January 28, 2020

3179 words 15 mins read

Paper Group ANR 866

Novel semi-metrics for multivariate change point analysis and anomaly detection. Fast and deep neuromorphic learning with time-to-first-spike coding. Multi-class Novelty Detection Using Mix-up Technique. Cross-Platform Performance Portability Using Highly Parametrized SYCL Kernels. Hierarchical approach to classify food scenes in egocentric photo-s …

Novel semi-metrics for multivariate change point analysis and anomaly detection


Title	Novel semi-metrics for multivariate change point analysis and anomaly detection
Authors	Nick James, Max Menzies, Lamiae Azizi, Jennifer Chan
Abstract	This paper proposes a new method for determining similarity and anomalies between time series, most practically effective in large collections of (likely related) time series, by measuring distances between structural breaks within such a collection. We introduce a class of \emph{semi-metric} distance measures, which we term \emph{MJ distances}. These semi-metrics provide an advantage over existing options such as the Hausdorff and Wasserstein metrics. We prove they have desirable properties, including better sensitivity to outliers, while experiments on simulated data demonstrate that they uncover similarity within collections of time series more effectively. Semi-metrics carry a potential disadvantage: without the triangle inequality, they may not satisfy a “transitivity property of closeness.” We analyse this failure with proof and introduce an computational method to investigate, in which we demonstrate that our semi-metrics violate transitivity infrequently and mildly. Finally, we apply our methods to cryptocurrency and measles data, introducing a judicious application of eigenvalue analysis.
Tasks	Anomaly Detection, Time Series
Published	2019-11-04
URL	https://arxiv.org/abs/1911.00995v2
PDF	https://arxiv.org/pdf/1911.00995v2.pdf
PWC	https://paperswithcode.com/paper/novel-semi-metrics-for-multivariate-change
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Fast and deep neuromorphic learning with time-to-first-spike coding


Title	Fast and deep neuromorphic learning with time-to-first-spike coding
Authors	Julian Göltz, Andreas Baumbach, Sebastian Billaudelle, Oliver Breitwieser, Dominik Dold, Laura Kriener, Akos Ferenc Kungl, Walter Senn, Johannes Schemmel, Karlheinz Meier, Mihai Alexandru Petrovici
Abstract	For a biological agent operating under environmental pressure, energy consumption and reaction times are of critical importance. Similarly, engineered systems also strive for short time-to-solution and low energy-to-solution characteristics. At the level of neuronal implementation, this implies achieving the desired results with as few and as early spikes as possible. In the time-to-first-spike coding framework, both of these goals are inherently emerging features of learning. Here, we describe a rigorous derivation of error-backpropagation-based learning for hierarchical networks of leaky integrate-and-fire neurons. We explicitly address two issues that are relevant for both biological plausibility and applicability to neuromorphic substrates by incorporating dynamics with finite time constants and by optimizing the backward pass with respect to substrate variability. This narrows the gap between previous models of first-spike-time learning and biological neuronal dynamics, thereby also enabling fast and energy-efficient inference on analog neuromorphic devices that inherit these dynamics from their biological archetypes, which we demonstrate on two generations of the BrainScaleS analog neuromorphic architecture.
Tasks
Published	2019-12-24
URL	https://arxiv.org/abs/1912.11443v1
PDF	https://arxiv.org/pdf/1912.11443v1.pdf
PWC	https://paperswithcode.com/paper/fast-and-deep-neuromorphic-learning-with-time
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Multi-class Novelty Detection Using Mix-up Technique


Title	Multi-class Novelty Detection Using Mix-up Technique
Authors	Supritam Bhattacharjee, Devraj Mandal, Soma Biswas
Abstract	Multi-class novelty detection is increasingly becoming an important area of research due to the continuous increase in the number of object categories. It tries to answer the pertinent question: given a test sample, should we even try to classify it? We propose a novel solution using the concept of mixup technique for novelty detection, termed as Segregation Network. During training, a pair of examples are selected from the training data and an interpolated data point using their convex combination is constructed. We develop a suitable loss function to train our model to predict its constituent classes. During testing, each input query is combined with the known class prototypes to generate mixed samples which are then passed through the trained network. Our model which is trained to reveal the constituent classes can then be used to determine whether the sample is novel or not. The intuition is that if a query comes from a known class and is mixed with the set of known class prototypes, then the prediction of the trained model for the correct class should be high. In contrast, for a query from a novel class, the predictions for all the known classes should be low. The proposed model is trained using only the available known class data and does not need access to any auxiliary dataset or attributes. Extensive experiments on two benchmark datasets, namely Caltech 256 and Stanford Dogs and comparisons with the state-of-the-art algorithms justifies the usefulness of our approach.
Tasks
Published	2019-05-11
URL	https://arxiv.org/abs/1905.04523v3
PDF	https://arxiv.org/pdf/1905.04523v3.pdf
PWC	https://paperswithcode.com/paper/segregation-network-for-multi-class-novelty
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Cross-Platform Performance Portability Using Highly Parametrized SYCL Kernels


