October 17, 2019

3323 words 16 mins read

Paper Group ANR 724

Senti-Attend: Image Captioning using Sentiment and Attention. Towards effective AI-powered agile project management. An Adversarial Approach for Explainable AI in Intrusion Detection Systems. Transfer Learning for Action Unit Recognition. IEST: WASSA-2018 Implicit Emotions Shared Task. Strongly polynomial efficient approximation scheme for segmenta …

Senti-Attend: Image Captioning using Sentiment and Attention


Title	Senti-Attend: Image Captioning using Sentiment and Attention
Authors	Omid Mohamad Nezami, Mark Dras, Stephen Wan, Cecile Paris
Abstract	There has been much recent work on image captioning models that describe the factual aspects of an image. Recently, some models have incorporated non-factual aspects into the captions, such as sentiment or style. However, such models typically have difficulty in balancing the semantic aspects of the image and the non-factual dimensions of the caption; in addition, it can be observed that humans may focus on different aspects of an image depending on the chosen sentiment or style of the caption. To address this, we design an attention-based model to better add sentiment to image captions. The model embeds and learns sentiment with respect to image-caption data, and uses both high-level and word-level sentiment information during the learning process. The model outperforms the state-of-the-art work in image captioning with sentiment using standard evaluation metrics. An analysis of generated captions also shows that our model does this by a better selection of the sentiment-bearing adjectives and adjective-noun pairs.
Tasks	Image Captioning
Published	2018-11-24
URL	http://arxiv.org/abs/1811.09789v1
PDF	http://arxiv.org/pdf/1811.09789v1.pdf
PWC	https://paperswithcode.com/paper/senti-attend-image-captioning-using-sentiment
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Towards effective AI-powered agile project management


Title	Towards effective AI-powered agile project management
Authors	Hoa Khanh Dam, Truyen Tran, John Grundy, Aditya Ghose, Yasutaka Kamei
Abstract	The rise of Artificial intelligence (AI) has the potential to significantly transform the practice of project management. Project management has a large socio-technical element with many uncertainties arising from variability in human aspects e.g., customers’ needs, developers’ performance and team dynamics. AI can assist project managers and team members by automating repetitive, high-volume tasks to enable project analytics for estimation and risk prediction, providing actionable recommendations, and even making decisions. AI is potentially a game changer for project management in helping to accelerate productivity and increase project success rates. In this paper, we propose a framework where AI technologies can be leveraged to offer support for managing agile projects, which have become increasingly popular in the industry.
Tasks
Published	2018-12-27
URL	http://arxiv.org/abs/1812.10578v1
PDF	http://arxiv.org/pdf/1812.10578v1.pdf
PWC	https://paperswithcode.com/paper/towards-effective-ai-powered-agile-project
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An Adversarial Approach for Explainable AI in Intrusion Detection Systems


Title	An Adversarial Approach for Explainable AI in Intrusion Detection Systems
Authors	Daniel L. Marino, Chathurika S. Wickramasinghe, Milos Manic
Abstract	Despite the growing popularity of modern machine learning techniques (e.g. Deep Neural Networks) in cyber-security applications, most of these models are perceived as a black-box for the user. Adversarial machine learning offers an approach to increase our understanding of these models. In this paper we present an approach to generate explanations for incorrect classifications made by data-driven Intrusion Detection Systems (IDSs). An adversarial approach is used to find the minimum modifications (of the input features) required to correctly classify a given set of misclassified samples. The magnitude of such modifications is used to visualize the most relevant features that explain the reason for the misclassification. The presented methodology generated satisfactory explanations that describe the reasoning behind the mis-classifications, with descriptions that match expert knowledge. The advantages of the presented methodology are: 1) applicable to any classifier with defined gradients. 2) does not require any modification of the classifier model. 3) can be extended to perform further diagnosis (e.g. vulnerability assessment) and gain further understanding of the system. Experimental evaluation was conducted on the NSL-KDD99 benchmark dataset using Linear and Multilayer perceptron classifiers. The results are shown using intuitive visualizations in order to improve the interpretability of the results.
Tasks	Intrusion Detection
Published	2018-11-28
URL	http://arxiv.org/abs/1811.11705v1
PDF	http://arxiv.org/pdf/1811.11705v1.pdf
PWC	https://paperswithcode.com/paper/an-adversarial-approach-for-explainable-ai-in
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Transfer Learning for Action Unit Recognition


