OpenRaiser/Intern-Atlas huggingface dataset:
The Potential of Resource Sharing in 5G Millimeter-Wave Bands
Venue: arXiv cs.NI 2016
Year: 2016

With the severe spectrum shortage in conventional cellular bands, the millimeter (mmWave) frequencies, roughly above 10~GHz, have been attracting growing attention for next-generation micro- and pico- cellular wireless networks. A fundamental and open question is how these bands should be used by cellular operators. Cellular spectrum has been traditionally allocated following an exclusive ownership model. However, in this paper we argue that the distinct nature of mmWave communication -- the massive bandwidth degrees of freedom, directional isolation and high susceptibility to blockage -- suggest that spectrum and infrastructure sharing between multiple operators may be necessary to exploit the full potential of these bands. High-level capacity analyses are presented that reveal significant possible gains under spectrum and infrastructure sharing, even under minimal coordination between operators. Moreover, we discuss how network technologies including software defined networks (SDNs) and network function virtualization (NFV) can easily enable resource sharing by having a programmable core entity provide transparent inter-operator access to the end user.
Interdependent Network Formation Games
Venue: arXiv cs.SI 2016
Year: 2016

Designing optimal interdependent networks is important for the robustness and efficiency of national critical infrastructures. Here, we establish a two-person game-theoretic model in which two network designers choose to maximize the global connectivity independently. This framework enables decentralized network design by using iterative algorithms. After a finite number of steps, the algorithm will converge to a Nash equilibrium, and yields the equilibrium topology of the network. We corroborate our results by using numerical experiments, and compare the Nash equilibrium solutions with their team solution counterparts. The experimental results of the game method and the team method provide design guidelines to increase the efficiency of the interdependent network formation games. Moreover, the proposed game framework can be generally applied to a diverse number of applications, including power system networks and international air transportation networks.
Big Data For Development: Applications and Techniques
Venue: arXiv cs.CY 2016
Year: 2016

With the explosion of social media sites and proliferation of digital computing devices and Internet access, massive amounts of public data is being generated on a daily basis. Efficient techniques/ algorithms to analyze this massive amount of data can provide near real-time information about emerging trends and provide early warning in case of an imminent emergency (such as the outbreak of a viral disease). In addition, careful mining of these data can reveal many useful indicators of socioeconomic and political events, which can help in establishing effective public policies. The focus of this study is to review the application of big data analytics for the purpose of human development. The emerging ability to use big data techniques for development (BD4D) promises to revolutionalize healthcare, education, and agriculture; facilitate the alleviation of poverty; and help to deal with humanitarian crises and violent conflicts. Besides all the benefits, the large-scale deployment of BD4D is beset with several challenges due to the massive size, fast-changing and diverse nature of big data. The most pressing concerns relate to efficient data acquisition and sharing, establishing of context (e.g., geolocation and time) and veracity of a dataset, and ensuring appropriate privacy. In this study, we provide a review of existing BD4D work to study the impact of big data on the development of society. In addition to reviewing the important works, we also highlight important challenges and open issues.
CNN for License Plate Motion Deblurring
Venue: arXiv cs.CV 2016
Year: 2016

In this work we explore the previously proposed approach of direct blind deconvolution and denoising with convolutional neural networks in a situation where the blur kernels are partially constrained. We focus on blurred images from a real-life traffic surveillance system, on which we, for the first time, demonstrate that neural networks trained on artificial data provide superior reconstruction quality on real images compared to traditional blind deconvolution methods. The training data is easy to obtain by blurring sharp photos from a target system with a very rough approximation of the expected blur kernels, thereby allowing custom CNNs to be trained for a specific application (image content and blur range). Additionally, we evaluate the behavior and limits of the CNNs with respect to blur direction range and length.
Firefly Algorithm for optimization problems with non-continuous variables: A Review and Analysis
Venue: arXiv cs.NE 2016
Year: 2016

Firefly algorithm is a swarm based metaheuristic algorithm inspired by the flashing behavior of fireflies. It is an effective and an easy to implement algorithm. It has been tested on different problems from different disciplines and found to be effective. Even though the algorithm is proposed for optimization problems with continuous variables, it has been modified and used for problems with non-continuous variables, including binary and integer valued problems. In this paper a detailed review of this modifications of firefly algorithm for problems with non-continuous variables will be discussed. The strength and weakness of the modifications along with possible future works will be presented.
Determination of sizes of optimal three-dimensional optical orthogonal codes of weight three with the AM-OPP restriction
Venue: arXiv cs.IT 2016
Year: 2016

