Paper Group ANR 1388
Modal clustering asymptotics with applications to bandwidth selection. Debiasing Word Embeddings Improves Multimodal Machine Translation. A-Phase classification using convolutional neural networks. Electroencephalography based Classification of Long-term Stress using Psychological Labeling. Machine learning without a feature set for detecting burst …
Modal clustering asymptotics with applications to bandwidth selection
| Title | Modal clustering asymptotics with applications to bandwidth selection |
| Authors | Alessandro Casa, José E. Chacón, Giovanna Menardi |
| Abstract | Density-based clustering relies on the idea of linking groups to some specific features of the probability distribution underlying the data. The reference to a true, yet unknown, population structure allows to frame the clustering problem in a standard inferential setting, where the concept of ideal population clustering is defined as the partition induced by the true density function. The nonparametric formulation of this approach, known as modal clustering, draws a correspondence between the groups and the domains of attraction of the density modes. Operationally, a nonparametric density estimate is required and a proper selection of the amount of smoothing, governing the shape of the density and hence possibly the modal structure, is crucial to identify the final partition. In this work, we address the issue of density estimation for modal clustering from an asymptotic perspective. A natural and easy to interpret metric to measure the distance between density-based partitions is discussed, its asymptotic approximation explored, and employed to study the problem of bandwidth selection for nonparametric modal clustering. |
| Tasks | Density Estimation |
| Published | 2019-01-22 |
| URL | http://arxiv.org/abs/1901.07300v1 |
| http://arxiv.org/pdf/1901.07300v1.pdf | |
| PWC | https://paperswithcode.com/paper/modal-clustering-asymptotics-with |
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Debiasing Word Embeddings Improves Multimodal Machine Translation
| Title | Debiasing Word Embeddings Improves Multimodal Machine Translation |
| Authors | Tosho Hirasawa, Mamoru Komachi |
| Abstract | In recent years, pretrained word embeddings have proved useful for multimodal neural machine translation (NMT) models to address the shortage of available datasets. However, the integration of pretrained word embeddings has not yet been explored extensively. Further, pretrained word embeddings in high dimensional spaces have been reported to suffer from the hubness problem. Although some debiasing techniques have been proposed to address this problem for other natural language processing tasks, they have seldom been studied for multimodal NMT models. In this study, we examine various kinds of word embeddings and introduce two debiasing techniques for three multimodal NMT models and two language pairs – English-German translation and English-French translation. With our optimal settings, the overall performance of multimodal models was improved by up to +1.93 BLEU and +2.02 METEOR for English-German translation and +1.73 BLEU and +0.95 METEOR for English-French translation. |
| Tasks | Machine Translation, Multimodal Machine Translation, Word Embeddings |
| Published | 2019-05-24 |
| URL | https://arxiv.org/abs/1905.10464v3 |
| https://arxiv.org/pdf/1905.10464v3.pdf | |
| PWC | https://paperswithcode.com/paper/debiasingword-embeddings-improves-multimodal |
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A-Phase classification using convolutional neural networks
| Title | A-Phase classification using convolutional neural networks |
| Authors | Edgar R. Arce-Santana, Alfonso Alba, Martin O. Mendez, Valdemar Arce-Guevara |
| Abstract | A series of short events, called A-phases, can be observed in the human electroencephalogram during NREM sleep. These events can be classified in three groups (A1, A2 and A3) according to their spectral contents, and are thought to play a role in the transitions between the different sleep stages. A-phase detection and classification is usually performed manually by a trained expert, but it is a tedious and time-consuming task. In the past two decades, various researchers have designed algorithms to automatically detect and classify the A-phases with varying degrees of success, but the problem remains open. In this paper, a different approach is proposed: instead of attempting to design a general classifier for all subjects, we propose to train ad-hoc classifiers for each subject using as little data as possible, in order to drastically reduce the amount of time required from the expert. The proposed classifiers are based on deep convolutional neural networks using the log-spectrogram of the EEG signal as input data. Results are encouraging, achieving average accuracies of 80.31% when discriminating between A-phases and non A-phases, and 71.87% when classifying among A-phase sub-types, with only 25% of the total A-phases used for training. When additional expert-validated data is considered, the sub-type classification accuracy increases to 78.92%. These results show that a semi-automatic annotation system with assistance from an expert could provide a better alternative to fully automatic classifiers. |