Title	Cross-Platform Performance Portability Using Highly Parametrized SYCL Kernels
Authors	John Lawson, Mehdi Goli, Duncan McBain, Daniel Soutar, Louis Sugy
Abstract	Over recent years heterogeneous systems have become more prevalent across HPC systems, with over 100 supercomputers in the TOP500 incorporating GPUs or other accelerators. These hardware platforms have different performance characteristics and optimization requirements. In order to make the most of multiple accelerators a developer has to provide implementations of their algorithms tuned for each device. Hardware vendors provide libraries targeting their devices specifically, which provide good performance but frequently have different API designs, hampering portability. The SYCL programming model allows users to write heterogeneous programs using completely standard C++, and so developers have access to the power of C++ templates when developing compute kernels. In this paper we show that by writing highly parameterized kernels for matrix multiplies and convolutions we achieve performance competitive with vendor implementations across different architectures. Furthermore, tuning for new devices amounts to choosing the combinations of kernel parameters that perform best on the hardware.
Tasks
Published	2019-04-10
URL	http://arxiv.org/abs/1904.05347v1
PDF	http://arxiv.org/pdf/1904.05347v1.pdf
PWC	https://paperswithcode.com/paper/cross-platform-performance-portability-using
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Hierarchical approach to classify food scenes in egocentric photo-streams


Title	Hierarchical approach to classify food scenes in egocentric photo-streams
Authors	Estefania Talavera, Maria Leyva-Vallina, Md. Mostafa Kamal Sarker, Domenec Puig, Nicolai Petkov, Petia Radeva
Abstract	Recent studies have shown that the environment where people eat can affect their nutritional behaviour. In this work, we provide automatic tools for a personalised analysis of a person’s health habits by the examination of daily recorded egocentric photo-streams. Specifically, we propose a new automatic approach for the classification of food-related environments, that is able to classify up to 15 such scenes. In this way, people can monitor the context around their food intake in order to get an objective insight into their daily eating routine. We propose a model that classifies food-related scenes organized in a semantic hierarchy. Additionally, we present and make available a new egocentric dataset composed of more than 33000 images recorded by a wearable camera, over which our proposed model has been tested. Our approach obtains an accuracy and F-score of 56% and 65%, respectively, clearly outperforming the baseline methods.
Tasks
Published	2019-05-10
URL	https://arxiv.org/abs/1905.04097v1
PDF	https://arxiv.org/pdf/1905.04097v1.pdf
PWC	https://paperswithcode.com/paper/hierarchical-approach-to-classify-food-scenes
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Video-based surgical skill assessment using 3D convolutional neural networks


