OpenRaiser/Intern-Atlas huggingface dataset:
Multi-Modal Hybrid Deep Neural Network for Speech Enhancement
Venue: arXiv cs.LG 2016
Year: 2016

Deep Neural Networks (DNN) have been successful in en- hancing noisy speech signals. Enhancement is achieved by learning a nonlinear mapping function from the features of the corrupted speech signal to that of the reference clean speech signal. The quality of predicted features can be improved by providing additional side channel information that is robust to noise, such as visual cues. In this paper we propose a novel deep learning model inspired by insights from human audio visual perception. In the proposed unified hybrid architecture, features from a Convolution Neural Network (CNN) that processes the visual cues and features from a fully connected DNN that processes the audio signal are integrated using a Bidirectional Long Short-Term Memory (BiLSTM) network. The parameters of the hybrid model are jointly learned using backpropagation. We compare the quality of enhanced speech from the hybrid models with those from traditional DNN and BiLSTM models.
Bind: a Partitioned Global Workflow Parallel Programming Model
Venue: arXiv cs.DC 2016
Year: 2016

High Performance Computing is notorious for its long and expensive software development cycle. To address this challenge, we present Bind: a "partitioned global workflow" parallel programming model for C++ applications that enables quick prototyping and agile development cycles for high performance computing software targeting heterogeneous distributed many-core architectures. We present applications of Bind to Linear Algebra and MapReduce algorithms alongside with performance results.
Sketched Floor plans versus SLAM maps: A Comparison
Venue: arXiv cs.HC 2016
Year: 2016

Maps --- specifically floor plans --- are useful for a variety of tasks from arranging furniture to designating conceptual or functional spaces (e.g., kitchen, walkway). We present a simple algorithm for quickly laying a floor plan (or other conceptual map) onto a SLAM map, creating a one-to-one mapping between them. Our goal was to enable using a floor plan (or other hand-drawn or annotated map) in robotic applications instead of the typical SLAM map created by the robot. We look at two use cases, specifying "no-go" regions within a room and locating objects within a scanned room. Although a user study showed no statistical difference between the two types of maps in terms of performance on this spatial memory task, we argue that floor plans are closer to the mental maps people would naturally draw to characterize spaces.
A necessary and sufficient condition for minimum phase and implications for phase retrieval
Venue: arXiv cs.IT 2016
Year: 2016

We give a necessary and sufficient condition for a function $E(t)$ being of minimum phase, and hence for its phase being univocally determined by its intensity $|E(t)|^2$. This condition is based on the knowledge of $E(t)$ alone and not of its analytic continuation in the complex plane, thus greatly simplifying its practical applicability. We apply these results to find the class of all band-limited signals that correspond to distinct receiver states when the detector is sensitive to the field intensity only and insensitive to the field phase, and discuss the performance of a recently proposed transmission scheme able to linearly detect all distinguishable states.
cltorch: a Hardware-Agnostic Backend for the Torch Deep Neural Network Library, Based on OpenCL
Venue: arXiv cs.NE 2016
Year: 2016

This paper presents cltorch, a hardware-agnostic backend for the Torch neural network framework. cltorch enables training of deep neural networks on GPUs from diverse hardware vendors, including AMD, NVIDIA, and Intel. cltorch contains sufficient implementation to run models such as AlexNet, VGG, Overfeat, and GoogleNet. It is written using the OpenCL language, a portable compute language, governed by the Khronos Group. cltorch is the top-ranked hardware-agnostic machine learning framework on Chintala's convnet-benchmarks page. This paper presents the technical challenges encountered whilst creating the cltorch backend for Torch, and looks in detail at the challenges related to obtaining a fast hardware-agnostic implementation. The convolutional layers are identified as the key area of focus for accelerating hardware-agnostic frameworks. Possible approaches to accelerating the convolutional implementation are identified including: implementation of the convolutions using the implicitgemm or winograd algorithm, using a GEMM implementation adapted to the geometries associated with the convolutional algorithm, or using a pluggable hardware-specific convolutional implementation.
Deep Learning for Music
Venue: arXiv cs.LG 2016
Year: 2016

