Paper Group ANR 512
Deep Neural Object Analysis by Interactive Auditory Exploration with a Humanoid Robot. Multiobjective Test Problems with Degenerate Pareto Fronts. High-Dimensional Inference for Cluster-Based Graphical Models. Global Convergence Analysis of the Flower Pollination Algorithm: A Discrete-Time Markov Chain Approach. On The Utility of Conditional Genera …
Deep Neural Object Analysis by Interactive Auditory Exploration with a Humanoid Robot
| Title | Deep Neural Object Analysis by Interactive Auditory Exploration with a Humanoid Robot |
| Authors | Manfred Eppe, Matthias Kerzel, Erik Strahl, Stefan Wermter |
| Abstract | We present a novel approach for interactive auditory object analysis with a humanoid robot. The robot elicits sensory information by physically shaking visually indistinguishable plastic capsules. It gathers the resulting audio signals from microphones that are embedded into the robotic ears. A neural network architecture learns from these signals to analyze properties of the contents of the containers. Specifically, we evaluate the material classification and weight prediction accuracy and demonstrate that the framework is fairly robust to acoustic real-world noise. |
| Tasks | Material Classification |
| Published | 2018-07-03 |
| URL | http://arxiv.org/abs/1807.01035v2 |
| http://arxiv.org/pdf/1807.01035v2.pdf | |
| PWC | https://paperswithcode.com/paper/deep-neural-object-analysis-by-interactive |
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Multiobjective Test Problems with Degenerate Pareto Fronts
| Title | Multiobjective Test Problems with Degenerate Pareto Fronts |
| Authors | Liangli Zhen, Miqing Li, Ran Cheng, Dezhong Peng, Xin Yao |
| Abstract | In multiobjective optimization, a set of scalable test problems with a variety of features allows researchers to investigate and evaluate abilities of different optimization algorithms, and thus can help them to design and develop more effective and efficient approaches. Existing, commonly-used test problem suites are mainly focused on the situations where all the objectives are conflicting with each other. However, in some many-objective optimization problems, there may be unexpected characteristics among objectives, e.g., redundancy. This leads to a degenerate problem. In this paper, we systematically study degenerate problems. We abstract three generic characteristics of degenerate problems, and on the basis of these characteristics we present a set of test problems, in order to support the investigation of multiobjective search algorithms on problems with redundant objectives. To assess the proposed test problems, ten representative multiobjective evolutionary algorithms are tested. The results indicate that none of the tested algorithms is able to effectively solve these proposed problems, calling for the need of developing new approaches to addressing degenerate multi-objective problems. |
| Tasks | Multiobjective Optimization |
| Published | 2018-06-07 |
| URL | http://arxiv.org/abs/1806.02706v1 |
| http://arxiv.org/pdf/1806.02706v1.pdf | |
| PWC | https://paperswithcode.com/paper/multiobjective-test-problems-with-degenerate |
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High-Dimensional Inference for Cluster-Based Graphical Models
| Title | High-Dimensional Inference for Cluster-Based Graphical Models |
| Authors | Carson Eisenach, Florentina Bunea, Yang Ning, Claudiu Dinicu |
| Abstract | Motivated by modern applications in which one constructs graphical models based on a very large number of features, this paper introduces a new class of cluster-based graphical models. Unlike standard graphical models, variable clustering is applied as an initial step for reducing the dimension of the feature space. We employ model assisted clustering, in which the clusters contain features that are similar to the same unobserved latent variable. Two different cluster-based Gaussian graphical models are considered: the latent variable graph, corresponding to the graphical model associated with the unobserved latent variables, and the cluster-average graph, corresponding to the vector of features averaged over clusters. We derive estimates tailored to these graphs, with the goal of pattern recovery under false discovery rate (FDR) control. Our study reveals that likelihood based inference for the latent graph is analytically intractable, and we develop alternative estimation and inference strategies. We replace the likelihood of the data by appropriate empirical risk functions that allow for valid inference in both graphical models under study. Our main results are Berry-Esseen central limit theorems for the proposed estimators, which are proved under weaker assumptions than those employed in the existing literature on Gaussian graphical model inference. We make explicit the implications of the asymptotic approximations on graph recovery under FDR control, and show when it can be controlled asymptotically. Our analysis takes into account the uncertainty induced by the initial clustering step. We find that the errors induced by clustering are asymptotically ignorable in the follow-up analysis, under no further restrictions on the parameter space for which inference is valid. The theoretical properties of the proposed procedures are verified on simulated data and an fMRI data analysis. |
