OpenRaiser/Intern-Atlas huggingface dataset:
Joint Tilt Angle Adaptation and Beamforming in Multicell Multiuser Cellular Networks
Venue: arXiv cs.IT 2017
Year: 2017

3D beamforming is a promising approach for interference coordination in cellular networks which brings significant improvements in comparison with conventional 2D beamforming techniques. This paper investigates the problem of joint beamforming design and tilt angle adaptation of the BS antenna array for maximizing energy efficiency (EE) in downlink of multi-cell multi-user coordinated cellular networks. An iterative algorithm based on fractional programming approach is introduced to solve the resulting non-convex optimization problem. In each iteration, users are clustered based on their elevation angle. Then, optimization of the tilt angle is carried out through a reduced complexity greedy search to find the best tilt angle for a given placement of the users. Numerical results show that the proposed algorithm achieves higher EE compared to the 2D beamforming techniques.
LIDE: Language Identification from Text Documents
Venue: arXiv cs.CL 2017
Year: 2017

The increase in the use of microblogging came along with the rapid growth on short linguistic data. On the other hand deep learning is considered to be the new frontier to extract meaningful information out of large amount of raw data in an automated manner. In this study, we engaged these two emerging fields to come up with a robust language identifier on demand, namely Language Identification Engine (LIDE). As a result, we achieved 95.12% accuracy in Discriminating between Similar Languages (DSL) Shared Task 2015 dataset, which is comparable to the maximum reported accuracy of 95.54% achieved so far.
Counting triangles, tunable clustering and the small-world property in random key graphs (Extended version)
Venue: arXiv cs.SI 2017
Year: 2017

Random key graphs were introduced to study various properties of the Eschenauer-Gligor key predistribution scheme for wireless sensor networks (WSNs). Recently this class of random graphs has received much attention in contexts as diverse as recommender systems, social network modeling, and clustering and classification analysis. This paper is devoted to analyzing various properties of random key graphs. In particular, we establish a zero-one law for the the existence of triangles in random key graphs, and identify the corresponding critical scaling. This zero-one law exhibits significant differences with the corresponding result in Erdos-Renyi (ER) graphs. We also compute the clustering coefficient of random key graphs, and compare it to that of ER graphs in the many node regime when their expected average degrees are asymptotically equivalent. For the parameter range of practical relevance in both wireless sensor network and social network applications, random key graphs are shown to be much more clustered than the corresponding ER graphs. We also explore the suitability of random key graphs as small world models in the sense of Watts and Strogatz.
The g-Good-Neighbor Conditional Diagnosability of Locally Twisted Cubes
Venue: arXiv cs.DM 2017
Year: 2017

In the work of Peng et al. in 2012, a new measure was proposed for fault diagnosis of systems: namely, g-good-neighbor conditional diagnosability, which requires that any fault-free vertex has at least g fault-free neighbors in the system. In this paper, we establish the g-good-neighbor conditional diagnosability of locally twisted cubes under the PMC model and the MM^* model.
On Higher Order Positive Differential Energy Operator
Venue: arXiv cs.SD 2017
Year: 2017

The higher order differential energy operator (DEO), denoted via $Υ_k(x)$, is an extension to the second order famous Teager-Kaiser operator. The DEO helps measuring the higher order gauge of energy of a signal which is useful for AM-FM demodulation. However, the energy criterion defined by the DEO is not compliant with the presumption of positivity of energy. In this paper we introduce a higher order operator called Positive Differential Energy Operator (PDEO). This operator which can be obtained using alternative recursive relations, resolves the energy sign problem. The simulations demonstrate that the proposed operator can outperform DEOs in terms of Average Signal to Error Ratio (ASER) in AM/FM demodulation.
Exploring Model Predictive Control to Generate Optimal Control Policies for HRI Dynamical Systems
Venue: arXiv cs.HC 2017
Year: 2017

