OpenRaiser/Intern-Atlas huggingface dataset:
An Adaptive Deep Learning Algorithm Based Autoencoder for Interference Channels
Venue: arXiv cs.LG 2019
Year: 2019

Deep learning (DL) based autoencoder has shown great potential to significantly enhance the physical layer performance. In this paper, we present a DL based autoencoder for interference channel. Based on a characterization of a k-user Gaussian interference channel, where the interferences are classified as different levels from weak to very strong interferences based on a coupling parameter α, a DL neural network (NN) based autoencoder is designed to train the data set and decode the received signals. The performance such a DL autoencoder for different interference scenarios are studied, with α known or partially known, where we assume that α is predictable but with a varying up to 10\% at the training stage. The results demonstrate that DL based approach has a significant capability to mitigate the effect induced by a poor signal-to-noise ratio (SNR) and a high interference-to-noise ratio (INR). However, the enhancement depends on the knowledge of α as well as the interference levels. The proposed DL approach performs well with α up to 10\% offset for weak interference level. For strong and very strong interference channel, the offset of α needs to be constrained to less than 5\% and 2\%, respectively, to maintain similar performance as α is known.
Channel Extrapolation in FDD Massive MIMO: Theoretical Analysis and Numerical Validation
Venue: arXiv cs.IT 2019
Year: 2019

Downlink channel estimation in massive MIMO systems is well known to generate a large overhead in frequency division duplex (FDD) mode as the amount of training generally scales with the number of transmit antennas. Using instead an extrapolation of the channel from the measured uplink estimates to the downlink frequency band completely removes this overhead. In this paper, we investigate the theoretical limits of channel extrapolation in frequency. We highlight the advantage of basing the extrapolation on high-resolution channel estimation. A lower bound (LB) on the mean squared error (MSE) of the extrapolated channel is derived. A simplified LB is also proposed, giving physical intuition on the SNR gain and extrapolation range that can be expected in practice. The validity of the simplified LB relies on the assumption that the paths are well separated. The SNR gain then linearly improves with the number of receive antennas while the extrapolation performance penalty quadratically scales with the ratio of the frequency and the training bandwidth. The theoretical LB is numerically evaluated using a 3GPP channel model and we show that the LB can be reached by practical high-resolution parameter extraction algorithms. Our results show that there are strong limitations on the extrapolation range than can be expected in SISO systems while much more promising results can be obtained in the multiple-antenna setting as the paths can be more easily separated in the delay-angle domain.
Challenging Environments for Traffic Sign Detection: Reliability Assessment under Inclement Conditions
Venue: arXiv cs.CV 2019
Year: 2019

State-of-the-art algorithms successfully localize and recognize traffic signs over existing datasets, which are limited in terms of challenging condition type and severity. Therefore, it is not possible to estimate the performance of traffic sign detection algorithms under overlooked challenging conditions. Another shortcoming of existing datasets is the limited utilization of temporal information and the unavailability of consecutive frames and annotations. To overcome these shortcomings, we generated the CURE-TSD video dataset and hosted the first IEEE Video and Image Processing (VIP) Cup within the IEEE Signal Processing Society. In this paper, we provide a detailed description of the CURE-TSD dataset, analyze the characteristics of the top performing algorithms, and provide a performance benchmark. Moreover, we investigate the robustness of the benchmarked algorithms with respect to sign size, challenge type and severity. Benchmarked algorithms are based on state-of-the-art and custom convolutional neural networks that achieved a precision of 0.55 and a recall of 0.32, F0.5 score of 0.48 and F2 score of 0.35. Experimental results show that benchmarked algorithms are highly sensitive to tested challenging conditions, which result in an average performance drop of 0.17 in terms of precision and a performance drop of 0.28 in recall under severe conditions. The dataset is publicly available at https://github.com/olivesgatech/CURE-TSD.
Downlink NOMA in Multi-UAV Networks over Bivariate Rician Shadowed Fading Channels
Venue: arXiv cs.IT 2019
Year: 2019

