OpenRaiser/Intern-Atlas huggingface dataset:
Using Complex Network Theory for Temporal Locality in Network Traffic Flows
Venue: arXiv cs.NI 2018
Year: 2018

Monitoring the interaction behaviors of network traffic flows and detecting unwanted Internet applications and anomalous flows have become a challenging problem, since many applications obfuscate their network traffic flow using unregistered port numbers or payload encryption. In this paper, the temporal locality complex network model--TLCN is proposed as a way to monitor, analyze and visualize network traffic flows. TLCNs model the interaction behaviors of large-scale network traffic flows, where the nodes and the edges can be defined to represent different flow levels and flow interactions separately. Then, the statistical characteristics and dynamic behaviors of the TLCNs are studied to represent TLCN's structure representing ability to the flow interactions. According to the analysis of TLCN statistical characteristics with different Internet applications, we found that the weak interaction flows prefer to form the small-world TLCN and the strong interaction flows prefer to the scale-free TLCN. In the studies of anomaly behaviors of TLCNs, the network structure of attacked TLCNs can have a remarkable feature for three attack patterns, and the evolution of TLCNs exhibits a good consistency between TLCN structure and attack process. With the introduction of TLCNs, we are able to harness a wealth of tools and graph modeling techniques from a diverse set of disciplines.
SceneEDNet: A Deep Learning Approach for Scene Flow Estimation
Venue: arXiv cs.CV 2018
Year: 2018

Estimating scene flow in RGB-D videos is attracting much interest of the computer vision researchers, due to its potential applications in robotics. The state-of-the-art techniques for scene flow estimation, typically rely on the knowledge of scene structure of the frame and the correspondence between frames. However, with the increasing amount of RGB-D data captured from sophisticated sensors like Microsoft Kinect, and the recent advances in the area of sophisticated deep learning techniques, introduction of an efficient deep learning technique for scene flow estimation, is becoming important. This paper introduces a first effort to apply a deep learning method for direct estimation of scene flow by presenting a fully convolutional neural network with an encoder-decoder (ED) architecture. The proposed network SceneEDNet involves estimation of three dimensional motion vectors of all the scene points from sequence of stereo images. The training for direct estimation of scene flow is done using consecutive pairs of stereo images and corresponding scene flow ground truth. The proposed architecture is applied on a huge dataset and provides meaningful results.
Learning a Single Tucker Decomposition Network for Lossy Image Compression with Multiple Bits-Per-Pixel Rates
Venue: arXiv cs.CV 2018
Year: 2018

Lossy image compression (LIC), which aims to utilize inexact approximations to represent an image more compactly, is a classical problem in image processing. Recently, deep convolutional neural networks (CNNs) have achieved interesting results in LIC by learning an encoder-quantizer-decoder network from a large amount of data. However, existing CNN-based LIC methods usually can only train a network for a specific bits-per-pixel (bpp). Such a "one network per bpp" problem limits the generality and flexibility of CNNs to practical LIC applications. In this paper, we propose to learn a single CNN which can perform LIC at multiple bpp rates. A simple yet effective Tucker Decomposition Network (TDNet) is developed, where there is a novel tucker decomposition layer (TDL) to decompose a latent image representation into a set of projection matrices and a core tensor. By changing the rank of the core tensor and its quantization, we can easily adjust the bpp rate of latent image representation within a single CNN. Furthermore, an iterative non-uniform quantization scheme is presented to optimize the quantizer, and a coarse-to-fine training strategy is introduced to reconstruct the decompressed images. Extensive experiments demonstrate the state-of-the-art compression performance of TDNet in terms of both PSNR and MS-SSIM indices.
An Adaptive Learning Method of Deep Belief Network by Layer Generation Algorithm
Venue: arXiv cs.NE 2018
Year: 2018

Deep Belief Network (DBN) has a deep architecture that represents multiple features of input patterns hierarchically with the pre-trained Restricted Boltzmann Machines (RBM). A traditional RBM or DBN model cannot change its network structure during the learning phase. Our proposed adaptive learning method can discover the optimal number of hidden neurons and weights and/or layers according to the input space. The model is an important method to take account of the computational cost and the model stability. The regularities to hold the sparse structure of network is considerable problem, since the extraction of explicit knowledge from the trained network should be required. In our previous research, we have developed the hybrid method of adaptive structural learning method of RBM and Learning Forgetting method to the trained RBM. In this paper, we propose the adaptive learning method of DBN that can determine the optimal number of layers during the learning. We evaluated our proposed model on some benchmark data sets.
DLOPT: Deep Learning Optimization Library
Venue: arXiv cs.LG 2018
Year: 2018