We model Human-Robot-Interaction (HRI) scenarios as linear dynamical systems and use Model Predictive Control (MPC) with mixed integer constraints to generate human-aware control policies. We motivate the approach by presenting two scenarios. The first involves an assistive robot that aims to maximize productivity while minimizing the human's workload, and the second involves a listening humanoid robot that manages its eye contact behavior to maximize "connection" and minimize social "awkwardness" with the human during the interaction. Our simulation results show that the robot generates useful behaviors as it finds control policies to minimize the specified cost function. Further, we implement the second scenario on a humanoid robot and test the eye contact scenario with 48 human participants to demonstrate and evaluate the desired controller behavior. The humanoid generated 25% more eye contact when it was told to maximize connection over when it was told to maximize awkwardness. However, despite showing the desired behavior, there was no statistical difference between the participant's perceived connection with the humanoid.
Long Timescale Credit Assignment in NeuralNetworks with External Memory
Venue: arXiv cs.AI 2017
Year: 2017

Credit assignment in traditional recurrent neural networks usually involves back-propagating through a long chain of tied weight matrices. The length of this chain scales linearly with the number of time-steps as the same network is run at each time-step. This creates many problems, such as vanishing gradients, that have been well studied. In contrast, a NNEM's architecture recurrent activity doesn't involve a long chain of activity (though some architectures such as the NTM do utilize a traditional recurrent architecture as a controller). Rather, the externally stored embedding vectors are used at each time-step, but no messages are passed from previous time-steps. This means that vanishing gradients aren't a problem, as all of the necessary gradient paths are short. However, these paths are extremely numerous (one per embedding vector in memory) and reused for a very long time (until it leaves the memory). Thus, the forward-pass information of each memory must be stored for the entire duration of the memory. This is problematic as this additional storage far surpasses that of the actual memories, to the extent that large memories on infeasible to back-propagate through in high dimensional settings. One way to get around the need to hold onto forward-pass information is to recalculate the forward-pass whenever gradient information is available. However, if the observations are too large to store in the domain of interest, direct reinstatement of a forward pass cannot occur. Instead, we rely on a learned autoencoder to reinstate the observation, and then use the embedding network to recalculate the forward-pass. Since the recalculated embedding vector is unlikely to perfectly match the one stored in memory, we try out 2 approximations to utilize error gradient w.r.t. the vector in memory.
Minimally Naturalistic Artificial Intelligence
Venue: arXiv cs.AI 2017
Year: 2017

The rapid advancement of machine learning techniques has re-energized research into general artificial intelligence. While the idea of domain-agnostic meta-learning is appealing, this emerging field must come to terms with its relationship to human cognition and the statistics and structure of the tasks humans perform. The position of this article is that only by aligning our agents' abilities and environments with those of humans do we stand a chance at developing general artificial intelligence (GAI). A broad reading of the famous 'No Free Lunch' theorem is that there is no universally optimal inductive bias or, equivalently, bias-free learning is impossible. This follows from the fact that there are an infinite number of ways to extrapolate data, any of which might be the one used by the data generating environment; an inductive bias prefers some of these extrapolations to others, which lowers performance in environments using these adversarial extrapolations. We may posit that the optimal GAI is the one that maximally exploits the statistics of its environment to create its inductive bias; accepting the fact that this agent is guaranteed to be extremely sub-optimal for some alternative environments. This trade-off appears benign when thinking about the environment as being the physical universe, as performance on any fictive universe is obviously irrelevant. But, we should expect a sharper inductive bias if we further constrain our environment. Indeed, we implicitly do so by defining GAI in terms of accomplishing that humans consider useful. One common version of this is need the for 'common-sense reasoning', which implicitly appeals to the statistics of physical universe as perceived by humans.
HoLiSwap: Reducing Wire Energy in L1 Caches
Venue: arXiv cs.AR 2017
Year: 2017

