January 26, 2020

3345 words 16 mins read

Paper Group ANR 1458

CAMLPAD: Cybersecurity Autonomous Machine Learning Platform for Anomaly Detection. Why Does a Visual Question Have Different Answers?. Semi-Automatic Crowdsourcing Tool for Online Food Image Collection and Annotation. Accelerated Neural Networks on OpenCL Devices Using SYCL-DNN. Election Manipulation on Social Networks: Seeding, Edge Removal, Edge …

CAMLPAD: Cybersecurity Autonomous Machine Learning Platform for Anomaly Detection


Title	CAMLPAD: Cybersecurity Autonomous Machine Learning Platform for Anomaly Detection
Authors	Ayush Hariharan, Ankit Gupta, Trisha Pal
Abstract	As machine learning and cybersecurity continue to explode in the context of the digital ecosystem, the complexity of cybersecurity data combined with complicated and evasive machine learning algorithms leads to vast difficulties in designing an end to end system for intelligent, automatic anomaly classification. On the other hand, traditional systems use elementary statistics techniques and are often inaccurate, leading to weak centralized data analysis platforms. In this paper, we propose a novel system that addresses these two problems, titled CAMLPAD, for Cybersecurity Autonomous Machine Learning Platform for Anomaly Detection. The CAMLPAD systems streamlined, holistic approach begins with retrieving a multitude of different species of cybersecurity data in real time using elasticsearch, then running several machine learning algorithms, namely Isolation Forest, Histogram Based Outlier Score (HBOS), Cluster Based Local Outlier Factor (CBLOF), and K Means Clustering, to process the data. Next, the calculated anomalies are visualized using Kibana and are assigned an outlier score, which serves as an indicator for whether an alert should be sent to the system administrator that there are potential anomalies in the network. After comprehensive testing of our platform in a simulated environment, the CAMLPAD system achieved an adjusted rand score of 95 percent, exhibiting the reliable accuracy and precision of the system. All in all, the CAMLPAD system provides an accurate, streamlined approach to real time cybersecurity anomaly detection, delivering a novel solution that has the potential to revolutionize the cybersecurity sector.
Tasks	Anomaly Detection
Published	2019-07-23
URL	https://arxiv.org/abs/1907.10442v1
PDF	https://arxiv.org/pdf/1907.10442v1.pdf
PWC	https://paperswithcode.com/paper/camlpad-cybersecurity-autonomous-machine
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Why Does a Visual Question Have Different Answers?


Title	Why Does a Visual Question Have Different Answers?
Authors	Nilavra Bhattacharya, Qing Li, Danna Gurari
Abstract	Visual question answering is the task of returning the answer to a question about an image. A challenge is that different people often provide different answers to the same visual question. To our knowledge, this is the first work that aims to understand why. We propose a taxonomy of nine plausible reasons, and create two labelled datasets consisting of ~45,000 visual questions indicating which reasons led to answer differences. We then propose a novel problem of predicting directly from a visual question which reasons will cause answer differences as well as a novel algorithm for this purpose. Experiments demonstrate the advantage of our approach over several related baselines on two diverse datasets. We publicly share the datasets and code at https://vizwiz.org.
Tasks	Question Answering, Visual Question Answering
Published	2019-08-12
URL	https://arxiv.org/abs/1908.04342v2
PDF	https://arxiv.org/pdf/1908.04342v2.pdf
PWC	https://paperswithcode.com/paper/why-does-a-visual-question-have-different
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Semi-Automatic Crowdsourcing Tool for Online Food Image Collection and Annotation


Title	Semi-Automatic Crowdsourcing Tool for Online Food Image Collection and Annotation
Authors	Zeman Shao, Runyu Mao, Fengqing Zhu
Abstract	Assessing dietary intake accurately remains an open and challenging research problem. In recent years, image-based approaches have been developed to automatically estimate food intake by capturing eat occasions with mobile devices and wearable cameras. To build a reliable machine-learning models that can automatically map pixels to calories, successful image-based systems need large collections of food images with high quality groundtruth labels to improve the learned models. In this paper, we introduce a semi-automatic system for online food image collection and annotation. Our system consists of a web crawler, an automatic food detection method and a web-based crowdsoucing tool. The web crawler is used to download large sets of online food images based on the given food labels. Since not all retrieved images contain foods, we introduce an automatic food detection method to remove irrelevant images. We designed a web-based crowdsourcing tool to assist the crowd or human annotators to locate and label all the foods in the images. The proposed semi-automatic online food image collection system can be used to build large food image datasets with groundtruth labels efficiently from scratch.
Tasks
Published	2019-10-11
URL	https://arxiv.org/abs/1910.05242v2
PDF	https://arxiv.org/pdf/1910.05242v2.pdf
PWC	https://paperswithcode.com/paper/semi-automatic-crowdsourcing-tool-for-online
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Accelerated Neural Networks on OpenCL Devices Using SYCL-DNN


