Paper Group ANR 531
Heterogeneous Network Motifs. Bilinear discriminant feature line analysis for image feature extraction. Estimating Mass Distribution of Articulated Objects through Non-prehensile Manipulation. Canada Protocol: an ethical checklist for the use of Artificial Intelligence in Suicide Prevention and Mental Health. Classification of Motorcycles using Ext …
Heterogeneous Network Motifs
| Title | Heterogeneous Network Motifs |
| Authors | Ryan A. Rossi, Nesreen K. Ahmed, Aldo Carranza, David Arbour, Anup Rao, Sungchul Kim, Eunyee Koh |
| Abstract | Many real-world applications give rise to large heterogeneous networks where nodes and edges can be of any arbitrary type (e.g., user, web page, location). Special cases of such heterogeneous graphs include homogeneous graphs, bipartite, k-partite, signed, labeled graphs, among many others. In this work, we generalize the notion of network motifs to heterogeneous networks. In particular, small induced typed subgraphs called typed graphlets (heterogeneous network motifs) are introduced and shown to be the fundamental building blocks of complex heterogeneous networks. Typed graphlets are a powerful generalization of the notion of graphlet (network motif) to heterogeneous networks as they capture both the induced subgraph of interest and the types associated with the nodes in the induced subgraph. To address this problem, we propose a fast, parallel, and space-efficient framework for counting typed graphlets in large networks. We discover the existence of non-trivial combinatorial relationships between lower-order ($k-1$)-node typed graphlets and leverage them for deriving many of the $k$-node typed graphlets in $o(1)$ constant time. Thus, we avoid explicit enumeration of those typed graphlets. Notably, the time complexity matches the best untyped graphlet counting algorithm. The experiments demonstrate the effectiveness of the proposed framework in terms of runtime, space-efficiency, parallel speedup, and scalability as it is able to handle large-scale networks. |
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| Published | 2019-01-28 |
| URL | https://arxiv.org/abs/1901.10026v3 |
| https://arxiv.org/pdf/1901.10026v3.pdf | |
| PWC | https://paperswithcode.com/paper/heterogeneous-network-motifs |
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Bilinear discriminant feature line analysis for image feature extraction
| Title | Bilinear discriminant feature line analysis for image feature extraction |
| Authors | Lijun Yan, Jun-Bao Li, Xiaorui Zhu, Jeng-Shyang Pan, Linlin Tang |
| Abstract | A novel bilinear discriminant feature line analysis (BDFLA) is proposed for image feature extraction. The nearest feature line (NFL) is a powerful classifier. Some NFL-based subspace algorithms were introduced recently. In most of the classical NFL-based subspace learning approaches, the input samples are vectors. For image classification tasks, the image samples should be transformed to vectors first. This process induces a high computational complexity and may also lead to loss of the geometric feature of samples. The proposed BDFLA is a matrix-based algorithm. It aims to minimise the within-class scatter and maximise the between-class scatter based on a two-dimensional (2D) NFL. Experimental results on two-image databases confirm the effectiveness. |
| Tasks | Image Classification |
| Published | 2019-05-03 |
| URL | https://arxiv.org/abs/1905.03710v1 |
| https://arxiv.org/pdf/1905.03710v1.pdf | |
| PWC | https://paperswithcode.com/paper/190503710 |
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Estimating Mass Distribution of Articulated Objects through Non-prehensile Manipulation
| Title | Estimating Mass Distribution of Articulated Objects through Non-prehensile Manipulation |
| Authors | K. Niranjan Kumar, Irfan Essa, C. Karen Liu |
| Abstract | We explore the problem of estimating the mass distribution of an articulated object by an interactive robotic agent. Our method predicts the mass distribution of an object by using limited sensing and actuating capabilities of a robotic agent during an interaction with the object. Inspired by the role of exploratory play in human infants, we take the combined approach of supervised and reinforcement learning to train an agent such that it learns to strategically interact with the object for estimating its mass distribution. Our method consists of two neural networks: (i) the policy network which decides how to interact with the object, and (ii) the predictor network that estimates the mass distribution given a history of observations and interactions. Using our method, we train a robotic arm to estimate the mass distribution of an object with moving parts (e.g. an articulated rigid body system) by pushing it on a surface with unknown friction properties. We show the robustness of our method across different physics simulators and robotic platforms. We further test our method on a real robot platform with 3D printed articulated chains with varying mass distributions. We present results that demonstrate that our method significantly outperforms the baseline agent that uses random pushes to interact with the object. |
