aid stringlengths 9 15 | mid stringlengths 7 10 | abstract stringlengths 78 2.56k | related_work stringlengths 92 1.77k | ref_abstract dict |
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1901.04982 | 2909529610 | Modern Intel CPUs reduce their frequency when executing wide vector operations (AVX2 and AVX-512 instructions), as these instructions increase power consumption. The frequency is only increased again two milliseconds after the last code section containing such instructions has been executed in order to prevent excessiv... | @cite_1 describe several scheduling algorithms for ISA-heterogeneous multiprocessors which share a common core ISA. They describe a system in which each task has a and where the scheduler prefers tasks whose task ISA ID closely matches the core's ISA. We apply a similar approach to software-defined heterogeneity on cur... | {
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"Heterogeneous MPSoC architectures can provide higher performance and flexibility with less power consumption and lower cost than homogeneous ones. However, as processor instruction sets of general heterogeneous MPSoCs are not ident... |
1901.04949 | 2963609467 | The Encoder-Decoder architecture is a main stream deep learning model for biomedical image segmentation. The encoder fully compresses the input and generates encoded features, and the decoder then produces dense predictions using encoded features. However, decoders are still under-explored in such architectures. In thi... | . A typical model-wise decoding structure is shown in Fig. (A). Its core idea is to treat the whole encoder as a blackbox" model, assuming that all the learned information is encoded into the output of the last layer of the encoder network. We call it model-wise" because the decoder takes the model-wise" output of the ... | {
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"Accurate localization and segmentation of intervertebral discs (IVDs) from volumetric data is a pre-requisite for clinical diagnosis and treatment planning. With the advance of deep learning, 2D fully convolutional networks (FCN) ... |
1901.04949 | 2963609467 | The Encoder-Decoder architecture is a main stream deep learning model for biomedical image segmentation. The encoder fully compresses the input and generates encoded features, and the decoder then produces dense predictions using encoded features. However, decoders are still under-explored in such architectures. In thi... | (1) Linear structure: The idea of this structure is to chain convolutions and pooling operations for feature extraction, such as in @cite_19 . (2) Residual structure: In such structure, the residual path element-wisely adds the input features to the output of the same block, making it a residual unit @cite_4 . This str... | {
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1901.04942 | 2908618619 | Code obfuscation is a popular approach to turn program comprehension and analysis harder, with the aim of mitigating threats related to malicious reverse engineering and code tampering. However, programming languages that compile to high level bytecode (e.g., Java) can be obfuscated only to a limited extent. In fact, h... | Identifier renaming @cite_21 is an instance of layout obfuscation that removes relevant information from the code by changing the names of classes, fields and operations into meaningless identifiers, so as to make it harder for an attacker to guess the functionalities implemented by different parts of the application. | {
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"There exist several obfuscation tools for preventing Java bytecode from being decompiled. Most of these tools, simply scramble the names of the identifiers stored in a bytecode by substituting the identifiers with meaningless name... |
1901.04942 | 2908618619 | Code obfuscation is a popular approach to turn program comprehension and analysis harder, with the aim of mitigating threats related to malicious reverse engineering and code tampering. However, programming languages that compile to high level bytecode (e.g., Java) can be obfuscated only to a limited extent. In fact, h... | Data obfuscation category of transforms targets data and data structures contained in the program. Using these transformations, data encoding can be changed @cite_13 , variables can be split or merged, and arrays can be split, folded, and merged. | {
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"Data obfuscations are program transformations used to complicate program understanding and conceal actual values of program variables. The possibility to hide constant values is a basic building block of several obfuscation techni... |
1901.04942 | 2908618619 | Code obfuscation is a popular approach to turn program comprehension and analysis harder, with the aim of mitigating threats related to malicious reverse engineering and code tampering. However, programming languages that compile to high level bytecode (e.g., Java) can be obfuscated only to a limited extent. In fact, h... | The objective of control-flow obfuscation is to alter the flow of control within the code. Reordering statements, methods, loops and hiding the actual control flow behind irrelevant conditional statements classify as control-flow obfuscation transforms. Obfuscation based on Opaque predicates @cite_18 is a control-flow ... | {
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"It has become common to distribute software in forms that are isomorphic to the original source code. An important example is Java bytecode. Since such codes are easy to decompile, they increase the risk of malicious reverse engin... |
1901.04942 | 2908618619 | Code obfuscation is a popular approach to turn program comprehension and analysis harder, with the aim of mitigating threats related to malicious reverse engineering and code tampering. However, programming languages that compile to high level bytecode (e.g., Java) can be obfuscated only to a limited extent. In fact, h... | With the increasing adoption of Java as a programming language, the idea of translating Java bytecode to C was investigated @cite_2 , but mainly as a way avoid the overhead due to the Java interpreter, i.e. by turning the whole Java program into a single machine-dependent executable. In our approach, we instead keep th... | {
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"The Java bytecode language is emerging as a software distribution standard. With major vendors committed to porting the Java run-time environment to their platforms, programs in Java bytecode are expected to run without modificatio... |
1901.04942 | 2908618619 | Code obfuscation is a popular approach to turn program comprehension and analysis harder, with the aim of mitigating threats related to malicious reverse engineering and code tampering. However, programming languages that compile to high level bytecode (e.g., Java) can be obfuscated only to a limited extent. In fact, h... | Other obfuscation approaches that rely on code translation are based on an obfuscated Virtual Machine @cite_11 @cite_5 (OVM for short). Binary machine-dependent code is translated to a custom opcodes that can be interpreted by the OVM. (A portion of) the clear code is replaced by the corresponding custom opcodes and th... | {
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"One of the most common forms of security attacks involves exploiting a vulnerability to inject malicious code into an executing application and then cause the injected code to be executed. A theore... |
1901.04942 | 2908618619 | Code obfuscation is a popular approach to turn program comprehension and analysis harder, with the aim of mitigating threats related to malicious reverse engineering and code tampering. However, programming languages that compile to high level bytecode (e.g., Java) can be obfuscated only to a limited extent. In fact, h... | This attack is possible, because the application itself is not dependent to the specific OVM. As a countermeasure, techniques have been proposed to inject such bindings @cite_16 . | {
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"Process-level Virtual machines (PVMs) often play a crucial role in program protection. In particular, virtualization-based tools like VMProtect and CodeVirtualizer have been shown to provide desirable obfuscation properties (i.e.,... |
1901.05219 | 2910436055 | Sentence embedding is a significant research topic in the field of natural language processing (NLP). Generating sentence embedding vectors reflecting the intrinsic meaning of a sentence is a key factor to achieve an enhanced performance in various NLP tasks such as sentence classification and document summarization. T... | Doc2vec @cite_3 is a representative model that does not need sentence sequence information. Paragraph vectors-distributed bag of words (PV-DBOW) and paragraph vector-distributed memory (PV-DM), two distinct learning methods of Doc2vec, train sentence vectors based on the same objective: maximizing the probability to pr... | {
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"Many machine learning algorithms require the input to be represented as a fixed-length feature vector. When it comes to texts, one of the most common fixed-length features is bag-of-words. Despite their popularity, bag-of-words fea... |
1901.05219 | 2910436055 | Sentence embedding is a significant research topic in the field of natural language processing (NLP). Generating sentence embedding vectors reflecting the intrinsic meaning of a sentence is a key factor to achieve an enhanced performance in various NLP tasks such as sentence classification and document summarization. T... | proposed a simple embedding model named SIF, which computes a sentence vector as a weighted average of fixed pre-trained word embedding vectors. Despite its simplicity, SIF accomplished improved performance in STS tasks and outperformed many complex models based on RNNs if word weights are properly adjusted. Sent2vec @... | {
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"The recent tremendous success of unsupervised word embeddings in a multitude of applications raises the obvious question if similar methods could be derived to improve embeddings (i.e. semantic representations) of word sequences a... |
1901.05219 | 2910436055 | Sentence embedding is a significant research topic in the field of natural language processing (NLP). Generating sentence embedding vectors reflecting the intrinsic meaning of a sentence is a key factor to achieve an enhanced performance in various NLP tasks such as sentence classification and document summarization. T... | Contrary to previously demonstrated sentence embedding models that exploit information from corpora to learn sentence vectors, C-PHRASE @cite_29 requires external information. C-PHRASE uses information from the syntactic parse tree of each sentence. This additional knowledge is included in the training objective of C-B... | {
