aid string | mid string | abstract string | related_work string | ref_abstract dict | title string | text_except_rw string | total_words int64 |
|---|---|---|---|---|---|---|---|
cs9809108 | 2949225035 | We present our approach to the problem of how an agent, within an economic Multi-Agent System, can determine when it should behave strategically (i.e. learn and use models of other agents), and when it should act as a simple price-taker. We provide a framework for the incremental implementation of modeling capabilities... | Within the MAS community, some work @cite_15 has focused on how artificial AI-based learning agents would fare in communities of similar agents. For example, @cite_6 and @cite_8 show how agents can learn the capabilities of others via repeated interactions, but these agents do not learn to predict what actions other mi... | {
"abstract": [
"In multi-agent environments, an intelligent agent often needs to interact with other individuals or groups of agents to achieve its goals. Agent tracking is one key capability required for intelligent interaction. It involves monitoring the observable actions of other agents and inferring their u... | Learning Nested Agent Models in an Information Economy | In open, multi-agent systems, agents can come and go without any central control or guidance, and thus how and which agents interact with each other will change dynamically. Agents might try to manipulate the interactions to their individual benefits, at the cost of the global efficiency. To avoid this, the protocols a... | 7,648 |
1903.05238 | 2963943458 | Abstract Interaction in virtual reality (VR) environments (e.g. grasping and manipulating virtual objects) is essential to ensure a pleasant and immersive experience. In this work, we propose a visually realistic, flexible and robust grasping system that enables real-time interactions in virtual environments. Resulting... | Grasping action is the most basic component of any interaction and it is composed of three major components @cite_21 . The first one is related to the process of approaching the arm and hand to the target object, considering the overall body movement. The second component focuses on the hand and body pre-shaping before... | {
"abstract": [
"Abstract This paper addresses the important issue of automating grasping movement in the animation of virtual actors, and presents a methodology and algorithm to generate realistic looking grasping motion of arbitrary shaped objects. A hybrid approach using both forward and inverse kinematics is ... | A Visually Plausible Grasping System for Object Manipulation and Interaction in Virtual Reality Environments | W ITH the advent of affordable VR headsets such as Oculus VR/Go and HTC Vive, many works and projects are using virtual environments for different purposes. Most of VR applications are related to the entertainment industry (i.e. games and 3D cinema) or architectural visualizations, where virtual scene realism is a corn... | 5,795 |
1903.05238 | 2963943458 | Abstract Interaction in virtual reality (VR) environments (e.g. grasping and manipulating virtual objects) is essential to ensure a pleasant and immersive experience. In this work, we propose a visually realistic, flexible and robust grasping system that enables real-time interactions in virtual environments. Resulting... | Grasping data-driven approaches have existed since a long time ago @cite_21 . These methods are based on large databases of predefined hand poses selected using user criteria or based on grasp taxonomies (i.e. final grasp poses when an object was successfully grasped) which provide us the ability to discriminate betwee... | {
"abstract": [
"Abstract This paper addresses the important issue of automating grasping movement in the animation of virtual actors, and presents a methodology and algorithm to generate realistic looking grasping motion of arbitrary shaped objects. A hybrid approach using both forward and inverse kinematics is ... | A Visually Plausible Grasping System for Object Manipulation and Interaction in Virtual Reality Environments | W ITH the advent of affordable VR headsets such as Oculus VR/Go and HTC Vive, many works and projects are using virtual environments for different purposes. Most of VR applications are related to the entertainment industry (i.e. games and 3D cinema) or architectural visualizations, where virtual scene realism is a corn... | 5,795 |
1903.05238 | 2963943458 | Abstract Interaction in virtual reality (VR) environments (e.g. grasping and manipulating virtual objects) is essential to ensure a pleasant and immersive experience. In this work, we propose a visually realistic, flexible and robust grasping system that enables real-time interactions in virtual environments. Resulting... | The selection process is also constrained by the hand high degree of freedom (DOF). In order to deal with dimensionality and redundancy many researchers have used techniques such as principal component analysis (PCA) @cite_1 @cite_28 . For the same purpose, @cite_22 studied the correlations between hand DOFs aiming to ... | {
"abstract": [
"In this paper, we build upon recent advances in neuroscience research which have shown that control of the human hand during grasping is dominated by movement in a configuration space of highly reduced dimensionality. We extend this concept to robotic hands and show how a similar dimensionality r... | A Visually Plausible Grasping System for Object Manipulation and Interaction in Virtual Reality Environments | W ITH the advent of affordable VR headsets such as Oculus VR/Go and HTC Vive, many works and projects are using virtual environments for different purposes. Most of VR applications are related to the entertainment industry (i.e. games and 3D cinema) or architectural visualizations, where virtual scene realism is a corn... | 5,795 |
1903.05238 | 2963943458 | Abstract Interaction in virtual reality (VR) environments (e.g. grasping and manipulating virtual objects) is essential to ensure a pleasant and immersive experience. In this work, we propose a visually realistic, flexible and robust grasping system that enables real-time interactions in virtual environments. Resulting... | In order to achieve realistic object interactions, physical simulations on the objects should also be considered @cite_29 @cite_11 @cite_26 . Moreover, hand and finger movement trajectories need to be both, kinematically and dynamically valid @cite_19 . @cite_29 simulate hand interaction, such as two hands grasping eac... | {
"abstract": [
"",
"Animated human characters in everyday scenarios must interact with the environment using their hands. Captured human motion can provide a database of realistic examples. However, examples involving contact are difficult to edit and retarget; realism can suffer when a grasp does not appear... | A Visually Plausible Grasping System for Object Manipulation and Interaction in Virtual Reality Environments | W ITH the advent of affordable VR headsets such as Oculus VR/Go and HTC Vive, many works and projects are using virtual environments for different purposes. Most of VR applications are related to the entertainment industry (i.e. games and 3D cinema) or architectural visualizations, where virtual scene realism is a corn... | 5,795 |
cmp-lg9804001 | 1742257591 | Graph Interpolation Grammars are a declarative formalism with an operational semantics. Their goal is to emulate salient features of the human parser, and notably incrementality. The parsing process defined by GIGs incrementally builds a syntactic representation of a sentence as each successive lexeme is read. A GIG ru... | Graph interpolation can be viewed as an extension of tree adjunction to parse graphs. And, indeed, TAGs @cite_2 , by introducing a 2-dimensional formalism into computational linguistics, have made a decisive step towards designing a syntactic theory that is both computationally tractable and linguistically realistic. I... | {
"abstract": [
"In this paper, a tree generating system called a tree adjunct grammar is described and its formal properties are studied relating them to the tree generating systems of Brainerd (Information and Control14 (1969), 217-231) and Rounds (Mathematical Systems Theory 4 (1970), 257-287) and to the recog... | 0 | ||
cmp-lg9804001 | 1742257591 | Graph Interpolation Grammars are a declarative formalism with an operational semantics. Their goal is to emulate salient features of the human parser, and notably incrementality. The parsing process defined by GIGs incrementally builds a syntactic representation of a sentence as each successive lexeme is read. A GIG ru... | In Lexical Functional Grammars @cite_4 , grammatical functions are loosely coupled with phrase structure, which seems to be just the opposite of what is done in a GIG, in which functional edges are part of the phrase structure. Nonetheless, these two approaches share the concern of bringing out a functional structure, ... | {
"abstract": [
"The editor of this volume, who is also author or coauthor of five of the contributions, has provided an introduction that not only affords an overview of the separate articles but also interrelates the basic issues in linguistics, psycholinguistics and cognitive studies that are addressed in this... | 0 | ||
cmp-lg9709004 | 1575569168 | Automatic text categorization is a complex and useful task for many natural language processing applications. Recent approaches to text categorization focus more on algorithms than on resources involved in this operation. In contrast to this trend, we present an approach based on the integration of widely available res... | To our knowledge, lexical databases have been used only once in TC. Hearst @cite_10 adapted a disambiguation algorithm by Yarowsky using WordNet to recognize category occurrences. Categories are made of WordNet terms, which is not the general case of standard or user-defined categories. It is a hard task to adapt WordN... | {
"abstract": [
"This dissertation investigates the role of contextual information in the automated retrieval and display of full-text documents, using robust natural language processing algorithms to automatically detect structure in and assign topic labels to texts. Many long texts are comprised of complex topi... | 0 | ||
cmp-lg9709004 | 1575569168 | Automatic text categorization is a complex and useful task for many natural language processing applications. Recent approaches to text categorization focus more on algorithms than on resources involved in this operation. In contrast to this trend, we present an approach based on the integration of widely available res... | Lexical databases have been employed recently in word sense disambiguation. For example, Agirre and Rigau @cite_3 make use of a semantic distance that takes into account structural factors in WordNet for achieving good results for this task. Additionally, Resnik @cite_2 combines the use of WordNet and a text collection... | {
"abstract": [
"In this paper, we present a new approach for word sense disambiguation (WSD) using an exemplar-based learning algorithm. This approach integrates a diverse set of knowledge sources to disambiguate word sense, including part of speech of neighboring words, morphological form, the unordered set of ... | 0 | ||
cmp-lg9706008 | 2951421399 | This paper describes an experimental comparison of three unsupervised learning algorithms that distinguish the sense of an ambiguous word in untagged text. The methods described in this paper, McQuitty's similarity analysis, Ward's minimum-variance method, and the EM algorithm, assign each instance of an ambiguous word... | Word--sense disambiguation has more commonly been cast as a problem in supervised learning (e.g., @cite_13 , @cite_2 , @cite_24 , @cite_6 , @cite_14 , @cite_5 , @cite_3 , @cite_16 , @cite_37 ). However, all of these methods require that manually sense tagged text be available to train the algorithm. For most domains su... | {
"abstract": [
"The Naive Mix is a new supervised learning algorithm that is based on a sequential method for selecting probabilistic models. The usual objective of model selection is to find a single model that adequately characterizes the data in a training sample. However, during model selection a sequence of... | Distinguishing Word Senses in Untagged Text | 0 | |
cmp-lg9706008 | 2951421399 | This paper describes an experimental comparison of three unsupervised learning algorithms that distinguish the sense of an ambiguous word in untagged text. The methods described in this paper, McQuitty's similarity analysis, Ward's minimum-variance method, and the EM algorithm, assign each instance of an ambiguous word... | A more recent bootstrapping approach is described in @cite_23 . This algorithm requires a small number of training examples to serve as a seed. There are a variety of options discussed for automatically selecting seeds; one is to identify collocations that uniquely distinguish between senses. For plant , the collocatio... | {
"abstract": [
"This paper presents an unsupervised learning algorithm for sense disambiguation that, when trained on unannotated English text, rivals the performance of supervised techniques that require time-consuming hand annotations. The algorithm is based on two powerful constraints---that words tend to hav... | Distinguishing Word Senses in Untagged Text | 0 | |
cmp-lg9706008 | 2951421399 | This paper describes an experimental comparison of three unsupervised learning algorithms that distinguish the sense of an ambiguous word in untagged text. The methods described in this paper, McQuitty's similarity analysis, Ward's minimum-variance method, and the EM algorithm, assign each instance of an ambiguous word... | While @cite_23 does not discuss distinguishing more than 2 senses of a word, there is no immediate reason to doubt that the one sense per collocation'' rule @cite_24 would still hold for a larger number of senses. In future work we will evaluate using the one sense per collocation'' rule to seed our various methods. Th... | {
"abstract": [
"Previous work [Gale, Church and Yarowsky, 1992] showed that with high probability a polysemous word has one sense per discourse. In this paper we show that for certain definitions of collocation, a polysemous word exhibits essentially only one sense per collocation. We test this empirical hypothe... | Distinguishing Word Senses in Untagged Text | 0 | |
cmp-lg9706008 | 2951421399 | This paper describes an experimental comparison of three unsupervised learning algorithms that distinguish the sense of an ambiguous word in untagged text. The methods described in this paper, McQuitty's similarity analysis, Ward's minimum-variance method, and the EM algorithm, assign each instance of an ambiguous word... | Clustering has most often been applied in natural language processing as a method for inducing syntactic or semantically related groupings of words (e.g., @cite_19 , @cite_26 , @cite_25 , @cite_34 , @cite_1 , @cite_35 ). | {
"abstract": [
"Word groupings useful for language processing tasks are increasingly available, as thesauri appear on-line, and as distributional word clustering techniques improve. However, for many tasks, one is interested in relationships among word senses, not words. This paper presents a method for automati... | Distinguishing Word Senses in Untagged Text | 0 | |
cmp-lg9706008 | 2951421399 | This paper describes an experimental comparison of three unsupervised learning algorithms that distinguish the sense of an ambiguous word in untagged text. The methods described in this paper, McQuitty's similarity analysis, Ward's minimum-variance method, and the EM algorithm, assign each instance of an ambiguous word... | An early application of clustering to word--sense disambiguation is described in @cite_29 . There words are represented in terms of the co-occurrence statistics of four letter sequences. This representation uses 97 features to characterize a word, where each feature is a linear combination of letter four-grams formulat... | {
