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[ "abstract: 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 modeli...	Within the MAS community, some work @cite_1 has focused on how artificial AI-based learning agents would fare in communities of similar agents. For example, @cite_2 and show how agents can learn the capabilities of others via repeated interactions, but these agents do not learn to predict what actions other might take....
[ "abstract: 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 environm...	Grasping action is the most basic component of any interaction and it is composed of three major components @cite_1 . 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 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 environm...	Grasping data-driven approaches have existed since a long time ago @cite_1 . 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 between...
[ "abstract: 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 environm...	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_2 . For the same purpose, studied the correlations between hand DOFs aiming to simplify h...
[ "abstract: 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 environm...	In order to achieve realistic object interactions, physical simulations on the objects should also be considered @cite_1 @cite_2 . Moreover, hand and finger movement trajectories need to be both, kinematically and dynamically valid @cite_3 . @cite_1 simulate hand interaction, such as two hands grasping each other in th...
[ "abstract: 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...	Graph interpolation can be viewed as an extension of tree adjunction to parse graphs. And, indeed, TAGs @cite_1 , 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: 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...	In Lexical Functional Grammars @cite_1 , 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: 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 widel...	To our knowledge, lexical databases have been used only once in TC. Hearst @cite_1 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 WordNe...
[ "abstract: 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 widel...	Lexical databases have been employed recently in word sense disambiguation. For example, Agirre and Rigau @cite_1 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: 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...	Word--sense disambiguation has more commonly been cast as a problem in supervised learning (e.g., @cite_1 , , @cite_2 , @cite_6 , @cite_4 , @cite_5 , @cite_6 , @cite_7 , @cite_8 ). However, all of these methods require that manually sense tagged text be available to train the algorithm. For most domains such text is no...
[ "abstract: 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...	A more recent bootstrapping approach is described in @cite_1 . 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 collocation...
[ "abstract: 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...	While @cite_1 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_2 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. This...
[ "abstract: 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...	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_2 , , @cite_3 , , @cite_4 ).
[ "abstract: 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...	An early application of clustering to word--sense disambiguation is described in @cite_1 . 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 formulate...
[ "abstract: 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...	The features used in this work are complex and difficult to interpret and it isn't clear that this complexity is required. @cite_1 compares his method to @cite_2 and shows that for four words the former performs significantly better in distinguishing between two senses.
[ "abstract: 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 =...	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: 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...	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: 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...	In order to utilize models with more complicated interactions among feature variables, @cite_1 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: 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...	Alternative probabilistic approaches have involved using a single contextual feature to perform disambiguation (e.g., @cite_6 , @cite_2 , and @cite_3 present techniques for identifying the optimal feature to use in disambiguation). Maximum Entropy models have been used to express the interactions among multiple feature...
[ "abstract: 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 discus...	In computational linguistics, on the other hand, positive imperatives have been extensively investigated, both from the point of view of interpretation @cite_3 @cite_2 @cite_3 and generation @cite_5 @cite_5 . Little work, however, has been directed at negative imperatives. (for exceptions see the work of in interpretat...
[ "abstract: 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, ...	Recently, VDSH @cite_1 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_2 proposed to substitute the Gaussian pri...
[ "abstract: 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 bli...	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_1 , anisotropic diffusion @cite_2 and wavelet coring @cite_3 use the statistical regular...
[ "abstract: 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 bli...	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: 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 rarel...	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_1 ...
[ "abstract: 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...	Modern neural networks that provide good performance tend to be large and overparameterised, fuelled by observations that larger @cite_1 @cite_2 @cite_3 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_4 @cite_5...
[ "abstract: 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...	Early works like @cite_1 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_3 and @cite_4 where sensitivity of the loss with respect to neurons and weights are used r...
[ "abstract: 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...	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_1 @cite_2 @cite_3 and variational pruning @cite_4 @cite_5 @cite_6 . Among these, magnitude-based weight p...