| Tasks | EEG |
| Published | 2019-07-22 |
| URL | https://arxiv.org/abs/1907.09296v1 |
| https://arxiv.org/pdf/1907.09296v1.pdf | |
| PWC | https://paperswithcode.com/paper/a-phase-classification-using-convolutional |
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Electroencephalography based Classification of Long-term Stress using Psychological Labeling
| Title | Electroencephalography based Classification of Long-term Stress using Psychological Labeling |
| Authors | Sanay Muhammad Umar Saeed, Syed Muhammad Anwar, Humaira Khalid, Muhammad Majid, Ulas Bagci |
| Abstract | Stress research is a rapidly emerging area in thefield of electroencephalography (EEG) based signal processing.The use of EEG as an objective measure for cost effective andpersonalized stress management becomes important in particularsituations such as the non-availability of mental health facilities.In this study, long-term stress is classified using baseline EEGsignal recordings. The labelling for the stress and control groupsis performed using two methods (i) the perceived stress scalescore and (ii) expert evaluation. The frequency domain featuresare extracted from five-channel EEG recordings in addition tothe frontal and temporal alpha and beta asymmetries. The alphaasymmetry is computed from four channels and used as a feature.Feature selection is also performed using a t-test to identifystatistically significant features for both stress and control groups.We found that support vector machine is best suited to classifylong-term human stress when used with alpha asymmetry asa feature. It is observed that expert evaluation based labellingmethod has improved the classification accuracy up to 85.20%.Based on these results, it is concluded that alpha asymmetry maybe used as a potential bio-marker for stress classification, when labels are assigned using expert evaluation. |
| Tasks | EEG |
| Published | 2019-07-17 |
| URL | https://arxiv.org/abs/1907.07671v1 |
| https://arxiv.org/pdf/1907.07671v1.pdf | |
| PWC | https://paperswithcode.com/paper/electroencephalography-based-classification |
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Machine learning without a feature set for detecting bursts in the EEG of preterm infants
| Title | Machine learning without a feature set for detecting bursts in the EEG of preterm infants |
| Authors | John M. O’Toole, Geraldine B. Boylan |
| Abstract | Deep neural networks enable learning directly on the data without the domain knowledge needed to construct a feature set. This approach has been extremely successful in almost all machine learning applications. We propose a new framework that also learns directly from the data, without extracting a feature set. We apply this framework to detecting bursts in the EEG of premature infants. The EEG is recorded within days of birth in a cohort of infants without significant brain injury and born <30 weeks of gestation. The method first transforms the time-domain signal to the time–frequency domain and then trains a machine learning method, a gradient boosting machine, on each time-slice of the time–frequency distribution. We control for oversampling the time–frequency distribution with a significant reduction (<1%) in memory and computational complexity. The proposed method achieves similar accuracy to an existing multi-feature approach: area under the characteristic curve of 0.98 (with 95% confidence interval of 0.96 to 0.99), with a median sensitivity of 95% and median specificity of 94%. The proposed framework presents an accurate, simple, and computational efficient implementation as an alternative to both the deep learning approach and to the manual generation of a feature set. |
| Tasks | EEG |
| Published | 2019-07-16 |
| URL | https://arxiv.org/abs/1907.06943v1 |
| https://arxiv.org/pdf/1907.06943v1.pdf | |
| PWC | https://paperswithcode.com/paper/machine-learning-without-a-feature-set-for |
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AMRec: An Intelligent System for Academic Method Recommendation
| Title | AMRec: An Intelligent System for Academic Method Recommendation |
| Authors | Shanshan Huang, Xiaojun Wan, Xuewei Tang |
| Abstract | Finding new academic Methods for research problems is the key task in a researcher’s research career. It is usually very difficult for new researchers to find good Methods for their research problems since they lack of research experiences. In order to help researchers carry out their researches in a more convenient way, we describe a novel recommendation system called AMRec to recommend new academic Methods for research problems in this paper. Our proposed system first extracts academic concepts (Tasks and Methods) and their relations from academic literatures, and then leverages the regularized matrix factorization Method for academic Method recommendation. Preliminary evaluation results verify the effectiveness of our proposed system. |
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| Published | 2019-04-10 |
| URL | http://arxiv.org/abs/1904.04995v1 |
| http://arxiv.org/pdf/1904.04995v1.pdf | |
