OpenRaiser/Intern-Atlas huggingface dataset:
Three-Stage Speaker Verification Architecture in Emotional Talking Environments
Venue: arXiv cs.SD 2018
Year: 2018

Speaker verification performance in neutral talking environment is usually high, while it is sharply decreased in emotional talking environments. This performance degradation in emotional environments is due to the problem of mismatch between training in neutral environment while testing in emotional environments. In this work, a three-stage speaker verification architecture has been proposed to enhance speaker verification performance in emotional environments. This architecture is comprised of three cascaded stages: gender identification stage followed by an emotion identification stage followed by a speaker verification stage. The proposed framework has been evaluated on two distinct and independent emotional speech datasets: in-house dataset and Emotional Prosody Speech and Transcripts dataset. Our results show that speaker verification based on both gender information and emotion information is superior to each of speaker verification based on gender information only, emotion information only, and neither gender information nor emotion information. The attained average speaker verification performance based on the proposed framework is very alike to that attained in subjective assessment by human listeners.
Geometry of Deep Learning for Magnetic Resonance Fingerprinting
Venue: arXiv cs.LG 2018
Year: 2018

Current popular methods for Magnetic Resonance Fingerprint (MRF) recovery are bottlenecked by the heavy storage and computation requirements of a dictionary-matching (DM) step due to the growing size and complexity of the fingerprint dictionaries in multi-parametric quantitative MRI applications. In this paper we study a deep learning approach to address these shortcomings. Coupled with a dimensionality reduction first layer, the proposed MRF-Net is able to reconstruct quantitative maps by saving more than 60 times in memory and computations required for a DM baseline. Fine-grid manifold enumeration i.e. the MRF dictionary is only used for training the network and not during image reconstruction. We show that the MRF-Net provides a piece-wise affine approximation to the Bloch response manifold projection and that rather than memorizing the dictionary, the network efficiently clusters this manifold and learns a set of hierarchical matched-filters for affine regression of the NMR characteristics in each segment.
Travel Speed Prediction with a Hierarchical Convolutional Neural Network and Long Short-Term Memory Model Framework
Venue: arXiv cs.LG 2018
Year: 2018

Advanced travel information and warning, if provided accurately, can help road users avoid traffic congestion through dynamic route planning and behavior change. It also enables traffic control centres mitigate the impact of congestion by activating Intelligent Transport System (ITS) proactively. Deep learning has become increasingly popular in recent years, following a surge of innovative GPU technology, high-resolution, big datasets and thriving machine learning algorithms. However, there are few examples exploiting this emerging technology to develop applications for traffic prediction. This is largely due to the difficulty in capturing random, seasonal, non-linear, and spatio-temporal correlated nature of traffic data. In this paper, we propose a data-driven modelling approach with a novel hierarchical D-CLSTM-t deep learning model for short-term traffic speed prediction, a framework combined with convolutional neural network (CNN) and long short-term memory (LSTM) models. A deep CNN model is employed to learn the spatio-temporal traffic patterns of the input graphs, which are then fed into a deep LSTM model for sequence learning. To capture traffic seasonal variations, time of the day and day of the week indicators are fused with trained features. The model is trained end-to-end to predict travel speed in 15 to 90 minutes in the future. We compare the model performance against other baseline models including CNN, LGBM, LSTM, and traditional speed-flow curves. Experiment results show that the D-CLSTM-t outperforms other models considerably. Model tests show that speed upstream also responds sensibly to a sudden accident occurring downstream. Our D-CLSTM-t model framework is also highly scalable for future extension such as for network-wide traffic prediction, which can also be improved by including additional features such as weather, long term seasonality and accident information.
Propheticus: Generalizable Machine Learning Framework
Venue: arXiv cs.LG 2018
Year: 2018

Due to recent technological developments, Machine Learning (ML), a subfield of Artificial Intelligence (AI), has been successfully used to process and extract knowledge from a variety of complex problems. However, a thorough ML approach is complex and highly dependent on the problem at hand. Additionally, implementing the logic required to execute the experiments is no small nor trivial deed, consequentially increasing the probability of faulty code which can compromise the results. Propheticus is a data-driven framework which results of the need for a tool that abstracts some of the inherent complexity of ML, whilst being easy to understand and use, as well as to adapt and expand to assist the user's specific needs. Propheticus systematizes and enforces various complex concepts of an ML experiment workflow, taking into account the nature of both the problem and the data. It contains functionalities to execute all the different tasks, from data preprocessing, to results analysis and comparison. Notwithstanding, it can be fairly easily adapted to different problems due to its flexible architecture, and customized as needed to address the user's needs.
Hands-on Experience with Gaussian Processes (GPs): Implementing GPs in Python - I
Venue: arXiv cs.LG 2018
Year: 2018

