We then build a further system , based on ensembles of two-layered bidirectional recurrent neural networks , to map structural motif predictions into a traditional 3-class ( helix , strand , coil ) secondary structure . This system achieves 79.5 % correct prediction using the " hard " CASP 3-class assignment , and 81.4 % with a more lenient assignment , outperforming a sophisticated state-of-the-art predictor ( Porter ) trained in the same experimental conditions . The structural motif predictor is publicly available at : http://distill.ucd.ie/porter+/. The paper presents an extension of the modified Smith predictor controller referred to as the flexible Smith predictor control scheme . The tuning procedure is based on the multiple integration method and requires only the process open-loop step response to calculate the parameters of the flexible Smith predictor . In this paper , we present two novel profile-guided static prediction techniques— Static Correlation Choice ( SCC ) prediction and Static Choice ( SC ) prediction for alleviating the energy consumption without compromising performance . Using our techniques , the hardware choice predictor of a hybrid predictor can be completely eliminated from the processor and replaced with our off-line profiling schemes . Our simulation results show an average 40 % power reduction compared to several hybrid predictors . Also , it is known that , for neural networks ( NNs ) , one can improve the generalization performance by applying random perturbations to each input in order to generate artificial input points and encouraging the model to assign similar outputs to the set of artificial inputs derived from the same point [ reference ] . Several studies have also confirmed that this philosophy of making the predictor robust against random and local perturbation is effective in semi - supervised learning . For selected examples , see [ reference ] , [ reference ] , [ reference ] , [ reference ] , [ reference ] . Finally , we evaluate domain - adversarial descriptor learning in the context of person re - identification application [ reference ] , where the task is to obtain good pedestrian image descriptors that are suitable for retrieval and verification . We apply domainadversarial learning , as we consider a descriptor predictor trained with a Siamese - like loss instead of the label predictor trained with a classification loss . In a series of experiments , we demonstrate that domain - adversarial learning can improve cross - data - set re - identification considerably . This improves over a similar approach of [ reference ] that simply trains a single deep autoencoder for both domains . In both approaches , the actual classifier / predictor is learned in a separate step using the feature representation learned by autoencoder ( s ) . In contrast to [ reference ][ reference ] , our approach performs feature learning , domain adaptation and classifier learning jointly , in a unified architecture , and using a single learning algorithm ( backpropagation ) . To introduce our network progressively , we start from a simple version and gradually add components . As in Fig 2 ( top ) , our network has an encoder stage and a predictor stage . The encoder maps the input pair of an image We further improve the design of the predictor branch to better accommodate large and smooth surfaces which are common in natural objects . The fully connected predictor as above can not make full use of such natural geometric statistics , since each point is predicted independently . The improved predictor in Fig 2 ( middle ) exploits this geometric smoothness property . Instead , we learn a model of the task dynamics that can be used to assess the novelty of a new state . Formally , let denote the encoding of the state , and let be a dynamics predictor parameterized by . takes an encoded version of a state at time and the agent ’s action at time and attempts to predict an encoded version of the agent As shown in Fig . [ reference ] , the model is composed of the following four modules : an encoder stack , a decoder stack , a newly added fertility predictor ( details in [ reference ] ) , and a translation predictor for token decoding . subsection : Encoder Stack These feature layers provide high - quality deep features of different granularity ( conv4_3 the finest and conv11 the coarsest ) . A convolutional predictor with kernels is added to each of the 6 layers to detect segments and links . We index the feature layers and the predictors by . SVM is able to identify the separated hyper plane that maximizes the margin between two different classes . However , SVM has a weakness for parameter selection or suitable feature . In this research , the researchers made an improvement toward the previous research using combined method of feature selection in SVM through comparing three-feature selection ; Genetic Algorithm , Particle Swarm Optimization , and Principal Component Analysis . Later on , it activates the remaining system modules like webcam , communication block , etc . The event-driven feature improves the system performance in terms of resources utilization and power consumption compared to the counter classical ones . A firstsystem implementation is realized and successfully tested . However , the quality of the learned features is not sufficiently good , so a complex back-end model , either neural or probabilistic , has to be used to address the residual uncertainty when applied to speaker verification , just as with raw features . This paper presents a convolutional time-delay deep neural network structure ( CT-DNN ) for speaker feature learning . Our experimental results on the Fisher database demonstrated that this CT-DNN can produce high-quality speaker features : even with a single feature ( 0.3 seconds including the context ) , the EER can be as low as 7.68 % . Key frame can dramatically reduce the data of the video indexing , and provide a framework for video abstraction and retrieval . In this paper , at first briefly discuss the existing approaches , and then present a motion feature based key frame extracting method using fuzzy reasoning theory for MPEG compressed video . By performing minimal decoding on compressed bit stream , computational complexity is reduced , and the processing speed can be highly improved . In this paper , depth estimation and semantic segmentation are addressed together from a single input image through a hybrid convolutional network . Different from the state of the art methods where features are extracted by a sole feature extraction network for both tasks , the proposed HybridNet improves the features extraction by separating the relevant features for one task from those which are relevant for both . Experimental results demonstrate that HybridNet results are comparable with the state of the art methods , as well as the single task methods that HybridNet is based on . Adversarial STN . In our Adversarial Spatial Transformer Network , we focus on feature map rotations . That is , given a feature map after the RoI - pooling layer as input , our ASTN will learn to rotate the feature map to make it harder to recognize . Any supervised machine learning algorithm requires a set of informative , discriminating , and independent features . In prediction problems on networks this means that one has to construct a feature vector representation for the nodes and edges . A typical solution involves hand - engineering domain - specific features based on expert knowledge . reference ][ reference ][ reference ][ reference ][ reference ] . For video face recognition , most of these methods either use pairwise frame feature similarity computation [ reference ][ reference ] or naive ( average / max ) frame feature pooling [ reference ][ reference ][ reference ] . This motivated us to seek for an adaptive aggregation approach . Adding larger scales for PASCAL only results in marginal improvement but significantly increases the computation . The feature extrapolating layer extrapolates 4 scales with equal intervals between every two input scales , so the final conv feature pyramid has 21 scales for KITTI and 16 scales for PASCAL . In the RoI generating layer , each location in a heat map generates 7 boxes with 7 