Methods and approaches are discussed that identify and filter off affecting factors (noise) above primary signals,based on the Adaptive-Nework-Based Fuzzy Inference System. Influences of the zonal winds in equatorial ...Methods and approaches are discussed that identify and filter off affecting factors (noise) above primary signals,based on the Adaptive-Nework-Based Fuzzy Inference System. Influences of the zonal winds in equatorial eastern and middle/western Pacific on the SSTA in the equatorial region and their contribution to the latter are diagnosed and verified with observations of a number of significant El Nio and La Nia episodes. New viewpoints are propsed. The methods of wavelet decomposition and reconstruction are used to build a predictive model based on independent domains of frequency,which shows some advantages in composite prediction and prediction validity.The methods presented above are of non-linearity, error-allowing and auto-adaptive/learning, in addition to rapid and easy access,illustrative and quantitative presentation,and analyzed results that agree generally with facts. They are useful in diagnosing and predicting the El Nio and La Nia problems that are just roughly described in dynamics.展开更多
Electrical resistivity tomography (ERT) has been used to experimentally detect shallow buried faults in urban areas in the past a few years, with some progress and experience obtained. According to the results from Ol...Electrical resistivity tomography (ERT) has been used to experimentally detect shallow buried faults in urban areas in the past a few years, with some progress and experience obtained. According to the results from Olympic Park, Beijing, Shandong Province, Gansu Province and Shanxi Province, we have generalized the method and procedure for inferring the discontinuity of electrical structures (DES) indicating a buried fault in urban areas from resistivity tomograms and its typical electrical features. In general, the layered feature of the electrical structure is first analyzed to preliminarily define whether or not a DES exists in the target area. Resistivity contours in resistivity tomograms are then analyzed from the deep to the shallow. If they extend upward from the deep to the shallow and shape into an integral dislocation, sharp flexure (convergence) or gradient zone, it is inferred that the DES exists, indicating a buried fault. Finally, horizontal tracing is be carried out to define the trend of the DES. The DES can be divided into three types-type AB, ABA and AC. In the present paper, the Zhangdian-Renhe fault system in Zibo city is used as an example to illustrate how to use the method to infer the location and spatial extension of a target fault. Geologic drilling holes are placed based on our research results, and the drilling logs testify that our results are correct. However, the method of this paper is not exclusive and inflexible. It is expected to provide reference and assistance for inferring the shallow buried faults in urban areas from resistivity tomograms in the future.展开更多
The research purpose is invention (construction) of a formal logical inference of the Law of Conservation of Energy within a logically formalized axiomatic epistemology-and-axiology theory Sigma from a precisely defin...The research purpose is invention (construction) of a formal logical inference of the Law of Conservation of Energy within a logically formalized axiomatic epistemology-and-axiology theory Sigma from a precisely defined assumption of a-priori-ness of knowledge. For realizing this aim, the following work has been done: 1) a two-valued algebraic system of formal axiology has been defined precisely and applied to proper-philosophy of physics, namely, to an almost unknown (not-recognized) formal-axiological aspect of the physical law of conservation of energy;2) the formal axiomatic epistemology-and-axiology theory Sigma has been defined precisely and applied to proper-physics for realizing the above-indicated purpose. Thus, a discrete mathematical model of relationship between philosophy of physics and universal epistemology united with formal axiology has been constructed. Results: 1) By accurate computing relevant compositions of evaluation-functions within the discrete mathematical model, it is demonstrated that a formal-axiological analog of the great conservation law of proper physics is a formal-axiological law of two-valued algebra of metaphysics. (A precise algorithmic definition of the unhabitual (not-well-known) notion “formal-axiological law of algebra of metaphysics” is given.) 2) The hitherto never published significantly new nontrivial scientific result of investigation presented in this article is a formal logical inference of the law of conservation of energy within the formal axiomatic theory Sigma from conjunction of the formal-axiological analog of the law of conservation of energy and the assumption of a-priori-ness of knowledge.展开更多
The global Internet is a complex network of interconnected autonomous systems(ASes).Understanding Internet inter-domain path information is crucial for understanding,managing,and improving the Internet.The path inform...The global Internet is a complex network of interconnected autonomous systems(ASes).Understanding Internet inter-domain path information is crucial for understanding,managing,and improving the Internet.The path information can also help protect user privacy and security.However,due to the complicated and heterogeneous structure of the Internet,path information is not publicly available.Obtaining path information is challenging due to the limited measurement probes and collectors.Therefore,inferring Internet inter-domain paths from the limited data is a supplementary approach to measure Internet inter-domain paths.The purpose of this survey is to provide an overview of techniques that have been conducted to infer Internet inter-domain paths from 2005 to 2023 and present the main lessons from these studies.To this end,we summarize the inter-domain path inference techniques based on the granularity of the paths,for each method,we describe the data sources,the key ideas,the advantages,and the limitations.To help readers understand the path inference techniques,we also summarize the background techniques for path inference,such as techniques to measure the Internet,infer AS relationships,resolve aliases,and map IP addresses to ASes.A case study of the existing techniques is also presented to show the real-world applications of inter-domain path inference.Additionally,we discuss the challenges and opportunities in inferring Internet inter-domain paths,the drawbacks of the state-of-the-art techniques,and the future directions.展开更多