Deep learning hyper-parameter optimization is a tough task. Finding an appropriate network configuration is a key to success, however most of the times this labor is roughly done. In this work we introduce a novel library to tackle this problem, the Deep Learning Optimization Library: DLOPT. We briefly describe its architecture and present a set of use examples. This is an open source project developed under the GNU GPL v3 license and it is freely available at https://github.com/acamero/dlopt
Two-Phase Dynamic Analysis of Message-Passing Go Programs based on Vector Clocks
Venue: arXiv cs.PL 2018
Year: 2018

Understanding the run-time behavior of concurrent programs is a challenging task. A popular approach is to establish a happens- before relation via vector clocks. Thus, we can identify bugs and per- formance bottlenecks, for example, by checking if two con icting events may happen concurrently. We employ a two-phase method to derive vector clock information for a wide range of concurrency features that includes all of the message-passing features in Go. The rst phase (instrumentation and tracing) yields a run-time trace that records all events related to message-passing concurrency that took place. The second phase (trace replay) is carried out o ine and replays the recorded traces to infer vector clock information. Trace replay operates on thread-local traces. Thus, we can observe behav- ior that might result from some alternative schedule. Our approach is not tied to any speci c language. We have built a prototype for the Go programming language and provide empirical evidence of the usefulness of our method.
Paired Comparison Sentiment Scores
Venue: arXiv cs.CL 2018
Year: 2018

The method of paired comparisons is an established method in psychology. In this article, it is applied to obtain continuous sentiment scores for words from comparisons made by test persons. We created an initial lexicon with $n=199$ German words from a two-fold all-pair comparison experiment with ten different test persons. From the probabilistic models taken into account, the logistic model showed the best agreement with the results of the comparison experiment. The initial lexicon can then be used in different ways. One is to create special purpose sentiment lexica through the addition of arbitrary words that are compared with some of the initial words by test persons. A cross-validation experiment suggests that only about 18 two-fold comparisons are necessary to estimate the score of a new, yet unknown word, provided these words are selected by a modification of a method by Silverstein & Farrell. Another application of the initial lexicon is the evaluation of automatically created corpus-based lexica. By such an evaluation, we compared the corpus-based lexica SentiWS, SenticNet, and SentiWordNet, of which SenticNet 4 performed best. This technical report is a corrected and extended version of a presentation made at the ICDM Sentire workshop in 2016.
Computing Kernels in Parallel: Lower and Upper Bounds
Venue: arXiv cs.CC 2018
Year: 2018

Parallel fixed-parameter tractability studies how parameterized problems can be solved in parallel. A surprisingly large number of parameterized problems admit a high level of parallelization, but this does not mean that we can also efficiently compute small problem kernels in parallel: known kernelization algorithms are typically highly sequential. In the present paper, we establish a number of upper and lower bounds concerning the sizes of kernels that can be computed in parallel. An intriguing finding is that there are complex trade-offs between kernel size and the depth of the circuits needed to compute them: For the vertex cover problem, an exponential kernel can be computed by AC$^0$-circuits, a quadratic kernel by TC$^0$-circuits, and a linear kernel by randomized NC-circuits with derandomization being possible only if it is also possible for the matching problem. Other natural problems for which similar (but quantitatively different) effects can be observed include tree decomposition problems parameterized by the vertex cover number, the undirected feedback vertex set problem, the matching problem, or the point line cover problem. We also present natural problems for which computing kernels is inherently sequential.
A Numerical Comparison of an Isogeometric and a Classical Higher-Order Approach to the Electric Field Integral Equation
Venue: arXiv cs.CE 2018
Year: 2018

In this paper, we advocate a novel spline-based isogeometric approach for boundary elements and its efficient implementation. We compare solutions obtained by both an isogeometric approach, and a classical parametric higher-order approach via Raviart-Thomas elements to the solution of the electric field integral equation; i.e., the solution to an electromagnetic scattering problem, promising high convergence orders w.r.t. pointwise error. We discuss both, the obtained accuracy per DOF, as well as the effort required to solve the corresponding system iteratively, on three numerical examples of varying complexity.
Study of Switched Max-Link Buffer-Aided Relay Selection for Cooperative MIMO Systems
Venue: arXiv cs.IT 2018
Year: 2018

In this paper, we investigate relay selection for cooperative multiple-antenna systems that are equipped with buffers, which increase the reliability of wireless links. In particular, we present a novel relay selection technique based on switching and the Max-Link protocol that is named Switched Max-Link. We also introduce a novel relay selection criterion based on the maximum likelihood (ML) principle denoted maximum minimum distance that is incorporated into. Simulations are then employed to evaluate the performance of the proposed and existing techniques.
Towards Head Motion Compensation Using Multi-Scale Convolutional Neural Networks
Venue: arXiv cs.CV 2018
Year: 2018

