For clinical applications , real-time and freehand manipulation of a probe is required . In a previous study , we developed the combined autocorrelation method ( CAM ) , which produces an elasticity image with high-speed processing and accuracy , and achieves a wide dynamic range for strain estimation . In the current study , we extended the CAM clinical uses to be robust for tissue sideslip and suited to freehand compression . This paper reports on a content addressable memory ( CAM ) employing a multi-Vdd scheme for low power pattern recognition applications . The complete design , simulation and testing of the chip is presented along with an exploration of the multi-Vdd design space . Due to its capability of parallel search , content addressable memory ( CAM ) has been widely used on the applications requiring high-speed data search . In recent years , the architectures and design techniques for CAM have been consistently evolving . Integral coefficients of the proposed evaluation of the effectiveness of development CAD / CAM systems , and computer program for calculation and processing of general results on the study group . The coefficients can quantitatively and objectively evaluate the quality of students ' knowledge on the complexity of performing various design tasks in computer systems and to conduct a comparative analysis of the design . Despite significant advances made in software and machines to CNC , there is the lack of a common infrastructure for information CAD / CAPP / CAM / CNC . This article describes the new standard STEP-NC ( ISO14649 ) , aspects of the development of CAD / CAM systems adhering to STEP-NC and integration of product data models and manufacturing using STEP-NC , STEP and OpenCNC architecture . A performance evaluation of a case study for lattice structure designs was conducted . The criteria used for the evaluation were CAD human-machine-interface , RAM consumption , data exchange between CAD , CAE and CAM tools and finite element analysis ( FEA ) duration and file sizes . The CAD tool was incapable of executing a repetition function for octet-truss lattice structures of 150 x 150 x 150 mm dimensions or larger and the software stopped working . The challenge of this research is to embed the application data model in the computer-aided CE environment which enforces the standard product definition ( STEP ) and provides the communication capabilities between engineering functions . A method to integrate CAE and CAM tools based on PDES/STEP has been developed . This research proposes an object-oriented approach to implement PDES/STEP compatible applications . All components are interfaced to the Control and Monitoring ( CAM ) system via the Karoo Array Telescope Communication Protocol ( KATCP ) . KATCP is used extensively for internal communications between CAM components and other subsystems [ 2 ] . A KATCP interface exposes requests and sensors [ 3 ] . The KATCP protocol is a text based , human-readable protocol build on TCP/IP and supports flexible , run-time configuration by providing interrogation of sensors and requests/commands ; these are used by the clients to discover the configuration of devices dynamically . The KATCP protocol is specified as the CAM interface for all subcontracted and internal hardware devices and subsystems , as well as internal communication between CAM components . In cases where the subcontractor can not deliver a KATCP interface , a Device Translator is Proceedings of ICALEPCS2013 , San Francisco , CA , USA MOCOAAB06 Project Status Reports ISBN 978 - 3 - 95450 - 139 - 7 19 C op yr ig ht c ○ 20 14 C C -B Y3 . To use Topsolid7 the machine should be determined first , second , determining the process , third , determining the process parameter , the last is determining the cutting strategy . In The Topsolid7 CAM package in Mechanical Engineering Departement FTI-ITS there is only one post processor for Fanuc 3 axis control . For other CNC Control , The G Code should be editted manually . In this paper the behavior of the SRL16 CAM design methodologywas described using VHDL and implemented using FPGA technique . Then , the performance of the method was compared to other traditional CAM design methods . The proposed RCAM is configured and used as the main part of different networkdevices and units ( Ethernet Address Filtering Unit , WLAN MAC Address Filtering Unit , Firewall on Chip ( FoC ) , QoS Packet Classification Unit , Routing Table Search Unit and Network Intrusion Detection System(NIDS ) search unit ) . Post-processing can translate the CAM programming operations to the specific CNC machine instructions , the quality of which determines the quality and efficiency of programming[1,2 ] . Post-processing in Edgecam integrates with CAM programming operations to easily generate NC programs for mainstream machine tools and CNC systems , construct kinematics models and geometry models of machine tools , which can simulate part machining in CAM programming and check collision . _ _ _ _ _ _ _ _ _ _ _ _ _ Tiegang Li , Mechanical School , Shenyang Institute of Engineering , 18 Pu Chang Street , Shenyang , Liaoning Province 110136 , P.R.C. Then , in order to optimize projection direction a of PPPR model efficiently , combining the cat mapping function , accelerating evolutionary mechanism and the GA algorithm , the chaotic efficient genetic algorithms ( CEGA ) is proposed for optimizing projection direction a. Finally , a port throughput forecasting approach , adopting CEGA algorithm to optimize the optimal projection direction a in inner layer while optimizing the ridge function number M in outer layer , employing correlation analysis method ( CAM ) method to determine the input variables , namely PPPRCEGA , is proposed . Subsequently , this study compiles numerical experimentation to evaluate the feasibility and performance of the proposed approach . Then , three filters , named blob-like structure enhancement ( BSE ) , line structure enhancement ( LSE ) , and central adaptive medialness ( CAM ) filters , are used for filtering the output of the last step . Afterward , the outputs of BSE , LSE , and CAM filters are used for initial candidate detection and candidate segmentation , respectively . Also , line tracking method is proposed for extending the candidate voxels , and then , it is used for candidate segmentation . Further analysis shows that the baseline learns to correlate the informative words in the question sentence and visual concepts in the image with the answer . Furthermore , such correlations can be used to compute reasonable spatial attention map with the help of the CAM technique proposed in . The source code and the visual QA demo based on the trained model are publicly available . ‘ hot dog ’ . To highlight the informative image regions relevant to the predicted answer we apply a technique called Class Activation Mapping ( CAM ) proposed in . The CAM technique leverages the linear relation between the softmax prediction and the final convolutional feature map , which allows us to identify the most discriminative image regions relevant to the predicted result . Since our technique increases classification accuracy , we wish to understand if the improvement is a result of enhanced CNN localization abilities due to PC . To measure the regions the CNN localizes on , we utilize Gradient - Weighted Class Activation Mapping ( Grad - CAM ) [ reference ] , a method that provides a heatmap of visual saliency as produced by the network . 1 . Introduction This paper describes a network architecture for supervised learning that combines techniques used in neural networks 1,7,8 with symbolic machine learning 3,4,6 to gain advantages of both approaches . In supervised learning the network is given a training set containing examples . Although Inductive Logic Programming ( ILP ) is generally thought of as a research area at the intersection of machine learning and computational logic , Bergadano and Gunetti propose that most of the research in ILP has in fact come from machine learning , particularly in the evolution of inductive reasoning from pattern recognition , through initial approaches to symbolic machine learning , to recent techniques for learning relational concepts . In this book they provide an extended , up-to-date survey of ILP , emphasizing methods and systems suitable for software engineering applications , including inductive program development , testing , and maintenance . In this paper application of symbolic , supervised learning in a multi-agent system is presented . As an environment Fish Bank game is used . It allows to generate a strategy for an agent in a situation , when the environment provides some feedback after the agent has acted . Symbolic , supervised learning is not so widely used in multi-agent systems . There are many methods belonging to this class that generate knowledge from data . Interpretability of the subtasks is critical in hierarchical decision-making as it increases the transparency of black-box-style DRL approach and helps the RL practitioners to understand the high-level behavior of the system better . In this paper , we introduce symbolic planning into DRL and propose a framework of Symbolic Deep Reinforcement Learning ( SDRL ) that can handle both high-dimensional sensory inputs and symbolic planning . The task-level interpretability is enabled by relating symbolic actions to options . Due to the large scale variation of surface defects of different types of strip steel , there are limitations in using threshold segmentation to locate objects , we propose a surface defect detection algorithm combining selective search and class activation mapping ( CAM ) to improve objects localization . First , we use selective search to generate defect bounding box in the image , and predicts the classification and CAM of the defect in the image through the trained model . Then , we selected some of these advances and introduced a new approach that combines multi-scale and multi-layer feature extraction with feature selection . We also propose an approach for local RMAC descriptor extraction based on class activation maps . Our parameter-free approach provides short descriptors and achieves state-of-the-art performance without the need of CNN finetuning or additional data in any way . It measures both positive and negative contributions of each spatial element in the feature maps produced from the last convolution layer leading to correct classification of an input image . A series of experiments on a “ multi-modality ” CNN model designed for classifying seven different types of image modalities shows that the proposed method is significantly better in detecting and localizing the discriminative ROIs than other state of the art class-activation methods . Further , to visualize its effectiveness we generate “ class-specific ” ROI maps by averaging the CRM scores of images in each modality class , and characterize the visual explanation through their different size , shape , and location for our multi-modality CNN model that achieved over 98 % performance on a dataset constructed from publicly available images . Our model uses a one-layer convolutional neural network ( CNN ) with different window size to find data races method . Then we adopt the class activation map function with global average pooling to extract the weights of the last convolutional layer and backpropagate it with the input source code to extract the line of codes with a data race . Thus , the DeepRace model can detect the data race bugs on a file and line of code level . The performance of these methods is highly dependent on the accuracy of body-part detection , which is a well known challenging problem in computer vision . Different from these part-based methods , we propose to recognize human attributes by using CAM ( Class Activation Map ) network and further improve the recognition by refining the attention heat map , which is an intermediate result in CAM and reflects relevant image regions for each attribute . The proposed method does not require the detection of body parts and the prior correspondence between body parts and attributes . In this paper , we propose a simple and effective way to improve one-look regression models for object counting from images . We use class activation map visualizations to illustrate the drawbacks of learning a pure one-look regression model for a counting task . Based on these insights , we enhance one-look regression counting models by regulating activation maps from the final convolution layer of the network with coarse ground-truth activation maps generated from simple dot annotations . In the simulation results , we achieve high performance