OpenRaiser/Intern-Atlas huggingface dataset:
Deep Attention Recurrent Q-Network
Venue: arXiv cs.LG 2015
Year: 2015

A deep learning approach to reinforcement learning led to a general learner able to train on visual input to play a variety of arcade games at the human and superhuman levels. Its creators at the Google DeepMind's team called the approach: Deep Q-Network (DQN). We present an extension of DQN by "soft" and "hard" attention mechanisms. Tests of the proposed Deep Attention Recurrent Q-Network (DARQN) algorithm on multiple Atari 2600 games show level of performance superior to that of DQN. Moreover, built-in attention mechanisms allow a direct online monitoring of the training process by highlighting the regions of the game screen the agent is focusing on when making decisions.
A Note on Approximating 2-Transmitters
Venue: arXiv cs.CG 2015
Year: 2015

A k-transmitter in a simple orthogonal polygon P is a mobile guard that travels back and forth along an orthogonal line segment s inside P. The k-transmitter can see a point p in P if there exists a point q on s such that the line segment pq is normal to s and pq intersects the boundary of P in at most k points. In this paper, we give a 2-approximation algorithm for the problem of guarding a monotone orthogonal polygon with the minimum number of 2-transmitters.
Emerging Cloud Computing Security Threats
Venue: arXiv cs.OH 2015
Year: 2015

Cloud computing is one of the latest emerging innovations of the modern internet and technological landscape. With everyone from the White house to major online technological leaders like Amazon and Google using or offering cloud computing services it is truly presents itself as an exciting and innovative method to store and use data on the internet.
Randomized Strategy for Walking in Streets for a Simple Robot
Venue: arXiv cs.CG 2015
Year: 2015

We consider the problem of walking in an unknown street, for a robot that has a minimal sensing capability. The robot is equipped with a sensor that only detects the discontinuities in depth information (gaps) and can locate the target point as enters in its visibility region. First, we propose an online deterministic search strategy that generates an optimal search path for the simple robot to reach the target t, starting from s. In contrast with previously known research, the path is designed without memorizing any portion of the scene has seen so far. Then, we present a randomized search strategy, based on the deterministic strategy. We prove that the expected distance traveled by the robot is at most a 5.33 times longer than the shortest path to reach the target.
THCHS-30 : A Free Chinese Speech Corpus
Venue: arXiv cs.CL 2015
Year: 2015

Speech data is crucially important for speech recognition research. There are quite some speech databases that can be purchased at prices that are reasonable for most research institutes. However, for young people who just start research activities or those who just gain initial interest in this direction, the cost for data is still an annoying barrier. We support the `free data' movement in speech recognition: research institutes (particularly supported by public funds) publish their data freely so that new researchers can obtain sufficient data to kick of their career. In this paper, we follow this trend and release a free Chinese speech database THCHS-30 that can be used to build a full- edged Chinese speech recognition system. We report the baseline system established with this database, including the performance under highly noisy conditions.
Diffusion in Colocation Contact Networks: the Impact of Nodal Spatiotemporal Dynamics
Venue: arXiv cs.SI 2015
Year: 2015

Temporal contact networks are studied to understand dynamic spreading phenomena such as communicable diseases or information dissemination. To establish how spatiotemporal dynamics of nodes impact spreading potential in colocation contact networks, we propose "inducement-shuffling" null models which break one or more correlations between times, locations and nodes. By reconfiguring the time and/or location of each node's presence in the network, these models induce alternative sets of colocation events giving rise to contact networks with varying spreading potential. This enables second-order causal reasoning about how correlations in nodes' spatiotemporal preferences not only lead to a given contact network but ultimately influence the network's spreading potential. We find the correlation between nodes and times to be the greatest impediment to spreading, while the correlation between times and locations slightly catalyzes spreading. Under each of the presented null models we measure both the number of contacts and infection prevalence as a function of time, with the surprising finding that the two have no direct causality.
Sparsifying Neural Network Connections for Face Recognition
Venue: arXiv cs.CV 2015
Year: 2015

This paper proposes to learn high-performance deep ConvNets with sparse neural connections, referred to as sparse ConvNets, for face recognition. The sparse ConvNets are learned in an iterative way, each time one additional layer is sparsified and the entire model is re-trained given the initial weights learned in previous iterations. One important finding is that directly training the sparse ConvNet from scratch failed to find good solutions for face recognition, while using a previously learned denser model to properly initialize a sparser model is critical to continue learning effective features for face recognition. This paper also proposes a new neural correlation-based weight selection criterion and empirically verifies its effectiveness in selecting informative connections from previously learned models in each iteration. When taking a moderately sparse structure (26%-76% of weights in the dense model), the proposed sparse ConvNet model significantly improves the face recognition performance of the previous state-of-the-art DeepID2+ models given the same training data, while it keeps the performance of the baseline model with only 12% of the original parameters.
Rademacher Complexity of the Restricted Boltzmann Machine
Venue: arXiv cs.LG 2015
Year: 2015