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"We introduce C-PHRASE, a distributional semantic model that learns word representations by optimizing context prediction for phrases at all levels in a syntactic tree, from single words to full se... |
1901.05219 | 2910436055 | Sentence embedding is a significant research topic in the field of natural language processing (NLP). Generating sentence embedding vectors reflecting the intrinsic meaning of a sentence is a key factor to achieve an enhanced performance in various NLP tasks such as sentence classification and document summarization. T... | FastSent @cite_8 , similar to SkipThought, is a sentence embedding model aimed at predicting the surrounding sentences of a given sentence. FastSent learns the source word embedding @math and target word embedding @math . When three consecutive sentences @math , @math , @math are given, @math , the representation vecto... | {
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"Unsupervised methods for learning distributed representations of words are ubiquitous in today's NLP research, but far less is known about the best ways to learn distributed phrase or sentence representations from unlabelled data. ... |
1901.05219 | 2910436055 | Sentence embedding is a significant research topic in the field of natural language processing (NLP). Generating sentence embedding vectors reflecting the intrinsic meaning of a sentence is a key factor to achieve an enhanced performance in various NLP tasks such as sentence classification and document summarization. T... | InferSent @cite_0 is a sentence embedding model trained through supervised tasks. Inspired by previous researches in computer vision, where a large number of models are pre-trained through a classification task based on the ImageNet @cite_18 dataset, performed a research to determine the effectiveness of supervised tas... | {
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"Many modern NLP systems rely on word embeddings, previously trained in an unsupervised manner on large corpora, as base features. Efforts to obtain embeddings for larger chunks of text, such as sen... |
1901.05061 | 2909169132 | In this paper we study deep learning-based music source separation, and explore using an alternative loss to the standard spectrogram pixel-level L2 loss for model training. Our main contribution is in demonstrating that adding a high-level feature loss term, extracted from the spectrograms using a VGG net, can improve... | To the best of our knowledge, there is no existing work on the application of such spectrogram feature losses to music source separation. The general idea of applying feature style reconstruction losses as proposed in @cite_4 for the visual domain, to an audio domain problem has been explored by some researchers, with ... | {
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"“Style transfer” among images has recently emerged as a very active research topic, fuelled by the power of convolution neural networks (CNNs), and has become fast a very popu... |
1901.04596 | 2909671697 | The success of deep neural networks often relies on a large amount of labeled examples, which can be difficult to obtain in many real scenarios. To address this challenge, unsupervised methods are strongly preferred for training neural networks without using any labeled data. In this paper, we present a novel paradigm ... | Generative Adversarial Nets. Besides the auto-encoders, Generative Adversarial Nets (GANs) become popular for training network representations of data in an unsupervised fashion. Unlike the auto-encoders, GANs attempt to directly generate data from noises drawn from a random distribution. By viewing the sampled noises ... | {
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"We introduce the adversarially learned inference (ALI) model, which jointly learns a generation network a... |
1901.04604 | 2910098502 | State-of-the-art methods for image-to-image translation with Generative Adversarial Networks (GANs) can learn a mapping from one domain to another domain using unpaired image data. However, these methods require the training of one specific model for every pair of image domains, which limits the scalability in dealing ... | @cite_20 are powerful generative models, which have achieved impressive results on different computer vision tasks, , image generation @cite_3 @cite_15 , editing @cite_49 @cite_22 and inpainting @cite_35 @cite_46 . However, GANs are difficult to train, since it is hard to keep the balance between the generator and the ... | {
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1901.04604 | 2910098502 | State-of-the-art methods for image-to-image translation with Generative Adversarial Networks (GANs) can learn a mapping from one domain to another domain using unpaired image data. However, these methods require the training of one specific model for every pair of image domains, which limits the scalability in dealing ... | CGAN models learn a translation between image inputs and image outputs using neutral networks. Isola al @cite_32 design the pix2pix framework which is a conditional framework using a CGAN to learn the mapping function. Based on pix2pix, Zhu al @cite_9 further present BicycleGAN which achieves multi-modal image-to-image... | {
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1901.04604 | 2910098502 | State-of-the-art methods for image-to-image translation with Generative Adversarial Networks (GANs) can learn a mapping from one domain to another domain using unpaired image data. However, these methods require the training of one specific model for every pair of image domains, which limits the scalability in dealing ... | To alleviate the issue of pairing training data, Zhu al @cite_42 introduce CycleGAN, which learns the mappings between two unpaired image domains without supervision with the aid of a cycle-consistency loss. Apart from CycleGAN, there are other variants proposed to tackle the problem. For instance, CoupledGAN @cite_21 ... | {
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1901.04604 | 2910098502 | State-of-the-art methods for image-to-image translation with Generative Adversarial Networks (GANs) can learn a mapping from one domain to another domain using unpaired image data. However, these methods require the training of one specific model for every pair of image domains, which limits the scalability in dealing ... | There are only very few recent methods attempting to implement multi-modal image-to-image translation in an efficient way. Anoosheh al propose a ComboGAN model @cite_38 , which only needs to train @math generator discriminator pairs for @math different image domains. To further reduce the model complexity, Choi al intr... | {
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"The past year alone has seen unprecedented leaps in the area of learning-based image translation, namely Cycle-GAN, by But experiments so far have been tailored to merely two domains at a time, an... |
1901.04555 | 2910489255 | Previous attempts at music artist classification use frame level audio features which summarize frequency content within short intervals of time. Comparatively, more recent music information retrieval tasks take advantage of temporal structure in audio spectrograms using deep convolutional and recurrent models. This pa... | Advances in artist classification with machine learning are limited. Furthermore, while exploratory attempts exist @cite_4 , a comprehensive study with deep learning is still absent. Prior academic works are almost a decade old and employ traditional algorithms which do not work well with high-dimensional and sequentia... | {
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"In this paper we demonstrate the artist detection component of Minnowmatch, a machine listening and music retrieval engine. Minnowmatch (Mi... |
1901.04555 | 2910489255 | Previous attempts at music artist classification use frame level audio features which summarize frequency content within short intervals of time. Comparatively, more recent music information retrieval tasks take advantage of temporal structure in audio spectrograms using deep convolutional and recurrent models. This pa... | Traditional MIR tasks relied heavily on MFCCs which extract frequency content in a short-window frame of audio; however, recent works are shifting towards spectrograms which take advantage of temporal structure and have shown better performance in classification tasks @cite_23 . A spectrogram is a representation of fre... | {
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"This document describes version 0.4.0 of librosa: a Python pack- age for audio and music signal processing. At a high level, libr... |
1901.04555 | 2910489255 | Previous attempts at music artist classification use frame level audio features which summarize frequency content within short intervals of time. Comparatively, more recent music information retrieval tasks take advantage of temporal structure in audio spectrograms using deep convolutional and recurrent models. This pa... | Since their early successes on ImageNet @cite_2 , CNNs have become a standard in deep learning when working with visual data. In a prior work, @cite_7 discuss how convolution can be used in MIR tasks; notably, they demonstrate that the layers in a CNN act as feature extractors. Generally, low-level layers describe soun... | {
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1901.04846 | 2910937629 | Soil texture is important for many environmental processes. In this paper, we study the classification of soil texture based on hyperspectral data. We develop and implement three 1-dimensional (1D) convolutional neural networks (CNN): the LucasCNN, the LucasResNet which contains an identity block as residual network, a... | In this section, we briefly review the published research which is related to the presented classification of soil texture based on hyperspectral data. A first review of geological remote sensing is given by @cite_7 . Traditional approaches like nearest mean, nearest neighbor, maximum likelihood, hidden Markov models a... | {
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"Abstract Improvements in optical remote sensing spectral resolution and increased data volumes necessitates the development of improved techniques for quantitative geological analysis. Laboratory spectral studies indicate that abso... |
