OpenRaiser/Intern-Atlas huggingface dataset:
DIFET: Distributed Feature Extraction Tool For High Spatial Resolution Remote Sensing Images
Venue: arXiv cs.DC 2018
Year: 2018

In this paper, we propose distributed feature extraction tool from high spatial resolution remote sensing images. Tool is based on Apache Hadoop framework and Hadoop Image Processing Interface. Two corner detection (Harris and Shi-Tomasi) algorithms and five feature descriptors (SIFT, SURF, FAST, BRIEF, and ORB) are considered. Robustness of the tool in the task of feature extraction from LandSat-8 imageries are evaluated in terms of horizontal scalability.
Big Data Privacy Context: Literature Effects On Secure Informational Assets
Venue: arXiv cs.CY 2018
Year: 2018

This article's objective is the identification of research opportunities in the current big data privacy domain, evaluating literature effects on secure informational assets. Until now, no study has analyzed such relation. Its results can foster science, technologies and businesses. To achieve these objectives, a big data privacy Systematic Literature Review (SLR) is performed on the main scientific peer reviewed journals in Scopus database. Bibliometrics and text mining analysis complement the SLR. This study provides support to big data privacy researchers on: most and least researched themes, research novelty, most cited works and authors, themes evolution through time and many others. In addition, TOPSIS and VIKOR ranks were developed to evaluate literature effects versus informational assets indicators. Secure Internet Servers (SIS) was chosen as decision criteria. Results show that big data privacy literature is strongly focused on computational aspects. However, individuals, societies, organizations and governments face a technological change that has just started to be investigated, with growing concerns on law and regulation aspects. TOPSIS and VIKOR Ranks differed in several positions and the only consistent country between literature and SIS adoption is the United States. Countries in the lowest ranking positions represent future research opportunities.
Label and Sample: Efficient Training of Vehicle Object Detector from Sparsely Labeled Data
Venue: arXiv cs.CV 2018
Year: 2018

Self-driving vehicle vision systems must deal with an extremely broad and challenging set of scenes. They can potentially exploit an enormous amount of training data collected from vehicles in the field, but the volumes are too large to train offline naively. Not all training instances are equally valuable though, and importance sampling can be used to prioritize which training images to collect. This approach assumes that objects in images are labeled with high accuracy. To generate accurate labels in the field, we exploit the spatio-temporal coherence of vehicle video. We use a near-to-far labeling strategy by first labeling large, close objects in the video, and tracking them back in time to induce labels on small distant presentations of those objects. In this paper we demonstrate the feasibility of this approach in several steps. First, we note that an optimal subset (relative to all the objects encountered and labeled) of labeled objects in images can be obtained by importance sampling using gradients of the recognition network. Next we show that these gradients can be approximated with very low error using the loss function, which is already available when the CNN is running inference. Then, we generalize these results to objects in a larger scene using an object detection system. Finally, we describe a self-labeling scheme using object tracking. Objects are tracked back in time (near-to-far) and labels of near objects are used to check accuracy of those objects in the far field. We then evaluate the accuracy of models trained on importance sampled data vs models trained on complete data.
Scientific Relation Extraction with Selectively Incorporated Concept Embeddings
Venue: arXiv cs.IR 2018
Year: 2018

This paper describes our submission for the SemEval 2018 Task 7 shared task on semantic relation extraction and classification in scientific papers. We extend the end-to-end relation extraction model of (Miwa and Bansal) with enhancements such as a character-level encoding attention mechanism on selecting pretrained concept candidate embeddings. Our official submission ranked the second in relation classification task (Subtask 1.1 and Subtask 2 Senerio 2), and the first in the relation extraction task (Subtask 2 Scenario 1).
Localized solar power prediction based on weather data from local history and global forecasts
Venue: arXiv cs.SY 2018
Year: 2018

