OpenRaiser/Intern-Atlas huggingface dataset:
Mapping to Declarative Knowledge for Word Problem Solving
Venue: arXiv cs.CL 2017
Year: 2017

Math word problems form a natural abstraction to a range of quantitative reasoning problems, such as understanding financial news, sports results, and casualties of war. Solving such problems requires the understanding of several mathematical concepts such as dimensional analysis, subset relationships, etc. In this paper, we develop declarative rules which govern the translation of natural language description of these concepts to math expressions. We then present a framework for incorporating such declarative knowledge into word problem solving. Our method learns to map arithmetic word problem text to math expressions, by learning to select the relevant declarative knowledge for each operation of the solution expression. This provides a way to handle multiple concepts in the same problem while, at the same time, support interpretability of the answer expression. Our method models the mapping to declarative knowledge as a latent variable, thus removing the need for expensive annotations. Experimental evaluation suggests that our domain knowledge based solver outperforms all other systems, and that it generalizes better in the realistic case where the training data it is exposed to is biased in a different way than the test data.
Advances in Pre-Training Distributed Word Representations
Venue: arXiv cs.CL 2017
Year: 2017

Many Natural Language Processing applications nowadays rely on pre-trained word representations estimated from large text corpora such as news collections, Wikipedia and Web Crawl. In this paper, we show how to train high-quality word vector representations by using a combination of known tricks that are however rarely used together. The main result of our work is the new set of publicly available pre-trained models that outperform the current state of the art by a large margin on a number of tasks.
On the Frequency Dependency of Radio Channel's Delay Spread: Analyses and Findings From mmMAGIC Multi-frequency Channel Sounding
Venue: arXiv cs.IT 2017
Year: 2017

This paper analyzes the frequency dependency of the radio propagation channel's root mean square (rms) delay spread (DS), based on the multi-frequency measurement campaigns in the mmMAGIC project. The campaigns cover indoor, outdoor, and outdoor-to-indoor (O2I) scenarios and a wide frequency range from 2 to 86 GHz. Several requirements have been identified that define the parameters which need to be aligned in order to make a reasonable comparison among the different channel sounders employed for this study. A new modelling approach enabling the evaluation of the statistical significance of the model parameters from different measurements and the establishment of a unified model is proposed. After careful analysis, the conclusion is that any frequency trend of the DS is small considering its confidence intervals. There is statistically significant difference from the 3GPP New Radio (NR) model TR 38.901, except for the O2I scenario.
Artificial-noise-aided Secure Multicast Precoding for Directional Modulation Systems
Venue: arXiv cs.IT 2017
Year: 2017

In multi-cast scenario, all desired users are divided into $K$ groups. Each group receives its own individual confidential message stream. Eavesdropper group aims to intercept $K$ confidential-message streams. To achieve a secure transmission, two secure schemes are proposed: maximum group receive power plus null-space (NS) projection (Max-GRP plus NSP) and leakage. The former obtains its precoding vector per group by maximizing its own group receive power subject to the orthogonal constraint, and its AN projection matrix consist of all bases of NS of all desired steering vectors from all groups. The latter attains its desired precoding vector per group by driving the current confidential message power to its group steering space and reducing its power leakage to eavesdropper group and other $K-1$ desired ones by maximizing signal to leakage and noise ratio (Max-SLNR). And its AN projection matrix is designed by forcing AN power into the eavesdropper steering space by viewing AN as a useful signal for eavesdropper group and maximizing AN to leakage-and-noise ratio (Max-ANLNR). Simulation results show that the proposed two methods are better than conventional method in terms of both bit-error-rate (BER) and secrecy sum-rate per group. Also, the leakage scheme performs better than Max-GRP-NSP , especially in the presence of direction measurement errors. However, the latter requires no channel statistical parameters and thus is simpler compared to the former.
Optimal Control Computation via Evolution Partial Differential Equation with Arbitrary Definite Conditions
Venue: arXiv cs.SY 2017
Year: 2017

The compact Variation Evolving Method (VEM) that originates from the continuous-time dynamics stability theory seeks the optimal solutions with variation evolution principle. It is further developed to be more flexible in solving the Optimal Control Problems (OCPs), by relaxing the definite conditions from a feasible solution to an arbitrary one for the derived Evolution Partial Differential Equation (EPDE). To guarantee the validity, an unconstrained Lyapunov functional that has the same minimum as the original OCP is constructed, and it ensures the evolution towards the optimal solution from infeasible solutions. With the semi-discrete method, the EPDE is transformed to the finite-dimensional Initial-value Problem (IVP), and then solved with common Ordinary Differential Equation (ODE) numerical integration methods. Illustrative examples are presented to show the effectiveness of the proposed method.
Learning to Customize Network Security Rules
Venue: arXiv cs.CR 2017
Year: 2017

