OpenRaiser/Intern-Atlas huggingface dataset:
PSO-Optimized Hopfield Neural Network-Based Multipath Routing for Mobile Ad-hoc Networks
Venue: arXiv cs.NE 2017
Year: 2017

Mobile ad-hoc network (MANET) is a dynamic collection of mobile computers without the need for any existing infrastructure. Nodes in a MANET act as hosts and routers. Designing of robust routing algorithms for MANETs is a challenging task. Disjoint multipath routing protocols address this problem and increase the reliability, security and lifetime of network. However, selecting an optimal multipath is an NP-complete problem. In this paper, Hopfield neural network (HNN) which its parameters are optimized by particle swarm optimization (PSO) algorithm is proposed as multipath routing algorithm. Link expiration time (LET) between each two nodes is used as the link reliability estimation metric. This approach can find either node-disjoint or link-disjoint paths in single phase route discovery. Simulation results confirm that PSO-HNN routing algorithm has better performance as compared to backup path set selection algorithm (BPSA) in terms of the path set reliability and number of paths in the set.
PAPR Reduction of OFDM Through Pilot Shifting
Venue: arXiv cs.IT 2017
Year: 2017

Peak to Average Power Ratio (PAPR) of Orthogonal Frequency Division Multiplexing (OFDM) is a long-standing problem which has been hindering its performance for decades. In this paper, we propose a new PAPR reduction scheme based on shifting pilot locations among the data symbols. Since no side information is sent to the receiver about the pilot locations, a novel pilot detection algorithm is devised here exploiting the pilot power and the relative constant distance property of pilots. The proposed scheme attains around 1.5 dB PAPR reduction. The pilot detection accuracy is shown to be very excellent ranging from 80% to 99% at 0 dB of Signal to Noise Ratio (SNR) in different parameters. This scheme is very spectrally efficient with reduced complexity without degrading BER performance significantly
Column Generation for Interaction Coverage in Combinatorial Software Testing
Venue: arXiv cs.AI 2017
Year: 2017

This paper proposes a novel column generation framework for combinatorial software testing. In particular, it combines Mathematical Programming and Constraint Programming in a hybrid decomposition to generate covering arrays. The approach allows generating parameterized test cases with coverage guarantees between parameter interactions of a given application. Compared to exhaustive testing, combinatorial test case generation reduces the number of tests to run significantly. Our column generation algorithm is generic and can accommodate mixed coverage arrays over heterogeneous alphabets. The algorithm is realized in practice as a cloud service and recognized as one of the five winners of the company-wide cloud application challenge at Oracle. The service is currently helping software developers from a range of different product teams in their testing efforts while exposing declarative constraint models and hybrid optimization techniques to a broader audience.
Improving End-to-End Speech Recognition with Policy Learning
Venue: arXiv cs.CL 2017
Year: 2017

Connectionist temporal classification (CTC) is widely used for maximum likelihood learning in end-to-end speech recognition models. However, there is usually a disparity between the negative maximum likelihood and the performance metric used in speech recognition, e.g., word error rate (WER). This results in a mismatch between the objective function and metric during training. We show that the above problem can be mitigated by jointly training with maximum likelihood and policy gradient. In particular, with policy learning we are able to directly optimize on the (otherwise non-differentiable) performance metric. We show that joint training improves relative performance by 4% to 13% for our end-to-end model as compared to the same model learned through maximum likelihood. The model achieves 5.53% WER on Wall Street Journal dataset, and 5.42% and 14.70% on Librispeech test-clean and test-other set, respectively.
Continual Prediction of Notification Attendance with Classical and Deep Network Approaches
Venue: arXiv cs.HC 2017
Year: 2017