"abstract": [
"The representation of documents and queries as vectors in a high-dimensional space is well-established in information retrieval. The author proposes that the semantics of words and contexts in a text be represented as vectors. The dimensions of the space are words and the initial vectors are dete... | Distinguishing Word Senses in Untagged Text | 0 | |
cmp-lg9706008 | 2951421399 | This paper describes an experimental comparison of three unsupervised learning algorithms that distinguish the sense of an ambiguous word in untagged text. The methods described in this paper, McQuitty's similarity analysis, Ward's minimum-variance method, and the EM algorithm, assign each instance of an ambiguous word... | The features used in this work are complex and difficult to interpret and it isn't clear that this complexity is required. @cite_23 compares his method to @cite_29 and shows that for four words the former performs significantly better in distinguishing between two senses. | {
"abstract": [
"The representation of documents and queries as vectors in a high-dimensional space is well-established in information retrieval. The author proposes that the semantics of words and contexts in a text be represented as vectors. The dimensions of the space are words and the initial vectors are dete... | Distinguishing Word Senses in Untagged Text | 0 | |
cmp-lg9511007 | 2950225692 | This paper presents a new measure of semantic similarity in an IS-A taxonomy, based on the notion of information content. Experimental evaluation suggests that the measure performs encouragingly well (a correlation of r = 0.79 with a benchmark set of human similarity judgments, with an upper bound of r = 0.90 for human... | The literature on corpus-based determination of word similarity has recently been growing by leaps and bounds, and is too extensive to discuss in detail here (for a review, see @cite_1 ), but most approaches to the problem share a common assumption: semantically similar words have similar distributional behavior in a c... | {
"abstract": [
"Selectional constraints are limitations on the applicability of predicates to arguments. For example, the statement \"The number two is blue\" may be syntactically well formed, but at some level it is anomalous-- scBLUE is not a predicate that can be applied to numbers. In this dissertation, I pr... | Using Information Content to Evaluate Semantic Similarity in a Taxonomy | Evaluating semantic relatedness using network representations is a problem with a long history in artificial intelligence and psychology, dating back to the spreading activation approach of Quillian [1968] and Collins and Loftus [1975]. Semantic similarity represents a special case of semantic relatedness: for example,... | 2,749 |
cmp-lg9702008 | 2950202165 | Statistical models of word-sense disambiguation are often based on a small number of contextual features or on a model that is assumed to characterize the interactions among a set of features. Model selection is presented as an alternative to these approaches, where a sequential search of possible models is conducted i... | Statistical analysis of NLP data has often been limited to the application of standard models, such as n-gram (Markov chain) models and the Naive Bayes model. While n-grams perform well in part--of--speech tagging and speech processing, they require a fixed interdependency structure that is inappropriate for the broad ... | {
"abstract": [
"This paper describes an experimental comparison of seven different learning algorithms on the problem of learning to disambiguate the meaning of a word from context. The algorithms tested include statistical, neural-network, decision-tree, rule-based, and case-based classification techniques. The... | Sequential Model Selection for Word Sense Disambiguation * | 0 | |
cmp-lg9702008 | 2950202165 | Statistical models of word-sense disambiguation are often based on a small number of contextual features or on a model that is assumed to characterize the interactions among a set of features. Model selection is presented as an alternative to these approaches, where a sequential search of possible models is conducted i... | In order to utilize models with more complicated interactions among feature variables, @cite_8 introduce the use of sequential model selection and decomposable models for word--sense disambiguation. They recommended a model selection procedure using BSS and the exact conditional test in combination with a test for mode... | {
"abstract": [
"Most probabilistic classifiers used for word-sense disambiguation have either been based on only one contextual feature or have used a model that is simply assumed to characterize the interdependencies among multiple contextual features. In this paper, a different approach to formulating a probab... | Sequential Model Selection for Word Sense Disambiguation * | 0 | |
cmp-lg9702008 | 2950202165 | Statistical models of word-sense disambiguation are often based on a small number of contextual features or on a model that is assumed to characterize the interactions among a set of features. Model selection is presented as an alternative to these approaches, where a sequential search of possible models is conducted i... | Alternative probabilistic approaches have involved using a single contextual feature to perform disambiguation (e.g., @cite_17 , @cite_20 , and @cite_14 present techniques for identifying the optimal feature to use in disambiguation). Maximum Entropy models have been used to express the interactions among multiple feat... | {
"abstract": [
"Previous work [Gale, Church and Yarowsky, 1992] showed that with high probability a polysemous word has one sense per discourse. In this paper we show that for certain definitions of collocation, a polysemous word exhibits essentially only one sense per collocation. We test this empirical hypothe... | Sequential Model Selection for Word Sense Disambiguation * | 0 | |
cmp-lg9607014 | 2950224005 | In this paper, we define the notion of a preventative expression and discuss a corpus study of such expressions in instructional text. We discuss our coding schema, which takes into account both form and function features, and present measures of inter-coder reliability for those features. We then discuss the correlati... | In computational linguistics, on the other hand, positive imperatives have been extensively investigated, both from the point of view of interpretation @cite_13 @cite_8 @cite_6 @cite_1 and generation @cite_9 @cite_10 @cite_4 @cite_7 . Little work, however, has been directed at negative imperatives. (for exceptions see ... | {
"abstract": [
"Currently, computational linguists and cognitive scientists working in the area of discourse and dialogue argue that their subjective judgments are reliable using several different statistics, none of which are easily interpretable or comparable to each other. Meanwhile, researchers in content an... | A Corpus Study of Negative Imperatives in Natural Language Instructions * | While interpreting instructions, an agent is continually faced with a number of possible actions to execute, the majority of which are not appropriate for the situation at hand. An instructor is therefore required not only to prescribe the appropriate actions to the reader, but also to prevent the reader from executing... | 3,031 |
1908.11078 | 2971016516 | Hashing is promising for large-scale information retrieval tasks thanks to the efficiency of distance evaluation between binary codes. Generative hashing is often used to generate hashing codes in an unsupervised way. However, existing generative hashing methods only considered the use of simple priors, like Gaussian a... | Recently, VDSH @cite_28 proposed to use a VAE to learn the latent representations of documents and then use a separate stage to cast the continuous representations into binary codes. While fairly successful, this generative hashing model requires a two-stage training. NASH @cite_11 proposed to substitute the Gaussian p... | {
"abstract": [
"As the amount of textual data has been rapidly increasing over the past decade, efficient similarity search methods have become a crucial component of large-scale information retrieval systems. A popular strategy is to represent original data samples by compact binary codes through hashing. A spe... | Document Hashing with Mixture-Prior Generative Models | Similarity search aims to find items that look most similar to the query one from a huge amount of data , and are found in extensive applications like plagiarism analysis (Stein et al., 2007), collaborative filtering (Koren, 2008;, content-based multimedia retrieval (Lew et al., 2006), web services (Dong et al., 2004) ... | 3,642 |
1908.11314 | 2970733215 | Blind image denoising is an important yet very challenging problem in computer vision due to the complicated acquisition process of real images. In this work we propose a new variational inference method, which integrates both noise estimation and image denoising into a unique Bayesian framework, for blind image denois... | Most classical image denoising methods belong to this category, through designing a MAP model with a fidelity loss term and a regularization one delivering the pre-known image prior. Along this line, total variation denoising @cite_30 , anisotropic diffusion @cite_40 and wavelet coring @cite_25 use the statistical regu... | {
"abstract": [
"A constrained optimization type of numerical algorithm for removing noise from images is presented. The total variation of the image is minimized subject to constraints involving the statistics of the noise. The constraints are imposed using Lagrange multipliers. The solution is obtained using th... | Variational Denoising Network: Toward Blind Noise Modeling and Removal | Image denoising is an important research topic in computer vision, aiming at recovering the underlying clean image from an observed noisy one. The noise contained in a real noisy image is generally accumulated from multiple different sources, e.g., capturing instruments, data transmission media, image quantization, etc... | 4,560 |
1908.11314 | 2970733215 | Blind image denoising is an important yet very challenging problem in computer vision due to the complicated acquisition process of real images. In this work we propose a new variational inference method, which integrates both noise estimation and image denoising into a unique Bayesian framework, for blind image denois... | Instead of pre-setting image prior, deep learning methods directly learn a denoiser (formed as a deep neural network) from noisy to clean ones on a large collection of noisy-clean image pairs. Jain and Seung @cite_1 firstly adopted a five layer convolution neural network (CNN) for the task. Then some auto-encoder based... | {
"abstract": [
"Due to the fast inference and good performance, discriminative learning methods have been widely studied in image denoising. However, these methods mostly learn a specific model for each noise level, and require multiple models for denoising images with different noise levels. They also lack flex... | Variational Denoising Network: Toward Blind Noise Modeling and Removal | Image denoising is an important research topic in computer vision, aiming at recovering the underlying clean image from an observed noisy one. The noise contained in a real noisy image is generally accumulated from multiple different sources, e.g., capturing instruments, data transmission media, image quantization, etc... | 4,560 |
1908.11057 | 2970096436 | Textual network embeddings aim to learn a low-dimensional representation for every node in the network so that both the structural and textual information from the networks can be well preserved in the representations. Traditionally, the structural and textual embeddings were learned by models that rarely take the mutu... | Text Embedding There has been various methods to embed textual information into vector representations for NLP tasks. The classical method for embedding textual information could be one-hot vector, term frequency inverse document frequency (TF-IDF), etc. Due to the high-dimension and sparsity problems in here, @cite_18... | {
"abstract": [
"We propose two novel model architectures for computing continuous vector representations of words from very large data sets. The quality of these representations is measured in a word similarity task, and the results are compared to the previously best performing techniques based on different typ... | A Deep Neural Information Fusion Architecture for Textual Network Embeddings | Networks provide an effective way to organize heterogeneous relevant data, which can often be leveraged to facilitate downstream applications. For example, the huge amount of textual and relationship data in social networks contains abundant information on people's preferences, and thus can be used for personalized adv... | 3,810 |
1908.10797 | 2971306187 | Recurrent Neural Network (RNN) has been deployed as the de facto model to tackle a wide variety of language generation problems and achieved state-of-the-art (SOTA) performance. However despite its impressive results, the large number of parameters in the RNN model makes deployment in mobile and embedded devices infeas... | Modern neural networks that provide good performance tend to be large and overparameterised, fuelled by observations that larger @cite_38 @cite_6 @cite_46 networks tend to be easier to train. This in turn drives numerous efforts to reduce model size using techniques such as weight pruning and quantisation @cite_34 @cit... | {
"abstract": [
"Convexity has recently received a lot of attention in the machine learning community, and the lack of convexity has been seen as a major disadvantage of many learning algorithms, such as multi-layer artificial neural networks. We show that training multi-layer neural networks in which the number ... | Image Captioning with Sparse Recurrent Neural Network | Automatically generating a caption that describes an image, a problem known as image captioning, is a challenging problem where computer vision (CV) meets natural language processing (NLP). A well performing model not only has to identify the objects in the image, but also capture the semantic relationship between them... | 4,912 |
1908.10797 | 2971306187 | Recurrent Neural Network (RNN) has been deployed as the de facto model to tackle a wide variety of language generation problems and achieved state-of-the-art (SOTA) performance. However despite its impressive results, the large number of parameters in the RNN model makes deployment in mobile and embedded devices infeas... | Early works like @cite_48 and @cite_2 explored pruning by computing the Hessian of the loss with respect to the parameters in order to assess the saliency of each parameter. Other works involving saliency computation include @cite_0 and @cite_33 where sensitivity of the loss with respect to neurons and weights are used... | {
"abstract": [
"The sensitivity of the global error (cost) function to the inclusion exclusion of each synapse in the artificial neural network is estimated. Introduced are shadow arrays which keep track of the incremental changes to the synaptic weights during a single pass of back-propagating learning. The syn... | Image Captioning with Sparse Recurrent Neural Network | Automatically generating a caption that describes an image, a problem known as image captioning, is a challenging problem where computer vision (CV) meets natural language processing (NLP). A well performing model not only has to identify the objects in the image, but also capture the semantic relationship between them... | 4,912 |
