OpenRaiser/Intern-Atlas huggingface dataset:
Driver Identification Using Automobile Sensor Data from a Single Turn
Venue: arXiv cs.HC 2017
Year: 2017

As automotive electronics continue to advance, cars are becoming more and more reliant on sensors to perform everyday driving operations. These sensors are omnipresent and help the car navigate, reduce accidents, and provide comfortable rides. However, they can also be used to learn about the drivers themselves. In this paper, we propose a method to predict, from sensor data collected at a single turn, the identity of a driver out of a given set of individuals. We cast the problem in terms of time series classification, where our dataset contains sensor readings at one turn, repeated several times by multiple drivers. We build a classifier to find unique patterns in each individual's driving style, which are visible in the data even on such a short road segment. To test our approach, we analyze a new dataset collected by AUDI AG and Audi Electronics Venture, where a fleet of test vehicles was equipped with automotive data loggers storing all sensor readings on real roads. We show that turns are particularly well-suited for detecting variations across drivers, especially when compared to straightaways. We then focus on the 12 most frequently made turns in the dataset, which include rural, urban, highway on-ramps, and more, obtaining accurate identification results and learning useful insights about driver behavior in a variety of settings.
Mobile Academy: A Ubiquitous Mobile Learning (mLearning) Platform
Venue: arXiv cs.CY 2017
Year: 2017

The paper reports on an ongoing research project into the development of "Mobile Academy", an Android-based mobile learning (mLearning) application (app). The project comprises three major phases: requirement analysis, application development and testing and evaluation. To satisfy the user requirement analysis, a detailed ethnographic study was conducted to investigate how people from different background use mobile devices for learning purposes. The initial analysis and evaluation of the first version of the projected app demonstrates very promising results. Making use of the app seemed to have, in general, a positive dimension in facilitating educational use of mobile devices.
Guiding Network Analysis using Graph Slepians: An Illustration for the C. Elegans Connectome
Venue: arXiv cs.LG 2017
Year: 2017

Spectral approaches of network analysis heavily rely upon the eigendecomposition of the graph Laplacian. For instance, in graph signal processing, the Laplacian eigendecomposition is used to define the graph Fourier transform and then transpose signal processing operations to graphs by implementing them in the spectral domain. Here, we build on recent work that generalized Slepian functions to the graph setting. In particular, graph Slepians are band-limited graph signals with maximal energy concentration in a given subgraph. We show how this approach can be used to guide network analysis; i.e., we propose a visualization that reveals network organization of a subgraph, but while striking a balance with global network structure. These developments are illustrated for the structural connectome of the C. Elegans.
Controlled Experiments with Student Participants in Software Engineering: Preliminary Results from a Systematic Mapping Study
Venue: arXiv cs.SE 2017
Year: 2017

[Context] In software engineering research, emphasis is given to sound evaluations of new approaches. While industry surveys or industrial case studies are preferred to evaluate industrial applicability, controlled experiments with student participants are commonly used to determine measurements such as effectiveness and efficiency of a proposed approach. [Objectives] In this paper, we elaborate on the current state of the art of controlled experiments using student participants. As student participants are commonly only reluctantly accepted in scientific communities and threats regarding the generalizability are quite obvious, we want to determine how widespread controlled experiments with student participants are and in which settings they are used. [Methods] This paper reports on a systematic mapping study using high-quality journals and conferences from the software engineering field as data sources. We scanned all papers published between 2010 and 2014 and investigated all papers reporting student experiments in detail. [Results] From 2788 papers under investigation 175 report results from controlled experiments. 109 (62.29%) of these controlled experiments have been conducted with student participants. Most experiments used undergraduate student participants, recruited students on a voluntary basis, and set them tasks to measure their comprehension. However, many experiments lack information regarding the students' recruitment and other important factors. [Conclusions] In conclusion, student participation in software engineering experiments can be seen as a common evaluation approach. In contrast, there seems to be little knowledge about the threats to validity in student experiments, as major drivers such as the recruitment are not reported at all.
New Directions: Wireless Robotic Materials
Venue: arXiv cs.RO 2017
Year: 2017

We describe opportunities and challenges with wireless robotic materials. Robotic materials are multi-functional composites that tightly integrate sensing, actuation, computation and communication to create smart composites that can sense their environment and change their physical properties in an arbitrary programmable manner. Computation and communication in such materials are based on miniature, possibly wireless, devices that are scattered in the material and interface with sensors and actuators inside the material. Whereas routing and processing of information within the material build upon results from the field of sensor networks, robotic materials are pushing the limits of sensor networks in both size (down to the order of microns) and numbers of devices (up to the order of millions). In order to solve the algorithmic and systems challenges of such an approach, which will involve not only computer scientists, but also roboticists, chemists and material scientists, the community requires a common platform - much like the "Mote" that bootstrapped the widespread adoption of the field of sensor networks - that is small, provides ample of computation, is equipped with basic networking functionalities, and preferably can be powered wirelessly.
Augmentor: An Image Augmentation Library for Machine Learning
Venue: arXiv cs.CV 2017
Year: 2017

