OpenRaiser/Intern-Atlas huggingface dataset:
Energy-Efficient Interactive Beam-Alignment for Millimeter-Wave Networks
Venue: arXiv cs.IT 2018
Year: 2018

Millimeter-wave will be a key technology in next-generation wireless networks thanks to abundant bandwidth availability. However, the use of large antenna arrays with beamforming demands precise beam-alignment between transmitter and receiver, and may entail huge overhead in mobile environments. This paper investigates the design of an optimal interactive beam-alignment and data communication protocol, with the goal of minimizing power consumption under a minimum rate constraint. The base-station selects beam-alignment or data communication and the beam parameters, based on feedback from the user-end. Based on the sectored antenna model and uniform prior on the angles of departure and arrival (AoD/AoA), the optimality of a fixed-length beam-alignment phase followed by a data-communication phase is demonstrated. Moreover, a decoupled fractional beam-alignment method is shown to be optimal, which decouples over time the alignment of AoD and AoA, and iteratively scans a fraction of their region of uncertainty. A heuristic policy is proposed for non-uniform prior on AoD/AoA, with provable performance guarantees, and it is shown that the uniform prior is the worst-case scenario. The performance degradation due to detection errors is studied analytically and via simulation. The numerical results with analog beams depict up to 4dB, 7.5dB, and 14 dB gains over a state-of-the-art bisection method, conventional and interactive exhaustive search policies, respectively, and demonstrate that the sectored model provides valuable insights for beam-alignment design.
Verifying Programs Under Custom Application-Specific Execution Models
Venue: arXiv cs.PL 2018
Year: 2018

Researchers have recently designed a number of application-specific fault tolerance mechanisms that enable applications to either be naturally resilient to errors or include additional detection and correction steps that can bring the overall execution of an application back into an envelope for which an acceptable execution is eventually guaranteed. A major challenge to building an application that leverages these mechanisms, however, is to verify that the implementation satisfies the basic invariants that these mechanisms require--given a model of how faults may manifest during the application's execution. To this end we present Leto, an SMT based automatic verification system that enables developers to verify their applications with respect to a first-class execution model specification. Namely, Leto enables software and platform developers to programmatically specify the execution semantics of the underlying hardware system as well as verify assertions about the behavior of the application's resulting execution. In this paper, we present the Leto programming language and its corresponding verification system. We also demonstrate Leto on several applications that leverage application-specific fault tolerance mechanisms.
FT-LADS: Fault-Tolerant Object-Logging based Big Data Transfer System using Layout-Aware Data Scheduling
Venue: arXiv cs.DC 2018
Year: 2018

Layout-Aware Data Scheduler (LADS) data transfer tool, identifies and addresses the issues that lead to congestion on the path of an end-to-end data transfer in the terabit network environments. It exploits the underlying storage layout at each endpoint to maximize throughput without negatively impacting the performance of shared storage resources for other users. LADS can avoid congested storage elements within the shared storage resource, improving input/output bandwidth, and hence the data transfer rates across the high speed networks. However, absence of FT (fault tolerance) support in LADS results in data re-transmission overhead along with the possible integrity issues upon errors. In this paper, we propose object based logging methods to avoid transmitting the objects which are successfully written to Parallel File System (PFS) at the sink end. Depending on the number of logger files created, for the whole dataset, we classified our fault tolerance mechanisms into three different categories: File logger, Transaction logger and Universal logger. Also, to address space overhead of these object based logging mechanisms, we have proposed different methods of populating logger files with the information of the completed objects. We have evaluated the data transfer performance and recovery time overhead of the proposed object based logging fault tolerant mechanisms on LADS data transfer tool. Our experimental results show that, LADS in conjunction with proposed object based fault tolerance mechanisms exhibit an overhead of less than 1% with respect to data transfer time and total recovery time overhead is around 10% of total data transfer time at any fault point.
On Computing Average Common Substring Over Run Length Encoded Sequences
Venue: arXiv cs.DS 2018
Year: 2018

The Average Common Substring (ACS) is a popular alignment-free distance measure for phylogeny reconstruction. The ACS can be computed in O(n) space and time, where n=x+y is the input size. The compressed string matching is the study of string matching problems with the following twist: the input data is in a compressed format and the underling task must be performed with little or no decompression. In this paper, we revisit the ACS problem under this paradigm where the input sequences are given in their run-length encoded format. We present an algorithm to compute ACS(X,Y) in O(Nlog N) time using O(N) space, where N is the total length of sequences after run-length encoding.
Covert Wireless Communications with Active Eavesdropper on AWGN Channels
Venue: arXiv cs.IT 2018
Year: 2018