1908.10797 | 2971306187 | Recurrent Neural Network (RNN) has been deployed as the de facto model to tackle a wide variety of language generation problems and achieved state-of-the-art (SOTA) performance. However despite its impressive results, the large number of parameters in the RNN model makes deployment in mobile and embedded devices infeas... | Most of the recent works in network pruning focused on vision-centric classification tasks using Convolutional Neural Networks (CNNs) and occasionally RNNs. Techniques proposed include magnitude-based pruning @cite_60 @cite_9 @cite_52 and variational pruning @cite_21 @cite_19 @cite_53 . Among these, magnitude-based wei... | {
"abstract": [
"We propose an efficient and unified framework, namely ThiNet, to simultaneously accelerate and compress CNN models in both training and inference stages. We focus on the filter level pruning, i.e., the whole filter would be discarded if it is less important. Our method does not change the origina... | Image Captioning with Sparse Recurrent Neural Network | Automatically generating a caption that describes an image, a problem known as image captioning, is a challenging problem where computer vision (CV) meets natural language processing (NLP). A well performing model not only has to identify the objects in the image, but also capture the semantic relationship between them... | 4,912 |
1908.10797 | 2971306187 | Recurrent Neural Network (RNN) has been deployed as the de facto model to tackle a wide variety of language generation problems and achieved state-of-the-art (SOTA) performance. However despite its impressive results, the large number of parameters in the RNN model makes deployment in mobile and embedded devices infeas... | [label= *)] Simple and fast. Our approach enables easy pruning of the RNN decoder equipped with visual attention, whereby the best number of weights to prune in each layer is automatically determined. Compared to works such as @cite_45 @cite_51 , our approach is simpler with a single hyperparameter versus @math - @math... | {
"abstract": [
"Long short-term memory (LSTM) has been widely used for sequential data modeling. Researchers have increased LSTM depth by stacking LSTM cells to improve performance. This incurs model redundancy, increases run-time delay, and makes the LSTMs more prone to overfitting. To address these problems, w... | Image Captioning with Sparse Recurrent Neural Network | Automatically generating a caption that describes an image, a problem known as image captioning, is a challenging problem where computer vision (CV) meets natural language processing (NLP). A well performing model not only has to identify the objects in the image, but also capture the semantic relationship between them... | 4,912 |
1908.10797 | 2971306187 | Recurrent Neural Network (RNN) has been deployed as the de facto model to tackle a wide variety of language generation problems and achieved state-of-the-art (SOTA) performance. However despite its impressive results, the large number of parameters in the RNN model makes deployment in mobile and embedded devices infeas... | While there are other works on compressing RNNs, most of the methods proposed either comes with structural constraints or are complementary to model pruning in principle. Examples include using low-rank matrix factorisations @cite_28 @cite_40 , product quantisation on embeddings @cite_14 , factorising word predictions ... | {
"abstract": [
"Long short-term memory (LSTM) has been widely used for sequential data modeling. Researchers have increased LSTM depth by stacking LSTM cells to improve performance. This incurs model redundancy, increases run-time delay, and makes the LSTMs more prone to overfitting. To address these problems, w... | Image Captioning with Sparse Recurrent Neural Network | Automatically generating a caption that describes an image, a problem known as image captioning, is a challenging problem where computer vision (CV) meets natural language processing (NLP). A well performing model not only has to identify the objects in the image, but also capture the semantic relationship between them... | 4,912 |
1908.10084 | 2971193649 | BERT (, 2018) and RoBERTa (, 2019) has set a new state-of-the-art performance on sentence-pair regression tasks like semantic textual similarity (STS). However, it requires that both sentences are fed into the network, which causes a massive computational overhead: Finding the most similar pair in a collection of 10,00... | BERT @cite_19 is a pre-trained transformer network @cite_11 , which set for various NLP tasks new state-of-the-art results, including question answering, sentence classification, and sentence-pair regression. The input for BERT for sentence-pair regression consists of the two sentences, separated by a special [SEP] tok... | {
"abstract": [
"Semantic Textual Similarity (STS) measures the meaning similarity of sentences. Applications include machine translation (MT), summarization, generation, question answering (QA), short answer grading, semantic search, dialog and conversational systems. The STS shared task is a venue for assessing... | Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks | In this publication, we present Sentence-BERT (SBERT), a modification of the BERT network using siamese and triplet networks that is able to derive semantically meaningful sentence embeddings 2 . This enables BERT to be used for certain new tasks, which up-to-now were not applicable for BERT. These tasks include large-... | 3,351 |
1908.10155 | 2970282336 | Video action recognition, which is topical in computer vision and video analysis, aims to allocate a short video clip to a pre-defined category such as brushing hair or climbing stairs. Recent works focus on action recognition with deep neural networks that achieve state-of-the-art results in need of high-performance p... | Pooling methods are requisite either in two-stream networks @cite_32 @cite_33 or in other feature fusion models. @cite_9 simply uses average pooling and outperforms others. @cite_28 proposes bilinear pooling to model local parts of object: two feature representations are learned separately and then multiplied using the... | {
"abstract": [
"Convolutional Neural Networks (CNNs) with Bilinear Pooling, initially in their full form and later using compact representations, have yielded impressive performance gains on a wide range of visual tasks, including fine-grained visual categorization, visual question answering, face recognition, a... | Mobile Video Action Recognition | Video analysis has drawn increasing attention from the computer vision community, for that videos occupy more than 75% of the global IP traffic [11]. With the development of deep learning methods, recent works achieve promising performance on video analysis tasks, including action recognition [6,21,24,28,37], emotion r... | 4,102 |
1908.10155 | 2970282336 | Video action recognition, which is topical in computer vision and video analysis, aims to allocate a short video clip to a pre-defined category such as brushing hair or climbing stairs. Recent works focus on action recognition with deep neural networks that achieve state-of-the-art results in need of high-performance p... | Recently, lightweight neural networks including SqeezeNet @cite_15 , Xception @cite_8 , ShuffleNet @cite_31 , ShuffleNetV2 @cite_7 , MobileNet @cite_12 , and MobileNetV2 @cite_10 have been proposed to run on mobile devices with the parameters and computation reduced significantly. Since we focus on mobile video action ... | {
"abstract": [
"We introduce an extremely computation-efficient CNN architecture named ShuffleNet, which is designed specially for mobile devices with very limited computing power (e.g., 10-150 MFLOPs). The new architecture utilizes two new operations, pointwise group convolution and channel shuffle, to greatly ... | Mobile Video Action Recognition | Video analysis has drawn increasing attention from the computer vision community, for that videos occupy more than 75% of the global IP traffic [11]. With the development of deep learning methods, recent works achieve promising performance on video analysis tasks, including action recognition [6,21,24,28,37], emotion r... | 4,102 |
1908.09941 | 2970931569 | In this paper, we study a family of non-convex and possibly non-smooth inf-projection minimization problems, where the target objective function is equal to minimization of a joint function over another variable. This problem includes difference of convex (DC) functions and a family of bi-convex functions as special ca... | Another important result is following the Bennett's inequality. Corollary 5 in @cite_7 shows that: where @math is the sample variance. It is notable that @math is equivalent (with a constant scaling) to the empirical variance @math . Similarly, the above uniform estimate can be extended to infinite loss classes using d... | {
"abstract": [
"We give improved constants for data dependent and variance sensitive confidence bounds, called empirical Bernstein bounds, and extend these inequalities to hold uniformly over classes of functions whose growth function is polynomial in the sample size n. The bounds lead us to consider sample vari... | Stochastic Optimization for Non-convex Inf-Projection Problems | 0 | |
1908.09941 | 2970931569 | In this paper, we study a family of non-convex and possibly non-smooth inf-projection minimization problems, where the target objective function is equal to minimization of a joint function over another variable. This problem includes difference of convex (DC) functions and a family of bi-convex functions as special ca... | An intuitive approach to considering the variance-based regularization is to include the first two terms on the right hand side into the objective, which is the formulation proposed in @cite_7 , i.e., sample variance penalty (SVP): An excess risk bound of @math may be achieved by solving the SVP. However, @cite_7 does ... | {
"abstract": [
"We give improved constants for data dependent and variance sensitive confidence bounds, called empirical Bernstein bounds, and extend these inequalities to hold uniformly over classes of functions whose growth function is polynomial in the sample size n. The bounds lead us to consider sample vari... | Stochastic Optimization for Non-convex Inf-Projection Problems | 0 | |
1908.09941 | 2970931569 | In this paper, we study a family of non-convex and possibly non-smooth inf-projection minimization problems, where the target objective function is equal to minimization of a joint function over another variable. This problem includes difference of convex (DC) functions and a family of bi-convex functions as special ca... | Recently, @cite_17 proposed a min-max formulation based on distributionally robust optimization for variance-based regularization as following: where @math is a hyper-parameter, @math , @math , and @math is called the @math -divergence based on @math . The above problem is convex-concave when the loss function @math is... | {
"abstract": [
"We develop an approach to risk minimization and stochastic optimization that provides a convex surrogate for variance, allowing near-optimal and computationally efficient trading between approximation and estimation error. Our approach builds off of techniques for distributionally robust optimiza... | Stochastic Optimization for Non-convex Inf-Projection Problems | 0 | |
1908.09941 | 2970931569 | In this paper, we study a family of non-convex and possibly non-smooth inf-projection minimization problems, where the target objective function is equal to minimization of a joint function over another variable. This problem includes difference of convex (DC) functions and a family of bi-convex functions as special ca... | To solve the above min-max formulation, @cite_6 proposed stochastic primal-dual algorithms based on the stochastic mirror prox methods proposed in for addressing convex-concave problems. When the loss function @math is non-convex (e.g., the hypothesis class is defined by deep neural networks), the resulting min-max pro... | {
"abstract": [
"Min-max saddle-point problems have broad applications in many tasks in machine learning, e.g., distributionally robust learning, learning with non-decomposable loss, or learning with uncertain data. Although convex-concave saddle-point problems have been broadly studied with efficient algorithms ... | Stochastic Optimization for Non-convex Inf-Projection Problems | 0 | |
1908.09506 | 2969721933 | When deploying autonomous agents in unstructured environments over sustained periods of time, adaptability and robustness oftentimes outweigh optimality as a primary consideration. In other words, safety and survivability constraints play a key role and in this paper, we present a novel, constraint-learning framework f... | Finding feasible control constraints that can be translated to a set of state constraints has been of particular interest both in the controls and machine learning communities. Early work includes the study of artificial potential functions in the context of obstacle avoidance, and the construction of so-called navigat... | {
"abstract": [
"Reinforcement learning is a powerful paradigm for learning optimal policies from experimental data. However, to find optimal policies, most reinforcement learning algorithms explore all possible actions, which may be harmful for real-world systems. As a consequence, learning algorithms are rarely... | Constraint Learning for Control Tasks with Limited Duration Barrier Functions | Acquiring an optimal policy that attains the maximum return over some time horizon is of primary interest in the literature of both reinforcement learning [1][2][3] and optimal control [4]. A large number of algorithms have been designed to successfully control systems with complex dynamics to accomplish specific tasks... | 7,890 |
1908.09506 | 2969721933 | When deploying autonomous agents in unstructured environments over sustained periods of time, adaptability and robustness oftentimes outweigh optimality as a primary consideration. In other words, safety and survivability constraints play a key role and in this paper, we present a novel, constraint-learning framework f... | On the other hand, control barrier functions (CBFs) @cite_45 @cite_17 @cite_1 @cite_8 @cite_46 @cite_43 @cite_12 @cite_15 @cite_32 @cite_7 were proposed to guarantee that an agent remains in a certain region of the state space (i.e., forward invariance @cite_42 ) by using a locally accurate model of the agent dynamics ... | {
"abstract": [
"Motivated by the need to simultaneously guarantee safety and stability of safety-critical dynamical systems, we construct permissive barrier certificates in this paper that explicitly maximize the region where the system can be stabilized without violating safety constraints. An iterative search ... | Constraint Learning for Control Tasks with Limited Duration Barrier Functions | Acquiring an optimal policy that attains the maximum return over some time horizon is of primary interest in the literature of both reinforcement learning [1][2][3] and optimal control [4]. A large number of algorithms have been designed to successfully control systems with complex dynamics to accomplish specific tasks... | 7,890 |
1908.09506 | 2969721933 | When deploying autonomous agents in unstructured environments over sustained periods of time, adaptability and robustness oftentimes outweigh optimality as a primary consideration. In other words, safety and survivability constraints play a key role and in this paper, we present a novel, constraint-learning framework f... | Moreover, our work is also related to safe reinforcement learning, such as Lyapunov-based safe learning (cf. @cite_40 @cite_28 ) and constrained Markov decision processes (CMDPs) (cf. @cite_31 @cite_50 ). The former is based on the fact that sublevel sets of a control Lyapunov function are forward invariant, and consid... | {
"abstract": [
