Paper Group ANR 1135
Automated Process Planning for Hybrid Manufacturing. Moving Vehicle Detection Using AdaBoost and Haar-Like Feature in Surveillance Videos. A Multi-Disciplinary Review of Knowledge Acquisition Methods: From Human to Autonomous Eliciting Agents. Diversity and degrees of freedom in regression ensembles. Embarrassingly Simple Model for Early Action Pro …
Automated Process Planning for Hybrid Manufacturing
| Title | Automated Process Planning for Hybrid Manufacturing |
| Authors | Morad Behandish, Saigopal Nelaturi, Johan de Kleer |
| Abstract | Hybrid manufacturing (HM) technologies combine additive and subtractive manufacturing (AM/SM) capabilities, leveraging AM’s strengths in fabricating complex geometries and SM’s precision and quality to produce finished parts. We present a systematic approach to automated computer-aided process planning (CAPP) for HM that can identify non-trivial, qualitatively distinct, and cost-optimal combinations of AM/SM modalities. A multimodal HM process plan is represented by a finite Boolean expression of AM and SM manufacturing primitives, such that the expression evaluates to an ‘as-manufactured’ artifact. We show that primitives that respect spatial constraints such as accessibility and collision avoidance may be constructed by solving inverse configuration space problems on the ‘as-designed’ artifact and manufacturing instruments. The primitives generate a finite Boolean algebra (FBA) that enumerates the entire search space for planning. The FBA’s canonical intersection terms (i.e., ‘atoms’) provide the complete domain decomposition to reframe manufacturability analysis and process planning into purely symbolic reasoning, once a subcollection of atoms is found to be interchangeable with the design target. The approach subsumes unimodal (all-AM or all-SM) process planning as special cases. We demonstrate the practical potency of our framework and its computational efficiency when applied to process planning of complex 3D parts with dramatically different AM and SM instruments. |
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| Published | 2018-05-18 |
| URL | http://arxiv.org/abs/1805.07035v1 |
| http://arxiv.org/pdf/1805.07035v1.pdf | |
| PWC | https://paperswithcode.com/paper/automated-process-planning-for-hybrid |
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Moving Vehicle Detection Using AdaBoost and Haar-Like Feature in Surveillance Videos
| Title | Moving Vehicle Detection Using AdaBoost and Haar-Like Feature in Surveillance Videos |
| Authors | Mohammad Mahdi Moghimi, Maryam Nayeri, Majid Pourahmadi, Mohammad Kazem Moghimi |
| Abstract | Vehicle detection is a technology which its aim is to locate and show the vehicle size in digital images. In this technology, vehicles are detected in presence of other things like trees and buildings. It has an important role in many computer vision applications such as vehicle tracking, analyzing the traffic scene and efficient traffic management. In this paper, vehicles detected based on the boosting technique by Viola Jones. Our proposed system is tested in some real scenes of surveillance videos with different light conditions. The experimental results show that the accuracy,completeness, and quality of the proposed vehicle detection method are better than the previous techniques (about 94%, 92%, and 87%, respectively). Thus, our proposed approach is robust and efficient to detect vehicles in surveillance videos and their applications. |
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| Published | 2018-01-05 |
| URL | http://arxiv.org/abs/1801.01698v1 |
| http://arxiv.org/pdf/1801.01698v1.pdf | |
| PWC | https://paperswithcode.com/paper/moving-vehicle-detection-using-adaboost-and |
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A Multi-Disciplinary Review of Knowledge Acquisition Methods: From Human to Autonomous Eliciting Agents
| Title | A Multi-Disciplinary Review of Knowledge Acquisition Methods: From Human to Autonomous Eliciting Agents |
| Authors | George Leu, Hussein Abbass |
| Abstract | This paper offers a multi-disciplinary review of knowledge acquisition methods in human activity systems. The review captures the degree of involvement of various types of agencies in the knowledge acquisition process, and proposes a classification with three categories of methods: the human agent, the human-inspired agent, and the autonomous machine agent methods. In the first two categories, the acquisition of knowledge is seen as a cognitive task analysis exercise, while in the third category knowledge acquisition is treated as an autonomous knowledge-discovery endeavour. The motivation for this classification stems from the continuous change over time of the structure, meaning and purpose of human activity systems, which are seen as the factor that fuelled researchers’ and practitioners’ efforts in knowledge acquisition for more than a century. We show through this review that the KA field is increasingly active due to the higher and higher pace of change in human activity, and conclude by discussing the emergence of a fourth category of knowledge acquisition methods, which are based on red-teaming and co-evolution. |