1901.04846 | 2910937629 | Soil texture is important for many environmental processes. In this paper, we study the classification of soil texture based on hyperspectral data. We develop and implement three 1-dimensional (1D) convolutional neural networks (CNN): the LucasCNN, the LucasResNet which contains an identity block as residual network, a... | The increasing popularity of deep learning approaches in many research disciplines has also reached the field of remote sensing. Deep learning approaches turn out to solve classification tasks better than shallow methods . @cite_8 give a detailed overview of deep learning in remote sensing and @cite_15 review the appli... | {
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"Spectral observations along the spectrum in many narrow spectral bands through hyperspectral imaging provides valuable information ... |
1901.04879 | 2910932954 | The popularity and projected growth of in-home smart speaker assistants, such as Amazon's Echo, has raised privacy concerns among consumers and privacy advocates. Notable questions regarding the collection and storage of user data by for-profit organizations include: what data is being collected and how is it being use... | How smart speaker manufacturers evaluate external data requests, how they store user data, how user Terms of Service are specified and updated, and what information is included in transparency reports are all entirely dependent upon the manufacturer and vary between each one. With software services becoming increasingl... | {
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"Smart speakers with voice assistants, like Amazon Echo and Google Home, provide benefits and convenience but also raise privacy concerns due to their continuously listening microphones. We studied people's reasons for and against ... |
1901.04787 | 2910090829 | Annotated datasets in different domains are critical for many supervised learning-based solutions to related problems and for the evaluation of the proposed solutions. Topics in natural language processing (NLP) similarly require annotated datasets to be used for such purposes. In this paper, we target at two NLP probl... | NER is a long-studied problem and its most common definition is the extraction of named entities such as person, location, and organization names from texts, in addition to some temporal and numeric expressions @cite_5 . Message Understanding Conference (MUC) series constitute an important milestone for NER research as... | {
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1901.04787 | 2910090829 | Annotated datasets in different domains are critical for many supervised learning-based solutions to related problems and for the evaluation of the proposed solutions. Topics in natural language processing (NLP) similarly require annotated datasets to be used for such purposes. In this paper, we target at two NLP probl... | Stance detection is a newly-emerging topic in NLP research, with an increasing number of on-topic studies conducted in recent years. It is usually defined as the automatic detection of whether the owner of a given piece of text is in favor of or against a given target. Therefore, a classification result as , , or is ge... | {
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1901.04678 | 2909165485 | Many real-life dynamical systems change abruptly followed by almost stationary periods. In this paper, we consider streams of data with such abrupt behavior and investigate the problem of tracking their statistical properties in an online manner. We devise a tracking procedure where an estimator that is suitable for a ... | When it comes to extensions of the sliding window, Koychev . proposed a new paradigm called Gradual Forgetting (GF) @cite_2 @cite_8 @cite_30 . According to the principles of GF, observations in the same window are treated unequally when computing the estimates based on weight assignment. Recent observations receive mor... | {
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1901.04678 | 2909165485 | Many real-life dynamical systems change abruptly followed by almost stationary periods. In this paper, we consider streams of data with such abrupt behavior and investigate the problem of tracking their statistical properties in an online manner. We devise a tracking procedure where an estimator that is suitable for a ... | In @cite_9 , Oommen and Rueda presented the SLWE to estimate the underlying parameters of time varying binomial multinomial distribution. The SLWE originally stems from the theory of variable structure Learning Automata @cite_17 , and more particularly, its reward-inaction flavor. The most appealing properties of the S... | {
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"In this paper, we formally present a novel estimation method, referred to as the Stochastic Learning Weak Estimator (SLWE), which... |
1901.04678 | 2909165485 | Many real-life dynamical systems change abruptly followed by almost stationary periods. In this paper, we consider streams of data with such abrupt behavior and investigate the problem of tracking their statistical properties in an online manner. We devise a tracking procedure where an estimator that is suitable for a ... | Gama . @cite_18 presents a clear distinction between memory management and forgetting mechanisms. Adaptive windowing @cite_27 works with the premise of growing the size the sliding window indefinitely until a change is detected via a change detection technique. In this situation, the size of the window is reset wheneve... | {
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"On-line learning in domains where the target concept depends on some hidden context poses serious problems. A changing context can induce changes in the target concepts, producing what is known as... |
1901.04678 | 2909165485 | Many real-life dynamical systems change abruptly followed by almost stationary periods. In this paper, we consider streams of data with such abrupt behavior and investigate the problem of tracking their statistical properties in an online manner. We devise a tracking procedure where an estimator that is suitable for a ... | Another interesting family of approaches assume that the true value of the parameter being estimated is revealed after some delay, which enables quantifying the error of the estimator. In such settings, some research @cite_23 have used ensemble methods where the output of different estimators is combined using weighted... | {
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1901.04678 | 2909165485 | Many real-life dynamical systems change abruptly followed by almost stationary periods. In this paper, we consider streams of data with such abrupt behavior and investigate the problem of tracking their statistical properties in an online manner. We devise a tracking procedure where an estimator that is suitable for a ... | In the same perspective, the estimated error can be used for re-initializing the estimation as performed in @cite_21 . In all brevity, changes are detected based on comparing sections of data, using statistical analysis to detect distributional changes, i.e., abrupt or gradual changes in the mean of the data points whe... | {
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1901.04636 | 2910887632 | The Search for Extra-terrestrial Intelligence (SETI) aims to find technological signals of extra-solar origin. Radio frequency SETI is characterized by large unlabeled datasets and complex interference environment. The infinite possibilities of potential signal types require generalizable signal processing techniques w... | Recurrent neural network (RNN) and long short-term memory (LSTM) models ( @cite_11 @cite_17 @cite_1 @cite_13 @cite_16 @cite_7 @cite_14 ) have driven many of the recent advances in predictive video generation. The LSTM encoder-decoder models proposed in works such as @cite_16 and @cite_13 provide a general framework for... | {
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1907.06223 | 2956688917 | This manuscript presents a fast direct solution technique for solving two dimensional wave scattering problems from quasi-periodic multilayered structures. The fast solver is built from the linear system that results from the discretization of a boundary integral formulation that is robust at Wood's anomalies. When the... | Direct discretization of ) is possible via finite difference or finite element methods @cite_13 but it faces two challenges, (i) meshing to the interfaces to maintain accuracy and (ii) enforcing the radiation condition. Meshing can be effectively be handled using mesh generation software such as GMESH @cite_40 . Techni... | {
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1907.06123 | 2961014033 | In this paper, we introduce the Preselection Bandit problem, in which the learner preselects a subset of arms (choice alternatives) for a user, which then chooses the final arm from this subset. The learner is not aware of the user's preferences, but can learn them from observed choices. In our concrete setting, we all... | The flexible Pre-Bandit problem has obvious connections to the dueling bandits resp. battling bandits setting, with the freedom of adjusting the size of comparison for each time instance. In @cite_2 , the effect of this flexibility is investigated in an active PAC-framework for finding the best arm under the PL model, ... | {
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1907.06214 | 2958892036 | One of the questions that arises when designing models that learn to solve multiple tasks simultaneously is how much of the available training budget should be devoted to each individual task. We refer to any formalized approach to addressing this problem (learned or otherwise) as a task selection policy. In this work ... | Multitask learning has been studied extensively, and can be motivated as a means of inductive bias learning @cite_32 @cite_16 , representation learning @cite_4 @cite_25 , or as a form of learning to learn @cite_37 @cite_39 @cite_30 @cite_33 . In the context of natural language processing, MTL has been used to improve t... | {
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1907.06214 | 2958892036 | One of the questions that arises when designing models that learn to solve multiple tasks simultaneously is how much of the available training budget should be devoted to each individual task. We refer to any formalized approach to addressing this problem (learned or otherwise) as a task selection policy. In this work ... | However, MTL in NLP is not always successful --- the GLUE baseline MTL models are significantly worse than the single task models @cite_42 , alonso2016 only find significant improvements with MTL in one of five tasks evaluated, and the multitask model in mccann2018 does not quite reach the performance of the same model... | {