different aspect ratios for KITTI and 5 aspect ratios for PASCAL , where each number indicates the ratio between the height and the width of the bounding box . subsection : Locality Sensitive Hashing ( LSH ) An alternative approach to BASS and DISCO is to use well - established feature generation methods that are agnostic about the type of input they receive . Such methods include polynomial bases , sparse distributed memories and locality sensitive hashing ( LSH ) . subsection : Multi - scale feature maps The main goal of constructing multi - scale feature maps is to add more context without increasing the computational cost . Figure [ reference ] illustrates the concept of multi - scale feature maps , where additional input feature maps contain a larger view of the context of the frame by downsampling larger context windows with different strides . subsection : Unimodal Feature Extraction For a fair comparison with the state - of - the - art method , conversational memory networks ( CMN ) , we follow identical feature extraction procedures . subsubsection : Textual Feature Extraction Such representations can be adapted to image classification by fine - tuning , or extracted as holistic features for classification with linear SVMs . When used as generic feature extractors , feature aggregation techniques designed for hand - crafted features can also work with CNN embeddings and achieve competitive performance . An alternative approach for object classification is via detection . Note that we could also consider a feature - level method which discriminates between the features or semantics from two image sets as viewed under a task network . This would amount to an additional feature level GAN loss ( see Figure [ reference ] orange portion ) : Taken together , these loss functions form our complete objective : This ultimately corresponds to solving for a target model according to the optimization problem We have introduced a method for unsupervised adaptation which generalizes adversarial objectives to be viewed as operating at the pixel or feature level . In addition , we introduce the use of cycle - consistency together with semantic transformation constraints to guide the mapping from one domain to another . Analysis of data from recent experiments leads to the observation that the distributions of peak signal levels and durations of underdense meteor trails differ from classic predictions . The authors show that if the trail arrival process is modeled by three independent random variables and the decay factor is modeled as a random process , the observed distribution functions for the amplitude and duration of underdense meteor trails can be more accurately synthesized . They conclude that the present findings have important ramifications in applications where it is desired to simulate more accurately the arrival process of meteor trails in order to predict the performance of meteor communication systems.<<ETX > > Abroad learners try multi-domain methods to study risk forecast [ 1 - 6 ] . Domestic learners establish financial risk forecasting system taking ST ( special treatment for abnormal situation including finance and others ) companies in domestic stock market as studying objects , adopting single variable analysis , Multiple Linear Regression Discriminant Model and multielement logical aggressive model [ 7 - 13 ] . But the current research has the following problems :  A serial of mathematical statistics is taken before model establishing , but this affects the effectiveness . For illustration , the collected copies are 100 enterprises , in which 97 copies are valid . The empirical results show that both the confirmation data of the independent variable system and the confirmation data of the dependent variable have the satisfying reliability . The organization support and the leadership support are first essential factors of affecting ERP implementation success in Chinese Enterprises . Accurate modeling of complex regional environments is achieved with parsimonious and interpretable models . While we ensure the causal variables of the previous study are considered for inclusion in the models developed , this paper focuses on the ability to accurately predict conflict over individual independent variable analysis . In order to better understand the effectiveness of AV technology , we present a systematic meta-study of 24 experimental studies . We pursue two separate analyses : an analysis ofindependent variables , in which we tie each study to a particular guiding learning theory in an attempt to determine which guiding theory has had the most predictive success ; and an analysis of dependent variables , which enables us to determine which measurement techniques have been most sensitive to the learning benefits of AV technology . Our most significant finding is that how students use AV technology has a greater impact on effectiveness than what AV technology shows them . This is a descriptive research with samples of undergraduate students of Unsoed as many as 334 students among 7,376 students . Thus , one variable analysis is used in order to see the value of the central tendency and then tested by using the rate value . The results showed that students ’ information literacy skills measuring by the standard information literacy competency standards for higher education from ACRL are in the skilled category . The analysis of software defect data is important for software quality assurance , project management and processes improvement.,but the data analysis functions of current software defect management tools are weak . The data types of software defect attributes are discussed , and then the basic analysis methods of software defect data , including one-variable data analysis and multiple-variable data analysis , are proposed . Moreover , several key technologies for implementing the methods of software defect data analysis are discussed . In this chapter we describe some of the available biasing methods and also those that are commonly used in applications . The single random variable case is treated here . The development is carried out by means of several illustrative examples . 3 . Independent variable selection Generally in a classification problem , the variable that is to be predicted is known as the dependent variable ( transportation mode in our case ) because its value depends upon , or is decided by , the values of all the other attributes . The other attributes that help to predict the value of the dependent variable , are known as the independent variables ( IVs ) in the dataset . Objectives : This article aims to produce a covariate adjustment method that allows for automatic variable selection , so that practitioners need not commit to any specific set of covariates prior to seeing the data . Results : In this article , we propose the “ leave-one-out potential outcomes ” estimator . Nevertheless , gait is sensitive to any changes . Up to date in the area of feature extraction , image feature representation method based on the spatial gradient is still lacking in efficiency especially for the covariate case like carrying bag and wearing a coat . Although the use of Histogram of orientation Gradient ( HOG ) in pedestrian detection is the most effective method , its accuracy is still considered low after testing on covariate dataset . We compute acquisition functions using the inference result from anyprobabilistic program or model framework , and perform efficient inference in sequential settings . We also describe the benefits of combining the above methods , present methodology for applying the embarrassingly parallel procedures when the number of machines is dynamic or unknown at inference time , illustrate how these methods can be applied for spatiotemporal analysis and in covariate dependent models , show ways tooptimize these methods by incorporating test-functions of interest , and demonstrate how these methods can be implemented in probabilistic programming frameworksfor automatic deployment . New methodological research offer strategies for studying and sometimes improving external validity in RDDs . This article examines four techniques : comparative RDD , covariate matching RDD , treatment effect derivatives , and statistical tests