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| Published | 2018-02-27 |
| URL | http://arxiv.org/abs/1802.09669v1 |
| http://arxiv.org/pdf/1802.09669v1.pdf | |
| PWC | https://paperswithcode.com/paper/a-multi-disciplinary-review-of-knowledge |
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Diversity and degrees of freedom in regression ensembles
| Title | Diversity and degrees of freedom in regression ensembles |
| Authors | Henry WJ Reeve, Gavin Brown |
| Abstract | Ensemble methods are a cornerstone of modern machine learning. The performance of an ensemble depends crucially upon the level of diversity between its constituent learners. This paper establishes a connection between diversity and degrees of freedom (i.e. the capacity of the model), showing that diversity may be viewed as a form of inverse regularisation. This is achieved by focusing on a previously published algorithm Negative Correlation Learning (NCL), in which model diversity is explicitly encouraged through a diversity penalty term in the loss function. We provide an exact formula for the effective degrees of freedom in an NCL ensemble with fixed basis functions, showing that it is a continuous, convex and monotonically increasing function of the diversity parameter. We demonstrate a connection to Tikhonov regularisation and show that, with an appropriately chosen diversity parameter, an NCL ensemble can always outperform the unregularised ensemble in the presence of noise. We demonstrate the practical utility of our approach by deriving a method to efficiently tune the diversity parameter. Finally, we use a Monte-Carlo estimator to extend the connection between diversity and degrees of freedom to ensembles of deep neural networks. |
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| Published | 2018-03-01 |
| URL | http://arxiv.org/abs/1803.00314v1 |
| http://arxiv.org/pdf/1803.00314v1.pdf | |
| PWC | https://paperswithcode.com/paper/diversity-and-degrees-of-freedom-in |
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Embarrassingly Simple Model for Early Action Proposal
| Title | Embarrassingly Simple Model for Early Action Proposal |
| Authors | Marcos Baptista-Ríos, Roberto J. López-Sastre, Franciso Javier Acevedo-Rodríguez, Saturnino Maldonado-Bascón |
| Abstract | Early action proposal consists in generating high quality candidate temporal segments that are likely to contain an action in a video stream, as soon as they happen. Many sophisticated approaches have been proposed for the action proposal problem but from the off-line perspective. On the contrary, we focus on the on-line version of the problem, proposing a simple classifier-based model, using standard 3D CNNs, that performs significantly better than the state of the art. |
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| Published | 2018-10-17 |
| URL | http://arxiv.org/abs/1810.07420v2 |
| http://arxiv.org/pdf/1810.07420v2.pdf | |
| PWC | https://paperswithcode.com/paper/embarrassingly-simple-model-for-early-action |
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Residual Network based Aggregation Model for Skin Lesion Classification
| Title | Residual Network based Aggregation Model for Skin Lesion Classification |
| Authors | Yongsheng Pan, Yong Xia |
| Abstract | We recognize that the skin lesion diagnosis is an essential and challenging sub-task in Image classification, in which the Fisher vector (FV) encoding algorithm and deep convolutional neural network (DCNN) are two of the most successful techniques. Since the joint use of FV and DCNN has demonstrated proven success, the joint techniques could have discriminatory power on skin lesion diagnosis as well. To this hypothesis, we propose the aggregation algorithm for skin lesion diagnosis that utilize the residual network to extract the local features and the Fisher vector method to aggregate the local features to image-level representation. We applied our algorithm on the International Skin Imaging Collaboration 2018 (ISIC2018) challenge and only focus on the third task, i.e., the disease classification. |
| Tasks | Image Classification, Skin Lesion Classification |