Boltzmann machine, as a fundamental construction block of deep belief network and deep Boltzmann machines, is widely used in deep learning community and great success has been achieved. However, theoretical understanding of many aspects of it is still far from clear. In this paper, we studied the Rademacher complexity of both the asymptotic restricted Boltzmann machine and the practical implementation with single-step contrastive divergence (CD-1) procedure. Our results disclose the fact that practical implementation training procedure indeed increased the Rademacher complexity of restricted Boltzmann machines. A further research direction might be the investigation of the VC dimension of a compositional function used in the CD-1 procedure.
Fast Optimization Algorithm on Riemannian Manifolds and Its Application in Low-Rank Representation
Venue: arXiv cs.NA 2015
Year: 2015

The paper addresses the problem of optimizing a class of composite functions on Riemannian manifolds and a new first order optimization algorithm (FOA) with a fast convergence rate is proposed. Through the theoretical analysis for FOA, it has been proved that the algorithm has quadratic convergence. The experiments in the matrix completion task show that FOA has better performance than other first order optimization methods on Riemannian manifolds. A fast subspace pursuit method based on FOA is proposed to solve the low-rank representation model based on augmented Lagrange method on the low rank matrix variety. Experimental results on synthetic and real data sets are presented to demonstrate that both FOA and SP-RPRG(ALM) can achieve superior performance in terms of faster convergence and higher accuracy.
A Novel Approach to Distributed Multi-Class SVM
Venue: arXiv cs.LG 2015
Year: 2015

With data sizes constantly expanding, and with classical machine learning algorithms that analyze such data requiring larger and larger amounts of computation time and storage space, the need to distribute computation and memory requirements among several computers has become apparent. Although substantial work has been done in developing distributed binary SVM algorithms and multi-class SVM algorithms individually, the field of multi-class distributed SVMs remains largely unexplored. This research proposes a novel algorithm that implements the Support Vector Machine over a multi-class dataset and is efficient in a distributed environment (here, Hadoop). The idea is to divide the dataset into half recursively and thus compute the optimal Support Vector Machine for this half during the training phase, much like a divide and conquer approach. While testing, this structure has been effectively exploited to significantly reduce the prediction time. Our algorithm has shown better computation time during the prediction phase than the traditional sequential SVM methods (One vs. One, One vs. Rest) and out-performs them as the size of the dataset grows. This approach also classifies the data with higher accuracy than the traditional multi-class algorithms.
On Konig-Egervary Collections of Maximum Critical Independent Sets
Venue: arXiv cs.DM 2015
Year: 2015

Let G be a simple graph with vertex set V(G). A set S is independent if no two vertices from S are adjacent. The graph G is known to be a Konig-Egervary if alpha(G)+mu(G)= |V(G)|, where alpha(G) denotes the size of a maximum independent set and mu(G) is the cardinality of a maximum matching. The number d(X)= |X|-|N(X)| is the difference of X, and an independent set A is critical if d(A) = max{d(I):I is an independent set in G} (Zhang; 1990). Let Omega(G) denote the family of all maximum independent sets. Let us say that a family Gamma of independent sets is a Konig-Egervary collection if |Union of Gamma| + |Intersection of Gamma| = 2alpha(G) (Jarden, Levit, Mandrescu; 2015). In this paper, we show that if the family of all maximum critical independent sets of a graph G is a Konig-Egervary collection, then G is a Konig-Egervary graph. It generalizes one of our conjectures recently validated in (Short; 2015).
Energy Efficiency of Massive MIMO: Coping with Daily Load Variation
Venue: arXiv cs.NI 2015
Year: 2015

Massive MIMO is a promising technique to meet the exponential growth of mobile traffic demand. However, contrary to the current systems, energy consumption of next generation networks is required to be load adaptive as the network load varies significantly throughout the day. In this paper, we propose a load adaptive multi-cell massive MIMO system where each base station (BS) adapts the number of antennas to the daily load profile (DLP) in order to maximize the downlink energy efficiency (EE). In order to incorporate the DLP, the load at each BS is modeled as an M/G/m/m state dependent queue under the assumption that the network is dimensioned to serve a maximum number of users at the peak load. The EE maximization problem is formulated in a game theoretic framework where the number of antennas to be used by a BS is determined through best response iteration. This load adaptive system achieves around 24% higher EE and saves around 40% energy compared to a baseline system where the BSs always run with the fixed number of antennas that is most energy efficient at the peak load and that can be switched off when there is no traffic.
Jointly Modeling Topics and Intents with Global Order Structure
Venue: arXiv cs.CL 2015
Year: 2015