"Reinforcement learning is a powerful paradigm for learning optimal policies from experimental data. However, to find optimal policies, most reinforcement learning algorithms explore all possible actions, which may be harmful for real-world systems. As a consequence, learning algorithms are rarely... | Constraint Learning for Control Tasks with Limited Duration Barrier Functions | Acquiring an optimal policy that attains the maximum return over some time horizon is of primary interest in the literature of both reinforcement learning [1][2][3] and optimal control [4]. A large number of algorithms have been designed to successfully control systems with complex dynamics to accomplish specific tasks... | 7,890 |
1908.09506 | 2969721933 | When deploying autonomous agents in unstructured environments over sustained periods of time, adaptability and robustness oftentimes outweigh optimality as a primary consideration. In other words, safety and survivability constraints play a key role and in this paper, we present a novel, constraint-learning framework f... | Besides, transfer learning (cf. @cite_4 ) aims at learning a new task by utilizing the knowledge already acquired via learning other tasks, and is sometimes referred to as "lifelong learning" @cite_54 and "learning to learn" @cite_57 . In transfer learning for reinforcement learning contexts, we first learn a set of so... | {
"abstract": [
"We develop a met alearning approach for learning hierarchically structured poli- cies, improving sample efficiency on unseen tasks through the use of shared primitives—policies that are executed for large numbers of timesteps. Specifi- cally, a set of primitives are shared within a distribution o... | Constraint Learning for Control Tasks with Limited Duration Barrier Functions | Acquiring an optimal policy that attains the maximum return over some time horizon is of primary interest in the literature of both reinforcement learning [1][2][3] and optimal control [4]. A large number of algorithms have been designed to successfully control systems with complex dynamics to accomplish specific tasks... | 7,890 |
1908.08704 | 2969244993 | We propose a self-supervised learning framework for visual odometry (VO) that incorporates correlation of consecutive frames and takes advantage of adversarial learning. Previous methods tackle self-supervised VO as a local structure from motion (SfM) problem that recovers depth from single image and relative poses fro... | Humans are capable of perceiving 3D environment and inferring ego-motion in a short time, but it is hard for an agent to be equipped with similar capabilities. VO SLAM has been considered as a multi-view geometric problem for decades. It is traditionally solved by minimizing photometric @cite_13 or geometric @cite_2 re... | {
"abstract": [
"We propose a direct (feature-less) monocular SLAM algorithm which, in contrast to current state-of-the-art regarding direct methods, allows to build large-scale, consistent maps of the environment. Along with highly accurate pose estimation based on direct image alignment, the 3D environment is r... | 0 | ||
1908.08704 | 2969244993 | We propose a self-supervised learning framework for visual odometry (VO) that incorporates correlation of consecutive frames and takes advantage of adversarial learning. Previous methods tackle self-supervised VO as a local structure from motion (SfM) problem that recovers depth from single image and relative poses fro... | Supervised methods formulate VO as a supervised learning problem and many methods with good results have been proposed. DeMoN @cite_39 jointly estimates pose and depth in an end-to-end manner. Inspired by the practice of parallel tracking and mapping in classic VO SLAM, DeepTAM @cite_29 utilizes two networks for pose a... | {
"abstract": [
"Convolutional neural networks (CNNs) have recently been very successful in a variety of computer vision tasks, especially on those linked to recognition. Optical flow estimation has not been among the tasks where CNNs were successful. In this paper we construct appropriate CNNs which are capable ... | 0 | ||
1908.08704 | 2969244993 | We propose a self-supervised learning framework for visual odometry (VO) that incorporates correlation of consecutive frames and takes advantage of adversarial learning. Previous methods tackle self-supervised VO as a local structure from motion (SfM) problem that recovers depth from single image and relative poses fro... | Self-supervised methods In order to alleviate the reliance on ground truth, recently many self-supervised methods have been proposed for VO. The key to self-supervised learning is to find the internal correlations and constraints in the training data. SfMLearner @cite_20 leverages the geometric correlation of depth and... | {
"abstract": [
"Monocular visual odometry approaches that purely rely on geometric cues are prone to scale drift and require sufficient motion parallax in successive frames for motion estimation and 3D reconstruction. In this paper, we propose to leverage deep monocular depth prediction to overcome limitations o... | 0 | ||
1908.08704 | 2969244993 | We propose a self-supervised learning framework for visual odometry (VO) that incorporates correlation of consecutive frames and takes advantage of adversarial learning. Previous methods tackle self-supervised VO as a local structure from motion (SfM) problem that recovers depth from single image and relative poses fro... | Despite its feasibility, self-supervised VO still underperforms supervised ones. Apart from the effectiveness of direct supervision, a key reason is that they focus mainly on geometric properties @cite_20 but pay little attention to the sequential nature of the problem. In these methods, only a few frames (no more than... | {
"abstract": [
"We present an unsupervised learning framework for the task of monocular depth and camera motion estimation from unstructured video sequences. In common with recent work [10, 14, 16], we use an end-to-end learning approach with view synthesis as the supervisory signal. In contrast to the previous ... | 0 | ||
1908.08326 | 2969493583 | In this paper, we study the problem of short sentence ranking for question answering. In order to get best score for all the sentences when given a query. We compute the representation for all the sentences in advance and leverage k-d tree to accelerate the speed. The experimental results shows that our methods beat th... | In recent years, neural information retrieval and neural question answering research has developed several effective ways to improve ranking accuracy. Interaction-based neural rankers match query and document pair using attention-based deep model; representation-based neural rankers output sentence representations and ... | {
"abstract": [
"This paper develops a model that addresses sentence embedding, a hot topic in current natural language processing research, using recurrent neural networks (RNN) with Long Short-Term Memory (LSTM) cells. The proposed LSTM-RNN model sequentially takes each word in a sentence, extracts its informat... | Revisiting Semantic Representation and Tree Search for Similar Question Retrieval | In retrieval-based question answering system (Wang, Hamza, and Florian 2017;Liu et al. 2018;Guo et al. 2019), we retrieve the answer or similar question from a large question-answer pairs. We compute the semantic similar score between question-question pairs or compute the semantic related score of question-answer pair... | 2,144 |
1908.08326 | 2969493583 | In this paper, we study the problem of short sentence ranking for question answering. In order to get best score for all the sentences when given a query. We compute the representation for all the sentences in advance and leverage k-d tree to accelerate the speed. The experimental results shows that our methods beat th... | Sentence embeddings is an important topic in this research area. Skip-Thought @cite_5 input one sentence to predict its previous and next sentence. InferSent @cite_9 outperforms Skip-Thought. @cite_16 is the methods that use unsupervised word vectors @cite_17 to construct the sentence vectors which is a strong baseline... | {
"abstract": [
"We present models for encoding sentences into embedding vectors that specifically target transfer learning to other NLP tasks. The models are efficient and result in accurate performance on diverse transfer tasks. Two variants of the encoding models allow for trade-offs between accuracy and compu... | Revisiting Semantic Representation and Tree Search for Similar Question Retrieval | In retrieval-based question answering system (Wang, Hamza, and Florian 2017;Liu et al. 2018;Guo et al. 2019), we retrieve the answer or similar question from a large question-answer pairs. We compute the semantic similar score between question-question pairs or compute the semantic related score of question-answer pair... | 2,144 |
1908.07715 | 2969758725 | In competitive parallel computing, the identical copies of a code in a phase of a sequential program are assigned to processor cores and the result of the fastest core is adopted. In the literature, it is reported that a superlinear speedup can be achieved if there is an enough fluctuation among the execution times con... | Wolfgang @cite_10 proposes random competition, in which the computations compete using the randomness in search algorithm. Although he analyzes speedups based on the variance of the measured execution times, there is no mention of CV. | {
"abstract": [
"We present a very simple parallel execution model suitable for inference systems with nondeterministic choices (OR-branching points). All the parallel processors solve the same task without any communication. Their programs only differ in the initialization of the random number generator used for... | A sufficient condition for a linear speedup in competitive parallel computing | Multi-core and many-core are in the mainstream of parallel computing and there is a steady increase in the number of their cores. However, in the near future, it is expected that the degree of parallelism is below the number of cores which the hardware provides due to the restriction of the problems to solve or the alg... | 2,985 |
1908.07715 | 2969758725 | In competitive parallel computing, the identical copies of a code in a phase of a sequential program are assigned to processor cores and the result of the fastest core is adopted. In the literature, it is reported that a superlinear speedup can be achieved if there is an enough fluctuation among the execution times con... | Without enough attention to the degree of the variance of the execution time among processors, using naively wastes computing resources. To overcome this problem, Cledat @cite_12 @cite_9 @cite_2 proposes the methods called and . The CV of WalkSAT, one of the application they adopted for evaluation, is less than one and... | {
"abstract": [
"With the advent of multi-cores and many-cores, traditional techniques that seek only to improve FLOPS of performance or the degree of parallelism have hit a roadblock with regards to providing even greater performance. In order to surmount this roadblock, techniques should more directly address t... | A sufficient condition for a linear speedup in competitive parallel computing | Multi-core and many-core are in the mainstream of parallel computing and there is a steady increase in the number of their cores. However, in the near future, it is expected that the degree of parallelism is below the number of cores which the hardware provides due to the restriction of the problems to solve or the alg... | 2,985 |
1908.04431 | 2967036800 | The interconnectivity of cyber and physical systems and Internet of things has created ubiquitous concerns of cyber threats for enterprise system managers. It is common that the asset owners and enterprise network operators need to work with cybersecurity professionals to manage the risk by remunerating them for their ... | Cybersecurity becomes a critical issue due to the large-scale deployment of smart devices and their integration with information and communication techologies (ICTs) @cite_6 @cite_54 . Hence, security risk management is an important task which has been investigated in different research fields, such as communications a... | {
"abstract": [
"",
"With the remarkable growth of the Internet and communication technologies over the past few decades, Internet of Things (IoTs) is enabling the ubiquitous connectivity of heterogeneous physical devices with software, sensors, and actuators. IoT networks are naturally two-layer with the clo... | Dynamic Contract Design for Systemic Cyber Risk Management of Interdependent Enterprise Networks | Cybersecurity is a critical issue in modern enterprise networks due to the adoption of advanced technologies, e.g., Internet of things (IoT), cloud and data centers, and supervisory control and data acquisition (SCADA) system, which create abundant surfaces for cyber attacks [19,37,46]. Due to the interconnections betw... | 12,021 |
1908.06893 | 2967126358 | With an increasing number of malicious attacks, the number of people and organizations falling prey to social engineering attacks is proliferating. Despite considerable research in mitigation systems, attackers continually improve their modus operandi by using sophisticated machine learning, natural language processing... | Natural language generation techniques have been widely popular for synthesizing unique pieces of textual content. NLG techniques proposed by @cite_25 @cite_28 rely on templates pre-constructed for specific purposes. The fake email generation system in @cite_6 uses a set of manually constructed rules to pre-define the ... | {
"abstract": [
"Malicious crowdsourcing forums are gaining traction as sources of spreading misinformation online, but are limited by the costs of hiring and managing human workers. In this paper, we identify a new class of attacks that leverage deep learning language models (Recurrent Neural Networks or RNNs) t... | Automated email Generation for Targeted Attacks using Natural Language | The continuous adversarial growth and learning has been one of the major challenges in the field of Cybersecurity. With the immense boom in usage and adaptation of the Internet, staggering numbers of individuals and organizations have fallen prey to targeted attacks like phishing and pharming. Such attacks result in di... | 4,536 |
1908.06893 | 2967126358 | With an increasing number of malicious attacks, the number of people and organizations falling prey to social engineering attacks is proliferating. Despite considerable research in mitigation systems, attackers continually improve their modus operandi by using sophisticated machine learning, natural language processing... | The system used for synthesizing emails in this work is somewhat aligned along the lines of the methodology described in @cite_14 @cite_9 . However, our proposed system has no manual labor involved and with some level of post processing has been shown to deceive an automated supervised classification system. | {
"abstract": [
"This paper describes a two-stage process for stochastic generation of email, in which the first stage structures the emails according to sender style and topic structure (high-level generation), and the second stage synthesizes text content based on the particulars of an email element and the goa... | Automated email Generation for Targeted Attacks using Natural Language | The continuous adversarial growth and learning has been one of the major challenges in the field of Cybersecurity. With the immense boom in usage and adaptation of the Internet, staggering numbers of individuals and organizations have fallen prey to targeted attacks like phishing and pharming. Such attacks result in di... | 4,536 |