Our goal is to be able to build a generative model from a deep neural network architecture to try to create music that has both harmony and melody and is passable as music composed by humans. Previous work in music generation has mainly been focused on creating a single melody. More recent work on polyphonic music modeling, centered around time series probability density estimation, has met some partial success. In particular, there has been a lot of work based off of Recurrent Neural Networks combined with Restricted Boltzmann Machines (RNN-RBM) and other similar recurrent energy based models. Our approach, however, is to perform end-to-end learning and generation with deep neural nets alone.
Distance geometry approach for special graph coloring problems
Venue: arXiv cs.DS 2016
Year: 2016

One of the most important combinatorial optimization problems is graph coloring. There are several variations of this problem involving additional constraints either on vertices or edges. They constitute models for real applications, such as channel assignment in mobile wireless networks. In this work, we consider some coloring problems involving distance constraints as weighted edges, modeling them as distance geometry problems. Thus, the vertices of the graph are considered as embedded on the real line and the coloring is treated as an assignment of positive integers to the vertices, while the distances correspond to line segments, where the goal is to find a feasible intersection of them. We formulate different such coloring problems and show feasibility conditions for some problems. We also propose implicit enumeration methods for some of the optimization problems based on branch-and-prune methods proposed for distance geometry problems in the literature. An empirical analysis was undertaken, considering equality and inequality constraints, uniform and arbitrary set of distances, and the performance of each variant of the method considering the handling and propagation of the set of distances involved.
Joint Data Compression and MAC Protocol Design for Smartgrids with Renewable Energy
Venue: arXiv cs.NI 2016
Year: 2016

In this paper, we consider the joint design of data compression and 802.15.4-based medium access control (MAC) protocol for smartgrids with renewable energy. We study the setting where a number of nodes, each of which comprises electricity load and/or renewable sources, report periodically their injected powers to a data concentrator. Our design exploits the correlation of the reported data in both time and space to efficiently design the data compression using the compressed sensing (CS) technique and theMAC protocol so that the reported data can be recovered reliably within minimum reporting time. Specifically, we perform the following design tasks: i) we employ the two-dimensional (2D) CS technique to compress the reported data in the distributed manner; ii) we propose to adapt the 802.15.4 MAC protocol frame structure to enable efficient data transmission and reliable data reconstruction; and iii) we develop an analytical model based on which we can obtain efficient MAC parameter configuration to minimize the reporting delay. Finally, numerical results are presented to demonstrate the effectiveness of our proposed framework compared to existing solutions.
SocialHBC: Social Networking and Secure Authentication using Interference-Robust Human Body Communication
Venue: arXiv cs.HC 2016
Year: 2016

With the advent of cheap computing through five decades of continued miniaturization following Moores Law, wearable devices are becoming increasingly popular. These wearable devices are typically interconnected using wireless body area network (WBAN). Human body communication (HBC) provides an alternate energy-efficient communication technique between on-body wearable devices by using the human body as a conducting medium. This allows order of magnitude lower communication power, compared to WBAN, due to lower loss and broadband signaling. Moreover, HBC is significantly more secure than WBAN, as the information is contained within the human body and cannot be snooped on unless the person is physically touched. In this paper, we highlight applications of HBC as (1) Social Networking (e.g. LinkedIn/Facebook friend request sent during Handshaking in a meeting/party), (2) Secure Authentication using human-human or human-machine dynamic HBC and (3) ultra-low power, secure BAN using intra-human HBC. One of the biggest technical bottlenecks of HBC has been the interference (e.g. FM) picked up by the human body acting like an antenna. In this work, for the first time, we introduce an integrating dual data rate (DDR) receiver technique, that allows notch filtering (>20 dB) of the interference for interference-robust HBC.
Accumulate then Forward: An Opportunistic Relaying Protocol for Wireless-Powered Cooperative Communications
Venue: arXiv cs.IT 2016
Year: 2016