Modeling document structure is of great importance for discourse analysis and related applications. The goal of this research is to capture the document intent structure by modeling documents as a mixture of topic words and rhetorical words. While the topics are relatively unchanged through one document, the rhetorical functions of sentences usually change following certain orders in discourse. We propose GMM-LDA, a topic modeling based Bayesian unsupervised model, to analyze the document intent structure cooperated with order information. Our model is flexible that has the ability to combine the annotations and do supervised learning. Additionally, entropic regularization can be introduced to model the significant divergence between topics and intents. We perform experiments in both unsupervised and supervised settings, results show the superiority of our model over several state-of-the-art baselines.
The Maximum Scatter TSP on a Regular Grid
Venue: arXiv cs.DM 2015
Year: 2015

In the maximum scatter traveling salesman problem the objective is to find a tour that maximizes the shortest distance between any two consecutive nodes. This model can be applied to manufacturing processes, particularly laser melting processes. We extend an algorithm by Arkin et al. that yields optimal solutions for nodes on a line to a regular $m \times n$-grid. The new algorithm $\textsc{Weave}(m,n)$ takes linear time to compute an optimal tour in some cases. It is asymptotically optimal and a $\frac{\sqrt{10}}{5}$-approximation for the $3\times 4$-grid, which is the worst case.
Cooperative Estimation for Synchronization of Heterogeneous Multi-Agent Systems Using Relative Information
Venue: arXiv cs.SY 2015
Year: 2015

In this paper, we present a distributed estimation setup where local agents estimate their states from relative measurements received from their neighbours. In the case of heterogeneous multi-agent systems, where only relative measurements are available, this is of high relevance. The objective is to improve the scalability of the existing distributed estimation algorithms by restricting the agents to estimating only their local states and those of immediate neighbours. The presented estimation algorithm also guarantees robust performance against model and measurement disturbances. It is shown that it can be integrated into output synchronization algorithms.
Contamination-Free Measures and Algebraic Operations
Venue: arXiv cs.AI 2015
Year: 2015

An open concept of rough evolution and an axiomatic approach to granules was also developed recently by the present author. Subsequently the concepts were used in the formal framework of rough Y-systems (RYS) for developing on granular correspondences by her. These have since been used for a new approach towards comparison of rough algebraic semantics across different semantic domains by way of correspondences that preserve rough evolution and try to avoid contamination. In this research paper, new methods are proposed and a semantics for handling possibly contaminated operations and structured bigness is developed. These would also be of natural interest for relative consistency of one collection of knowledge relative other.
Learning Efficient Correlated Equilibria
Venue: arXiv cs.GT 2015
Year: 2015

The majority of distributed learning literature focuses on convergence to Nash equilibria. Correlated equilibria, on the other hand, can often characterize more efficient collective behavior than even the best Nash equilibrium. However, there are no existing distributed learning algorithms that converge to specific correlated equilibria. In this paper, we provide one such algorithm which guarantees that the agents' collective joint strategy will constitute an efficient correlated equilibrium with high probability. The key to attaining efficient correlated behavior through distributed learning involves incorporating a common random signal into the learning environment.
The Teaching Dimension of Linear Learners
Venue: arXiv cs.LG 2015
Year: 2015

Teaching dimension is a learning theoretic quantity that specifies the minimum training set size to teach a target model to a learner. Previous studies on teaching dimension focused on version-space learners which maintain all hypotheses consistent with the training data, and cannot be applied to modern machine learners which select a specific hypothesis via optimization. This paper presents the first known teaching dimension for ridge regression, support vector machines, and logistic regression. We also exhibit optimal training sets that match these teaching dimensions. Our approach generalizes to other linear learners.
The complexity of partitioning into disjoint cliques and a triangle-free graph
Venue: arXiv cs.DM 2015
Year: 2015

Motivated by Chudnovsky's structure theorem of bull-free graphs, Abu-Khzam, Feghali, and Müller have recently proved that deciding if a graph has a vertex partition into disjoint cliques and a triangle-free graph is NP-complete for five graph classes. The problem is trivial for the intersection of these five classes. We prove that the problem is NP-complete for the intersection of two subsets of size four among the five classes. We also show NP-completeness for other small classes, such as graphs with maximum degree 4 and line graphs.
Deep Learning for Single and Multi-Session i-Vector Speaker Recognition
Venue: arXiv cs.SD 2015
Year: 2015