1908.06893 | 2967126358 | With an increasing number of malicious attacks, the number of people and organizations falling prey to social engineering attacks is proliferating. Despite considerable research in mitigation systems, attackers continually improve their modus operandi by using sophisticated machine learning, natural language processing... | In this paper, we focus primarily on generation of fake emails specifically engineered for phishing and scamming victims. Additionally, we also look at some state-of-the-art phishing email detection systems. Researchers in @cite_24 extract a large number of text body, URL and HTML features from emails, which are then f... | {
"abstract": [
"Phishing causes billions of dollars in damage every year and poses a serious threat to the Internet economy. Email is still the most commonly used medium to launch phishing attacks [1]. In this paper, we present a comprehensive natural language based scheme to detect phishing emails using feature... | Automated email Generation for Targeted Attacks using Natural Language | The continuous adversarial growth and learning has been one of the major challenges in the field of Cybersecurity. With the immense boom in usage and adaptation of the Internet, staggering numbers of individuals and organizations have fallen prey to targeted attacks like phishing and pharming. Such attacks result in di... | 4,536 |
1908.06851 | 2968588162 | Fingerprinting techniques, which are a common method for indoor localization, have been recently applied with success into outdoor settings. Particularly, the communication signals of Low Power Wide Area Networks (LPWAN) such as Sigfox, have been used for localization. In this rather recent field of study, not many pub... | The proliferation of Low Power Wide Area Networks (LPWAN), such as Sigfox and LoRaWAN, has brought a new domain of application of the fingerprinting methods. A recent study @cite_10 has experimentally verified the intuitive assumption that fingerprinting methods outperform, in terms of accuracy, proximity or ranging po... | {
"abstract": [
"Location-based services play an important role in Internet of Things (IoT) applications. However, a trade-off has to be made between the location estimation error and the battery lifetime of an IoT device. As IoT devices communicate over Low Power Wide Area Networks (LPWAN), signal strength local... | A Reproducible Analysis of RSSI Fingerprinting for Outdoor Localization Using Sigfox: Preprocessing and Hyperparameter Tuning | The recent emergence of Internet of Things (IoT) technologies has made so that a plethora of low power devices make their appearance worldwide, in people's everyday life. The concept of smart cities becomes familiar to the broad public, and numerous applications are being proposed, implemented and deployed in domains s... | 4,732 |
1908.06388 | 2968455557 | In this paper, we design a drug release mechanism for dynamic time division multiple access (TDMA)-based molecular communication via diffusion (MCvD). In the proposed scheme, the communication frame is divided into several time slots over each of which a transmitter nanomachine is scheduled to convey its information by... | Researchers study the TDMA optimization in neuron-based MC, which employs neurons to communicate and built in-body sensor-actuator networks (IBSANs) @cite_23 . They use an evolutionary multi-objective optimization algorithm to design the TDMA schedule. The resource allocation in MC has already studied for two transmitt... | {
"abstract": [
"Molecular communication is a new nano-scale communication paradigm that enables nanomachines to communicate with each other by emitting molecules to their surrounding environment. Nanonetworks are also envisioned to be composed of a number of nanomachines with molecular communication capability t... | 0 | ||
1908.05908 | 2969050910 | In this paper, we report our method for the Information Extraction task in 2019 Language and Intelligence Challenge. We incorporate BERT into the multi-head selection framework for joint entity-relation extraction. This model extends existing approaches from three perspectives. First, BERT is adopted as a feature extra... | Recent years, great efforts have been made on extracting relational fact from unstructured raw texts to build large structural knowledge bases. A relational fact is often represented as a triplet which consists of two entities (subject and object) and semantic relation between them. Early works @cite_13 @cite_0 @cite_1... | {
"abstract": [
"The state-of-the-art methods used for relation classification are primarily based on statistical machine learning, and their performance strongly depends on the quality of the extracted features. The extracted features are often derived from the output of pre-existing natural language processing ... | BERT-Based Multi-Head Selection for Joint Entity-Relation Extraction | Given a sentence and a list of pre-defined schemas which define the relation P and the classes of its corresponding subject S and object O, for example, (S TYPE: Person, P: wife, O TYPE: Person), (S TYPE: Company, P: founder, O TYPE: Person), a participating information extraction (IE) system is expected to output all ... | 1,958 |
1908.05666 | 2967081197 | Distributed computing frameworks such as MapReduce are often used to process large computational jobs. They operate by partitioning each job into smaller tasks executed on different servers. The servers also need to exchange intermediate values to complete the computation. Experimental evidence suggests that this so-ca... | The work of @cite_22 introduced ShuffleWatcher", a MapReduce scheduler that reduces throughput and job completion time. The scheme replicates Map tasks and delays or elongates a job's communication time depending on the network load. Their technique also judiciously assigns Reduce tasks to workers based on the Map assi... | {
"abstract": [
"The data placement strategy greatly affects the efficiency of MapReduce. The current strategy only takes the map phase into account to optimize the map time. But the ignored shuffle phase may increase the total running time significantly in many jobs. We propose a new data placement strategy, nam... | Resolvable Designs for Speeding up Distributed Computing | 0 | |
1908.05666 | 2967081197 | Distributed computing frameworks such as MapReduce are often used to process large computational jobs. They operate by partitioning each job into smaller tasks executed on different servers. The servers also need to exchange intermediate values to complete the computation. Experimental evidence suggests that this so-ca... | The recent work of @cite_7 introduces a scheme to handle the case when each Reduce function is computed by @math workers by utilizing a hybercube structure which controls the allocation of Map and Reduce tasks. Their work is motivated by distributed applications that require multiple rounds of Map and Reduce computatio... | {
"abstract": [
"Coded distributed computing introduced by in 2015 is an efficient approach to trade computing power to reduce the communication load in general distributed computing frameworks such as MapReduce. In particular, show that increasing the computation load in the Map phase by a factor of @math can cr... | Resolvable Designs for Speeding up Distributed Computing | 0 | |
1908.05666 | 2967081197 | Distributed computing frameworks such as MapReduce are often used to process large computational jobs. They operate by partitioning each job into smaller tasks executed on different servers. The servers also need to exchange intermediate values to complete the computation. Experimental evidence suggests that this so-ca... | Another approach that re-examines the computation - communication tradeoff from an alternate viewpoint has been investigated in @cite_9 . In this case, the assumption is that a server does not need to process all locally available files and storage constraints do not necessarily imply computation constraints. A lower b... | {
"abstract": [
"In this paper, we revisit the communication vs. distributed computing trade-off, studied within the framework of MapReduce in [1]. An implicit assumption in the aforementioned work is that each server performs all possible computations on all the files stored in its memory. Our starting observati... | Resolvable Designs for Speeding up Distributed Computing | 0 | |
1908.05666 | 2967081197 | Distributed computing frameworks such as MapReduce are often used to process large computational jobs. They operate by partitioning each job into smaller tasks executed on different servers. The servers also need to exchange intermediate values to complete the computation. Experimental evidence suggests that this so-ca... | In @cite_24 , the authors propose a scheme which gives each server access to a random subset of the input files and not all Reduce functions depend on the entire data set. This fact changes the policy according to which we decide which server computes which function. | {
"abstract": [
"In wireless distributed computing, networked nodes perform intermediate computations over data placed in their memory and exchange these intermediate values to calculate function values. In this paper we consider an asymmetric setting where each node has access to a random subset of the data, i.e... | Resolvable Designs for Speeding up Distributed Computing | 0 | |
1908.05666 | 2967081197 | Distributed computing frameworks such as MapReduce are often used to process large computational jobs. They operate by partitioning each job into smaller tasks executed on different servers. The servers also need to exchange intermediate values to complete the computation. Experimental evidence suggests that this so-ca... | As discussed above both @cite_16 and @cite_5 require a certain problem dimension to be very large. In particular, @cite_5 considers a single job and requires it to be split into a number of tasks that grows exponentially in the problem parameters. On the other hand @cite_16 considers functions that can be aggregated bu... | {
"abstract": [
"How can we optimally trade extra computing power to reduce the communication load in distributed computing? We answer this question by characterizing a fundamental tradeoff between computation and communication in distributed computing, i.e., the two are inversely proportional to each other. More... | Resolvable Designs for Speeding up Distributed Computing | 0 | |
1908.05498 | 2967155990 | Detecting scene text of arbitrary shapes has been a challenging task over the past years. In this paper, we propose a novel segmentation-based text detector, namely SAST, which employs a context attended multi-task learning framework based on a Fully Convolutional Network (FCN) to learn various geometric properties for... | Scene text is regarded as a special type of object, several methods @cite_10 @cite_43 @cite_57 @cite_54 @cite_20 @cite_45 are based on Faster R-CNN @cite_4 , SSD @cite_32 and DenseBox @cite_16 , which generates text bounding boxes by regressing coordinates of boxes directly. TextBoxes @cite_43 and RRD @cite_44 adopt SS... | {
"abstract": [
"State-of-the-art object detection networks depend on region proposal algorithms to hypothesize object locations. Advances like SPPnet [7] and Fast R-CNN [5] have reduced the running time of these detection networks, exposing region proposal computation as a bottleneck. In this work, we introduce ... | A Single-Shot Arbitrarily-Shaped Text Detector based on Context Attended Multi-Task Learning ACM Reference Format | Recently, scene text reading has attracted extensive attention in both academia and industry for its numerous applications, such as scene understanding, image and video retrieval, and robot navigation. As the prerequisite in textual information extraction and understanding, text detection is of great importance. Thanks... | 4,360 |
1908.05498 | 2967155990 | Detecting scene text of arbitrary shapes has been a challenging task over the past years. In this paper, we propose a novel segmentation-based text detector, namely SAST, which employs a context attended multi-task learning framework based on a Fully Convolutional Network (FCN) to learn various geometric properties for... | Instance segmentation is a challenging task, which involves both segmentation and classification tasks. The most recent and successful two-stage representative is Mask R-CNN @cite_0 , which achieves amazing results on public benchmarks, but requires relatively long execution time due to the per-proposal computation and... | {
"abstract": [
"The dominant sequence transduction models are based on complex recurrent or convolutional neural networks that include an encoder and a decoder. The best performing models also connect the encoder and decoder through an attention mechanism. We propose a new simple network architecture, the Transf... | A Single-Shot Arbitrarily-Shaped Text Detector based on Context Attended Multi-Task Learning ACM Reference Format | Recently, scene text reading has attracted extensive attention in both academia and industry for its numerous applications, such as scene understanding, image and video retrieval, and robot navigation. As the prerequisite in textual information extraction and understanding, text detection is of great importance. Thanks... | 4,360 |
1908.04867 | 2968869838 | Abstract We introduce a game-theoretic model to investigate the strategic interaction between a cyber insurance policyholder whose premium depends on her self-reported security level and an insurer with the power to audit the security level upon receiving an indemnity claim. Audits can reveal fraudulent (or simply care... | This paper continues the trend towards rectifying the substantial discrepancy'' @cite_19 between early cyber insurance models and informal claims about the insurance market. Early research considered factors relevant to the viability of a market. Interdependent security occurs when the risk depends on the actions of ot... | {
"abstract": [
"High correlation in failure of information systems due to worms and viruses has been cited as major impediment to cyber-insurance. However, of the many cyber-risk classes that influence failure of information systems, not all exhibit similar correlation properties. In this paper, we introduce a n... | Post-Incident Audits on Cyber Insurance Discounts | No amount of investment in security eliminates the risk of loss [1]. Driven by the frequency of cyber attacks, riskaverse organizations increasingly transfer residual risk by purchasing cyber insurance. As a result, the cyber-insurance market is predicted to grow to between $7.5 and $20 billion by 2020, as identified i... | 5,971 |
1908.04867 | 2968869838 | Abstract We introduce a game-theoretic model to investigate the strategic interaction between a cyber insurance policyholder whose premium depends on her self-reported security level and an insurer with the power to audit the security level upon receiving an indemnity claim. Audits can reveal fraudulent (or simply care... | The timing of the insurer's intervention plays is an important strategic aspect. Ex-ante interventions for the insurer include risk assessments and security investments before the policy term begins. @cite_6 investigated an insurer who could assess security levels perfectly or not at all, concluding that the latter can... | {
"abstract": [
"An insurer has to know the risks faced by a potential client to accurately determine an insurance premium offer. However, while the potential client might have a good understanding of its own security practices, it may also have an incentive not to disclose them honestly since the resulting infor... | Post-Incident Audits on Cyber Insurance Discounts | No amount of investment in security eliminates the risk of loss [1]. Driven by the frequency of cyber attacks, riskaverse organizations increasingly transfer residual risk by purchasing cyber insurance. As a result, the cyber-insurance market is predicted to grow to between $7.5 and $20 billion by 2020, as identified i... | 5,971 |