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1907.06214 | 2958892036 | One of the questions that arises when designing models that learn to solve multiple tasks simultaneously is how much of the available training budget should be devoted to each individual task. We refer to any formalized approach to addressing this problem (learned or otherwise) as a task selection policy. In this work ... | Counterfactual estimation @cite_0 tries to answer the question what would have happened if an agent had taken different actions?'', and so is closely related to the problem of off-policy evaluation in the context of reinforcement learning Counterfactual estimation has also been studied under the names counterfactual re... | {
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1907.06226 | 2962669974 | Lexical simplification (LS) aims to replace complex words in a given sentence with their simpler alternatives of equivalent meaning. Recently unsupervised lexical simplification approaches only rely on the complex word itself regardless of the given sentence to generate candidate substitutions, which will inevitably pr... | As complex and simplified parallel corpora are available, especially, the 'ordinary' English Wikipedia (EW) in combination with the 'simple' English Wikipedia (SEW), the paradigm shift of LS systems is from knowledge-based to data-driven simplification @cite_5 @cite_23 @cite_0 . (2010) identified lexical simplification... | {
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1907.06226 | 2962669974 | Lexical simplification (LS) aims to replace complex words in a given sentence with their simpler alternatives of equivalent meaning. Recently unsupervised lexical simplification approaches only rely on the complex word itself regardless of the given sentence to generate candidate substitutions, which will inevitably pr... | In this paper, we will first present a BERT-based LS approach that requires only a sufficiently large corpus of regular text without any manual efforts. Pre-training language models @cite_15 @cite_18 @cite_12 have attracted wide attention and has shown to be effective for improving many downstream natural language proc... | {
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1907.06167 | 2957307908 | Food classification is a challenging problem due to the large number of categories, high visual similarity between different foods, as well as the lack of datasets for training state-of-the-art deep models. Solving this problem will require advances in both computer vision models as well as datasets for evaluating thes... | Earlier works have tried to tackle the issue of limited datasets for food classification by collecting training data using human annotators or crowd-sourcing platforms @cite_30 @cite_27 @cite_9 @cite_14 @cite_10 . Such data curation is expensive and limits the scalability in terms of number of training categories as we... | {
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1907.06167 | 2957307908 | Food classification is a challenging problem due to the large number of categories, high visual similarity between different foods, as well as the lack of datasets for training state-of-the-art deep models. Solving this problem will require advances in both computer vision models as well as datasets for evaluating thes... | UEC256 @cite_9 consists of @math categories with bounding box indicating the location of its category label. However, it mostly contains Japanese food items. ChineseFoodNet @cite_24 consists of @math images from @math categories but is restricted to Chinese food items only. NutriNet dataset @cite_29 contains @math imag... | {
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"In this paper, we introduce a new and challenging large-scale food image dataset called \"ChineseFoodNet\", which aims to automatically recognizing pictured Chines... |
1907.05905 | 2738314890 | This paper describes a preliminary investigation of Voice Pathology Detection using Deep Neural Networks (DNN). We used voice recordings of sustained vowel a produced at normal pitch from German corpus Saarbruecken Voice Database (SVD). This corpus contains voice recordings and electroglottograph signals of more than 2... | The results vary greatly between the published papers mainly due to differences between sets of data that were used for the experiment. Mart ' i in @cite_21 reported 72 , | {
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1907.05945 | 2958639220 | We propose NH-TTC, a general method for fast, anticipatory collision avoidance for autonomous robots having arbitrary equations of motions. Our proposed approach exploits implicit differentiation and subgradient descent to locally optimize the non-convex and non-smooth cost functions that arise from planning over the a... | More closely related to our work is the Generalized Reciprocal Velocity Obstacles (GRVO) approach of @cite_5 that provides a generalized framework for local navigation supporting robots with both linear and nonlinear equations of motions. Similar to GVO, GRVO plans over potential robot controls, but does so through an ... | {
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1907.06085 | 2956903327 | We consider a quantity that measures the roundness of a bounded, convex @math -polytope in @math . We majorise this quantity in terms of the smallest singular value of the matrix of outer unit normals to the facets of the polytope. | Similar quantities appear also in the literature on finite volume methods. For example, in @cite_3 , a finite volume method based on Voronoi tessellations of the sphere is constructed, and a regularity norm' is defined for the Voronoi tessellation by minimising the ratio @math over all neighbours of each Voronoi cell @... | {
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"We first develop and analyze a finite volume scheme for the discretization of partial differential equations (PDEs) on the sphere; the scheme uses Voronoi tessellations of the sphere. For a model convection–diffusion problem, the f... |
1907.06078 | 2956637491 | Despite the widespread use of supervised deep learning methods for affect recognition from speech, they are severely limited by the lack of a sufficient amount of labelled speech data. Considering the abundant availability of unlabelled data, this paper proposed a semi-supervised model that can effectively utilise the ... | Multi-task learning (MTL) has been successful for simultaneously modelling multiple related tasks utilising shared representation @cite_28 @cite_44 . It aims to improve generalisation by learning the similarities as well as the differences among the given tasks from the training data @cite_69 . The standard methodology... | {
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1907.06078 | 2956637491 | Despite the widespread use of supervised deep learning methods for affect recognition from speech, they are severely limited by the lack of a sufficient amount of labelled speech data. Considering the abundant availability of unlabelled data, this paper proposed a semi-supervised model that can effectively utilise the ... | Another stream of research, instead of using different emotional attributes as auxiliary tasks, has utilised other available attributes, such as speaker and gender to improve the performance of SER @cite_52 . For instance, @cite_33 used gender and naturalness (natural or acted corpus) recognition as auxiliary tasks to ... | {
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1907.05820 | 2958586587 | We present GLNet, a self-supervised framework for learning depth, optical flow, camera pose and intrinsic parameters from monocular video -- addressing the difficulty of acquiring realistic ground-truth for such tasks. We propose three contributions: 1) we design new loss functions that capture multiple geometric const... | Understanding the geometry and dynamics of 3D scenes usually involves estimating multiple properties including depth, camera motion or optical flow. (SfM) @cite_34 @cite_33 @cite_14 @cite_21 @cite_40 @cite_19 or scene flow estimation @cite_25 @cite_42 are well-established methodologies with a solid record of fundamenta... | {
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1907.05820 | 2958586587 | We present GLNet, a self-supervised framework for learning depth, optical flow, camera pose and intrinsic parameters from monocular video -- addressing the difficulty of acquiring realistic ground-truth for such tasks. We propose three contributions: 1) we design new loss functions that capture multiple geometric const... | To address the bottleneck of accurate feature matching, recent work has focused on deep-learning-based approaches for geometric reasoning. Several methods train networks based on ground-truth labels and have been successfully applied to many tasks, including monocular depth estimation @cite_57 @cite_23 @cite_30 @cite_3... | {
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1907.05820 | 2958586587 | We present GLNet, a self-supervised framework for learning depth, optical flow, camera pose and intrinsic parameters from monocular video -- addressing the difficulty of acquiring realistic ground-truth for such tasks. We propose three contributions: 1) we design new loss functions that capture multiple geometric const... | To train using ground-truth labels, different authors rely either on synthetic dataset creation @cite_55 or on specialized equipment ( LIDAR) for data collection @cite_52 @cite_27 , which is expensive in practice. To reduce the amount of ground-truth labels required for training, unsupervised approaches recently emerge... | {
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1907.05820 | 2958586587 | We present GLNet, a self-supervised framework for learning depth, optical flow, camera pose and intrinsic parameters from monocular video -- addressing the difficulty of acquiring realistic ground-truth for such tasks. We propose three contributions: 1) we design new loss functions that capture multiple geometric const... | In this work, we focus on learning from monocular video. Many recent methods go along similar lines. Zhou al @cite_49 couple the learning of monocular depth and ego-motion. Vijayanarasimhan al @cite_8 learn object masks and rigid motion parameters of several objects. Subsequent methods further improved performance usin... | {