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| Published | 2018-06-13 |
| URL | http://arxiv.org/abs/1806.05139v1 |
| http://arxiv.org/pdf/1806.05139v1.pdf | |
| PWC | https://paperswithcode.com/paper/high-dimensional-inference-for-cluster-based |
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Global Convergence Analysis of the Flower Pollination Algorithm: A Discrete-Time Markov Chain Approach
| Title | Global Convergence Analysis of the Flower Pollination Algorithm: A Discrete-Time Markov Chain Approach |
| Authors | Xingshi He, Xin-She Yang, Mehmet Karamanoglu, Yuxin Zhao |
| Abstract | Flower pollination algorithm is a recent metaheuristic algorithm for solving nonlinear global optimization problems. The algorithm has also been extended to solve multiobjective optimization with promising results. In this work, we analyze this algorithm mathematically and prove its convergence properties by using Markov chain theory. By constructing the appropriate transition probability for a population of flower pollen and using the homogeneity property, it can be shown that the constructed stochastic sequences can converge to the optimal set. Under the two proper conditions for convergence, it is proved that the simplified flower pollination algorithm can indeed satisfy these convergence conditions and thus the global convergence of this algorithm can be guaranteed. Numerical experiments are used to demonstrate that the flower pollination algorithm can converge quickly in practice and can thus achieve global optimality efficiently. |
| Tasks | Multiobjective Optimization |
| Published | 2018-04-21 |
| URL | http://arxiv.org/abs/1804.07995v1 |
| http://arxiv.org/pdf/1804.07995v1.pdf | |
| PWC | https://paperswithcode.com/paper/global-convergence-analysis-of-the-flower |
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On The Utility of Conditional Generation Based Mutual Information for Characterizing Adversarial Subspaces
| Title | On The Utility of Conditional Generation Based Mutual Information for Characterizing Adversarial Subspaces |
| Authors | Chia-Yi Hsu, Pei-Hsuan Lu, Pin-Yu Chen, Chia-Mu Yu |
| Abstract | Recent studies have found that deep learning systems are vulnerable to adversarial examples; e.g., visually unrecognizable adversarial images can easily be crafted to result in misclassification. The robustness of neural networks has been studied extensively in the context of adversary detection, which compares a metric that exhibits strong discriminate power between natural and adversarial examples. In this paper, we propose to characterize the adversarial subspaces through the lens of mutual information (MI) approximated by conditional generation methods. We use MI as an information-theoretic metric to strengthen existing defenses and improve the performance of adversary detection. Experimental results on MagNet defense demonstrate that our proposed MI detector can strengthen its robustness against powerful adversarial attacks. |
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| Published | 2018-09-24 |
| URL | http://arxiv.org/abs/1809.08986v1 |
| http://arxiv.org/pdf/1809.08986v1.pdf | |
| PWC | https://paperswithcode.com/paper/on-the-utility-of-conditional-generation |
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On Ternary Coding and Three-Valued Logic
| Title | On Ternary Coding and Three-Valued Logic |
| Authors | Subhash Kak |
| Abstract | Mathematically, ternary coding is more efficient than binary coding. It is little used in computation because technology for binary processing is already established and the implementation of ternary coding is more complicated, but remains relevant in algorithms that use decision trees and in communications. In this paper we present a new comparison of binary and ternary coding and their relative efficiencies are computed both for number representation and decision trees. The implications of our inability to use optimal representation through mathematics or logic are examined. Apart from considerations of representation efficiency, ternary coding appears preferable to binary coding in classification of many real-world problems of artificial intelligence (AI) and medicine. We examine the problem of identifying appropriate three classes for domain-specific applications. |
