OpenRaiser/Intern-Atlas huggingface dataset:
Physical Layer Security for RF Satellite Channels in the Finite-length Regime
Venue: arXiv cs.IT 2018
Year: 2018

Secure communications is becoming increasingly relevant in the development of space technology. Well established cryptographic technology is already in place and is expected to continue to be so. On the other hand, information theoretical security emerges as a post-quantum versatile candidate to complement overall security strength. In order to prove such potential, performance analysis methods are needed that consider realistic legitimate and eavesdropper system assumptions and non-asymptotic coding lengths. In this paper we propose the design of secure radio frequency (RF) satellite links with realistic system assumptions. Our contribution is three-fold. First, we propose a wiretap channel model for the finite-length regime. The model includes an stochastic wiretap encoding method using existing practical linear error correcting codes and hash codes. Secrecy is provided with privacy amplification, for which the finite-length secrecy metric is given that upper bounds semantic secrecy. Second, we derive a novel RF (broadcast) satellite wiretap channel model that parameterizes the stochastic degraded channel around the legitimate channel, a necessary condition to enable secure communication. Finally, we show the design of a secure satellite physical layer and finite-length performance evaluation. In doing so, we define as sacrifice rate the fixed fraction of the overall coding rate budget for reliability that needs to be allocated to secrecy. Our methodology does not make use of channel side information of the eavesdropper, only assumes worst case system assumptions. We illustrate our proposed design method with numerical results using practical error correcting codes in current standards of satellite communication.
Hardness of Approximate Nearest Neighbor Search
Venue: arXiv cs.CC 2018
Year: 2018

We prove conditional near-quadratic running time lower bounds for approximate Bichromatic Closest Pair with Euclidean, Manhattan, Hamming, or edit distance. Specifically, unless the Strong Exponential Time Hypothesis (SETH) is false, for every $δ>0$ there exists a constant $ε>0$ such that computing a $(1+ε)$-approximation to the Bichromatic Closest Pair requires $n^{2-δ}$ time. In particular, this implies a near-linear query time for Approximate Nearest Neighbor search with polynomial preprocessing time. Our reduction uses the Distributed PCP framework of [ARW'17], but obtains improved efficiency using Algebraic Geometry (AG) codes. Efficient PCPs from AG codes have been constructed in other settings before [BKKMS'16, BCGRS'17], but our construction is the first to yield new hardness results.
An Effective Multi-Cue Positioning System for Agricultural Robotics
Venue: arXiv cs.RO 2018
Year: 2018

The self-localization capability is a crucial component for Unmanned Ground Vehicles (UGV) in farming applications. Approaches based solely on visual cues or on low-cost GPS are easily prone to fail in such scenarios. In this paper, we present a robust and accurate 3D global pose estimation framework, designed to take full advantage of heterogeneous sensory data. By modeling the pose estimation problem as a pose graph optimization, our approach simultaneously mitigates the cumulative drift introduced by motion estimation systems (wheel odometry, visual odometry, ...), and the noise introduced by raw GPS readings. Along with a suitable motion model, our system also integrates two additional types of constraints: (i) a Digital Elevation Model and (ii) a Markov Random Field assumption. We demonstrate how using these additional cues substantially reduces the error along the altitude axis and, moreover, how this benefit spreads to the other components of the state. We report exhaustive experiments combining several sensor setups, showing accuracy improvements ranging from 37% to 76% with respect to the exclusive use of a GPS sensor. We show that our approach provides accurate results even if the GPS unexpectedly changes positioning mode. The code of our system along with the acquired datasets are released with this paper.
Power Control and Channel Allocation for D2D Underlaid Cellular Networks
Venue: arXiv cs.IT 2018
Year: 2018