Security is a major concern for organizations who wish to leverage cloud computing. In order to reduce security vulnerabilities, public cloud providers offer firewall functionalities. When properly configured, a firewall protects cloud networks from cyber-attacks. However, proper firewall configuration requires intimate knowledge of the protected system, high expertise and on-going maintenance. As a result, many organizations do not use firewalls effectively, leaving their cloud resources vulnerable. In this paper, we present a novel supervised learning method, and prototype, which compute recommendations for firewall rules. Recommendations are based on sampled network traffic meta-data (NetFlow) collected from a public cloud provider. Labels are extracted from firewall configurations deemed to be authored by experts. NetFlow is collected from network routers, avoiding expensive collection from cloud VMs, as well as relieving privacy concerns. The proposed method captures network routines and dependencies between resources and firewall configuration. The method predicts IPs to be allowed by the firewall. A grouping algorithm is subsequently used to generate a manageable number of IP ranges. Each range is a parameter for a firewall rule. We present results of experiments on real data, showing ROC AUC of 0.92, compared to 0.58 for an unsupervised baseline. The results prove the hypothesis that firewall rules can be automatically generated based on router data, and that an automated method can be effective in blocking a high percentage of malicious traffic.
Automated Formal Equivalence Verification of Pipelined Nested Loops in Datapath Designs
Venue: arXiv cs.AR 2017
Year: 2017

In this paper, we present an efficient formal approach to check the equivalence of synthesized RTL against the high-level specification in the presence of pipelining transformations. To increase the scalability of our proposed method, we dynamically divide the designs into several smaller parts called segments by introducing cut-points. Then we employ Modular Horner Expansion Diagram (M-HED) to check whether the specification and implementation are equivalent or not. In an iterative manner, the equivalence checking for each segment is performed. At each step, the equivalent nodes and those nodes which have an impact on them are removed until the whole design is covered. Our proposed method enables us to deal with the equivalence checking problem for behaviorally synthesized designs even in the presence of pipelines for nested loops. The empirical results demonstrate the efficiency and scalability of our proposed method in terms of run-time and memory usage for several large designs synthesized by a commercial behavioral synthesis tool. Average improvements in terms of the memory usage and run time in comparison with SMT- and SAT-based equivalence checking are 16.7x and 111.9x, respectively.
TEDS: A Trusted Entropy and Dempster Shafer Mechanism for Routing in Wireless Mesh Networks
Venue: arXiv cs.CR 2017
Year: 2017

Wireless Mesh Networks (WMNs) have emerged as a key technology for the next generation of wireless networking due to its self-forming, self-organizing and self-healing properties. However, due to the multi-hop nature of communications in WMN, we cannot assume that all nodes in the network are cooperative. Nodes may drop all of the data packets they received to mount a Denial of Service (DoS) attack. In this paper, we proposed a lightweight trust detection mechanism called Trusted Entropy and Dempster Shafer (TEDS) to mitigate the effects of blackhole attacks. This novel idea combines entropy function and Dempster Shafer belief theory to derive a trust rating for a node. If the trust rating of a node is less than a threshold, it will be blacklisted and isolated from the network. In this way, the network can be assured of a secure end to end path free of malicious nodes for data forwarding. Our proposed idea has been extensively tested in simulation using network simulator NS-3 and simulation results show that we are able to improve the packet delivery ratio with slight increase in normalized routing overhead.
Abstract Interpretation using a Language of Symbolic Approximation
Venue: arXiv cs.SE 2017
Year: 2017

The traditional abstract domain framework for imperative programs suffers from several shortcomings; in particular it does not allow precise symbolic abstractions. To solve these problems, we propose a new abstract interpretation framework, based on symbolic expressions used both as an abstraction of the program, and as the input analyzed by abstract domains. We demonstrate new applications of the frame- work: an abstract domain that efficiently propagates constraints across the whole program; a new formalization of functor domains as approximate translation, which allows the production of approximate programs, on which we can perform classical symbolic techniques. We used these to build a complete analyzer for embedded C programs, that demonstrates the practical applicability of the framework.
Modelling Noise-Resilient Single-Switch Scanning Systems
Venue: arXiv cs.HC 2017
Year: 2017

