OpenRaiser/Intern-Atlas huggingface dataset:
Video Pixel Networks
Venue: arXiv cs.CV 2016
Year: 2016

We propose a probabilistic video model, the Video Pixel Network (VPN), that estimates the discrete joint distribution of the raw pixel values in a video. The model and the neural architecture reflect the time, space and color structure of video tensors and encode it as a four-dimensional dependency chain. The VPN approaches the best possible performance on the Moving MNIST benchmark, a leap over the previous state of the art, and the generated videos show only minor deviations from the ground truth. The VPN also produces detailed samples on the action-conditional Robotic Pushing benchmark and generalizes to the motion of novel objects.
Dynamical system-based robot reaching motions by para-model control approach - A preliminary study
Venue: arXiv cs.SY 2016
Year: 2016

In this report, we apply the proposed "para-model" framework in order to control the trajectory of a dynamical system-based robot. The optimization of the dynamical performances in closed-loop is performed using a derivative-free optimization algorithm.
Wireless Vehicular Networks in Emergencies: A Single Frequency Network Approach
Venue: arXiv cs.IT 2016
Year: 2016

Obtaining high quality sensor information is critical in vehicular emergencies. However, existing standards such as IEEE 802.11p/DSRC and LTE-A cannot support either the required data rates or the latency requirements. One solution to this problem is for municipalities to invest in dedicated base stations to ensure that drivers have the information they need to make safe decisions in or near accidents. In this paper we further propose that these municipality-owned base stations form a Single Frequency Network (SFN). In order to ensure that transmissions are reliable, we derive tight bounds on the outage probability when the SFN is overlaid on an existing cellular network. Using our bounds, we propose a transmission power allocation algorithm. We show that our power allocation model can reduce the total instantaneous SFN transmission power up to $20$ times compared to a static uniform power allocation solution, for the considered scenarios. The result is particularly important when base stations rely on an off-grid power source (i.e., batteries).
MatLM: a Matrix Formulation for Probabilistic Language Models
Venue: arXiv cs.IR 2016
Year: 2016

Probabilistic language models are widely used in Information Retrieval (IR) to rank documents by the probability that they generate the query. However, the implementation of the probabilistic representations with programming languages that favor matrix calculations is challenging. In this paper, we utilize matrix representations to reformulate the probabilistic language models. The matrix representation is a superstructure for the probabilistic language models to organize the calculated probabilities and a potential formalism for standardization of language models and for further mathematical analysis. It facilitates implementations by matrix friendly programming languages. In this paper, we consider the matrix formulation of conventional language model with Dirichlet smoothing, and two language models based on Latent Dirichlet Allocation (LDA), i.e., LBDM and LDI. We release a Java software package--MatLM--implementing the proposed models. Code is available at: https://github.com/yanshanwang/JGibbLDA-v.1.0-MatLM.
Ensemble Maximum Entropy Classification and Linear Regression for Author Age Prediction
Venue: arXiv cs.LG 2016
Year: 2016

The evolution of the internet has created an abundance of unstructured data on the web, a significant part of which is textual. The task of author profiling seeks to find the demographics of people solely from their linguistic and content-based features in text. The ability to describe traits of authors clearly has applications in fields such as security and forensics, as well as marketing. Instead of seeing age as just a classification problem, we also frame age as a regression one, but use an ensemble chain method that incorporates the power of both classification and regression to learn the authors exact age.
Code Design for Short Blocks: A Survey
Venue: arXiv cs.IT 2016
Year: 2016

The design of block codes for short information blocks (e.g., a thousand or less information bits) is an open research problem which is gaining relevance thanks to emerging applications in wireless communication networks. In this work, we review some of the most recent code constructions targeting the short block regime, and we compare then with both finite-length performance bounds and classical error correction coding schemes. We will see how it is possible to effectively approach the theoretical bounds, with different performance vs. decoding complexity trade-offs.
A Computational Approach to Automatic Prediction of Drunk Texting
Venue: arXiv cs.CL 2016
Year: 2016

Alcohol abuse may lead to unsociable behavior such as crime, drunk driving, or privacy leaks. We introduce automatic drunk-texting prediction as the task of identifying whether a text was written when under the influence of alcohol. We experiment with tweets labeled using hashtags as distant supervision. Our classifiers use a set of N-gram and stylistic features to detect drunk tweets. Our observations present the first quantitative evidence that text contains signals that can be exploited to detect drunk-texting.
Online wheel speed filtering for periodic disturbance reduction: a strategy for an advanced bicycle application
Venue: arXiv cs.SY 2016
Year: 2016