Unmanned aerial vehicles (UAVs) are set to feature heavily in upcoming fifth generation (5G) networks. Yet, the adoption of multi-UAV networks means that spectrum scarcity in UAV communications is an issue in need of urgent solutions. Towards this end, downlink non-orthogonal multiple access (NOMA) is investigated in this paper for multi-UAV networks to improve spectrum utilization. Using the bivariate Rician shadowed fading model, closed-form expressions for the joint probability density function (PDF), marginal cumulative distribution functions (CDFs), and outage probability expressions are derived. Under a stochastic geometry framework for downlink NOMA at the UAVs, an outage probability analysis of the multi-UAV network is conducted, where it is shown that downlink NOMA attains lower outage probability than orthogonal multiple access (OMA). Furthermore, it is shown that NOMA is less susceptible to shadowing than OMA.
Predicting city safety perception based on visual image content
Venue: arXiv cs.CV 2019
Year: 2019

Safety perception measurement has been a subject of interest in many cities of the world. This is due to its social relevance, and to its effect on some local economic activities. Even though people safety perception is a subjective topic, sometimes it is possible to find out common patterns given a restricted geographical and sociocultural context. This paper presents an approach that makes use of image processing and machine learning techniques to detect with high accuracy urban environment patterns that could affect citizen's safety perception.
P-Reverb: Perceptual Characterization of Early and Late Reflections for Auditory Displays
Venue: arXiv cs.SD 2019
Year: 2019

We introduce a novel, perceptually derived metric (P-Reverb) that relates the just-noticeable difference (JND) of the early sound field(also called early reflections) to the late sound field (known as late reflections or reverberation). Early and late reflections are crucial components of the sound field and provide multiple perceptual cues for auditory displays. We conduct two extensive user evaluations that relate the JNDs of early reflections and late reverberation in terms of the mean-free path of the environment and present a novel P-Reverb metric. Our metric is used to estimate dynamic reverberation characteristics efficiently in terms of important parameters like reverberation time (RT60). We show the numerical accuracy of our P-Reverb metric in estimating RT60. Finally, we use our metric to design an interactive sound propagation algorithm and demonstrate its effectiveness on various benchmarks.
Covering Vectors by Spaces in Perturbed Graphic Matroids and Their Duals
Venue: arXiv cs.DS 2019
Year: 2019

Perturbed graphic matroids are binary matroids that can be obtained from a graphic matroid by adding a noise of small rank. More precisely, r-rank perturbed graphic matroid M is a binary matroid that can be represented in the form I +P, where I is the incidence matrix of some graph and P is a binary matrix of rank at most r. Such matroids naturally appear in a number of theoretical and applied settings. The main motivation behind our work is an attempt to understand which parameterized algorithms for various problems on graphs could be lifted to perturbed graphic matroids. We study the parameterized complexity of a natural generalization (for matroids) of the following fundamental problems on graphs: Steiner Tree and Multiway Cut. In this generalization, called the Space Cover problem, we are given a binary matroid M with a ground set E, a set of terminals T\subseteq E, and a non-negative integer k. The task is to decide whether T can be spanned by a subset of E\setminus T of size at most k. We prove that on graphic matroid perturbations, for every fixed r, Space Cover is fixed-parameter tractable parameterized by k. On the other hand, the problem becomes W[1]-hard when parameterized by r+k+|T| and it is NP-complete for r\leq 2 and |T|\leq 2. On cographic matroids, that are the duals of graphic matroids, Space Cover generalizes another fundamental and well-studied problem, namely Multiway Cut. We show that on the duals of perturbed graphic matroids the Space Cover problem is fixed-parameter tractable parameterized by r+k.
Effectiveness of Crypto-Transcoding for H.264/AVC and HEVC Video Bit-streams
Venue: arXiv cs.MM 2019
Year: 2019