Single-switch scanning systems allow nonspeaking individuals with motor disabilities to communicate by triggering a single switch (e.g., raising an eye brow). A problem with current single-switch scanning systems is that while they result in reasonable performance in noiseless conditions, for instance via simulation or tests with able-bodied users, they fail to accurately model the noise sources that are introduced when a non-speaking individual with motor disabilities is triggering the switch in a realistic use context. To help assist the development of more noise-resilient single-switch scanning systems we have developed a mathematical model of scanning systems which incorporates extensive noise modelling. Our model includes an improvement to the standard scanning method, which we call fast-scan, which we show via simulation can be more suitable for certain users of scanning systems.
Low-Level Augmented Bayesian Optimization for Finding the Best Cloud VM
Venue: arXiv cs.DC 2017
Year: 2017

With the advent of big data applications, which tends to have longer execution time, choosing the right cloud VM to run these applications has significant performance as well as economic implications. For example, in our large-scale empirical study of 107 different workloads on three popular big data systems, we found that a wrong choice can lead to a 20 times slowdown or an increase in cost by 10 times. Bayesian optimization is a technique for optimizing expensive (black-box) functions. Previous attempts have only used instance-level information (such as # of cores, memory size) which is not sufficient to represent the search space. In this work, we discover that this may lead to the fragility problem---either incurs high search cost or finds only the sub-optimal solution. The central insight of this paper is to use low-level performance information to augment the process of Bayesian Optimization. Our novel low-level augmented Bayesian Optimization is rarely worse than current practices and often performs much better (in 46 of 107 cases). Further, it significantly reduces the search cost in nearly half of our case studies. Based on this work, we conclude that it is often insufficient to use general-purpose off-the-shelf methods for configuring cloud instances without augmenting those methods with essential systems knowledge such as CPU utilization, working memory size and I/O wait time.
Exploring the significance of using perceptually relevant image decolorization method for scene classification
Venue: arXiv cs.CV 2017
Year: 2017

A color image contains luminance and chrominance components representing the intensity and color information respectively. The objective of the work presented in this paper is to show the significance of incorporating the chrominance information for the task of scene classification. An improved color-to-grayscale image conversion algorithm by effectively incorporating the chrominance information is proposed using color-to-gay structure similarity index (C2G-SSIM) and singular value decomposition (SVD) to improve the perceptual quality of the converted grayscale images. The experimental result analysis based on the image quality assessment for image decolorization called C2G-SSIM and success rate (Cadik and COLOR250 datasets) shows that the proposed image decolorization technique performs better than 8 existing benchmark algorithms for image decolorization. In the second part of the paper, the effectiveness of incorporating the chrominance component in scene classification task is demonstrated using the deep belief network (DBN) based image classification system developed using dense scale invariant feature transform (SIFT) as features. The levels of chrominance information incorporated by the proposed image decolorization technique is confirmed by the improvement in the overall scene classification accuracy . Also, the overall scene classification performance is improved by the combination of models obtained using the proposed and the conventional decolorization methods.
RedDwarfData: a simplified dataset of StarCraft matches
Venue: arXiv cs.AI 2017
Year: 2017

The game Starcraft is one of the most interesting arenas to test new machine learning and computational intelligence techniques; however, StarCraft matches take a long time and creating a good dataset for training can be hard. Besides, analyzing match logs to extract the main characteristics can also be done in many different ways to the point that extracting and processing data itself can take an inordinate amount of time and of course, depending on what you choose, can bias learning algorithms. In this paper we present a simplified dataset extracted from the set of matches published by Robinson and Watson, which we have called RedDwarfData, containing several thousand matches processed to frames, so that temporal studies can also be undertaken. This dataset is available from GitHub under a free license. An initial analysis and appraisal of these matches is also made.
A multi-candidate electronic voting scheme with unlimited participants
Venue: arXiv cs.CR 2017
Year: 2017

In this paper a new multi-candidate electronic voting scheme is constructed with unlimited participants. The main idea is to express a ballot to allow voting for up to k out of the m candidates and unlimited participants. The purpose of vote is to select more than one winner among $m$ candidates. Our result is complementary to the result by Sun peiyong$'$ s scheme, in the sense, their scheme is not amenable for large-scale electronic voting due to flaw of ballot structure. In our scheme the vote is split and hidden, and tallying is made for $G\ddot{o}del$ encoding in decimal base without any trusted third party, and the result does not rely on any traditional cryptography or computational intractable assumption. Thus the proposed scheme not only solves the problem of ballot structure, but also achieves the security including perfect ballot secrecy, receipt-free, robustness, fairness and dispute-freeness.
On Asymptotic Analysis of Zero-Delay Energy-Distortion Tradeoff Under Additive White Gaussian Noise
Venue: arXiv cs.IT 2017
Year: 2017