Covert wireless communication can prevent an adversary from knowing the existence of user's transmission, thus provide stronger security protection. In AWGN channels, a square root law was obtained and the result shows that Alice can reliably and covertly transmit $\mathcal{O}(\sqrt{n})$ bits to Bob in n channel uses in the presence of a passive eavesdropper (Willie). However, existing work presupposes that Willie is static and only samples the channels at a fixed place. If Willie can dynamically adjust the testing distance between him and Alice according to his sampling values, his detection probability of error can be reduced significantly via a trend test. We found that, if Alice has no prior knowledge about Willie, she cannot hide her transmission behavior in the presence of an active Willie, and the square root law does not hold in this situation. We then proposed a novel countermeasure to deal with the active Willie. Through randomized transmission scheduling, Willie cannot detect Alice's transmission attempts if Alice can set her transmission probability below a threshold. Additionally, we systematically evaluated the security properties of covert communications in a dense wireless network, and proposed a density-based routing scheme to deal with multi-hop covert communication in a wireless network. As the network grows denser, Willie's uncertainty increases, and finally resulting in a "shadow" network to Willie.
Convolutional Neural Network Architecture for Recovering Watermark Synchronization
Venue: arXiv cs.MM 2018
Year: 2018

Since real-time contents can be captured and downloaded very easily, copyright infringement has become a serious problem. In order to reduce the loss caused by copyright infringement, copyright owners insert a watermark in the content to protect the copyright using illegal distribution route tracking and copyright authentication. However, whereas many existing watermarking techniques are robust to signal distortion such as compression, they are vulnerable to geometric distortion that causes synchronization errors. In particular, capturing real-time content in Internet browsers and smartphone applications is problematic because geometric distortion such as scaling and translation frequently occurs. In this paper, we propose a convolutional neural network-based template architecture that compensates for the disadvantages of existing watermarking techniques that are vulnerable to geometric distortion. The proposed template consists of a template generation network, a template extraction network, and a template matching network. The template generation network generates a template in the form of noise and the template is inserted into certain pre-defined spatial locations of the image. The extraction network detects spatial locations where the template is inserted in the image. Finally, the template matching network estimates the parameters of the geometric distortion by comparing the shape of spatial locations where the template was inserted with the locations where the template was detected. It is possible to recover an image in its original geometrical form using the estimated parameters, and as a result, watermarks applied using existing watermarking techniques that are vulnerable to geometric distortion can be decoded normally.
Distributed Hypothesis Testing with Concurrent Detections
Venue: arXiv cs.IT 2018
Year: 2018

A detection system with a single sensor and $\mathsf{K}$ detectors is considered, where each of the terminals observes a memoryless source sequence and the sensor sends a common message to all the detectors. The communication of this message is assumed error-free but rate-limited. The joint probability mass function (pmf) of the source sequences observed at the terminals depends on an $\mathsf{M}$-ary hypothesis $(\mathsf{M} \geq \mathsf{K})$, and the goal of the communication is that each detector can guess the underlying hypothesis. Each detector $k$ aims to maximize the error exponent under hypothesis $k$, while ensuring a small probability of error under all other hypotheses. This paper presents an achievable exponents region for the case of positive communication rate, and characterizes the optimal exponents region for the case of zero communication rate. All results extend also to a composite hypothesis testing scenario.
Self-X Design of Wireless Networks: Exploiting Artificial Intelligence and Guided Learning
Venue: arXiv cs.NI 2018
Year: 2018

In this work, we develop a framework that jointly decides on the optimal location of wireless extenders and the channel configuration of extenders and access points (APs) in a Wireless Mesh Network (WMN). Typically, the rule-based approaches in the literature result in limited exploration while reinforcement learning based approaches result in slow convergence. Therefore, Artificial Intelligence (AI) is adopted to support network autonomy and to capture insights on system and environment evolution. We propose a Self-X (self-optimizing and self-learning) framework that encapsulates both environment and intelligent agent to reach optimal operation through sensing, perception, reasoning and learning in a truly autonomous fashion. The agent derives adequate knowledge from previous actions improving the quality of future decisions. Domain experience was provided to guide the agent while exploring and exploiting the set of possible actions in the environment. Thus, it guarantees a low-cost learning and achieves a near-optimal network configuration addressing the non-deterministic polynomial-time hardness (NP-hard) problem of joint channel assignment and location optimization in WMNs. Extensive simulations are run to validate its fast convergence, high throughput and resilience to dynamic interference conditions. We deploy the framework on off-the-shelf wireless devices to enable autonomous self-optimization and self-deployment, using APs and wireless extenders.
Data driven discrete-time parsimonious identification of a nonlinear state-space model for a weakly nonlinear system with short data record
Venue: arXiv cs.SY 2018
Year: 2018