for local selection bias . The goal of the article is to help evaluators understand the logic , assumptions , data requirements , and reach of the new methods . The authors describe covariate and noncovariate bridging methods and evaluate their efficacy . The authors conceptualized covariate procedures as finite mixture models that researchers may use in either single or multiple imputation . A simulation study of single-imputation bridging indicated that the use of covariates notably improves the efficacy of bridging . In this paper , we assume that the outcome is recorded as current status failure time data . Despite well-developed literature the routine practical use of many current status data modeling methods remains infrequent due to the lack of specialized statistical software , the difficulty to assess model goodness-of-fit , as well as the possible loss of information caused by covariate grouping or discretization . We propose a model based on pseudo-observations that is convenient to implement and that allows for flexibility in the choice of the outcome . Results The search revealed 217 relevant articles . Seven methodological frameworks were identified : time-dependent covariate modelling , generalised estimating equations , landmark analysis , two-stage modelling , joint-modelling , trajectory classification and machine learning . Each of these frameworks satisfies at least one of three aims : to better represent the predictor-outcome relationship over time , to infer a covariate value at a pre-specified time and to account for the effect of covariate change . MOTIVATION Using gene expression data to classify ( or predict ) tumor types has received much research attention recently . Due to some special features of gene expression data , several new methods have been proposed , including the weighted voting scheme of Golub et al. , the compound covariate method of Hedenfalk et al. ( originally proposed by Tukey ) , and the shrunken centroids method of Tibshirani et al. These methods look different and are more or less ad hoc . RESULTS We call these subjects ' cured ' , and we consider the case where the data are modeled using a Cox proportional hazards ( CPH ) mixture cure model . We study covariate imputation approaches using fully conditional specification . We derive the exact conditional distribution and suggest a sampling scheme for imputing partially observed covariates in the CPH cure model setting . subsection : Batch Normalization Review Batch Normalization has been introduced in as an effective tool to reduce internal covariate shift in deep networks and accelerate the training process . Ever since , BN plays a key role in most modern deep learning architectures . Note that most of these approaches assume some form of ignorability . One of the most widely used approaches to estimating ATE is covariate adjustment , also known as back - door adjustment or the G - computation formula . In its basic version , covariate adjustment amounts to estimating the functions , . Therefore , covariate adjustment methods are the most natural candidates for estimating ITE as well as ATE , using the estimates of . However , most previous work on this subject focused on asymptotic consistency , and so far there has not been much work on the generalization - error of such a procedure . subsection : Batch Normalization Batch normalization has been recently proposed in the machine learning community and addresses the so - called internal covariate shift problem by normalizing the mean and the variance of each layer ’s pre - activations for each training mini - batch . Several works have already shown that this technique is effective both to improve the system performance and to speed - up the training procedure . That is , we want to make the distributions and to be similar . Under the covariate shift assumption , this would make the label prediction accuracy on the target domain to be the same as on the source domain . Measuring the dissimilarity of the distributions and is however non - trivial , given that is high - dimensional , and that the distributions themselves are constantly changing as learning progresses . I would also like to thank Professor Keith Stenning of the Human Communications Research Centre at Edinburgh , and Alan Ainsworth , formerly of British Airways PLC , now at Imperial College Planning Applications Research Centre . I should like to express my gratitude to Keith Rapley ( now Innovation Manager ) at British Airways for arranging sponsorship of this work , to Mark Raskino and Neil Morrison for early stimulation of ideas concerning neural networks ; and to Dr Maurice Barr , Louis Busuttil , Paul Summerbell , Dr Himadri Chatterjee and Rupert Blackley for making possible the forecasting and attribute work . Thanks are due to Garry Copeland , Steve Mott , David Barnes and Rick Magaldi for open and helpful support on the engine condition monitoring projects . AttKGCN integrates both attribute prediction and Re-ID learning together in a unified end-to-end framework which can boost their performances , respectively . AttKGCN first builds a directed attribute KG whose nodes denote attributes and edges encode the co-occurrence relationships of different attributes . Then , AttKGCN learns a set of inter-dependent attribute classifiers which are combined with person visual descriptors for attribute prediction . Taking the expert system toolbox C-ADVISOR3.0 as the development platform , a scheme evaluation and Decision-making system of intelligent scheme design for large span structures is developed . The multi-level multiple factor fuzzy comprehensive evaluation and fuzzy multiple attribute decision making procedure based on the similar approach degree of large-span spatial structural scheme design is realized . The application example shows that the system can be efficiently applied in the evaluation and the decision-making procession of real structures . According to the characteristics of operation between matrixes , the computation was simplified by introducing only the concept of zero value . This method was proved to be equal to attribute reduction by discernable matrix . At last , the method was applied to complete decision table . ABSTRACT A general approach to building an effective justification process around a multi — attribute decision model is suggested which incorporates factory performance measurements to focus on difficult to quantify strategic and tactical benefits from Computer Integrated Manufacturing . This approach differs from previous work in that : attributes are rationally designed performance measures which form the basis for a formal feedback system , and attributes are selected to represent the spectrum of benefits known to be relevant to CIM systems . It is widely used in signal processing , statistics , data compression and other fields . The paper introduces a SVD method to reduce dimension of original dataset and makes use of the attribute of LSA technique to combine SVD method with LSA technique , and then presents new methods for dual private protection data mining . Finally we conduct experiments to test and verify the proposed approach and get good results . In-line with the basic objective and viewpoint of the smart power grids , transmission line fault pattern recognition and fault clearance must be done out more intelligently , judiciously and automatically , with less operator intrusion . The present paper proposes an organized and smart fault classifier with a combination of reliable preprocessing technique for key attribute selection from the power system recorded waveforms and employs a dependable decision tree based classification algorithm to acquire fault detection precision , even when the power network under consideration is large . PSCAD/EMTDC has been used for conducting a case study on simulated IEEE 30-bus system . An efficient adaptive plane segmentation algorithm is employed to extract planar patches which is based on voxel growing . A novel projected-image-based method is utilized for fast plane attribute calculation . The attributes of individual planes and relation constrains between the planes are employed to robustly build plane correspondences . The algorithms are efficient in the sense