Device-to-Device (D2D) communications underlaying cellular networks is a viable network technology that can potentially increase spectral utilization and improve power efficiency for proximitybased wireless applications and services. However, a major challenge in such deployment scenarios is the interference caused by D2D links when sharing the same resources with cellular users. In this work, we propose a channel allocation (CA) scheme together with a set of three power control (PC) schemes to mitigate interference in a D2D underlaid cellular system modeled as a random network using the mathematical tool of stochastic geometry. The novel aspect of the proposed CA scheme is that it enables D2D links to share resources with multiple cellular users as opposed to one as previously considered in the literature. Moreover, the accompanying distributed PC schemes further manage interference during link establishment and maintenance. The first two PC schemes compensate for large-scale path-loss effects and maximize the D2D sum rate by employing distance-dependent pathloss parameters of the D2D link and the base station, including an error estimation margin. The third scheme is an adaptive PC scheme based on a variable target signal-to-interference-plus-noise ratio, which limits the interference caused by D2D users and provides sufficient coverage probability for cellular users. Closed-form expressions for the coverage probability of cellular links, D2D links, and sum rate of D2D links are derived in terms of the allocated power, density of D2D links, and path-loss exponent. The impact of these key system parameters on network performance is analyzed and compared with previous work. Simulation results demonstrate an enhancement in cellular and D2D coverage probabilities, and an increase in spectral and power efficiency.
Model-Free Control for Distributed Stream Data Processing using Deep Reinforcement Learning
Venue: arXiv cs.DC 2018
Year: 2018

In this paper, we focus on general-purpose Distributed Stream Data Processing Systems (DSDPSs), which deal with processing of unbounded streams of continuous data at scale distributedly in real or near-real time. A fundamental problem in a DSDPS is the scheduling problem with the objective of minimizing average end-to-end tuple processing time. A widely-used solution is to distribute workload evenly over machines in the cluster in a round-robin manner, which is obviously not efficient due to lack of consideration for communication delay. Model-based approaches do not work well either due to the high complexity of the system environment. We aim to develop a novel model-free approach that can learn to well control a DSDPS from its experience rather than accurate and mathematically solvable system models, just as a human learns a skill (such as cooking, driving, swimming, etc). Specifically, we, for the first time, propose to leverage emerging Deep Reinforcement Learning (DRL) for enabling model-free control in DSDPSs; and present design, implementation and evaluation of a novel and highly effective DRL-based control framework, which minimizes average end-to-end tuple processing time by jointly learning the system environment via collecting very limited runtime statistics data and making decisions under the guidance of powerful Deep Neural Networks. To validate and evaluate the proposed framework, we implemented it based on a widely-used DSDPS, Apache Storm, and tested it with three representative applications. Extensive experimental results show 1) Compared to Storm's default scheduler and the state-of-the-art model-based method, the proposed framework reduces average tuple processing by 33.5% and 14.0% respectively on average. 2) The proposed framework can quickly reach a good scheduling solution during online learning, which justifies its practicability for online control in DSDPSs.
High-Dynamic-Range Imaging for Cloud Segmentation
Venue: arXiv cs.CV 2018
Year: 2018

Sky/cloud images obtained from ground-based sky-cameras are usually captured using a fish-eye lens with a wide field of view. However, the sky exhibits a large dynamic range in terms of luminance, more than a conventional camera can capture. It is thus difficult to capture the details of an entire scene with a regular camera in a single shot. In most cases, the circumsolar region is over-exposed, and the regions near the horizon are under-exposed. This renders cloud segmentation for such images difficult. In this paper, we propose HDRCloudSeg -- an effective method for cloud segmentation using High-Dynamic-Range (HDR) imaging based on multi-exposure fusion. We describe the HDR image generation process and release a new database to the community for benchmarking. Our proposed approach is the first using HDR radiance maps for cloud segmentation and achieves very good results.
Low-Complexity Concatenated LDPC-Staircase Codes
Venue: arXiv cs.IT 2018
Year: 2018