Title	Transfer Learning for Action Unit Recognition
Authors	Yen Khye Lim, Zukang Liao, Stavros Petridis, Maja Pantic
Abstract	This paper presents a classifier ensemble for Facial Expression Recognition (FER) based on models derived from transfer learning. The main experimentation work is conducted for facial action unit detection using feature extraction and fine-tuning convolutional neural networks (CNNs). Several classifiers for extracted CNN codes such as Linear Discriminant Analysis (LDA), Support Vector Machines (SVMs) and Long Short-Term Memory (LSTM) are compared and evaluated. Multi-model ensembles are also used to further improve the performance. We have found that VGG-Face and ResNet are the relatively optimal pre-trained models for action unit recognition using feature extraction and the ensemble of VGG-Net variants and ResNet achieves the best result.
Tasks	Action Unit Detection, Facial Action Unit Detection, Facial Expression Recognition, Transfer Learning
Published	2018-07-19
URL	http://arxiv.org/abs/1807.07556v1
PDF	http://arxiv.org/pdf/1807.07556v1.pdf
PWC	https://paperswithcode.com/paper/transfer-learning-for-action-unit-recognition
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IEST: WASSA-2018 Implicit Emotions Shared Task


Title	IEST: WASSA-2018 Implicit Emotions Shared Task
Authors	Roman Klinger, Orphée De Clercq, Saif M. Mohammad, Alexandra Balahur
Abstract	Past shared tasks on emotions use data with both overt expressions of emotions (I am so happy to see you!) as well as subtle expressions where the emotions have to be inferred, for instance from event descriptions. Further, most datasets do not focus on the cause or the stimulus of the emotion. Here, for the first time, we propose a shared task where systems have to predict the emotions in a large automatically labeled dataset of tweets without access to words denoting emotions. Based on this intention, we call this the Implicit Emotion Shared Task (IEST) because the systems have to infer the emotion mostly from the context. Every tweet has an occurrence of an explicit emotion word that is masked. The tweets are collected in a manner such that they are likely to include a description of the cause of the emotion - the stimulus. Altogether, 30 teams submitted results which range from macro F1 scores of 21 % to 71 %. The baseline (MaxEnt bag of words and bigrams) obtains an F1 score of 60 % which was available to the participants during the development phase. A study with human annotators suggests that automatic methods outperform human predictions, possibly by honing into subtle textual clues not used by humans. Corpora, resources, and results are available at the shared task website at http://implicitemotions.wassa2018.com.
Tasks
Published	2018-09-04
URL	http://arxiv.org/abs/1809.01083v2
PDF	http://arxiv.org/pdf/1809.01083v2.pdf
PWC	https://paperswithcode.com/paper/iest-wassa-2018-implicit-emotions-shared-task
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Strongly polynomial efficient approximation scheme for segmentation


Title	Strongly polynomial efficient approximation scheme for segmentation
Authors	Nikolaj Tatti
Abstract	Partitioning a sequence of length $n$ into $k$ coherent segments (Seg) is one of the classic optimization problems. As long as the optimization criterion is additive, Seg can be solved exactly in $O(n^2k)$ time using a classic dynamic program. Due to the quadratic term, computing the exact segmentation may be too expensive for long sequences, which has led to development of approximate solutions. We consider an existing estimation scheme that computes $(1 + \epsilon)$ approximation in polylogarithmic time. We augment this algorithm, making it strongly polynomial. We do this by first solving a slightly different segmentation problem (MaxSeg), where the quality of the segmentation is the maximum penalty of an individual segment. By using this solution to initialize the estimation scheme, we are able to obtain a strongly polynomial algorithm. In addition, we consider a cumulative version of Seg, where we are asked to discover the optimal segmentation for each prefix of the input sequence. We propose a strongly polynomial algorithm that yields $(1 + \epsilon)$ approximation in $O(nk^2 / \epsilon)$ time. Finally, we consider a cumulative version of MaxSeg, and show that we can solve the problem in $O(nk \log k)$ time.
Tasks
Published	2018-05-28
URL	http://arxiv.org/abs/1805.11170v2
PDF	http://arxiv.org/pdf/1805.11170v2.pdf
PWC	https://paperswithcode.com/paper/strongly-polynomial-efficient-approximation
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The Intelligent ICU Pilot Study: Using Artificial Intelligence Technology for Autonomous Patient Monitoring