This document serves to complement our website which was developed with the aim of exposing the students to Gaussian Processes (GPs). GPs are non-parametric Bayesian regression models that are largely used by statisticians and geospatial data scientists for modeling spatial data. Several open source libraries spanning from Matlab [1], Python [2], R [3] etc., are already available for simple plug-and-use. The objective of this handout and in turn the website was to allow the users to develop stand-alone GPs in Python by relying on minimal external dependencies. To this end, we only use the default python modules and assist the users in developing their own GPs from scratch giving them an in-depth knowledge of what goes on under the hood. The module covers GP inference using maximum likelihood estimation (MLE) and gives examples of 1D (dummy) spatial data.
Planning with Arithmetic and Geometric Attributes
Venue: arXiv cs.AI 2018
Year: 2018

A desirable property of an intelligent agent is its ability to understand its environment to quickly generalize to novel tasks and compose simpler tasks into more complex ones. If the environment has geometric or arithmetic structure, the agent should exploit these for faster generalization. Building on recent work that augments the environment with user-specified attributes, we show that further equipping these attributes with the appropriate geometric and arithmetic structure brings substantial gains in sample complexity.
On-Orbit Smart Camera System to Observe Illuminated and Unilluminated Space Objects
Venue: arXiv cs.CV 2018
Year: 2018

The wide availability of Commercial Off-The-Shelf (COTS) electronics that can withstand Low Earth Orbit conditions has opened avenue for wide deployment of CubeSats and small-satellites. CubeSats thanks to their low developmental and launch costs offer new opportunities for rapidly demonstrating on-orbit surveillance capabilities. In our earlier work, we proposed development of SWIMSat (Space based Wide-angle Imaging of Meteors) a 3U CubeSat demonstrator that is designed to observe illuminated objects entering the Earth's atmosphere. The spacecraft would operate autonomously using a smart camera with vision algorithms to detect, track and report of objects. Several CubeSats can track an object in a coordinated fashion to pinpoint an object's trajectory. An extension of this smart camera capability is to track unilluminated objects utilizing capabilities we have been developing to track and navigate to Near Earth Objects (NEOs). This extension enables detecting and tracking objects that can't readily be detected by humans. The system maintains a dense star map of the night sky and performs round the clock observations. Standard optical flow algorithms are used to obtain trajectories of all moving objects in the camera field of view. Through a process of elimination, certain stars maybe occluded by a transiting unilluminated object which is then used to first detect and obtain a trajectory of the object. Using multiple cameras observing the event from different points of view, it may be possible then to triangulate the position of the object in space and obtain its orbital trajectory. In this work, the performance of our space object detection algorithm coupled with a spacecraft guidance, navigation, and control system is demonstrated.
Deep neural network marketplace recommenders in online experiments
Venue: arXiv cs.IR 2018
Year: 2018

Recommendations are broadly used in marketplaces to match users with items relevant to their interests and needs. To understand user intent and tailor recommendations to their needs, we use deep learning to explore various heterogeneous data available in marketplaces. This paper focuses on the challenge of measuring recommender performance and summarizes the online experiment results with several promising types of deep neural network recommenders - hybrid item representation models combining features from user engagement and content, sequence-based models, and multi-armed bandit models that optimize user engagement by re-ranking proposals from multiple submodels. The recommenders are currently running in production at the leading Norwegian marketplace FINN.no and serves over one million visitors everyday.
Obstacle Detection Quality as a Problem-Oriented Approach to Stereo Vision Algorithms Estimation in Road Situation Analysis
Venue: arXiv cs.CV 2018
Year: 2018

In this work we present a method for performance evaluation of stereo vision based obstacle detection techniques that takes into account the specifics of road situation analysis to minimize the effort required to prepare a test dataset. This approach has been designed to be implemented in systems such as self-driving cars or driver assistance and can also be used as problem-oriented quality criterion for evaluation of stereo vision algorithms.
Distributed and Optimal Resilient Planning of Large-Scale Interdependent Critical Infrastructures
Venue: arXiv cs.SY 2018
Year: 2018

The complex interconnections between heterogeneous critical infrastructure sectors make the system of systems (SoS) vulnerable to natural or human-made disasters and lead to cascading failures both within and across sectors. Hence, the robustness and resilience of the interdependent critical infrastructures (ICIs) against extreme events are essential for delivering reliable and efficient services to our society. To this end, we first establish a holistic probabilistic network model to model the interdependencies between infrastructure components. To capture the underlying failure and recovery dynamics of ICIs, we further propose a Markov decision processes (MDP) model in which the repair policy determines a long-term performance of the ICIs. To address the challenges that arise from the curse of dimensionality of the MDP, we reformulate the problem as an approximate linear program and then simplify it using factored graphs. We further obtain the distributed optimal control for ICIs under mild assumptions. Finally, we use a case study of the interdependent power and subway systems to corroborate the results and show that the optimal resilience resource planning and allocation can reduce the failure probability and mitigate the impact of failures caused by natural or artificial disasters.
Slotted ALOHA Overlay on LoRaWAN: a Distributed Synchronization Approach
Venue: arXiv cs.NI 2018
Year: 2018