A low-complexity soft-decision concatenated FEC scheme, consisting of an inner LDPC code and an outer staircase code is proposed. The inner code is tasked with reducing the bit error probability below the outer-code threshold. The concatenated code is obtained by optimizing the degree distribution of the inner-code ensemble to minimize estimated data-flow, for various choices of outer staircase codes. A key feature that emerges from this optimization is that it pays to leave some inner codeword bits completely uncoded, thereby greatly reducing a significant portion of the decoding complexity. The trade-off between required SNR and decoding complexity of the designed codes is characterized by a Pareto frontier. Computer simulations of the resulting codes reveals that the net coding-gains of existing designs can be achieved with up to 71\% reduction in complexity. A hardware-friendly quasi-cyclic construction is given for the inner codes, which can realize an energy-efficient decoder implementation, and even further complexity reductions via a layered message-passing decoder schedule.
SpeechPy - A Library for Speech Processing and Recognition
Venue: arXiv cs.SD 2018
Year: 2018

SpeechPy is an open source Python package that contains speech preprocessing techniques, speech features, and important post-processing operations. It provides most frequent used speech features including MFCCs and filterbank energies alongside with the log-energy of filter-banks. The aim of the package is to provide researchers with a simple tool for speech feature extraction and processing purposes in applications such as Automatic Speech Recognition and Speaker Verification.
Scout: An Experienced Guide to Find the Best Cloud Configuration
Venue: arXiv cs.DC 2018
Year: 2018

Finding the right cloud configuration for workloads is an essential step to ensure good performance and contain running costs. A poor choice of cloud configuration decreases application performance and increases running cost significantly. While Bayesian Optimization is effective and applicable to any workloads, it is fragile because performance and workload are hard to model (to predict). In this paper, we propose a novel method, SCOUT. The central insight of SCOUT is that using prior measurements, even those for different workloads, improves search performance and reduces search cost. At its core, SCOUT extracts search hints (inference of resource requirements) from low-level performance metrics. Such hints enable SCOUT to navigate through the search space more efficiently---only spotlight region will be searched. We evaluate SCOUT with 107 workloads on Apache Hadoop and Spark. The experimental results demonstrate that our approach finds better cloud configurations with a lower search cost than state of the art methods. Based on this work, we conclude that (i) low-level performance information is necessary for finding the right cloud configuration in an effective, efficient and reliable way, and (ii) a search method can be guided by historical data, thereby reducing cost and improving performance.
CAESAR: Context Awareness Enabled Summary-Attentive Reader
Venue: arXiv cs.CL 2018
Year: 2018

Comprehending meaning from natural language is a primary objective of Natural Language Processing (NLP), and text comprehension is the cornerstone for achieving this objective upon which all other problems like chat bots, language translation and others can be achieved. We report a Summary-Attentive Reader we designed to better emulate the human reading process, along with a dictiontary-based solution regarding out-of-vocabulary (OOV) words in the data, to generate answer based on machine comprehension of reading passages and question from the SQuAD benchmark. Our implementation of these features with two popular models (Match LSTM and Dynamic Coattention) was able to reach close to matching the results obtained from humans.
Applied Erasure Coding in Networks and Distributed Storage
Venue: arXiv cs.IT 2018
Year: 2018

The amount of digital data is rapidly growing. There is an increasing use of a wide range of computer systems, from mobile devices to large-scale data centers, and important for reliable operation of all computer systems is mitigating the occurrence and the impact of errors in digital data. The demand for new ultra-fast and highly reliable coding techniques for data at rest and for data in transit is a major research challenge. Reliability is one of the most important design requirements. The simplest way of providing a degree of reliability is by using data replication techniques. However, replication is highly inefficient in terms of capacity utilization. Erasure coding has therefore become a viable alternative to replication since it provides the same level of reliability as replication with significantly less storage overhead. The present thesis investigates efficient constructions of erasure codes for different applications. Methods from both coding and information theory have been applied to network coding, Optical Packet Switching (OPS) networks and distributed storage systems. The following four issues are addressed: - Construction of binary and non-binary erasure codes; - Reduction of the header overhead due to the encoding coefficients in network coding; - Construction and implementation of new erasure codes for large-scale distributed storage systems that provide savings in the storage and network resources compared to state-of-the-art codes; and - Provision of a unified view on Quality of Service (QoS) in OPS networks when erasure codes are used, with the focus on Packet Loss Rate (PLR), survivability and secrecy.
Comparing Downward Fragments of the Relational Calculus with Transitive Closure on Trees
Venue: arXiv cs.DB 2018
Year: 2018