Title	The Intelligent ICU Pilot Study: Using Artificial Intelligence Technology for Autonomous Patient Monitoring
Authors	Anis Davoudi, Kumar Rohit Malhotra, Benjamin Shickel, Scott Siegel, Seth Williams, Matthew Ruppert, Emel Bihorac, Tezcan Ozrazgat-Baslanti, Patrick J. Tighe, Azra Bihorac, Parisa Rashidi
Abstract	Currently, many critical care indices are repetitively assessed and recorded by overburdened nurses, e.g. physical function or facial pain expressions of nonverbal patients. In addition, many essential information on patients and their environment are not captured at all, or are captured in a non-granular manner, e.g. sleep disturbance factors such as bright light, loud background noise, or excessive visitations. In this pilot study, we examined the feasibility of using pervasive sensing technology and artificial intelligence for autonomous and granular monitoring of critically ill patients and their environment in the Intensive Care Unit (ICU). As an exemplar prevalent condition, we also characterized delirious and non-delirious patients and their environment. We used wearable sensors, light and sound sensors, and a high-resolution camera to collected data on patients and their environment. We analyzed collected data using deep learning and statistical analysis. Our system performed face detection, face recognition, facial action unit detection, head pose detection, facial expression recognition, posture recognition, actigraphy analysis, sound pressure and light level detection, and visitation frequency detection. We were able to detect patient’s face (Mean average precision (mAP)=0.94), recognize patient’s face (mAP=0.80), and their postures (F1=0.94). We also found that all facial expressions, 11 activity features, visitation frequency during the day, visitation frequency during the night, light levels, and sound pressure levels during the night were significantly different between delirious and non-delirious patients (p-value<0.05). In summary, we showed that granular and autonomous monitoring of critically ill patients and their environment is feasible and can be used for characterizing critical care conditions and related environment factors.
Tasks	Action Unit Detection, Face Detection, Face Recognition, Facial Action Unit Detection, Facial Expression Recognition
Published	2018-04-25
URL	http://arxiv.org/abs/1804.10201v2
PDF	http://arxiv.org/pdf/1804.10201v2.pdf
PWC	https://paperswithcode.com/paper/the-intelligent-icu-pilot-study-using
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Flow-based Network Traffic Generation using Generative Adversarial Networks


Title	Flow-based Network Traffic Generation using Generative Adversarial Networks
Authors	Markus Ring, Daniel Schlör, Dieter Landes, Andreas Hotho
Abstract	Flow-based data sets are necessary for evaluating network-based intrusion detection systems (NIDS). In this work, we propose a novel methodology for generating realistic flow-based network traffic. Our approach is based on Generative Adversarial Networks (GANs) which achieve good results for image generation. A major challenge lies in the fact that GANs can only process continuous attributes. However, flow-based data inevitably contain categorical attributes such as IP addresses or port numbers. Therefore, we propose three different preprocessing approaches for flow-based data in order to transform them into continuous values. Further, we present a new method for evaluating the generated flow-based network traffic which uses domain knowledge to define quality tests. We use the three approaches for generating flow-based network traffic based on the CIDDS-001 data set. Experiments indicate that two of the three approaches are able to generate high quality data.
Tasks	Image Generation, Intrusion Detection
Published	2018-09-27
URL	http://arxiv.org/abs/1810.07795v1
PDF	http://arxiv.org/pdf/1810.07795v1.pdf
PWC	https://paperswithcode.com/paper/flow-based-network-traffic-generation-using
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Learning Transposition-Invariant Interval Features from Symbolic Music and Audio


Title	Learning Transposition-Invariant Interval Features from Symbolic Music and Audio
Authors	Stefan Lattner, Maarten Grachten, Gerhard Widmer
Abstract	Many music theoretical constructs (such as scale types, modes, cadences, and chord types) are defined in terms of pitch intervals—relative distances between pitches. Therefore, when computer models are employed in music tasks, it can be useful to operate on interval representations rather than on the raw musical surface. Moreover, interval representations are transposition-invariant, valuable for tasks like audio alignment, cover song detection and music structure analysis. We employ a gated autoencoder to learn fixed-length, invertible and transposition-invariant interval representations from polyphonic music in the symbolic domain and in audio. An unsupervised training method is proposed yielding an organization of intervals in the representation space which is musically plausible. Based on the representations, a transposition-invariant self-similarity matrix is constructed and used to determine repeated sections in symbolic music and in audio, yielding competitive results in the MIREX task “Discovery of Repeated Themes and Sections”.
Tasks
Published	2018-06-21
URL	http://arxiv.org/abs/1806.08236v2
PDF	http://arxiv.org/pdf/1806.08236v2.pdf
PWC	https://paperswithcode.com/paper/learning-transposition-invariant-interval
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Fast Lexically Constrained Decoding with Dynamic Beam Allocation for Neural Machine Translation