of 91 % accuracy using the cascaded CNN . We also demonstrate the distinction of our system compared to conventional network using classification activation map for visualizing the region that the network focuses on . A key problem is how to obtain high quality objects location from image-level category . Classification activation mapping is a common method which can be used to generate high-precise object location cues . However these location cues are generally very sparse and small such that they can not provide effective information for image segmentation . When applied to image classification , this relation helps approximate the point-wise mutual information between an input image and a label without modifying the network structure . To this end , we propose infoCAM , informative class activation map , which highlights regions of the input image that are the most relevant to a given label based on differences in information . The activation map helps localise the target object in an input image . ‘ hot dog ’ . To highlight the informative image regions relevant to the predicted answer we apply a technique called Class Activation Mapping ( CAM ) proposed in . The CAM technique leverages the linear relation between the softmax prediction and the final convolutional feature map , which allows us to identify the most discriminative image regions relevant to the predicted result . We evaluate the Res2Net block on all these models and demonstrate consistent performance gains over baseline models on widely - used datasets , e.g. , CIFAR - 100 and ImageNet . Further ablation studies and experimental results on representative computer vision tasks , i.e. , object detection , class activation mapping , and salient object detection , further verify the superiority of the Res2Net over the state - of - the - art baseline methods . The source code and trained models will be made publicly available . As summarized in Table 4 , we observe an average 3.4 % improvement across five different networks , implying better localization accuracy . Change in Class - Activation Mapping : To qualitatively study the improvement in localization due to PC , we obtain samples from the CUB - 200 - 2011 dataset and visualize the localization regions returned from Grad - CAM for both the baseline and PC - trained VGG - 16 model . Abstract : This paper describes design considerations for the implementation of a decision-support system based on a Common Decision Exchange Protocol ( CDEP ) , which is an XML- , REST-based protocol for representing generic human decisions in a simple , interoperable format . The paper also describes characteristics of decisions that should be expressed using CDEP and specifies a proposed XML format . The CDEP will enable war fighters to track and manage the decision-making process better , to enable improved information-flow metrics in networks , to maintain an archive of the decisions and the decision-making process , to enable semi-automation of certain decision-making processes , to improve information sharing across networks , and ultimately , to support better and faster decision making . The CDEP format should provide concise , generic , structured assessments and decisions that enable " drill down , " support pedigree and confidence , enable approvals and vetting , define options considered , link to previous decisions , and support flexible structuring . The format recommended here is a first step toward such a CDEP . Too often , the litany of proposed explainable deep learning methods stop at the first step , providing practitioners with insight into a model , but no way to act on it . In this paper , we propose contextual decomposition explanation penalization ( CDEP ) , a method which enables practitioners to leverage existing explanation methods in order to increase the predictive accuracy of deep learning models . In particular , when shown that a model has incorrectly assigned importance to some features , CDEP enables practitioners to correct these errors by directly regularizing the provided explanations . Despite these efforts , there remain significant challenges to effectively increasing the statistical power and decreasing the Type I error rate while pooling the heterogeneous datasets from public resources . The objective of this study is to develop a novel meta-analysis approach , Consistent Differential Expression Pattern ( CDEP ) , to identify genes with common differential expression patterns across different datasets . ResultsWe combined False Discovery Rate ( FDR ) estimation and the non-parametric RankProd approach to estimate the Type I error rate in each microarray dataset of the meta-analysis . To enable the recording and retrieval of decisions , a conceptual model is presented that can be used as a set of requirements for a data structure for decision storage . The approach conforms partly to the proposed Common Decision Exchange Protocol ( CDEP ) standard , but we extend it to capture decision attributes , decision making stages , decision makers and other collaborators , and the information and tools used . Capturing the linkages between decision elements enables a wide range of organizational decision making processes to be encoded for ontological reasoning . This paper contains a completeness proof for the system ILW , a rather bewildering axiom system belonging to the family of interpretability logics . We have treasured this little proof for a considerable time , keeping it just for ourselves . The provability logic of a theory T is the set of modal formulas , which under any arithmetical realization are provable in T . We slightly modify this notion by requiring the arithmetical realizations to come from a specified set Γ. We make an analogous modification for interpretability logics . This is a paper from 2012 . Moreover , we prove that Z follows from a restricted form of B . Finally , we give an overview of the known relationships of IL ( PRA ) to important other interpretability principles . The relevant modal logic , in addition to the usual provability operator « □ » , has a binary operator « ⊇ » to be interpreted as the formalized interpretability relation . We give an axiomatization and a decision procedure for the class of those modal formulas that express valid interpretability principles Our results demonstrate that psychophysical