| Published | 2018-07-24 |
| URL | http://arxiv.org/abs/1807.09150v1 |
| http://arxiv.org/pdf/1807.09150v1.pdf | |
| PWC | https://paperswithcode.com/paper/residual-network-based-aggregation-model-for |
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A One-Class Classification Decision Tree Based on Kernel Density Estimation
| Title | A One-Class Classification Decision Tree Based on Kernel Density Estimation |
| Authors | Sarah Itani, Fabian Lecron, Philippe Fortemps |
| Abstract | One-class Classification (OCC) is an area of machine learning which addresses prediction based on unbalanced datasets. Basically, OCC algorithms achieve training by means of a single class sample, with potentially some additional counter-examples. The current OCC models give satisfaction in terms of performance, but there is an increasing need for the development of interpretable models. In the present work, we propose a one-class model which addresses concerns of both performance and interpretability. Our hybrid OCC method relies on density estimation as part of a tree-based learning algorithm, called One-Class decision Tree (OC-Tree). Within a greedy and recursive approach, our proposal rests on kernel density estimation to split a data subset on the basis of one or several intervals of interest. Thus, the OC-Tree encloses data within hyper-rectangles of interest which can be described by a set of rules. Against state-of-the-art methods such as Cluster Support Vector Data Description (ClusterSVDD), One-Class Support Vector Machine (OCSVM) and isolation Forest (iForest), the OC-Tree performs favorably on a range of benchmark datasets. Furthermore, we propose a real medical application for which the OC-Tree has demonstrated its effectiveness, through the ability to tackle interpretable diagnosis aid based on unbalanced datasets. |
| Tasks | Density Estimation |
| Published | 2018-05-14 |
| URL | https://arxiv.org/abs/1805.05021v3 |
| https://arxiv.org/pdf/1805.05021v3.pdf | |
| PWC | https://paperswithcode.com/paper/a-one-class-decision-tree-based-on-kernel |
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Prototypical Clustering Networks for Dermatological Disease Diagnosis
| Title | Prototypical Clustering Networks for Dermatological Disease Diagnosis |
| Authors | Viraj Prabhu, Anitha Kannan, Murali Ravuri, Manish Chablani, David Sontag, Xavier Amatriain |
| Abstract | We consider the problem of image classification for the purpose of aiding doctors in dermatological diagnosis. Dermatological diagnosis poses two major challenges for standard off-the-shelf techniques: First, the data distribution is typically extremely long tailed. Second, intra-class variability is often large. To address the first issue, we formulate the problem as low-shot learning, where once deployed, a base classifier must rapidly generalize to diagnose novel conditions given very few labeled examples. To model diverse classes effectively, we propose Prototypical Clustering Networks (PCN), an extension to Prototypical Networks that learns a mixture of prototypes for each class. Prototypes are initialized for each class via clustering and refined via an online update scheme. Classification is performed by measuring similarity to a weighted combination of prototypes within a class, where the weights are the inferred cluster responsibilities. We demonstrate the strengths of our approach in effective diagnosis on a realistic dataset of dermatological conditions. |
| Tasks | Image Classification |
| Published | 2018-11-07 |
| URL | http://arxiv.org/abs/1811.03066v1 |
| http://arxiv.org/pdf/1811.03066v1.pdf | |
| PWC | https://paperswithcode.com/paper/prototypical-clustering-networks-for |
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Blockwise Parallel Decoding for Deep Autoregressive Models
| Title | Blockwise Parallel Decoding for Deep Autoregressive Models |
| Authors | Mitchell Stern, Noam Shazeer, Jakob Uszkoreit |
| Abstract | Deep autoregressive sequence-to-sequence models have demonstrated impressive performance across a wide variety of tasks in recent years. While common architecture classes such as recurrent, convolutional, and self-attention networks make different trade-offs between the amount of computation needed per layer and the length of the critical path at training time, generation still remains an inherently sequential process. To overcome this limitation, we propose a novel blockwise parallel decoding scheme in which we make predictions for multiple time steps in parallel then back off to the longest prefix validated by a scoring model. This allows for substantial theoretical improvements in generation speed when applied to architectures that can process output sequences in parallel. We verify our approach empirically through a series of experiments using state-of-the-art self-attention models for machine translation and image super-resolution, achieving iteration reductions of up to 2x over a baseline greedy decoder with no loss in quality, or up to 7x in exchange for a slight decrease in performance. In terms of wall-clock time, our fastest models exhibit real-time speedups of up to 4x over standard greedy decoding. |