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| Published | 2018-07-13 |
| URL | http://arxiv.org/abs/1807.06419v1 |
| http://arxiv.org/pdf/1807.06419v1.pdf | |
| PWC | https://paperswithcode.com/paper/on-ternary-coding-and-three-valued-logic |
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Learning from past scans: Tomographic reconstruction to detect new structures
| Title | Learning from past scans: Tomographic reconstruction to detect new structures |
| Authors | Preeti Gopal, Sharat Chandran, Imants Svalbe, Ajit Rajwade |
| Abstract | The need for tomographic reconstruction from sparse measurements arises when the measurement process is potentially harmful, needs to be rapid, or is uneconomical. In such cases, prior information from previous longitudinal scans of the same or similar objects helps to reconstruct the current object whilst requiring significantly fewer `updating’ measurements. However, a significant limitation of all prior-based methods is the possible dominance of the prior over the reconstruction of new localised information that has evolved within the test object. In this paper, we improve the state of the art by (1) detecting potential regions where new changes may have occurred, and (2) effectively reconstructing both the old and new structures by computing regional weights that moderate the local influence of the priors. We have tested the efficacy of our method on synthetic as well as real volume data. The results demonstrate that using weighted priors significantly improves the overall quality of the reconstructed data whilst minimising their impact on regions that contain new information. | |
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| Published | 2018-12-23 |
| URL | http://arxiv.org/abs/1812.10998v1 |
| http://arxiv.org/pdf/1812.10998v1.pdf | |
| PWC | https://paperswithcode.com/paper/learning-from-past-scans-tomographic |
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Multi-D Kneser-Ney Smoothing Preserving the Original Marginal Distributions
| Title | Multi-D Kneser-Ney Smoothing Preserving the Original Marginal Distributions |
| Authors | András Dobó |
| Abstract | Smoothing is an essential tool in many NLP tasks, therefore numerous techniques have been developed for this purpose in the past. One of the most widely used smoothing methods are the Kneser-Ney smoothing (KNS) and its variants, including the Modified Kneser-Ney smoothing (MKNS), which are widely considered to be among the best smoothing methods available. Although when creating the original KNS the intention of the authors was to develop such a smoothing method that preserves the marginal distributions of the original model, this property was not maintained when developing the MKNS. In this article I would like to overcome this and propose such a refined version of the MKNS that preserves these marginal distributions while keeping the advantages of both previous versions. Beside its advantageous properties, this novel smoothing method is shown to achieve about the same results as the MKNS in a standard language modelling task. |
| Tasks | Language Modelling |
| Published | 2018-07-10 |
| URL | http://arxiv.org/abs/1807.03583v1 |
| http://arxiv.org/pdf/1807.03583v1.pdf | |
| PWC | https://paperswithcode.com/paper/multi-d-kneser-ney-smoothing-preserving-the |
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A Corpus of English-Hindi Code-Mixed Tweets for Sarcasm Detection
| Title | A Corpus of English-Hindi Code-Mixed Tweets for Sarcasm Detection |
| Authors | Sahil Swami, Ankush Khandelwal, Vinay Singh, Syed Sarfaraz Akhtar, Manish Shrivastava |
| Abstract | Social media platforms like twitter and facebook have be- come two of the largest mediums used by people to express their views to- wards different topics. Generation of such large user data has made NLP tasks like sentiment analysis and opinion mining much more important. Using sarcasm in texts on social media has become a popular trend lately. Using sarcasm reverses the meaning and polarity of what is implied by the text which poses challenge for many NLP tasks. The task of sarcasm detection in text is gaining more and more importance for both commer- cial and security services. We present the first English-Hindi code-mixed dataset of tweets marked for presence of sarcasm and irony where each token is also annotated with a language tag. We present a baseline su- pervised classification system developed using the same dataset which achieves an average F-score of 78.4 after using random forest classifier and performing 10-fold cross validation. |
| Tasks | Opinion Mining, Sarcasm Detection, Sentiment Analysis |
| Published | 2018-05-30 |
| URL | http://arxiv.org/abs/1805.11869v1 |
| http://arxiv.org/pdf/1805.11869v1.pdf | |