The generation of artificial data based on existing observations, known as data augmentation, is a technique used in machine learning to improve model accuracy, generalisation, and to control overfitting. Augmentor is a software package, available in both Python and Julia versions, that provides a high level API for the expansion of image data using a stochastic, pipeline-based approach which effectively allows for images to be sampled from a distribution of augmented images at runtime. Augmentor provides methods for most standard augmentation practices as well as several advanced features such as label-preserving, randomised elastic distortions, and provides many helper functions for typical augmentation tasks used in machine learning.
Deep Learning for Passive Synthetic Aperture Radar
Venue: arXiv cs.CV 2017
Year: 2017

We introduce a deep learning (DL) framework for inverse problems in imaging, and demonstrate the advantages and applicability of this approach in passive synthetic aperture radar (SAR) image reconstruction. We interpret image recon- struction as a machine learning task and utilize deep networks as forward and inverse solvers for imaging. Specifically, we design a recurrent neural network (RNN) architecture as an inverse solver based on the iterations of proximal gradient descent optimization methods. We further adapt the RNN architecture to image reconstruction problems by transforming the network into a recurrent auto-encoder, thereby allowing for unsupervised training. Our DL based inverse solver is particularly suitable for a class of image formation problems in which the forward model is only partially known. The ability to learn forward models and hyper parameters combined with unsupervised training approach establish our recurrent auto-encoder suitable for real world applications. We demonstrate the performance of our method in passive SAR image reconstruction. In this regime a source of opportunity, with unknown location and transmitted waveform, is used to illuminate a scene of interest. We investigate recurrent auto- encoder architecture based on the 1 and 0 constrained least- squares problem. We present a projected stochastic gradient descent based training scheme which incorporates constraints of the unknown model parameters. We demonstrate through extensive numerical simulations that our DL based approach out performs conventional sparse coding methods in terms of computation and reconstructed image quality, specifically, when no information about the transmitter is available.
Evaluating Word Embeddings for Sentence Boundary Detection in Speech Transcripts
Venue: arXiv cs.CL 2017
Year: 2017

This paper is motivated by the automation of neuropsychological tests involving discourse analysis in the retellings of narratives by patients with potential cognitive impairment. In this scenario the task of sentence boundary detection in speech transcripts is important as discourse analysis involves the application of Natural Language Processing tools, such as taggers and parsers, which depend on the sentence as a processing unit. Our aim in this paper is to verify which embedding induction method works best for the sentence boundary detection task, specifically whether it be those which were proposed to capture semantic, syntactic or morphological similarities.
Self-Powered Wireless Sensor
Venue: arXiv cs.SY 2017
Year: 2017

This paper develops a novel power harvesting system to harvest ambient RF energy to power a wireless sensor. Harvesting ambient RF energy is a very difficult task as the power levels are extremely weak. Simulation results show zero threshold MOSFETs are essential in the RF to DC conversion process. 0.5VDC at the output of the RF to DC conversion stage is the minimum voltage which must be achieved for the micro-power sensor circuitry to operate. The weakest power level the proposed system can successfully harvest is -37dBm. The measured available power from the FM band has been measured to fluctuate between -33 to -43dBm using a ribbon FM dipole antenna. Ambient RF energy would best be used in conjunction with other forms of harvested ambient energy to increase diversity and dependability. The potential economic and environmental benefits make such endeavors truly worthwhile.
Language Identification Using Deep Convolutional Recurrent Neural Networks
Venue: arXiv cs.CV 2017
Year: 2017

Language Identification (LID) systems are used to classify the spoken language from a given audio sample and are typically the first step for many spoken language processing tasks, such as Automatic Speech Recognition (ASR) systems. Without automatic language detection, speech utterances cannot be parsed correctly and grammar rules cannot be applied, causing subsequent speech recognition steps to fail. We propose a LID system that solves the problem in the image domain, rather than the audio domain. We use a hybrid Convolutional Recurrent Neural Network (CRNN) that operates on spectrogram images of the provided audio snippets. In extensive experiments we show, that our model is applicable to a range of noisy scenarios and can easily be extended to previously unknown languages, while maintaining its classification accuracy. We release our code and a large scale training set for LID systems to the community.
Balanced Allocation Through Random Walk
Venue: arXiv cs.DS 2017
Year: 2017