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1907.06013 | 2961188780 | This paper describes Motion Planning Networks (MPNet), a computationally efficient, learning-based neural planner for solving motion planning problems. MPNet uses neural networks to learn general near-optimal heuristics for path planning in seen and unseen environments. It receives environment information as point-clou... | Biased sampling heuristics adaptively sample the robot state-space to overcome limitations caused by random uniform exploration in underlying SMP methods. For instance, P-RRT* @cite_15 @cite_61 incorporates artificial potential fields @cite_42 into RRT* to generate goal directed trees for rapid convergence to an optima... | {
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1907.06013 | 2961188780 | This paper describes Motion Planning Networks (MPNet), a computationally efficient, learning-based neural planner for solving motion planning problems. MPNet uses neural networks to learn general near-optimal heuristics for path planning in seen and unseen environments. It receives environment information as point-clou... | Lazy edge evaluation methods, on the other hand, have shown to exhibit significant improvements in computation speeds by evaluating edges only along the potential path solutions. However, these methods are critically dependent on the underlying edge selector and tend to exhibit limited performance in cluttered environm... | {
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1907.06013 | 2961188780 | This paper describes Motion Planning Networks (MPNet), a computationally efficient, learning-based neural planner for solving motion planning problems. MPNet uses neural networks to learn general near-optimal heuristics for path planning in seen and unseen environments. It receives environment information as point-clou... | There has also been attempts towards building learning-based motion planners. For instance, Value Iteration Networks (VIN) @cite_5 approximates a planner by emulating value iteration using recurrent convolutional neural networks and max-pooling. However, VIN is only applicable for discrete planning tasks. Universal Pla... | {
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"We introduce the value iteration network (VIN): a fully differentiable neural network with a planning module' embedded within. VINs can learn to plan, and are suita... |
1907.05792 | 2957380905 | Goal-oriented dialog systems, which can be trained end-to-end without manually encoding domain-specific features, show tremendous promise in the customer support use-case e.g. flight booking, hotel reservation, technical support, student advising etc. These dialog systems must learn to interact with external domain kno... | There have been several studies on incorporating unstructured external information into dialog models. ghazvininejad2017knowledge proposed a knowledge-grounded neural conversation model by improving the seq2seq approach to produce more contentful responses. The model was trained using Twitter Dialog Dataset @cite_13 as... | {
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1907.06005 | 2959491548 | The ever evolving informatics technology has gradually bounded human and computer in a compact way. Understanding user behavior becomes a key enabler in many fields such as sedentary-related healthcare, human-computer interaction (HCI) and affective computing. Traditional sensor-based and vision-based user behavior ana... | WiFi-based behavior sensing technology has many advantages over traditional behavior sensing technology (e.g. vision-based sensing technology, infrared-based sensing technology and dedicated sensor based sensing technology) in terms of non-line-of-sight, passive sensing (no need to carry sensors), low cost, easy deploy... | {
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1907.06005 | 2959491548 | The ever evolving informatics technology has gradually bounded human and computer in a compact way. Understanding user behavior becomes a key enabler in many fields such as sedentary-related healthcare, human-computer interaction (HCI) and affective computing. Traditional sensor-based and vision-based user behavior ana... | In the early days, WiFi-based behavior sensing mainly uses RSS (Received Signal Strength). Sigg @cite_7 use a software radio to transmit RF signals and determine human motion based on changes in RSS. Abdelnasser leverage RSS to identify 7 different gestures @cite_3 and respiratory detection @cite_22 . We also built a s... | {
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1907.06005 | 2959491548 | The ever evolving informatics technology has gradually bounded human and computer in a compact way. Understanding user behavior becomes a key enabler in many fields such as sedentary-related healthcare, human-computer interaction (HCI) and affective computing. Traditional sensor-based and vision-based user behavior ana... | These schemes basically rely on high-level features like location, velocity and direction of a motion for behavior computing. In particular, Wisee @cite_9 and Widance @cite_26 use the Doppler shift to extract the direction of motion and then use this direction information to classify different motions. Widir @cite_12 u... | {
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1907.06005 | 2959491548 | The ever evolving informatics technology has gradually bounded human and computer in a compact way. Understanding user behavior becomes a key enabler in many fields such as sedentary-related healthcare, human-computer interaction (HCI) and affective computing. Traditional sensor-based and vision-based user behavior ana... | Since human behavior is complex and fuzzy in nature, recently there is a trend to explore computational intelligence, which is a specialized paradigm to deal with this kind of problems @cite_23 @cite_31 @cite_6 . These methods extract features or directly input waveform data into the deep network model and train the ne... | {
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1907.06005 | 2959491548 | The ever evolving informatics technology has gradually bounded human and computer in a compact way. Understanding user behavior becomes a key enabler in many fields such as sedentary-related healthcare, human-computer interaction (HCI) and affective computing. Traditional sensor-based and vision-based user behavior ana... | To this end, we present BeSense, which leverages signal processing to handle the micro-gestures and computational intelligence for behavior analysis. It extends our previous work @cite_14 with non-trivial improvement. In particular, we have improved the signal segmentation algorithm, which results in better performance... | {
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"In this paper, we present WoSense, a device-free and real-time behavior analysis system leveraging only WiFi infrastructures. WoSense aims to remotely recognize various human behaviors like surfing, gaming and working around compu... |
1907.06071 | 2971318102 | Dense depth completion is essential for autonomous systems and 3D reconstruction. In this paper, a lightweight yet efficient network (S &CNet) is proposed to obtain a good trade-off between efficiency and accuracy for the dense depth completion. A dual-stream attention module (S &C enhancer) is introduced to measure bo... | From past to now, there has been a significant effort in prior works to design efficient networks. For image classification task, the MobileNet @cite_0 can achieve comparable top-5 accuarcy with VGG-16 @cite_8 on ImageNet benchmark with a @math times fewer weights. Then, ShuffleNet @cite_6 further boost the performance... | {
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1901.04452 | 2909445821 | The FoundationDB Record Layer is an open source library that provides a record-oriented data store with semantics similar to a relational database implemented on top of FoundationDB, an ordered, transactional key-value store. The Record Layer provides a lightweight, highly extensible way to store structured data. It of... | There is a broad literature on query optimization, starting with the seminal work of @cite_13 on System R. Since then, much of the focus has been on efficient search-space exploration. Most notably, Cascades @cite_15 introduced a clean separation of logical and physical query plans, and proposed operators and transform... | {
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"abstract": [
"This paper describes a new extensible query optimization framework that resolves many of the shortcomings of the EXODUS and Volcano optimizer generators. In additio... |
1901.04268 | 2909259341 | In this paper, we propose to learn shared semantic space with correlation alignment ( @math ) for multimodal data representations, which aligns nonlinear correlations of multimodal data distributions in deep neural networks designed for heterogeneous data. In the context of cross-modal (event) retrieval, we design a ne... | Statistical correlation models are designed to learn a subspace where cross-modal data are aligned from the perspective of statistics. Canonical correlation analysis (CCA) @cite_6 is a representative work, which projects two sets of data to a common subspace where their correlations are maximized. Similar to CCA, cross... | {
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"abstract": [
"This paper investigates the problem of ... |
1901.04268 | 2909259341 | In this paper, we propose to learn shared semantic space with correlation alignment ( @math ) for multimodal data representations, which aligns nonlinear correlations of multimodal data distributions in deep neural networks designed for heterogeneous data. In the context of cross-modal (event) retrieval, we design a ne... | The intuition of ranking models is that relevant pairs in the retrieved results should rank higher than the irrelevant pairs @cite_12 . Recently, neural networks combined with ranking loss become popular in the context of cross-modal retrieval. @cite_10 @cite_22 designed a two-branch neural network with multiple layers... | {
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"abstract": [
"In this paper, we introduce Recipe1M, a new large-scale, structured corpus of over 1m cooking recipes an... |
1901.04379 | 2908724672 | Deep neural acoustic models benefit from context-dependent (CD) modeling of output symbols. We consider direct training of CTC networks with CD outputs, and identify two issues. The first one is frame-level normalization of probabilities in CTC, which induces strong language modeling behavior that leads to overfitting ... | Several authors have proposed to use multicharacter, or multiphoneme output tokens to reduce the number of emissions. The DeepSpeech 2 model used non-overlappling character bigrams @cite_16 , while @cite_15 and @cite_10 dynamically chose a decomposition of the output sequence. This idea was also explored in @cite_11 in... | {
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"abstract": [