Head pose estimation and tracking is useful in variety of medical applications. With the advent of RGBD cameras like Kinect, it has become feasible to do markerless tracking by estimating the head pose directly from the point clouds. One specific medical application is robot assisted transcranial magnetic stimulation (TMS) where any patient motion is compensated with the help of a robot. For increased patient comfort, it is important to track the head without markers. In this regard, we address the head pose estimation problem using two different approaches. In the first approach, we build upon the more traditional approach of model based head tracking, where a head model is morphed according to the particular head to be tracked and the morphed model is used to track the head in the point cloud streams. In the second approach, we propose a new multi-scale convolutional neural network architecture for more accurate pose regression. Additionally, we outline a systematic data set acquisition strategy using a head phantom mounted on the robot and ground-truth labels generated using a highly accurate tracking system.
Deep Learning for Audio Transcription on Low-Resource Datasets
Venue: arXiv cs.LG 2018
Year: 2018

In training a deep learning system to perform audio transcription, two practical problems may arise. Firstly, most datasets are weakly labelled, having only a list of events present in each recording without any temporal information for training. Secondly, deep neural networks need a very large amount of labelled training data to achieve good quality performance, yet in practice it is difficult to collect enough samples for most classes of interest. In this paper, we propose factorising the final task of audio transcription into multiple intermediate tasks in order to improve the training performance when dealing with this kind of low-resource datasets. We evaluate three data-efficient approaches of training a stacked convolutional and recurrent neural network for the intermediate tasks. Our results show that different methods of training have different advantages and disadvantages.
Medical Technologies and Challenges of Robot Assisted Minimally Invasive Intervention and Diagnostics
Venue: arXiv cs.RO 2018
Year: 2018

Emerging paradigms furthering the reach of medical technology deeper into human anatomy present unique modeling, control and sensing problems. This paper discusses a brief history of medical robotics leading to the current trend of minimally invasive intervention and diagnostics in confined spaces. Robotics for natural orifice and single port access surgery, capsule and magnetically actuated robotics and microrobotics are discussed with the aim of elucidating the state of the art. Works on modeling, sensing and control of mechanical architectures of robots for natural orifice and single port access surgery are discussed, followed by a presentation of works on magnetic actuation, sensing and localization for capsule robotics and microrobotics. Finally challenges and open problems in each one of these areas are presented.
Navigating Diverse Data Science Learning: Critical Reflections Towards Future Practice
Venue: arXiv cs.GL 2018
Year: 2018

Data Science is currently a popular field of science attracting expertise from very diverse backgrounds. Current learning practices need to acknowledge this and adapt to it. This paper summarises some experiences relating to such learning approaches from teaching a postgraduate Data Science module, and draws some learned lessons that are of relevance to others teaching Data Science.
SimArch: A Multi-agent System For Human Path Simulation In Architecture Design
Venue: arXiv cs.AI 2018
Year: 2018

Human moving path is an important feature in architecture design. By studying the path, architects know where to arrange the basic elements (e.g. structures, glasses, furniture, etc.) in the space. This paper presents SimArch, a multi-agent system for human moving path simulation. It involves a behavior model built by using a Markov Decision Process. The model simulates human mental states, target range detection, and collision prediction when agents are on the floor, in a particular small gallery, looking at an exhibit, or leaving the floor. It also models different kinds of human characteristics by assigning different transition probabilities. A modified weighted A* search algorithm quickly plans the sub-optimal path of the agents. In an experiment, SimArch takes a series of preprocessed floorplans as inputs, simulates the moving path, and outputs a density map for evaluation. The density map provides the prediction that how likely a person will occur in a location. A following discussion illustrates how architects can use the density map to improve their floorplan design.
Deep learning for comprehensive forecasting of Alzheimer's Disease progression
Venue: arXiv cs.LG 2018
Year: 2018

Most approaches to machine learning from electronic health data can only predict a single endpoint. Here, we present an alternative that uses unsupervised deep learning to simulate detailed patient trajectories. We use data comprising 18-month trajectories of 44 clinical variables from 1908 patients with Mild Cognitive Impairment or Alzheimer's Disease to train a model for personalized forecasting of disease progression. We simulate synthetic patient data including the evolution of each sub-component of cognitive exams, laboratory tests, and their associations with baseline clinical characteristics, generating both predictions and their confidence intervals. Our unsupervised model predicts changes in total ADAS-Cog scores with the same accuracy as specifically trained supervised models and identifies sub-components associated with word recall as predictive of progression. The ability to simultaneously simulate dozens of patient characteristics is a crucial step towards personalized medicine for Alzheimer's Disease.
A Hierarchical Bayesian Linear Regression Model with Local Features for Stochastic Dynamics Approximation
Venue: arXiv cs.LG 2018
Year: 2018