We consider the allocation problem in which $m \leq (1-ε) dn $ items are to be allocated to $n$ bins with capacity $d$. The items $x_1,x_2,\ldots,x_m$ arrive sequentially and when item $x_i$ arrives it is given two possible bin locations $p_i=h_1(x_i),q_i=h_2(x_i)$ via hash functions $h_1,h_2$. We consider a random walk procedure for inserting items and show that the expected time insertion time is constant provided $ε= Ω\left(\sqrt{ \frac{ \log d}{d}} \right).$
Visualizing and Exploring Dynamic High-Dimensional Datasets with LION-tSNE
Venue: arXiv cs.AI 2017
Year: 2017

T-distributed stochastic neighbor embedding (tSNE) is a popular and prize-winning approach for dimensionality reduction and visualizing high-dimensional data. However, tSNE is non-parametric: once visualization is built, tSNE is not designed to incorporate additional data into existing representation. It highly limits the applicability of tSNE to the scenarios where data are added or updated over time (like dashboards or series of data snapshots). In this paper we propose, analyze and evaluate LION-tSNE (Local Interpolation with Outlier coNtrol) - a novel approach for incorporating new data into tSNE representation. LION-tSNE is based on local interpolation in the vicinity of training data, outlier detection and a special outlier mapping algorithm. We show that LION-tSNE method is robust both to outliers and to new samples from existing clusters. We also discuss multiple possible improvements for special cases. We compare LION-tSNE to a comprehensive list of possible benchmark approaches that include multiple interpolation techniques, gradient descent for new data, and neural network approximation.
AntibIoTic: Protecting IoT Devices Against DDoS Attacks
Venue: arXiv cs.CR 2017
Year: 2017

The 2016 is remembered as the year that showed to the world how dangerous Distributed Denial of Service attacks can be. Gauge of the disruptiveness of DDoS attacks is the number of bots involved: the bigger the botnet, the more powerful the attack. This character, along with the increasing availability of connected and insecure IoT devices, makes DDoS and IoT the perfect pair for the malware industry. In this paper we present the main idea behind AntibIoTic, a palliative solution to prevent DDoS attacks perpetrated through IoT devices.
Importance of Image Enhancement Techniques in Color Image Segmentation: A Comprehensive and Comparative Study
Venue: arXiv cs.CV 2017
Year: 2017

Color image segmentation is a very emerging research topic in the area of color image analysis and pattern recognition. Many state-of-the-art algorithms have been developed for this purpose. But, often the segmentation results of these algorithms seem to be suffering from miss-classifications and over-segmentation. The reasons behind these are the degradation of image quality during the acquisition, transmission and color space conversion. So, here arises the need of an efficient image enhancement technique which can remove the redundant pixels or noises from the color image before proceeding for final segmentation. In this paper, an effort has been made to study and analyze different image enhancement techniques and thereby finding out the better one for color image segmentation. Also, this comparative study is done on two well-known color spaces HSV and LAB separately to find out which color space supports segmentation task more efficiently with respect to those enhancement techniques.
Amplify-and-Forward Two-Way Relaying System over Free-Space Optics Channels
Venue: arXiv cs.IT 2017
Year: 2017

In this paper, we analyze the performance of a two- way subcarrier intensity-modulated (SIM) amplify-and-forward (AF) relaying system over free-space optics (FSO) communication channels. The analysis takes into consideration attenuations due to atmospheric turbulence, geometric spread and pointing errors at the same time. We derive, in generalized infinite power series expressions, the tight upper and lower bounds of the overall outage probability and average probability of errors of the system. The study finds that this two-way subcarrier intensity- modulated AF relaying system using a binary phase shift keying (BPSK) modulation could be used for practical applications in case of a weak turbulence regime in which the required SNR is about 30 dB to obtain the average bit error probability of 10-6. It is also noted that the pointing errors clearly degrade the performance of the two-way subcarrier intensity-modulated AF relaying system.
Evaluating Visual Conversational Agents via Cooperative Human-AI Games
Venue: arXiv cs.HC 2017
Year: 2017

As AI continues to advance, human-AI teams are inevitable. However, progress in AI is routinely measured in isolation, without a human in the loop. It is crucial to benchmark progress in AI, not just in isolation, but also in terms of how it translates to helping humans perform certain tasks, i.e., the performance of human-AI teams. In this work, we design a cooperative game - GuessWhich - to measure human-AI team performance in the specific context of the AI being a visual conversational agent. GuessWhich involves live interaction between the human and the AI. The AI, which we call ALICE, is provided an image which is unseen by the human. Following a brief description of the image, the human questions ALICE about this secret image to identify it from a fixed pool of images. We measure performance of the human-ALICE team by the number of guesses it takes the human to correctly identify the secret image after a fixed number of dialog rounds with ALICE. We compare performance of the human-ALICE teams for two versions of ALICE. Our human studies suggest a counterintuitive trend - that while AI literature shows that one version outperforms the other when paired with an AI questioner bot, we find that this improvement in AI-AI performance does not translate to improved human-AI performance. This suggests a mismatch between benchmarking of AI in isolation and in the context of human-AI teams.
Securing Wireless Communications of the Internet of Things from the Physical Layer, An Overview
Venue: arXiv cs.CR 2017
Year: 2017