experiments allow for robust quality assessment of transparency in machine learning . Interestingly the quality metrics computed without humans in the loop did not provide a consistent ranking of interpretability methods nor were they representative for how useful an explanation was for humans . These findings highlight the potential of methods from classical psychophysics for modern machine learning applications . Topic models , like many other unsupervised machine learning methods , requite input from the researchers in the form of parameters used with these models . Through a user study , this article demonstrates that the commonly used interpretability-focused approaches can lead to different outcomes . Also differences emerging from parameter selection are discovered in the outcomes of the analysis . It utilizes an attention mechanism that relates the encoded representations to samples in order to determine prototypes . The resulting model outperforms state of the art in three high impact problems without sacrificing accuracy of the original model : ( 1 ) it enables high-quality interpretability that outputs samples most relevant to the decision-making ( i.e. a sample-based interpretability method ) ; ( 2 ) it achieves state of the art confidence estimation by quantifying the mismatch across prototype labels ; and ( 3 ) it obtains state of the art in distribution mismatch detection . All this can be achieved with minimal additional test time and a practically viable training time computational cost . There exist two contradictory requirements with fuzzy modeling , namely , accuracy and interpretability . In this study , the subtractive clustering and a multi-objective evolutionary algorithm are used to develop a novel fuzzy modeling scheme based on the FDT classifier to construct an accurate and interpretable system that is defined as the interpretability-based fuzzy decision tree classifier . Two interpretability measures , namely , complexity interpretability and semantics interpretability are considered to reach acceptable accuracy . Based On Properties Of Volume Elements -- Discovering of Part-Whole Relations Used in Architectural Prototyping of Project Tasks -- Multi-level ontological model of Big Data processing -- Ontological Modeling for Industrial Enterprise Engineering -- Designing The Knowledge Base For The Intelligent Inertial Regulator Based On Quasi-optimal Synthesis Of Controls Using The Combined Maximum Principle -- Assessing the software developer 's quality using fuzzy estimates -- A Fuzzy Control Method for Priority Driven Embedded Device -- Interpretability of fuzzy temporal models -- Control Of The Cognitive Process In Hard Real-time Environment In The Context Of The Expanded Stepping Theories Of Active Logic -- Method of the Maximum Dynamic Flow Finding in the Fuzzy Graph with Gains -- Hybrid bioinspired algorithm of 1.5 dimensional bin-packing . In response , humans need to be able to determine the “ fitness for use ” of a given model or dataset , and to assess the methodology that was used to produce it . To address this need , we propose to develop interpretability and transparency tools based on the concept of a nutritional label , drawing an analogy to the food industry , where simple , standard labels convey information about the ingredients and production processes . Nutritional labels are derived automatically or semi-automatically as part of the complex process that gave rise to the data or model they describe , embodying the paradigm of interpretability-by-design . Too often , the litany of proposed explainable deep learning methods stop at the first step , providing practitioners with insight into a model , but no way to act on it . In this paper , we propose contextual decomposition explanation penalization ( CDEP ) , a method which enables practitioners to leverage existing explanation methods in order to increase the predictive accuracy of deep learning models . In particular , when shown that a model has incorrectly assigned importance to some features , CDEP enables practitioners to correct these errors by directly regularizing the provided explanations .
For clinical applications , real-time and freehand manipulation of a probe is required . In a previous study , we developed the combined autocorrelation method ( CAM ) , which produces an elasticity image with high-speed processing and accuracy , and achieves a wide dynamic range for strain estimation . In the current study , we extended the CAM clinical uses to be robust for tissue sideslip and suited to freehand compression .
This paper reports on a content addressable memory ( CAM ) employing a multi-Vdd scheme for low power pattern recognition applications . The complete design , simulation and testing of the chip is presented along with an exploration of the multi-Vdd design space .
Due to its capability of parallel search , content addressable memory ( CAM ) has been widely used on the applications requiring high-speed data search . In recent years , the architectures and design techniques for CAM have been consistently evolving .
Integral coefficients of the proposed evaluation of the effectiveness of development CAD / CAM systems , and computer program for calculation and processing of general results on the study group . The coefficients can quantitatively and objectively evaluate the quality of students ' knowledge on the complexity of performing various design tasks in computer systems and to conduct a comparative analysis of the design .
Despite significant advances made in software and machines to CNC , there is the lack of a common infrastructure for information CAD / CAPP / CAM / CNC . This article describes the new standard STEP-NC ( ISO14649 ) , aspects of the development of CAD / CAM systems adhering to STEP-NC and integration of product data models and manufacturing using STEP-NC , STEP and OpenCNC architecture .
A performance evaluation of a case study for lattice structure designs was conducted . The criteria used for the evaluation were CAD human-machine-interface , RAM consumption , data exchange between CAD , CAE and CAM tools and finite element analysis ( FEA ) duration and file sizes . The CAD tool was incapable of executing a repetition function for octet-truss lattice structures of 150 x 150 x 150 mm dimensions or larger and the software stopped working .