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| Published | 2019-07-09 |
| URL | https://arxiv.org/abs/1907.03964v3 |
| https://arxiv.org/pdf/1907.03964v3.pdf | |
| PWC | https://paperswithcode.com/paper/estimating-mass-distribution-of-articulated |
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Canada Protocol: an ethical checklist for the use of Artificial Intelligence in Suicide Prevention and Mental Health
| Title | Canada Protocol: an ethical checklist for the use of Artificial Intelligence in Suicide Prevention and Mental Health |
| Authors | Carl-Maria Mörch, Abhishek Gupta, Brian L. Mishara |
| Abstract | Introduction: To improve current public health strategies in suicide prevention and mental health, governments, researchers and private companies increasingly use information and communication technologies, and more specifically Artificial Intelligence and Big Data. These technologies are promising but raise ethical challenges rarely covered by current legal systems. It is essential to better identify, and prevent potential ethical risks. Objectives: The Canada Protocol - MHSP is a tool to guide and support professionals, users, and researchers using AI in mental health and suicide prevention. Methods: A checklist was constructed based upon ten international reports on AI and ethics and two guides on mental health and new technologies. 329 recommendations were identified, of which 43 were considered as applicable to Mental Health and AI. The checklist was validated, using a two round Delphi Consultation. Results: 16 experts participated in the first round of the Delphi Consultation and 8 participated in the second round. Of the original 43 items, 38 were retained. They concern five categories: “Description of the Autonomous Intelligent System” (n=8), “Privacy and Transparency” (n=8), “Security” (n=6), “Health-Related Risks” (n=8), “Biases” (n=8). The checklist was considered relevant by most users, and could need versions tailored to each category of target users. |
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| Published | 2019-07-17 |
| URL | https://arxiv.org/abs/1907.07493v1 |
| https://arxiv.org/pdf/1907.07493v1.pdf | |
| PWC | https://paperswithcode.com/paper/canada-protocol-an-ethical-checklist-for-the |
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Classification of Motorcycles using Extracted Images of Traffic Monitoring Videos
| Title | Classification of Motorcycles using Extracted Images of Traffic Monitoring Videos |
| Authors | Adriano Belletti Felicio, André Luiz Cunha |
| Abstract | Due to the great growth of motorcycles in the urban fleet and the growth of the study on its behavior and of how this vehicle affects the flow of traffic becomes necessary the development of tools and techniques different from the conventional ones to identify its presence in the traffic flow and be able to extract your information. The article in question attempts to contribute to the study on this type of vehicle by generating a motorcycle image bank and developing and calibrating a motorcycle classifier by combining the LBP techniques to create the characteristic vectors and the classification technique LinearSVC to perform the predictions. In this way the classifier of vehicles of the type motorcycle developed in this research can classify the images of vehicles extracted of videos of monitoring between two classes motorcycles and non-motorcycles with a precision and an accuracy superior to 0,9. |
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| Published | 2019-03-30 |
| URL | https://arxiv.org/abs/1904.00247v2 |
| https://arxiv.org/pdf/1904.00247v2.pdf | |
| PWC | https://paperswithcode.com/paper/classification-of-motorcycles-using-extracted |
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Temporal Graph Convolutional Networks for Automatic Seizure Detection
| Title | Temporal Graph Convolutional Networks for Automatic Seizure Detection |
| Authors | Ian Covert, Balu Krishnan, Imad Najm, Jiening Zhan, Matthew Shore, John Hixson, Ming Jack Po |
| Abstract | Seizure detection from EEGs is a challenging and time consuming clinical problem that would benefit from the development of automated algorithms. EEGs can be viewed as structural time series, because they are multivariate time series where the placement of leads on a patient’s scalp provides prior information about the structure of interactions. Commonly used deep learning models for time series don’t offer a way to leverage structural information, but this would be desirable in a model for structural time series. To address this challenge, we propose the temporal graph convolutional network (TGCN), a model that leverages structural information and has relatively few parameters. TGCNs apply feature extraction operations that are localized and shared over both time and space, thereby providing a useful inductive bias in tasks where one expects similar features to be discriminative across the different sequences. In our experiments we focus on metrics that are most important to seizure detection, and demonstrate that TGCN matches the performance of related models that have been shown to be state of the art in other tasks. Additionally, we investigate interpretability advantages of TGCN by exploring approaches for helping clinicians determine when precisely seizures occur, and the parts of the brain that are most involved. |