[ "abstract: 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...	[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_1 @cite_2 , our approach is simpler with a single hyperparameter versus @math - @math h...
[ "abstract: 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...	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_1 @cite_2 , product quantisation on embeddings , factorising word predictions into multip...
[ "abstract: 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 coll...	BERT @cite_1 is a pre-trained transformer network @cite_2 , 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] token...
[ "abstract: 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 hig...	Pooling methods are requisite either in two-stream networks @cite_1 or in other feature fusion models. @cite_2 simply uses average pooling and outperforms others. @cite_3 proposes bilinear pooling to model local parts of object: two feature representations are learned separately and then multiplied using the outer prod...
[ "abstract: 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 hig...	Recently, lightweight neural networks including SqeezeNet @cite_1 , Xception @cite_2 , ShuffleNet @cite_3 , ShuffleNetV2 @cite_4 , MobileNet @cite_5 , and MobileNetV2 @cite_6 have been proposed to run on mobile devices with the parameters and computation reduced significantly. Since we focus on mobile video action reco...
[ "abstract: 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 function...	Another important result is following the Bennett's inequality. Corollary 5 in @cite_1 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: 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 function...	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_1 , i.e., sample variance penalty (SVP): An excess risk bound of @math may be achieved by solving the SVP. However, @cite_1 does ...
[ "abstract: 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 function...	Recently, @cite_1 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: 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 function...	To solve the above min-max formulation, @cite_1 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: 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-learn...	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: 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-learn...	On the other hand, control barrier functions (CBFs) @cite_3 @cite_2 @cite_3 @cite_4 @cite_5 @cite_6 @cite_8 were proposed to guarantee that an agent remains in a certain region of the state space (i.e., forward invariance ) by using a locally accurate model of the agent dynamics (i.e., a model that accurately predicts ...
[ "abstract: 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-learn...	Moreover, our work is also related to safe reinforcement learning, such as Lyapunov-based safe learning (cf. @cite_1 @cite_2 ) and constrained Markov decision processes (CMDPs) (cf. @cite_3 @cite_4 ). The former is based on the fact that sublevel sets of a control Lyapunov function are forward invariant, and considers ...
[ "abstract: 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-learn...	Besides, transfer learning (cf. @cite_1 ) 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_2 and "learning to learn" @cite_3 . In transfer learning for reinforcement learning contexts, we first learn a set of sour...
[ "abstract: 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 rel...	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_1 or geometric @cite_2 rep...
[ "abstract: 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 rel...	Supervised methods formulate VO as a supervised learning problem and many methods with good results have been proposed. DeMoN @cite_1 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_2 utilizes two networks for pose and...
[ "abstract: 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 rel...	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_1 leverages the geometric correlation of depth and ...
[ "abstract: 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 rel...	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_1 but pay little attention to the sequential nature of the problem. In these methods, only a few frames (no more than ...
[ "abstract: 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 ...	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: 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 ...	Sentence embeddings is an important topic in this research area. Skip-Thought @cite_1 input one sentence to predict its previous and next sentence. InferSent @cite_2 outperforms Skip-Thought. @cite_3 is the methods that use unsupervised word vectors @cite_4 to construct the sentence vectors which is a strong baseline. ...
[ "abstract: 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 exec...	Wolfgang @cite_1 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: 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 exec...	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_1 proposes the methods called and . The CV of WalkSAT, one of the application they adopted for evaluation, is less than one and the speedup is w...
[ "abstract: 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 ...	Cybersecurity becomes a critical issue due to the large-scale deployment of smart devices and their integration with information and communication techologies (ICTs) @cite_1 @cite_2 . Hence, security risk management is an important task which has been investigated in different research fields, such as communications an...
[ "abstract: 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 lang...	Natural language generation techniques have been widely popular for synthesizing unique pieces of textual content. NLG techniques proposed by @cite_1 @cite_2 rely on templates pre-constructed for specific purposes. The fake email generation system in @cite_3 uses a set of manually constructed rules to pre-define the st...