The security of the Internet of Things (IoT) is receiving considerable interest as the low power constraints and complexity features of many IoT devices are limiting the use of conventional cryptographic techniques. This article provides an overview of recent research efforts on alternative approaches for securing IoT wireless communications at the physical layer, specifically the key topics of key generation and physical layer encryption. These schemes can be implemented and are lightweight, and thus offer practical solutions for providing effective IoT wireless security. Future research to make IoT-based physical layer security more robust and pervasive is also covered.
The Effect of Planarization on Width
Venue: arXiv cs.DM 2017
Year: 2017

We study the effects of planarization (the construction of a planar diagram $D$ from a non-planar graph $G$ by replacing each crossing by a new vertex) on graph width parameters. We show that for treewidth, pathwidth, branchwidth, clique-width, and tree-depth there exists a family of $n$-vertex graphs with bounded parameter value, all of whose planarizations have parameter value $Ω(n)$. However, for bandwidth, cutwidth, and carving width, every graph with bounded parameter value has a planarization of linear size whose parameter value remains bounded. The same is true for the treewidth, pathwidth, and branchwidth of graphs of bounded degree.
Energy-based Models for Video Anomaly Detection
Venue: arXiv cs.CV 2017
Year: 2017

Automated detection of abnormalities in data has been studied in research area in recent years because of its diverse applications in practice including video surveillance, industrial damage detection and network intrusion detection. However, building an effective anomaly detection system is a non-trivial task since it requires to tackle challenging issues of the shortage of annotated data, inability of defining anomaly objects explicitly and the expensive cost of feature engineering procedure. Unlike existing appoaches which only partially solve these problems, we develop a unique framework to cope the problems above simultaneously. Instead of hanlding with ambiguous definition of anomaly objects, we propose to work with regular patterns whose unlabeled data is abundant and usually easy to collect in practice. This allows our system to be trained completely in an unsupervised procedure and liberate us from the need for costly data annotation. By learning generative model that capture the normality distribution in data, we can isolate abnormal data points that result in low normality scores (high abnormality scores). Moreover, by leverage on the power of generative networks, i.e. energy-based models, we are also able to learn the feature representation automatically rather than replying on hand-crafted features that have been dominating anomaly detection research over many decades. We demonstrate our proposal on the specific application of video anomaly detection and the experimental results indicate that our method performs better than baselines and are comparable with state-of-the-art methods in many benchmark video anomaly detection datasets.
S$^3$FD: Single Shot Scale-invariant Face Detector
Venue: arXiv cs.CV 2017
Year: 2017

This paper presents a real-time face detector, named Single Shot Scale-invariant Face Detector (S$^3$FD), which performs superiorly on various scales of faces with a single deep neural network, especially for small faces. Specifically, we try to solve the common problem that anchor-based detectors deteriorate dramatically as the objects become smaller. We make contributions in the following three aspects: 1) proposing a scale-equitable face detection framework to handle different scales of faces well. We tile anchors on a wide range of layers to ensure that all scales of faces have enough features for detection. Besides, we design anchor scales based on the effective receptive field and a proposed equal proportion interval principle; 2) improving the recall rate of small faces by a scale compensation anchor matching strategy; 3) reducing the false positive rate of small faces via a max-out background label. As a consequence, our method achieves state-of-the-art detection performance on all the common face detection benchmarks, including the AFW, PASCAL face, FDDB and WIDER FACE datasets, and can run at 36 FPS on a Nvidia Titan X (Pascal) for VGA-resolution images.
Designing and building the mlpack open-source machine learning library
Venue: arXiv cs.MS 2017
Year: 2017

mlpack is an open-source C++ machine learning library with an emphasis on speed and flexibility. Since its original inception in 2007, it has grown to be a large project implementing a wide variety of machine learning algorithms, from standard techniques such as decision trees and logistic regression to modern techniques such as deep neural networks as well as other recently-published cutting-edge techniques not found in any other library. mlpack is quite fast, with benchmarks showing mlpack outperforming other libraries' implementations of the same methods. mlpack has an active community, with contributors from around the world---including some from PUST. This short paper describes the goals and design of mlpack, discusses how the open-source community functions, and shows an example usage of mlpack for a simple data science problem.