Title	Accelerated Neural Networks on OpenCL Devices Using SYCL-DNN
Authors	Rod Burns, John Lawson, Duncan McBain, Daniel Soutar
Abstract	Over the past few years machine learning has seen a renewed explosion of interest, following a number of studies showing the effectiveness of neural networks in a range of tasks which had previously been considered incredibly hard. Neural networks’ effectiveness in the fields of image recognition and natural language processing stems primarily from the vast amounts of data available to companies and researchers, coupled with the huge amounts of compute power available in modern accelerators such as GPUs, FPGAs and ASICs. There are a number of approaches available to developers for utilizing GPGPU technologies such as SYCL, OpenCL and CUDA, however many applications require the same low level mathematical routines. Libraries dedicated to accelerating these common routines allow developers to easily make full use of the available hardware without requiring low level knowledge of the hardware themselves, however such libraries are often provided by hardware manufacturers for specific hardware such as cuDNN for Nvidia hardware or MIOpen for AMD hardware. SYCL-DNN is a new open-source library dedicated to providing accelerated routines for neural network operations which are hardware and vendor agnostic. Built on top of the SYCL open standard and written entirely in standard C++, SYCL-DNN allows a user to easily accelerate neural network code for a wide range of hardware using a modern C++ interface. The library is tested on AMD’s OpenCL for GPU, Intel’s OpenCL for CPU and GPU, ARM’s OpenCL for Mali GPUs as well as ComputeAorta’s OpenCL for R-Car CV engine and host CPU. In this talk we will present performance figures for SYCL-DNN on this range of hardware, and discuss how high performance was achieved on such a varied set of accelerators with such different hardware features.
Tasks
Published	2019-04-08
URL	http://arxiv.org/abs/1904.04174v1
PDF	http://arxiv.org/pdf/1904.04174v1.pdf
PWC	https://paperswithcode.com/paper/accelerated-neural-networks-on-opencl-devices
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Title	Election Manipulation on Social Networks: Seeding, Edge Removal, Edge Addition
Authors	Matteo Castiglioni, Nicola Gatti, Giulia Landriani, Diodato Ferraioli
Abstract	We focus on the election manipulation problem through social influence, where a manipulator exploits a social network to make her most preferred candidate win an election. Influence is due to information in favor of and/or against one or multiple candidates, sent by seeds and spreading through the network according to the independent cascade model. We provide a comprehensive study of the election control problem, investigating two forms of manipulations: seeding to buy influencers given a social network, and removing or adding edges in the social network given the seeds and the information sent. In particular, we study a wide range of cases distinguishing for the number of candidates or the kind of information spread over the network. Our main result is positive for democracy, and it shows that the election manipulation problem is not affordable in the worst-case except for trivial classes of instances, even when one accepts to approximate the margin of victory. In the case of seeding, we also show that the manipulation is hard even if the graph is a line and that a large class of algorithms, including most of the approaches recently adopted for social-influence problems, fail to compute a bounded approximation even on elementary networks, as undirected graphs with every node having a degree at most two or directed trees. In the case of edge removal or addition, our hardness results also apply to the basic case of social influence maximization/minimization. In contrast, the hardness of election manipulation holds even when the manipulator has an unlimited budget, being allowed to remove or add an arbitrary number of edges.
Tasks
Published	2019-11-14
URL	https://arxiv.org/abs/1911.06198v2
PDF	https://arxiv.org/pdf/1911.06198v2.pdf
PWC	https://paperswithcode.com/paper/election-control-in-social-networks-via-edge
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Distance-Guided GA-Based Approach to Distributed Data-Intensive Web Service Composition