Due to geometrical errors and possible misalignment of the sensors, wheel speed measurements provided by incremental encoders in road vehicles are usually affected by significant periodic noises. This paper presents an online wheel speed filtering procedure, based on a model of the sensor, aimed at processing the speed measurement to make it suitable for advanced vehicle dynamics applications. In particular, differently from low-pass and notch filtering, this strategy is reliable for the cycling cadence estimation from the wheel speed on bicycles. Experimental data are used to show the effectiveness of the proposed approach.
Is Neural Machine Translation Ready for Deployment? A Case Study on 30 Translation Directions
Venue: arXiv cs.CL 2016
Year: 2016

In this paper we provide the largest published comparison of translation quality for phrase-based SMT and neural machine translation across 30 translation directions. For ten directions we also include hierarchical phrase-based MT. Experiments are performed for the recently published United Nations Parallel Corpus v1.0 and its large six-way sentence-aligned subcorpus. In the second part of the paper we investigate aspects of translation speed, introducing AmuNMT, our efficient neural machine translation decoder. We demonstrate that current neural machine translation could already be used for in-production systems when comparing words-per-second ratios.
A Tour of TensorFlow
Venue: arXiv cs.LG 2016
Year: 2016

Deep learning is a branch of artificial intelligence employing deep neural network architectures that has significantly advanced the state-of-the-art in computer vision, speech recognition, natural language processing and other domains. In November 2015, Google released $\textit{TensorFlow}$, an open source deep learning software library for defining, training and deploying machine learning models. In this paper, we review TensorFlow and put it in context of modern deep learning concepts and software. We discuss its basic computational paradigms and distributed execution model, its programming interface as well as accompanying visualization toolkits. We then compare TensorFlow to alternative libraries such as Theano, Torch or Caffe on a qualitative as well as quantitative basis and finally comment on observed use-cases of TensorFlow in academia and industry.
Towards Air Quality Estimation Using Collected Multimodal Environmental Data
Venue: arXiv cs.CY 2016
Year: 2016

This paper presents an open platform, which collects multimodal environmental data related to air quality from several sources including official open sources, social media and citizens. Collecting and fusing different sources of air quality data into a unified air quality indicator is a highly challenging problem, leveraging recent advances in image analysis, open hardware, machine learning and data fusion and is expected to result in increased geographical coverage and temporal granularity of air quality data.
Seer: Empowering Software Defined Networking with Data Analytics
Venue: arXiv cs.NI 2016
Year: 2016

Network complexity is increasing, making network control and orchestration a challenging task. The proliferation of network information and tools for data analytics can provide an important insight into resource provisioning and optimisation. The network knowledge incorporated in software defined networking can facilitate the knowledge driven control, leveraging the network programmability. We present Seer: a flexible, highly configurable data analytics platform for network intelligence based on software defined networking and big data principles. Seer combines a computational engine with a distributed messaging system to provide a scalable, fault tolerant and real-time platform for knowledge extraction. Our first prototype uses Apache Spark for streaming analytics and open network operating system (ONOS) controller to program a network in real-time. The first application we developed aims to predict the mobility pattern of mobile devices inside a smart city environment.
Channel Selection Scheme for Cooperative Routing Protocols in Cognitive Radio Networks
Venue: arXiv cs.NI 2016
Year: 2016

Cognitive radio networks (CRNs) propose a smart solution for spectrum usage inefficiency. Routing protocols for CRNs follow different criteria to choose the best route to the destination and to avoid the interference with primary users. Some protocols use cooperative communication techniques to achieve the full coexistence between secondary users (SUs) and primary users (PUs). Although using such cross-layer techniques have a great impact on the quality of the chosen routes, the existing work do not choose the best channel to send the data over. Thus, the available spectrum is not utilized efficiently. In this work, we propose CSCR, a channel selection scheme for cooperation-based routing protocols in CRNs. The proposed scheme increases the spectrum utilization through integrating the channels selection in the route discovery phase of the cooperation-based routing protocols. The best channels, that are less congested with primary users and that lead to minimum switching overhead, are chosen while constructing the cooperative group. Evaluating CSCR via NS2 simulations shows that it outperforms its counterparts in terms of goodput, end-to-end delay, and packet delivery ratio. The proposed scheme can enhance the network goodput, in some cases, by more than 150%, as compared to other related protocols.
The Simulation Model Partitioning Problem: an Adaptive Solution Based on Self-Clustering (Extended Version)
Venue: arXiv cs.DC 2016
Year: 2016