We investigate to what extent mobile use patterns can predict -- at the moment it is posted -- whether a notification will be clicked within the next 10 minutes. We use a data set containing the detailed mobile phone usage logs of 279 users, who over the course of 5 weeks received 446,268 notifications from a variety of apps. Besides using classical gradient-boosted trees, we demonstrate how to make continual predictions using a recurrent neural network (RNN). The two approaches achieve a similar AUC of ca. 0.7 on unseen users, with a possible operation point of 50% sensitivity and 80% specificity considering all notification types (an increase of 40% with respect to a probabilistic baseline). These results enable automatic, intelligent handling of mobile phone notifications without the need for user feedback or personalization. Furthermore, they showcase how forego feature-extraction by using RNNs for continual predictions directly on mobile usage logs. To the best of our knowledge, this is the first work that leverages mobile sensor data for continual, context-aware predictions of interruptibility using deep neural networks.
Plug-in Electric Vehicle Charging Congestion Analysis Using Taxi Travel Data in the Central Area of Beijing
Venue: arXiv cs.SY 2017
Year: 2017

Recharging a plug-in electric vehicle is more time-consuming than refueling an internal combustion engine vehicle. As a result, charging stations may face serious congestion problems during peak traffic hours in the near future with the rapid growth of plug-in electric vehicle population. Considering that drivers' time costs are usually expensive, charging congestion will be a dominant factor that affect a charging station's quality of service. Hence, it is indispensable to conduct adequate congestion analysis when designing charging stations in order to guarantee acceptable quality of service in the future. This paper proposes a data-driven approach for charging congestion analysis of plug-in electric vehicle charging stations. Based on a data-driven plug-in electric vehicle charging station planning model, we adopt the queuing theory to model and analyze the charging congestion phenomenon in these planning results. We simulate and analyze the proposed method for charging stations servicing shared-use electric taxis in the central area of Beijing leveraging real-world taxi travel data.
Molecular Signal Modeling of a Partially Counting Absorbing Spherical Receiver
Venue: arXiv cs.ET 2017
Year: 2017

To communicate at the nanoscale, researchers have proposed molecular communication as an energy-efficient solution. The drawback to this solution is that the histogram of the molecules' hitting times, which constitute the molecular signal at the receiver, has a heavy tail. Reducing the effects of this heavy tail, inter-symbol interference (ISI), has been the focus of most prior research. In this paper, a novel way of decreasing the ISI by defining a counting region on the spherical receiver's surface facing towards the transmitter node is proposed. The beneficial effect comes from the fact that the molecules received from the back lobe of the receiver are more likely to be coming through longer paths that contribute to ISI. In order to justify this idea, the joint distribution of the arrival molecules with respect to angle and time is derived. Using this distribution, the channel model function is approximated for the proposed system, i.e., the partially counting absorbing spherical receiver. After validating the channel model function, the characteristics of the molecular signal are investigated and improved performance is presented. Moreover, the optimal counting region in terms of bit error rate is found analytically.
Attribute CNNs for Word Spotting in Handwritten Documents
Venue: arXiv cs.CV 2017
Year: 2017

Word spotting has become a field of strong research interest in document image analysis over the last years. Recently, AttributeSVMs were proposed which predict a binary attribute representation. At their time, this influential method defined the state-of-the-art in segmentation-based word spotting. In this work, we present an approach for learning attribute representations with Convolutional Neural Networks (CNNs). By taking a probabilistic perspective on training CNNs, we derive two different loss functions for binary and real-valued word string embeddings. In addition, we propose two different CNN architectures, specifically designed for word spotting. These architectures are able to be trained in an end-to-end fashion. In a number of experiments, we investigate the influence of different word string embeddings and optimization strategies. We show our Attribute CNNs to achieve state-of-the-art results for segmentation-based word spotting on a large variety of data sets.
Recognizing Generalized Transmission Graphs of Line Segments and Circular Sectors
Venue: arXiv cs.CG 2017
Year: 2017