that their worst-case running time is proportional to the cost of computing the fixed points of only those strongly connected components containing affected attributes or attributes directly dependent on affected attributes . When the attribute grammar is noncircular or the specific dependency graph under consideration is acyclic , both algorithms reduce to the standard optimal algorithms for noncircular attribute evaluation . Then , incremental attribute reduction algorithms for incomplete decision systems when adding multiple objects and when deleting multiple objects are proposed respectively . Finally , comparative experiments on different real-life data sets are conducted to demonstrate the effectiveness and efficiency of the proposed incremental algorithms for updating attribute reducts with the variation of multiple objects in incomplete decision systems . The contributions of this paper are two - fold . First , we propose a fully trainable attribute - based neural network founded upon the CNN + RNN architecture , that can be applied to multiple V2L problems . We do this by inserting an explicit representation of attributes of the scene which are meaningful to humans . The drawback is that they still rely on hard - coded visual concepts and suffer the implied limits on the variety of the output . Fang et al . won the 2015 COCO Captioning Challenge with an approach that is similar to ours in as much as it applies a visual concept ( i.e. , attribute ) detection process before generating sentences . They first learned independent detectors for visual words based on a multi - instance learning framework and then used a maximum entropy language model conditioned on the set of visually detected words directly to generate captions . Finally , a cross hypothesis max - pooling is applied to integrate the outputs into a single prediction vector . Since we formulate the attribute prediction as a multi - label problem , our attributes prediction network can be replaced by any other multi - label classification framework and it also can be benefit from the development of the multi - label classification researches . For example , to address the computational inefficiency of using a large numbers of proposed regions , we can apply an ‘ R - CNN ’ architecture so that we do not need to compute the convolutional feature map multiple times . We first tested several aspects of our models on the validation set ( we randomly choose 5000 images from the validation set as our val set , with the remainder testing ) . Inspecting Table [ reference ] , results the on VQA validation set , we see that the attribute - based Att - LSTM is a significant improvement over our VggNet + LSTM baseline . We also evaluate another baseline , the VggNet + ft + LSTM , which uses the penultimate layer of the attributes prediction CNN ( in Section [ reference ] ) as the input to the LSTM . Instead of adopting any pre - trained CNNs , we suggest to define the perceptual loss on the attribute discriminator , which can be adaptively trained along with the learning procedure , and is named as adaptive perceptual loss . Compared with conventional perceptual loss , ours is more effective in computation , tailored to our specific attribute transfer task , and can serve as a kind of hidden - layer supervision or regularization to ease the training of the DIAT model . To further encourage the identity keeping property , we add an identity loss by minimizing the distance between feature representations of the generated face and the reference . Perarnau et al . adopt an encoder - decoder architecture , where attribute transfer can be conducted by editing the latent representation . Shen et al . learn the residual image in the GAN framework , and adopt dual learning to learn two reverse attribute transfer models simultaneously . Zhou et al . propose a model to learn object transfiguration from two sets of unpaired images that have the opposite attribute . In the following , we provide more details on these losses and regularization terms . Adversarial attribute loss . Adversarial strategy is a common stratey that is widely used in security problems . For the threshold in the perceptual regularization , we set it to be a value in the range of . As for in the attribute ration regularization , we set it to be ( i ) for small local attributes ( e . g . , mouth ) , ( ii ) for large local attributes ( e . g . , eyes ) , and ( iii ) for global attributes ( e . g . , gender and age ) . subsection : Local attribute transfer Attribute learning is an active topic . There have been works on first learning attribute classifiers and using attribute predictions for face recognition . What we have tried in this paper is the inverse , by first predicting the identities , and then using the learned identity - related features to predict attributes . Furthermore , GHM - C has one more merit that Focal loss ignores : down - weighting the gradient contribution of outliers . With our GHM - C loss , the huge number of very easy examples are largely down - weighted and the outliers are slightly down - weighted as well , which simultaneously addresses the attribute imbalance problem and the outliers problem . From the right figure in Fig .
We then build a further system , based on ensembles of two-layered bidirectional recurrent neural networks , to map structural motif predictions into a traditional 3-class ( helix , strand , coil ) secondary structure . This system achieves 79.5 % correct prediction using the " hard " CASP 3-class assignment , and 81.4 % with a more lenient assignment , outperforming a sophisticated state-of-the-art predictor ( Porter ) trained in the same experimental conditions . The structural motif predictor is publicly available at : http://distill.ucd.ie/porter+/.
The paper presents an extension of the modified Smith predictor controller referred to as the flexible Smith predictor control scheme . The tuning procedure is based on the multiple integration method and requires only the process open-loop step response to calculate the parameters of the flexible Smith predictor .
In this paper , we present two novel profile-guided static prediction techniques— Static Correlation Choice ( SCC ) prediction and Static Choice ( SC ) prediction for alleviating the energy consumption without compromising performance . Using our techniques , the hardware choice predictor of a hybrid predictor can be completely eliminated from the processor and replaced with our off-line profiling schemes . Our simulation results show an average 40 % power reduction compared to several hybrid predictors .
Also , it is known that , for neural networks ( NNs ) , one can improve the generalization performance by applying random perturbations to each input in order to generate artificial input points and encouraging the model to assign similar outputs to the set of artificial inputs derived from the same point [ reference ] . Several studies have also confirmed that this philosophy of making the predictor robust against random and local perturbation is effective in semi - supervised learning . For selected examples , see [ reference ] , [ reference ] , [ reference ] , [ reference ] , [ reference ] .
Finally , we evaluate domain - adversarial descriptor learning in the context of person re - identification application [ reference ] , where the task is to obtain good pedestrian image descriptors that are suitable for retrieval and verification . We apply domainadversarial learning , as we consider a descriptor predictor trained with a Siamese - like loss instead of the label predictor trained with a classification loss . In a series of experiments , we demonstrate that domain - adversarial learning can improve cross - data - set re - identification considerably .
This improves over a similar approach of [ reference ] that simply trains a single deep autoencoder for both domains . In both approaches , the actual classifier / predictor is learned in a separate step using the feature representation learned by autoencoder ( s ) . In contrast to [ reference ][ reference ] , our approach performs feature learning , domain adaptation and classifier learning jointly , in a unified architecture , and using a single learning algorithm ( backpropagation ) .