The challenge of this research is to embed the application data model in the computer-aided CE environment which enforces the standard product definition ( STEP ) and provides the communication capabilities between engineering functions . A method to integrate CAE and CAM tools based on PDES/STEP has been developed . This research proposes an object-oriented approach to implement PDES/STEP compatible applications .
All components are interfaced to the Control and Monitoring ( CAM ) system via the Karoo Array Telescope Communication Protocol ( KATCP ) . KATCP is used extensively for internal communications between CAM components and other subsystems [ 2 ] . A KATCP interface exposes requests and sensors [ 3 ] .
The KATCP protocol is a text based , human-readable protocol build on TCP/IP and supports flexible , run-time configuration by providing interrogation of sensors and requests/commands ; these are used by the clients to discover the configuration of devices dynamically . The KATCP protocol is specified as the CAM interface for all subcontracted and internal hardware devices and subsystems , as well as internal communication between CAM components . In cases where the subcontractor can not deliver a KATCP interface , a Device Translator is Proceedings of ICALEPCS2013 , San Francisco , CA , USA MOCOAAB06 Project Status Reports ISBN 978 - 3 - 95450 - 139 - 7 19 C op yr ig ht c ○ 20 14 C C -B Y3 .
To use Topsolid7 the machine should be determined first , second , determining the process , third , determining the process parameter , the last is determining the cutting strategy . In The Topsolid7 CAM package in Mechanical Engineering Departement FTI-ITS there is only one post processor for Fanuc 3 axis control . For other CNC Control , The G Code should be editted manually .
In this paper the behavior of the SRL16 CAM design methodologywas described using VHDL and implemented using FPGA technique . Then , the performance of the method was compared to other traditional CAM design methods . The proposed RCAM is configured and used as the main part of different networkdevices and units ( Ethernet Address Filtering Unit , WLAN MAC Address Filtering Unit , Firewall on Chip ( FoC ) , QoS Packet Classification Unit , Routing Table Search Unit and Network Intrusion Detection System(NIDS ) search unit ) .
Post-processing can translate the CAM programming operations to the specific CNC machine instructions , the quality of which determines the quality and efficiency of programming[1,2 ] . Post-processing in Edgecam integrates with CAM programming operations to easily generate NC programs for mainstream machine tools and CNC systems , construct kinematics models and geometry models of machine tools , which can simulate part machining in CAM programming and check collision . _ _ _ _ _ _ _ _ _ _ _ _ _ Tiegang Li , Mechanical School , Shenyang Institute of Engineering , 18 Pu Chang Street , Shenyang , Liaoning Province 110136 , P.R.C.
Then , in order to optimize projection direction a of PPPR model efficiently , combining the cat mapping function , accelerating evolutionary mechanism and the GA algorithm , the chaotic efficient genetic algorithms ( CEGA ) is proposed for optimizing projection direction a. Finally , a port throughput forecasting approach , adopting CEGA algorithm to optimize the optimal projection direction a in inner layer while optimizing the ridge function number M in outer layer , employing correlation analysis method ( CAM ) method to determine the input variables , namely PPPRCEGA , is proposed . Subsequently , this study compiles numerical experimentation to evaluate the feasibility and performance of the proposed approach .
Then , three filters , named blob-like structure enhancement ( BSE ) , line structure enhancement ( LSE ) , and central adaptive medialness ( CAM ) filters , are used for filtering the output of the last step . Afterward , the outputs of BSE , LSE , and CAM filters are used for initial candidate detection and candidate segmentation , respectively . Also , line tracking method is proposed for extending the candidate voxels , and then , it is used for candidate segmentation .
Further analysis shows that the baseline learns to correlate the informative words in the question sentence and visual concepts in the image with the answer . Furthermore , such correlations can be used to compute reasonable spatial attention map with the help of the CAM technique proposed in . The source code and the visual QA demo based on the trained model are publicly available .
‘ hot dog ’ . To highlight the informative image regions relevant to the predicted answer we apply a technique called Class Activation Mapping ( CAM ) proposed in . The CAM technique leverages the linear relation between the softmax prediction and the final convolutional feature map , which allows us to identify the most discriminative image regions relevant to the predicted result .
Since our technique increases classification accuracy , we wish to understand if the improvement is a result of enhanced CNN localization abilities due to PC . To measure the regions the CNN localizes on , we utilize Gradient - Weighted Class Activation Mapping ( Grad - CAM ) [ reference ] , a method that provides a heatmap of visual saliency as produced by the network .
1 . Introduction This paper describes a network architecture for supervised learning that combines techniques used in neural networks 1,7,8 with symbolic machine learning 3,4,6 to gain advantages of both approaches . In supervised learning the network is given a training set containing examples .