1908.04867 | 2968869838 | Abstract We introduce a game-theoretic model to investigate the strategic interaction between a cyber insurance policyholder whose premium depends on her self-reported security level and an insurer with the power to audit the security level upon receiving an indemnity claim. Audits can reveal fraudulent (or simply care... | The literature on economic theory of insurance fraud has developed two main approaches: and @cite_2 . The costly state falsification approach assesses the client's behaviour towards a claim. We consider the costly state verification approach, which focuses on the insurer identifying fraudulent claims. The insurer can v... | {
"abstract": [
"Abstract This paper characterizes the equilibrium of an insurance market where opportunist policyholders may file fraudulent claims. We assume that insurance policies are traded in a competitive market where insurers cannot distinguish honest policyholders from opportunists. The insurer-policyhol... | Post-Incident Audits on Cyber Insurance Discounts | No amount of investment in security eliminates the risk of loss [1]. Driven by the frequency of cyber attacks, riskaverse organizations increasingly transfer residual risk by purchasing cyber insurance. As a result, the cyber-insurance market is predicted to grow to between $7.5 and $20 billion by 2020, as identified i... | 5,971 |
1908.04867 | 2968869838 | Abstract We introduce a game-theoretic model to investigate the strategic interaction between a cyber insurance policyholder whose premium depends on her self-reported security level and an insurer with the power to audit the security level upon receiving an indemnity claim. Audits can reveal fraudulent (or simply care... | Our contribution to the literature is the first theoretical consideration of post-incident claims management. Our model captures the trade-off between the incentive to exaggerate security posture to receive a premium discount and the possibility of punishment for non-compliance with the reported security policies. We c... | {
"abstract": [
"We consider arbitrary risk-averse users, whose costs of improving security are given by an arbitrary convex function. In our model, user probability to incur damage (from an attack) depends on both his own security and network security: thus, security is interdependent. We introduce two user type... | Post-Incident Audits on Cyber Insurance Discounts | No amount of investment in security eliminates the risk of loss [1]. Driven by the frequency of cyber attacks, riskaverse organizations increasingly transfer residual risk by purchasing cyber insurance. As a result, the cyber-insurance market is predicted to grow to between $7.5 and $20 billion by 2020, as identified i... | 5,971 |
1908.04686 | 2967058823 | We show how to build several data structures of central importance to string processing, taking as input the Burrows-Wheeler transform (BWT) and using small extra working space. Let @math be the text length and @math be the alphabet size. We first provide two algorithms that enumerate all LCP values and suffix tree int... | As far as the CSA is concerned, this component can be easily built from the BWT using small space as it is formed (in its simplest design) by just a BWT with rank select functionality enhanced with a suffix array sampling, see also @cite_21 . | {
"abstract": [
"We show that the compressed suffix array and the compressed suffix tree for a string of length n over an integer alphabet of size σ ≤ n can both be built in O(n) (randomized) time using only O(n log σ) bits of working space. The previously fastest construction algorithms that used O(n log σ) bits... | 0 | ||
1908.04686 | 2967058823 | We show how to build several data structures of central importance to string processing, taking as input the Burrows-Wheeler transform (BWT) and using small extra working space. Let @math be the text length and @math be the alphabet size. We first provide two algorithms that enumerate all LCP values and suffix tree int... | We are aware of only one work building the LCP array in small space from the BWT: @cite_38 show how to build the LCP array in @math time and @math bits of working space on top of the input BWT and the output. Other works @cite_15 @cite_21 show how to build the LCP array directly from the text in @math time and @math bi... | {
"abstract": [
"Many sequence analysis tasks can be accomplished with a suffix array, and several of them additionally need the longest common prefix array. In large scale applications, suffix arrays are being replaced with full-text indexes that are based on the Burrows-Wheeler transform. In this paper, we pres... | 0 | ||
1908.04686 | 2967058823 | We show how to build several data structures of central importance to string processing, taking as input the Burrows-Wheeler transform (BWT) and using small extra working space. Let @math be the text length and @math be the alphabet size. We first provide two algorithms that enumerate all LCP values and suffix tree int... | K "a rkk "a @cite_26 show that the PLCP bitvector can be built in @math time using @math bits of working space on top of the text, the suffix array, and the output PLCP. Kasai at al.'s lemma also stands at the basis of a more space-efficient algorithm from V " a lim " a @cite_18 , which computes the PLCP from a CSA in ... | {
"abstract": [
"Suffix tree is one of the most important data structures in string algorithms and biological sequence analysis. Unfortunately, when it comes to implementing those algorithms and applying them to real genomic sequences, often the main memory size becomes the bottleneck. This is easily explained by... | 0 | ||
1908.04686 | 2967058823 | We show how to build several data structures of central importance to string processing, taking as input the Burrows-Wheeler transform (BWT) and using small extra working space. Let @math be the text length and @math be the alphabet size. We first provide two algorithms that enumerate all LCP values and suffix tree int... | The remaining component required to build a compressed suffix tree (in the version described by Sadakane @cite_10 ) is the suffix tree topology, represented either in BPS @cite_19 (balanced parentheses) or DFUDS @cite_41 (depth first unary degree sequence), using @math bits. As far as the BPS representation is concerne... | {
"abstract": [
"Suffix tree is one of the most important data structures in string algorithms and biological sequence analysis. Unfortunately, when it comes to implementing those algorithms and applying them to real genomic sequences, often the main memory size becomes the bottleneck. This is easily explained by... | 0 | ||
1908.04686 | 2967058823 | We show how to build several data structures of central importance to string processing, taking as input the Burrows-Wheeler transform (BWT) and using small extra working space. Let @math be the text length and @math be the alphabet size. We first provide two algorithms that enumerate all LCP values and suffix tree int... | In this paper, we give new space-time trade-offs that allow building the CST's components in smaller working space (and in some cases even faster) with respect to the existing solutions. We start by combining 's algorithm @cite_38 with the suffix-tree enumeration procedure of Belazzougui @cite_21 to obtain an algorithm... | {
"abstract": [
"Many sequence analysis tasks can be accomplished with a suffix array, and several of them additionally need the longest common prefix array. In large scale applications, suffix arrays are being replaced with full-text indexes that are based on the Burrows-Wheeler transform. In this paper, we pres... | 0 | ||
1908.04686 | 2967058823 | We show how to build several data structures of central importance to string processing, taking as input the Burrows-Wheeler transform (BWT) and using small extra working space. Let @math be the text length and @math be the alphabet size. We first provide two algorithms that enumerate all LCP values and suffix tree int... | Also contribution ) improves the state-of-the-art, due to @cite_21 @cite_31 . In those papers, the authors show how to merge the BWTs of two texts @math and obtain the BWT of the collection @math in @math time and @math bits of working space for any @math [Thm. 7] belazzougui2016linear . When @math , this running time ... | {
"abstract": [
"The field of succinct data structures has flourished over the last 16 years. Starting from the compressed suffix array (CSA) by Grossi and Vitter (STOC 2000) and the FM-index by Ferragina and Manzini (FOCS 2000), a number of generalizations and applications of string indexes based on the Burrows-... | 0 | ||
1908.04628 | 2968100992 | Many real-world prediction tasks have outcome (a.k.a. target or response) variables that have characteristic heavy-tail distributions. Examples include copies of books sold, auction prices of art pieces, etc. By learning heavy-tailed distributions, big and rare'' instances (e.g., the best-sellers) will have accurate pr... | In real-world applications like search engines and recommendation systems, systems provide ranked lists tailored to users and their queries @cite_11 @cite_16 @cite_5 . In some cases, mapping those preferences into an ordinal variable leads to better user experience. Such tasks require the use of regression and multi-cl... | {
"abstract": [
"With Web mail services offering larger and larger storage capacity, most users do not feel the need to systematically delete messages anymore and inboxes keep growing. It is quite surprising that in spite of the huge progress of relevance ranking in Web Search, mail search results are still typic... | L2P: LEARNING TO PLACE FOR ESTIMATING HEAVY-TAILED DISTRIBUTED OUTCOMES | Heavy-tailed distributions are prevalent in real world data. By heavy-tailed, we mean a variable whose distribution has a heavier tail than the exponential distribution. Many real-world applications involve predicting heavy-tailed distributed outcomes. For example, publishers want to predict a book's sales number befor... | 5,394 |
1908.04628 | 2968100992 | Many real-world prediction tasks have outcome (a.k.a. target or response) variables that have characteristic heavy-tail distributions. Examples include copies of books sold, auction prices of art pieces, etc. By learning heavy-tailed distributions, big and rare'' instances (e.g., the best-sellers) will have accurate pr... | Regression problems are known to suffer from under-predicting rare instances @cite_17 . Approaches proposed to correct fitting models consider prior correction that introduces terms capturing a fraction of rare events in the observations and weighting the data to compensate for differences @cite_0 @cite_4 . Hsu and Sab... | {
"abstract": [
"Disease and trait-associated variants represent a tiny minority of all known genetic variation, and therefore there is necessarily an imbalance between the small set of available disease-associated and the much larger set of non-deleterious genomic variation, especially in non-coding regulatory r... | L2P: LEARNING TO PLACE FOR ESTIMATING HEAVY-TAILED DISTRIBUTED OUTCOMES | Heavy-tailed distributions are prevalent in real world data. By heavy-tailed, we mean a variable whose distribution has a heavier tail than the exponential distribution. Many real-world applications involve predicting heavy-tailed distributed outcomes. For example, publishers want to predict a book's sales number befor... | 5,394 |
1908.04628 | 2968100992 | Many real-world prediction tasks have outcome (a.k.a. target or response) variables that have characteristic heavy-tail distributions. Examples include copies of books sold, auction prices of art pieces, etc. By learning heavy-tailed distributions, big and rare'' instances (e.g., the best-sellers) will have accurate pr... | In literature, efficient methodologies were proposed to learn pairwise relations more efficiently than comparing all @math pairs exhaustively. Qian proposed using two-step hashing framework to retrieve relevant instance and nominate pairs whose ranking is uncertain @cite_21 . Similar approaches to efficiently search si... | {
"abstract": [
"We introduce a method that enables scalable similarity search for learned metrics. Given pairwise similarity and dissimilarity constraints between some examples, we learn a Mahalanobis distance function that captures the examples' underlying relationships well. To allow sublinear time similarity ... | L2P: LEARNING TO PLACE FOR ESTIMATING HEAVY-TAILED DISTRIBUTED OUTCOMES | Heavy-tailed distributions are prevalent in real world data. By heavy-tailed, we mean a variable whose distribution has a heavier tail than the exponential distribution. Many real-world applications involve predicting heavy-tailed distributed outcomes. For example, publishers want to predict a book's sales number befor... | 5,394 |
1908.03477 | 2968848930 | We address the problem of cross-modal fine-grained action retrieval between text and video. Cross-modal retrieval is commonly achieved through learning a shared embedding space, that can indifferently embed modalities. In this paper, we propose to enrich the embedding by disentangling parts-of-speech (PoS) in the accom... | Recently, neural networks trained with a ranking loss considering image pairs @cite_13 , triplets @cite_26 , quadruplets @cite_31 or beyond @cite_2 , have been considered for metric learning @cite_4 @cite_26 and for a broad range of search tasks such as face person identification @cite_20 @cite_31 @cite_28 @cite_1 or i... | {
"abstract": [
"We describe a novel cross-modal embedding space for actions, named Action2Vec, which combines linguistic cues from class labels with spatio-temporal features derived from video clips. Our approach uses a hierarchical recurrent network to capture the temporal structure of video features. We train ... | Fine-Grained Action Retrieval Through Multiple Parts-of-Speech Embeddings | With the onset of the digital age, millions of hours of video are being recorded and searching this data is becoming a monumental task. It is even more tedious when searching shifts from video-level labels, such as 'dancing' or 'skiing', to short action segments like 'cracking eggs' or 'tightening a screw'. In this pap... | 4,495 |
1908.03477 | 2968848930 | We address the problem of cross-modal fine-grained action retrieval between text and video. Cross-modal retrieval is commonly achieved through learning a shared embedding space, that can indifferently embed modalities. In this paper, we propose to enrich the embedding by disentangling parts-of-speech (PoS) in the accom... | Representing text Early works in image-to-text cross-modal retrieval @cite_12 @cite_3 @cite_25 used TF-IDF as a weighted bag-of-words model for text representations (either from a word embedding model or one-hot vectors) in order to aggregate variable length text captions into a single fixed sized representation. With ... | {
"abstract": [
"We describe a novel cross-modal embedding space for actions, named Action2Vec, which combines linguistic cues from class labels with spatio-temporal features derived from video clips. Our approach uses a hierarchical recurrent network to capture the temporal structure of video features. We train ... | Fine-Grained Action Retrieval Through Multiple Parts-of-Speech Embeddings | With the onset of the digital age, millions of hours of video are being recorded and searching this data is becoming a monumental task. It is even more tedious when searching shifts from video-level labels, such as 'dancing' or 'skiing', to short action segments like 'cracking eggs' or 'tightening a screw'. In this pap... | 4,495 |
1908.03477 | 2968848930 | We address the problem of cross-modal fine-grained action retrieval between text and video. Cross-modal retrieval is commonly achieved through learning a shared embedding space, that can indifferently embed modalities. In this paper, we propose to enrich the embedding by disentangling parts-of-speech (PoS) in the accom... | Hahn al @cite_35 use two LSTMs to directly project videos into the Word2Vec embedding space. This method is evaluated on higher-level activities, showing that such a visual embedding aligns well with the learned space of Word2Vec to perform zero-shot recognition of these coarser-grained classes. Miech al @cite_11 found... | {
