In such a case , the dereverberation algorithm would perform poorly . We discuss the influence of overlapping zeros on the dereverberation performance of LIME . Spatial information can be used to deal with the problem of overlapping zeros . The set of tuples accessible by a particular agent residing on a given host is altered transparently in response to changes in the connectivity pattern among the mobile hosts . In this paper we present the key design concepts behind the LIME system . This work builds on the LIME ( Linda in mobile environment ) tuple space framework to implement a system that offers clustering and routing capabilities for mobile ad hoc network ( MANET ) environments , and provide an agent-like architecture for running distributed and collaborative applications on mobile devices . This paper describes the components that were added to the LIME system , which were necessary to implement engagement and disengagement of hosts into and out of spaces , and illustrates the developed engagement mechanism and routing protocol with the aid of example scenarios . The paper then discusses the system performance obtained from implementing its functions using the ns-2 network simulation software . Static and dynamical aspects of the application are specified by the UML . For the system implementation we used the LIME middleware and Java technology This technique allows developers to build highly-tailored coordination models while leveraging established models and middleware . We present three such veneers , the coordination models they embody , and the manner in which they were implemented The LIME middleware , which supplies tuple space based coordination in the ad hoc environment , serves as the implementation base for our veneers . These veneers cover diverse areas in ad hoc mobility : service discovery and provision , event registration and distribution , and secure tuple space access . In the first part of this paper we analyse current learner-monitoring techniques as an initialization phase for eLearning recommender systems . We next review standardization efforts in this area ; finally , we focus on xAPI and the potential interaction with the LIME model , which will be also summarized below . The common linear interpolation solution used for regression on grids or look-up-tables is shown to solve a related maximum entropy problem . LIME simulation results support use of the method , and performance on a pipeline integrity classification problem demonstrates that the proposed algorithm has practical value . Latest Advances and New Domains . Vocabularies , Schemas , Ontologies.- Requirements for and Evaluation of User Support for Large-Scale Ontology Alignment.- RODI : A Benchmark for Automatic Mapping Generation in Relational-to-Ontology Data Integration.- VocBench : a Web Application for Collaborative Development of Multilingual Thesauri.- Leveraging and Balancing Heterogeneous Sources of Evidence in Ontology Learning.- Reasoning.- A Context-Based Semantics for SPARQL Property Paths over the Web.- Distributed and Scalable OWL EL Reasoning.- Large scale rule-based Reasoning using a Laptop.- RDF Digest : Efficient Summarization of RDF/S KBs.- Linked Data.- A Comparison of Data Structures to Manage URIs on the Web of Data.- Heuristics for Fixing Common Errors in Deployed schema.org Microdata.- Semantic Web and Web Science.- LOD-based Disambiguation of Named Entities in @tweets through Context enrichment.- Knowledge Enabled Approach to Predict the Location of Twitter Users.- Semantic Data Management , Big data , Scalability.- A Compact In-Memory Dictionary for RDF data.- Quality Assessment of Linked Datasets using Probabilistic Approximations.- Cooperative Techniques for SPARQL Query Relaxation in RDF Databases.- HDT-MR : A Scalable Solution for RDF Compression with HDT and MapReduce.- Processing Aggregate Queries in a Federation of SPARQL Endpoints.- A survey of HTTP caching implementations on the open Semantic Web.- Query Execution Optimization for Clients of Triple Pattern Fragments.- Natural Language Processing and Information Retrieval LIME : the Metadata Module for OntoLex.- Learning a Cross-Lingual Semantic Representation of Relations Expressed in Text.- HAWK Hybrid Question Answering using Linked Data.- Machine Learning.- Automating RDF Dataset Transformation and Enrichment.- Semi-supervised Instance Matching Using Boosted Classifiers.- Assigning Semantic Labels to Data Sources.- Inductive Classification through Evidence-based Models and their Ensembles.- Mobile Web , Internet of Things and Semantic Streams.- Standardized and Efficient RDF Encoding for Constrained Embedded Networks.- Services , Web APIs , and the Web of Things SPSC : Efficient Composition of Semantic Services in Unstructured P2P Networks.- Data as a Service : The Semantic Web Redeployed.- Cognition and Semantic Web.- On Coherent Indented Tree Visualization of RDF Graphs.- Gagg : A Graph Aggregation Operator.