Although Inductive Logic Programming ( ILP ) is generally thought of as a research area at the intersection of machine learning and computational logic , Bergadano and Gunetti propose that most of the research in ILP has in fact come from machine learning , particularly in the evolution of inductive reasoning from pattern recognition , through initial approaches to symbolic machine learning , to recent techniques for learning relational concepts . In this book they provide an extended , up-to-date survey of ILP , emphasizing methods and systems suitable for software engineering applications , including inductive program development , testing , and maintenance .
In this paper application of symbolic , supervised learning in a multi-agent system is presented . As an environment Fish Bank game is used .
It allows to generate a strategy for an agent in a situation , when the environment provides some feedback after the agent has acted . Symbolic , supervised learning is not so widely used in multi-agent systems . There are many methods belonging to this class that generate knowledge from data .
Interpretability of the subtasks is critical in hierarchical decision-making as it increases the transparency of black-box-style DRL approach and helps the RL practitioners to understand the high-level behavior of the system better . In this paper , we introduce symbolic planning into DRL and propose a framework of Symbolic Deep Reinforcement Learning ( SDRL ) that can handle both high-dimensional sensory inputs and symbolic planning . The task-level interpretability is enabled by relating symbolic actions to options .
Due to the large scale variation of surface defects of different types of strip steel , there are limitations in using threshold segmentation to locate objects , we propose a surface defect detection algorithm combining selective search and class activation mapping ( CAM ) to improve objects localization . First , we use selective search to generate defect bounding box in the image , and predicts the classification and CAM of the defect in the image through the trained model .
Then , we selected some of these advances and introduced a new approach that combines multi-scale and multi-layer feature extraction with feature selection . We also propose an approach for local RMAC descriptor extraction based on class activation maps . Our parameter-free approach provides short descriptors and achieves state-of-the-art performance without the need of CNN finetuning or additional data in any way .
It measures both positive and negative contributions of each spatial element in the feature maps produced from the last convolution layer leading to correct classification of an input image . A series of experiments on a “ multi-modality ” CNN model designed for classifying seven different types of image modalities shows that the proposed method is significantly better in detecting and localizing the discriminative ROIs than other state of the art class-activation methods . Further , to visualize its effectiveness we generate “ class-specific ” ROI maps by averaging the CRM scores of images in each modality class , and characterize the visual explanation through their different size , shape , and location for our multi-modality CNN model that achieved over 98 % performance on a dataset constructed from publicly available images .
Our model uses a one-layer convolutional neural network ( CNN ) with different window size to find data races method . Then we adopt the class activation map function with global average pooling to extract the weights of the last convolutional layer and backpropagate it with the input source code to extract the line of codes with a data race . Thus , the DeepRace model can detect the data race bugs on a file and line of code level .
The performance of these methods is highly dependent on the accuracy of body-part detection , which is a well known challenging problem in computer vision . Different from these part-based methods , we propose to recognize human attributes by using CAM ( Class Activation Map ) network and further improve the recognition by refining the attention heat map , which is an intermediate result in CAM and reflects relevant image regions for each attribute . The proposed method does not require the detection of body parts and the prior correspondence between body parts and attributes .
In this paper , we propose a simple and effective way to improve one-look regression models for object counting from images . We use class activation map visualizations to illustrate the drawbacks of learning a pure one-look regression model for a counting task . Based on these insights , we enhance one-look regression counting models by regulating activation maps from the final convolution layer of the network with coarse ground-truth activation maps generated from simple dot annotations .
In the simulation results , we achieve high performance of 91 % accuracy using the cascaded CNN . We also demonstrate the distinction of our system compared to conventional network using classification activation map for visualizing the region that the network focuses on .
A key problem is how to obtain high quality objects location from image-level category . Classification activation mapping is a common method which can be used to generate high-precise object location cues . However these location cues are generally very sparse and small such that they can not provide effective information for image segmentation .
When applied to image classification , this relation helps approximate the point-wise mutual information between an input image and a label without modifying the network structure . To this end , we propose infoCAM , informative class activation map , which highlights regions of the input image that are the most relevant to a given label based on differences in information . The activation map helps localise the target object in an input image .
‘ hot dog ’ . To highlight the informative image regions relevant to the predicted answer we apply a technique called Class Activation Mapping ( CAM ) proposed in . The CAM technique leverages the linear relation between the softmax prediction and the final convolutional feature map , which allows us to identify the most discriminative image regions relevant to the predicted result .
We evaluate the Res2Net block on all these models and demonstrate consistent performance gains over baseline models on widely - used datasets , e.g. , CIFAR - 100 and ImageNet . Further ablation studies and experimental results on representative computer vision tasks , i.e. , object detection , class activation mapping , and salient object detection , further verify the superiority of the Res2Net over the state - of - the - art baseline methods . The source code and trained models will be made publicly available .
As summarized in Table 4 , we observe an average 3.4 % improvement across five different networks , implying better localization accuracy . Change in Class - Activation Mapping : To qualitatively study the improvement in localization due to PC , we obtain samples from the CUB - 200 - 2011 dataset and visualize the localization regions returned from Grad - CAM for both the baseline and PC - trained VGG - 16 model .