[ "abstract: 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 lang...	The system used for synthesizing emails in this work is somewhat aligned along the lines of the methodology described in @cite_1 @cite_2 . 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: 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 lang...	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_1 extract a large number of text body, URL and HTML features from emails, which are then fe...
[ "abstract: 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 stud...	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_1 has experimentally verified the intuitive assumption that fingerprinting methods outperform, in terms of accuracy, proximity or ranging pos...
[ "abstract: 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...	Researchers study the TDMA optimization in neuron-based MC, which employs neurons to communicate and built in-body sensor-actuator networks (IBSANs) @cite_1 . They use an evolutionary multi-objective optimization algorithm to design the TDMA schedule. The resource allocation in MC has already studied for two transmitte...
[ "abstract: 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 ...	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_1 @cite_2 @cite_3 ...
[ "abstract: 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 ...	The work of @cite_1 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 assig...
[ "abstract: 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 ...	The recent work of @cite_1 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: 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 ...	Another approach that re-examines the computation - communication tradeoff from an alternate viewpoint has been investigated in @cite_1 . 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: 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 ...	In @cite_1 , 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: 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 ...	As discussed above both @cite_1 and require a certain problem dimension to be very large. In particular, 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_1 considers functions that can be aggregated but requires the num...
[ "abstract: 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...	Scene text is regarded as a special type of object, several methods @cite_1 @cite_2 are based on Faster R-CNN @cite_9 , SSD @cite_4 and DenseBox @cite_5 , which generates text bounding boxes by regressing coordinates of boxes directly. TextBoxes @cite_2 and RRD @cite_7 adopt SSD as a base detector and adjust the anchor...
[ "abstract: 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...	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_1 , which achieves amazing results on public benchmarks, but requires relatively long execution time due to the per-proposal computation and...
[ "abstract: 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 ...	This paper continues the trend towards rectifying the substantial discrepancy'' 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 others'' @c...
[ "abstract: 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 ...	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_1 investigated an insurer who could assess security levels perfectly or not at all, concluding that the latter can...
[ "abstract: 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 ...	The literature on economic theory of insurance fraud has developed two main approaches: and @cite_1 . 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 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 ...	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 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 ...	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_1 .
[ "abstract: 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 ...	We are aware of only one work building the LCP array in small space from the BWT: @cite_1 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_2 @cite_3 show how to build the LCP array directly from the text in @math time and @math bits ...
[ "abstract: 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 ...	K "a rkk "a @cite_1 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_2 , which computes the PLCP from a CSA in @m...
[ "abstract: 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 ...	The remaining component required to build a compressed suffix tree (in the version described by Sadakane @cite_1 ) is the suffix tree topology, represented either in BPS @cite_2 (balanced parentheses) or DFUDS @cite_3 (depth first unary degree sequence), using @math bits. As far as the BPS representation is concerned, ...
[ "abstract: 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 ...	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_1 with the suffix-tree enumeration procedure of Belazzougui @cite_2 to obtain an algorithm t...
[ "abstract: 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 ...	Also contribution ) improves the state-of-the-art, due to @cite_1 @cite_2 . 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 is...
[ "abstract: 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 h...	In real-world applications like search engines and recommendation systems, systems provide ranked lists tailored to users and their queries @cite_1 @cite_2 @cite_3 . In some cases, mapping those preferences into an ordinal variable leads to better user experience. Such tasks require the use of regression and multi-clas...
[ "abstract: 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 h...	Regression problems are known to suffer from under-predicting rare instances @cite_1 . 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_2 @cite_3 . Hsu and Saba...
[ "abstract: 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 h...	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_1 . Similar approaches to efficiently search sim...
[ "abstract: 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 (Po...	Recently, neural networks trained with a ranking loss considering image pairs , triplets , quadruplets @cite_11 or beyond , have been considered for metric learning @cite_4 and for a broad range of search tasks such as face person identification @cite_6 @cite_11 @cite_15 or instance retrieval @cite_9 . These learning-t...