To avoid delays arising from a need to decrypt a video prior to transcoding and then re-encrypt it afterwards, this paper assesses a selective encryption (SE) content protection scheme. The scheme is suited to both recent standardized codecs, namely H.264/Advanced Video Coding (AVC) and High Efficiency Video Coding (HEVC). Specifically, the paper outlines a joint crypto-transcoding scheme for secure transrating of a video bitstream. That is to say it generates new video bitrates, possibly as part of an HTTP Adaptive Streaming (HAS) content delivery network. The scheme will reduce the bitrate to one or more lower desired bit-rate without consuming time in the encryption/decryption process, which would be the case when full encryption is used. In addition, the decryption key no longer needs to be exposed at intermediate middleboxes, including when transrating is performed in a cloud datacenter. The effectiveness of the scheme is variously evaluated: by examination of the SE generated visual distortion; by the extent of computational and bitrate overheads; and by choice of cipher when encrypting the selected elements within the bitstream. Results indicate that there remains: a content; quantization level (after transrating of an encrypted video); and codec-type dependency to any distortion introduced. A further recommendation is that the Advanced Encryption Standard (AES) is preferred for SE to lightweight XOR encryption, despite it being taken up elsewhere as a real-time encryption method.
A Mobility-Aware Vehicular Caching Scheme in Content Centric Networks: Model and Optimization
Venue: arXiv cs.NI 2019
Year: 2019

Edge caching is being explored as a promising technology to alleviate the network burden of cellular networks by separating the computing functionalities away from cellular base stations. However, the service capability of existing caching scheme is limited by fixed edge infrastructure when facing the uncertainties of users' requests and locations. The vehicular caching, which uses the moving vehicles as cache carriers, is regard as an efficient method to solve the problem above. This paper studies the effectiveness of vehicular caching scheme in content centric networks by developing optimization model towards the minimization of network energy consumption. Particularly, we model the interactions between caching vehicles and mobile users as a 2-D Markov process, in order to characterize the network availability of mobile users. Based on the developed model, we propose an online vehicular caching design by optimizing network energy efficiency. Specifically, the problem of caching decision making is firstly formulated as a fractional optimization model, towards the optimal energy efficiency. Using nonlinear fractional programming technology and Lyapunov optimization theory, we derive the theoretical solution for the optimization model. An online caching algorithm to enable the optimal vehicular caching is developed based on the solution. Finally, extensive simulations are conducted to examine the performance of our proposal. By comparison, our online caching scheme outperforms the existing scheme in terms of energy efficiency, hit ratio, cache utilization, and system gain.
Travelling on Graphs with Small Highway Dimension
Venue: arXiv cs.DS 2019
Year: 2019

We study the Travelling Salesperson (TSP) and the Steiner Tree problem (STP) in graphs of low highway dimension. This graph parameter was introduced by Abraham et al. [SODA 2010] as a model for transportation networks, on which TSP and STP naturally occur for various applications in logistics. It was previously shown [Feldmann et al. ICALP 2015] that these problems admit a quasi-polynomial time approximation scheme (QPTAS) on graphs of constant highway dimension. We demonstrate that a significant improvement is possible in the special case when the highway dimension is 1, for which we present a fully-polynomial time approximation scheme (FPTAS). We also prove that STP is weakly NP-hard for these restricted graphs. For TSP we show NP-hardness for graphs of highway dimension 6, which answers an open problem posed in [Feldmann et al. ICALP 2015].
Jamming Suppression in Massive MIMO Systems
Venue: arXiv cs.IT 2019
Year: 2019

In this paper, we propose a framework for protecting the uplink transmission of a massive multiple-input multiple-output (mMIMO) system from a jamming attack. Our framework includes a novel minimum mean-squared error based jamming suppression (MMSE-JS) estimator for channel training and a linear zero-forcing jamming suppression (ZFJS) detector for uplink combining. The MMSE-JS exploits some intentionally unused pilots to reduce the pilot contamination caused by the jammer. The ZFJS suppresses the jamming interference during the detection of the legitimate users' data symbols. The implementation of the proposed framework is practical, since the complexities of computing the MMSE-JS and the ZFJS are linear (not exponential) with respect to the number of antennas at the base station and linear detectors with the same complexities as the ZFJS have been already fabricated using 28 nm FD-SOI (Fully Depleted Silicon On Insulator) technology in [12] and Xilinx Virtex-7 XC7VX690T FPGA in [13] for the mMIMO systems. Our analysis shows that the jammer cannot dramatically affect the performance of a mMIMO system equipped with the combination of MMSE-JS and ZFJS. Numerical results confirm our analysis.
Towards Optimal Depth Reductions for Syntactically Multilinear Circuits
Venue: arXiv cs.CC 2019
Year: 2019