- FrameBase : The experimental results show the accuracy of about 73 % , and the false alarm related to the reliability of this abnormality diagnosis model can be minimized to about 17 % . Finally , to solve the problem of reliability and analysis of the model itself due to the problem of blackbox , which is a disadvantage of the deep learning model , the LIME analysis method is applied to list the sensor data that affected the determination of the abnormal state . Experts can easily make professional judgments about abnormal conditions and build a model in which known data about faults and symptoms are continuously increasing . We first discuss the interaction potential of LIME interfaces . Then we introduce the development of LIME cells and the design of some LIME widgets . The danger now reaches out to our own lives : a plenty of individual data is accessible to interpersonal organizations and cell phone suppliers and is in a roundabout way exchanged to dishonest outsider and fourth gathering applications . In this work , we introduce a bland information genealogy system LIME for information stream over numerous elements that take two trademark , key parts ( i.e. , proprietor and purchaser ) . We characterize the correct security ensures required by such an information heredity component toward distinguishing proof of a blameworthy substance , and recognize the disentangling non-disavowal and genuineness suppositions . In this work , we propose anchor-LIME ( aLIME ) , a model-agnostic technique that produces high-precision rule-based explanations for which the coverage boundaries are very clear . We compare aLIME to linear LIME with simulated experiments , and demonstrate the flexibility of aLIME with qualitative examples from a variety of domains and tasks . This paper presents the bootstrapping solution used on the embedded bytecode processor SHAP [ 6 ] . Although it employs load-time bytecode patching to resolve constant pool indirections , it is itself most comfortably implemented in Java and executed as bytecode by SHAP . Of course , the processor startup sequence initiates the plain loading of the bootloader code – but it does not undertake any conversions on it . Its VC capabilities are comparable to the other examined devices . The functionality of the concept is assessed through the Southampton Hand Assessment Procedure ( SHAP ) and its score is compared with scores of body-powered hooks from literature . The SHAP tasks are performed in both VO and VC mode and the individual preferences are noted . It features a novel mark-and-copy GC strategy on a segmented memory , thereby overcoming the tremendous space overhead of two-space copying and the compaction race of mark-and-compact approaches . The proposed GC architecture has been practically implemented and proven using the embedded bytecode processor SHAP as a sample testbed . The synthesis results for settings up to three SHAP mutator cores are given and online functional measurements are presented . Of existing work on interpreting complex models , Shapley values is the only method with a solid theoretical foundation . Kernel SHAP is a computationally efficient approximation to Shapley values in higher dimensions . Like most other existing methods , this approach assumes independent features , which may give very wrong explanations . Furthermore , the different interpretations between different kinds of classifiers can also help security experts better design the structures of the IDSs . More importantly , this work is unique in the intrusion detection field , presenting the first use of the SHAP method to give explanations for IDSs . Experimental results show that our LightGBM method is effective in predicting extubation failure and outperform other machine learning methods such as artificial neural network ( ANN ) , logistic regression ( LR ) and support vector machine ( SVM ) . The results of feature importance and SHAP analysis are also proved effective and accurate . However , there has been little evaluation of the extent to which these tools achieve this goal . We study data scientists ' use of two existing interpretability tools , the InterpretML implementation of GAMs and the SHAP Python package . We conduct a contextual inquiry ( N=11 ) and a survey ( N=197 ) of data scientists to observe how they use interpretability tools to uncover common issues that arise when building and evaluating ML models . Case Studies Appendix 6.1 Mathematical Background Appendix 6.2 Demonstration 7 CHARACTER ANIMATION 7.1 Introduction 7.2 Vertex Animation And Blending 7.3 Skeleton Animation 7.4 Low-Level Animation Management Appendix 7.1 Using Quaternions To Represent Rotation Appendix 7.2 Implementing Quaternions Appendix 7.3 Efficiency Consideration In Character Animation ( Optimising For SIMD Pentium3 Instructions ) 8 ANIMATING SHAP - METHODS 8.1 Introduction 8.2 Spline Cages 8.3 Free Form Deformation ( FFD ) 8.4 Extended Free Form Deformations ( Effds ) 8.5 Curve Deformers - Wires 8.6 Skinning Revisited Appendix 