One of the challenges in model-based control of stochastic dynamical systems is that the state transition dynamics are involved, and it is not easy or efficient to make good-quality predictions of the states. Moreover, there are not many representational models for the majority of autonomous systems, as it is not easy to build a compact model that captures the entire dynamical subtleties and uncertainties. In this work, we present a hierarchical Bayesian linear regression model with local features to learn the dynamics of a micro-robotic system as well as two simpler examples, consisting of a stochastic mass-spring damper and a stochastic double inverted pendulum on a cart. The model is hierarchical since we assume non-stationary priors for the model parameters. These non-stationary priors make the model more flexible by imposing priors on the priors of the model. To solve the maximum likelihood (ML) problem for this hierarchical model, we use the variational expectation maximization (EM) algorithm, and enhance the procedure by introducing hidden target variables. The algorithm yields parsimonious model structures, and consistently provides fast and accurate predictions for all our examples involving large training and test sets. This demonstrates the effectiveness of the method in learning stochastic dynamics, which makes it suitable for future use in a paradigm, such as model-based reinforcement learning, to compute optimal control policies in real time.
Instance-based entropy fuzzy support vector machine for imbalanced data
Venue: arXiv cs.LG 2018
Year: 2018

Imbalanced classification has been a major challenge for machine learning because many standard classifiers mainly focus on balanced datasets and tend to have biased results towards the majority class. We modify entropy fuzzy support vector machine (EFSVM) and introduce instance-based entropy fuzzy support vector machine (IEFSVM). Both EFSVM and IEFSVM use the entropy information of k-nearest neighbors to determine the fuzzy membership value for each sample which prioritizes the importance of each sample. IEFSVM considers the diversity of entropy patterns for each sample when increasing the size of neighbors, k, while EFSVM uses single entropy information of the fixed size of neighbors for all samples. By varying k, we can reflect the component change of sample's neighbors from near to far distance in the determination of fuzzy value membership. Numerical experiments on 35 public and 12 real-world imbalanced datasets are performed to validate IEFSVM and area under the receiver operating characteristic curve (AUC) is used to compare its performance with other SVMs and machine learning methods. IEFSVM shows a much higher AUC value for datasets with high imbalance ratio, implying that IEFSVM is effective in dealing with the class imbalance problem.
Knowledge Extracted from Recurrent Deep Belief Network for Real Time Deterministic Control
Venue: arXiv cs.NE 2018
Year: 2018

Recently, the market on deep learning including not only software but also hardware is developing rapidly. Big data is collected through IoT devices and the industry world will analyze them to improve their manufacturing process. Deep Learning has the hierarchical network architecture to represent the complicated features of input patterns. Although deep learning can show the high capability of classification, prediction, and so on, the implementation on GPU devices are required. We may meet the trade-off between the higher precision by deep learning and the higher cost with GPU devices. We can success the knowledge extraction from the trained deep learning with high classification capability. The knowledge that can realize faster inference of pre-trained deep network is extracted as IF-THEN rules from the network signal flow given input data. Some experiment results with benchmark tests for time series data sets showed the effectiveness of our proposed method related to the computational speed.
Testing Global Constraints
Venue: arXiv cs.AI 2018
Year: 2018

Every Constraint Programming (CP) solver exposes a library of constraints for solving combinatorial problems. In order to be useful, CP solvers need to be bug-free. Therefore the testing of the solver is crucial to make developers and users confident. We present a Java library allowing any JVM based solver to test that the implementations of the individual constraints are correct. The library can be used in a test suite executed in a continuous integration tool or it can also be used to discover minimalist instances violating some properties (arc-consistency, etc) in order to help the developer to identify the origin of the problem using standard debuggers.
Do altmetrics assess societal impact in a comparable way to case studies? An empirical test of the convergent validity of altmetrics based on data from the UK Research Excellence Framework (REF)
Venue: arXiv cs.DL 2018
Year: 2018

Altmetrics have been proposed as a way to assess the societal impact of research. Although altmetrics are already in use as impact or attention metrics in different contexts, it is still not clear whether they really capture or reflect societal impact. This study is based on altmetrics, citation counts, research output and case study data from the UK Research Excellence Framework (REF), and peers' REF assessments of research output and societal impact. We investigated the convergent validity of altmetrics by using two REF datasets: publications submitted as research output (PRO) to the REF and publications referenced in case studies (PCS). Case studies, which are intended to demonstrate societal impact, should cite the most relevant research papers. We used the MHq' indicator for assessing impact - an indicator which has been introduced for count data with many zeros. The results of the first part of the analysis show that news media as well as mentions on Facebook, in blogs, in Wikipedia, and in policy-related documents have higher MHq' values for PCS than for PRO. Thus, the altmetric indicators seem to have convergent validity for these data. In the second part of the analysis, altmetrics have been correlated with REF reviewers' average scores on PCS. The negative or close to zero correlations question the convergent validity of altmetrics in that context. We suggest that they may capture a different aspect of societal impact (which can be called unknown attention) to that seen by reviewers (who are interested in the causal link between research and action in society).