This paper investigates a wireless-powered cooperative communication network consisting of a source, a destination and a multi-antenna decode-and-forward relay. We consider the relay as a wireless-powered node that has no external power supply; but it is equipped with an energy harvesting (EH) unit and a rechargeable battery such that it can harvest and accumulate energy from radio-frequency signals broadcast by the source. By fully incorporating the EH feature of the relay, we develop an opportunistic relaying protocol, termed accumulate-then-forward (ATF), for the considered WPCCN. We then adopt the discrete Markov chain to model the dynamic charging and discharging behaviors of the relay battery. Based on this, we derive a closed-form expression for the exact outage probability of the proposed ATF protocol. Numerical results show that the ATF scheme can outperform the direct transmission one, especially when the amount of energy consumed by relay for information forwarding is optimized.
A 0.3-2.6 TOPS/W Precision-Scalable Processor for Real-Time Large-Scale ConvNets
Venue: arXiv cs.AR 2016
Year: 2016

A low-power precision-scalable processor for ConvNets or convolutional neural networks (CNN) is implemented in a 40nm technology. Its 256 parallel processing units achieve a peak 102GOPS running at 204MHz. To minimize energy consumption while maintaining throughput, this works is the first to both exploit the sparsity of convolutions and to implement dynamic precision-scalability enabling supply- and energy scaling. The processor is fully C-programmable, consumes 25-288mW at 204 MHz and scales efficiency from 0.3-2.6 real TOPS/W. This system hereby outperforms the state-of-the-art up to 3.9x in energy efficiency.
Sentiment Aggregate Functions for Political Opinion Polling using Microblog Streams
Venue: arXiv cs.SI 2016
Year: 2016

The automatic content analysis of mass media in the social sciences has become necessary and possible with the raise of social media and computational power. One particularly promising avenue of research concerns the use of sentiment analysis in microblog streams. However, one of the main challenges consists in aggregating sentiment polarity in a timely fashion that can be fed to the prediction method. We investigated a large set of sentiment aggregate functions and performed a regression analysis using political opinion polls as gold standard. Our dataset contains nearly 233 000 tweets, classified according to their polarity (positive, negative or neutral), regarding the five main Portuguese political leaders during the Portuguese bailout (2011-2014). Results show that different sentiment aggregate functions exhibit different feature importance over time while the error keeps almost unchanged.
Networked Control under Random and Malicious Packet Losses
Venue: arXiv cs.SY 2016
Year: 2016

We study cyber security issues in networked control of a linear dynamical system. Specifically, the dynamical system and the controller are assumed to be connected through a communication channel that face malicious attacks as well as random packet losses due to unreliability of transmissions. We provide a probabilistic characterization for the link failures which allows us to study combined effects of malicious and random packet losses. We first investigate almost sure stabilization under an event-triggered control law, where we utilize Lyapunov-like functions to characterize the triggering times at which the plant and the controller attempt to exchange state and control data over the network. We then provide a look at the networked control problem from the attacker's perspective and explore malicious attacks that cause instability. Finally, we demonstrate the efficacy of our results with numerical examples.
Isometry Groups of Combinatorial Codes
Venue: arXiv cs.IT 2016
Year: 2016

Two isometry groups of combinatorial codes are described: the group of automorphisms and the group of monomial automorphisms, which is the group of those automorphisms that extend to monomial maps. Unlike the case of classical linear codes, where these groups are the same, it is shown that for combinatorial codes the groups can be arbitrary different. Particularly, there exist codes with the full automorphism group and the trivial monomial automorphism group. In the paper the two groups are characterized and codes with predefined isometry groups are constructed.
TSSort: Probabilistic Noise Resistant Sorting
Venue: arXiv cs.DS 2016
Year: 2016