This paper is about partitioning in parallel and distributed simulation. That means decomposing the simulation model into a numberof components and to properly allocate them on the execution units. An adaptive solution based on self-clustering, that considers both communication reduction and computational load-balancing, is proposed. The implementation of the proposed mechanism is tested using a simulation model that is challenging both in terms of structure and dynamicity. Various configurations of the simulation model and the execution environment have been considered. The obtained performance results are analyzed using a reference cost model. The results demonstrate that the proposed approach is promising and that it can reduce the simulation execution time in both parallel and distributed architectures.
Computationally Secure Optical Transmission Systems with Optical Encryption at Line Rate
Venue: arXiv cs.CR 2016
Year: 2016

We propose a novel system for optical encryption based on an optical XOR and optical Linear Feedback Shift Register (oLFSRs). Though we choose LFSR for its ability to process optical signals at line rate, we consider the fact that it offers no cryptographic security. To address the security shortfall, we propose implementation of parallel oLFSRs, whereby the resulting key-stream at line rate is controlled electronically by a nonlinear random number generator at speeds much lower than the optical line rate, which makes the system practically relevant. The analysis of computational security shows that the proposed system is secure against wiretapping and can be engineered with the state of the art optical components.
A cumulative approach to quantification for sentiment analysis
Venue: arXiv cs.IR 2016
Year: 2016

We estimate sentiment categories proportions for retrieval within large retrieval sets. In general, estimates are produced by counting the classification outcomes and then by adjusting such category sizes taking into account misclassification error matrix. However, both the accuracy of the classifier and the precision of the retrieval produce a large number of errors that makes difficult the application of an aggregative approach to sentiment analysis as a reliable and efficient estimation of proportions for sentiment categories. The challenge for real time analytics during retrieval is thus to overcome misclassification errors, and more importantly, to apply sentiment classification or any other similar post-processing analytics at retrieval time. We present a non-aggregative approach that can be applied to very large retrieval sets of queries.
Domain Adaptation with Soft-margin multiple feature-kernel learning beats Deep Learning for surveillance face recognition
Venue: arXiv cs.CV 2016
Year: 2016

Face recognition (FR) is the most preferred mode for biometric-based surveillance, due to its passive nature of detecting subjects, amongst all different types of biometric traits. FR under surveillance scenario does not give satisfactory performance due to low contrast, noise and poor illumination conditions on probes, as compared to the training samples. A state-of-the-art technology, Deep Learning, even fails to perform well in these scenarios. We propose a novel soft-margin based learning method for multiple feature-kernel combinations, followed by feature transformed using Domain Adaptation, which outperforms many recent state-of-the-art techniques, when tested using three real-world surveillance face datasets.
A short introduction to process theory
Venue: arXiv cs.LO 2016
Year: 2016

These lecture notes concern the basics of the theory of process behaviour. First the concept of a (labelled) transition system receives ample treatment and then the following issues concerning process behaviour are elaborated in the setting of transition systems: (i) concurrency and interaction, (ii) abstraction, (iii) sequential composition, alternative composition, and iteration, (iv) expressions that represent process behaviours, and (v) structural operational semantics and equational laws for such expressions. To quicken an intuitive understanding, direct connections with programs and automata are established wherever appropriate. For the interested reader, direct connections with Petri nets are also established. In each chapter, except the last one, it shows that what has been dealt with so far still has certain limitations. Each time, the next chapter is devoted to reducing the limitations concerned.
LAYERS: Yet another Neural Network toolkit
Venue: arXiv cs.NE 2016
Year: 2016

Layers is an open source neural network toolkit aim at providing an easy way to implement modern neural networks. The main user target are students and to this end layers provides an easy scriptting language that can be early adopted. The user has to focus only on design details as network totpology and parameter tunning.
Effective Low-Complexity Optimization Methods for Joint Phase Noise and Channel Estimation in OFDM
Venue: arXiv cs.IT 2016
Year: 2016

Phase noise correction is crucial to exploit full advantage of orthogonal frequency division multiplexing (OFDM) in modern high-data-rate communications. OFDM channel estimation with simultaneous phase noise compensation has therefore drawn much attention and stimulated continuing efforts. Existing methods, however, either have not taken into account the fundamental properties of phase noise or are only able to provide estimates of limited applicability owing to considerable computational complexity. In this paper, we have reformulated the joint estimation problem in the time domain as opposed to existing frequency-domain approaches, which enables us to develop much more efficient algorithms using the majorization-minimization technique. In addition, we propose a method based on dimensionality reduction and the Bayesian Information Criterion (BIC) that can adapt to various phase noise levels and accomplish much lower mean squared error than the benchmarks without incurring much additional computational cost. Several numerical examples with phase noise generated by free-running oscillators or phase-locked loops demonstrate that our proposed algorithms outperform existing methods with respect to both computational efficiency and mean squared error within a large range of signal-to-noise ratios.
Nonlinear Systems Identification Using Deep Dynamic Neural Networks
Venue: arXiv cs.NE 2016
Year: 2016