Many real world systems exhibit a quasi linear or weakly nonlinear behavior during normal operation, and a hard saturation effect for high peaks of the input signal. In this paper, a methodology to identify a parsimonious discrete-time nonlinear state space model (NLSS) for the nonlinear dynamical system with relatively short data record is proposed. The capability of the NLSS model structure is demonstrated by introducing two different initialisation schemes, one of them using multivariate polynomials. In addition, a method using first-order information of the multivariate polynomials and tensor decomposition is employed to obtain the parsimonious decoupled representation of the set of multivariate real polynomials estimated during the identification of NLSS model. Finally, the experimental verification of the model structure is done on the cascaded water-benchmark identification problem.
Image Classification Based on Quantum KNN Algorithm
Venue: arXiv cs.CV 2018
Year: 2018

Image classification is an important task in the field of machine learning and image processing. However, the usually used classification method --- the K Nearest-Neighbor algorithm has high complexity, because its two main processes: similarity computing and searching are time-consuming. Especially in the era of big data, the problem is prominent when the amount of images to be classified is large. In this paper, we try to use the powerful parallel computing ability of quantum computers to optimize the efficiency of image classification. The scheme is based on quantum K Nearest-Neighbor algorithm. Firstly, the feature vectors of images are extracted on classical computers. Then the feature vectors are inputted into a quantum superposition state, which is used to achieve parallel computing of similarity. Next, the quantum minimum search algorithm is used to speed up searching process for similarity. Finally, the image is classified by quantum measurement. The complexity of the quantum algorithm is only O((kM)^(1/2)), which is superior to the classical algorithms. Moreover, the measurement step is executed only once to ensure the validity of the scheme. The experimental results show that, the classification accuracy is 83.1% on Graz-01 dataset and 78% on Caltech-101 dataset, which is close to existing classical algorithms. Hence, our quantum scheme has a good classification performance while greatly improving the efficiency.
A Context-based Approach for Dialogue Act Recognition using Simple Recurrent Neural Networks
Venue: arXiv cs.CL 2018
Year: 2018

Dialogue act recognition is an important part of natural language understanding. We investigate the way dialogue act corpora are annotated and the learning approaches used so far. We find that the dialogue act is context-sensitive within the conversation for most of the classes. Nevertheless, previous models of dialogue act classification work on the utterance-level and only very few consider context. We propose a novel context-based learning method to classify dialogue acts using a character-level language model utterance representation, and we notice significant improvement. We evaluate this method on the Switchboard Dialogue Act corpus, and our results show that the consideration of the preceding utterances as a context of the current utterance improves dialogue act detection.
Graph Based Semi-supervised Learning with Convolution Neural Networks to Classify Crisis Related Tweets
Venue: arXiv cs.CY 2018
Year: 2018

During time-critical situations such as natural disasters, rapid classification of data posted on social networks by affected people is useful for humanitarian organizations to gain situational awareness and to plan response efforts. However, the scarcity of labeled data in the early hours of a crisis hinders machine learning tasks thus delays crisis response. In this work, we propose to use an inductive semi-supervised technique to utilize unlabeled data, which is often abundant at the onset of a crisis event, along with fewer labeled data. Specif- ically, we adopt a graph-based deep learning framework to learn an inductive semi-supervised model. We use two real-world crisis datasets from Twitter to evaluate the proposed approach. Our results show significant improvements using unlabeled data as compared to only using labeled data.
Fully Associative Patch-based 1-to-N Matcher for Face Recognition
Venue: arXiv cs.CV 2018
Year: 2018

This paper focuses on improving face recognition performance by a patch-based 1-to-N signature matcher that learns correlations between different facial patches. A Fully Associative Patch-based Signature Matcher (FAPSM) is proposed so that the local matching identity of each patch contributes to the global matching identities of all the patches. The proposed matcher consists of three steps. First, based on the signature, the local matching identity and the corresponding matching score of each patch are computed. Then, a fully associative weight matrix is learned to obtain the global matching identities and scores of all the patches. At last, the l1-regularized weighting is applied to combine the global matching identity of each patch and obtain a final matching identity. The proposed matcher has been integrated with the UR2D system for evaluation. The experimental results indicate that the proposed matcher achieves better performance than the current UR2D system. The Rank-1 accuracy is improved significantly by 3% and 0.55% on the UHDB31 dataset and the IJB-A dataset, respectively.
Robust 6D Object Pose Estimation with Stochastic Congruent Sets
Venue: arXiv cs.CV 2018
Year: 2018