To introduce our network progressively , we start from a simple version and gradually add components . As in Fig 2 ( top ) , our network has an encoder stage and a predictor stage . The encoder maps the input pair of an image
We further improve the design of the predictor branch to better accommodate large and smooth surfaces which are common in natural objects . The fully connected predictor as above can not make full use of such natural geometric statistics , since each point is predicted independently . The improved predictor in Fig 2 ( middle ) exploits this geometric smoothness property .
Instead , we learn a model of the task dynamics that can be used to assess the novelty of a new state . Formally , let denote the encoding of the state , and let be a dynamics predictor parameterized by . takes an encoded version of a state at time and the agent ’s action at time and attempts to predict an encoded version of the agent
As shown in Fig . [ reference ] , the model is composed of the following four modules : an encoder stack , a decoder stack , a newly added fertility predictor ( details in [ reference ] ) , and a translation predictor for token decoding . subsection : Encoder Stack
These feature layers provide high - quality deep features of different granularity ( conv4_3 the finest and conv11 the coarsest ) . A convolutional predictor with kernels is added to each of the 6 layers to detect segments and links . We index the feature layers and the predictors by .
SVM is able to identify the separated hyper plane that maximizes the margin between two different classes . However , SVM has a weakness for parameter selection or suitable feature . In this research , the researchers made an improvement toward the previous research using combined method of feature selection in SVM through comparing three-feature selection ; Genetic Algorithm , Particle Swarm Optimization , and Principal Component Analysis .
Later on , it activates the remaining system modules like webcam , communication block , etc . The event-driven feature improves the system performance in terms of resources utilization and power consumption compared to the counter classical ones . A firstsystem implementation is realized and successfully tested .
However , the quality of the learned features is not sufficiently good , so a complex back-end model , either neural or probabilistic , has to be used to address the residual uncertainty when applied to speaker verification , just as with raw features . This paper presents a convolutional time-delay deep neural network structure ( CT-DNN ) for speaker feature learning . Our experimental results on the Fisher database demonstrated that this CT-DNN can produce high-quality speaker features : even with a single feature ( 0.3 seconds including the context ) , the EER can be as low as 7.68 % .
Key frame can dramatically reduce the data of the video indexing , and provide a framework for video abstraction and retrieval . In this paper , at first briefly discuss the existing approaches , and then present a motion feature based key frame extracting method using fuzzy reasoning theory for MPEG compressed video . By performing minimal decoding on compressed bit stream , computational complexity is reduced , and the processing speed can be highly improved .
In this paper , depth estimation and semantic segmentation are addressed together from a single input image through a hybrid convolutional network . Different from the state of the art methods where features are extracted by a sole feature extraction network for both tasks , the proposed HybridNet improves the features extraction by separating the relevant features for one task from those which are relevant for both . Experimental results demonstrate that HybridNet results are comparable with the state of the art methods , as well as the single task methods that HybridNet is based on .
Adversarial STN . In our Adversarial Spatial Transformer Network , we focus on feature map rotations . That is , given a feature map after the RoI - pooling layer as input , our ASTN will learn to rotate the feature map to make it harder to recognize .
Any supervised machine learning algorithm requires a set of informative , discriminating , and independent features . In prediction problems on networks this means that one has to construct a feature vector representation for the nodes and edges . A typical solution involves hand - engineering domain - specific features based on expert knowledge .
reference ][ reference ][ reference ][ reference ][ reference ] . For video face recognition , most of these methods either use pairwise frame feature similarity computation [ reference ][ reference ] or naive ( average / max ) frame feature pooling [ reference ][ reference ][ reference ] . This motivated us to seek for an adaptive aggregation approach .
Adding larger scales for PASCAL only results in marginal improvement but significantly increases the computation . The feature extrapolating layer extrapolates 4 scales with equal intervals between every two input scales , so the final conv feature pyramid has 21 scales for KITTI and 16 scales for PASCAL . In the RoI generating layer , each location in a heat map generates 7 boxes with 7 different aspect ratios for KITTI and 5 aspect ratios for PASCAL , where each number indicates the ratio between the height and the width of the bounding box .
subsection : Locality Sensitive Hashing ( LSH ) An alternative approach to BASS and DISCO is to use well - established feature generation methods that are agnostic about the type of input they receive . Such methods include polynomial bases , sparse distributed memories and locality sensitive hashing ( LSH ) .
subsection : Multi - scale feature maps The main goal of constructing multi - scale feature maps is to add more context without increasing the computational cost . Figure [ reference ] illustrates the concept of multi - scale feature maps , where additional input feature maps contain a larger view of the context of the frame by downsampling larger context windows with different strides .
subsection : Unimodal Feature Extraction For a fair comparison with the state - of - the - art method , conversational memory networks ( CMN ) , we follow identical feature extraction procedures . subsubsection : Textual Feature Extraction
Such representations can be adapted to image classification by fine - tuning , or extracted as holistic features for classification with linear SVMs . When used as generic feature extractors , feature aggregation techniques designed for hand - crafted features can also work with CNN embeddings and achieve competitive performance . An alternative approach for object classification is via detection .
Note that we could also consider a feature - level method which discriminates between the features or semantics from two image sets as viewed under a task network . This would amount to an additional feature level GAN loss ( see Figure [ reference ] orange portion ) : Taken together , these loss functions form our complete objective : This ultimately corresponds to solving for a target model according to the optimization problem We have introduced a method for unsupervised adaptation which generalizes adversarial objectives to be viewed as operating at the pixel or feature level . In addition , we introduce the use of cycle - consistency together with semantic transformation constraints to guide the mapping from one domain to another .
Analysis of data from recent experiments leads to the observation that the distributions of peak signal levels and durations of underdense meteor trails differ from classic predictions . The authors show that if the trail arrival process is modeled by three independent random variables and the decay factor is modeled as a random process , the observed distribution functions for the amplitude and duration of underdense meteor trails can be more accurately synthesized . They conclude that the present findings have important ramifications in applications where it is desired to simulate more accurately the arrival process of meteor trails in order to predict the performance of meteor communication systems.<<ETX > >
Abroad learners try multi-domain methods to study risk forecast [ 1 - 6 ] . Domestic learners establish financial risk forecasting system taking ST ( special treatment for abnormal situation including finance and others ) companies in domestic stock market as studying objects , adopting single variable analysis , Multiple Linear Regression Discriminant Model and multielement logical aggressive model [ 7 - 13 ] . But the current research has the following problems :  A serial of mathematical statistics is taken before model establishing , but this affects the effectiveness .