Suppose we have an arrangement $\mathcal{A}$ of $n$ geometric objects $x_1, \dots, x_n \subseteq \mathbb{R}^2$ in the plane, with a distinguished point $p_i$ in each object $x_i$. The generalized transmission graph of $\mathcal{A}$ has vertex set $\{x_1, \dots, x_n\}$ and a directed edge $x_ix_j$ if and only if $p_j \in x_i$. Generalized transmission graphs provide a generalized model of the connectivity in networks of directional antennas. The complexity class $\exists \mathbb{R}$ contains all problems that can be reduced in polynomial time to an existential sentence of the form $\exists x_1, \dots, x_n: ϕ(x_1,\dots, x_n)$, where $x_1,\dots, x_n$ range over $\mathbb{R}$ and $ϕ$ is a propositional formula with signature $(+, -, \cdot, 0, 1)$. The class $\exists \mathbb{R}$ aims to capture the complexity of the existential theory of the reals. It lies between $\mathbf{NP}$ and $\mathbf{PSPACE}$. Many geometric decision problems, such as recognition of disk graphs and of intersection graphs of lines, are complete for $\exists \mathbb{R}$. Continuing this line of research, we show that the recognition problem of generalized transmission graphs of line segments and of circular sectors is hard for $\exists \mathbb{R}$. As far as we know, this constitutes the first such result for a class of directed graphs.
Optimal Discrete Spatial Compression for Beamspace Massive MIMO Signals
Venue: arXiv cs.IT 2017
Year: 2017

Deploying massive number of antennas at the base station side can boost the cellular system performance dramatically. Meanwhile, it however involves significant additional radio-frequency (RF) front-end complexity, hardware cost and power consumption. To address this issue, the beamspace-multiple-input-multiple-output (beamspace-MIMO) based approach is considered as a promising solution. In this paper, we first show that the traditional beamspace-MIMO suffers from spatial power leakage and imperfect channel statistics estimation. A beam combination module is hence proposed, which consists of a small number (compared with the number of antenna elements) of low-resolution (possibly one-bit) digital (discrete) phase shifters after the beamspace transformation to further compress the beamspace signal dimensionality, such that the number of RF chains can be reduced beyond beamspace transformation and beam selection. The optimum discrete beam combination weights for the uplink are obtained based on the branch-and-bound (BB) approach. The key to the BB-based solution is to solve the embodied sub-problem, whose solution is derived in a closed-form. Based on the solution, a sequential greedy beam combination scheme with linear-complexity (w.r.t. the number of beams in the beamspace) is proposed. Link-level simulation results based on realistic channel models and long-term-evolution (LTE) parameters are presented which show that the proposed schemes can reduce the number of RF chains by up to $25\%$ with a one-bit digital phase-shifter-network.
Image Segmentation to Distinguish Between Overlapping Human Chromosomes
Venue: arXiv cs.CV 2017
Year: 2017

In medicine, visualizing chromosomes is important for medical diagnostics, drug development, and biomedical research. Unfortunately, chromosomes often overlap and it is necessary to identify and distinguish between the overlapping chromosomes. A segmentation solution that is fast and automated will enable scaling of cost effective medicine and biomedical research. We apply neural network-based image segmentation to the problem of distinguishing between partially overlapping DNA chromosomes. A convolutional neural network is customized for this problem. The results achieved intersection over union (IOU) scores of 94.7% for the overlapping region and 88-94% on the non-overlapping chromosome regions.
Automatic Estimation of Ice Bottom Surfaces from Radar Imagery
Venue: arXiv cs.CV 2017
Year: 2017

Ground-penetrating radar on planes and satellites now makes it practical to collect 3D observations of the subsurface structure of the polar ice sheets, providing crucial data for understanding and tracking global climate change. But converting these noisy readings into useful observations is generally done by hand, which is impractical at a continental scale. In this paper, we propose a computer vision-based technique for extracting 3D ice-bottom surfaces by viewing the task as an inference problem on a probabilistic graphical model. We first generate a seed surface subject to a set of constraints, and then incorporate additional sources of evidence to refine it via discrete energy minimization. We evaluate the performance of the tracking algorithm on 7 topographic sequences (each with over 3000 radar images) collected from the Canadian Arctic Archipelago with respect to human-labeled ground truth.
From Dissipativity Theory to Compositional Construction of Finite Markov Decision Processes
Venue: arXiv cs.SY 2017
Year: 2017