LoRaWAN is one of the most promising standards for IoT applications. Nevertheless, the high density of end-devices expected for each gateway, the absence of an effective synchronization scheme between gateway and end-devices, challenge the scalability of these networks. In this article, we propose to regulate the communication of LoRaWAN networks using a Slotted-ALOHA (S-ALOHA) instead of the classic ALOHA approach used by LoRa. The implementation is an overlay on top of the standard LoRaWAN; thus no modification in pre-existing LoRaWAN firmware and libraries is necessary. Our method is based on a novel distributed synchronization service that is suitable for low-cost IoT end-nodes. S-ALOHA supported by our synchronization service significantly improves the performance of traditional LoRaWAN networks regarding packet loss rate and network throughput.
82 Treebanks, 34 Models: Universal Dependency Parsing with Multi-Treebank Models
Venue: arXiv cs.CL 2018
Year: 2018

We present the Uppsala system for the CoNLL 2018 Shared Task on universal dependency parsing. Our system is a pipeline consisting of three components: the first performs joint word and sentence segmentation; the second predicts part-of- speech tags and morphological features; the third predicts dependency trees from words and tags. Instead of training a single parsing model for each treebank, we trained models with multiple treebanks for one language or closely related languages, greatly reducing the number of models. On the official test run, we ranked 7th of 27 teams for the LAS and MLAS metrics. Our system obtained the best scores overall for word segmentation, universal POS tagging, and morphological features.
Unsupervised Cross-lingual Word Embedding by Multilingual Neural Language Models
Venue: arXiv cs.CL 2018
Year: 2018

We propose an unsupervised method to obtain cross-lingual embeddings without any parallel data or pre-trained word embeddings. The proposed model, which we call multilingual neural language models, takes sentences of multiple languages as an input. The proposed model contains bidirectional LSTMs that perform as forward and backward language models, and these networks are shared among all the languages. The other parameters, i.e. word embeddings and linear transformation between hidden states and outputs, are specific to each language. The shared LSTMs can capture the common sentence structure among all languages. Accordingly, word embeddings of each language are mapped into a common latent space, making it possible to measure the similarity of words across multiple languages. We evaluate the quality of the cross-lingual word embeddings on a word alignment task. Our experiments demonstrate that our model can obtain cross-lingual embeddings of much higher quality than existing unsupervised models when only a small amount of monolingual data (i.e. 50k sentences) are available, or the domains of monolingual data are different across languages.
Term-Mouse-Fixations as an Additional Indicator for Topical User Interests in Domain-Specific Search
Venue: arXiv cs.IR 2018
Year: 2018

Models in Interactive Information Retrieval (IIR) are grounded very much on the user's task in order to give system support based on different task types and topics. However, the automatic recognition of user interests from log data in search systems is not trivial. Search queries entered by users a surely one such source. However, queries may be short, or users are only browsing. In this paper, we propose a method of term-mouse-fixations which takes the fixations on terms users are hovering over with the mouse into consideration to estimate topical user interests. We analyzed 22,259 search sessions of a domain-specific digital library over a period of about four months. We compared these mouse fixations to user-entered search terms and to titles and keywords from documents the user showed an interest in. These terms were found in 87.12% of all analyzed sessions; in this subset of sessions, per session on average 11.46 term-mouse-fixations from queries and viewed documents were found. These terms were fixated significantly longer with about 7 seconds than other terms with about 4.4 seconds. This means, term-mouse-fixations provide indicators for topical user interests and it is possible to extract them based on fixation time.
Challenges for Measuring Usefulness of Interactive IR Systems with Log-based Approaches
Venue: arXiv cs.IR 2018
Year: 2018

The usefulness evaluation model proposed by Cole et al. in 2009 [2] focuses on the evaluation of interactive IR systems by their support towards the user's overall goal, sub goals and tasks. This is a more human focus of the IR evaluation process than with classical TREC-oriented studies and gives a more holistic view on the IR evaluation process. However, yet there is no formal framework how the usefulness model can be operationalized. Additionally, a lot of information needed for the operationalization is only available in explicit user studies where for example the overall goal and the tasks are prompted from the users or are predefined. Measuring the usefulness of IR systems outside the laboratory is a challenging task as most often only log data of user interaction is available. But, an operationalization of the usefulness model based on interaction data could be applied to diverse systems and evaluation results would be comparable. In this article we discuss the challenges for measuring the usefulness of IIR systems with log-based approaches.
Metamorphic Relation Based Adversarial Attacks on Differentiable Neural Computer
Venue: arXiv cs.LG 2018
Year: 2018