For illustration , the collected copies are 100 enterprises , in which 97 copies are valid . The empirical results show that both the confirmation data of the independent variable system and the confirmation data of the dependent variable have the satisfying reliability . The organization support and the leadership support are first essential factors of affecting ERP implementation success in Chinese Enterprises .
Accurate modeling of complex regional environments is achieved with parsimonious and interpretable models . While we ensure the causal variables of the previous study are considered for inclusion in the models developed , this paper focuses on the ability to accurately predict conflict over individual independent variable analysis .
In order to better understand the effectiveness of AV technology , we present a systematic meta-study of 24 experimental studies . We pursue two separate analyses : an analysis ofindependent variables , in which we tie each study to a particular guiding learning theory in an attempt to determine which guiding theory has had the most predictive success ; and an analysis of dependent variables , which enables us to determine which measurement techniques have been most sensitive to the learning benefits of AV technology . Our most significant finding is that how students use AV technology has a greater impact on effectiveness than what AV technology shows them .
This is a descriptive research with samples of undergraduate students of Unsoed as many as 334 students among 7,376 students . Thus , one variable analysis is used in order to see the value of the central tendency and then tested by using the rate value . The results showed that students ’ information literacy skills measuring by the standard information literacy competency standards for higher education from ACRL are in the skilled category .
The analysis of software defect data is important for software quality assurance , project management and processes improvement.,but the data analysis functions of current software defect management tools are weak . The data types of software defect attributes are discussed , and then the basic analysis methods of software defect data , including one-variable data analysis and multiple-variable data analysis , are proposed . Moreover , several key technologies for implementing the methods of software defect data analysis are discussed .
In this chapter we describe some of the available biasing methods and also those that are commonly used in applications . The single random variable case is treated here . The development is carried out by means of several illustrative examples .
3 . Independent variable selection Generally in a classification problem , the variable that is to be predicted is known as the dependent variable ( transportation mode in our case ) because its value depends upon , or is decided by , the values of all the other attributes . The other attributes that help to predict the value of the dependent variable , are known as the independent variables ( IVs ) in the dataset .
Objectives : This article aims to produce a covariate adjustment method that allows for automatic variable selection , so that practitioners need not commit to any specific set of covariates prior to seeing the data . Results : In this article , we propose the “ leave-one-out potential outcomes ” estimator .
Nevertheless , gait is sensitive to any changes . Up to date in the area of feature extraction , image feature representation method based on the spatial gradient is still lacking in efficiency especially for the covariate case like carrying bag and wearing a coat . Although the use of Histogram of orientation Gradient ( HOG ) in pedestrian detection is the most effective method , its accuracy is still considered low after testing on covariate dataset .
We compute acquisition functions using the inference result from anyprobabilistic program or model framework , and perform efficient inference in sequential settings . We also describe the benefits of combining the above methods , present methodology for applying the embarrassingly parallel procedures when the number of machines is dynamic or unknown at inference time , illustrate how these methods can be applied for spatiotemporal analysis and in covariate dependent models , show ways tooptimize these methods by incorporating test-functions of interest , and demonstrate how these methods can be implemented in probabilistic programming frameworksfor automatic deployment .
New methodological research offer strategies for studying and sometimes improving external validity in RDDs . This article examines four techniques : comparative RDD , covariate matching RDD , treatment effect derivatives , and statistical tests for local selection bias . The goal of the article is to help evaluators understand the logic , assumptions , data requirements , and reach of the new methods .
The authors describe covariate and noncovariate bridging methods and evaluate their efficacy . The authors conceptualized covariate procedures as finite mixture models that researchers may use in either single or multiple imputation . A simulation study of single-imputation bridging indicated that the use of covariates notably improves the efficacy of bridging .
In this paper , we assume that the outcome is recorded as current status failure time data . Despite well-developed literature the routine practical use of many current status data modeling methods remains infrequent due to the lack of specialized statistical software , the difficulty to assess model goodness-of-fit , as well as the possible loss of information caused by covariate grouping or discretization . We propose a model based on pseudo-observations that is convenient to implement and that allows for flexibility in the choice of the outcome .
Results The search revealed 217 relevant articles . Seven methodological frameworks were identified : time-dependent covariate modelling , generalised estimating equations , landmark analysis , two-stage modelling , joint-modelling , trajectory classification and machine learning . Each of these frameworks satisfies at least one of three aims : to better represent the predictor-outcome relationship over time , to infer a covariate value at a pre-specified time and to account for the effect of covariate change .
MOTIVATION Using gene expression data to classify ( or predict ) tumor types has received much research attention recently . Due to some special features of gene expression data , several new methods have been proposed , including the weighted voting scheme of Golub et al. , the compound covariate method of Hedenfalk et al. ( originally proposed by Tukey ) , and the shrunken centroids method of Tibshirani et al. These methods look different and are more or less ad hoc . RESULTS
We call these subjects ' cured ' , and we consider the case where the data are modeled using a Cox proportional hazards ( CPH ) mixture cure model . We study covariate imputation approaches using fully conditional specification . We derive the exact conditional distribution and suggest a sampling scheme for imputing partially observed covariates in the CPH cure model setting .
subsection : Batch Normalization Review Batch Normalization has been introduced in as an effective tool to reduce internal covariate shift in deep networks and accelerate the training process . Ever since , BN plays a key role in most modern deep learning architectures .
Note that most of these approaches assume some form of ignorability . One of the most widely used approaches to estimating ATE is covariate adjustment , also known as back - door adjustment or the G - computation formula . In its basic version , covariate adjustment amounts to estimating the functions , . Therefore , covariate adjustment methods are the most natural candidates for estimating ITE as well as ATE , using the estimates of . However , most previous work on this subject focused on asymptotic consistency , and so far there has not been much work on the generalization - error of such a procedure .
subsection : Batch Normalization Batch normalization has been recently proposed in the machine learning community and addresses the so - called internal covariate shift problem by normalizing the mean and the variance of each layer ’s pre - activations for each training mini - batch . Several works have already shown that this technique is effective both to improve the system performance and to speed - up the training procedure .