In this paper we present TSSort, a probabilistic, noise resistant, quickly converging comparison sort algorithm based on Microsoft TrueSkill. The algorithm combines TrueSkill's updating rules with a newly developed next item pair selection strategy, enabling it to beat standard sorting algorithms w.r.t. convergence speed and noise resistance, as shown in simulations. TSSort is useful if comparisons of items are expensive or noisy, or if intermediate results shall be approximately ordered.
Spatio-temporal Interference Correlation and Joint Coverage in Cellular Networks
Venue: arXiv cs.IT 2016
Year: 2016

This paper provides an analytical framework with foundations in stochastic geometry to characterize the spatio-temporal interference correlation as well as the joint coverage probability at two spatial locations in a cellular network. In particular, modeling the locations of cellular base stations (BSs) as a Poisson Point Process (PPP), we study interference correlation at two spatial locations $\ell_1$ and $\ell_2$ separated by a distance $v$, when the user follows \emph{closest BS association policy} at both spatial locations and moves from $\ell_1$ to $\ell_2$. With this user displacement, two scenarios can occur: i) the user is handed off to a new serving BS at $\ell_2$, or ii) no handoff occurs and the user is served by the same BS at both locations. After providing intermediate results such as probability of handoff and distance distributions of the serving BS at the two user locations, we use them to derive exact expressions for spatio-temporal interference correlation coefficient and joint coverage probability for any distance separation $v$. We also study two different handoff strategies: i) \emph{handoff skipping}, and ii) \emph{conventional handoffs}, and derive the expressions of joint coverage probability for both strategies. The exact analysis is not straightforward and involves a careful treatment of the neighborhood of the two spatial locations and the resulting handoff scenarios. To provide analytical insights, we also provide easy-to-use expressions for two special cases: i) static user ($v =0$) and ii) highly mobile user ($v \rightarrow \infty)$. As expected, our analysis shows that the interference correlation and joint coverage probability decrease with increasing $v$, with $v \rightarrow \infty$ corresponding to a completely uncorrelated scenario.
CMS-RCNN: Contextual Multi-Scale Region-based CNN for Unconstrained Face Detection
Venue: arXiv cs.CV 2016
Year: 2016

Robust face detection in the wild is one of the ultimate components to support various facial related problems, i.e. unconstrained face recognition, facial periocular recognition, facial landmarking and pose estimation, facial expression recognition, 3D facial model construction, etc. Although the face detection problem has been intensely studied for decades with various commercial applications, it still meets problems in some real-world scenarios due to numerous challenges, e.g. heavy facial occlusions, extremely low resolutions, strong illumination, exceptionally pose variations, image or video compression artifacts, etc. In this paper, we present a face detection approach named Contextual Multi-Scale Region-based Convolution Neural Network (CMS-RCNN) to robustly solve the problems mentioned above. Similar to the region-based CNNs, our proposed network consists of the region proposal component and the region-of-interest (RoI) detection component. However, far apart of that network, there are two main contributions in our proposed network that play a significant role to achieve the state-of-the-art performance in face detection. Firstly, the multi-scale information is grouped both in region proposal and RoI detection to deal with tiny face regions. Secondly, our proposed network allows explicit body contextual reasoning in the network inspired from the intuition of human vision system. The proposed approach is benchmarked on two recent challenging face detection databases, i.e. the WIDER FACE Dataset which contains high degree of variability, as well as the Face Detection Dataset and Benchmark (FDDB). The experimental results show that our proposed approach trained on WIDER FACE Dataset outperforms strong baselines on WIDER FACE Dataset by a large margin, and consistently achieves competitive results on FDDB against the recent state-of-the-art face detection methods.
Abducing Compliance of Incomplete Event Logs
Venue: arXiv cs.AI 2016
Year: 2016