| Tasks | Image Super-Resolution, Machine Translation, Super-Resolution |
| Published | 2018-11-07 |
| URL | http://arxiv.org/abs/1811.03115v1 |
| http://arxiv.org/pdf/1811.03115v1.pdf | |
| PWC | https://paperswithcode.com/paper/blockwise-parallel-decoding-for-deep |
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Fast and Efficient Depth Map Estimation from Light Fields
| Title | Fast and Efficient Depth Map Estimation from Light Fields |
| Authors | Yuriy Anisimov, Didier Stricker |
| Abstract | The paper presents an algorithm for depth map estimation from the light field images in relatively small amount of time, using only single thread on CPU. The proposed method improves existing principle of line fitting in 4-dimensional light field space. Line fitting is based on color values comparison using kernel density estimation. Our method utilizes result of Semi-Global Matching (SGM) with Census transform-based matching cost as a border initialization for line fitting. It provides a significant reduction of computations needed to find the best depth match. With the suggested evaluation metric we show that proposed method is applicable for efficient depth map estimation while preserving low computational time compared to others. |
| Tasks | Density Estimation |
| Published | 2018-05-01 |
| URL | http://arxiv.org/abs/1805.00264v1 |
| http://arxiv.org/pdf/1805.00264v1.pdf | |
| PWC | https://paperswithcode.com/paper/fast-and-efficient-depth-map-estimation-from |
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Currency exchange prediction using machine learning, genetic algorithms and technical analysis
| Title | Currency exchange prediction using machine learning, genetic algorithms and technical analysis |
| Authors | Gonçalo Abreu, Rui Neves, Nuno Horta |
| Abstract | Technical analysis is used to discover investment opportunities. To test this hypothesis we propose an hybrid system using machine learning techniques together with genetic algorithms. Using technical analysis there are more ways to represent a currency exchange time series than the ones it is possible to test computationally, i.e., it is unfeasible to search the whole input feature space thus a genetic algorithm is an alternative. In this work, an architecture for automatic feature selection is proposed to optimize the cross validated performance estimation of a Naive Bayes model using a genetic algorithm. The proposed architecture improves the return on investment of the unoptimized system from 0,43% to 10,29% in the validation set. The features selected and the model decision boundary are visualized using the algorithm t-Distributed Stochastic Neighbor embedding. |
| Tasks | Feature Selection, Time Series |
| Published | 2018-05-29 |
| URL | http://arxiv.org/abs/1805.11232v1 |
| http://arxiv.org/pdf/1805.11232v1.pdf | |
| PWC | https://paperswithcode.com/paper/currency-exchange-prediction-using-machine |
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A-CCNN: adaptive ccnn for density estimation and crowd counting
| Title | A-CCNN: adaptive ccnn for density estimation and crowd counting |
| Authors | Saeed Amirgholipour Kasmani, Xiangjian He, Wenjing Jia, Dadong Wang, Michelle Zeibots |
| Abstract | Crowd counting, for estimating the number of people in a crowd using vision-based computer techniques, has attracted much interest in the research community. Although many attempts have been reported, real-world problems, such as huge variation in subjects’ sizes in images and serious occlusion among people, make it still a challenging problem. In this paper, we propose an Adaptive Counting Convolutional Neural Network (A-CCNN) and consider the scale variation of objects in a frame adaptively so as to improve the accuracy of counting. Our method takes advantages of contextual information to provide more accurate and adaptive density maps and crowd counting in a scene. Extensively experimental evaluation is conducted using different benchmark datasets for object-counting and shows that the proposed approach is effective and outperforms state-of-the-art approaches. |
| Tasks | Crowd Counting, Density Estimation, Object Counting |