"Most existing sequence labelling models rely on a fixed decomposition of a target sequence into a sequence of basic units. These... |
1901.04379 | 2908724672 | Deep neural acoustic models benefit from context-dependent (CD) modeling of output symbols. We consider direct training of CTC networks with CD outputs, and identify two issues. The first one is frame-level normalization of probabilities in CTC, which induces strong language modeling behavior that leads to overfitting ... | Perhaps most similar to our work is @cite_0 in which a neural network is trained to replace a state-tying decision tree. However, this implies a multistage training procedure in which both classical GMM-HMM and neural systems are built. In contrast, we strive to keep a simple, one stage training procedure that can be s... | {
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"abstract": [
"In this paper, we present a novel Deep Triphone Embedding (DTE) representation derived from Deep Neural Network (DNN) to encapsulate the discriminative information present in the adjoining speech frames. DTEs are generated using a ... |
1901.04210 | 2950381856 | Visual SLAM shows significant progress in recent years due to high attention from vision community but still, challenges remain for low-textured environments. Feature based visual SLAMs do not produce reliable camera and structure estimates due to insufficient features in a low-textured environment. Moreover, existing ... | Direct SLAM: Direct methods @cite_4 gains popularity for its semi dense map creation. Recently, Engel al present LSD SLAM, @cite_7 a direct SLAM pipeline that maintains a semi-dense map and minimizes the photometric error of pixels between images. LSD SLAM @cite_7 initializes the depth of pixels with a random value of ... | {
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"abstract": [
"We present a new parametrization for point features within monocular simultaneous localization and mapping (SLAM) that permits efficient and accurate representation... |
1901.04210 | 2950381856 | Visual SLAM shows significant progress in recent years due to high attention from vision community but still, challenges remain for low-textured environments. Feature based visual SLAMs do not produce reliable camera and structure estimates due to insufficient features in a low-textured environment. Moreover, existing ... | Edge based Visual Odometry: Tarrio and Pedre @cite_14 present an edge-based visual odometry pipeline that uses edges as a feature for depth estimation. But camera estimation is erroneous because odometry works only on pairwise consistency, global consistency checking is very important for accurate camera estimation in ... | {
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"abstract": [
"This paper presents ORB-SLAM, a feature-based monocular simultaneous localization and mapping (SLAM) system that operates in real time, in small and large indoor a... |
1901.04248 | 2898695505 | The continuing monitoring and surveying of the nearby space to detect Near Earth Objects (NEOs) and Near Earth Asteroids (NEAs) are essential because of the threats that this kind of objects impose on the future of our planet. We need more computational resources and advanced algorithms to deal with the exponential gro... | The Near-Earth Asteroid Tracking (NEAT) program was among the first to implement a fully automated system for detecting asteroids, including controlling the telescope, acquiring wide-field images, and detecting NEOs using on-site computing power provided by a Sun Sparc 20 and a Sun Enterprise 450 computer @cite_10 . | {
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"abstract": [
"The Near-Earth Asteroid Tracking (NEAT) system operates autonomously at the Maui Space Surveillance Site on the summit of the extinct Haleakala Volcano Crater, Hawaii. The program began in 1995 December and continues with an obser... |
1901.04248 | 2898695505 | The continuing monitoring and surveying of the nearby space to detect Near Earth Objects (NEOs) and Near Earth Asteroids (NEAs) are essential because of the threats that this kind of objects impose on the future of our planet. We need more computational resources and advanced algorithms to deal with the exponential gro... | @cite_3 developed a highly automated moving object detection software package. Their approach maintains high efficiency while producing low false-detection rates by using two independent detection algorithms and combining the results, reducing the operator time associated with searching huge data sets. | {
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"abstract": [
"With the deployment of large CCD mosaic cameras and their use in large-scale surveys to discover Solar system objects, there is a need for fast detection algorithms that can handle large data loads in a nearly automatic way. We pre... |
1901.04248 | 2898695505 | The continuing monitoring and surveying of the nearby space to detect Near Earth Objects (NEOs) and Near Earth Asteroids (NEAs) are essential because of the threats that this kind of objects impose on the future of our planet. We need more computational resources and advanced algorithms to deal with the exponential gro... | The first automatic detection improved algorithm, that cleans all stars in all individual images before combining them, was developed by @cite_13 . The algorithm uses many CCD images in order to detect very dark moving objects that are invisible on a single CCD image. It was applied for the first time on a very small 3... | {
"cite_N": [
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"abstract": [
"We have devised an automatic detection algorithm for unresolved moving objects, such as asteroids and comets. The algorithm uses many CCD images in order to detect very dark moving objects that are invisible on a single CCD image.... |
1901.04248 | 2898695505 | The continuing monitoring and surveying of the nearby space to detect Near Earth Objects (NEOs) and Near Earth Asteroids (NEAs) are essential because of the threats that this kind of objects impose on the future of our planet. We need more computational resources and advanced algorithms to deal with the exponential gro... | Pan-STARRS Moving Object Processing System (MOPS) @cite_0 is a modern software package that produces automatic asteroid discoveries and identifications from catalogs of transient detections from next-generation astronomical survey telescopes. MOPS achieves 99:5 Besides major surveys, automated asteroid detection system... | {
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"abstract": [
"ABSTRACT.We describe the Pan-STARRS Moving Object Processing System (MOPS), a modern software package that produces automatic asteroid discoveries and identifications... |
1901.04248 | 2898695505 | The continuing monitoring and surveying of the nearby space to detect Near Earth Objects (NEOs) and Near Earth Asteroids (NEAs) are essential because of the threats that this kind of objects impose on the future of our planet. We need more computational resources and advanced algorithms to deal with the exponential gro... | Cop ^ a @cite_6 propose an automated pipeline prototype for asteroids detection, written in Python under Linux, which calls some 3rd party astrophysics libraries. The current version of the proposed pipeline prototype is tightly coupled with the data obtained from the 2.5 meters diameter Isaac Newton Telescope (INT) lo... | {
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"abstract": [
"Near Earth Asteroids (NEAs) are discovered daily, mainly by few major surveys, nevertheless many of them remain unobserved for years, even decades. Even so, there is room for new discoveries, including those submitted by smaller pr... |
1901.04359 | 2908905630 | Distributed synchronous stochastic gradient descent (S-SGD) has been widely used in training large-scale deep neural networks (DNNs), but it typically requires very high communication bandwidth between computational workers (e.g., GPUs) to exchange gradients iteratively. Recently, Top- @math sparsification techniques h... | In terms of gradient sparsification, which zero-outs a large proportion of gradients to reduce the communication size, @cite_17 and @cite_30 empirically demonstrate that up to $99 Focusing on the study of sparsification, the other two work in @cite_16 and @cite_6 are mostly related to our work. They have realized that ... | {
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"abstract": [
"Highly distributed training of Deep Neural Networks (DNNs) on future compute platforms (offering 100 of TeraOps s of computational capacity... |
1901.04364 | 2910001187 | In critical care, intensivists are required to continuously monitor high dimensional vital signs and lab measurements to detect and diagnose acute patient conditions. This has always been a challenging task. In this study, we propose a novel self-correcting deep learning prediction approach to address this challenge. W... | AKI prediction with utilization of features from EHR data is attracting a widespread research interest @cite_11 @cite_8 . In particular, much research in recent years has focused on predictive modeling on a broad population to identify high-risk subjects as early as possible @cite_8 . Initially, AKI prediction was mode... | {
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"abstract": [
"Background:A major problem in treating acute kidney injury (AKI) is that ... |
1901.03865 | 2768363122 | What is the impact of software engineering research on current practices in industry? In this paper, I report on my direct experience as a PhD post-doc working in software engineering research projects, and then spending the following five years as an engineer in two different companies (the first one being the same I ... | The fact that academic research has only limited impact on practice is a fact that has been long discussed in academia. For example, Briand @cite_28 shared his 20 year experience of collaborating with around 30 different companies and public institutions. Being rewarded for number of publications, in contrast to other ... | {
"cite_N": [
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"2088697603"
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"abstract": [
"The author provides, based on 20 years of research and industrial experience, his assessment of software engineering research. He then builds on such analysis to provide recommendations on how we need to change as a research commu... |