The capability to store data about business processes execution in so-called Event Logs has brought to the diffusion of tools for the analysis of process executions and for the assessment of the goodness of a process model. Nonetheless, these tools are often very rigid in dealing with with Event Logs that include incomplete information about the process execution. Thus, while the ability of handling incomplete event data is one of the challenges mentioned in the process mining manifesto, the evaluation of compliance of an execution trace still requires an end-to-end complete trace to be performed. This paper exploits the power of abduction to provide a flexible, yet computationally effective, framework to deal with different forms of incompleteness in an Event Log. Moreover it proposes a refinement of the classical notion of compliance into strong and conditional compliance to take into account incomplete logs. Finally, performances evaluation in an experimental setting shows the feasibility of the presented approach.
Gender Inference using Statistical Name Characteristics in Twitter
Venue: arXiv cs.CL 2016
Year: 2016

Much attention has been given to the task of gender inference of Twitter users. Although names are strong gender indicators, the names of Twitter users are rarely used as a feature; probably due to the high number of ill-formed names, which cannot be found in any name dictionary. Instead of relying solely on a name database, we propose a novel name classifier. Our approach extracts characteristics from the user names and uses those in order to assign the names to a gender. This enables us to classify international first names as well as ill-formed names.
4G Mobile Communication Systems: Key Technology and Evolution
Venue: arXiv cs.NI 2016
Year: 2016

With the worldwide third-generation mobile communication system gradually implemented, the future development of mobile communications has become a hot topic and evolution of the problem. This paper introduces the fourth generation mobile communication system and its performance and network structure and OFDM, software defined radio, smart antennas, IPv6 and other key technologies, and analyzes the relationship between 4G mobile communication system for mobile communications and 3G, and the evolution of communication systems do Prospect.
Homogeneous Equations of Algebraic Petri Nets
Venue: arXiv cs.LO 2016
Year: 2016

Algebraic Petri nets are a formalism for modeling distributed systems and algorithms, describing control and data flow by combining Petri nets and algebraic specification. One way to specify correctness of an algebraic Petri net model $N$ is to specify a linear equation $E$ over the places of $N$ based on term substitution, and coefficients from an abelian group $G$. Then, $E$ is valid in $N$ iff $E$ is valid in each reachable marking of $N$ . Due to the expressive power of Algebraic Petri nets, validity is generally undecidable. Stable linear equations form a class of linear equations for which validity is decidable. Place invariants yield a well-understood but incomplete characterization of all stable linear equations. In this paper, we provide a complete characterization of stability for the subclass of homogeneous linear equations, by restricting ourselves to the interpretation of terms over the Herbrand structure without considering further equality axioms. Based thereon, we show that stability is decidable for homogeneous linear equations if $G$ is a cyclic group.
Introspective Agents: Confidence Measures for General Value Functions
Venue: arXiv cs.AI 2016
Year: 2016

Agents of general intelligence deployed in real-world scenarios must adapt to ever-changing environmental conditions. While such adaptive agents may leverage engineered knowledge, they will require the capacity to construct and evaluate knowledge themselves from their own experience in a bottom-up, constructivist fashion. This position paper builds on the idea of encoding knowledge as temporally extended predictions through the use of general value functions. Prior work has focused on learning predictions about externally derived signals about a task or environment (e.g. battery level, joint position). Here we advocate that the agent should also predict internally generated signals regarding its own learning process - for example, an agent's confidence in its learned predictions. Finally, we suggest how such information would be beneficial in creating an introspective agent that is able to learn to make good decisions in a complex, changing world.
Greedy palindromic lengths
Venue: arXiv cs.FL 2016
Year: 2016

In [A. Frid, S. Puzynina, L.Q. Zamboni, \textit{On palindromic factorization of words}, Adv. in Appl. Math. 50 (2013), 737-748], it was conjectured that any infinite word whose palindromic lengths of factors are bounded is ultimately periodic. We introduce variants of this conjecture and prove this conjecture in particular cases. Especially we introduce left and right greedy palindromic lengths. These lengths are always greater than or equals to the initial palindromic length. When the greedy left (or right) palindromic lengths of prefixes of a word are bounded then this word is ultimately periodic.