8.1 Its weight including a socket was 245 g. We improved the durability of the finger joints , the interface of the drive unit , the mechanism of the wrist joint , the appearance of the F3Hand . An evaluation using a Southampton Hand Assessment Procedure ( SHAP ) test and a test on the manipulation of the daily objects demonstrated that an amputee was able to manipulate various objects . SHAP ~ E was created in August 1955 . From the beginning its aim has been to reduce redundant effort among various users of the IBM 704 , insofar as the preparation of commonly used routines is concerned . Both signal processing and statistic methods are used to extract information from the first 24 hours after the admission in the ICU ; then a long short term memory ( LSTM ) model is used for classification and regression tasks . Finally , SHapley Additive exPlanations approach is used to increase the interpretability of the model in order to understand which variables require more attention and to help doctors and technicians in the choice of the best treatment pathway . Regardless of which task the model was designed to , age of admission is the most important feature ; vitals signs generally account the most with respect to arterial blood gas ( ABG ) measurements and no significant improvements are present when features from empirical mode decomposition ( EMD ) are added to the feature space . We propose to add an interpretability layer ( iSTS for short ) formalized as the alignment between pairs of segments across the two sentences , where the relation between the segments is labeled with a relation type and a similarity score . This way , a system performing STS could use the interpretability layer to explain to users why it returned that specific score for the given sentence pair . We present a publicly available dataset of sentence pairs annotated following the formalization . However , due to the lack of legal value and institutional design support , the technological advantages of interpretable algorithms to prevent and control algorithm black boxes , “ Offside order ” and coping with a risky society can not be confirmed and guided by law . Therefore , it is an effective way to solve the risks in the age of artificial intelligence by taking the technical critical theory of risk society as the value basis and taking the interpretability of algorithms as a necessary condition for algorithm regulation . Unfortunately , there is little consensus on what interpretability in machine learning is and how to evaluate it for benchmarking . Current interpretability evaluation typically falls into two categories . The first evaluates interpretability in the context of an application : if the system is useful in either a practical application or a simplified version of it , then it must be somehow interpretable ( e.g. Ribeiro et al. [ 2016 ] , Lei et al. [ 2016 ] , Kim et al. [ 2015a ] , Doshi-Velez et al. [ 2015 ] , Kim et al. [ 2015b ] ) . Of the non trivial logics for axioms as evaluated , none is tautologous . Hence the interpretability logic IL is refuted , and forms a non tautologous fragment of the universal logic VŁ4 . The transient part of the EMG signal , which is recorded at the surface of the skin of the forearm , has been exploited to provide the recognition of three finger movements . The objective of the paper is to describe the identification procedure , based on Transparent ( interpretability ) Fuzzy System and accuracy , which allow the cooperation between expert rules and induced rules . An initial fuzzy rule system is generated using the statistic ’s trimmed mean method [ 7 ] , rather clustering , which fulfils many criteria for transparency and semantic . To more deeply interpret commentcontroversy model decisions we frame prediction as binary classification and evaluate baselines and multi-task CNNs that use an auxiliary news-genre-encoder . Finally , we use ablation and interpretability methods to determine the impacts of topic , discourse and sentiment indicators , contextual vs. global word influence , as well as genre-keywords vs. per-genrecontroversy keywords – to find that the models learn plausible controversy features using only incidentally supervised signals . Illustrating First Principles in Neural Network Physical Interpretability . In the last years , deep learning has solved seemingly intractable problems , boosting the hope to find ( approximate ) solutions to problems that now are considered unsolvable . In this work , we develop SMILES2vec , a deep RNN that automatically learns features from SMILES strings to predict a broad range of chemical properties , including toxicity , activity , solubility and solvation energy . Furthermore , we trained an interpretability mask for SMILES2vec solubility prediction , which identifies specific parts of a chemical that is consistent with groundtruth knowledge with an accuracy of 88 % , demonstrating that neural networks can learn technically accurate chemical concepts .