| PWC | https://paperswithcode.com/paper/a-corpus-of-english-hindi-code-mixed-tweets |
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CascadeCNN: Pushing the performance limits of quantisation
| Title | CascadeCNN: Pushing the performance limits of quantisation |
| Authors | Alexandros Kouris, Stylianos I. Venieris, Christos-Savvas Bouganis |
| Abstract | This work presents CascadeCNN, an automated toolflow that pushes the quantisation limits of any given CNN model, to perform high-throughput inference by exploiting the computation time-accuracy trade-off. Without the need for retraining, a two-stage architecture tailored for any given FPGA device is generated, consisting of a low- and a high-precision unit. A confidence evaluation unit is employed between them to identify misclassified cases at run time and forward them to the high-precision unit or terminate computation. Experiments demonstrate that CascadeCNN achieves a performance boost of up to 55% for VGG-16 and 48% for AlexNet over the baseline design for the same resource budget and accuracy. |
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| Published | 2018-05-22 |
| URL | http://arxiv.org/abs/1805.08743v1 |
| http://arxiv.org/pdf/1805.08743v1.pdf | |
| PWC | https://paperswithcode.com/paper/cascadecnn-pushing-the-performance-limits-of |
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Sparse One-Time Grab Sampling of Inliers
| Title | Sparse One-Time Grab Sampling of Inliers |
| Authors | Maryam Jaberi, Marianna Pensky, Hassan Foroosh |
| Abstract | Estimating structures in “big data” and clustering them are among the most fundamental problems in computer vision, pattern recognition, data mining, and many other other research fields. Over the past few decades, many studies have been conducted focusing on different aspects of these problems. One of the main approaches that is explored in the literature to tackle the problems of size and dimensionality is sampling subsets of the data in order to estimate the characteristics of the whole population, e.g. estimating the underlying clusters or structures in the data. In this paper, we propose a one-time-grab' sampling algorithm\cite{jaberi2015swift,jaberi2018sparse}. This method can be used as the front end to any supervised or unsupervised clustering method. Rather than focusing on the strategy of maximizing the probability of sampling inliers, our goal is to minimize the number of samples needed to instantiate all underlying model instances. More specifically, our goal is to answer the following question: {\em Given a very large population of points with $C$ embedded structures and gross outliers, what is the minimum number of points $r$ to be selected randomly in one grab in order to make sure with probability $P$ that at least $\varepsilon$ points are selected on each structure, where $\varepsilon$ is the number of degrees of freedom of each structure.'} |
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| Published | 2018-12-21 |
| URL | http://arxiv.org/abs/1901.02338v1 |
| http://arxiv.org/pdf/1901.02338v1.pdf | |
| PWC | https://paperswithcode.com/paper/sparse-one-time-grab-sampling-of-inliers |
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Image computing for fibre-bundle endomicroscopy: A review
| Title | Image computing for fibre-bundle endomicroscopy: A review |
| Authors | Antonios Perperidis, Kevin Dhaliwal, Stephen McLaughlin, Tom Vercauteren |
| Abstract | Endomicroscopy is an emerging imaging modality, that facilitates the acquisition of in vivo, in situ optical biopsies, assisting diagnostic and potentially therapeutic interventions. While there is a diverse and constantly expanding range of commercial and experimental optical biopsy platforms available, fibre-bundle endomicroscopy is currently the most widely used platform and is approved for clinical use in a range of clinical indications. Miniaturised, flexible fibre-bundles, guided through the working channel of endoscopes, needles and catheters, enable high-resolution imaging across a variety of organ systems. Yet, the nature of image acquisition though a fibre-bundle gives rise to several inherent characteristics and limitations necessitating novel and effective image pre- and post-processing algorithms, ranging from image formation, enhancement and mosaicing to pathology detection and quantification. This paper introduces the underlying technology and most prevalent clinical applications of fibre-bundle endomicroscopy, and provides a comprehensive, up-to-date, review of relevant image reconstruction, analysis and understanding/inference methodologies. Furthermore, current limitations as well as future challenges and opportunities in fibre-bundle endomicroscopy computing are identified and discussed. |
| Tasks | Image Reconstruction |
| Published | 2018-09-03 |
| URL | http://arxiv.org/abs/1809.00604v1 |