Neural networks are known to be effective function approximators. Recently, deep neural networks have proven to be very effective in pattern recognition, classification tasks and human-level control to model highly nonlinear realworld systems. This paper investigates the effectiveness of deep neural networks in the modeling of dynamical systems with complex behavior. Three deep neural network structures are trained on sequential data, and we investigate the effectiveness of these networks in modeling associated characteristics of the underlying dynamical systems. We carry out similar evaluations on select publicly available system identification datasets. We demonstrate that deep neural networks are effective model estimators from input-output data
Scheduling Feasibility of Energy Management in Micro-grids Based on Significant Moment Analysis
Venue: arXiv cs.SY 2016
Year: 2016

This paper studies the operation and scheduling of electric loads in micro-grid, a highly automated and distributed cyber-physical energy system (CPES). We establish rigorous mathematical expressions for electric loads and battery banks in the micro-grid by considering their characteristics and constraints. Based on these mathematical models, we propose a novel real-time scheduling analysis method for priority-based energy management in micro-grid, named Significant Moments Analysis (SMA). SMA pinpoints all the crucial moments when electrical operations are requested among the micro-grid and establishes a dynamic model to describe the scheduling behavior of electric loads. Using SMA, we can check the scheduling feasibility and predict whether the micro-grid can generate enough power to support the execution of electric loads. In the case where the power is insufficient to supply load demands, SMA can provide accurate information about the amount of insufficient power and the time when the insufficiency happens. Simulated results are presented to show the effectiveness of the proposed analysis method.
Cooperative Output Regulation of Linear Multi-agent Systems with Communication Constraints
Venue: arXiv cs.SY 2016
Year: 2016

In this paper, we consider the cooperative output regulation problem for heterogeneous linear multi-agent systems in the presence of communication constraints. Under standard assumptions on the agents dynamics, we propose a distributed control algorithm relying on intermittent and asynchronous discrete-time information exchange that can be subject to unknown time-varying delays and information losses. We show that cooperative output regulation can be reached for arbitrary characteristics of the discrete-time communication process and under mild assumptions on the interconnection topology between agents. A numerical example is given to illustrate the effectiveness of our theoretical results.
$\ell_1$ Regularized Gradient Temporal-Difference Learning
Venue: arXiv cs.AI 2016
Year: 2016

In this paper, we study the Temporal Difference (TD) learning with linear value function approximation. It is well known that most TD learning algorithms are unstable with linear function approximation and off-policy learning. Recent development of Gradient TD (GTD) algorithms has addressed this problem successfully. However, the success of GTD algorithms requires a set of well chosen features, which are not always available. When the number of features is huge, the GTD algorithms might face the problem of overfitting and being computationally expensive. To cope with this difficulty, regularization techniques, in particular $\ell_1$ regularization, have attracted significant attentions in developing TD learning algorithms. The present work combines the GTD algorithms with $\ell_1$ regularization. We propose a family of $\ell_1$ regularized GTD algorithms, which employ the well known soft thresholding operator. We investigate convergence properties of the proposed algorithms, and depict their performance with several numerical experiments.
Modeling Data-Plane Power Consumption of Future Internet Architectures
Venue: arXiv cs.NI 2016
Year: 2016

With current efforts to design Future Internet Architectures (FIAs), the evaluation and comparison of different proposals is an interesting research challenge. Previously, metrics such as bandwidth or latency have commonly been used to compare FIAs to IP networks. We suggest the use of power consumption as a metric to compare FIAs. While low power consumption is an important goal in its own right (as lower energy use translates to smaller environmental impact as well as lower operating costs), power consumption can also serve as a proxy for other metrics such as bandwidth and processor load. Lacking power consumption statistics about either commodity FIA routers or widely deployed FIA testbeds, we propose models for power consumption of FIA routers. Based on our models, we simulate scenarios for measuring power consumption of content delivery in different FIAs. Specifically, we address two questions: 1) which of the proposed FIA candidates achieves the lowest energy footprint; and 2) which set of design choices yields a power-efficient network architecture? Although the lack of real-world data makes numerous assumptions necessary for our analysis, we explore the uncertainty of our calculations through sensitivity analysis of input parameters.