In such a case , the dereverberation algorithm would perform poorly . We discuss the influence of overlapping zeros on the dereverberation performance of LIME . Spatial information can be used to deal with the problem of overlapping zeros .
The set of tuples accessible by a particular agent residing on a given host is altered transparently in response to changes in the connectivity pattern among the mobile hosts . In this paper we present the key design concepts behind the LIME system .
This work builds on the LIME ( Linda in mobile environment ) tuple space framework to implement a system that offers clustering and routing capabilities for mobile ad hoc network ( MANET ) environments , and provide an agent-like architecture for running distributed and collaborative applications on mobile devices . This paper describes the components that were added to the LIME system , which were necessary to implement engagement and disengagement of hosts into and out of spaces , and illustrates the developed engagement mechanism and routing protocol with the aid of example scenarios . The paper then discusses the system performance obtained from implementing its functions using the ns-2 network simulation software .
Static and dynamical aspects of the application are specified by the UML . For the system implementation we used the LIME middleware and Java technology
This technique allows developers to build highly-tailored coordination models while leveraging established models and middleware . We present three such veneers , the coordination models they embody , and the manner in which they were implemented The LIME middleware , which supplies tuple space based coordination in the ad hoc environment , serves as the implementation base for our veneers . These veneers cover diverse areas in ad hoc mobility : service discovery and provision , event registration and distribution , and secure tuple space access .
In the first part of this paper we analyse current learner-monitoring techniques as an initialization phase for eLearning recommender systems . We next review standardization efforts in this area ; finally , we focus on xAPI and the potential interaction with the LIME model , which will be also summarized below .
The common linear interpolation solution used for regression on grids or look-up-tables is shown to solve a related maximum entropy problem . LIME simulation results support use of the method , and performance on a pipeline integrity classification problem demonstrates that the proposed algorithm has practical value .
Latest Advances and New Domains . Vocabularies , Schemas , Ontologies.- Requirements for and Evaluation of User Support for Large-Scale Ontology Alignment.- RODI : A Benchmark for Automatic Mapping Generation in Relational-to-Ontology Data Integration.- VocBench : a Web Application for Collaborative Development of Multilingual Thesauri.- Leveraging and Balancing Heterogeneous Sources of Evidence in Ontology Learning.- Reasoning.- A Context-Based Semantics for SPARQL Property Paths over the Web.- Distributed and Scalable OWL EL Reasoning.- Large scale rule-based Reasoning using a Laptop.- RDF Digest : Efficient Summarization of RDF/S KBs.- Linked Data.- A Comparison of Data Structures to Manage URIs on the Web of Data.- Heuristics for Fixing Common Errors in Deployed schema.org Microdata.- Semantic Web and Web Science.- LOD-based Disambiguation of Named Entities in @tweets through Context enrichment.- Knowledge Enabled Approach to Predict the Location of Twitter Users.- Semantic Data Management , Big data , Scalability.- A Compact In-Memory Dictionary for RDF data.- Quality Assessment of Linked Datasets using Probabilistic Approximations.- Cooperative Techniques for SPARQL Query Relaxation in RDF Databases.- HDT-MR : A Scalable Solution for RDF Compression with HDT and MapReduce.- Processing Aggregate Queries in a Federation of SPARQL Endpoints.- A survey of HTTP caching implementations on the open Semantic Web.- Query Execution Optimization for Clients of Triple Pattern Fragments.- Natural Language Processing and Information Retrieval LIME : the Metadata Module for OntoLex.- Learning a Cross-Lingual Semantic Representation of Relations Expressed in Text.- HAWK Hybrid Question Answering using Linked Data.- Machine Learning.