Mobile phones are becoming a primary platform for information access. A major aspect of ubiquitous computing is context-aware applications which collect information about the environment that the user is in and use th...Mobile phones are becoming a primary platform for information access. A major aspect of ubiquitous computing is context-aware applications which collect information about the environment that the user is in and use this information to provide better service and improve user experience. Location awareness makes certain applications possible, e.g., recommending nearby businesses and tracking estimated routes. An Android application is able to collect useful Wi-Fi information without registering a location listener with a network-based provider. We passively collected the data of the IDs of Wi-Fi access points and the received signal strengths. We developed and implemented an algorithm to analyse the data;and designed heuristics to infer the location of the device over time—all without ever connecting to the network thus maximally preserving the privacy of the user.展开更多
Human Immunodeficiency Virus (HIV) dynamics in Africa are purely characterised by sparse sampling of DNA sequences for individuals who are infected. There are some sub-groups that are more at risk than the general pop...Human Immunodeficiency Virus (HIV) dynamics in Africa are purely characterised by sparse sampling of DNA sequences for individuals who are infected. There are some sub-groups that are more at risk than the general population. These sub-groups have higher infectivity rates. We came up with a likelihood inference model of multi-type birth-death process that can be used to make inference for HIV epidemic in an African setting. We employ a likelihood inference that incorporates a probability of removal from infectious pool in the model. We have simulated trees and made parameter inference on the simulated trees as well as investigating whether the model distinguishes between heterogeneous and homogeneous dynamics. The model makes fairly good parameter inference. It distinguishes between heterogeneous and homogeneous dynamics well. Parameter estimation was also performed under sparse sampling scenario. We investigated whether trees obtained from a structured population are more balanced than those from a non-structured host population using tree statistics that measure tree balance and imbalance. Trees from non-structured population were more balanced basing on Colless and Sackin indices.展开更多
Deciding the penalty of a law case has always been a complex process, which may involve with much coordination. Despite the judicial study based on the rules and conditions, artificial intelligence and machine learnin...Deciding the penalty of a law case has always been a complex process, which may involve with much coordination. Despite the judicial study based on the rules and conditions, artificial intelligence and machine learning has rarely been used to study the problem of penalty inferring, leaving the large amount of law cases as well as various factors among them untouched. This paper aims to incorporate the state-of-the-art artificial intelligence methods to exploit to what extent this problem can be alleviated. We first analyze 145 000 law cases and observe that there are two sorts of labels, temporal labels and spatial labels, which have unique characteristics. Temporal labels and spatial labels tend to converge towards the final penalty, on condition that the cases are of the same category. In light of this, we propose a latent-class probabilistic generative model, namely Penalty Topic Model （PTM）, to infer the topic of law cases, and the temporal and spatial patterns of topics embedded in the case judgment. Then, the learnt knowledge is utilized to automatically cluster all cases accordingly in a unified way. We conduct extensive experiments to evaluate the performance of the proposed PTM on a real large-scale dataset of law cases. The experimental results show the superiority of our proposed PTM.展开更多
In the words of the late Sir Colin Blakemore,neurologists have historically sought to infer brain functions in a manner akin to to king a hammer to a computeranalyzing localized anatomical lesions caused by trauma,tum...In the words of the late Sir Colin Blakemore,neurologists have historically sought to infer brain functions in a manner akin to to king a hammer to a computeranalyzing localized anatomical lesions caused by trauma,tumors,or strokes,noting deficits,and inferring what functions certain brain regions may be responsible for.This approach exemplifies a deletion heuristic,where the absence of a specific function reveals insights about the underlying structures or mechanisms responsible for it.By observing what is lost when a particular brain region is damaged,throughout the history of the field,neurologists have pieced together the intricate relationship between anatomy and function.展开更多
Inferring gene regulatory networks （GRNs） is a challenging task in Bioinformatics. In this paper, an algorithm, PCHMS, is introduced to infer GRNs. This method applies the path consistency （PC） algorithm based on ...Inferring gene regulatory networks （GRNs） is a challenging task in Bioinformatics. In this paper, an algorithm, PCHMS, is introduced to infer GRNs. This method applies the path consistency （PC） algorithm based on conditional mutual information test （PCA-CMI）. In the PC-based algorithms the separator set is determined to detect the dependency between variables. The PCHMS algorithm attempts to select the set in the smart way. For this purpose, the edges of resulted skeleton are directed based on PC algorithm direction rule and mutual information test （MIT） score. Then the separator set is selected according to the directed network by considering a suitable sequential order of genes. The effectiveness of this method is benchmarked through several networks from the DREAM challenge and the widely used SOS DNA repair network of Escherichia coll. Results show that applying the PCHMS algorithm improves the precision of learning the structure of the GRNs in comparison with current popular approaches.展开更多
The formation and growth of cracks in concrete dams are mainly induced by hydrostatic and temperature loads.As cracks es-pecially unstable cracks are of great danger to the safety of dams,it is critical to avoid extre...The formation and growth of cracks in concrete dams are mainly induced by hydrostatic and temperature loads.As cracks es-pecially unstable cracks are of great danger to the safety of dams,it is critical to avoid extremely adverse load combinations during the dam operations to achieve the stability of cracks.Conventionally,the adverse load combinations have to be deter-mined empirically by experts based on specific dam site conditions.Therefore,it is attractive to apply quantitative instead of empirical methods to identify the adverse loading conditions.In this study,we employ an adaptive neuro-fuzzy inference sys-tem(ANFIS) to Chencun concrete dam.The ANFIS is able to help us build a relationship between the model inputs(reservoir water level and air temperature) and the model output(crack opening displacement).Based on this relationship,the rules of the adverse load combinations to the crack are generated directly from the monitoring data.The accuracy of the trained ANFIS is proved by comparing the modeling results and the monitoring data.Our work demonstrates that the ANFIS is a useful ap-proach for accurately recognizing the rules of the adverse load combinations that can be used in the knowledge base of dam safety expert system.展开更多