"abstract": [
"We describe a novel cross-modal embedding space for actions, named Action2Vec, which combines linguistic cues from class labels with spatio-temporal features derived from video clips. Our approach uses a hierarchical recurrent network to capture the temporal structure of video features. We train ... | Fine-Grained Action Retrieval Through Multiple Parts-of-Speech Embeddings | With the onset of the digital age, millions of hours of video are being recorded and searching this data is becoming a monumental task. It is even more tedious when searching shifts from video-level labels, such as 'dancing' or 'skiing', to short action segments like 'cracking eggs' or 'tightening a screw'. In this pap... | 4,495 |
1908.03477 | 2968848930 | We address the problem of cross-modal fine-grained action retrieval between text and video. Cross-modal retrieval is commonly achieved through learning a shared embedding space, that can indifferently embed modalities. In this paper, we propose to enrich the embedding by disentangling parts-of-speech (PoS) in the accom... | Fine-grained action recognition Recently, several large-scale datasets have been published for the task of fine-grained action recognition @cite_17 @cite_15 @cite_23 @cite_36 @cite_24 . These generally focus on a closed vocabulary of class labels describing short and or specific actions. | {
"abstract": [
"Computer vision has a great potential to help our daily lives by searching for lost keys, watering flowers or reminding us to take a pill. To succeed with such tasks, computer vision methods need to be trained from real and diverse examples of our daily dynamic scenes. While most of such scenes a... | Fine-Grained Action Retrieval Through Multiple Parts-of-Speech Embeddings | With the onset of the digital age, millions of hours of video are being recorded and searching this data is becoming a monumental task. It is even more tedious when searching shifts from video-level labels, such as 'dancing' or 'skiing', to short action segments like 'cracking eggs' or 'tightening a screw'. In this pap... | 4,495 |
1908.03477 | 2968848930 | We address the problem of cross-modal fine-grained action retrieval between text and video. Cross-modal retrieval is commonly achieved through learning a shared embedding space, that can indifferently embed modalities. In this paper, we propose to enrich the embedding by disentangling parts-of-speech (PoS) in the accom... | Rohrbach al @cite_24 investigate hand and pose estimation techniques for fine-grained activity recognition. By compositing separate actions, and treating them as attributes, they can predict unseen activities via novel combinations of seen actions. Mahdisoltani al @cite_9 train for four different tasks, including both ... | {
"abstract": [
"Activity recognition has shown impressive progress in recent years. However, the challenges of detecting fine-grained activities and understanding how they are combined into composite activities have been largely overlooked. In this work we approach both tasks and present a dataset which provides... | Fine-Grained Action Retrieval Through Multiple Parts-of-Speech Embeddings | With the onset of the digital age, millions of hours of video are being recorded and searching this data is becoming a monumental task. It is even more tedious when searching shifts from video-level labels, such as 'dancing' or 'skiing', to short action segments like 'cracking eggs' or 'tightening a screw'. In this pap... | 4,495 |
1908.03121 | 2966129987 | We study the simulation of stellar mergers, which requires complex simulations with high computational demands. We have developed Octo-Tiger, a finite volume grid-based hydrodynamics simulation code with Adaptive Mesh Refinement which is unique in conserving both linear and angular momentum to machine precision. To fac... | There are several studies that investigate the structure of mass loss in V1309 Scorpii through computer simulation. One approach to modeling this system is smoothed-particle hydrodynamics (SPH). Notable SPH applications include StarSmasher @cite_44 @cite_18 (a fork of StarCrash @cite_8 ) and an unpublished code develop... | {
"abstract": [
"A new code for astrophysical magnetohydrodynamics (MHD) is described. The code has been designed to be easily extensible for use with static and adaptive mesh refinement. It combines higher order Godunov methods with the constrained transport (CT) technique to enforce the divergence-free constrai... | From Piz Daint to the Stars: Simulation of Stellar Mergers using High-Level Abstractions | Astrophysical simulations are among the classical drivers for exascale computing. They require multiple scales of physics and cover vast scales in space and time. Even the next generation of high-performance computing (HPC) systems will be insufficient to solve more than a fraction of the many conceivable scenarios.
H... | 8,475 |
1908.03121 | 2966129987 | We study the simulation of stellar mergers, which requires complex simulations with high computational demands. We have developed Octo-Tiger, a finite volume grid-based hydrodynamics simulation code with Adaptive Mesh Refinement which is unique in conserving both linear and angular momentum to machine precision. To fac... | Adaptive multithreading systems such as HPX expose concurrency by using user-level threads. Some other notable solutions that take such an approach are Uintah @cite_2 , Chapel @cite_55 , Charm++ @cite_12 , Kokkos @cite_36 , Legion @cite_5 , and PaRSEC @cite_23 . Note that we only refer to distributed memory capable sol... | {
"abstract": [
"Abstract The manycore revolution can be characterized by increasing thread counts, decreasing memory per thread, and diversity of continually evolving manycore architectures. High performance computing (HPC) applications and libraries must exploit increasingly finer levels of parallelism within t... | From Piz Daint to the Stars: Simulation of Stellar Mergers using High-Level Abstractions | Astrophysical simulations are among the classical drivers for exascale computing. They require multiple scales of physics and cover vast scales in space and time. Even the next generation of high-performance computing (HPC) systems will be insufficient to solve more than a fraction of the many conceivable scenarios.
H... | 8,475 |
1908.03121 | 2966129987 | We study the simulation of stellar mergers, which requires complex simulations with high computational demands. We have developed Octo-Tiger, a finite volume grid-based hydrodynamics simulation code with Adaptive Mesh Refinement which is unique in conserving both linear and angular momentum to machine precision. To fac... | There are several particle-based FMM implementations utilizing task-based programming available. The approach described in @cite_15 uses the Quark runtime environment @cite_21 , the implementation in @cite_10 @cite_42 uses StarPu @cite_30 , whilst @cite_11 uses OpenMP @cite_35 , and @cite_16 compares Cilk @cite_3 , HPX... | {
"abstract": [
"In the field of HPC, the current hardware trend is to design multiprocessor architectures featuring heterogeneous technologies such as specialized coprocessors (e.g. Cell BE) or data-parallel accelerators (e.g. GPUs). Approaching the theoretical performance of these architectures is a complex iss... | From Piz Daint to the Stars: Simulation of Stellar Mergers using High-Level Abstractions | Astrophysical simulations are among the classical drivers for exascale computing. They require multiple scales of physics and cover vast scales in space and time. Even the next generation of high-performance computing (HPC) systems will be insufficient to solve more than a fraction of the many conceivable scenarios.
H... | 8,475 |
1908.02711 | 2965409261 | Adversarial training has been recently employed for realizing structured semantic segmentation, in which the aim is to preserve higher-level scene structural consistencies in dense predictions. However, as we show, value-based discrimination between the predictions from the segmentation network and ground-truth annotat... | . Adversarial training schemes have been extensively employed in the literature to impose structural consistencies for semantic segmentation @cite_30 @cite_14 @cite_33 @cite_2 @cite_23 @cite_1 @cite_8 @cite_48 @cite_24 @cite_20 @cite_5 . @cite_14 incorporate a discriminator network trained to distinguish the real label... | {
"abstract": [
"In this paper, we propose perceptual adversarial networks (PANs) for image-to-image transformations. Different from existing application driven algorithms, PAN provides a generic framework of learning to map from input images to desired images (Fig. 1), such as a rainy image to its de-rained coun... | I Bet You Are Wrong: Gambling Adversarial Networks for Structured Semantic Segmentation | In the past years, deep neural networks have obtained substantial success in various visual recognition tasks including semantic segmentation [12,15]. Despite the success of the frequently used (fully) convolutional neural networks [31] on semantic segmentation, they lack a built-in mechanism to enforce global structur... | 4,106 |
1908.02711 | 2965409261 | Adversarial training has been recently employed for realizing structured semantic segmentation, in which the aim is to preserve higher-level scene structural consistencies in dense predictions. However, as we show, value-based discrimination between the predictions from the segmentation network and ground-truth annotat... | @cite_14 also discuss the value-based discrimination issue, which they attempt to alleviate by feeding the discriminator with a Cartesian product of the prediction maps and the input image channels. However, their followed strategy resulted in no improvements as reported. This can be attributed to remaining value-based... | {
"abstract": [
"Adversarial training has been shown to produce state of the art results for generative image modeling. In this paper we propose an adversarial training approach to train semantic segmentation models. We train a convolutional semantic segmentation network along with an adversarial network that dis... | I Bet You Are Wrong: Gambling Adversarial Networks for Structured Semantic Segmentation | In the past years, deep neural networks have obtained substantial success in various visual recognition tasks including semantic segmentation [12,15]. Despite the success of the frequently used (fully) convolutional neural networks [31] on semantic segmentation, they lack a built-in mechanism to enforce global structur... | 4,106 |
1908.02256 | 2966257000 | Recently, the field of adversarial machine learning has been garnering attention by showing that state-of-the-art deep neural networks are vulnerable to adverserial examples, stemming from small perturbations being added to the input image. Adversarial examples are generated by a malicious adversary by obtaining access... | is the technique of injecting adverserial examples and the corresponding gold standard labels into the training set @cite_8 @cite_18 @cite_12 . The motivation of this methodology is that the network will learn the adverserial perturbations introduced by the attacker. The problem with adverserial training is that it dou... | {
"abstract": [
"Several machine learning models, including neural networks, consistently misclassify adversarial examples---inputs formed by applying small but intentionally worst-case perturbations to examples from the dataset, such that the perturbed input results in the model outputting an incorrect answer wi... | BlurNet: Defense by Filtering the Feature Maps | Machine learning has been ubiquitous in various fields like computer vision and speech recognition. [12,10] However, despite these advancements, neural network classifiers have been found to be susceptible to so called adversarial images [23]. These images are created by altering some pixels in the input space so that ... | 4,058 |
1908.01823 | 2965547769 | We propose a general approach for change-point detection in dynamic networks. The proposed method is model-free and covers a wide range of dynamic networks. The key idea behind our approach is to effectively utilize the network structure in designing change-point detection algorithms. This is done via an initial step o... | @cite_5 proposes a novel estimator for estimating the link probability matrix @math of an undirected network by neighborhood smoothing (NBS). The essential idea consists of the following: Given an adjacent matrix @math , the link probability @math between node @math and @math is estimated by where @math is a certain se... | {
"abstract": [
"SummaryThe estimation of probabilities of network edges from the observed adjacency matrix has important applications to the prediction of missing links and to network denoising. It is usually addressed by estimating the graphon, a function that determines the matrix of edge probabilities, but th... | Change-point detection in dynamic networks via graphon estimation | The last few decades have witnessed rapid advancement in models, computational algorithms and theories for inference of networks. This is largely motivated by the increasing prevalence of network data in diverse fields of science, engineering and society, and the need to extract meaningful scientific information out of... | 9,660 |
1908.01823 | 2965547769 | We propose a general approach for change-point detection in dynamic networks. The proposed method is model-free and covers a wide range of dynamic networks. The key idea behind our approach is to effectively utilize the network structure in designing change-point detection algorithms. This is done via an initial step o... | Another related area of research is anomaly detection in dynamic networks, where the task is to detect short abrupt deviation of the network behavior from its norm. This is not the focus of our paper and we refer the readers to @cite_1 for a comprehensive survey. | {
"abstract": [
"Anomaly detection is an important problem with multiple applications, and thus has been studied for decades in various research domains. In the past decade there has been a growing interest in anomaly detection in data represented as networks, or graphs, largely because of their robust expressive... | Change-point detection in dynamic networks via graphon estimation | The last few decades have witnessed rapid advancement in models, computational algorithms and theories for inference of networks. This is largely motivated by the increasing prevalence of network data in diverse fields of science, engineering and society, and the need to extract meaningful scientific information out of... | 9,660 |
1908.01536 | 2965290354 | Current techniques for explainable AI have been applied with some success to image processing. The recent rise of research in video processing has called for similar work n deconstructing and explaining spatio-temporal models. While many techniques are designed for 2D convolutional models, others are inherently applica... | Inflating convolutional layers to 3D for video tasks was first explored in @cite_15 , in which the authors chose to optimise an architecture for the video task, rather than adapt one from an image problem. Both @cite_1 and @cite_5 have adapted large image classification models (Inception and ResNet respectively) to act... | {
"abstract": [