Title	Video-based surgical skill assessment using 3D convolutional neural networks
Authors	Isabel Funke, Sören Torge Mees, Jürgen Weitz, Stefanie Speidel
Abstract	Purpose: A profound education of novice surgeons is crucial to ensure that surgical interventions are effective and safe. One important aspect is the teaching of technical skills for minimally invasive or robot-assisted procedures. This includes the objective and preferably automatic assessment of surgical skill. Recent studies presented good results for automatic, objective skill evaluation by collecting and analyzing motion data such as trajectories of surgical instruments. However, obtaining the motion data generally requires additional equipment for instrument tracking or the availability of a robotic surgery system to capture kinematic data. In contrast, we investigate a method for automatic, objective skill assessment that requires video data only. This has the advantage that video can be collected effortlessly during minimally invasive and robot-assisted training scenarios. Methods: Our method builds on recent advances in deep learning-based video classification. Specifically, we propose to use an inflated 3D ConvNet to classify snippets, i.e., stacks of a few consecutive frames, extracted from surgical video. The network is extended into a Temporal Segment Network during training. Results: We evaluate the method on the publicly available JIGSAWS dataset, which consists of recordings of basic robot-assisted surgery tasks performed on a dry lab bench-top model. Our approach achieves high skill classification accuracies ranging from 95.1% to 100.0%. Conclusions: Our results demonstrate the feasibility of deep learning-based assessment of technical skill from surgical video. Notably, the 3D ConvNet is able to learn meaningful patterns directly from the data, alleviating the need for manual feature engineering. Further evaluation will require more annotated data for training and testing.
Tasks	Feature Engineering, Optical Flow Estimation, Video Classification
Published	2019-03-06
URL	https://arxiv.org/abs/1903.02306v3
PDF	https://arxiv.org/pdf/1903.02306v3.pdf
PWC	https://paperswithcode.com/paper/video-based-surgical-skill-assessment-using
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One-to-X analogical reasoning on word embeddings: a case for diachronic armed conflict prediction from news texts


Title	One-to-X analogical reasoning on word embeddings: a case for diachronic armed conflict prediction from news texts
Authors	Andrey Kutuzov, Erik Velldal, Lilja Øvrelid
Abstract	We extend the well-known word analogy task to a one-to-X formulation, including one-to-none cases, when no correct answer exists. The task is cast as a relation discovery problem and applied to historical armed conflicts datasets, attempting to predict new relations of type `location:armed-group’ based on data about past events. As the source of semantic information, we use diachronic word embedding models trained on English news texts. A simple technique to improve diachronic performance in such task is demonstrated, using a threshold based on a function of cosine distance to decrease the number of false positives; this approach is shown to be beneficial on two different corpora. Finally, we publish a ready-to-use test set for one-to-X analogy evaluation on historical armed conflicts data. \|
Tasks	Word Embeddings
Published	2019-07-29
URL	https://arxiv.org/abs/1907.12674v1
PDF	https://arxiv.org/pdf/1907.12674v1.pdf
PWC	https://paperswithcode.com/paper/one-to-x-analogical-reasoning-on-word-1
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When is ACL’s Deadline? A Scientific Conversational Agent


Title	When is ACL’s Deadline? A Scientific Conversational Agent
Authors	Mohsen Mesgar, Paul Youssef, Lin Li, Dominik Bierwirth, Yihao Li, Christian M. Meyer, Iryna Gurevych
Abstract	Our conversational agent UKP-ATHENA assists NLP researchers in finding and exploring scientific literature, identifying relevant authors, planning or post-processing conference visits, and preparing paper submissions using a unified interface based on natural language inputs and responses. UKP-ATHENA enables new access paths to our swiftly evolving research area with its massive amounts of scientific information and high turnaround times. UKP-ATHENA’s responses connect information from multiple heterogeneous sources which researchers currently have to explore manually one after another. Unlike a search engine, UKP-ATHENA maintains the context of a conversation to allow for efficient information access on papers, researchers, and conferences. Our architecture consists of multiple components with reference implementations that can be easily extended by new skills and domains. Our user-based evaluation shows that UKP-ATHENA already responds 45% of different formulations of defined intents with 37% information coverage rate.
Tasks
Published	2019-11-23
URL	https://arxiv.org/abs/1911.10392v1
PDF	https://arxiv.org/pdf/1911.10392v1.pdf
PWC	https://paperswithcode.com/paper/when-is-acls-deadline-a-scientific
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Framework

Relational Memory-based Knowledge Graph Embedding


Title	Relational Memory-based Knowledge Graph Embedding
Authors	Dai Quoc Nguyen, Tu Dinh Nguyen, Dinh Phung
Abstract	Knowledge graph embedding models often suffer from a limitation of remembering existing triples to predict new triples. To overcome this issue, we introduce a novel embedding model, named R-MeN, that explores a relational memory network to model relationship triples. In R-MeN, we simply represent each triple as a sequence of 3 input vectors which recurrently interact with a relational memory. This memory network is constructed to incorporate new information using a self-attention mechanism over the memory and input vectors to return a corresponding output vector for every timestep. Consequently, we obtain 3 output vectors which are then multiplied element-wisely into a single one; and finally, we feed this vector to a linear neural layer to produce a scalar score for the triple. Experimental results show that our proposed R-MeN obtains state-of-the-art results on two well-known benchmark datasets WN11 and FB13 for triple classification task.
Tasks	Graph Embedding, Knowledge Graph Embedding
Published	2019-07-13
URL	https://arxiv.org/abs/1907.06080v1
PDF	https://arxiv.org/pdf/1907.06080v1.pdf
PWC	https://paperswithcode.com/paper/relational-memory-based-knowledge-graph
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A Deep Framework for Bone Age Assessment based on Finger Joint Localization