This paper is concerned with a compositional approach for constructing finite Markov decision processes of interconnected discrete-time stochastic control systems. The proposed approach leverages the interconnection topology and a notion of so-called stochastic storage functions describing joint dissipativity-type properties of subsystems and their abstractions. In the first part of the paper, we derive dissipativity-type compositional conditions for quantifying the error between the interconnection of stochastic control subsystems and that of their abstractions. In the second part of the paper, we propose an approach to construct finite Markov decision processes together with their corresponding stochastic storage functions for classes of discrete-time control systems satisfying some incremental passivablity property. Under this property, one can construct finite Markov decision processes by a suitable discretization of the input and state sets. Moreover, we show that for linear stochastic control systems, the aforementioned property can be readily checked by some matrix inequality. We apply our proposed results to the temperature regulation in a circular building by constructing compositionally a finite Markov decision process of a network containing 200 rooms in which the compositionality condition does not require any constraint on the number or gains of the subsystems. We employ the constructed finite Markov decision process as a substitute to synthesize policies regulating the temperature in each room for a bounded time horizon.
Wolf in Sheep's Clothing - The Downscaling Attack Against Deep Learning Applications
Venue: arXiv cs.CR 2017
Year: 2017

This paper considers security risks buried in the data processing pipeline in common deep learning applications. Deep learning models usually assume a fixed scale for their training and input data. To allow deep learning applications to handle a wide range of input data, popular frameworks, such as Caffe, TensorFlow, and Torch, all provide data scaling functions to resize input to the dimensions used by deep learning models. Image scaling algorithms are intended to preserve the visual features of an image after scaling. However, common image scaling algorithms are not designed to handle human crafted images. Attackers can make the scaling outputs look dramatically different from the corresponding input images. This paper presents a downscaling attack that targets the data scaling process in deep learning applications. By carefully crafting input data that mismatches with the dimension used by deep learning models, attackers can create deceiving effects. A deep learning application effectively consumes data that are not the same as those presented to users. The visual inconsistency enables practical evasion and data poisoning attacks to deep learning applications. This paper presents proof-of-concept attack samples to popular deep-learning-based image classification applications. To address the downscaling attacks, the paper also suggests multiple potential mitigation strategies.
Exploring Models and Data for Remote Sensing Image Caption Generation
Venue: arXiv cs.CV 2017
Year: 2017

Inspired by recent development of artificial satellite, remote sensing images have attracted extensive attention. Recently, noticeable progress has been made in scene classification and target detection.However, it is still not clear how to describe the remote sensing image content with accurate and concise sentences. In this paper, we investigate to describe the remote sensing images with accurate and flexible sentences. First, some annotated instructions are presented to better describe the remote sensing images considering the special characteristics of remote sensing images. Second, in order to exhaustively exploit the contents of remote sensing images, a large-scale aerial image data set is constructed for remote sensing image caption. Finally, a comprehensive review is presented on the proposed data set to fully advance the task of remote sensing caption. Extensive experiments on the proposed data set demonstrate that the content of the remote sensing image can be completely described by generating language descriptions. The data set is available at https://github.com/201528014227051/RSICD_optimal
PHOEG Helps Obtaining Extremal Graphs
Venue: arXiv cs.DM 2017
Year: 2017

Extremal Graph Theory aims to determine bounds for graph invariants as well as the graphs attaining those bounds. We are currently developping PHOEG, an ecosystem of tools designed to help researchers in Extremal Graph Theory. It uses a big relational database of undirected graphs and works with the convex hull of the graphs as points in the invariants space in order to exactly obtain the extremal graphs and optimal bounds on the invariants for some fixed parameters. The results obtained on the restricted finite class of graphs can later be used to infer conjectures. This database also allows us to make queries on those graphs. Once the conjecture defined, PHOEG goes one step further by helping in the process of designing a proof guided by successive applications of transformations from any graph to an extremal graph. To this aim, we use a second database based on a graph data model. The paper presents ideas and techniques used in PHOEG to assist the study of Extremal Graph Theory.
The Pyramid Scheme: Oblivious RAM for Trusted Processors
Venue: arXiv cs.CR 2017
Year: 2017