Deep neural networks (DNN), while becoming the driving force of many novel technology and achieving tremendous success in many cutting-edge applications, are still vulnerable to adversarial attacks. Differentiable neural computer (DNC) is a novel computing machine with DNN as its central controller operating on an external memory module for data processing. The unique architecture of DNC contributes to its state-of-the-art performance in tasks which requires the ability to represent variables and data structure as well as to store data over long timescales. However, there still lacks a comprehensive study on how adversarial examples affect DNC in terms of robustness. In this paper, we propose metamorphic relation based adversarial techniques for a range of tasks described in the natural processing language domain. We show that the near-perfect performance of the DNC in bAbI logical question answering tasks can be degraded by adversarially injected sentences. We further perform in-depth study on the role of DNC's memory size in its robustness and analyze the potential reason causing why DNC fails. Our study demonstrates the current challenges and potential opportunities towards constructing more robust DNCs.
The largest cognitive systems will be optoelectronic
Venue: arXiv cs.ET 2018
Year: 2018

Electrons and photons offer complementary strengths for information processing. Photons are excellent for communication, while electrons are superior for computation and memory. Cognition requires distributed computation to be communicated across the system for information integration. We present reasoning from neuroscience, network theory, and device physics supporting the conjecture that large-scale cognitive systems will benefit from electronic devices performing synaptic, dendritic, and neuronal information processing operating in conjunction with photonic communication. On the chip scale, integrated dielectric waveguides enable fan-out to thousands of connections. On the system scale, fiber and free-space optics can be employed. The largest cognitive systems will be limited by the distance light can travel during the period of a network oscillation. We calculate that optoelectronic networks the area of a large data center ($10^5$\,m$^2$) will be capable of system-wide information integration at $1$\,MHz. At frequencies of cortex-wide integration in the human brain ($4$\,Hz, theta band), optoelectronic systems could integrate information across the surface of the earth.
Machine Learning Methods for Network Intrusion Detection
Venue: arXiv cs.NI 2018
Year: 2018

Network security engineers work to keep services available all the time by handling intruder attacks. Intrusion Detection System (IDS) is one of the obtainable mechanisms that is used to sense and classify any abnormal actions. Therefore, the IDS must be always up to date with the latest intruder attacks signatures to preserve confidentiality, integrity, and availability of the services. The speed of the IDS is a very important issue as well learning the new attacks. This research work illustrates how the Knowledge Discovery and Data Mining (or Knowledge Discovery in Databases) KDD dataset is very handy for testing and evaluating different Machine Learning Techniques. It mainly focuses on the KDD preprocess part in order to prepare a decent and fair experimental data set. The J48, MLP, and Bayes Network classifiers have been chosen for this study. It has been proven that the J48 classifier has achieved the highest accuracy rate for detecting and classifying all KDD dataset attacks, which are of type DOS, R2L, U2R, and PROBE.
Hybrid Statistical Estimation of Mutual Information and its Application to Information Flow
Venue: arXiv cs.IT 2018
Year: 2018

Analysis of a probabilistic system often requires to learn the joint probability distribution of its random variables. The computation of the exact distribution is usually an exhaustive precise analysis on all executions of the system. To avoid the high computational cost of such an exhaustive search, statistical analysis has been studied to efficiently obtain approximate estimates by analyzing only a small but representative subset of the system's behavior. In this paper we propose a hybrid statistical estimation method that combines precise and statistical analyses to estimate mutual information, Shannon entropy, and conditional entropy, together with their confidence intervals. We show how to combine the analyses on different components of a discrete system with different accuracy to obtain an estimate for the whole system. The new method performs weighted statistical analysis with different sample sizes over different components and dynamically finds their optimal sample sizes. Moreover, it can reduce sample sizes by using prior knowledge about systems and a new abstraction-then-sampling technique based on qualitative analysis. To apply the method to the source code of a system, we show how to decompose the code into components and to determine the analysis method for each component by overviewing the implementation of those techniques in the HyLeak tool. We demonstrate with case studies that the new method outperforms the state of the art in quantifying information leakage.
Reservoir Computing based Neural Image Filters
Venue: arXiv cs.CV 2018
Year: 2018

Clean images are an important requirement for machine vision systems to recognize visual features correctly. However, the environment, optics, electronics of the physical imaging systems can introduce extreme distortions and noise in the acquired images. In this work, we explore the use of reservoir computing, a dynamical neural network model inspired from biological systems, in creating dynamic image filtering systems that extracts signal from noise using inverse modeling. We discuss the possibility of implementing these networks in hardware close to the sensors.