Motivated by the continuing interest in the tree data model, we study the expressive power of downward navigational query languages on trees and chains. Basic navigational queries are built from the identity relation and edge relations using composition and union. We study the effects on relative expressiveness when we add transitive closure, projections, coprojections, intersection, and difference; this for boolean queries and path queries on labeled and unlabeled structures. In all cases, we present the complete Hasse diagram. In particular, we establish, for each query language fragment that we study on trees, whether it is closed under difference and intersection.
Exploring Novel Game Spaces with Fluidic Games
Venue: arXiv cs.AI 2018
Year: 2018

With the growing integration of smartphones into our daily lives, and their increased ease of use, mobile games have become highly popular across all demographics. People listen to music, play games or read the news while in transit or bridging gap times. While mobile gaming is gaining popularity, mobile expression of creativity is still in its early stages. We present here a new type of mobile app -- fluidic games -- and illustrate our iterative approach to their design. This new type of app seamlessly integrates exploration of the design space into the actual user experience of playing the game, and aims to enrich the user experience. To better illustrate the game domain and our approach, we discuss one specific fluidic game, which is available as a commercial product. We also briefly discuss open challenges such as player support and how generative techniques can aid the exploration of the game space further.
Design of a Low Voltage Analog-to-Digital Converter using Voltage Controlled Stochastic Switching of Low Barrier Nanomagnets
Venue: arXiv cs.ET 2018
Year: 2018

The inherent stochasticity in many nano-scale devices makes them prospective candidates for low-power computations. Such devices have been demonstrated to exhibit probabilistic switching between two stable states to achieve stochastic behavior. Recently, superparamagnetic nanomagnets (having low energy barrier EB $\sim$ 1kT) have shown promise of achieving stochastic switching at GHz rates, with very low currents. On the other hand, voltage-controlled switching of nanomagnets through the Magneto-electric (ME) effect has shown further improvements in energy efficiency. In this simulation paper, we first analyze the stochastic switching characteristics of such super-paramagnetic nanomagnets in a voltage-controlled spintronic device. We study the influence of external bias on the switching behavior. Subsequently, we show that our proposed device leverages the voltage controlled stochasticity in performing low-voltage 8-bit analog to digital conversions. This eliminates the need for comparators, unlike the Complementary Metal-Oxide Semiconductor (CMOS)-based flash Analog-to-Digital converters (ADC). This device allows for a simple and compact design which can potentially be applied in implementing sensors which desire low voltage conversions.
Deep Continuous Clustering
Venue: arXiv cs.LG 2018
Year: 2018

Clustering high-dimensional datasets is hard because interpoint distances become less informative in high-dimensional spaces. We present a clustering algorithm that performs nonlinear dimensionality reduction and clustering jointly. The data is embedded into a lower-dimensional space by a deep autoencoder. The autoencoder is optimized as part of the clustering process. The resulting network produces clustered data. The presented approach does not rely on prior knowledge of the number of ground-truth clusters. Joint nonlinear dimensionality reduction and clustering are formulated as optimization of a global continuous objective. We thus avoid discrete reconfigurations of the objective that characterize prior clustering algorithms. Experiments on datasets from multiple domains demonstrate that the presented algorithm outperforms state-of-the-art clustering schemes, including recent methods that use deep networks.
Teaching the Formalization of Mathematical Theories and Algorithms via the Automatic Checking of Finite Models
Venue: arXiv cs.LO 2018
Year: 2018