- Automating RDF Dataset Transformation and Enrichment.- Semi-supervised Instance Matching Using Boosted Classifiers.- Assigning Semantic Labels to Data Sources.- Inductive Classification through Evidence-based Models and their Ensembles.- Mobile Web , Internet of Things and Semantic Streams.- Standardized and Efficient RDF Encoding for Constrained Embedded Networks.- Services , Web APIs , and the Web of Things SPSC : Efficient Composition of Semantic Services in Unstructured P2P Networks.- Data as a Service : The Semantic Web Redeployed.- Cognition and Semantic Web.- On Coherent Indented Tree Visualization of RDF Graphs.- Gagg : A Graph Aggregation Operator.- FrameBase :
The experimental results show the accuracy of about 73 % , and the false alarm related to the reliability of this abnormality diagnosis model can be minimized to about 17 % . Finally , to solve the problem of reliability and analysis of the model itself due to the problem of blackbox , which is a disadvantage of the deep learning model , the LIME analysis method is applied to list the sensor data that affected the determination of the abnormal state . Experts can easily make professional judgments about abnormal conditions and build a model in which known data about faults and symptoms are continuously increasing .
We first discuss the interaction potential of LIME interfaces . Then we introduce the development of LIME cells and the design of some LIME widgets .
The danger now reaches out to our own lives : a plenty of individual data is accessible to interpersonal organizations and cell phone suppliers and is in a roundabout way exchanged to dishonest outsider and fourth gathering applications . In this work , we introduce a bland information genealogy system LIME for information stream over numerous elements that take two trademark , key parts ( i.e. , proprietor and purchaser ) . We characterize the correct security ensures required by such an information heredity component toward distinguishing proof of a blameworthy substance , and recognize the disentangling non-disavowal and genuineness suppositions .
In this work , we propose anchor-LIME ( aLIME ) , a model-agnostic technique that produces high-precision rule-based explanations for which the coverage boundaries are very clear . We compare aLIME to linear LIME with simulated experiments , and demonstrate the flexibility of aLIME with qualitative examples from a variety of domains and tasks .
This paper presents the bootstrapping solution used on the embedded bytecode processor SHAP [ 6 ] . Although it employs load-time bytecode patching to resolve constant pool indirections , it is itself most comfortably implemented in Java and executed as bytecode by SHAP . Of course , the processor startup sequence initiates the plain loading of the bootloader code – but it does not undertake any conversions on it .
Its VC capabilities are comparable to the other examined devices . The functionality of the concept is assessed through the Southampton Hand Assessment Procedure ( SHAP ) and its score is compared with scores of body-powered hooks from literature . The SHAP tasks are performed in both VO and VC mode and the individual preferences are noted .
It features a novel mark-and-copy GC strategy on a segmented memory , thereby overcoming the tremendous space overhead of two-space copying and the compaction race of mark-and-compact approaches . The proposed GC architecture has been practically implemented and proven using the embedded bytecode processor SHAP as a sample testbed . The synthesis results for settings up to three SHAP mutator cores are given and online functional measurements are presented .
Of existing work on interpreting complex models , Shapley values is the only method with a solid theoretical foundation . Kernel SHAP is a computationally efficient approximation to Shapley values in higher dimensions . Like most other existing methods , this approach assumes independent features , which may give very wrong explanations .
Furthermore , the different interpretations between different kinds of classifiers can also help security experts better design the structures of the IDSs . More importantly , this work is unique in the intrusion detection field , presenting the first use of the SHAP method to give explanations for IDSs .