With the recent interest in net-zero sustainability for commercial buildings, integration of photovoltaic (PV) assets becomes even more important. This integration remains a challenge due to high solar variability and uncertainty in the prediction of PV output. Most existing methods predict PV output using either local power/weather history or global weather forecasts, thereby ignoring either the impending global phenomena or the relevant local characteristics, respectively. This work proposes to leverage weather data from both local weather history and global forecasts based on time series modeling with exogenous inputs. The proposed model results in eighteen hour ahead forecasts with a mean accuracy of $\approx$ 80\% and uses data from the National Ocean and Atmospheric Administration's (NOAA) High-Resolution Rapid Refresh (HRRR) model.
Exploring the Applications of Faster R-CNN and Single-Shot Multi-box Detection in a Smart Nursery Domain
Venue: arXiv cs.CV 2018
Year: 2018

The ultimate goal of a baby detection task concerns detecting the presence of a baby and other objects in a sequence of 2D images, tracking them and understanding the semantic contents of the scene. Recent advances in deep learning and computer vision offer various powerful tools in general object detection and can be applied to a baby detection task. In this paper, the Faster Region-based Convolutional Neural Network and the Single-Shot Multi-Box Detection approaches are explored. They are the two state-of-the-art object detectors based on the region proposal tactic and the multi-box tactic. The presence of a baby in the scene obtained from these detectors, tested using different pre-trained models, are discussed. This study is important since the behaviors of these detectors in a baby detection task using different pre-trained models are still not well understood. This exploratory study reveals many useful insights into the applications of these object detectors in the smart nursery domain.
Augmenting Bottleneck Features of Deep Neural Network Employing Motor State for Speech Recognition at Humanoid Robots
Venue: arXiv cs.SD 2018
Year: 2018

As for the humanoid robots, the internal noise, which is generated by motors, fans and mechanical components when the robot is moving or shaking its body, severely degrades the performance of the speech recognition accuracy. In this paper, a novel speech recognition system robust to ego-noise for humanoid robots is proposed, in which on/off state of the motor is employed as auxiliary information for finding the relevant input features. For this, we consider the bottleneck features, which have been successfully applied to deep neural network (DNN) based automatic speech recognition (ASR) system. When learning the bottleneck features to catch, we first exploit the motor on/off state data as supplementary information in addition to the acoustic features as the input of the first deep neural network (DNN) for preliminary acoustic modeling. Then, the second DNN for primary acoustic modeling employs both the bottleneck features tossed from the first DNN and the acoustics features. When the proposed method is evaluated in terms of phoneme error rate (PER) on TIMIT database, the experimental results show that achieve obvious improvement (11% relative) is achieved by our algorithm over the conventional systems.
Adaptive Structural Learning of Deep Belief Network for Medical Examination Data and Its Knowledge Extraction by using C4.5
Venue: arXiv cs.NE 2018
Year: 2018

Deep Learning has a hierarchical network architecture to represent the complicated feature of input patterns. The adaptive structural learning method of Deep Belief Network (DBN) has been developed. The method can discover an optimal number of hidden neurons for given input data in a Restricted Boltzmann Machine (RBM) by neuron generation-annihilation algorithm, and generate a new hidden layer in DBN by the extension of the algorithm. In this paper, the proposed adaptive structural learning of DBN was applied to the comprehensive medical examination data for the cancer prediction. The prediction system shows higher classification accuracy (99.8% for training and 95.5% for test) than the traditional DBN. Moreover, the explicit knowledge with respect to the relation between input and output patterns was extracted from the trained DBN network by C4.5. Some characteristics extracted in the form of IF-THEN rules to find an initial cancer at the early stage were reported in this paper.
A Directed Information Learning Framework for Event-Driven M2M Traffic Prediction
Venue: arXiv cs.IT 2018
Year: 2018