Education in the practical applications of logic and proving such as the formal specification and verification of computer programs is substantially hampered by the fact that most time and effort that is invested in proving is actually wasted in vain: because of errors in the specifications respectively algorithms that students have developed, their proof attempts are often pointless (because the proposition proved is actually not of interest) or a priori doomed to fail (because the proposition to be proved does actually not hold), this is a frequent source of frustration and gives formal methods a bad reputation. RISCAL (RISC Algorithm Language) is a formal specification language and associated software system that attempts to overcome this problem by making logic formalization fun rather than a burden. To this end, RISCAL allows students to easily validate the correctness of instances of propositions respectively algorithms by automatically evaluating/executing and checking them on (small) finite models. Thus many/most errors can be quickly detected and subsequent proof attempts can be focused on propositions that are more/most likely to be both meaningful and true.
Optimizing Learned Bloom Filters by Sandwiching
Venue: arXiv cs.DS 2018
Year: 2018

We provide a simple method for improving the performance of the recently introduced learned Bloom filters, by showing that they perform better when the learned function is sandwiched between two Bloom filters.
Local Distance Metric Learning for Nearest Neighbor Algorithm
Venue: arXiv cs.CV 2018
Year: 2018

Distance metric learning is a successful way to enhance the performance of the nearest neighbor classifier. In most cases, however, the distribution of data does not obey a regular form and may change in different parts of the feature space. Regarding that, this paper proposes a novel local distance metric learning method, namely Local Mahalanobis Distance Learning (LMDL), in order to enhance the performance of the nearest neighbor classifier. LMDL considers the neighborhood influence and learns multiple distance metrics for a reduced set of input samples. The reduced set is called as prototypes which try to preserve local discriminative information as much as possible. The proposed LMDL can be kernelized very easily, which is significantly desirable in the case of highly nonlinear data. The quality as well as the efficiency of the proposed method assesses through a set of different experiments on various datasets and the obtained results show that LDML as well as the kernelized version is superior to the other related state-of-the-art methods.
Explanatory relations in arbitrary logics based on satisfaction systems, cutting and retraction
Venue: arXiv cs.AI 2018
Year: 2018

The aim of this paper is to introduce a new framework for defining abductive reasoning operators based on a notion of retraction in arbitrary logics defined as satisfaction systems. We show how this framework leads to the design of explanatory relations satisfying properties of abductive reasoning, and discuss its application to several logics. This extends previous work on propositional logics where retraction was defined as a morphological erosion. Here weaker properties are required for retraction, leading to a larger set of suitable operators for abduction for different logics.
OpenMath and SMT-LIB
Venue: arXiv cs.SC 2018
Year: 2018

OpenMath and SMT-LIB are languages with very different origins, but both "represent mathematics". We describe SMT-LIB for the OpenMath community and consider adaptations for both languages to support the growing SC-Square initiative.
An Offline-Sampling SMPC Framework with Application to Automated Space Maneuvers
Venue: arXiv cs.SY 2018
Year: 2018

In this paper, a sampling-based Stochastic Model Predictive Control algorithm is proposed for discrete-time linear systems subject to both parametric uncertainties and additive disturbances. One of the main drivers for the development of the proposed control strategy is the need of real-time implementability of guidance and control strategies for automated rendezvous and proximity operations between spacecraft. The paper presents considers the validation of the proposed control algorithm on an experimental testbed, showing how it may indeed be implemented in a realistic framework. Parametric uncertainties due to the mass variations during operations, linearization errors, and disturbances due to external space environment are simultaneously considered. The approach enables to suitably tighten the constraints to guarantee robust recursive feasibility when bounds on the uncertain variables are provided, and under mild assumptions, asymptotic stability in probability of the origin can be established. The offline sampling approach in the control design phase is shown to reduce the computational cost, which usually constitutes the main limit for the adoption of Stochastic Model Predictive Control schemes, especially for low-cost on-board hardware. These characteristics are demonstrated both through simulations and by means of experimental results.