| PWC | https://paperswithcode.com/paper/amrec-an-intelligent-system-for-academic |
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Fast mmwave Beam Alignment via Correlated Bandit Learning
| Title | Fast mmwave Beam Alignment via Correlated Bandit Learning |
| Authors | Wen Wu, Nan Cheng, Ning Zhang, Peng Yang, Weihua Zhuang, Xuemin, Shen |
| Abstract | Beam alignment (BA) is to ensure the transmitter and receiver beams are accurately aligned to establish a reliable communication link in millimeter-wave (mmwave) systems. Existing BA methods search the entire beam space to identify the optimal transmit-receive beam pair, which incurs significant BA latency on the order of seconds in the worst case. In this paper, we develop a learning algorithm to reduce BA latency, namely Hierarchical Beam Alignment (HBA) algorithm. We first formulate the BA problem as a stochastic multi-armed bandit problem with the objective to maximize the cumulative received signal strength within a certain period. The proposed algorithm takes advantage of the correlation structure among beams such that the information from nearby beams is extracted to identify the optimal beam, instead of searching the entire beam space. Furthermore, the prior knowledge on the channel fluctuation is incorporated in the proposed algorithm to further accelerate the BA process. Theoretical analysis indicates that the proposed algorithm is asymptotically optimal. Extensive simulation results demonstrate that the proposed algorithm can identify the optimal beam with a high probability and reduce the BA latency from hundreds of milliseconds to a few milliseconds in the multipath channel, as compared to the existing BA method in IEEE 802.11ad. |
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| Published | 2019-09-07 |
| URL | https://arxiv.org/abs/1909.03313v1 |
| https://arxiv.org/pdf/1909.03313v1.pdf | |
| PWC | https://paperswithcode.com/paper/fast-mmwave-beam-alignment-via-correlated |
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Suitability of an inter-burst detection method for grading hypoxic-ischemic encephalopathy in newborn EEG
| Title | Suitability of an inter-burst detection method for grading hypoxic-ischemic encephalopathy in newborn EEG |
| Authors | Sumit A. Raurale, Saif Nalband, Geraldine B. Boylan, Gordon Lightbody, John M. O’Toole |
| Abstract | Electroencephalography (EEG) is an important clinical tool for grading injury caused by lack of oxygen or blood to the brain during birth. Characteristics of low-voltage waveforms, known as inter-bursts, are related to different grades of injury. This study assesses the suitability of an existing inter-burst detection method, developed from preterm infants born <30 weeks of gestational age, to detect inter-bursts in term infants. Different features from the temporal organisation of the inter-bursts are combined using a multi-layer perceptron (MLP) machine learning algorithm to classify four grades of injury in the EEG. We find that the best performing feature, percentage of inter-bursts, has an accuracy of 59.3%. Combining this with the maximum duration of inter-bursts in the MLP produces a testing accuracy of 77.8%, with similar performance to existing multi-feature methods. These results validate the use of the preterm detection method in term EEG and show how simple measures of the inter-burst interval can be used to classify different grades of injury. |
| Tasks | EEG |
| Published | 2019-07-05 |
| URL | https://arxiv.org/abs/1907.02877v1 |
| https://arxiv.org/pdf/1907.02877v1.pdf | |
| PWC | https://paperswithcode.com/paper/suitability-of-an-inter-burst-detection |
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Learning Image Information for eCommerce Queries
| Title | Learning Image Information for eCommerce Queries |
| Authors | Utkarsh Porwal |
| Abstract | Computing similarity between a query and a document is fundamental in any information retrieval system. In search engines, computing query-document similarity is an essential step in both retrieval and ranking stages. In eBay search, document is an item and the query-item similarity can be computed by comparing different facets of the query-item pair. Query text can be compared with the text of the item title. Likewise, a category constraint applied on the query can be compared with the listing category of the item. However, images are one signal that are usually present in the items but are not present in the query. Images are one of the most intuitive signals used by users to determine the relevance of the item given a query. Including this signal in estimating similarity between the query-item pair is likely to improve the relevance of the search engine. We propose a novel way of deriving image information for queries. We attempt to learn image information for queries from item images instead of generating explicit image features or an image for queries. We use canonical correlation analysis (CCA) to learn a new subspace where projecting the original data will give us a new query and item representation. We hypothesize that this new query representation will also have image information about the query. We estimate the query-item similarity using a vector space model and report the performance of the proposed method on eBay’s search data. We show 11.89% relevance improvement over the baseline using area under the receiver operating characteristic curve (AUROC) as the evaluation metric. We also show 3.1% relevance improvement over the baseline with area under the precision recall curve (AUPRC) . |