Burst of transmissions stemming from event-driven traffic in machine type communication (MTC) can lead to congestion of random access resources, packet collisions, and long delays. In this paper, a directed information (DI) learning framework is proposed to predict the source traffic in event-driven MTC. By capturing the history of transmissions during past events by a sequence of binary random variables, the DI between different machine type devices (MTDs) is calculated and used for predicting the set of possible MTDs that are likely to report an event. Analytical and simulation results show that the proposed DI learning method can reveal the correlation between transmission from different MTDs that report the same event, and the order in which they transmit their data. The proposed algorithm and the presented results show that the DI can be used to implement effective predictive resource allocation for event-driven MTC.
Data Poisoning Attacks against Online Learning
Venue: arXiv cs.LG 2018
Year: 2018

We consider data poisoning attacks, a class of adversarial attacks on machine learning where an adversary has the power to alter a small fraction of the training data in order to make the trained classifier satisfy certain objectives. While there has been much prior work on data poisoning, most of it is in the offline setting, and attacks for online learning, where training data arrives in a streaming manner, are not well understood. In this work, we initiate a systematic investigation of data poisoning attacks for online learning. We formalize the problem into two settings, and we propose a general attack strategy, formulated as an optimization problem, that applies to both with some modifications. We propose three solution strategies, and perform extensive experimental evaluation. Finally, we discuss the implications of our findings for building successful defenses.
Modeling and Simulation of Spark Streaming
Venue: arXiv cs.DC 2018
Year: 2018

As more and more devices connect to Internet of Things, unbounded streams of data will be generated, which have to be processed "on the fly" in order to trigger automated actions and deliver real-time services. Spark Streaming is a popular realtime stream processing framework. To make efficient use of Spark Streaming and achieve stable stream processing, it requires a careful interplay between different parameter configurations. Mistakes may lead to significant resource overprovisioning and bad performance. To alleviate such issues, this paper develops an executable and configurable model named SSP (stands for Spark Streaming Processing) to model and simulate Spark Streaming. SSP is written in ABS, which is a formal, executable, and object-oriented language for modeling distributed systems by means of concurrent object groups. SSP allows users to rapidly evaluate and compare different parameter configurations without deploying their applications on a cluster/cloud. The simulation results show that SSP is able to mimic Spark Streaming in different scenarios.
Natural Language Generation with Neural Variational Models
Venue: arXiv cs.CL 2018
Year: 2018

In this thesis, we explore the use of deep neural networks for generation of natural language. Specifically, we implement two sequence-to-sequence neural variational models - variational autoencoders (VAE) and variational encoder-decoders (VED). VAEs for text generation are difficult to train due to issues associated with the Kullback-Leibler (KL) divergence term of the loss function vanishing to zero. We successfully train VAEs by implementing optimization heuristics such as KL weight annealing and word dropout. We also demonstrate the effectiveness of this continuous latent space through experiments such as random sampling, linear interpolation and sampling from the neighborhood of the input. We argue that if VAEs are not designed appropriately, it may lead to bypassing connections which results in the latent space being ignored during training. We show experimentally with the example of decoder hidden state initialization that such bypassing connections degrade the VAE into a deterministic model, thereby reducing the diversity of generated sentences. We discover that the traditional attention mechanism used in sequence-to-sequence VED models serves as a bypassing connection, thereby deteriorating the model's latent space. In order to circumvent this issue, we propose the variational attention mechanism where the attention context vector is modeled as a random variable that can be sampled from a distribution. We show empirically using automatic evaluation metrics, namely entropy and distinct measures, that our variational attention model generates more diverse output sentences than the deterministic attention model. A qualitative analysis with human evaluation study proves that our model simultaneously produces sentences that are of high quality and equally fluent as the ones generated by the deterministic attention counterpart.
SD-WAN Internet Census
Venue: arXiv cs.CR 2018
Year: 2018