We show that any $n$-variate polynomial computable by a syntactically multilinear circuit of size $\operatorname{poly}(n)$ can be computed by a depth-$4$ syntactically multilinear ($ΣΠΣΠ$) circuit of size at most $\exp\left({O\left(\sqrt{n\log n}\right)}\right)$. For degree $d = ω(n/\log n)$, this improves upon the upper bound of $\exp\left({O(\sqrt{d}\log n)}\right)$ obtained by Tavenas~\cite{T15} for general circuits, and is known to be asymptotically optimal in the exponent when $d < n^ε$ for a small enough constant $ε$. Our upper bound matches the lower bound of $\exp\left({Ω\left(\sqrt{n\log n}\right)}\right)$ proved by Raz and Yehudayoff~\cite{RY09}, and thus cannot be improved further in the exponent. Our results hold over all fields and also generalize to circuits of small individual degree. More generally, we show that an $n$-variate polynomial computable by a syntactically multilinear circuit of size $\operatorname{poly}(n)$ can be computed by a syntactically multilinear circuit of product-depth $Δ$ of size at most $\exp\left(O\left(Δ\cdot (n/\log n)^{1/Δ} \cdot \log n\right)\right)$. It follows from the lower bounds of Raz and Yehudayoff (CC 2009) that in general, for constant $Δ$, the exponent in this upper bound is tight and cannot be improved to $o\left(\left(n/\log n\right)^{1/Δ}\cdot \log n\right)$.
Do zealots increase or decrease the polarization in social networks?
Venue: arXiv cs.SI 2019
Year: 2019

Zealots are the vertices in a social network who do not change their opinions under social pressure, and are crucial to the study of opinion dynamics on complex networks. In this paper, we study the effect of zealots on the polarization dynamics of a deterministic majority-rule model using the configuration model as a substrate. To this end, we propose a novel quantifier, called `correlated polarization', for measuring the amount of polarization in the network when vertices can exists in two opposite states. The quantifier takes into account not only the fraction of vertices with each opinion, but also how they are connected to each other. We then show that the presence of zealots does not have a fixed effect on the polarization, and can change it in positive, negative or neutral way depending upon their topological characteristics like degree, their total fraction in the network, density and degree heterogeneity of the network, and the type of initial conditions of the dynamics. Our results particularly highlight the importance of the role played by the initial conditions in drifting the polarization towards lower or higher values as the total number of zealots is increased.
Predicting US State-Level Agricultural Sentiment as a Measure of Food Security with Tweets from Farming Communities
Venue: arXiv cs.CL 2019
Year: 2019

The ability to obtain accurate food security metrics in developing areas where relevant data can be sparse is critically important for policy makers tasked with implementing food aid programs. As a result, a great deal of work has been dedicated to predicting important food security metrics such as annual crop yields using a variety of methods including simulation, remote sensing, weather models, and human expert input. As a complement to existing techniques in crop yield prediction, this work develops neural network models for predicting the sentiment of Twitter feeds from farming communities. Specifically, we investigate the potential of both direct learning on a small dataset of agriculturally-relevant tweets and transfer learning from larger, well-labeled sentiment datasets from other domains (e.g.~politics) to accurately predict agricultural sentiment, which we hope would ultimately serve as a useful crop yield predictor. We find that direct learning from small, relevant datasets outperforms transfer learning from large, fully-labeled datasets, that convolutional neural networks broadly outperform recurrent neural networks on Twitter sentiment classification, and that these models perform substantially less well on ternary sentiment problems characteristic of practical settings than on binary problems often found in the literature.
Directional Regularized Tensor Modeling for Video Rain Streaks Removal
Venue: arXiv cs.CV 2019
Year: 2019