Asymptotic energy-distortion performance of zero-delay communication scenarios under additive white Gaussian noise is investigated. Using high-resolution analysis for quantizer design, the higher-order term in the logarithm of the distortion (termed the {\em energy-distortion dispersion}) is optimized while keeping the leading term (i.e., {\em energy-distortion exponent}) at its optimal value. For uniform and Gaussian sources, significant gains are observed compared to naïvely performed quantization, i.e., aimed at optimizing the source coding performance instead of the end-to-end distortion in joint source-channel coding.
Dependence Structure Analysis Of Meta-level Metrics in YouTube Videos: A Vine Copula Approach
Venue: arXiv cs.SI 2017
Year: 2017

This paper uses vine copula to analyze the multivariate statistical dependence in a massive YouTube dataset consisting of 6 million videos over 25 thousand channels. Specifically we study the statistical dependency of 7 YouTube meta-level metrics: view count, number of likes, number of comments, length of video title, number of subscribers, click rates, and average percentage watching. Dependency parameters such as the Kendall's tau and tail dependence coefficients are computed to evaluate the pair-wise dependence of these meta-level metrics. The vine copula model yields several interesting dependency structures. We show that view count and number of likes' are in the central position of the dependence structure. Conditioned on these two metrics, the other five meta-level metrics are virtually independent of each other. Also, Sports, Gaming, Fashion, Comedy videos have similar dependence structure to each other, while the News category exhibits a strong tail dependence. We also study Granger causality effects and upload dynamics and their impact on view count. Our findings provide a useful understanding of user engagement in YouTube.
Methods for Detecting Paraphrase Plagiarism
Venue: arXiv cs.IR 2017
Year: 2017

Paraphrase plagiarism is one of the difficult challenges facing plagiarism detection systems. Paraphrasing occur when texts are lexically or syntactically altered to look different, but retain their original meaning. Most plagiarism detection systems (many of which are commercial based) are designed to detect word co-occurrences and light modifications, but are unable to detect severe semantic and structural alterations such as what is seen in many academic documents. Hence many paraphrase plagiarism cases go undetected. In this paper, we approached the problem of paraphrase plagiarism by proposing methods for detecting the most common techniques (phenomena) used in paraphrasing texts (namely; lexical substitution, insertion/deletion and word and phrase reordering), and combined the methods into a paraphrase detection model. We evaluated our proposed methods and model on collections containing paraphrase texts. Experimental results show significant improvement in performance when the methods were combined (the proposed model) as opposed to running them individually. The results also show that the proposed paraphrase detection model outperformed a standard baseline (based on greedy string tilling), and previous studies.
Randomized Communication in Radio Networks
Venue: arXiv cs.NI 2017
Year: 2017

A communication network is called a radio network if its nodes exchange messages in the following restricted way. First, a send operation performed by a node delivers copies of the same message to all directly reachable nodes. Secondly, a node can successfully receive an incoming message only if exactly one of its neighbors sent a message in that step. It is this semantics of how ports at nodes send and receive messages that defines the networks rather than the fact that only radio waves are used as a medium of communication; but if that is the case then just a single frequency is used. We discuss algorithmic aspects of exchanging information in such networks, concentrating on distributed randomized protocols. Specific problems and solutions depend a lot on the topology of the underlying reachability graph and how much the nodes know about it. In single-hop networks each pair of nodes can communicate directly. This kind of networks is also known as the multiple access channel. Popular broadcasting protocols used on such channels are Aloha and the exponential backoff. Multi-hop networks may have arbitrary topology and packets need to be routed hopping through a sequence of adjacent nodes. Distributed protocols run by such networks are usually robust enough not to expect the nodes to know their neighbors. These ad-hoc networks and protocols model the situation when nodes are mobile and do not rely on a fixed infrastructure.
Face Synthesis from Visual Attributes via Sketch using Conditional VAEs and GANs
Venue: arXiv cs.CV 2017
Year: 2017

Automatic synthesis of faces from visual attributes is an important problem in computer vision and has wide applications in law enforcement and entertainment. With the advent of deep generative convolutional neural networks (CNNs), attempts have been made to synthesize face images from attributes and text descriptions. In this paper, we take a different approach, where we formulate the original problem as a stage-wise learning problem. We first synthesize the facial sketch corresponding to the visual attributes and then we reconstruct the face image based on the synthesized sketch. The proposed Attribute2Sketch2Face framework, which is based on a combination of deep Conditional Variational Autoencoder (CVAE) and Generative Adversarial Networks (GANs), consists of three stages: (1) Synthesis of facial sketch from attributes using a CVAE architecture, (2) Enhancement of coarse sketches to produce sharper sketches using a GAN-based framework, and (3) Synthesis of face from sketch using another GAN-based network. Extensive experiments and comparison with recent methods are performed to verify the effectiveness of the proposed attribute-based three stage face synthesis method.