Object pose estimation is frequently achieved by first segmenting an RGB image and then, given depth data, registering the corresponding point cloud segment against the object's 3D model. Despite the progress due to CNNs, semantic segmentation output can be noisy, especially when the CNN is only trained on synthetic data. This causes registration methods to fail in estimating a good object pose. This work proposes a novel stochastic optimization process that treats the segmentation output of CNNs as a confidence probability. The algorithm, called Stochastic Congruent Sets (StoCS), samples pointsets on the point cloud according to the soft segmentation distribution and so as to agree with the object's known geometry. The pointsets are then matched to congruent sets on the 3D object model to generate pose estimates. StoCS is shown to be robust on an APC dataset, despite the fact the CNN is trained only on synthetic data. In the YCB dataset, StoCS outperforms a recent network for 6D pose estimation and alternative pointset matching techniques.
Fast Retinomorphic Event Stream for Video Recognition and Reinforcement Learning
Venue: arXiv cs.CV 2018
Year: 2018

Good temporal representations are crucial for video understanding, and the state-of-the-art video recognition framework is based on two-stream networks. In such framework, besides the regular ConvNets responsible for RGB frame inputs, a second network is introduced to handle the temporal representation, usually the optical flow (OF). However, OF or other task-oriented flow is computationally costly, and is thus typically pre-computed. Critically, this prevents the two-stream approach from being applied to reinforcement learning (RL) applications such as video game playing, where the next state depends on current state and action choices. Inspired by the early vision systems of mammals and insects, we propose a fast event-driven representation (EDR) that models several major properties of early retinal circuits: (1) logarithmic input response, (2) multi-timescale temporal smoothing to filter noise, and (3) bipolar (ON/OFF) pathways for primitive event detection[12]. Trading off the directional information for fast speed (> 9000 fps), EDR en-ables fast real-time inference/learning in video applications that require interaction between an agent and the world such as game-playing, virtual robotics, and domain adaptation. In this vein, we use EDR to demonstrate performance improvements over state-of-the-art reinforcement learning algorithms for Atari games, something that has not been possible with pre-computed OF. Moreover, with UCF-101 video action recognition experiments, we show that EDR performs near state-of-the-art in accuracy while achieving a 1,500x speedup in input representation processing, as compared to optical flow.
Text classification based on ensemble extreme learning machine
Venue: arXiv cs.IR 2018
Year: 2018

In this paper, we propose a novel approach based on cost-sensitive ensemble weighted extreme learning machine; we call this approach AE1-WELM. We apply this approach to text classification. AE1-WELM is an algorithm including balanced and imbalanced multiclassification for text classification. Weighted ELM assigning the different weights to the different samples improves the classification accuracy to a certain extent, but weighted ELM considers the differences between samples in the different categories only and ignores the differences between samples within the same categories. We measure the importance of the documents by the sample information entropy, and generate cost-sensitive matrix and factor based on the document importance, then embed the cost-sensitive weighted ELM into the AdaBoost.M1 framework seamlessly. Vector space model(VSM) text representation produces the high dimensions and sparse features which increase the burden of ELM. To overcome this problem, we develop a text classification framework combining the word vector and AE1-WELM. The experimental results show that our method provides an accurate, reliable and effective solution for text classification.
ARUM: Polar Coded HARQ Scheme based on Incremental Channel Polarization
Venue: arXiv cs.IT 2018
Year: 2018

A hybrid ARQ (HARQ) scheme for polar code, which is called active-bit relocation under masks (ARUM), is proposed. In each transmission, the data bits are encoded and bit-wisely XOR-masked using a binary vector before being transmitted through the channel. The masking process combines multiple transmissions together which forms another step of inter-transmission channel transform. The reliabilities are updated after every transmission, and the less reliable bits in earlier ones are relocated to the more reliable positions at the latest transmitted block. ARUM is a very flexible HARQ scheme which allows each transmission to have a different mother code length and to adopt independent rate-matching scheme with sufficient channel state feedback in HARQ process. Simulation shows that ARUM can obtain near-optimal coding gain.
A Formulation of Recursive Self-Improvement and Its Possible Efficiency
Venue: arXiv cs.AI 2018
Year: 2018

Recursive self-improving (RSI) systems have been dreamed of since the early days of computer science and artificial intelligence. However, many existing studies on RSI systems remain philosophical, and lacks clear formulation and results. In this paper, we provide a formal definition for one class of RSI systems, and then demonstrate the existence of computable and efficient RSI systems on a restricted version. We use simulation to empirically show that we achieve logarithmic runtime complexity with respect to the size of the search space, and these results suggest it is possible to achieve an efficient recursive self-improvement.