The promising performance of Deep Learning (DL) in speech recognition has motivated the use of DL in other speech technology applications such as speaker recognition. Given i-vectors as inputs, the authors proposed an impostor selection algorithm and a universal model adaptation process in a hybrid system based on Deep Belief Networks (DBN) and Deep Neural Networks (DNN) to discriminatively model each target speaker. In order to have more insight into the behavior of DL techniques in both single and multi-session speaker enrollment tasks, some experiments have been carried out in this paper in both scenarios. Additionally, the parameters of the global model, referred to as universal DBN (UDBN), are normalized before adaptation. UDBN normalization facilitates training DNNs specifically with more than one hidden layer. Experiments are performed on the NIST SRE 2006 corpus. It is shown that the proposed impostor selection algorithm and UDBN adaptation process enhance the performance of conventional DNNs 8-20 % and 16-20 % in terms of EER for the single and multi-session tasks, respectively. In both scenarios, the proposed architectures outperform the baseline systems obtaining up to 17 % reduction in EER.
Reinforcement Control with Hierarchical Backpropagated Adaptive Critics
Venue: arXiv cs.NE 2015
Year: 2015

Present incremental learning methods are limited in the ability to achieve reliable credit assignment over a large number time steps (or events). However, this situation is typical for cases where the dynamical system to be controlled requires relatively frequent control updates in order to maintain stability or robustness yet has some action-consequences which must be established over relatively long periods of time. To address this problem, the learning capabilities of a control architecture comprised of two Backpropagated Adaptive Critics (BACs) in a two-level hierarchy with continuous actions are explored. The high-level BAC updates less frequently than the low-level BAC and controls the latter to some degree. The response of the low-level to high-level signals can either be determined a priori or it can emerge during learning. A general approach called Response Induction Learning is introduced to address the latter case.
A Case-Based Look at Integrating Social Context into Software Quality
Venue: arXiv cs.SE 2015
Year: 2015

Ensuring high-quality software requires considering the social climate within which the applications will be deployed and used. This can be done by designing quality goals and objectives that are consistent with changing social and ethical landscapes. Using principles of technological determinism, this article presents three cases that illustrate why it is becoming even more important to integrate these concerns into software design and quality assurance. With these examples in mind, this article explains how to consider technological determinism in software design and quality assurance practices to achieve this enhanced sensitivity on a practical level.
Information Resources Management Framework for Virtual Enterprise
Venue: arXiv cs.CY 2015
Year: 2015

Virtual enterprise is a new form of organization in recent years which adapt to the IT environment. Information resources management implemented in the virtual enterprise is determined by the form of business organization and information exchange mechanisms. According to the present characteristics of virtual enterprise management, it puts forward the strategies and measures of information resources management framework for virtual enterprise.
Plane Bichromatic Trees of Low Degree
Venue: arXiv cs.CG 2015
Year: 2015

Let $R$ and $B$ be two disjoint sets of points in the plane such that $|B|\leqslant |R|$, and no three points of $R\cup B$ are collinear. We show that the geometric complete bipartite graph $K(R,B)$ contains a non-crossing spanning tree whose maximum degree is at most $\max\left\{3, \left\lceil \frac{|R|-1}{|B|}\right\rceil + 1\right\}$; this is the best possible upper bound on the maximum degree. This solves an open problem posed by Abellanas et al. at the Graph Drawing Symposium, 1996.
Design Strategies for Playful Technologies to Support Light-intensity Physical Activity in the Workplace
Venue: arXiv cs.HC 2015
Year: 2015

Moderate to vigorous intensity physical activity has an established preventative role in obesity, cardiovascular disease, and diabetes. However recent evidence suggests that sitting time affects health negatively independent of whether adults meet prescribed physical activity guidelines. Since many of us spend long hours daily sitting in front of a host of electronic screens, this is cause for concern. In this paper, we describe a set of three prototype digital games created for encouraging light-intensity physical activity during short breaks at work. The design of these kinds of games is a complex process that must consider motivation strategies, interaction methodology, usability and ludic aspects. We present design guidelines for technologies that encourage physical activity in the workplace that we derived from a user evaluation using the prototypes. Although the design guidelines can be seen as general principles, we conclude that they have to be considered differently for different workplace cultures and workspaces. Our study was conducted with users who have some experience playing casual games on their mobile devices and were able and willing to increase their physical activity.
Frequency Hopping on a 5G Millimeter-Wave Uplink
Venue: arXiv cs.IT 2015
Year: 2015

In order to overcome the anticipated tremendous growth in the volume of mobile data traffic, the next generation of cellular networks will need to exploit the large bandwidth offered by the millimeter-wave (mmWave) band. A key distinguishing characteristic of mmWave is its use of highly directional and steerable antennas. In addition, future networks will be highly densified through the proliferation of base stations and their supporting infrastructure. With the aim of further improving the overall throughput of the network by mitigating the effect of frequency-selective fading and co-channel interference, 5G cellular networks are also expected to aggressively use frequency-hopping. This paper outlines an analytical framework that captures the main characteristics of a 5G cellular uplink. This framework is used to emphasize the benefits of network infrastructure densification, antenna directivity, mmWave propagation characteristics, and frequency hopping.