Humans regularly interact with their surrounding objects.Such interactions often result in strongly correlated motions between humans and the interacting objects.We thus ask:"Is it possible to infer object proper...Humans regularly interact with their surrounding objects.Such interactions often result in strongly correlated motions between humans and the interacting objects.We thus ask:"Is it possible to infer object properties from skeletal motion alone,even without seeing the interacting object itself?"In this paper,we present a fine-grained action recognition method that learns to infer such latent object properties from human interaction motion alone.This inference allows us to disentangle the motion from the object property and transfer object properties to a given motion.We collected a large number of videos and 3 D skeletal motions of performing actors using an inertial motion capture device.We analyzed similar actions and learned subtle differences between them to reveal latent properties of the interacting objects.In particular,we learned to identify the interacting object,by estimating its weight,or its spillability.Our results clearly demonstrate that motions and interacting objects are highly correlated and that related object latent properties can be inferred from 3 D skeleton sequences alone,leading to new synthesis possibilities for motions involving human interaction.Our dataset is available at http://vcc.szu.edu.cn/research/2020/IT.html.展开更多
Identification of significant biological relationships or patterns is central to many metagenomic studies.Methods that estimate association networks have been proposed for this purpose;however,they assume that associa...Identification of significant biological relationships or patterns is central to many metagenomic studies.Methods that estimate association networks have been proposed for this purpose;however,they assume that associations are static,neglecting the fact that relationships in a microbial ecosystem may vary with changes in environmental factors(EFs),which can result in inaccurate estimations.Therefore,in this study,we propose a computational model,called the k-Lognormal-Dirichlet-Multinomial(kLDM)model,which estimates multiple association networks that correspond to specific environmental conditions,and simultaneously infers microbe-microbe and EF-microbe associations for each network.The effectiveness of the kLDM model was demonstrated on synthetic data,a colorectal cancer(CRC)dataset,the Tara Oceans dataset,and the American Gut Project dataset.The results revealed that the widely-used Spearman’s rank correlation coefficient method performed much worse than the other methods,indicating the importance of separating samples by environmental conditions.Cancer fecal samples were then compared with cancer-free samples,and the estimation achieved by kLDM exhibited fewer associations among microbes but stronger associations between specific bacteria,especially five CRC-associated operational taxonomic units,indicating gut microbe translocation in cancer patients.Some EF-dependent associations were then found within a marine eukaryotic community.Finally,the gut microbial heterogeneity of inflammatory bowel disease patients was detected.These results demonstrate that kLDM can elucidate the complex associations within microbial ecosystems.The kLDM program,R,and Python scripts,together with all experimental datasets,are accessible at https://github.com/tinglab/kLDM.git.展开更多
Global Navigation Satellite System(GNSS)data is an inexpensive and ubiquitous source of activity data.Global Positioning System(GPS)is an example of such data.Although there have been several studies about inferring d...Global Navigation Satellite System(GNSS)data is an inexpensive and ubiquitous source of activity data.Global Positioning System(GPS)is an example of such data.Although there have been several studies about inferring device activity using GPS data from a consumer device,freight GPS data presents unique challenges for example having low and variable frequency,long transmission gaps,and frequent and unpredictable device ID resetting for preserving privacy.This study aims to provide an end-to-end,generic data analytical framework to infer multiple aspects of truck activity such as stops,trips,and tours.We use popular existing methods to construct the data processing pipeline and provide insights into their practical usage.We also propose improved data filters to different aspects of the data processing pipeline to address challenges found in privacy-preserving freight GPS data.We use freight data across four weeks from the greater Philadelphia region with variable transmission frequency ranging from one second to several hours to perform experiments and validate our methods.Our findings indicate that auxiliary information such as land use can be helpful in fine tuning stop inference,but spatio-temporal information contained in timestamped GPS pings is still the most powerful source of false stop identification.We also find that a combination of simple clustering techniques can provide a way to perform fast and reasonable clustering of the same stop.展开更多
Robustness against measurement uncertainties is crucial for gas turbine engine diagnosis.While current research focuses mainly on measurement noise,measurement bias remains challenging.This study proposes a novel perf...Robustness against measurement uncertainties is crucial for gas turbine engine diagnosis.While current research focuses mainly on measurement noise,measurement bias remains challenging.This study proposes a novel performance-based fault detection and identification(FDI)strategy for twin-shaft turbofan gas turbine engines and addresses these uncertainties through a first-order Takagi-Sugeno-Kang fuzzy inference system.To handle ambient condition changes,we use parameter correction to preprocess the raw measurement data,which reduces the FDI’s system complexity.Additionally,the power-level angle is set as a scheduling parameter to reduce the number of rules in the TSK-based FDI system.The data for designing,training,and testing the proposed FDI strategy are generated using a component-level turbofan engine model.The antecedent and consequent parameters of the TSK-based FDI system are optimized using the particle swarm optimization algorithm and ridge regression.A robust structure combining a specialized fuzzy inference system with the TSK-based FDI system is proposed to handle measurement biases.The performance of the first-order TSK-based FDI system and robust FDI structure are evaluated through comprehensive simulation studies.Comparative studies confirm the superior accuracy of the first-order TSK-based FDI system in fault detection,isolation,and identification.The robust structure demonstrates a 2%-8%improvement in the success rate index under relatively large measurement bias conditions,thereby indicating excellent robustness.Accuracy against significant bias values and computation time are also evaluated,suggesting that the proposed robust structure has desirable online performance.This study proposes a novel FDI strategy that effectively addresses measurement uncertainties.展开更多