This paper describes HoLiSwap a method to reduce L1 cache wire energy, a significant fraction of total cache energy, by swapping hot lines to the cache way nearest to the processor. We observe that (i) a small fraction (<3%) of cache lines (hot lines) serve over 60% of the L1 cache accesses and (ii) the difference in wire energy between the nearest and farthest cache subarray can be over 6$\times$. Our method exploits this difference in wire energy to dynamically identify hot lines and swap them to the nearest physical way in a set-associative L1 cache. This provides up to 44% improvement in the wire energy (1.82% saving in overall system energy) with no impact on the cache miss rate and 0.13% performance drop. We also show that HoLiSwap can simplify way-prediction.
Analysis of Distributed ADMM Algorithm for Consensus Optimization in Presence of Error
Venue: arXiv cs.DC 2017
Year: 2017

ADMM is a popular algorithm for solving convex optimization problems. Applying this algorithm to distributed consensus optimization problem results in a fully distributed iterative solution which relies on processing at the nodes and communication between neighbors. Local computations usually suffer from different types of errors, due to e.g., observation or quantization noise, which can degrade the performance of the algorithm. In this work, we focus on analyzing the convergence behavior of distributed ADMM for consensus optimization in presence of additive node error. We specifically show that (a noisy) ADMM converges linearly under certain conditions and also examine the associated convergence point. Numerical results are provided which demonstrate the effectiveness of the presented analysis.
Multipermutation Ulam Sphere Analysis Toward Characterizing Maximal Code Size
Venue: arXiv cs.IT 2017
Year: 2017

Permutation codes, in the form of rank modulation, have shown promise for applications such as flash memory. One of the metrics recently suggested as appropriate for rank modulation is the Ulam metric, which measures the minimum translocation distance between permutations. Multipermutation codes have also been proposed as a generalization of permutation codes that would improve code size (and consequently the code rate). In this paper we analyze the Ulam metric in the context of multipermutations, noting some similarities and differences between the Ulam metric in the context of permutations. We also consider sphere sizes for multipermutations under the Ulam metric and resulting bounds on code size.
A feasibility study on SSVEP-based interaction with motivating and immersive virtual and augmented reality
Venue: arXiv cs.HC 2017
Year: 2017

Non-invasive steady-state visual evoked potential (SSVEP) based brain-computer interface (BCI) systems offer high bandwidth compared to other BCI types and require only minimal calibration and training. Virtual reality (VR) has been already validated as effective, safe, affordable and motivating feedback modality for BCI experiments. Augmented reality (AR) enhances the physical world by superimposing informative, context sensitive, computer generated content. In the context of BCI, AR can be used as a friendlier and more intuitive real-world user interface, thereby facilitating a more seamless and goal directed interaction. This can improve practicality and usability of BCI systems and may help to compensate for their low bandwidth. In this feasibility study, three healthy participants had to finish a complex navigation task in immersive VR and AR conditions using an online SSVEP BCI. Two out of three subjects were successful in all conditions. To our knowledge, this is the first work to present an SSVEP BCI that operates using target stimuli integrated in immersive VR and AR (head-mounted display and camera). This research direction can benefit patients by introducing more intuitive and effective real-world interaction (e.g. smart home control). It may also be relevant for user groups that require or benefit from hands free operation (e.g. due to temporary situational disability).
The Adaptive $s$-step Conjugate Gradient Method
Venue: arXiv cs.NA 2017
Year: 2017