1901.03865 | 2768363122 | What is the impact of software engineering research on current practices in industry? In this paper, I report on my direct experience as a PhD post-doc working in software engineering research projects, and then spending the following five years as an engineer in two different companies (the first one being the same I ... | The problem of science vs. engineering does also have impact on teaching and education. As Offutt stated: Isn't it just a little strange that we prepare software engineers by teaching them computer science?'' @cite_37 . Practical software engineering is different from science, and needs different teaching methods. | {
"cite_N": [
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"abstract": [
"Based on over 20 years of teaching and research experience, the author provides his assessment of software engineering education. He then builds on the analysis to provide recommendations on how we need to diverge from computer sc... |
1901.03865 | 2768363122 | What is the impact of software engineering research on current practices in industry? In this paper, I report on my direct experience as a PhD post-doc working in software engineering research projects, and then spending the following five years as an engineer in two different companies (the first one being the same I ... | One way to improve the state of software engineering research is to have close collaborations with industrial partners. In this regard, @cite_17 performed a systematic literature review on the topic of industry-academia collaborations, collecting and discussing 33 articles published between 1995 and 2014. On the other ... | {
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"abstract": [
"The level of industry-academia collaboration (IAC) in software engineering in general and in software testing in particular is quite low. Many researchers and pra... |
1901.03865 | 2768363122 | What is the impact of software engineering research on current practices in industry? In this paper, I report on my direct experience as a PhD post-doc working in software engineering research projects, and then spending the following five years as an engineer in two different companies (the first one being the same I ... | More recently, @cite_13 made a survey about industry-academia collaborations in computer science and software engineering, involving 60 academics and 66 people in industry. One conclusion was: There is a lack of communication and understanding between academia and industry ( @math ) there is a lot of mistrust of academ... | {
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"IT-driven innovation is an enormous factor in the worldwide economic leadership of the United States. It is larger than finance, construction, or transportation, and it employs nearly 6 of the US workforce. The top three companies... |
1901.03865 | 2768363122 | What is the impact of software engineering research on current practices in industry? In this paper, I report on my direct experience as a PhD post-doc working in software engineering research projects, and then spending the following five years as an engineer in two different companies (the first one being the same I ... | The discrepancy between industry and academia is also clear when looking at what topics are discussed at practitioner conferences compared to the academic ones @cite_32 . For example, in the context of testing, practitioners are more interested on mobile and agile testing, whereas academics seems to focus more on model... | {
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"To determine how industry and academia approach software testing, researchers compared the titles of presentations from selected conferences in each of the two communities. The results shed light on the root cause of low industry–... |
1901.03865 | 2768363122 | What is the impact of software engineering research on current practices in industry? In this paper, I report on my direct experience as a PhD post-doc working in software engineering research projects, and then spending the following five years as an engineer in two different companies (the first one being the same I ... | In the past, there were attempts from SIGSOFT (the Impact Project https: www.sigsoft.org impact.html ) to keep track of and promote academic impact on software engineering practice @cite_5 . Different success stories were discussed, where ideas'' investigated in academia were then considered and adopted in industry. Ho... | {
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"The impact project provides a solid and scholarly assessment of the impact software engineering research has had on software engineering practice. The assessment takes the form of a series of studies and briefings, each involving l... |
1901.03865 | 2768363122 | What is the impact of software engineering research on current practices in industry? In this paper, I report on my direct experience as a PhD post-doc working in software engineering research projects, and then spending the following five years as an engineer in two different companies (the first one being the same I ... | As of time of writing, one of the most famous success stories is @cite_1 : a company making static dynamic analysis tools that was founded at Stanford University, and then sold for more than $300 millions. http: www.coverity.com press-releases synopsys-completes-coverity-acquisition | {
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"abstract": [
"How Coverity built a bug-finding tool, and a business, around the unlimited supply of bugs in software systems."
]
} |
1901.03865 | 2768363122 | What is the impact of software engineering research on current practices in industry? In this paper, I report on my direct experience as a PhD post-doc working in software engineering research projects, and then spending the following five years as an engineer in two different companies (the first one being the same I ... | The importance of industry-academia collaborations and the aim of achieving usable results for practitioners are not something that is specific only for software engineering, but they are also significant for many other fields such as Computer-Human Interaction @cite_18 , Data Mining @cite_27 and even medicine @cite_34... | {
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"",
"Data mining (DM) research has successfully developed advanced DM techniques and algorit... |
1901.04095 | 2910109410 | Network embedding aims to learn a latent, low-dimensional vector representations of network nodes, effective in supporting various network analytic tasks. While prior arts on network embedding focus primarily on preserving network topology structure to learn node representations, recently proposed attributed network em... | Existing research work on network embedding can be roughly divided into two categories @cite_25 : structure preserving network embedding that leverages network structure only, and attributed network embedding that couples network structure with node attributes to improve network embedding. | {
"cite_N": [
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"abstract": [
"With the widespread use of information technologies, information networks are becoming increasingly popular to capture complex relationships across various disciplines. In reality, the large scale of information networks often mak... |
1901.03900 | 2909951054 | We propose a genetic algorithm (GA) for hyperparameter optimization of artificial neural networks which includes chromosomal crossover as well as a decoupling of parameters (i.e., weights and biases) from hyperparameters (e.g., learning rate, weight decay, and dropout) during sexual reproduction. Children are produced ... | Previous work addressing hyperparameter optimization in a high-performance computing setting spans a wide range of complexities and efficacies, from simple approaches like random search and grid search to more complicated approaches such as @cite_13 and (LTFB) @cite_26 . While some approaches, such as (MENNDL) @cite_23... | {
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"abstract": [
"We propose a new framework for parallelizing deep neural network training that maximize the amo... |
1901.03900 | 2909951054 | We propose a genetic algorithm (GA) for hyperparameter optimization of artificial neural networks which includes chromosomal crossover as well as a decoupling of parameters (i.e., weights and biases) from hyperparameters (e.g., learning rate, weight decay, and dropout) during sexual reproduction. Children are produced ... | This work first frames PBT as a genetic algorithm and then generalizes and extends it in Section . The original version of PBT can be thought of as a genetic algorithm with the following characteristics: overlapping generations, genes comprised of hyperparameters and parameters, GA generations running concurrently with... | {
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"Human evaluations of machine translation are extensive but expensive. Human evaluations can take months to finish and involve human labor that can not be reused. We propose a method of automatic machine translation evaluation that... |
1901.03872 | 2908589198 | Direct design of a robot's rendered dynamics, such as in impedance control, is now a well-established control mode in uncertain environments. When the physical interaction port variables are not measured directly, dynamic and kinematic models are required to relate the measured variables to the interaction port variabl... | Adaptive control uses input and output data of the system to (in some variants) realize on-line identification of model parameters @cite_15 . However, adaptive techniques require a structured or parameterized model, making nonlinear friction and non-conventional dynamics difficult to compensate. | {
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"2069913713"
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"abstract": [
"This article presents a general methodology for the design of adaptive control systems which can learn to operate efficiently in dynamical environments possessing a high degree of uncertainty. Multiple models are used to describe ... |
1901.03872 | 2908589198 | Direct design of a robot's rendered dynamics, such as in impedance control, is now a well-established control mode in uncertain environments. When the physical interaction port variables are not measured directly, dynamic and kinematic models are required to relate the measured variables to the interaction port variabl... | Nonparametric modeling techniques have found application to inverse dynamics learning @cite_11 . These models can generalize from historical data to new trajectories, although the extent to which this is practically achieved is both application and parameter dependent. Being constructed from historical data, they can c... | {