| Published | 2018-04-19 |
| URL | http://arxiv.org/abs/1804.06958v2 |
| http://arxiv.org/pdf/1804.06958v2.pdf | |
| PWC | https://paperswithcode.com/paper/a-ccnn-adaptive-ccnn-for-density-estimation |
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TGE-viz : Transition Graph Embedding for Visualization of Plan Traces and Domains
| Title | TGE-viz : Transition Graph Embedding for Visualization of Plan Traces and Domains |
| Authors | Sriram Gopalakrishnan, Subbarao Kambhampati |
| Abstract | Existing work for plan trace visualization in automated planning uses pipeline-style visualizations, similar to plans in Gantt charts. Such visualization do not capture the domain structure or dependencies between the various fluents and actions. Additionally, plan traces in such visualizations cannot be easily compared with one another without parsing the details of individual actions, which imposes a higher cognitive load. We introduce TGE-viz, a technique to visualize plan traces within an embedding of the entire transition graph of a domain in low dimensional space. TGE-viz allows users to visualize and criticize plans more intuitively for mixed-initiative planning. It also allows users to visually appraise the structure of domains and the dependencies in it. |
| Tasks | Graph Embedding |
| Published | 2018-11-24 |
| URL | http://arxiv.org/abs/1811.09900v1 |
| http://arxiv.org/pdf/1811.09900v1.pdf | |
| PWC | https://paperswithcode.com/paper/tge-viz-transition-graph-embedding-for |
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Artificial Intelligence (AI) Methods in Optical Networks: A Comprehensive Survey
| Title | Artificial Intelligence (AI) Methods in Optical Networks: A Comprehensive Survey |
| Authors | Javier Mata, Ignacio de Miguel, Ramó n J. Durá n, Noemí Merayo, Sandeep Kumar Singh, Admela Jukan, Mohit Chamania |
| Abstract | Artificial intelligence (AI) is an extensive scientific discipline which enables computer systems to solve problems by emulating complex biological processes such as learning, reasoning and self-correction. This paper presents a comprehensive review of the application of AI techniques for improving performance of optical communication systems and networks. The use of AI-based techniques is first studied in applications related to optical transmission, ranging from the characterization and operation of network components to performance monitoring, mitigation of nonlinearities, and quality of transmission estimation. Then, applications related to optical network control and management are also reviewed, including topics like optical network planning and operation in both transport and access networks. Finally, the paper also presents a summary of opportunities and challenges in optical networking where AI is expected to play a key role in the near future. |
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| Published | 2018-01-05 |
| URL | http://arxiv.org/abs/1801.01704v2 |
| http://arxiv.org/pdf/1801.01704v2.pdf | |
| PWC | https://paperswithcode.com/paper/artificial-intelligence-ai-methods-in-optical |
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Parameter Sharing Reinforcement Learning Architecture for Multi Agent Driving Behaviors
| Title | Parameter Sharing Reinforcement Learning Architecture for Multi Agent Driving Behaviors |
| Authors | Meha Kaushik, Phaniteja S, K. Madhava Krishna |
| Abstract | Multi-agent learning provides a potential framework for learning and simulating traffic behaviors. This paper proposes a novel architecture to learn multiple driving behaviors in a traffic scenario. The proposed architecture can learn multiple behaviors independently as well as simultaneously. We take advantage of the homogeneity of agents and learn in a parameter sharing paradigm. To further speed up the training process asynchronous updates are employed into the architecture. While learning different behaviors simultaneously, the given framework was also able to learn cooperation between the agents, without any explicit communication. We applied this framework to learn two important behaviors in driving: 1) Lane-Keeping and 2) Over-Taking. Results indicate faster convergence and learning of a more generic behavior, that is scalable to any number of agents. When compared the results with existing approaches, our results indicate equal and even better performance in some cases. |
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| Published | 2018-11-17 |
| URL | http://arxiv.org/abs/1811.07214v1 |
| http://arxiv.org/pdf/1811.07214v1.pdf | |
| PWC | https://paperswithcode.com/paper/parameter-sharing-reinforcement-learning |
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