"We introduce UCF101 which is currently the largest dataset of human actions. It consists of 101 action classes, over 13k clips and 27 hours of video data. The database consists of realistic user uploaded videos containing camera motion and cluttered background. Additionally, we provide baseline a... | Discriminating Spatial and Temporal Relevance in Deep Taylor Decompositions for Explainable Activity Recognition | Recent success in solving image recognition problems can be attributed to the application to these problems of increasingly complex convolutional neural networks (CNNs) that make use of spatial convolutional feature extractors. This success has been closely followed by a call for explainability and transparency of thes... | 2,297 |
1908.01536 | 2965290354 | Current techniques for explainable AI have been applied with some success to image processing. The recent rise of research in video processing has called for similar work n deconstructing and explaining spatio-temporal models. While many techniques are designed for 2D convolutional models, others are inherently applica... | A variety of approaches have been attempted for explaining decisions made by deep neural networks. For example, in @cite_8 the authors propose feature visualisation for CNNs, in which the input images are optimised to maximally activate each filter in the CNN convolutional layers, following work in @cite_3 on non-convo... | {
"abstract": [
"",
"We aim to model the top-down attention of a convolutional neural network (CNN) classifier for generating task-specific attention maps. Inspired by a top-down human visual attention model, we propose a new backpropagation scheme, called Excitation Backprop, to pass along top-down signals d... | Discriminating Spatial and Temporal Relevance in Deep Taylor Decompositions for Explainable Activity Recognition | Recent success in solving image recognition problems can be attributed to the application to these problems of increasingly complex convolutional neural networks (CNNs) that make use of spatial convolutional feature extractors. This success has been closely followed by a call for explainability and transparency of thes... | 2,297 |
1908.01536 | 2965290354 | Current techniques for explainable AI have been applied with some success to image processing. The recent rise of research in video processing has called for similar work n deconstructing and explaining spatio-temporal models. While many techniques are designed for 2D convolutional models, others are inherently applica... | Layer-wise relevance propagation (LRP) rules, as defined in @cite_10 , have found moderate success in explaining image recognition tasks. Multiple implementations and improvements have been made to these rules, with marginal winning probability (MWP) @cite_4 , to our knowledge being the first implementation of the rule... | {
"abstract": [
"Compressed domain human action recognition algorithms are extremely efficient, because they only require a partial decoding of the video bit stream. However, the question what exactly makes these algorithms decide for a particular action is still a mystery. In this paper, we present a general met... | Discriminating Spatial and Temporal Relevance in Deep Taylor Decompositions for Explainable Activity Recognition | Recent success in solving image recognition problems can be attributed to the application to these problems of increasingly complex convolutional neural networks (CNNs) that make use of spatial convolutional feature extractors. This success has been closely followed by a call for explainability and transparency of thes... | 2,297 |
1908.01536 | 2965290354 | Current techniques for explainable AI have been applied with some success to image processing. The recent rise of research in video processing has called for similar work n deconstructing and explaining spatio-temporal models. While many techniques are designed for 2D convolutional models, others are inherently applica... | In addition to MWP, the authors in @cite_4 also show that removing relevance for the dual of the signal improves the focus of the explanation. This contrastive MWP (cMWP) effectively removes relevance to all classes, by explaining all other outputs at the second logits layer, leaving only relevance contributing to the ... | {
"abstract": [
"We aim to model the top-down attention of a convolutional neural network (CNN) classifier for generating task-specific attention maps. Inspired by a top-down human visual attention model, we propose a new backpropagation scheme, called Excitation Backprop, to pass along top-down signals downwards... | Discriminating Spatial and Temporal Relevance in Deep Taylor Decompositions for Explainable Activity Recognition | Recent success in solving image recognition problems can be attributed to the application to these problems of increasingly complex convolutional neural networks (CNNs) that make use of spatial convolutional feature extractors. This success has been closely followed by a call for explainability and transparency of thes... | 2,297 |
1908.01536 | 2965290354 | Current techniques for explainable AI have been applied with some success to image processing. The recent rise of research in video processing has called for similar work n deconstructing and explaining spatio-temporal models. While many techniques are designed for 2D convolutional models, others are inherently applica... | Work on explainability methods outside of image tasks is still developing. Papers such as @cite_1 use feature visualisation techniques to provide insight into the models they have trained, but to our knowledge @cite_16 is still one of the only instances of an LRP based method applied to a video task. In this work, the ... | {
"abstract": [
"In this work, we revisit the global average pooling layer proposed in [13], and shed light on how it explicitly enables the convolutional neural network to have remarkable localization ability despite being trained on image-level labels. While this technique was previously proposed as a means for... | Discriminating Spatial and Temporal Relevance in Deep Taylor Decompositions for Explainable Activity Recognition | Recent success in solving image recognition problems can be attributed to the application to these problems of increasingly complex convolutional neural networks (CNNs) that make use of spatial convolutional feature extractors. This success has been closely followed by a call for explainability and transparency of thes... | 2,297 |
1908.00948 | 2966220025 | Spurred by the potential of deep learning, computational music generation has gained renewed academic interest. A crucial issue in music generation is that of user control, especially in scenarios where the music generation process is conditioned on existing musical material. Here we propose a model for conditional kic... | In addition to the VAE-based methods for control over music generation processes mentioned above, a number of other studies have applied deep learning methods to address the problem of music generation in general, as reviewed in @cite_19 . Drum track generation has been tackled using recurrent architectures @cite_4 @ci... | {
"abstract": [
"",
"Machine learning has shown a successful component of methods for automatic music composition. Considering music as a sequence of events with multiple complex dependencies on various levels of a composition, the long short-term memory-based (LSTM) architectures have been proven to be very ... | HIGH-LEVEL CONTROL OF DRUM TRACK GENERATION USING LEARNED PATTERNS OF RHYTHMIC INTERACTION | A crucial issue in music generation is that of user control. Especially for problems where musical material is to be generated conditioned on existing musical material (so-called conditional generation), it is not desirable for a system to produce its output deterministically. Typically there are multiple valid ways to... | 2,716 |
1908.00355 | 2966623897 | In order to mimic the human ability of continual acquisition and transfer of knowledge across various tasks, a learning system needs the capability for continual learning, effectively utilizing the previously acquired skills. As such, the key challenge is to transfer and generalize the knowledge learned from one task t... | Recently, a number of approaches have been proposed to adapt a DNN model to the continual learning setting, from an adaptive model architecture perspective such as adding columns or neurons for new tasks @cite_4 @cite_6 @cite_9 ; model parameter adjustment or regularization techniques like, imposing restrictions on par... | {
"abstract": [
"",
"Methods and systems for performing a sequence of machine learning tasks. One system includes a sequence of deep neural networks (DNNs), including: a first DNN corresponding to a first machine learning task, wherein the first DNN comprises a first plurality of indexed layers, and each laye... | Continual Learning via Online Leverage Score Sampling | It is a typical practice to design and optimize machine learning (ML) models to solve a single task. On the other hand, humans, instead of learning over isolated complex tasks, are capable of generalizing and transferring knowledge and skills learned from one task to another. This ability to remember, learn and transfe... | 3,697 |
1908.00355 | 2966623897 | In order to mimic the human ability of continual acquisition and transfer of knowledge across various tasks, a learning system needs the capability for continual learning, effectively utilizing the previously acquired skills. As such, the key challenge is to transfer and generalize the knowledge learned from one task t... | In order to demonstrate our idea in comparison with the state-of-the-art techniques, we briefly discuss the following three popular approaches to continual learning: I) : It constrains or regularizes the model parameters by adding additional terms in the loss function that prevent the model from deviating significantly... | {
"abstract": [
"Methods and systems for performing a sequence of machine learning tasks. One system includes a sequence of deep neural networks (DNNs), including: a first DNN corresponding to a first machine learning task, wherein the first DNN comprises a first plurality of indexed layers, and each layer in the... | Continual Learning via Online Leverage Score Sampling | It is a typical practice to design and optimize machine learning (ML) models to solve a single task. On the other hand, humans, instead of learning over isolated complex tasks, are capable of generalizing and transferring knowledge and skills learned from one task to another. This ability to remember, learn and transfe... | 3,697 |
1908.00222 | 2965084509 | Recently, there has been growing interest in developing learning-based methods to detect and utilize salient semi-global or global structures, such as junctions, lines, planes, cuboids, smooth surfaces, and all types of symmetries, for 3D scene modeling and understanding. However, the ground truth annotations are often... | @PARASPLIT Note that our dataset is very different from other popular large-scale 3D datasets, such as NYU v2 @cite_16 , SUN RGB-D @cite_34 , 2D-3D-S @cite_19 @cite_22 , ScanNet @cite_2 , and Matterport3D @cite_17 , in which the ground truth 3D information is stored in the format of point clouds or meshes. These datase... | {
"abstract": [
"",
"We present a dataset of large-scale indoor spaces that provides a variety of mutually registered modalities from 2D, 2.5D and 3D domains, with instance-level semantic and geometric annotations. The dataset covers over 6,000m2 and contains over 70,000 RGB images, along with the correspondi... | Structured3D: A Large Photo-realistic Dataset for Structured 3D Modeling | Inferring 3D information from 2D sensory data such as images and videos has long been a central research topic in computer vision. Conventional approach to build 3D models of a scene typically relies on detecting, matching, and triangulating local image features (e.g., patches, superpixels, edges, and SIFT features). A... | 3,766 |
1908.00222 | 2965084509 | Recently, there has been growing interest in developing learning-based methods to detect and utilize salient semi-global or global structures, such as junctions, lines, planes, cuboids, smooth surfaces, and all types of symmetries, for 3D scene modeling and understanding. However, the ground truth annotations are often... | In recent years, synthetic datasets have played an important role in successful training of deep neural networks. Notable examples for indoor scene understanding include SUNCG @cite_10 , SceneNet RGB-D @cite_21 , and InteriorNet @cite_23 . These datasets exceed real datasets in terms of scene diversity and frame number... | {
"abstract": [
"We introduce SceneNet RGB-D, a dataset providing pixel-perfect ground truth for scene understanding problems such as semantic segmentation, instance segmentation, and object detection. It also provides perfect camera poses and depth data, allowing investigation into geometric computer vision prob... | Structured3D: A Large Photo-realistic Dataset for Structured 3D Modeling | Inferring 3D information from 2D sensory data such as images and videos has long been a central research topic in computer vision. Conventional approach to build 3D models of a scene typically relies on detecting, matching, and triangulating local image features (e.g., patches, superpixels, edges, and SIFT features). A... | 3,766 |
1908.00222 | 2965084509 | Recently, there has been growing interest in developing learning-based methods to detect and utilize salient semi-global or global structures, such as junctions, lines, planes, cuboids, smooth surfaces, and all types of symmetries, for 3D scene modeling and understanding. However, the ground truth annotations are often... | Room layout estimation. Room layout estimation aims to reconstruct the enclosing structure of the indoor scene, consisting of walls, floor, and ceiling. Existing public datasets ( , PanoContext @cite_3 and LayoutNet @cite_0 ) assume a simple cuboid-shape layout. PanoContext @cite_3 collects about 500 panoramas from the... | {
"abstract": [
"We present a dataset of large-scale indoor spaces that provides a variety of mutually registered modalities from 2D, 2.5D and 3D domains, with instance-level semantic and geometric annotations. The dataset covers over 6,000m2 and contains over 70,000 RGB images, along with the corresponding depth... | Structured3D: A Large Photo-realistic Dataset for Structured 3D Modeling | Inferring 3D information from 2D sensory data such as images and videos has long been a central research topic in computer vision. Conventional approach to build 3D models of a scene typically relies on detecting, matching, and triangulating local image features (e.g., patches, superpixels, edges, and SIFT features). A... | 3,766 |
1907.13594 | 2965144741 | Being motivated by ceiling inspection applications via unmanned aerial vehicles (UAVs) which require close proximity flight to surfaces, a systematic control approach enabling safe and accurate close proximity flight is proposed in this work. There are two main challenges for close proximity flights: (i) the trust char... | The available approaches can handle the control of the flying robot when it does not engage with an interaction. However, the challenges associated with the aerodynamic interaction require the system to be more responsive, adaptive and resilient @cite_12 @cite_29 @cite_31 @cite_21 . This operation also brings system an... | {
"abstract": [
"This paper presents a novel control algorithm to regulate the aerodynamic thrust produced by fixed-pitch rotors commonly used on small-scale electrically powered multirotor aerial vehicles. The proposed controller significantly improves the disturbance rejection and gust tolerance of rotor thrust... | Aerial Robot Control in Close Proximity to Ceiling: A Force Estimation-based Nonlinear MPC | In recent years, in-site UAV inspection has gained momentum as an application area in robotic research [1]. Typical inspection application requires a robot to achieve accurate motions in close proximity to the environment for long periods of measurement [2]. This is a challenging control task for conventional controlle... | 3,285 |
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