| Tasks | Seizure Detection, Time Series |
| Published | 2019-05-03 |
| URL | https://arxiv.org/abs/1905.01375v1 |
| https://arxiv.org/pdf/1905.01375v1.pdf | |
| PWC | https://paperswithcode.com/paper/temporal-graph-convolutional-networks-for |
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Parallel Hardware for Faster Morphological Analysis
| Title | Parallel Hardware for Faster Morphological Analysis |
| Authors | Issam Damaj, Mahmoud Imdoukh, Rached Zantout |
| Abstract | Morphological analysis in the Arabic language is computationally intensive, has numerous forms and rules, and is intrinsically parallel. The investigation presented in this paper confirms that the effective development of parallel algorithms and the derivation of corresponding processors in hardware enable implementations with appealing performance characteristics. The presented developments of parallel hardware comprise the application of a variety of algorithm modelling techniques, strategies for concurrent processing, and the creation of pioneering hardware implementations that target modern programmable devices. The investigation includes the creation of a linguistic-based stemmer for Arabic verb root extraction with extended infix processing to attain high-levels of accuracy. The implementations comprise three versions, namely, software, non-pipelined processor, and pipelined processor with high throughput. The targeted systems are high-performance multi-core processors for software implementations and high-end Field Programmable Gate Array systems for hardware implementations. The investigation includes a thorough evaluation of the methodology, and performance and accuracy analyses of the developed software and hardware implementations. The pipelined processor achieved a significant speedup of 5571.4 over the software implementation. The developed stemmer for verb root extraction with infix processing attained accuracies of 87% and 90.7% for analyzing the texts of the Holy Quran and its Chapter 29 - Surat Al-Ankabut. |
| Tasks | Morphological Analysis |
| Published | 2019-04-09 |
| URL | http://arxiv.org/abs/1904.07148v1 |
| http://arxiv.org/pdf/1904.07148v1.pdf | |
| PWC | https://paperswithcode.com/paper/190407148 |
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Non-negative matrix factorization based on generalized dual divergence
| Title | Non-negative matrix factorization based on generalized dual divergence |
| Authors | Karthik Devarajan |
| Abstract | A theoretical framework for non-negative matrix factorization based on generalized dual Kullback-Leibler divergence, which includes members of the exponential family of models, is proposed. A family of algorithms is developed using this framework and its convergence proven using the Expectation-Maximization algorithm. The proposed approach generalizes some existing methods for different noise structures and contrasts with the recently proposed quasi-likelihood approach, thus providing a useful alternative for non-negative matrix factorizations. A measure to evaluate the goodness-of-fit of the resulting factorization is described. This framework can be adapted to include penalty, kernel and discriminant functions as well as tensors. |
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| Published | 2019-05-16 |
| URL | https://arxiv.org/abs/1905.07034v1 |
| https://arxiv.org/pdf/1905.07034v1.pdf | |
| PWC | https://paperswithcode.com/paper/non-negative-matrix-factorization-based-on |
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Epileptic seizure classification using statistical sampling and a novel feature selection algorithm
| Title | Epileptic seizure classification using statistical sampling and a novel feature selection algorithm |
| Authors | Md Mursalin, Syed Shamsul Islam, Md Kislu Noman, Adel Ali Al-Jumaily |
| Abstract | Epilepsy is a well-known neuronal disorder that can be identified by interpretation of the electroencephalogram (EEG) signal. Usually, the length of an EEG signal is quite long which is challenging to interpret manually. In this work, we propose an automated epileptic seizure detection method by applying a two-step minimization technique: first, we reduce the data points using a statistical sampling technique and then, we minimize the number of features using our novel feature selection algorithm. We then apply different machine learning algorithms for performance measurement of the proposed feature selection algorithm. The experimental results outperform some of the state-of-the-art methods for seizure detection using the reduced data points and the least number of features. |