Existing blockwise empirical likelihood(BEL)method blocks the observations or their analogues,which is proven useful under some dependent data settings.In this paper,we introduce a new BEL(NBEL)method by blocking the ...Existing blockwise empirical likelihood(BEL)method blocks the observations or their analogues,which is proven useful under some dependent data settings.In this paper,we introduce a new BEL(NBEL)method by blocking the scoring functions under high dimensional cases.We study the construction of confidence regions for the parameters in spatial autoregressive models with spatial autoregressive disturbances(SARAR models)with high dimension of parameters by using the NBEL method.It is shown that the NBEL ratio statistics are asymptoticallyχ^(2)-type distributed,which are used to obtain the NBEL based confidence regions for the parameters in SARAR models.A simulation study is conducted to compare the performances of the NBEL and the usual EL methods.展开更多
In 6G,artificial intelligence represented by deep nerual network(DNN)will unleash its potential and empower IoT applications to transform into intelligent IoT applications.However,whole DNNbased inference is difficult...In 6G,artificial intelligence represented by deep nerual network(DNN)will unleash its potential and empower IoT applications to transform into intelligent IoT applications.However,whole DNNbased inference is difficult to carry out on resourceconstrained intelligent IoT devices and will suffer privacy leakage when offloading to the cloud or mobile edge computation server(MECs).In this paper,we formulate a privacy and delay dual-driven device-edge collaborative inference(P4DE-CI)system to preserve the privacy of raw data while accelerating the intelligent inference process,where the intelligent IoT devices run the front-end part of DNN model and the MECs execute the back-end part of DNN model.Considering three typical privacy leakage models and the end-to-end delay of collaborative DNN-based inference,we define a novel intelligent inference Quality of service(I2-QoS)metric as the weighted summation of the inference latency and privacy preservation level.Moreover,we propose a DDPG-based joint DNN model optimization and resource allocation algorithm to maximize I2-QoS,by optimizing the association relationship between intelligent IoT devices and MECs,the DNN model placement decision,and the DNN model partition decision.Experiments carried out on the AlexNet model reveal that the proposed algorithm has better performance in both privacy-preserving and inference-acceleration.展开更多
1.Introduction Data inference(DInf)is a data security threat in which critical information is inferred from low-sensitivity data.Once regarded as an advanced professional threat limited to intelligence analysts,DInf h...1.Introduction Data inference(DInf)is a data security threat in which critical information is inferred from low-sensitivity data.Once regarded as an advanced professional threat limited to intelligence analysts,DInf has become a widespread risk in the artificial intelligence(AI)era.展开更多
The primary objective of this study is to measure fluoride levels in groundwater samples using machine learning approaches alongside traditional and fuzzy logic models based health risk assessment in the hard rock Arj...The primary objective of this study is to measure fluoride levels in groundwater samples using machine learning approaches alongside traditional and fuzzy logic models based health risk assessment in the hard rock Arjunanadi River basin,South India.Fluoride levels in the study area vary between 0.1 and 3.10 mg/L,with 32 samples exceeding the World Health Organization(WHO)standard of 1.5 mg/L.Hydrogeochemical analyses(Durov and Gibbs)clearly show that the overall water chemistry is primarily influenced by simple dissolution,mixing,and rock-water interactions,indicating that geogenic sources are the predominant contributors to fluoride in the study area.Around 446.5 km^(2)is considered at risk.In predictive analysis,five Machine Learning(ML)models were used,with the AdaBoost model performing better than the other models,achieving 96%accuracy and 4%error rate.The Traditional Health Risk Assessment(THRA)results indicate that 65%of samples pose highly susceptible for dental fluorosis,while 12%of samples pose highly susceptible for skeletal fluorosis in young age groups.The Fuzzy Inference System(FIS)model effectively manages ambiguity and linguistic factors,which are crucial when addressing health risks linked to groundwater fluoride contamination.In this model,input variables include fluoride concentration,individual age,and ingestion rate,while output variables consist of dental caries risk,dental fluorosis,and skeletal fluorosis.The overall results indicate that increased ingestion rates and prolonged exposure to contaminated water make adults and the elderly people vulnerable to dental and skeletal fluorosis,along with very young and young age groups.This study is an essential resource for local authorities,healthcare officials,and communities,aiding in the mitigation of health risks associated with groundwater contamination and enhancing quality of life through improved water management and health risk assessment,aligning with Sustainable Development Goals(SDGs)3 and 6,thereby contributing to a cleaner and healthier society.展开更多
文摘Methods and approaches are discussed that identify and filter off affecting factors (noise) above primary signals,based on the Adaptive-Nework-Based Fuzzy Inference System. Influences of the zonal winds in equatorial eastern and middle/western Pacific on the SSTA in the equatorial region and their contribution to the latter are diagnosed and verified with observations of a number of significant El Nio and La Nia episodes. New viewpoints are propsed. The methods of wavelet decomposition and reconstruction are used to build a predictive model based on independent domains of frequency,which shows some advantages in composite prediction and prediction validity.The methods presented above are of non-linearity, error-allowing and auto-adaptive/learning, in addition to rapid and easy access,illustrative and quantitative presentation,and analyzed results that agree generally with facts. They are useful in diagnosing and predicting the El Nio and La Nia problems that are just roughly described in dynamics.