Outdoor videos sometimes contain unexpected rain streaks due to the rainy weather, which bring negative effects on subsequent computer vision applications, e.g., video surveillance, object recognition and tracking, etc. In this paper, we propose a directional regularized tensor-based video deraining model by taking into consideration the arbitrary direction of rain streaks. In particular, the sparsity of rain streaks in spatial and derivative domains, the spatiotemporal sparsity and low-rank property of video background are incorporated into the proposed method. Different from many previous methods under the assumption of vertically falling rain streaks, we consider a more realistic assumption that all the rain streaks in a video fall in an approximately similar arbitrary direction. The resulting complicated optimization problem will be effectively solved through an alternating direction method. Comprehensive experiments on both synthetic and realistic datasets have demonstrated the superiority of the proposed deraining method.
An entropic feature selection method in perspective of Turing formula
Venue: arXiv cs.LG 2019
Year: 2019

Health data are generally complex in type and small in sample size. Such domain-specific challenges make it difficult to capture information reliably and contribute further to the issue of generalization. To assist the analytics of healthcare datasets, we develop a feature selection method based on the concept of Coverage Adjusted Standardized Mutual Information (CASMI). The main advantages of the proposed method are: 1) it selects features more efficiently with the help of an improved entropy estimator, particularly when the sample size is small, and 2) it automatically learns the number of features to be selected based on the information from sample data. Additionally, the proposed method handles feature redundancy from the perspective of joint-distribution. The proposed method focuses on non-ordinal data, while it works with numerical data with an appropriate binning method. A simulation study comparing the proposed method to six widely cited feature selection methods shows that the proposed method performs better when measured by the Information Recovery Ratio, particularly when the sample size is small.
UPCASE - A Method for Self-Assessing the Capability of the Usability Process in Small Organizations
Venue: arXiv cs.SE 2019
Year: 2019

Designing usable products is important to provide a competitive edge through user satisfaction. A first step to establish or improve a usability process is to perform a process assessment. As this may be costly, an alternative for organizations seeking for lighter assessments, especially small organizations, may be self-assessments. They can be carried out by an organization on its own to assess the capability of its process. Although there are specific assessment methods to assess the usability process, none of them provides a self-assessment method considering the specific characteristics of small organizations. The objective of this research is to propose a method for self-assessing the capability of the usability process in small organizations. The method consists of a usability process reference model, a measurement framework, an assessment model, and a self-assessment process supported by an online tool. Based on systematic mapping studies on usability capability/maturity models and software process self-assessment methods, we identified the specific requirements of such a method. The UPCASE method was systematically developed using a multi-method approach based on the ISO/IEC TR 29110 and ISO/TR 18529 standard. The method has been applied and evaluated with respect to its reliability, usability, comprehensibility and internal consistency through a series of case studies. First results indicate that the method may be reliable. Feedback also indicates that the method is easy to use and understandable even for non-software process improvement experts. The UPCASE method is a first step to the self-assessment of the usability process in small organizations supporting the systematic establishment and improvement of the usability process contributing to the improvement of the usability of their software products.
Discovery of Natural Language Concepts in Individual Units of CNNs
Venue: arXiv cs.CL 2019
Year: 2019

Although deep convolutional networks have achieved improved performance in many natural language tasks, they have been treated as black boxes because they are difficult to interpret. Especially, little is known about how they represent language in their intermediate layers. In an attempt to understand the representations of deep convolutional networks trained on language tasks, we show that individual units are selectively responsive to specific morphemes, words, and phrases, rather than responding to arbitrary and uninterpretable patterns. In order to quantitatively analyze such an intriguing phenomenon, we propose a concept alignment method based on how units respond to the replicated text. We conduct analyses with different architectures on multiple datasets for classification and translation tasks and provide new insights into how deep models understand natural language.