| Tasks | EEG, Feature Selection, Seizure Detection |
| Published | 2019-02-25 |
| URL | http://arxiv.org/abs/1902.09962v2 |
| http://arxiv.org/pdf/1902.09962v2.pdf | |
| PWC | https://paperswithcode.com/paper/epileptic-seizure-classification-using |
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Understanding Art through Multi-Modal Retrieval in Paintings
| Title | Understanding Art through Multi-Modal Retrieval in Paintings |
| Authors | Noa Garcia, Benjamin Renoust, Yuta Nakashima |
| Abstract | In computer vision, visual arts are often studied from a purely aesthetics perspective, mostly by analysing the visual appearance of an artistic reproduction to infer its style, its author, or its representative features. In this work, however, we explore art from both a visual and a language perspective. Our aim is to bridge the gap between the visual appearance of an artwork and its underlying meaning, by jointly analysing its aesthetics and its semantics. We introduce the use of multi-modal techniques in the field of automatic art analysis by 1) collecting a multi-modal dataset with fine-art paintings and comments, and 2) exploring robust visual and textual representations in artistic images. |
| Tasks | Art Analysis |
| Published | 2019-04-24 |
| URL | http://arxiv.org/abs/1904.10615v1 |
| http://arxiv.org/pdf/1904.10615v1.pdf | |
| PWC | https://paperswithcode.com/paper/understanding-art-through-multi-modal |
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Non-Monotonic Sequential Text Generation
| Title | Non-Monotonic Sequential Text Generation |
| Authors | Sean Welleck, Kianté Brantley, Hal Daumé III, Kyunghyun Cho |
| Abstract | Standard sequential generation methods assume a pre-specified generation order, such as text generation methods which generate words from left to right. In this work, we propose a framework for training models of text generation that operate in non-monotonic orders; the model directly learns good orders, without any additional annotation. Our framework operates by generating a word at an arbitrary position, and then recursively generating words to its left and then words to its right, yielding a binary tree. Learning is framed as imitation learning, including a coaching method which moves from imitating an oracle to reinforcing the policy’s own preferences. Experimental results demonstrate that using the proposed method, it is possible to learn policies which generate text without pre-specifying a generation order, while achieving competitive performance with conventional left-to-right generation. |
| Tasks | Imitation Learning, Text Generation |
| Published | 2019-02-05 |
| URL | https://arxiv.org/abs/1902.02192v3 |
| https://arxiv.org/pdf/1902.02192v3.pdf | |
| PWC | https://paperswithcode.com/paper/non-monotonic-sequential-text-generation |
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Scalable Nonlinear Planning with Deep Neural Network Learned Transition Models
| Title | Scalable Nonlinear Planning with Deep Neural Network Learned Transition Models |
| Authors | Ga Wu, Buser Say, Scott Sanner |
| Abstract | In many real-world planning problems with factored, mixed discrete and continuous state and action spaces such as Reservoir Control, Heating Ventilation, and Air Conditioning, and Navigation domains, it is difficult to obtain a model of the complex nonlinear dynamics that govern state evolution. However, the ubiquity of modern sensors allows us to collect large quantities of data from each of these complex systems and build accurate, nonlinear deep neural network models of their state transitions. But there remains one major problem for the task of control – how can we plan with deep network learned transition models without resorting to Monte Carlo Tree Search and other black-box transition model techniques that ignore model structure and do not easily extend to mixed discrete and continuous domains? In this paper, we introduce two types of nonlinear planning methods that can leverage deep neural network learned transition models: Hybrid Deep MILP Planner (HD-MILP-Plan) and Tensorflow Planner (TF-Plan). In HD-MILP-Plan, we make the critical observation that the Rectified Linear Unit transfer function for deep networks not only allows faster convergence of model learning, but also permits a direct compilation of the deep network transition model to a Mixed-Integer Linear Program encoding. Further, we identify deep network specific optimizations for HD-MILP-Plan that improve performance over a base encoding and show that we can plan optimally with respect to the learned deep networks. In TF-Plan, we take advantage of the efficiency of auto-differentiation tools and GPU-based computation where we encode a subclass of purely continuous planning problems as Recurrent Neural Networks and directly optimize the actions through backpropagation. We compare both planners and show that TF-Plan is able to approximate the optimal plans found by HD-MILP-Plan in less computation time… |