基金The project entitled "Urban Active Fault Surveying Project"(143623) funded by the National Development and Roform Commission of China"Active Faults Exploration and Seismic Hazard Assessment in Zibo City"(SD1501) funded by the Department of Science & Technology of Shangdong Province,China
文摘Electrical resistivity tomography (ERT) has been used to experimentally detect shallow buried faults in urban areas in the past a few years, with some progress and experience obtained. According to the results from Olympic Park, Beijing, Shandong Province, Gansu Province and Shanxi Province, we have generalized the method and procedure for inferring the discontinuity of electrical structures (DES) indicating a buried fault in urban areas from resistivity tomograms and its typical electrical features. In general, the layered feature of the electrical structure is first analyzed to preliminarily define whether or not a DES exists in the target area. Resistivity contours in resistivity tomograms are then analyzed from the deep to the shallow. If they extend upward from the deep to the shallow and shape into an integral dislocation, sharp flexure (convergence) or gradient zone, it is inferred that the DES exists, indicating a buried fault. Finally, horizontal tracing is be carried out to define the trend of the DES. The DES can be divided into three types-type AB, ABA and AC. In the present paper, the Zhangdian-Renhe fault system in Zibo city is used as an example to illustrate how to use the method to infer the location and spatial extension of a target fault. Geologic drilling holes are placed based on our research results, and the drilling logs testify that our results are correct. However, the method of this paper is not exclusive and inflexible. It is expected to provide reference and assistance for inferring the shallow buried faults in urban areas from resistivity tomograms in the future.
文摘The research purpose is invention (construction) of a formal logical inference of the Law of Conservation of Energy within a logically formalized axiomatic epistemology-and-axiology theory Sigma from a precisely defined assumption of a-priori-ness of knowledge. For realizing this aim, the following work has been done: 1) a two-valued algebraic system of formal axiology has been defined precisely and applied to proper-philosophy of physics, namely, to an almost unknown (not-recognized) formal-axiological aspect of the physical law of conservation of energy;2) the formal axiomatic epistemology-and-axiology theory Sigma has been defined precisely and applied to proper-physics for realizing the above-indicated purpose. Thus, a discrete mathematical model of relationship between philosophy of physics and universal epistemology united with formal axiology has been constructed. Results: 1) By accurate computing relevant compositions of evaluation-functions within the discrete mathematical model, it is demonstrated that a formal-axiological analog of the great conservation law of proper physics is a formal-axiological law of two-valued algebra of metaphysics. (A precise algorithmic definition of the unhabitual (not-well-known) notion “formal-axiological law of algebra of metaphysics” is given.) 2) The hitherto never published significantly new nontrivial scientific result of investigation presented in this article is a formal logical inference of the law of conservation of energy within the formal axiomatic theory Sigma from conjunction of the formal-axiological analog of the law of conservation of energy and the assumption of a-priori-ness of knowledge.
基金supported by the National Natural Science Foundation of China(61471343)the National Key Technology Research and Development Program of the Ministry of Science and Technology of China(2014BAK14B03)
基金the China Postdoctoral Science Foundation(2023TQ0089)the National Natural Science Foundation of China(Nos.62072465,62172155)the Science and Technology Innovation Program of Hunan Province(Nos.2022RC3061,2023RC3027).
文摘The global Internet is a complex network of interconnected autonomous systems(ASes).Understanding Internet inter-domain path information is crucial for understanding,managing,and improving the Internet.The path information can also help protect user privacy and security.However,due to the complicated and heterogeneous structure of the Internet,path information is not publicly available.Obtaining path information is challenging due to the limited measurement probes and collectors.Therefore,inferring Internet inter-domain paths from the limited data is a supplementary approach to measure Internet inter-domain paths.The purpose of this survey is to provide an overview of techniques that have been conducted to infer Internet inter-domain paths from 2005 to 2023 and present the main lessons from these studies.To this end,we summarize the inter-domain path inference techniques based on the granularity of the paths,for each method,we describe the data sources,the key ideas,the advantages,and the limitations.To help readers understand the path inference techniques,we also summarize the background techniques for path inference,such as techniques to measure the Internet,infer AS relationships,resolve aliases,and map IP addresses to ASes.A case study of the existing techniques is also presented to show the real-world applications of inter-domain path inference.Additionally,we discuss the challenges and opportunities in inferring Internet inter-domain paths,the drawbacks of the state-of-the-art techniques,and the future directions.