| Tasks | Information Retrieval |
| Published | 2019-04-29 |
| URL | http://arxiv.org/abs/1904.12856v1 |
| http://arxiv.org/pdf/1904.12856v1.pdf | |
| PWC | https://paperswithcode.com/paper/learning-image-information-for-ecommerce |
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Protecting Privacy of Users in Brain-Computer Interface Applications
| Title | Protecting Privacy of Users in Brain-Computer Interface Applications |
| Authors | Anisha Agarwal, Rafael Dowsley, Nicholas D. McKinney, Dongrui Wu, Chin-Teng Lin, Martine De Cock, Anderson C. A. Nascimento |
| Abstract | Machine learning (ML) is revolutionizing research and industry. Many ML applications rely on the use of large amounts of personal data for training and inference. Among the most intimate exploited data sources is electroencephalogram (EEG) data, a kind of data that is so rich with information that application developers can easily gain knowledge beyond the professed scope from unprotected EEG signals, including passwords, ATM PINs, and other intimate data. The challenge we address is how to engage in meaningful ML with EEG data while protecting the privacy of users. Hence, we propose cryptographic protocols based on Secure Multiparty Computation (SMC) to perform linear regression over EEG signals from many users in a fully privacy-preserving (PP) fashion, i.e.~such that each individual’s EEG signals are not revealed to anyone else. To illustrate the potential of our secure framework, we show how it allows estimating the drowsiness of drivers from their EEG signals as would be possible in the unencrypted case, and at a very reasonable computational cost. Our solution is the first application of commodity-based SMC to EEG data, as well as the largest documented experiment of secret sharing based SMC in general, namely with 15 players involved in all the computations. |
| Tasks | EEG |
| Published | 2019-07-02 |
| URL | https://arxiv.org/abs/1907.01586v1 |
| https://arxiv.org/pdf/1907.01586v1.pdf | |
| PWC | https://paperswithcode.com/paper/protecting-privacy-of-users-in-brain-computer |
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Learning to Link
| Title | Learning to Link |
| Authors | Maria-Florina Balcan, Travis Dick, Manuel Lang |
| Abstract | Clustering is an important part of many modern data analysis pipelines, including network analysis and data retrieval. There are many different clustering algorithms developed by various communities, and it is often not clear which algorithm will give the best performance on a specific clustering task. Similarly, we often have multiple ways to measure distances between data points, and the best clustering performance might require a non-trivial combination of those metrics. In this work, we study data-driven algorithm selection and metric learning for clustering problems, where the goal is to simultaneously learn the best algorithm and metric for a specific application. The family of clustering algorithms we consider is parameterized linkage based procedures that includes single and complete linkage. The family of distance functions we learn over are convex combinations of base distance functions. We design efficient learning algorithms which receive samples from an application-specific distribution over clustering instances and simultaneously learn both a near-optimal distance and clustering algorithm from these classes. We also carry out a comprehensive empirical evaluation of our techniques showing that they can lead to significantly improved clustering performance. |
| Tasks | Metric Learning |
| Published | 2019-07-01 |
| URL | https://arxiv.org/abs/1907.00533v3 |
| https://arxiv.org/pdf/1907.00533v3.pdf | |
| PWC | https://paperswithcode.com/paper/learning-to-link |
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Scenario approach for minmax optimization with emphasis on the nonconvex case: positive results and caveats
| Title | Scenario approach for minmax optimization with emphasis on the nonconvex case: positive results and caveats |
| Authors | Mishal Assif P K, Debasish Chatterjee, Ravi Banavar |
| Abstract | We treat the so-called scenario approach, a popular probabilistic approximation method for robust minmax optimization problems via independent and indentically distributed (i.i.d) sampling from the uncertainty set, from various perspectives. The scenario approach is well-studied in the important case of convex robust optimization problems, and here we examine how the phenomenon of concentration of measures affects the i.i.d sampling aspect of the scenario approach in high dimensions and its relation with the optimal values. Moreover, we perform a detailed study of both the asymptotic behaviour (consistency) and finite time behaviour of the scenario approach in the more general setting of nonconvex minmax optimization problems. In the direction of the asymptotic behaviour of the scenario approach, we present an obstruction to consistency that arises when the decision set is noncompact. In the direction of finite sample guarantees, we establish a general methodology for extracting `probably approximately correct’ type estimates for the finite sample behaviour of the scenario approach for a large class of nonconvex problems. | |