Title	Fast Lexically Constrained Decoding with Dynamic Beam Allocation for Neural Machine Translation
Authors	Matt Post, David Vilar
Abstract	The end-to-end nature of neural machine translation (NMT) removes many ways of manually guiding the translation process that were available in older paradigms. Recent work, however, has introduced a new capability: lexically constrained or guided decoding, a modification to beam search that forces the inclusion of pre-specified words and phrases in the output. However, while theoretically sound, existing approaches have computational complexities that are either linear (Hokamp and Liu, 2017) or exponential (Anderson et al., 2017) in the number of constraints. We present a algorithm for lexically constrained decoding with a complexity of O(1) in the number of constraints. We demonstrate the algorithms remarkable ability to properly place these constraints, and use it to explore the shaky relationship between model and BLEU scores. Our implementation is available as part of Sockeye.
Tasks	Machine Translation
Published	2018-04-18
URL	http://arxiv.org/abs/1804.06609v2
PDF	http://arxiv.org/pdf/1804.06609v2.pdf
PWC	https://paperswithcode.com/paper/fast-lexically-constrained-decoding-with
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Deep learning generalizes because the parameter-function map is biased towards simple functions


Title	Deep learning generalizes because the parameter-function map is biased towards simple functions
Authors	Guillermo Valle-Pérez, Chico Q. Camargo, Ard A. Louis
Abstract	Deep neural networks (DNNs) generalize remarkably well without explicit regularization even in the strongly over-parametrized regime where classical learning theory would instead predict that they would severely overfit. While many proposals for some kind of implicit regularization have been made to rationalise this success, there is no consensus for the fundamental reason why DNNs do not strongly overfit. In this paper, we provide a new explanation. By applying a very general probability-complexity bound recently derived from algorithmic information theory (AIT), we argue that the parameter-function map of many DNNs should be exponentially biased towards simple functions. We then provide clear evidence for this strong simplicity bias in a model DNN for Boolean functions, as well as in much larger fully connected and convolutional networks applied to CIFAR10 and MNIST. As the target functions in many real problems are expected to be highly structured, this intrinsic simplicity bias helps explain why deep networks generalize well on real world problems. This picture also facilitates a novel PAC-Bayes approach where the prior is taken over the DNN input-output function space, rather than the more conventional prior over parameter space. If we assume that the training algorithm samples parameters close to uniformly within the zero-error region then the PAC-Bayes theorem can be used to guarantee good expected generalization for target functions producing high-likelihood training sets. By exploiting recently discovered connections between DNNs and Gaussian processes to estimate the marginal likelihood, we produce relatively tight generalization PAC-Bayes error bounds which correlate well with the true error on realistic datasets such as MNIST and CIFAR10 and for architectures including convolutional and fully connected networks.
Tasks	Gaussian Processes
Published	2018-05-22
URL	http://arxiv.org/abs/1805.08522v5
PDF	http://arxiv.org/pdf/1805.08522v5.pdf
PWC	https://paperswithcode.com/paper/deep-learning-generalizes-because-the
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HASP: A High-Performance Adaptive Mobile Security Enhancement Against Malicious Speech Recognition


Title	HASP: A High-Performance Adaptive Mobile Security Enhancement Against Malicious Speech Recognition
Authors	Zirui Xu, Fuxun Yu, Chenchen Liu, Xiang Chen
Abstract	Nowadays, machine learning based Automatic Speech Recognition (ASR) technique has widely spread in smartphones, home devices, and public facilities. As convenient as this technology can be, a considerable security issue also raises – the users’ speech content might be exposed to malicious ASR monitoring and cause severe privacy leakage. In this work, we propose HASP – a high-performance security enhancement approach to solve this security issue on mobile devices. Leveraging ASR systems’ vulnerability to the adversarial examples, HASP is designed to cast human imperceptible adversarial noises to real-time speech and effectively perturb malicious ASR monitoring by increasing the Word Error Rate (WER). To enhance the practical performance on mobile devices, HASP is also optimized for effective adaptation to the human speech characteristics, environmental noises, and mobile computation scenarios. The experiments show that HASP can achieve optimal real-time security enhancement: it can lead an average WER of 84.55% for perturbing the malicious ASR monitoring, and the data processing speed is 15x to 40x faster compared to the state-of-the-art methods. Moreover, HASP can effectively perturb various ASR systems, demonstrating a strong transferability.
Tasks	Mobile Security, Speech Recognition
Published	2018-09-04
URL	http://arxiv.org/abs/1809.01697v1
PDF	http://arxiv.org/pdf/1809.01697v1.pdf
PWC	https://paperswithcode.com/paper/hasp-a-high-performance-adaptive-mobile
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Domain Adaptive Transfer Learning with Specialist Models