文摘Mobile phones are becoming a primary platform for information access. A major aspect of ubiquitous computing is context-aware applications which collect information about the environment that the user is in and use this information to provide better service and improve user experience. Location awareness makes certain applications possible, e.g., recommending nearby businesses and tracking estimated routes. An Android application is able to collect useful Wi-Fi information without registering a location listener with a network-based provider. We passively collected the data of the IDs of Wi-Fi access points and the received signal strengths. We developed and implemented an algorithm to analyse the data;and designed heuristics to infer the location of the device over time—all without ever connecting to the network thus maximally preserving the privacy of the user.
文摘Human Immunodeficiency Virus (HIV) dynamics in Africa are purely characterised by sparse sampling of DNA sequences for individuals who are infected. There are some sub-groups that are more at risk than the general population. These sub-groups have higher infectivity rates. We came up with a likelihood inference model of multi-type birth-death process that can be used to make inference for HIV epidemic in an African setting. We employ a likelihood inference that incorporates a probability of removal from infectious pool in the model. We have simulated trees and made parameter inference on the simulated trees as well as investigating whether the model distinguishes between heterogeneous and homogeneous dynamics. The model makes fairly good parameter inference. It distinguishes between heterogeneous and homogeneous dynamics well. Parameter estimation was also performed under sparse sampling scenario. We investigated whether trees obtained from a structured population are more balanced than those from a non-structured host population using tree statistics that measure tree balance and imbalance. Trees from non-structured population were more balanced basing on Colless and Sackin indices.
基金This work is supported in part by the National Key Research and Development Program of China under Grant No. 2016YFC0800805 and the National Natural Science Foundation of China under Grant No. 61690201.
文摘Deciding the penalty of a law case has always been a complex process, which may involve with much coordination. Despite the judicial study based on the rules and conditions, artificial intelligence and machine learning has rarely been used to study the problem of penalty inferring, leaving the large amount of law cases as well as various factors among them untouched. This paper aims to incorporate the state-of-the-art artificial intelligence methods to exploit to what extent this problem can be alleviated. We first analyze 145 000 law cases and observe that there are two sorts of labels, temporal labels and spatial labels, which have unique characteristics. Temporal labels and spatial labels tend to converge towards the final penalty, on condition that the cases are of the same category. In light of this, we propose a latent-class probabilistic generative model, namely Penalty Topic Model （PTM）, to infer the topic of law cases, and the temporal and spatial patterns of topics embedded in the case judgment. Then, the learnt knowledge is utilized to automatically cluster all cases accordingly in a unified way. We conduct extensive experiments to evaluate the performance of the proposed PTM on a real large-scale dataset of law cases. The experimental results show the superiority of our proposed PTM.
文摘In the words of the late Sir Colin Blakemore,neurologists have historically sought to infer brain functions in a manner akin to to king a hammer to a computeranalyzing localized anatomical lesions caused by trauma,tumors,or strokes,noting deficits,and inferring what functions certain brain regions may be responsible for.This approach exemplifies a deletion heuristic,where the absence of a specific function reveals insights about the underlying structures or mechanisms responsible for it.By observing what is lost when a particular brain region is damaged,throughout the history of the field,neurologists have pieced together the intricate relationship between anatomy and function.
文摘Inferring gene regulatory networks （GRNs） is a challenging task in Bioinformatics. In this paper, an algorithm, PCHMS, is introduced to infer GRNs. This method applies the path consistency （PC） algorithm based on conditional mutual information test （PCA-CMI）. In the PC-based algorithms the separator set is determined to detect the dependency between variables. The PCHMS algorithm attempts to select the set in the smart way. For this purpose, the edges of resulted skeleton are directed based on PC algorithm direction rule and mutual information test （MIT） score. Then the separator set is selected according to the directed network by considering a suitable sequential order of genes. The effectiveness of this method is benchmarked through several networks from the DREAM challenge and the widely used SOS DNA repair network of Escherichia coll. Results show that applying the PCHMS algorithm improves the precision of learning the structure of the GRNs in comparison with current popular approaches.
基金supported by the National Natural Science Foundation of China (Grant Nos.50909041 and 41072217)
文摘The formation and growth of cracks in concrete dams are mainly induced by hydrostatic and temperature loads.As cracks es-pecially unstable cracks are of great danger to the safety of dams,it is critical to avoid extremely adverse load combinations during the dam operations to achieve the stability of cracks.Conventionally,the adverse load combinations have to be deter-mined empirically by experts based on specific dam site conditions.Therefore,it is attractive to apply quantitative instead of empirical methods to identify the adverse loading conditions.In this study,we employ an adaptive neuro-fuzzy inference sys-tem(ANFIS) to Chencun concrete dam.The ANFIS is able to help us build a relationship between the model inputs(reservoir water level and air temperature) and the model output(crack opening displacement).Based on this relationship,the rules of the adverse load combinations to the crack are generated directly from the monitoring data.The accuracy of the trained ANFIS is proved by comparing the modeling results and the monitoring data.Our work demonstrates that the ANFIS is a useful ap-proach for accurately recognizing the rules of the adverse load combinations that can be used in the knowledge base of dam safety expert system.