Title	Distance-Guided GA-Based Approach to Distributed Data-Intensive Web Service Composition
Authors	Soheila Sadeghiram, Hui MA, Gang Chen
Abstract	Distributed computing which uses Web services as fundamental elements, enables high-speed development of software applications through composing many interoperating, distributed, re-usable, and autonomous services. As a fundamental challenge for service developers, service composition must fulfil functional requirements and optimise Quality of Service (QoS) attributes, simultaneously. On the other hand, huge amounts of data have been created by advances in technologies, which may be exchanged between services. Data-intensive Web services are of great interest to implement data-intensive processes. However, current approaches to Web service composition have omitted either the effect of data, or the distribution of services. Evolutionary Computing (EC) techniques allow for the creation of compositions that meet all the above factors. In this paper, we will develop Genetic Algorithm (GA)-based approach for solving the problem of distributed data-intensive Web service composition (DWSC). In particular, we will introduce two new heuristics, i.e. Longest Common Subsequence(LCS) distance of services, in designing crossover operators. Additionally, a new local search technique incorporating distance of services will be proposed.
Tasks
Published	2019-01-16
URL	http://arxiv.org/abs/1901.05564v1
PDF	http://arxiv.org/pdf/1901.05564v1.pdf
PWC	https://paperswithcode.com/paper/distance-guided-ga-based-approach-to
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Online Inference and Detection of Curbs in Partially Occluded Scenes with Sparse LIDAR


Title	Online Inference and Detection of Curbs in Partially Occluded Scenes with Sparse LIDAR
Authors	Tarlan Suleymanov, Lars Kunze, Paul Newman
Abstract	Road boundaries, or curbs, provide autonomous vehicles with essential information when interpreting road scenes and generating behaviour plans. Although curbs convey important information, they are difficult to detect in complex urban environments (in particular in comparison to other elements of the road such as traffic signs and road markings). These difficulties arise from occlusions by other traffic participants as well as changing lighting and/or weather conditions. Moreover, road boundaries have various shapes, colours and structures while motion planning algorithms require accurate and precise metric information in real-time to generate their plans. In this paper, we present a real-time LIDAR-based approach for accurate curb detection around the vehicle (360 degree). Our approach deals with both occlusions from traffic and changing environmental conditions. To this end, we project 3D LIDAR pointcloud data into 2D bird’s-eye view images (akin to Inverse Perspective Mapping). These images are then processed by trained deep networks to infer both visible and occluded road boundaries. Finally, a post-processing step filters detected curb segments and tracks them over time. Experimental results demonstrate the effectiveness of the proposed approach on real-world driving data. Hence, we believe that our LIDAR-based approach provides an efficient and effective way to detect visible and occluded curbs around the vehicles in challenging driving scenarios.
Tasks	Autonomous Vehicles, Motion Planning
Published	2019-07-11
URL	https://arxiv.org/abs/1907.05375v1
PDF	https://arxiv.org/pdf/1907.05375v1.pdf
PWC	https://paperswithcode.com/paper/online-inference-and-detection-of-curbs-in
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UW-BHI at MEDIQA 2019: An Analysis of Representation Methods for Medical Natural Language Inference


Title	UW-BHI at MEDIQA 2019: An Analysis of Representation Methods for Medical Natural Language Inference
Authors	William R. Kearns, Wilson Lau, Jason A. Thomas
Abstract	Recent advances in distributed language modeling have led to large performance increases on a variety of natural language processing (NLP) tasks. However, it is not well understood how these methods may be augmented by knowledge-based approaches. This paper compares the performance and internal representation of an Enhanced Sequential Inference Model (ESIM) between three experimental conditions based on the representation method: Bidirectional Encoder Representations from Transformers (BERT), Embeddings of Semantic Predications (ESP), or Cui2Vec. The methods were evaluated on the Medical Natural Language Inference (MedNLI) subtask of the MEDIQA 2019 shared task. This task relied heavily on semantic understanding and thus served as a suitable evaluation set for the comparison of these representation methods.
Tasks	Language Modelling, Natural Language Inference
Published	2019-07-09
URL	https://arxiv.org/abs/1907.04286v1
PDF	https://arxiv.org/pdf/1907.04286v1.pdf
PWC	https://paperswithcode.com/paper/uw-bhi-at-mediqa-2019-an-analysis-of
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Representation, Exploration and Recommendation of Music Playlists