In this paper, we further investigate the constructions on three-dimensional $(u\times v\times w,k,1)$ optical orthogonal codes with the at most one optical pulse per wavelength/time plane restriction (briefly AM-OPP $3$-D $(u\times v\times w,k,1)$-OOCs) by way of the corresponding designs. Several new auxiliary designs such as incomplete holey group divisible designs and incomplete group divisible packings are introduced and therefore new constructions are presented. As a consequence, the exact number of codewords of an optimal AM-OPP $3$-D $(u\times v\times w,3,1)$-OOC is finally determined for any positive integers $v,w$ and $u\geq3$.
Can one quantum bit separate any pair of words with zero-error?
Venue: arXiv cs.FL 2016
Year: 2016

Determining the minimum number of states required by a finite automaton to separate a given pair of different words is an important problem. In this paper, we consider this problem for quantum automata (QFAs). We show that 2-state QFAs can separate any pair of words in nondeterministic acceptance mode and conjecture that they can separate any pair also with zero-error. Then, we focus on (a more general problem) separating a pair of two disjoint finite set of words. We show that QFAs can separate them efficiently in nondeterministic acceptance mode, i.e. the number of states is two to the power of the size of the small set. Additionally, we examine affine finite automata (AfAs) and show that two states are enough to separate any pair with zero-error. Moreover, AfAs can separate any pair of disjoint finite sets of words with one-sided bounded error efficiently like QFAs in nondeterministic mode.
Causal Discovery from Subsampled Time Series Data by Constraint Optimization
Venue: arXiv cs.AI 2016
Year: 2016

This paper focuses on causal structure estimation from time series data in which measurements are obtained at a coarser timescale than the causal timescale of the underlying system. Previous work has shown that such subsampling can lead to significant errors about the system's causal structure if not properly taken into account. In this paper, we first consider the search for the system timescale causal structures that correspond to a given measurement timescale structure. We provide a constraint satisfaction procedure whose computational performance is several orders of magnitude better than previous approaches. We then consider finite-sample data as input, and propose the first constraint optimization approach for recovering the system timescale causal structure. This algorithm optimally recovers from possible conflicts due to statistical errors. More generally, these advances allow for a robust and non-parametric estimation of system timescale causal structures from subsampled time series data.
Adaptive Frequency Cepstral Coefficients for Word Mispronunciation Detection
Venue: arXiv cs.SD 2016
Year: 2016

Systems based on automatic speech recognition (ASR) technology can provide important functionality in computer assisted language learning applications. This is a young but growing area of research motivated by the large number of students studying foreign languages. Here we propose a Hidden Markov Model (HMM)-based method to detect mispronunciations. Exploiting the specific dialog scripting employed in language learning software, HMMs are trained for different pronunciations. New adaptive features have been developed and obtained through an adaptive warping of the frequency scale prior to computing the cepstral coefficients. The optimization criterion used for the warping function is to maximize separation of two major groups of pronunciations (native and non-native) in terms of classification rate. Experimental results show that the adaptive frequency scale yields a better coefficient representation leading to higher classification rates in comparison with conventional HMMs using Mel-frequency cepstral coefficients.
Robust Localization and Tracking of Simultaneous Moving Sound Sources Using Beamforming and Particle Filtering
Venue: arXiv cs.RO 2016
Year: 2016

Mobile robots in real-life settings would benefit from being able to localize and track sound sources. Such a capability can help localizing a person or an interesting event in the environment, and also provides enhanced processing for other capabilities such as speech recognition. To give this capability to a robot, the challenge is not only to localize simultaneous sound sources, but to track them over time. In this paper we propose a robust sound source localization and tracking method using an array of eight microphones. The method is based on a frequency-domain implementation of a steered beamformer along with a particle filter-based tracking algorithm. Results show that a mobile robot can localize and track in real-time multiple moving sources of different types over a range of 7 meters. These new capabilities allow a mobile robot to interact using more natural means with people in real life settings.
Autonomous navigation for low-altitude UAVs in urban areas
Venue: arXiv cs.RO 2016
Year: 2016

In recent years, consumer Unmanned Aerial Vehicles have become very popular, everyone can buy and fly a drone without previous experience, which raises concern in regards to regulations and public safety. In this paper, we present a novel approach towards enabling safe operation of such vehicles in urban areas. Our method uses geodetically accurate dataset images with Geographical Information System (GIS) data of road networks and buildings provided by Google Maps, to compute a weighted A* shortest path from start to end locations of a mission. Weights represent the potential risk of injuries for individuals in all categories of land-use, i.e. flying over buildings is considered safer than above roads. We enable safe UAV operation in regards to 1- land-use by computing a static global path dependent on environmental structures, and 2- avoiding flying over moving objects such as cars and pedestrians by dynamically optimizing the path locally during the flight. As all input sources are first geo-registered, pixels and GPS coordinates are equivalent, it therefore allows us to generate an automated and user-friendly mission with GPS waypoints readable by consumer drones' autopilots. We simulated 54 missions and show significant improvement in maximizing UAV's standoff distance to moving objects with a quantified safety parameter over 40 times better than the naive straight line navigation.
Machine Agency in Human-Machine Networks; Impacts and Trust Implications
Venue: arXiv cs.HC 2016
Year: 2016