Title	A Deep Framework for Bone Age Assessment based on Finger Joint Localization
Authors	Xiaoman Zhang, Ziyuan Zhao, Cen Chen, Songyou Peng, Min Wu, Zhongyao Cheng, Singee Teo, Le Zhang, Zeng Zeng
Abstract	Bone age assessment is an important clinical trial to measure skeletal child maturity and diagnose of growth disorders. Conventional approaches such as the Tanner-Whitehouse (TW) and Greulich and Pyle (GP) may not perform well due to their large inter-observer and intra-observer variations. In this paper, we propose a finger joint localization strategy to filter out most non-informative parts of images. When combining with the conventional full image-based deep network, we observe a much-improved performance. % Our approach utilizes full hand and specific joints images for skeletal maturity prediction. In this study, we applied powerful deep neural network and explored a process in the forecast of skeletal bone age with the specifically combine joints images to increase the performance accuracy compared with the whole hand images.
Tasks
Published	2019-05-07
URL	https://arxiv.org/abs/1905.13124v2
PDF	https://arxiv.org/pdf/1905.13124v2.pdf
PWC	https://paperswithcode.com/paper/190513124
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Learning-based Resource Optimization in Ultra Reliable Low Latency HetNets


Title	Learning-based Resource Optimization in Ultra Reliable Low Latency HetNets
Authors	Mohammad Yousefvand, Kenza Hamidouche, Narayan B. Mandayam
Abstract	In this paper, the problems of user offloading and resource optimization are jointly addressed to support ultra-reliable and low latency communications (URLLC) in HetNets. In particular, a multi-tier network with a single macro base station (MBS) and multiple overlaid small cell base stations (SBSs) is considered that includes users with different latency and reliability constraints. Modeling the latency and reliability constraints of users with probabilistic guarantees, the joint problem of user offloading and resource allocation (JUR) in a URLLC setting is formulated as an optimization problem to minimize the cost of serving users for the MBS. In the considered scheme, SBSs bid to serve URLLC users under their coverage at a given price, and the MBS decides whether to serve each user locally or to offload it to one of the overlaid SBSs. Since the JUR optimization is NP-hard, we propose a low complexity learning-based heuristic method (LHM) which includes a support vector machine-based user association model and a convex resource optimization (CRO) algorithm. To further reduce the delay, we propose an alternating direction method of multipliers (ADMM)-based solution to the CRO problem. Simulation results show that using LHM, the MBS significantly decreases the spectrum access delay for users (by $\sim$ 93%) as compared to JUR, while also reducing its bandwidth and power costs in serving users (by $\sim$ 33%) as compared to directly serving users without offloading.
Tasks
Published	2019-05-12
URL	https://arxiv.org/abs/1905.04788v1
PDF	https://arxiv.org/pdf/1905.04788v1.pdf
PWC	https://paperswithcode.com/paper/learning-based-resource-optimization-in-ultra
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Generative Adversarial Networks for Financial Trading Strategies Fine-Tuning and Combination


Title	Generative Adversarial Networks for Financial Trading Strategies Fine-Tuning and Combination
Authors	Adriano Koshiyama, Nick Firoozye, Philip Treleaven
Abstract	Systematic trading strategies are algorithmic procedures that allocate assets aiming to optimize a certain performance criterion. To obtain an edge in a highly competitive environment, the analyst needs to proper fine-tune its strategy, or discover how to combine weak signals in novel alpha creating manners. Both aspects, namely fine-tuning and combination, have been extensively researched using several methods, but emerging techniques such as Generative Adversarial Networks can have an impact into such aspects. Therefore, our work proposes the use of Conditional Generative Adversarial Networks (cGANs) for trading strategies calibration and aggregation. To this purpose, we provide a full methodology on: (i) the training and selection of a cGAN for time series data; (ii) how each sample is used for strategies calibration; and (iii) how all generated samples can be used for ensemble modelling. To provide evidence that our approach is well grounded, we have designed an experiment with multiple trading strategies, encompassing 579 assets. We compared cGAN with an ensemble scheme and model validation methods, both suited for time series. Our results suggest that cGANs are a suitable alternative for strategies calibration and combination, providing outperformance when the traditional techniques fail to generate any alpha.
Tasks	Calibration, Time Series
Published	2019-01-07
URL	http://arxiv.org/abs/1901.01751v3
PDF	http://arxiv.org/pdf/1901.01751v3.pdf
PWC	https://paperswithcode.com/paper/generative-adversarial-networks-for-financial
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Deep reinforcement learning for scheduling in large-scale networked control systems