| http://arxiv.org/pdf/1809.00604v1.pdf | |
| PWC | https://paperswithcode.com/paper/image-computing-for-fibre-bundle |
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An Event Detection Approach Based On Twitter Hashtags
| Title | An Event Detection Approach Based On Twitter Hashtags |
| Authors | Shih-Feng Yang, Julia Taylor Rayz |
| Abstract | Twitter is one of the most popular microblogging services in the world. The great amount of information within Twitter makes it an important information channel for people to learn and share news. Twitter hashtag is an popular feature that can be viewed as human-labeled information which people use to identify the topic of a tweet. Many researchers have proposed event-detection approaches that can monitor Twitter data and determine whether special events, such as accidents, extreme weather, earthquakes, or crimes take place. Although many approaches use hashtags as one of their features, few of them explicitly focus on the effectiveness of using hashtags on event detection. In this study, we proposed an event detection approach that utilizes hashtags in tweets. We adopted the feature extraction used in STREAMCUBE and applied a clustering K-means approach to it. The experiments demonstrated that the K-means approach performed better than STREAMCUBE in the clustering results. A discussion on optimal K values for the K-means approach is also provided. |
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| Published | 2018-04-02 |
| URL | http://arxiv.org/abs/1804.11243v1 |
| http://arxiv.org/pdf/1804.11243v1.pdf | |
| PWC | https://paperswithcode.com/paper/an-event-detection-approach-based-on-twitter |
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DRE-Bot: A Hierarchical First Person Shooter Bot Using Multiple Sarsa(λ) Reinforcement Learners
| Title | DRE-Bot: A Hierarchical First Person Shooter Bot Using Multiple Sarsa(λ) Reinforcement Learners |
| Authors | Frank G. Glavin, Michael G. Madden |
| Abstract | This paper describes an architecture for controlling non-player characters (NPC) in the First Person Shooter (FPS) game Unreal Tournament 2004. Specifically, the DRE-Bot architecture is made up of three reinforcement learners, Danger, Replenish and Explore, which use the tabular Sarsa({\lambda}) algorithm. This algorithm enables the NPC to learn through trial and error building up experience over time in an approach inspired by human learning. Experimentation is carried to measure the performance of DRE-Bot when competing against fixed strategy bots that ship with the game. The discount parameter, {\gamma}, and the trace parameter, {\lambda}, are also varied to see if their values have an effect on the performance. |
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| Published | 2018-06-13 |
| URL | http://arxiv.org/abs/1806.05106v1 |
| http://arxiv.org/pdf/1806.05106v1.pdf | |
| PWC | https://paperswithcode.com/paper/dre-bot-a-hierarchical-first-person-shooter |
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What Level of Quality can Neural Machine Translation Attain on Literary Text?
| Title | What Level of Quality can Neural Machine Translation Attain on Literary Text? |
| Authors | Antonio Toral, Andy Way |
| Abstract | Given the rise of a new approach to MT, Neural MT (NMT), and its promising performance on different text types, we assess the translation quality it can attain on what is perceived to be the greatest challenge for MT: literary text. Specifically, we target novels, arguably the most popular type of literary text. We build a literary-adapted NMT system for the English-to-Catalan translation direction and evaluate it against a system pertaining to the previous dominant paradigm in MT: statistical phrase-based MT (PBSMT). To this end, for the first time we train MT systems, both NMT and PBSMT, on large amounts of literary text (over 100 million words) and evaluate them on a set of twelve widely known novels spanning from the the 1920s to the present day. According to the BLEU automatic evaluation metric, NMT is significantly better than PBSMT (p < 0.01) on all the novels considered. Overall, NMT results in a 11% relative improvement (3 points absolute) over PBSMT. A complementary human evaluation on three of the books shows that between 17% and 34% of the translations, depending on the book, produced by NMT (versus 8% and 20% with PBSMT) are perceived by native speakers of the target language to be of equivalent quality to translations produced by a professional human translator. |
| Tasks | Machine Translation |
| Published | 2018-01-15 |
| URL | http://arxiv.org/abs/1801.04962v1 |
| http://arxiv.org/pdf/1801.04962v1.pdf | |
| PWC | https://paperswithcode.com/paper/what-level-of-quality-can-neural-machine |
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