[ "abstract: 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 (Po...	Representing text Early works in image-to-text cross-modal retrieval @cite_1 @cite_2 @cite_3 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 th...
[ "abstract: 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 (Po...	Hahn al @cite_1 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_2 found t...
[ "abstract: 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 (Po...	Fine-grained action recognition Recently, several large-scale datasets have been published for the task of fine-grained action recognition @cite_1 @cite_2 @cite_3 @cite_4 . These generally focus on a closed vocabulary of class labels describing short and or specific actions.
[ "abstract: 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 (Po...	Rohrbach al @cite_1 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_2 train for four different tasks, including both c...
[ "abstract: 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 pr...	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 (a fork of StarCrash ) and an unpublished code developed by a collaboration of r...
[ "abstract: 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 pr...	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_1 , Chapel @cite_2 , Charm++ @cite_3 , Kokkos @cite_4 , Legion @cite_5 , and PaRSEC @cite_6 . Note that we only refer to distributed memory capable solutio...
[ "abstract: 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 pr...	There are several particle-based FMM implementations utilizing task-based programming available. The approach described in @cite_1 uses the Quark runtime environment , the implementation in @cite_2 @cite_7 uses StarPu @cite_4 , whilst @cite_5 uses OpenMP , and @cite_6 compares Cilk @cite_7 , HPX-5, and OpenMP tasks . O...
[ "abstract: 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 groun...	. Adversarial training schemes have been extensively employed in the literature to impose structural consistencies for semantic segmentation @cite_10 @cite_3 @cite_3 @cite_4 @cite_10 @cite_6 @cite_7 @cite_8 @cite_9 @cite_10 . @cite_3 incorporate a discriminator network trained to distinguish the real labels and network...
[ "abstract: 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 groun...	@cite_1 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: 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 o...	is the technique of injecting adverserial examples and the corresponding gold standard labels into the training set @cite_1 @cite_2 . 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 doubles the t...
[ "abstract: 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...	@cite_1 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: 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...	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: 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 inh...	Inflating convolutional layers to 3D for video tasks was first explored in @cite_2 , in which the authors chose to optimise an architecture for the video task, rather than adapt one from an image problem. Both @cite_2 and @cite_3 have adapted large image classification models (Inception and ResNet respectively) to acti...
[ "abstract: 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 inh...	A variety of approaches have been attempted for explaining decisions made by deep neural networks. For example, in @cite_1 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 on non-convolutional...
[ "abstract: 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 inh...	Layer-wise relevance propagation (LRP) rules, as defined in @cite_1 , 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_2 , to our knowledge being the first implementation of the rules...
[ "abstract: 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 inh...	In addition to MWP, the authors in @cite_1 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: 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 inh...	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_2 is still one of the only instances of an LRP based method applied to a video task. In this work, the d...
[ "abstract: 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 ...	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_6 . Drum track generation has been tackled using recurrent architectures @cite_9 , Re...
[ "abstract: 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 ...	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 ; model parameter adjustment or regularization techniques like, imposing restrictions on parameter updates @cite_2 ;...
[ "abstract: 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 ...	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: 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 annota...	@PARASPLIT Note that our dataset is very different from other popular large-scale 3D datasets, such as NYU v2 @cite_1 , SUN RGB-D @cite_5 , 2D-3D-S @cite_3 , ScanNet @cite_5 , and Matterport3D @cite_6 , in which the ground truth 3D information is stored in the format of point clouds or meshes. These datasets lack groun...
[ "abstract: 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 annota...	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_1 , SceneNet RGB-D @cite_2 , and InteriorNet . These datasets exceed real datasets in terms of scene diversity and frame numbers. But just...
[ "abstract: 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 annota...	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_1 and LayoutNet @cite_2 ) assume a simple cuboid-shape layout. PanoContext @cite_1 collects about 500 panoramas from the...
[ "abstract: 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 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_1 @cite_2 @cite_3 @cite_4 . This operation also brings system and en...

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