Abstract : This paper describes design considerations for the implementation of a decision-support system based on a Common Decision Exchange Protocol ( CDEP ) , which is an XML- , REST-based protocol for representing generic human decisions in a simple , interoperable format . The paper also describes characteristics of decisions that should be expressed using CDEP and specifies a proposed XML format . The CDEP will enable war fighters to track and manage the decision-making process better , to enable improved information-flow metrics in networks , to maintain an archive of the decisions and the decision-making process , to enable semi-automation of certain decision-making processes , to improve information sharing across networks , and ultimately , to support better and faster decision making . The CDEP format should provide concise , generic , structured assessments and decisions that enable " drill down , " support pedigree and confidence , enable approvals and vetting , define options considered , link to previous decisions , and support flexible structuring . The format recommended here is a first step toward such a CDEP .
Too often , the litany of proposed explainable deep learning methods stop at the first step , providing practitioners with insight into a model , but no way to act on it . In this paper , we propose contextual decomposition explanation penalization ( CDEP ) , a method which enables practitioners to leverage existing explanation methods in order to increase the predictive accuracy of deep learning models . In particular , when shown that a model has incorrectly assigned importance to some features , CDEP enables practitioners to correct these errors by directly regularizing the provided explanations .
Despite these efforts , there remain significant challenges to effectively increasing the statistical power and decreasing the Type I error rate while pooling the heterogeneous datasets from public resources . The objective of this study is to develop a novel meta-analysis approach , Consistent Differential Expression Pattern ( CDEP ) , to identify genes with common differential expression patterns across different datasets . ResultsWe combined False Discovery Rate ( FDR ) estimation and the non-parametric RankProd approach to estimate the Type I error rate in each microarray dataset of the meta-analysis .
To enable the recording and retrieval of decisions , a conceptual model is presented that can be used as a set of requirements for a data structure for decision storage . The approach conforms partly to the proposed Common Decision Exchange Protocol ( CDEP ) standard , but we extend it to capture decision attributes , decision making stages , decision makers and other collaborators , and the information and tools used . Capturing the linkages between decision elements enables a wide range of organizational decision making processes to be encoded for ontological reasoning .
This paper contains a completeness proof for the system ILW , a rather bewildering axiom system belonging to the family of interpretability logics . We have treasured this little proof for a considerable time , keeping it just for ourselves .
The provability logic of a theory T is the set of modal formulas , which under any arithmetical realization are provable in T . We slightly modify this notion by requiring the arithmetical realizations to come from a specified set Γ. We make an analogous modification for interpretability logics . This is a paper from 2012 .
Moreover , we prove that Z follows from a restricted form of B . Finally , we give an overview of the known relationships of IL ( PRA ) to important other interpretability principles .
The relevant modal logic , in addition to the usual provability operator « □ » , has a binary operator « ⊇ » to be interpreted as the formalized interpretability relation . We give an axiomatization and a decision procedure for the class of those modal formulas that express valid interpretability principles
Our results demonstrate that psychophysical experiments allow for robust quality assessment of transparency in machine learning . Interestingly the quality metrics computed without humans in the loop did not provide a consistent ranking of interpretability methods nor were they representative for how useful an explanation was for humans . These findings highlight the potential of methods from classical psychophysics for modern machine learning applications .
Topic models , like many other unsupervised machine learning methods , requite input from the researchers in the form of parameters used with these models . Through a user study , this article demonstrates that the commonly used interpretability-focused approaches can lead to different outcomes . Also differences emerging from parameter selection are discovered in the outcomes of the analysis .
It utilizes an attention mechanism that relates the encoded representations to samples in order to determine prototypes . The resulting model outperforms state of the art in three high impact problems without sacrificing accuracy of the original model : ( 1 ) it enables high-quality interpretability that outputs samples most relevant to the decision-making ( i.e. a sample-based interpretability method ) ; ( 2 ) it achieves state of the art confidence estimation by quantifying the mismatch across prototype labels ; and ( 3 ) it obtains state of the art in distribution mismatch detection . All this can be achieved with minimal additional test time and a practically viable training time computational cost .
There exist two contradictory requirements with fuzzy modeling , namely , accuracy and interpretability . In this study , the subtractive clustering and a multi-objective evolutionary algorithm are used to develop a novel fuzzy modeling scheme based on the FDT classifier to construct an accurate and interpretable system that is defined as the interpretability-based fuzzy decision tree classifier . Two interpretability measures , namely , complexity interpretability and semantics interpretability are considered to reach acceptable accuracy .