Title	Domain Adaptive Transfer Learning with Specialist Models
Authors	Jiquan Ngiam, Daiyi Peng, Vijay Vasudevan, Simon Kornblith, Quoc V. Le, Ruoming Pang
Abstract	Transfer learning is a widely used method to build high performing computer vision models. In this paper, we study the efficacy of transfer learning by examining how the choice of data impacts performance. We find that more pre-training data does not always help, and transfer performance depends on a judicious choice of pre-training data. These findings are important given the continued increase in dataset sizes. We further propose domain adaptive transfer learning, a simple and effective pre-training method using importance weights computed based on the target dataset. Our method to compute importance weights follow from ideas in domain adaptation, and we show a novel application to transfer learning. Our methods achieve state-of-the-art results on multiple fine-grained classification datasets and are well-suited for use in practice.
Tasks	Domain Adaptation, Fine-Grained Image Classification, Transfer Learning
Published	2018-11-16
URL	http://arxiv.org/abs/1811.07056v2
PDF	http://arxiv.org/pdf/1811.07056v2.pdf
PWC	https://paperswithcode.com/paper/domain-adaptive-transfer-learning-with
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Query Focused Abstractive Summarization: Incorporating Query Relevance, Multi-Document Coverage, and Summary Length Constraints into seq2seq Models


Title	Query Focused Abstractive Summarization: Incorporating Query Relevance, Multi-Document Coverage, and Summary Length Constraints into seq2seq Models
Authors	Tal Baumel, Matan Eyal, Michael Elhadad
Abstract	Query Focused Summarization (QFS) has been addressed mostly using extractive methods. Such methods, however, produce text which suffers from low coherence. We investigate how abstractive methods can be applied to QFS, to overcome such limitations. Recent developments in neural-attention based sequence-to-sequence models have led to state-of-the-art results on the task of abstractive generic single document summarization. Such models are trained in an end to end method on large amounts of training data. We address three aspects to make abstractive summarization applicable to QFS: (a)since there is no training data, we incorporate query relevance into a pre-trained abstractive model; (b) since existing abstractive models are trained in a single-document setting, we design an iterated method to embed abstractive models within the multi-document requirement of QFS; (c) the abstractive models we adapt are trained to generate text of specific length (about 100 words), while we aim at generating output of a different size (about 250 words); we design a way to adapt the target size of the generated summaries to a given size ratio. We compare our method (Relevance Sensitive Attention for QFS) to extractive baselines and with various ways to combine abstractive models on the DUC QFS datasets and demonstrate solid improvements on ROUGE performance.
Tasks	Abstractive Text Summarization, Document Summarization
Published	2018-01-23
URL	http://arxiv.org/abs/1801.07704v2
PDF	http://arxiv.org/pdf/1801.07704v2.pdf
PWC	https://paperswithcode.com/paper/query-focused-abstractive-summarization
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Framework

Non-Local Compressive Sensing Based SAR Tomography


Title	Non-Local Compressive Sensing Based SAR Tomography
Authors	Yilei Shi, Xiao Xiang Zhu, Richard Bamler
Abstract	Tomographic SAR (TomoSAR) inversion of urban areas is an inherently sparse reconstruction problem and, hence, can be solved using compressive sensing (CS) algorithms. This paper proposes solutions for two notorious problems in this field: 1) TomoSAR requires a high number of data sets, which makes the technique expensive. However, it can be shown that the number of acquisitions and the signal-to-noise ratio (SNR) can be traded off against each other, because it is asymptotically only the product of the number of acquisitions and SNR that determines the reconstruction quality. We propose to increase SNR by integrating non-local estimation into the inversion and show that a reasonable reconstruction of buildings from only seven interferograms is feasible. 2) CS-based inversion is computationally expensive and therefore barely suitable for large-scale applications. We introduce a new fast and accurate algorithm for solving the non-local L1-L2-minimization problem, central to CS-based reconstruction algorithms. The applicability of the algorithm is demonstrated using simulated data and TerraSAR-X high-resolution spotlight images over an area in Munich, Germany.
Tasks	Compressive Sensing
Published	2018-11-05
URL	http://arxiv.org/abs/1811.02046v1
PDF	http://arxiv.org/pdf/1811.02046v1.pdf
PWC	https://paperswithcode.com/paper/non-local-compressive-sensing-based-sar
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