Modern processors, e.g., Intel SGX, allow applications to isolate secret code and data in encrypted memory regions called enclaves. While encryption effectively hides the contents of memory, the sequence of address references issued by the secret code leaks information. This is a serious problem because these leaks can easily break the confidentiality guarantees of enclaves. In this paper, we explore Oblivious RAM (ORAM) designs that prevent these information leaks under the constraints of modern SGX processors. Most ORAMs are a poor fit for these processors because they have high constant overhead factors or require large private memories, which are not available in these processors. We address these limitations with a new hierarchical ORAM construction, the Pyramid ORAM, that is optimized towards online bandwidth cost and small blocks. It uses a new hashing scheme that circumvents the complexity of previous hierarchical schemes. We present an efficient x64-optimized implementation of Pyramid ORAM that uses only the processor's registers as private memory. We compare Pyramid ORAM with Circuit ORAM, a state-of-the-art tree-based ORAM scheme that also uses constant private memory. Pyramid ORAM has better online asymptotical complexity than Circuit ORAM. Our implementation of Pyramid ORAM and Circuit ORAM validates this: as all hierarchical schemes, Pyramid ORAM has high variance of access latencies; although latency can be high for some accesses, for typical configurations Pyramid ORAM provides access latencies that are 8X better than Circuit ORAM for 99% of accesses. Although the best known hierarchical ORAM has better asymptotical complexity, Pyramid ORAM has significantly lower constant overhead factors, making it the preferred choice in practice.
A non-commutative algorithm for multiplying (7 $\times$ 7) matrices using 250 multiplications
Venue: arXiv cs.SC 2017
Year: 2017

We present a non-commutative algorithm for multiplying (7x7) matrices using 250 multiplications and a non-commutative algorithm for multiplying (9x9) matrices using 520 multiplications. These algorithms are obtained using the same divide-and-conquer technique.
Multiview Deep Learning for Predicting Twitter Users' Location
Venue: arXiv cs.LG 2017
Year: 2017

The problem of predicting the location of users on large social networks like Twitter has emerged from real-life applications such as social unrest detection and online marketing. Twitter user geolocation is a difficult and active research topic with a vast literature. Most of the proposed methods follow either a content-based or a network-based approach. The former exploits user-generated content while the latter utilizes the connection or interaction between Twitter users. In this paper, we introduce a novel method combining the strength of both approaches. Concretely, we propose a multi-entry neural network architecture named MENET leveraging the advances in deep learning and multiview learning. The generalizability of MENET enables the integration of multiple data representations. In the context of Twitter user geolocation, we realize MENET with textual, network, and metadata features. Considering the natural distribution of Twitter users across the concerned geographical area, we subdivide the surface of the earth into multi-scale cells and train MENET with the labels of the cells. We show that our method outperforms the state of the art by a large margin on three benchmark datasets.
Reachable Set Computation and Safety Verification for Neural Networks with ReLU Activations
Venue: arXiv cs.LG 2017
Year: 2017

Neural networks have been widely used to solve complex real-world problems. Due to the complicate, nonlinear, non-convex nature of neural networks, formal safety guarantees for the output behaviors of neural networks will be crucial for their applications in safety-critical systems.In this paper, the output reachable set computation and safety verification problems for a class of neural networks consisting of Rectified Linear Unit (ReLU) activation functions are addressed. A layer-by-layer approach is developed to compute output reachable set. The computation is formulated in the form of a set of manipulations for a union of polyhedra, which can be efficiently applied with the aid of polyhedron computation tools. Based on the output reachable set computation results, the safety verification for a ReLU neural network can be performed by checking the intersections of unsafe regions and output reachable set described by a union of polyhedra. A numerical example of a randomly generated ReLU neural network is provided to show the effectiveness of the approach developed in this paper.