Title	Representation, Exploration and Recommendation of Music Playlists
Authors	Piyush Papreja, Hemanth Venkateswara, Sethuraman Panchanathan
Abstract	Playlists have become a significant part of our listening experience because of the digital cloud-based services such as Spotify, Pandora, Apple Music. Owing to the meteoric rise in the usage of playlists, recommending playlists is crucial to music services today. Although there has been a lot of work done in playlist prediction, the area of playlist representation hasn’t received that level of attention. Over the last few years, sequence-to-sequence models, especially in the field of natural language processing, have shown the effectiveness of learned embeddings in capturing the semantic characteristics of sequences. We can apply similar concepts to music to learn fixed length representations for playlists and use those representations for downstream tasks such as playlist discovery, browsing, and recommendation. In this work, we formulate the problem of learning a fixed-length playlist representation in an unsupervised manner, using Sequence-to-sequence (Seq2seq) models, interpreting playlists as sentences and songs as words. We compare our model with two other encoding architectures for baseline comparison. We evaluate our work using the suite of tasks commonly used for assessing sentence embeddings, along with a few additional tasks pertaining to music, and a recommendation task to study the traits captured by the playlist embeddings and their effectiveness for the purpose of music recommendation.
Tasks	Sentence Embeddings
Published	2019-07-01
URL	https://arxiv.org/abs/1907.01098v1
PDF	https://arxiv.org/pdf/1907.01098v1.pdf
PWC	https://paperswithcode.com/paper/representation-exploration-and-recommendation
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Understanding urban landuse from the above and ground perspectives: a deep learning, multimodal solution


Title	Understanding urban landuse from the above and ground perspectives: a deep learning, multimodal solution
Authors	Shivangi Srivastava, John E. Vargas-Muñoz, Devis Tuia
Abstract	Landuse characterization is important for urban planning. It is traditionally performed with field surveys or manual photo interpretation, two practices that are time-consuming and labor-intensive. Therefore, we aim to automate landuse mapping at the urban-object level with a deep learning approach based on data from multiple sources (or modalities). We consider two image modalities: overhead imagery from Google Maps and ensembles of ground-based pictures (side-views) per urban-object from Google Street View (GSV). These modalities bring complementary visual information pertaining to the urban-objects. We propose an end-to-end trainable model, which uses OpenStreetMap annotations as labels. The model can accommodate a variable number of GSV pictures for the ground-based branch and can also function in the absence of ground pictures at prediction time. We test the effectiveness of our model over the area of ^Ile-de-France, France, and test its generalization abilities on a set of urban-objects from the city of Nantes, France. Our proposed multimodal Convolutional Neural Network achieves considerably higher accuracies than methods that use a single image modality, making it suitable for automatic landuse map updates. Additionally, our approach could be easily scaled to multiple cities, because it is based on data sources available for many cities worldwide.
Tasks
Published	2019-05-05
URL	https://arxiv.org/abs/1905.01752v1
PDF	https://arxiv.org/pdf/1905.01752v1.pdf
PWC	https://paperswithcode.com/paper/understanding-urban-landuse-from-the-above
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One-Shot Induction of Generalized Logical Concepts via Human Guidance


Title	One-Shot Induction of Generalized Logical Concepts via Human Guidance
Authors	Mayukh Das, Nandini Ramanan, Janardhan Rao Doppa, Sriraam Natarajan
Abstract	We consider the problem of learning generalized first-order representations of concepts from a single example. To address this challenging problem, we augment an inductive logic programming learner with two novel algorithmic contributions. First, we define a distance measure between candidate concept representations that improves the efficiency of search for target concept and generalization. Second, we leverage richer human inputs in the form of advice to improve the sample-efficiency of learning. We prove that the proposed distance measure is semantically valid and use that to derive a PAC bound. Our experimental analysis on diverse concept learning tasks demonstrates both the effectiveness and efficiency of the proposed approach over a first-order concept learner using only examples.
Tasks
Published	2019-12-15
URL	https://arxiv.org/abs/1912.07060v1
PDF	https://arxiv.org/pdf/1912.07060v1.pdf
PWC	https://paperswithcode.com/paper/one-shot-induction-of-generalized-logical
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Ultrasound segmentation using U-Net: learning from simulated data and testing on real data