基金supported in part by Shenzhen Innovation Program(JCYJ20180305125709986)National Natural Science Foundation of China(61861130365,61761146002)+1 种基金GD Science and Technology Program(2020A0505100064,2015A030312015)DEGP Key Project(2018KZDXM058)。
文摘Humans regularly interact with their surrounding objects.Such interactions often result in strongly correlated motions between humans and the interacting objects.We thus ask:"Is it possible to infer object properties from skeletal motion alone,even without seeing the interacting object itself?"In this paper,we present a fine-grained action recognition method that learns to infer such latent object properties from human interaction motion alone.This inference allows us to disentangle the motion from the object property and transfer object properties to a given motion.We collected a large number of videos and 3 D skeletal motions of performing actors using an inertial motion capture device.We analyzed similar actions and learned subtle differences between them to reveal latent properties of the interacting objects.In particular,we learned to identify the interacting object,by estimating its weight,or its spillability.Our results clearly demonstrate that motions and interacting objects are highly correlated and that related object latent properties can be inferred from 3 D skeleton sequences alone,leading to new synthesis possibilities for motions involving human interaction.Our dataset is available at http://vcc.szu.edu.cn/research/2020/IT.html.
基金supported by the National Natural Science Foundation of China(Grant Nos.61872218,61673241,and 61721003)the Tsinghua-Fuzhou Institute Research ProgramBeijing National Research Center for Information Science and Technology(BNRist),China。
文摘Identification of significant biological relationships or patterns is central to many metagenomic studies.Methods that estimate association networks have been proposed for this purpose;however,they assume that associations are static,neglecting the fact that relationships in a microbial ecosystem may vary with changes in environmental factors(EFs),which can result in inaccurate estimations.Therefore,in this study,we propose a computational model,called the k-Lognormal-Dirichlet-Multinomial(kLDM)model,which estimates multiple association networks that correspond to specific environmental conditions,and simultaneously infers microbe-microbe and EF-microbe associations for each network.The effectiveness of the kLDM model was demonstrated on synthetic data,a colorectal cancer(CRC)dataset,the Tara Oceans dataset,and the American Gut Project dataset.The results revealed that the widely-used Spearman’s rank correlation coefficient method performed much worse than the other methods,indicating the importance of separating samples by environmental conditions.Cancer fecal samples were then compared with cancer-free samples,and the estimation achieved by kLDM exhibited fewer associations among microbes but stronger associations between specific bacteria,especially five CRC-associated operational taxonomic units,indicating gut microbe translocation in cancer patients.Some EF-dependent associations were then found within a marine eukaryotic community.Finally,the gut microbial heterogeneity of inflammatory bowel disease patients was detected.These results demonstrate that kLDM can elucidate the complex associations within microbial ecosystems.The kLDM program,R,and Python scripts,together with all experimental datasets,are accessible at https://github.com/tinglab/kLDM.git.
文摘Global Navigation Satellite System(GNSS)data is an inexpensive and ubiquitous source of activity data.Global Positioning System(GPS)is an example of such data.Although there have been several studies about inferring device activity using GPS data from a consumer device,freight GPS data presents unique challenges for example having low and variable frequency,long transmission gaps,and frequent and unpredictable device ID resetting for preserving privacy.This study aims to provide an end-to-end,generic data analytical framework to infer multiple aspects of truck activity such as stops,trips,and tours.We use popular existing methods to construct the data processing pipeline and provide insights into their practical usage.We also propose improved data filters to different aspects of the data processing pipeline to address challenges found in privacy-preserving freight GPS data.We use freight data across four weeks from the greater Philadelphia region with variable transmission frequency ranging from one second to several hours to perform experiments and validate our methods.Our findings indicate that auxiliary information such as land use can be helpful in fine tuning stop inference,but spatio-temporal information contained in timestamped GPS pings is still the most powerful source of false stop identification.We also find that a combination of simple clustering techniques can provide a way to perform fast and reasonable clustering of the same stop.
文摘Robustness against measurement uncertainties is crucial for gas turbine engine diagnosis.While current research focuses mainly on measurement noise,measurement bias remains challenging.This study proposes a novel performance-based fault detection and identification(FDI)strategy for twin-shaft turbofan gas turbine engines and addresses these uncertainties through a first-order Takagi-Sugeno-Kang fuzzy inference system.To handle ambient condition changes,we use parameter correction to preprocess the raw measurement data,which reduces the FDI’s system complexity.Additionally,the power-level angle is set as a scheduling parameter to reduce the number of rules in the TSK-based FDI system.The data for designing,training,and testing the proposed FDI strategy are generated using a component-level turbofan engine model.The antecedent and consequent parameters of the TSK-based FDI system are optimized using the particle swarm optimization algorithm and ridge regression.A robust structure combining a specialized fuzzy inference system with the TSK-based FDI system is proposed to handle measurement biases.The performance of the first-order TSK-based FDI system and robust FDI structure are evaluated through comprehensive simulation studies.Comparative studies confirm the superior accuracy of the first-order TSK-based FDI system in fault detection,isolation,and identification.The robust structure demonstrates a 2%-8%improvement in the success rate index under relatively large measurement bias conditions,thereby indicating excellent robustness.Accuracy against significant bias values and computation time are also evaluated,suggesting that the proposed robust structure has desirable online performance.This study proposes a novel FDI strategy that effectively addresses measurement uncertainties.