Title	Deep reinforcement learning for scheduling in large-scale networked control systems
Authors	Adrian Redder, Arunselvan Ramaswamy, Daniel E. Quevedo
Abstract	This work considers the problem of control and resource scheduling in networked systems. We present DIRA, a Deep reinforcement learning based Iterative Resource Allocation algorithm, which is scalable and control-aware. Our algorithm is tailored towards large-scale problems where control and scheduling need to act jointly to optimize performance. DIRA can be used to schedule general time-domain optimization based controllers. In the present work, we focus on control designs based on suitably adapted linear quadratic regulators. We apply our algorithm to networked systems with correlated fading communication channels. Our simulations show that DIRA scales well to large scheduling problems.
Tasks
Published	2019-05-15
URL	https://arxiv.org/abs/1905.05992v2
PDF	https://arxiv.org/pdf/1905.05992v2.pdf
PWC	https://paperswithcode.com/paper/deep-reinforcement-learning-for-scheduling-in
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Framework

A Comparative Analysis of Android Malware


Title	A Comparative Analysis of Android Malware
Authors	Neeraj Chavan, Fabio Di Troia, Mark Stamp
Abstract	In this paper, we present a comparative analysis of benign and malicious Android applications, based on static features. In particular, we focus our attention on the permissions requested by an application. We consider both binary classification of malware versus benign, as well as the multiclass problem, where we classify malware samples into their respective families. Our experiments are based on substantial malware datasets and we employ a wide variety of machine learning techniques, including decision trees and random forests, support vector machines, logistic model trees, AdaBoost, and artificial neural networks. We find that permissions are a strong feature and that by careful feature engineering, we can significantly reduce the number of features needed for highly accurate detection and classification.
Tasks	Feature Engineering
Published	2019-01-21
URL	http://arxiv.org/abs/1904.00735v1
PDF	http://arxiv.org/pdf/1904.00735v1.pdf
PWC	https://paperswithcode.com/paper/a-comparative-analysis-of-android-malware
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CNN-based Dual-Chain Models for Knowledge Graph Learning


Title	CNN-based Dual-Chain Models for Knowledge Graph Learning
Authors	Bo Peng, Renqiang Min, Xia Ning
Abstract	Knowledge graph learning plays a critical role in integrating domain specific knowledge bases when deploying machine learning and data mining models in practice. Existing methods on knowledge graph learning primarily focus on modeling the relations among entities as translations among the relations and entities, and many of these methods are not able to handle zero-shot problems, when new entities emerge. In this paper, we present a new convolutional neural network (CNN)-based dual-chain model. Different from translation based methods, in our model, interactions among relations and entities are directly captured via CNN over their embeddings. Moreover, a secondary chain of learning is conducted simultaneously to incorporate additional information and to enable better performance. We also present an extension of this model, which incorporates descriptions of entities and learns a second set of entity embeddings from the descriptions. As a result, the extended model is able to effectively handle zero-shot problems. We conducted comprehensive experiments, comparing our methods with 15 methods on 8 benchmark datasets. Extensive experimental results demonstrate that our proposed methods achieve or outperform the state-of-the-art results on knowledge graph learning, and outperform other methods on zero-shot problems. In addition, our methods applied to real-world biomedical data are able to produce results that conform to expert domain knowledge.
Tasks	Entity Embeddings
Published	2019-11-15
URL	https://arxiv.org/abs/1911.06910v2
PDF	https://arxiv.org/pdf/1911.06910v2.pdf
PWC	https://paperswithcode.com/paper/cnn-based-dual-chain-models-for-knowledge
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