On modern large-scale parallel computers, the performance of Krylov subspace iterative methods is limited by global synchronization. This has inspired the development of $s$-step Krylov subspace method variants, in which iterations are computed in blocks of $s$, which can reduce the number of global synchronizations per iteration by a factor of $O(s)$. Although the $s$-step variants are mathematically equivalent to their classical counterparts, they can behave quite differently in finite precision depending on the parameter $s$. If $s$ is chosen too large, the $s$-step method can suffer a convergence delay and a decrease in attainable accuracy relative to the classical method. This makes it difficult for a potential user of such methods - the $s$ value that minimizes the time per iteration may not be the best $s$ for minimizing the overall time-to-solution, and further may cause an unacceptable decrease in accuracy. Towards improving the reliability and usability of $s$-step Krylov subspace methods, in this work we derive the \emph{adaptive $s$-step CG method}, a variable $s$-step CG method where in block $k$, the parameter $s_k$ is determined automatically such that a user-specified accuracy is attainable. The method for determining $s_k$ is based on a bound on growth of the residual gap within block $k$, from which we derive a constraint on the condition numbers of the computed $O(s_k)$-dimensional Krylov subspace bases. The computations required for determining the block size $s_k$ can be performed without increasing the number of global synchronizations per block. Our numerical experiments demonstrate that the adaptive $s$-step CG method is able to attain up to the same accuracy as classical CG while still significantly reducing the total number of global synchronizations.
Towards Economic Models for MOOC Pricing Strategy Design
Venue: arXiv cs.CY 2017
Year: 2017

MOOCs have brought unprecedented opportunities of making high-quality courses accessible to everybody. However, from the business point of view, MOOCs are often challenged for lacking of sustainable business models, and academic research for marketing strategies of MOOCs is also a blind spot currently. In this work, we try to formulate the business models and pricing strategies in a structured and scientific way. Based on both theoretical research and real marketing data analysis from a MOOC platform, we present the insights of the pricing strategies for existing MOOC markets. We focus on the pricing strategies for verified certificates in the B2C markets, and also give ideas of modeling the course sub-licensing services in B2B markets.
The German eID as an Authentication Token on Android Devices
Venue: arXiv cs.CR 2017
Year: 2017

Due to the rapid increase of digitization within our society, digital identities gain more and more importance. Provided by the German eID solution, every citizen has the ability to identify himself against various governmental and private organizations with the help of his personal electronic ID card and a corresponding card reader. While there are several solutions available for desktop use of the eID infrastructure, mobile approaches have to be payed more attention. In this paper we present a new approach for using the German eID concept on an Android device without the need of the actual identity card and card reader. A security evaluation of our approach reveals that two non-critical vulnerabilities on the architecture can't be avoided. Nevertheless, no sensitive information are compromised. A proof of concept shows that an actual implementation faces some technical issues which have to be solved in the future.
Using data-compressors for statistical analysis of problems on homogeneity testing and classification
Venue: arXiv cs.IT 2017
Year: 2017

Nowadays data compressors are applied to many problems of text analysis, but many such applications are developed outside of the framework of mathematical statistics. In this paper we overcome this obstacle and show how several methods of classical mathematical statistics can be developed based on applications of the data compressors.
Iterative Block Tensor Singular Value Thresholding for Extraction of Low Rank Component of Image Data
Venue: arXiv cs.CV 2017
Year: 2017

Tensor principal component analysis (TPCA) is a multi-linear extension of principal component analysis which converts a set of correlated measurements into several principal components. In this paper, we propose a new robust TPCA method to extract the princi- pal components of the multi-way data based on tensor singular value decomposition. The tensor is split into a number of blocks of the same size. The low rank component of each block tensor is extracted using iterative tensor singular value thresholding method. The prin- cipal components of the multi-way data are the concatenation of all the low rank components of all the block tensors. We give the block tensor incoherence conditions to guarantee the successful decom- position. This factorization has similar optimality properties to that of low rank matrix derived from singular value decomposition. Ex- perimentally, we demonstrate its effectiveness in two applications, including motion separation for surveillance videos and illumination normalization for face images.
On the Asymptotic Behavior of Ultra-Densification under a Bounded Dual-Slope Path Loss Model
Venue: arXiv cs.NI 2017
Year: 2017