基金Supported by the National Natural Science Foundation of China(12061017,12361055)the Research Fund of Guangxi Key Lab of Multi-source Information Mining&Security(22-A-01-01)。
文摘Existing blockwise empirical likelihood(BEL)method blocks the observations or their analogues,which is proven useful under some dependent data settings.In this paper,we introduce a new BEL(NBEL)method by blocking the scoring functions under high dimensional cases.We study the construction of confidence regions for the parameters in spatial autoregressive models with spatial autoregressive disturbances(SARAR models)with high dimension of parameters by using the NBEL method.It is shown that the NBEL ratio statistics are asymptoticallyχ^(2)-type distributed,which are used to obtain the NBEL based confidence regions for the parameters in SARAR models.A simulation study is conducted to compare the performances of the NBEL and the usual EL methods.
基金supported by the National Natural Science Foundation of China(No.62201079)the Beijing Natural Science Foundation(No.L232051).
文摘In 6G,artificial intelligence represented by deep nerual network(DNN)will unleash its potential and empower IoT applications to transform into intelligent IoT applications.However,whole DNNbased inference is difficult to carry out on resourceconstrained intelligent IoT devices and will suffer privacy leakage when offloading to the cloud or mobile edge computation server(MECs).In this paper,we formulate a privacy and delay dual-driven device-edge collaborative inference(P4DE-CI)system to preserve the privacy of raw data while accelerating the intelligent inference process,where the intelligent IoT devices run the front-end part of DNN model and the MECs execute the back-end part of DNN model.Considering three typical privacy leakage models and the end-to-end delay of collaborative DNN-based inference,we define a novel intelligent inference Quality of service(I2-QoS)metric as the weighted summation of the inference latency and privacy preservation level.Moreover,we propose a DDPG-based joint DNN model optimization and resource allocation algorithm to maximize I2-QoS,by optimizing the association relationship between intelligent IoT devices and MECs,the DNN model placement decision,and the DNN model partition decision.Experiments carried out on the AlexNet model reveal that the proposed algorithm has better performance in both privacy-preserving and inference-acceleration.
基金supported by the National Key Research and Development Program of China(2022YFB2703503)the National Natural Science Foundation of China(62293501,62525210,and 62293502)the China Scholarship Council(202306280318).
文摘1.Introduction Data inference(DInf)is a data security threat in which critical information is inferred from low-sensitivity data.Once regarded as an advanced professional threat limited to intelligence analysts,DInf has become a widespread risk in the artificial intelligence(AI)era.
基金the Anusandhan National Research Foundation(ANRF),New Delhi[Erstwhile,Science and Engineering Research Board(SERB)]Department of Science and Technology(DST)(Government of India)(File No.:CRG/2022/002618 Dated:22.08.2023)for providing the grant and support to carry out this work effectively.
文摘The primary objective of this study is to measure fluoride levels in groundwater samples using machine learning approaches alongside traditional and fuzzy logic models based health risk assessment in the hard rock Arjunanadi River basin,South India.Fluoride levels in the study area vary between 0.1 and 3.10 mg/L,with 32 samples exceeding the World Health Organization(WHO)standard of 1.5 mg/L.Hydrogeochemical analyses(Durov and Gibbs)clearly show that the overall water chemistry is primarily influenced by simple dissolution,mixing,and rock-water interactions,indicating that geogenic sources are the predominant contributors to fluoride in the study area.Around 446.5 km^(2)is considered at risk.In predictive analysis,five Machine Learning(ML)models were used,with the AdaBoost model performing better than the other models,achieving 96%accuracy and 4%error rate.The Traditional Health Risk Assessment(THRA)results indicate that 65%of samples pose highly susceptible for dental fluorosis,while 12%of samples pose highly susceptible for skeletal fluorosis in young age groups.The Fuzzy Inference System(FIS)model effectively manages ambiguity and linguistic factors,which are crucial when addressing health risks linked to groundwater fluoride contamination.In this model,input variables include fluoride concentration,individual age,and ingestion rate,while output variables consist of dental caries risk,dental fluorosis,and skeletal fluorosis.The overall results indicate that increased ingestion rates and prolonged exposure to contaminated water make adults and the elderly people vulnerable to dental and skeletal fluorosis,along with very young and young age groups.This study is an essential resource for local authorities,healthcare officials,and communities,aiding in the mitigation of health risks associated with groundwater contamination and enhancing quality of life through improved water management and health risk assessment,aligning with Sustainable Development Goals(SDGs)3 and 6,thereby contributing to a cleaner and healthier society.