Experimental results show that our LightGBM method is effective in predicting extubation failure and outperform other machine learning methods such as artificial neural network ( ANN ) , logistic regression ( LR ) and support vector machine ( SVM ) . The results of feature importance and SHAP analysis are also proved effective and accurate .
However , there has been little evaluation of the extent to which these tools achieve this goal . We study data scientists ' use of two existing interpretability tools , the InterpretML implementation of GAMs and the SHAP Python package . We conduct a contextual inquiry ( N=11 ) and a survey ( N=197 ) of data scientists to observe how they use interpretability tools to uncover common issues that arise when building and evaluating ML models .
Case Studies Appendix 6.1 Mathematical Background Appendix 6.2 Demonstration 7 CHARACTER ANIMATION 7.1 Introduction 7.2 Vertex Animation And Blending 7.3 Skeleton Animation 7.4 Low-Level Animation Management Appendix 7.1 Using Quaternions To Represent Rotation Appendix 7.2 Implementing Quaternions Appendix 7.3 Efficiency Consideration In Character Animation ( Optimising For SIMD Pentium3 Instructions ) 8 ANIMATING SHAP - METHODS 8.1 Introduction 8.2 Spline Cages 8.3 Free Form Deformation ( FFD ) 8.4 Extended Free Form Deformations ( Effds ) 8.5 Curve Deformers - Wires 8.6 Skinning Revisited Appendix 8.1
Its weight including a socket was 245 g. We improved the durability of the finger joints , the interface of the drive unit , the mechanism of the wrist joint , the appearance of the F3Hand . An evaluation using a Southampton Hand Assessment Procedure ( SHAP ) test and a test on the manipulation of the daily objects demonstrated that an amputee was able to manipulate various objects .
SHAP ~ E was created in August 1955 . From the beginning its aim has been to reduce redundant effort among various users of the IBM 704 , insofar as the preparation of commonly used routines is concerned .
Both signal processing and statistic methods are used to extract information from the first 24 hours after the admission in the ICU ; then a long short term memory ( LSTM ) model is used for classification and regression tasks . Finally , SHapley Additive exPlanations approach is used to increase the interpretability of the model in order to understand which variables require more attention and to help doctors and technicians in the choice of the best treatment pathway . Regardless of which task the model was designed to , age of admission is the most important feature ; vitals signs generally account the most with respect to arterial blood gas ( ABG ) measurements and no significant improvements are present when features from empirical mode decomposition ( EMD ) are added to the feature space .
We propose to add an interpretability layer ( iSTS for short ) formalized as the alignment between pairs of segments across the two sentences , where the relation between the segments is labeled with a relation type and a similarity score . This way , a system performing STS could use the interpretability layer to explain to users why it returned that specific score for the given sentence pair . We present a publicly available dataset of sentence pairs annotated following the formalization .
However , due to the lack of legal value and institutional design support , the technological advantages of interpretable algorithms to prevent and control algorithm black boxes , “ Offside order ” and coping with a risky society can not be confirmed and guided by law . Therefore , it is an effective way to solve the risks in the age of artificial intelligence by taking the technical critical theory of risk society as the value basis and taking the interpretability of algorithms as a necessary condition for algorithm regulation .
Unfortunately , there is little consensus on what interpretability in machine learning is and how to evaluate it for benchmarking . Current interpretability evaluation typically falls into two categories . The first evaluates interpretability in the context of an application : if the system is useful in either a practical application or a simplified version of it , then it must be somehow interpretable ( e.g. Ribeiro et al. [ 2016 ] , Lei et al. [ 2016 ] , Kim et al. [ 2015a ] , Doshi-Velez et al. [ 2015 ] , Kim et al. [ 2015b ] ) .
Of the non trivial logics for axioms as evaluated , none is tautologous . Hence the interpretability logic IL is refuted , and forms a non tautologous fragment of the universal logic VŁ4 .