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"Humans demonstrate a remarkable ability to generate accurate and appropriate motor behavior under many different and often uncertain environmental conditions. We previously proposed a new modular architecture, the modular selectio... |
1901.03872 | 2908589198 | Direct design of a robot's rendered dynamics, such as in impedance control, is now a well-established control mode in uncertain environments. When the physical interaction port variables are not measured directly, dynamic and kinematic models are required to relate the measured variables to the interaction port variabl... | Several authors have investigated inverse dynamic modeling for changing payload mass @cite_21 @cite_2 , one of the most common ways interactive robots become multimodal, but here considerations of modeling and interactive performance are taken (modeling limitations, passivity, external input), and validation is in inte... | {
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"Accurate dynamic models can be very difficult to compute analytically for complex robots; moreover, using a precomputed fixed model does not... |
1901.03895 | 2910901898 | We present a novel predictive model architecture based on the principles of predictive coding that enables open loop prediction of future observations over extended horizons. There are two key innovations. First, whereas current methods typically learn to make long-horizon open-loop predictions using a multi-step cost ... | Recent work in developing predictive models include @cite_14 , where a model learns to predict future video frames by observing sequences of observation and actions. The model is then used to generate robotic trajectories using model predictive control (MPC), choosing the trajectory that ends with an image that best ma... | {
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"abstract": [
"The predictive learning of spatiotemporal sequences aims to generate future images by learning from the historical frames, where spatial appe... |
1901.03895 | 2910901898 | We present a novel predictive model architecture based on the principles of predictive coding that enables open loop prediction of future observations over extended horizons. There are two key innovations. First, whereas current methods typically learn to make long-horizon open-loop predictions using a multi-step cost ... | As for using predictive models to accelerate reinforcement learning, in @cite_1 , the authors use an action conditional model to predict the future states of agents operating in various openAI gym environments with high dimensional state spaces, and use the model in a model predictive control algorithm that quickly lea... | {
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"@cite_1",
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"abstract": [
"Model-free deep reinforcement learning algorithms have been shown to be capable of learning a wide range of robotic skills, but typically require a very large number of samples to achieve good perf... |
1901.03895 | 2910901898 | We present a novel predictive model architecture based on the principles of predictive coding that enables open loop prediction of future observations over extended horizons. There are two key innovations. First, whereas current methods typically learn to make long-horizon open-loop predictions using a multi-step cost ... | @cite_2 accelerates learning a state-action value function by using a neural network based model to generate synthetic rollouts up to a horizon of H steps. The rewards beyond H steps are replaced by the current estimate of the value of the penultimate observation in the synthetic rollouts, as in an H-step temporal diff... | {
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"abstract": [
"We adapt the ideas underlying the success of Deep Q-Learning to the continuous action domain. We present an actor-critic, model-free algorithm based on the deterministic policy gradient that can ope... |
1901.04016 | 2908711039 | Our middleware approach, Context-Oriented Software Middleware (COSM), supports context-dependent software with self-adaptability and dependability in a mobile computing environment. The COSM-middleware is a generic and platform-independent adaptation engine, which performs a runtime composition of the software's contex... | For a more complex context-aware system, the same context information would be triggered in different parts of an application and would trigger the invocation of additional behaviours. In this way, context handling becomes a concern that spans several application units, essentially crosscutting into the main applicatio... | {
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"abstract": [
"Context-aware applications behave differently depending on the context ... |
1907.05628 | 2957359913 | Disease-gene prediction (DGP) refers to the computational challenge of predicting associations between genes and diseases. Effective solutions to the DGP problem have the potential to accelerate the therapeutic development pipeline at early stages via efficient prioritization of candidate genes for various diseases. In... | The foundation of linkage methods is built on the combination of candidate gene identification through network community analysis, knowledge of chromosomal locations of the candidate genes, and knowledge of certain disease loci (regions of the genome that are likely to be associated with a particular disease). In @cite... | {
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"2020301095"
],
"abstract": [
"Background: The responsible genes have not yet been identified for many genetically mapped disease loci. Physically interacting proteins tend to be involved in the same cellular process, and mutations in their genes may lead to si... |
1907.05628 | 2957359913 | Disease-gene prediction (DGP) refers to the computational challenge of predicting associations between genes and diseases. Effective solutions to the DGP problem have the potential to accelerate the therapeutic development pipeline at early stages via efficient prioritization of candidate genes for various diseases. In... | Module-based methods are built on the concept that biological networks can be divided into modules or neighborhoods, which are roughly characterized by topological proximity. Candidate genes for a particular disease are assumed to belong to the same module as other genes that are known to be linked to that same disease... | {
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],
"abstract": [
"The observation that disease associated proteins often interact with each other has fueled the development of network-based approaches to elucidate the molecular mechanisms of human disease. Such approaches build on the assumption ... |
1907.05628 | 2957359913 | Disease-gene prediction (DGP) refers to the computational challenge of predicting associations between genes and diseases. Effective solutions to the DGP problem have the potential to accelerate the therapeutic development pipeline at early stages via efficient prioritization of candidate genes for various diseases. In... | Diffusion methods start with a set of seed genes which are known to be associated with a disease, however rather than computing a connectivity significance for each candidate gene in the network, random walk methods are used to find novel candidate genes @cite_4 @cite_17 @cite_24 @cite_18 @cite_9 . | {
"cite_N": [
"@cite_18",
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"@cite_9",
"@cite_24",
"@cite_17"
],
"mid": [
"2100582962",
"2038122249",
"1981949323",
"2097498614",
"2119220491"
],
"abstract": [
"Motivation: Clinical diseases are characterized by distinct phenotypes. To identify disease gene... |
1907.05538 | 2960971247 | We present a novel framework for collaboration amongst a team of robots performing Pose Graph Optimization (PGO) that addresses two important challenges for multi-robot SLAM: that of enabling information exchange "on-demand" via active rendezvous, and that of rejecting outlier measurements with high probability. Our ke... | As noted in @cite_19 , Pose Graph Optimization (PGO) is one of the most common modern techniques for SLAM, and supports many crucial tasks in robotics. A variety of techniques exist to extend PGO methods to multi-robot mapping problems. choudhary2017distributed address the limited communication challenge in @cite_17 by... | {
"cite_N": [
"@cite_1",
"@cite_6",
"@cite_19",
"@cite_2",
"@cite_5",
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"mid": [
"",
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"2461937780",
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"2102233102",
"2593712938"
],
"abstract": [
"",
"Multi-robot deployments have the potential for completing tasks mo... |
1907.05538 | 2960971247 | We present a novel framework for collaboration amongst a team of robots performing Pose Graph Optimization (PGO) that addresses two important challenges for multi-robot SLAM: that of enabling information exchange "on-demand" via active rendezvous, and that of rejecting outlier measurements with high probability. Our ke... | Additionally, there is a large body of work focused on light-weight position estimation from Radio Frequency (RF) signals. For instance, RF localization has been successfully applied to positioning when access to GPS signals are unavailable, such as indoor environments @cite_10 @cite_8 , but requires the presence of kn... | {
"cite_N": [
"@cite_26",
"@cite_8",
"@cite_29",
"@cite_0",
"@cite_24",
"@cite_16",
"@cite_10",
"@cite_25",
"@cite_20"
],
"mid": [
"2065860562",
"2124822240",
"2141775911",
"2013503669",
"2117909688",
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"2169126686",
"2158090937",
"20018130... |
1907.05538 | 2960971247 | We present a novel framework for collaboration amongst a team of robots performing Pose Graph Optimization (PGO) that addresses two important challenges for multi-robot SLAM: that of enabling information exchange "on-demand" via active rendezvous, and that of rejecting outlier measurements with high probability. Our ke... | As a supplement to some of the challenges of passive estimation, active SLAM approaches attempt to incorporate estimation uncertainty into the control of agents. This can include monitoring the estimation divergence during exploration @cite_28 and estimating information-gain from exploration versus uncertainty reductio... | {
"cite_N": [
"@cite_28",
"@cite_27",
"@cite_4"
],
"mid": [
"1972624884",
"",
"1535541145"
],
"abstract": [
"Autonomous exploration under uncertain robot location requires the robot to use active strategies to trade-off between the contrasting tasks of exploring the unknown scenari... |
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