We live in an emerging hyper-connected era in which people are in contact and interacting with an increasing number of other people and devices. Increasingly, modern IT systems form networks of humans and machines that interact with one another. As machines take a more active role in such networks, they exert an in-creasing level of influence on other participants. We review the existing literature on agency and propose a definition of agency that is practical for describing the capabilities and impact human and machine actors may have in a human-machine network. On this basis, we discuss and demonstrate the impact and trust implica-tions for machine actors in human-machine networks for emergency decision support, healthcare and future smart homes. We maintain that machine agency not only facilitates human to machine trust, but also interpersonal trust; and that trust must develop to be able to seize the full potential of future technology.
Three Theorems on odd degree Chebyshev polynomials and more generalized permutation polynomials over a ring of module $2^w$
Venue: arXiv cs.IT 2016
Year: 2016

Odd degree Chebyshev polynomials over a ring of modulo $2^w$ have two kinds of period. One is an "orbital period". Odd degree Chebyshev polynomials are bijection over the ring. Therefore, when an odd degree Chebyshev polynomial iterate affecting a factor of the ring, we can observe an orbit over the ring. The "orbital period" is a period of the orbit. The other is a "degree period". It is observed when changing the degree of Chebyshev polynomials with a fixed argument of polynomials. Both kinds of period have not been completely studied. In this paper, we clarify completely both of them. The knowledge about them enables us to efficiently solve degree decision problem of Chebyshev polynomial over the ring, and so a key-exchange protocol with Chebyshev polynomial over the ring is not secure. In addition, we generalize the discussion and show that a key-exchange protocol with more generalized permutation polynomials which belong to a certain class is not secure.
Victory Sign Biometric for Terrorists Identification
Venue: arXiv cs.CV 2016
Year: 2016

Covering the face and all body parts, sometimes the only evidence to identify a person is their hand geometry, and not the whole hand- only two fingers (the index and the middle fingers) while showing the victory sign, as seen in many terrorists videos. This paper investigates for the first time a new way to identify persons, particularly (terrorists) from their victory sign. We have created a new database in this regard using a mobile phone camera, imaging the victory signs of 50 different persons over two sessions. Simple measurements for the fingers, in addition to the Hu Moments for the areas of the fingers were used to extract the geometric features of the shown part of the hand shown after segmentation. The experimental results using the KNN classifier were encouraging for most of the recorded persons; with about 40% to 93% total identification accuracy, depending on the features, distance metric and K used.
Certified Universal Gathering in $R^2$ for Oblivious Mobile Robots
Venue: arXiv cs.DC 2016
Year: 2016

We present a unified formal framework for expressing mobile robots models, protocols, and proofs, and devise a protocol design/proof methodology dedicated to mobile robots that takes advantage of this formal framework. As a case study, we present the first formally certified protocol for oblivious mobile robots evolving in a two-dimensional Euclidean space. In more details, we provide a new algorithm for the problem of universal gathering mobile oblivious robots (that is, starting from any initial configuration that is not bivalent, using any number of robots, the robots reach in a finite number of steps the same position, not known beforehand) without relying on a common orientation nor chirality. We give very strong guaranties on the correctness of our algorithm by proving formally that it is correct, using the COQ proof assistant. This result demonstrates both the effectiveness of the approach to obtain new algorithms that use as few assumptions as necessary, and its manageability since the amount of developed code remains human readable.
Exact Weighted Minwise Hashing in Constant Time
Venue: arXiv cs.DS 2016
Year: 2016

Weighted minwise hashing (WMH) is one of the fundamental subroutine, required by many celebrated approximation algorithms, commonly adopted in industrial practice for large scale-search and learning. The resource bottleneck of the algorithms is the computation of multiple (typically a few hundreds to thousands) independent hashes of the data. The fastest hashing algorithm is by Ioffe \cite{Proc:Ioffe_ICDM10}, which requires one pass over the entire data vector, $O(d)$ ($d$ is the number of non-zeros), for computing one hash. However, the requirement of multiple hashes demands hundreds or thousands passes over the data. This is very costly for modern massive dataset. In this work, we break this expensive barrier and show an expected constant amortized time algorithm which computes $k$ independent and unbiased WMH in time $O(k)$ instead of $O(dk)$ required by Ioffe's method. Moreover, our proposal only needs a few bits (5 - 9 bits) of storage per hash value compared to around $64$ bits required by the state-of-art-methodologies. Experimental evaluations, on real datasets, show that for computing 500 WMH, our proposal can be 60000x faster than the Ioffe's method without losing any accuracy. Our method is also around 100x faster than approximate heuristics capitalizing on the efficient "densified" one permutation hashing schemes \cite{Proc:OneHashLSH_ICML14}. Given the simplicity of our approach and its significant advantages, we hope that it will replace existing implementations in practice.
Towards Least Privilege Containers with Cimplifier
Venue: arXiv cs.CR 2016
Year: 2016