The transient part of the EMG signal , which is recorded at the surface of the skin of the forearm , has been exploited to provide the recognition of three finger movements . The objective of the paper is to describe the identification procedure , based on Transparent ( interpretability ) Fuzzy System and accuracy , which allow the cooperation between expert rules and induced rules . An initial fuzzy rule system is generated using the statistic ’s trimmed mean method [ 7 ] , rather clustering , which fulfils many criteria for transparency and semantic .
To more deeply interpret commentcontroversy model decisions we frame prediction as binary classification and evaluate baselines and multi-task CNNs that use an auxiliary news-genre-encoder . Finally , we use ablation and interpretability methods to determine the impacts of topic , discourse and sentiment indicators , contextual vs. global word influence , as well as genre-keywords vs. per-genrecontroversy keywords – to find that the models learn plausible controversy features using only incidentally supervised signals .
Illustrating First Principles in Neural Network Physical Interpretability . In the last years , deep learning has solved seemingly intractable problems , boosting the hope to find ( approximate ) solutions to problems that now are considered unsolvable .
In this work , we develop SMILES2vec , a deep RNN that automatically learns features from SMILES strings to predict a broad range of chemical properties , including toxicity , activity , solubility and solvation energy . Furthermore , we trained an interpretability mask for SMILES2vec solubility prediction , which identifies specific parts of a chemical that is consistent with groundtruth knowledge with an accuracy of 88 % , demonstrating that neural networks can learn technically accurate chemical concepts .
Precise Dereverberation Using Multichannel Linear Prediction LIME: Linda meets mobility CRUST: Implementation of clustering and routing functions for mobile ad hoc networks using reactive tuple-spaces Application design for ad hoc collaboration environment based on dependency injection pattern Coordination middleware supporting rapid deployment of ad hoc mobile systems Review of Current Student-Monitoring Techniques used in eLearning-Focused recommender Systems and Learning analytics. The Experience API & LIME model Case Study Nonparametric supervised learning by linear interpolation with maximum entropy The Semantic Web. Latest Advances and New Domains The Anomaly Detection of 2.4L Diesel Engine Using One-Class SVM with Variational Autoencoder LIME: LIquid MEtal Interfaces for Non-Rigid Interaction Lime-A Generic Data Lineage Framework In Malicious Environment Nothing Else Matters: Model-Agnostic Explanations By Identifying Prediction Invariance Java-Programmed Bootloading in Spite of Load-Time Code Patching on a Minimal Embedded Bytecode Processor Design and evaluation of a novel upper limb body-powered terminal device with voluntary opening and voluntary closing capabilities An Embedded GC Module with Support for Multiple Mutators and Weak References Explaining individual predictions when features are dependent: More accurate approximations to Shapley values An Explainable Machine Learning Framework for Intrusion Detection Systems Prediction of Extubation Failure for Intensive Care Unit Patients Using Light Gradient Boosting Machine Interpreting Interpretability: Understanding Data Scientists' Use of Interpretability Tools for Machine Learning 3D games : animation and advanced real-time rendering F3Hand II: A Flexible Five-Fingered Prosthetic Hand Using Curved Pneumatic Artificial Muscles SHARE 709 system: a cooperative effort Multimodal physiological time series analysis for outcome prediction in the intensive care unit. Interpretable Semantic Textual Similarity: Finding and explaining differences between sentences Legal Regulation of Algorithms From the Perspective of Interpretability A Roadmap for a Rigorous Science of Interpretability Refutation of Interpretability Logics EMG-based Finger Movement Classification using transparent Fuzzy System Learning Comment Controversy Prediction in Web Discussions Using Incidentally Supervised Multi-Task CNNs A Deeper Look into 'Deep Learning of Aftershock Patterns Following Large Earthquakes': Illustrating First Principles in Neural Network Physical Interpretability SMILES2vec: Predicting Chemical Properties from Text Representations
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ICSE ICDCS Workshops The Semantic Web. Latest Advances and New Domains UIST ESA ARCS CHI SII EUSFLAT Conf. WASSA@EMNLP IWANN (1)
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