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| Published | 2019-04-05 |
| URL | https://arxiv.org/abs/1904.02873v3 |
| https://arxiv.org/pdf/1904.02873v3.pdf | |
| PWC | https://paperswithcode.com/paper/scalable-nonlinear-planning-with-deep-neural |
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Designing Normative Theories of Ethical and Legal Reasoning: LogiKEy Framework, Methodology, and Tool Support
| Title | Designing Normative Theories of Ethical and Legal Reasoning: LogiKEy Framework, Methodology, and Tool Support |
| Authors | Christoph Benzmüller, Xavier Parent, Leendert van der Torre |
| Abstract | A framework and methodology—termed LogiKEy—for the design and engineering of ethical reasoners, normative theories and deontic logics is presented. The overall motivation is the development of suitable means for the control and governance of intelligent autonomous systems. LogiKEy’s unifying formal framework is based on semantical embeddings of deontic logics, logic combinations and ethico-legal domain theories in expressive classic higher-order logic (HOL). This meta-logical approach enables the provision of powerful tool support in LogiKEy: off-the-shelf theorem provers and model finders for HOL are assisting the LogiKEy designer of ethical intelligent agents to flexibly experiment with underlying logics and their combinations, with ethico-legal domain theories, and with concrete examples—all at the same time. Continuous improvements of these off-the-shelf provers, without further ado, leverage the reasoning performance in LogiKEy. Case studies, in which the LogiKEy framework and methodology has been applied and tested, give evidence that HOL’s undecidability often does not hinder efficient experimentation. |
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| Published | 2019-03-25 |
| URL | https://arxiv.org/abs/1903.10187v5 |
| https://arxiv.org/pdf/1903.10187v5.pdf | |
| PWC | https://paperswithcode.com/paper/designing-normative-theories-of-ethical |
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GPU-based parallelism for ASP-solving
| Title | GPU-based parallelism for ASP-solving |
| Authors | Agostino Dovier, Andrea Formisano, Flavio Vella |
| Abstract | Answer Set Programming (ASP) has become, the paradigm of choice in the field of logic programming and non-monotonic reasoning. Thanks to the availability of efficient solvers, ASP has been successfully employed in a large number of application domains. The term GPU-computing indicates a recent programming paradigm aimed at enabling the use of modern parallel Graphical Processing Units (GPUs) for general purpose computing. In this paper we describe an approach to ASP-solving that exploits GPU parallelism. The design of a GPU-based solver poses various challenges due to the peculiarities of GPUs’ software and hardware architectures and to the intrinsic nature of the satisfiability problem. |
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| Published | 2019-09-04 |
| URL | https://arxiv.org/abs/1909.01786v1 |
| https://arxiv.org/pdf/1909.01786v1.pdf | |
| PWC | https://paperswithcode.com/paper/gpu-based-parallelism-for-asp-solving |
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An Automated Testing Framework for Conversational Agents
| Title | An Automated Testing Framework for Conversational Agents |
| Authors | Soodeh Atefi, Mohammad Amin Alipour |
| Abstract | Conversational agents are systems with a conversational interface that afford interaction in spoken language. These systems are becoming prevalent and are preferred in various contexts and for many users. Despite their increasing success, the automated testing infrastructure to support the effective and efficient development of such systems compared to traditional software systems is still limited. Automated testing framework for conversational systems can improve the quality of these systems by assisting developers to write, execute, and maintain test cases. In this paper, we introduce our work-in-progress automated testing framework, and its realization in the Python programming language. We discuss some research problems in the development of such an automated testing framework for conversational agents. In particular, we point out the problems of the specification of the expected behavior, known as test oracles, and semantic comparison of utterances. |
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| Published | 2019-02-17 |
| URL | http://arxiv.org/abs/1902.06193v1 |
| http://arxiv.org/pdf/1902.06193v1.pdf | |
| PWC | https://paperswithcode.com/paper/an-automated-testing-framework-for |
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