The concept of software defined wide area network (SD-WAN or SDWAN) is central to modern computer networking, particularly in enterprise networks. By definition, these systems form network perimeter and connect Internet, WAN, extranet, and branches that makes them crucial from cybersecurity point of view. The goal of this paper is to provide the results of passive and active fingerprinting for SD-WAN systems using a common threat intelligence approach. We explore Internet-based and cloud-based publicly available SD-WAN systems using well-known Shodan and Censys search engines and custom developed automation tools and show that most of the SD-WAN systems have known vulnerabilities related to outdated software and insecure configuration.
ParsRec: Meta-Learning Recommendations for Bibliographic Reference Parsing
Venue: arXiv cs.IR 2018
Year: 2018

Bibliographic reference parsers extract metadata (e.g. author names, title, year) from bibliographic reference strings. No reference parser consistently gives the best results in every scenario. For instance, one tool may be best in extracting titles, and another tool in extracting author names. In this paper, we address the problem of reference parsing from a recommender-systems perspective. We propose ParsRec, a meta-learning approach that recommends the potentially best parser(s) for a given reference string. We evaluate ParsRec on 105k references from chemistry. We propose two approaches to meta-learning recommendations. The first approach learns the best parser for an entire reference string. The second approach learns the best parser for each field of a reference string. The second approach achieved a 2.6% increase in F1 (0.909 vs. 0.886, p < 0.001) over the best single parser (GROBID), reducing the false positive rate by 20.2% (0.075 vs. 0.094), and the false negative rate by 18.9% (0.107 vs. 0.132).
Closed-Form Word Error Rate Analysis for Successive Interference Cancellation Decoders
Venue: arXiv cs.IT 2018
Year: 2018

We consider the estimation of an integer vector $\hbx\in \mathbb{Z}^n$ from the linear observation $\y=\A\hbx+\v$, where $\A\in\mathbb{R}^{m\times n}$ is a random matrix with independent and identically distributed (i.i.d.) standard Gaussian $\mathcal{N}(0,1)$ entries, and $\v\in \mathbb{R}^m$ is a noise vector with i.i.d. $\mathcal{N}(0,σ^2 )$ entries with given $σ$. In digital communications, $\hbx$ is typically uniformly distributed over an $n$-dimensional box $\mathcal{B}$. For this estimation problem, successive interference cancellation (SIC) decoders are popular due to their low complexity, and a detailed analysis of their word error rates (WERs) is highly useful. In this paper, we derive closed-form WER expressions for two cases: (1) $\hbx\in \mathbb{Z}^n$ is fixed and (2) $\hbx$ is uniformly distributed over $\mathcal{B}$. We also investigate some of their properties in detail and show that they agree closely with simulated word error probabilities.
Evaluating the Utility of Hand-crafted Features in Sequence Labelling
Venue: arXiv cs.CL 2018
Year: 2018

Conventional wisdom is that hand-crafted features are redundant for deep learning models, as they already learn adequate representations of text automatically from corpora. In this work, we test this claim by proposing a new method for exploiting handcrafted features as part of a novel hybrid learning approach, incorporating a feature auto-encoder loss component. We evaluate on the task of named entity recognition (NER), where we show that including manual features for part-of-speech, word shapes and gazetteers can improve the performance of a neural CRF model. We obtain a $F_1$ of 91.89 for the CoNLL-2003 English shared task, which significantly outperforms a collection of highly competitive baseline models. We also present an ablation study showing the importance of auto-encoding, over using features as either inputs or outputs alone, and moreover, show including the autoencoder components reduces training requirements to 60\%, while retaining the same predictive accuracy.
TRINITY: Coordinated Performance, Energy and Temperature Management in 3D Processor-Memory Stacks
Venue: arXiv cs.AR 2018
Year: 2018