That is , we want to make the distributions and to be similar . Under the covariate shift assumption , this would make the label prediction accuracy on the target domain to be the same as on the source domain . Measuring the dissimilarity of the distributions and is however non - trivial , given that is high - dimensional , and that the distributions themselves are constantly changing as learning progresses .
I would also like to thank Professor Keith Stenning of the Human Communications Research Centre at Edinburgh , and Alan Ainsworth , formerly of British Airways PLC , now at Imperial College Planning Applications Research Centre . I should like to express my gratitude to Keith Rapley ( now Innovation Manager ) at British Airways for arranging sponsorship of this work , to Mark Raskino and Neil Morrison for early stimulation of ideas concerning neural networks ; and to Dr Maurice Barr , Louis Busuttil , Paul Summerbell , Dr Himadri Chatterjee and Rupert Blackley for making possible the forecasting and attribute work . Thanks are due to Garry Copeland , Steve Mott , David Barnes and Rick Magaldi for open and helpful support on the engine condition monitoring projects .
AttKGCN integrates both attribute prediction and Re-ID learning together in a unified end-to-end framework which can boost their performances , respectively . AttKGCN first builds a directed attribute KG whose nodes denote attributes and edges encode the co-occurrence relationships of different attributes . Then , AttKGCN learns a set of inter-dependent attribute classifiers which are combined with person visual descriptors for attribute prediction .
Taking the expert system toolbox C-ADVISOR3.0 as the development platform , a scheme evaluation and Decision-making system of intelligent scheme design for large span structures is developed . The multi-level multiple factor fuzzy comprehensive evaluation and fuzzy multiple attribute decision making procedure based on the similar approach degree of large-span spatial structural scheme design is realized . The application example shows that the system can be efficiently applied in the evaluation and the decision-making procession of real structures .
According to the characteristics of operation between matrixes , the computation was simplified by introducing only the concept of zero value . This method was proved to be equal to attribute reduction by discernable matrix . At last , the method was applied to complete decision table .
ABSTRACT A general approach to building an effective justification process around a multi — attribute decision model is suggested which incorporates factory performance measurements to focus on difficult to quantify strategic and tactical benefits from Computer Integrated Manufacturing . This approach differs from previous work in that : attributes are rationally designed performance measures which form the basis for a formal feedback system , and attributes are selected to represent the spectrum of benefits known to be relevant to CIM systems .
It is widely used in signal processing , statistics , data compression and other fields . The paper introduces a SVD method to reduce dimension of original dataset and makes use of the attribute of LSA technique to combine SVD method with LSA technique , and then presents new methods for dual private protection data mining . Finally we conduct experiments to test and verify the proposed approach and get good results .
In-line with the basic objective and viewpoint of the smart power grids , transmission line fault pattern recognition and fault clearance must be done out more intelligently , judiciously and automatically , with less operator intrusion . The present paper proposes an organized and smart fault classifier with a combination of reliable preprocessing technique for key attribute selection from the power system recorded waveforms and employs a dependable decision tree based classification algorithm to acquire fault detection precision , even when the power network under consideration is large . PSCAD/EMTDC has been used for conducting a case study on simulated IEEE 30-bus system .
An efficient adaptive plane segmentation algorithm is employed to extract planar patches which is based on voxel growing . A novel projected-image-based method is utilized for fast plane attribute calculation . The attributes of individual planes and relation constrains between the planes are employed to robustly build plane correspondences .
The algorithms are efficient in the sense that their worst-case running time is proportional to the cost of computing the fixed points of only those strongly connected components containing affected attributes or attributes directly dependent on affected attributes . When the attribute grammar is noncircular or the specific dependency graph under consideration is acyclic , both algorithms reduce to the standard optimal algorithms for noncircular attribute evaluation .
Then , incremental attribute reduction algorithms for incomplete decision systems when adding multiple objects and when deleting multiple objects are proposed respectively . Finally , comparative experiments on different real-life data sets are conducted to demonstrate the effectiveness and efficiency of the proposed incremental algorithms for updating attribute reducts with the variation of multiple objects in incomplete decision systems .
The contributions of this paper are two - fold . First , we propose a fully trainable attribute - based neural network founded upon the CNN + RNN architecture , that can be applied to multiple V2L problems . We do this by inserting an explicit representation of attributes of the scene which are meaningful to humans .
The drawback is that they still rely on hard - coded visual concepts and suffer the implied limits on the variety of the output . Fang et al . won the 2015 COCO Captioning Challenge with an approach that is similar to ours in as much as it applies a visual concept ( i.e. , attribute ) detection process before generating sentences . They first learned independent detectors for visual words based on a multi - instance learning framework and then used a maximum entropy language model conditioned on the set of visually detected words directly to generate captions .
Finally , a cross hypothesis max - pooling is applied to integrate the outputs into a single prediction vector . Since we formulate the attribute prediction as a multi - label problem , our attributes prediction network can be replaced by any other multi - label classification framework and it also can be benefit from the development of the multi - label classification researches . For example , to address the computational inefficiency of using a large numbers of proposed regions , we can apply an ‘ R - CNN ’ architecture so that we do not need to compute the convolutional feature map multiple times .
We first tested several aspects of our models on the validation set ( we randomly choose 5000 images from the validation set as our val set , with the remainder testing ) . Inspecting Table [ reference ] , results the on VQA validation set , we see that the attribute - based Att - LSTM is a significant improvement over our VggNet + LSTM baseline . We also evaluate another baseline , the VggNet + ft + LSTM , which uses the penultimate layer of the attributes prediction CNN ( in Section [ reference ] ) as the input to the LSTM .
Instead of adopting any pre - trained CNNs , we suggest to define the perceptual loss on the attribute discriminator , which can be adaptively trained along with the learning procedure , and is named as adaptive perceptual loss . Compared with conventional perceptual loss , ours is more effective in computation , tailored to our specific attribute transfer task , and can serve as a kind of hidden - layer supervision or regularization to ease the training of the DIAT model . To further encourage the identity keeping property , we add an identity loss by minimizing the distance between feature representations of the generated face and the reference .