Title	Ultrasound segmentation using U-Net: learning from simulated data and testing on real data
Authors	Bahareh Behboodi, Hassan Rivaz
Abstract	Segmentation of ultrasound images is an essential task in both diagnosis and image-guided interventions given the ease-of-use and low cost of this imaging modality. As manual segmentation is tedious and time consuming, a growing body of research has focused on the development of automatic segmentation algorithms. Deep learning algorithms have shown remarkable achievements in this regard; however, they need large training datasets. Unfortunately, preparing large labeled datasets in ultrasound images is prohibitively difficult. Therefore, in this study, we propose the use of simulated ultrasound (US) images for training the U-Net deep learning segmentation architecture and test on tissue-mimicking phantom data collected by an ultrasound machine. We demonstrate that the trained architecture on the simulated data is transferrable to real data, and therefore, simulated data can be considered as an alternative training dataset when real datasets are not available. The second contribution of this paper is that we train our U- Net network on envelope and B-mode images of the simulated dataset, and test the trained network on real envelope and B- mode images of phantom, respectively. We show that test results are superior for the envelope data compared to B-mode image.
Tasks
Published	2019-04-24
URL	http://arxiv.org/abs/1904.11031v1
PDF	http://arxiv.org/pdf/1904.11031v1.pdf
PWC	https://paperswithcode.com/paper/190411031
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Deep Classification Network for Monocular Depth Estimation


Title	Deep Classification Network for Monocular Depth Estimation
Authors	Azeez Oluwafemi, Yang Zou, B. V. K. Vijaya Kumar
Abstract	Monocular Depth Estimation is usually treated as a supervised and regression problem when it actually is very similar to semantic segmentation task since they both are fundamentally pixel-level classification tasks. We applied depth increments that increases with depth in discretizing depth values and then applied Deeplab v2 and the result was higher accuracy. We were able to achieve a state-of-the-art result on the KITTI dataset and outperformed existing architecture by an 8% margin.
Tasks	Depth Estimation, Monocular Depth Estimation, Semantic Segmentation
Published	2019-10-23
URL	https://arxiv.org/abs/1910.10369v1
PDF	https://arxiv.org/pdf/1910.10369v1.pdf
PWC	https://paperswithcode.com/paper/deep-classification-network-for-monocular
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Analyzing the benefits of communication channels between deep learning models


Title	Analyzing the benefits of communication channels between deep learning models
Authors	Philippe Lacaille
Abstract	As artificial intelligence systems spread to more diverse and larger tasks in many domains, the machine learning algorithms, and in particular the deep learning models and the databases required to train them are getting bigger themselves. Some algorithms do allow for some scaling of large computations by leveraging data parallelism. However, they often require a large amount of data to be exchanged in order to ensure the shared knowledge throughout the compute nodes is accurate. In this work, the effect of different levels of communications between deep learning models is studied, in particular how it affects performance. The first approach studied looks at decentralizing the numerous computations that are done in parallel in training procedures such as synchronous and asynchronous stochastic gradient descent. In this setting, a simplified communication that consists of exchanging low bandwidth outputs between compute nodes can be beneficial. In the following chapter, the communication protocol is slightly modified to further include training instructions. Indeed, this is studied in a simplified setup where a pre-trained model, analogous to a teacher, can customize a randomly initialized model’s training procedure to accelerate learning. Finally, a communication channel where two deep learning models can exchange a purposefully crafted language is explored while allowing for different ways of optimizing that language.
Tasks
Published	2019-04-19
URL	http://arxiv.org/abs/1904.09211v1
PDF	http://arxiv.org/pdf/1904.09211v1.pdf
PWC	https://paperswithcode.com/paper/analyzing-the-benefits-of-communication
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Reducing Anomaly Detection in Images to Detection in Noise


Title	Reducing Anomaly Detection in Images to Detection in Noise
Authors	Axel Davy, Thibaud Ehret, Jean-Michel Morel, Mauricio Delbracio
Abstract	Anomaly detectors address the difficult problem of detecting automatically exceptions in an arbitrary background image. Detection methods have been proposed by the thousands because each problem requires a different background model. By analyzing the existing approaches, we show that the problem can be reduced to detecting anomalies in residual images (extracted from the target image) in which noise and anomalies prevail. Hence, the general and impossible background modeling problem is replaced by simpler noise modeling, and allows the calculation of rigorous thresholds based on the a contrario detection theory. Our approach is therefore unsupervised and works on arbitrary images.
Tasks	Anomaly Detection
Published	2019-04-25
URL	http://arxiv.org/abs/1904.11276v1
PDF	http://arxiv.org/pdf/1904.11276v1.pdf
PWC	https://paperswithcode.com/paper/reducing-anomaly-detection-in-images-to
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