The consistent demand for better performance has lead to innovations at hardware and microarchitectural levels. 3D stacking of memory and logic dies delivers an order of magnitude improvement in available memory bandwidth. The price paid however is, tight thermal constraints. In this paper, we study the complex multiphysics interactions between performance, energy and temperature. Using a cache coherent multicore processor cycle level simulator coupled with power and thermal estimation tools, we investigate the interactions between (a) thermal behaviors (b) compute and memory microarchitecture and (c) application workloads. The key insights from this exploration reveal the need to manage performance, energy and temperature in a coordinated fashion. Furthermore, we identify the concept of "effective heat capacity" i.e. the heat generated beyond which no further gains in performance is observed with increases in voltage-frequency of the compute logic. Subsequently, a real-time, numerical optimization based, application agnostic controller (TRINITY) is developed which intelligently manages the three parameters of interest. We observe up to $30\%$ improvement in Energy Delay$^2$ Product and up to $8$ Kelvin lower core temperatures as compared to fixed frequencies. Compared to the \texttt{ondemand} Linux CPU DVFS governor, for similar energy efficiency, TRINITY keeps the cores cooler by $6$ Kelvin which increases the lifetime reliability by up to 59\%.
Localization Guided Learning for Pedestrian Attribute Recognition
Venue: arXiv cs.CV 2018
Year: 2018

Pedestrian attribute recognition has attracted many attentions due to its wide applications in scene understanding and person analysis from surveillance videos. Existing methods try to use additional pose, part or viewpoint information to complement the global feature representation for attribute classification. However, these methods face difficulties in localizing the areas corresponding to different attributes. To address this problem, we propose a novel Localization Guided Network which assigns attribute-specific weights to local features based on the affinity between proposals pre-extracted proposals and attribute locations. The advantage of our model is that our local features are learned automatically for each attribute and emphasized by the interaction with global features. We demonstrate the effectiveness of our Localization Guided Network on two pedestrian attribute benchmarks (PA-100K and RAP). Our result surpasses the previous state-of-the-art in all five metrics on both datasets.
Random Matrices from Linear Codes and Wigner's semicircle law
Venue: arXiv cs.IT 2018
Year: 2018

In this paper we consider a new normalization of matrices obtained by choosing distinct codewords at random from linear codes over finite fields and find that under some natural algebraic conditions of the codes their empirical spectral distribution converges to Wigner's semicircle law as the length of the codes goes to infinity. One such condition is that the dual distance of the codes is at least 5. This is analogous to previous work on the empirical spectral distribution of similar matrices obtained in this fashion that converges to the Marchenko-Pastur law.
Humans and Machines can be Jointly Spatially Multiplexed by Massive MIMO
Venue: arXiv cs.IT 2018
Year: 2018

Future cellular networks are expected to support new communication paradigms such as machine-type communication (MTC) services along with conventional human-type communication (HTC) services. This requires base stations to serve a large number of devices in relatively short channel coherence intervals, which renders allocation of orthogonal pilot sequences per-device in each cell impractical. Furthermore, the stringent power constraints, place-and-play type connectivity and various data rate requirements of MTC devices make it impossible for the traditional cellular architecture to accommodate MTC and HTC services together. Massive multiple-input-multiple-output (mMIMO) technology has the potential to allow the coexistence of HTC and MTC services, thanks to its inherent spatial multiplexing properties and low transmission power requirements. In this work, we first tackle the optimal non-orthogonal pilot design problem and demonstrate that the optimal pilot sequences are Welch bound equality sequences. In the second part, we investigate the performance of a single cell under a shared physical channel assumption for MTC and HTC services and propose a novel scheme for sharing the time-frequency resources. The analysis reveals that mMIMO can significantly enhance the performance of such a setup and allow the inclusion of MTC services into the cellular networks without requiring additional resources.
Representation Learning for Image-based Music Recommendation
Venue: arXiv cs.MM 2018
Year: 2018

Image perception is one of the most direct ways to provide contextual information about a user concerning his/her surrounding environment; hence images are a suitable proxy for contextual recommendation. We propose a novel representation learning framework for image-based music recommendation that bridges the heterogeneity gap between music and image data; the proposed method is a key component for various contextual recommendation tasks. Preliminary experiments show that for an image-to-song retrieval task, the proposed method retrieves relevant or conceptually similar songs for input images.