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| Published | 2019-06-04 |
| URL | https://arxiv.org/abs/1906.01476v2 |
| https://arxiv.org/pdf/1906.01476v2.pdf | |
| PWC | https://paperswithcode.com/paper/scenario-approach-for-minmax-optimization |
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Differential Privacy for Eye-Tracking Data
| Title | Differential Privacy for Eye-Tracking Data |
| Authors | Ao Liu, Lirong Xia, Andrew Duchowski, Reynold Bailey, Kenneth Holmqvist, Eakta Jain |
| Abstract | As large eye-tracking datasets are created, data privacy is a pressing concern for the eye-tracking community. De-identifying data does not guarantee privacy because multiple datasets can be linked for inferences. A common belief is that aggregating individuals’ data into composite representations such as heatmaps protects the individual. However, we analytically examine the privacy of (noise-free) heatmaps and show that they do not guarantee privacy. We further propose two noise mechanisms that guarantee privacy and analyze their privacy-utility tradeoff. Analysis reveals that our Gaussian noise mechanism is an elegant solution to preserve privacy for heatmaps. Our results have implications for interdisciplinary research to create differentially private mechanisms for eye tracking. |
| Tasks | Eye Tracking |
| Published | 2019-04-15 |
| URL | http://arxiv.org/abs/1904.06809v1 |
| http://arxiv.org/pdf/1904.06809v1.pdf | |
| PWC | https://paperswithcode.com/paper/differential-privacy-for-eye-tracking-data |
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Detecting Fake News with Weak Social Supervision
| Title | Detecting Fake News with Weak Social Supervision |
| Authors | Kai Shu, Ahmed Hassan Awadallah, Susan Dumais, Huan Liu |
| Abstract | Limited labeled data is becoming the largest bottleneck for supervised learning systems. This is especially the case for many real-world tasks where large scale annotated examples are either too expensive to acquire or unavailable due to privacy or data access constraints. Weak supervision has shown to be a good means to mitigate the scarcity of annotated data by leveraging weak labels or injecting constraints from heuristic rules and/or external knowledge sources. Social media has little labeled data but possesses unique characteristics that make it suitable for generating weak supervision, resulting in a new type of weak supervision, i.e., weak social supervision. In this article, we illustrate how various aspects of social media can be used to generate weak social supervision. Specifically, we use the recent research on fake news detection as the use case, where social engagements are abundant but annotated examples are scarce, to show that weak social supervision is effective when facing the little labeled data problem. This article opens the door for learning with weak social supervision for other emerging tasks. |
| Tasks | Fake News Detection |
| Published | 2019-10-24 |
| URL | https://arxiv.org/abs/1910.11430v1 |
| https://arxiv.org/pdf/1910.11430v1.pdf | |
| PWC | https://paperswithcode.com/paper/detecting-fake-news-with-weak-social |
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Learning Coupled Spatial-temporal Attention for Skeleton-based Action Recognition
| Title | Learning Coupled Spatial-temporal Attention for Skeleton-based Action Recognition |
| Authors | Jiayun Wang |
| Abstract | In this paper, we propose a coupled spatial-temporal attention (CSTA) model for skeleton-based action recognition, which aims to figure out the most discriminative joints and frames in spatial and temporal domains simultaneously. Conventional approaches usually consider all the joints or frames in a skeletal sequence equally important, which are unrobust to ambiguous and redundant information. To address this, we first learn two sets of weights for different joints and frames through two subnetworks respectively, which enable the model to have the ability of “paying attention to” the relatively informative section. Then, we calculate the cross product based on the weights of joints and frames for the coupled spatial-temporal attention. Moreover, our CSTA mechanisms can be easily plugged into existing hierarchical CNN models (CSTA-CNN) to realize their function. Extensive experimental results on the recently collected UESTC dataset and the currently largest NTU dataset have shown the effectiveness of our proposed method for skeleton-based action recognition. |
| Tasks | Skeleton Based Action Recognition |
| Published | 2019-09-23 |
| URL | https://arxiv.org/abs/1909.10214v1 |
| https://arxiv.org/pdf/1909.10214v1.pdf | |
| PWC | https://paperswithcode.com/paper/190910214 |
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