In this paper, we investigate the impact of network densification on the performance in terms of downlink signal-to-interference (SIR) coverage probability and network area spectral efficiency (ASE). A sophisticated bounded dual-slope path loss model and practical user equipment (UE) densities are incorporated in the analysis, which have never been jointly considered before. By using stochastic geometry, we derive an integral expression along with closed-form bounds of the coverage probability and ASE, validated by simulation results. Through these, we provide the asymptotic behavior of ultra-densification. The coverage probability and ASE have non-zero convergence in asymptotic regions unless UE density goes to infinity (full load). Meanwhile, the effect of UE density on the coverage probability is analyzed. The coverage probability will reveal an U-shape for large UE densities due to interference fall into the near-field, but it will keep increasing for low UE densites. Furthermore, our results indicate that the performance is overestimated without applying the bounded dual-slope path loss model. The derived expressions and results in this work pave the way for future network provisioning.
On Optimal Service Differentiation in Congested Network Markets
Venue: arXiv cs.NI 2017
Year: 2017

As Internet applications have become more diverse in recent years, users having heavy demand for online video services are more willing to pay higher prices for better services than light users that mainly use e-mails and instant messages. This encourages the Internet Service Providers (ISPs) to explore service differentiations so as to optimize their profits and allocation of network resources. Much prior work has focused on the viability of network service differentiation by comparing with the case of a single-class service. However, the optimal service differentiation for an ISP subject to resource constraints has remained unsolved. In this work, we establish an optimal control framework to derive the analytical solution to an ISP's optimal service differentiation, i.e. the optimal service qualities and associated prices. By analyzing the structures of the solution, we reveal how an ISP should adjust the service qualities and prices in order to meet varying capacity constraints and users' characteristics. We also obtain the conditions under which ISPs have strong incentives to implement service differentiation and whether regulators should encourage such practices.
Embedding Watermarks into Deep Neural Networks
Venue: arXiv cs.CV 2017
Year: 2017

Deep neural networks have recently achieved significant progress. Sharing trained models of these deep neural networks is very important in the rapid progress of researching or developing deep neural network systems. At the same time, it is necessary to protect the rights of shared trained models. To this end, we propose to use a digital watermarking technology to protect intellectual property or detect intellectual property infringement of trained models. Firstly, we formulate a new problem: embedding watermarks into deep neural networks. We also define requirements, embedding situations, and attack types for watermarking to deep neural networks. Secondly, we propose a general framework to embed a watermark into model parameters using a parameter regularizer. Our approach does not hurt the performance of networks into which a watermark is embedded. Finally, we perform comprehensive experiments to reveal the potential of watermarking to deep neural networks as a basis of this new problem. We show that our framework can embed a watermark in the situations of training a network from scratch, fine-tuning, and distilling without hurting the performance of a deep neural network. The embedded watermark does not disappear even after fine-tuning or parameter pruning; the watermark completely remains even after removing 65% of parameters were pruned. The implementation of this research is: https://github.com/yu4u/dnn-watermark
The complexity of quantified constraints using the algebraic formulation
Venue: arXiv cs.LO 2017
Year: 2017

Let A be an idempotent algebra on a finite domain. We combine results of Chen, Zhuk and Carvalho et al. to argue that if A satisfies the polynomially generated powers property (PGP), then QCSP(Inv(A)) is in NP. We then use the result of Zhuk to prove a converse, that if QCSP(Inv(A)) satisfies the exponentially generated powers property (EGP), then QCSP(Inv(A)) is co-NP-hard. Since Zhuk proved that only PGP and EGP are possible, we derive a full dichotomy for the QCSP, justifying the moral correctness of what we term the Chen Conjecture. We examine in closer detail the situation for domains of size three. Over any finite domain, the only type of PGP that can occur is switchability. Switchability was introduced by Chen as a generalisation of the already-known Collapsibility. For three-element domain algebras A that are Switchable, we prove that for every finite subset Delta of Inv(A), Pol(Delta) is Collapsible. The significance of this is that, for QCSP on finite structures (over three-element domain), all QCSP tractability explained by Switchability is already explained by Collapsibility. Finally, we present a three-element domain complexity classification vignette, using known as well as derived results.