Application containers, such as Docker containers, have recently gained popularity as a solution for agile and seamless deployment of applications. These light-weight virtualization environments run applications that are packed together with their resources and configuration information, and thus can be deployed across various software platforms. However, these software ecosystems are not conducive to the true and tried security principles of privilege separation (PS) and principle of least privilege (PLP). We propose algorithms and a tool Cimplifier, which address these concerns in the context of containers. Specifically, given a container our tool partitions them into simpler containers, which are only provided enough resources to perform their functionality. As part our solution, we develop techniques for analyzing resource usage, for performing partitioning, and gluing the containers together to preserve functionality. Our evaluation on real-world containers demonstrates that Cimplifier can preserve the original functionality, leads to reduction in image size of 58-95%, and processes even large containers in under thirty seconds.
On Equivalence and Uniformisation Problems for Finite Transducers
Venue: arXiv cs.FL 2016
Year: 2016

Transductions are binary relations of finite words. For rational transductions, i.e., transductions defined by finite transducers, the inclusion, equivalence and sequential uniformisation problems are known to be undecidable. In this paper, we investigate stronger variants of inclusion, equivalence and sequential uniformisation, based on a general notion of transducer resynchronisation, and show their decidability. We also investigate the classes of finite-valued rational transductions and deterministic rational transductions, which are known to have a decidable equivalence problem. We show that sequential uniformisation is also decidable for them.
Opportunistic Jamming for Enhancing Security: Stochastic Geometry Modeling and Analysis
Venue: arXiv cs.IT 2016
Year: 2016

This correspondence studies the secrecy communication of the single-input single-output multi-eavesdropper (SISOME) channel with multiple single-antenna jammers, where the jammers and eavesdroppers are distributed according to the independent two-dimensional homogeneous Poisson point process (PPP). For enhancing the physical layer security, we propose an opportunistic multiple jammer selection scheme, where the jammers whose channel gains to the legitimate receiver less than a threshold, are selected to transmit independent and identically distributed (\emph{i.i.d.}) Gaussian jamming signals to confound the eavesdroppers. We characterize the secrecy throughput achieved by our proposed jammer selection scheme, and show that the secrecy throughput is a quasi-concave function of the selection threshold.
Perceptually-Motivated Nonlinear Channel Decorrelation For Stereo Acoustic Echo Cancellation
Venue: arXiv cs.SD 2016
Year: 2016

Acoustic echo cancellation with stereo signals is generally an under-determined problem because of the high coherence between the left and right channels. In this paper, we present a novel method of significantly reducing inter-channel coherence without affecting the audio quality. Our work takes into account psychoacoustic masking and binaural auditory cues. The proposed non-linear processing combines a shaped comb-allpass (SCAL) filter with the injection of psychoacoustically masked noise. We show that the proposed method performs significantly better than other known methods for reducing inter-channel coherence.
Lie Access Neural Turing Machine
Venue: arXiv cs.NE 2016
Year: 2016

Following the recent trend in explicit neural memory structures, we present a new design of an external memory, wherein memories are stored in an Euclidean key space $\mathbb R^n$. An LSTM controller performs read and write via specialized read and write heads. It can move a head by either providing a new address in the key space (aka random access) or moving from its previous position via a Lie group action (aka Lie access). In this way, the "L" and "R" instructions of a traditional Turing Machine are generalized to arbitrary elements of a fixed Lie group action. For this reason, we name this new model the Lie Access Neural Turing Machine, or LANTM. We tested two different configurations of LANTM against an LSTM baseline in several basic experiments. We found the right configuration of LANTM to outperform the baseline in all of our experiments. In particular, we trained LANTM on addition of $k$-digit numbers for $2 \le k \le 16$, but it was able to generalize almost perfectly to $17 \le k \le 32$, all with the number of parameters 2 orders of magnitude below the LSTM baseline.
Optimizing the Learning Order of Chinese Characters Using a Novel Topological Sort Algorithm
Venue: arXiv cs.CL 2016
Year: 2016

We present a novel algorithm for optimizing the order in which Chinese characters are learned, one that incorporates the benefits of learning them in order of usage frequency and in order of their hierarchal structural relationships. We show that our work outperforms previously published orders and algorithms. Our algorithm is applicable to any scheduling task where nodes have intrinsic differences in importance and must be visited in topological order.