Based On Properties Of Volume Elements -- Discovering of Part-Whole Relations Used in Architectural Prototyping of Project Tasks -- Multi-level ontological model of Big Data processing -- Ontological Modeling for Industrial Enterprise Engineering -- Designing The Knowledge Base For The Intelligent Inertial Regulator Based On Quasi-optimal Synthesis Of Controls Using The Combined Maximum Principle -- Assessing the software developer 's quality using fuzzy estimates -- A Fuzzy Control Method for Priority Driven Embedded Device -- Interpretability of fuzzy temporal models -- Control Of The Cognitive Process In Hard Real-time Environment In The Context Of The Expanded Stepping Theories Of Active Logic -- Method of the Maximum Dynamic Flow Finding in the Fuzzy Graph with Gains -- Hybrid bioinspired algorithm of 1.5 dimensional bin-packing .
In response , humans need to be able to determine the “ fitness for use ” of a given model or dataset , and to assess the methodology that was used to produce it . To address this need , we propose to develop interpretability and transparency tools based on the concept of a nutritional label , drawing an analogy to the food industry , where simple , standard labels convey information about the ingredients and production processes . Nutritional labels are derived automatically or semi-automatically as part of the complex process that gave rise to the data or model they describe , embodying the paradigm of interpretability-by-design .
Too often , the litany of proposed explainable deep learning methods stop at the first step , providing practitioners with insight into a model , but no way to act on it . In this paper , we propose contextual decomposition explanation penalization ( CDEP ) , a method which enables practitioners to leverage existing explanation methods in order to increase the predictive accuracy of deep learning models . In particular , when shown that a model has incorrectly assigned importance to some features , CDEP enables practitioners to correct these errors by directly regularizing the provided explanations .
Clinical assessment of real-time, freehand elasticity imaging system based on the combined autocorrelation method A content addressable memory with multi-Vdd scheme for low power tunable operation Testing methods for quaternary content addressable memory using charge-sharing sensing scheme Комплексные показатели оценки эффективности освоения конструкторско-технологических компьютерных систем Use neutral data interfaces for Numerical Controllers Computerized CAD TOOLS AND FILE FORMAT PERFORMANCE EVALUATION IN DESIGNING LATTICE STRUCTURES FOR ADDITIVE MANUFACTURING An object-oriented assembly application methodology for PDES/STEP based mechanical systems Overview of the Monitoring Data Archive used on MeerKAT MeerKAT Control and Monitoring - Design Concepts and Status Aplikasi CAM Topsolid7 untuk Membantu Proses Pemesinan Komponen Design & Implementation of High Speed Network Devices Using SRL16 Reconfigurable Content Addressable Memory (RCAM) Research on Postprocessor of DMU50 CNC Milling Machine Tool Based on Edgecam Port Throughput Forecasting by Using PPPR with Chaotic Efficient Genetic Algorithms and CMA Automatic detection and segmentation of blood vessels and pulmonary nodules based on a line tracking method and generalized linear regression model Simple Baseline for Visual Question Answering Pairwise Confusion for Fine-Grained Visual Classification Generalization By Controlled Intersection Of Examples Software Engineering Applications Learning in a Multi-agent Approach to a Fish Bank Game Supervised Rule Learning and Reinforcement Learning in A Multi-Agent System for the Fish Banks Game Logic-Based Sequential Decision-Making Weak Supervised Surface Defect Detection Method Based on Selective Search and CAM Towards Good Practices for Image Retrieval Based on CNN Features Visual Interpretation of Convolutional Neural Network Predictions in Classifying Medical Image Modalities DeepRace: Finding Data Race Bugs via Deep Learning Human attribute recognition by refining attention heat map Improving Object Counting with Heatmap Regulation Multi-View Image-based Vehicle Brand Recognition System with Cascaded Convolutional Neural Network Saliency guided deep network for weakly-supervised image segmentation Rethinking Softmax with Cross-Entropy: Neural Network Classifier as Mutual Information Estimator Simple Baseline for Visual Question Answering Res2Net: A New Multi-scale Backbone Architecture Pairwise Confusion for Fine-Grained Visual Classification Decision-Acquisition System Based on a Common Decision-Exchange Protocol Interpretations are useful: penalizing explanations to align neural networks with prior knowledge Consistent Differential Expression Pattern (CDEP) on microarray to identify genes related to metastatic behavior Decisions as Units of Organizational Knowledge to Manage Complexity Modal completeness of ILW Provability and Interpretability Logics with Restricted Realizations Interpretability in PRA The Interpretability Logic of Peano Arithmetic A psychophysics approach for quantitative comparison of interpretable computer vision models Aihemallinnus sekä muut ohjaamattomat koneoppimismenetelmät yhteiskuntatieteellisessä tutkimuksessa: kriittisiä havaintoja ProtoAttend: Attention-Based Prototypical Learning Interpretability-based fuzzy decision tree classifier a hybrid of the subtractive clustering and the multi-objective evolutionary algorithm Proceedings of the Third International Scientific Conference “Intelligent Information Technologies for Industry” (IITI’18) Volume 1 Nutritional Labels for Data and Models Interpretations are useful: penalizing explanations to align neural networks with prior knowledge
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