Perarnau et al . adopt an encoder - decoder architecture , where attribute transfer can be conducted by editing the latent representation . Shen et al . learn the residual image in the GAN framework , and adopt dual learning to learn two reverse attribute transfer models simultaneously . Zhou et al . propose a model to learn object transfiguration from two sets of unpaired images that have the opposite attribute .
In the following , we provide more details on these losses and regularization terms . Adversarial attribute loss . Adversarial strategy is a common stratey that is widely used in security problems .
For the threshold in the perceptual regularization , we set it to be a value in the range of . As for in the attribute ration regularization , we set it to be ( i ) for small local attributes ( e . g . , mouth ) , ( ii ) for large local attributes ( e . g . , eyes ) , and ( iii ) for global attributes ( e . g . , gender and age ) . subsection : Local attribute transfer
Attribute learning is an active topic . There have been works on first learning attribute classifiers and using attribute predictions for face recognition . What we have tried in this paper is the inverse , by first predicting the identities , and then using the learned identity - related features to predict attributes .
Furthermore , GHM - C has one more merit that Focal loss ignores : down - weighting the gradient contribution of outliers . With our GHM - C loss , the huge number of very easy examples are largely down - weighted and the outliers are slightly down - weighted as well , which simultaneously addresses the attribute imbalance problem and the outliers problem . From the right figure in Fig .
Protein Structural Motif Prediction in Multidimensional ø-Psi Space Leads to Improved Secondary Structure Prediction Automatic tuning of the flexible Smith predictor controller Choice Predictor for Free Virtual Adversarial Training: A Regularization Method for Supervised and Semi-Supervised Learning Domain-Adversarial Training of Neural Networks A Point Set Generation Network for 3D Object Reconstruction from a Single Image Incentivizing Exploration In Reinforcement Learning With Deep Predictive Models Non-Autoregressive Neural Machine Translation Detecting Oriented Text in Natural Images by Linking Segments Feature selection based on Genetic algorithm, particle swarm optimization and principal component analysis for opinion mining cosmetic product review An Event Driven Attendance Tracker Deep Speaker Feature Learning for Text-Independent Speaker Verification Rapid method of key frame extraction for MPEG compressed video Hybridnet for Depth Estimation and Semantic Segmentation A-Fast-RCNN: Hard Positive Generation via Adversary for Object Detection node2vec: Scalable Feature Learning for Networks Neural Aggregation Network for Video Face Recognition Subcategory-Aware Convolutional Neural Networks for Object Proposals and Detection The Arcade Learning Environment: An Evaluation Platform for General Agents (Extended Abstract) Very deep multilingual convolutional neural networks for LVCSR DialogueRNN: An Attentive RNN for Emotion Detection in Conversations ProNet: Learning to Propose Object-Specific Boxes for Cascaded Neural Networks CyCADA: Cycle-Consistent Adversarial Domain Adaptation Some observations on the distributions of amplitude and duration of underdense meteor trails and its application to design of meteor scatter protocols Application of Optimized Combination Method to Financial Risk Forecasting for Listed Companies The Empirical Analysis on the Successful Factors of Implementing Enterprise Resource Plan System in Chinese Enterprises Predictive models of world conflict: accounting for regional and conflict-state differences A Meta-Study of Algorithm Visualization Effectiveness KETERAMPILAN LITERASI INFORMASI MAHASISWA MENURUT STANDAR KOMPETENSI LITERASI INFORMASI ASSOCIATION OF COLLEGE & RESEARCH LIBRARIES (ACRL) Method of Software Defect Data Analysis and its Implementation Methods of Importance Sampling Inferring hybrid transportation modes from sparse GPS data using a moving window SVM classification The LOOP Estimator: Adjusting for Covariates in Randomized Experiments Gait Recognition based on Inverse Fast Fourier Transform Gaussian and Enhancement Histogram Oriented of Gradient Post-inference Methods for Scalable Probabilistic Modeling and Sequential Decision Making Regression Discontinuity and Beyond On the Use of Covariates in Bridging Single- and Multiple-Race Categories A Flexible Modeling Approach for Current Status Survival Data via Pseudo-Observations Harnessing repeated measurements of predictor variables for clinical risk prediction: a review of existing methods Linear regression and two-class classification with gene expression data Multiple imputation of missing covariates for the Cox proportional hazards cure model. In-place Activated BatchNorm for Memory-Optimized Training of DNNs Estimating individual treatment effect: generalization bounds and algorithms Light Gated Recurrent Units for Speech Recognition Unsupervised Domain Adaptation by Backpropagation The application of neural computation methods to forecasting and monitoring in an airline context AttKGCN: Attribute Knowledge Graph Convolutional Network for Person Re-identification Evaluation and decision system for intelligent scheme design of large-span spatial structures Attribute reduction method based on zero value principle Multi-Attribute Decision Models in the Justification of CIM Systems SVD-Based Factorization Technique for Dual Privacy Protection Data Mining Transmission line fault pattern recognition using decision tree based smart fault classifier in a large power network A Fast and Accurate Planar-Feature-Based Global Scan Registration Method Efficient evaluation of circular attribute grammars Knowledge granularity based incremental attribute reduction for incomplete decision systems Image Captioning and Visual Question Answering Based on Attributes and External Knowledge Deep Identity-aware Transfer of Facial Attributes Deeply learned face representations are sparse, selective, and robust Gradient Harmonized Single-stage Detector
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Asia-Pacific Computer Systems Architecture Conference IEEE Transactions on Pattern Analysis and Machine Intelligence J. Mach. Learn. Res. 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) ArXiv ICLR 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) ICASSP 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) KDD 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2017 IEEE Winter Conference on Applications of Computer Vision (WACV) IJCAI 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) AAAI 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) ICML Comput. Environ. Urban Syst. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition ICML IEEE Transactions on Emerging Topics in Computational Intelligence ICML IEEE Transactions on Pattern Analysis and Machine Intelligence ArXiv 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) AAAI
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