OpenRaiser/Intern-Atlas huggingface dataset:
Wireless Sensor Networks Security: State of the Art
Venue: arXiv cs.NI 2018
Year: 2018

Wireless sensor networks (WSNs) have become one of the main research topics in computer science in recent years, primarily owing to the significant challenges imposed by these networks and their immense applicability. WSNs have been employed for a diverse group of monitoring applications, with emphasis on industrial control scenarios, traffic management, rescue operations, public safety, residential automation, weather forecasting, and several other fields. These networks constitute resource-constrained sensors for which security and energy efficiency are essential concerns. In this context, many research efforts have been focused on increasing the security levels and reducing the energy consumption in the network. This paper provides a state-of-the-art survey of recent works in this direction, proposing a new taxonomy for the security attacks and requirements of WSNs.
Improved Chord Recognition by Combining Duration and Harmonic Language Models
Venue: arXiv cs.SD 2018
Year: 2018

Chord recognition systems typically comprise an acoustic model that predicts chords for each audio frame, and a temporal model that casts these predictions into labelled chord segments. However, temporal models have been shown to only smooth predictions, without being able to incorporate musical information about chord progressions. Recent research discovered that it might be the low hierarchical level such models have been applied to (directly on audio frames) which prevents learning musical relationships, even for expressive models such as recurrent neural networks (RNNs). However, if applied on the level of chord sequences, RNNs indeed can become powerful chord predictors. In this paper, we disentangle temporal models into a harmonic language model---to be applied on chord sequences---and a chord duration model that connects the chord-level predictions of the language model to the frame-level predictions of the acoustic model. In our experiments, we explore the impact of each model on the chord recognition score, and show that using harmonic language and duration models improves the results.
Simple Load Balancing
Venue: arXiv cs.DC 2018
Year: 2018

We consider the following load balancing process for $m$ tokens distributed arbitrarily among $n$ nodes connected by a complete graph: In each time step a pair of nodes is selected uniformly at random. Let $\ell_1$ and $\ell_2$ be their respective number of tokens. The two nodes exchange tokens such that they have $\lceil(\ell_1 + \ell_2)/2\rceil$ and $\lfloor(\ell_1 + \ell_2)/2\rfloor$ tokens, respectively. We provide a simple analysis showing that this process reaches almost perfect balance within $O(n\log{n} + n \logΔ)$ steps, where $Δ$ is the maximal initial load difference between any two nodes.
Aligator.jl - A Julia Package for Loop Invariant Generation
Venue: arXiv cs.SC 2018
Year: 2018

We describe the Aligator.jl software package for automatically generating all polynomial invariants of the rich class of extended P-solvable loops with nested conditionals. Aligator.jl is written in the programming language Julia and is open-source. Aligator.jl transforms program loops into a system of algebraic recurrences and implements techniques from symbolic computation to solve recurrences, derive closed form solutions of loop variables and infer the ideal of polynomial invariants by variable elimination based on Gröbner basis computation.
DRLGENCERT: Deep Learning-based Automated Testing of Certificate Verification in SSL/TLS Implementations
Venue: arXiv cs.CR 2018
Year: 2018

The Secure Sockets Layer (SSL) and Transport Layer Security (TLS) protocols are the foundation of network security. The certificate verification in SSL/TLS implementations is vital and may become the weak link in the whole network ecosystem. In previous works, some research focused on the automated testing of certificate verification, and the main approaches rely on generating massive certificates through randomly combining parts of seed certificates for fuzzing. Although the generated certificates could meet the semantic constraints, the cost is quite heavy, and the performance is limited due to the randomness. To fill this gap, in this paper, we propose DRLGENCERT, the first framework of applying deep reinforcement learning to the automated testing of certificate verification in SSL/TLS implementations. DRLGENCERT accepts ordinary certificates as input and outputs newly generated certificates which could trigger discrepancies with high efficiency. Benefited by the deep reinforcement learning, when generating certificates, our framework could choose the best next action according to the result of a previous modification, instead of simple random combinations. At the same time, we developed a set of new techniques to support the overall design, like new feature extraction method for X.509 certificates, fine-grained differential testing, and so forth. Also, we implemented a prototype of DRLGENCERT and carried out a series of real-world experiments. The results show DRLGENCERT is quite efficient, and we obtained 84,661 discrepancy-triggering certificates from 181,900 certificate seeds, say around 46.5% effectiveness. Also, we evaluated six popular SSL/TLS implementations, including GnuTLS, MatrixSSL, MbedTLS, NSS, OpenSSL, and wolfSSL. DRLGENCERT successfully discovered 23 serious certificate verification flaws, and most of them were previously unknown.
Network Decoupling: From Regular to Depthwise Separable Convolutions
Venue: arXiv cs.CV 2018
Year: 2018

Depthwise separable convolution has shown great efficiency in network design, but requires time-consuming training procedure with full training-set available. This paper first analyzes the mathematical relationship between regular convolutions and depthwise separable convolutions, and proves that the former one could be approximated with the latter one in closed form. We show depthwise separable convolutions are principal components of regular convolutions. And then we propose network decoupling (ND), a training-free method to accelerate convolutional neural networks (CNNs) by transferring pre-trained CNN models into the MobileNet-like depthwise separable convolution structure, with a promising speedup yet negligible accuracy loss. We further verify through experiments that the proposed method is orthogonal to other training-free methods like channel decomposition, spatial decomposition, etc. Combining the proposed method with them will bring even larger CNN speedup. For instance, ND itself achieves about 2X speedup for the widely used VGG16, and combined with other methods, it reaches 3.7X speedup with graceful accuracy degradation. We demonstrate that ND is widely applicable to classification networks like ResNet, and object detection network like SSD300.
Distributionally Adversarial Attack
Venue: arXiv cs.LG 2018
Year: 2018

Recent work on adversarial attack has shown that Projected Gradient Descent (PGD) Adversary is a universal first-order adversary, and the classifier adversarially trained by PGD is robust against a wide range of first-order attacks. It is worth noting that the original objective of an attack/defense model relies on a data distribution $p(\mathbf{x})$, typically in the form of risk maximization/minimization, e.g., $\max/\min\mathbb{E}_{p(\mathbf(x))}\mathcal{L}(\mathbf{x})$ with $p(\mathbf{x})$ some unknown data distribution and $\mathcal{L}(\cdot)$ a loss function. However, since PGD generates attack samples independently for each data sample based on $\mathcal{L}(\cdot)$, the procedure does not necessarily lead to good generalization in terms of risk optimization. In this paper, we achieve the goal by proposing distributionally adversarial attack (DAA), a framework to solve an optimal {\em adversarial-data distribution}, a perturbed distribution that satisfies the $L_\infty$ constraint but deviates from the original data distribution to increase the generalization risk maximally. Algorithmically, DAA performs optimization on the space of potential data distributions, which introduces direct dependency between all data points when generating adversarial samples. DAA is evaluated by attacking state-of-the-art defense models, including the adversarially-trained models provided by {\em MIT MadryLab}. Notably, DAA ranks {\em the first place} on MadryLab's white-box leaderboards, reducing the accuracy of their secret MNIST model to $88.79\%$ (with $l_\infty$ perturbations of $ε= 0.3$) and the accuracy of their secret CIFAR model to $44.71\%$ (with $l_\infty$ perturbations of $ε= 8.0$). Code for the experiments is released on \url{https://github.com/tianzheng4/Distributionally-Adversarial-Attack}.
LARNN: Linear Attention Recurrent Neural Network
Venue: arXiv cs.LG 2018
Year: 2018

The Linear Attention Recurrent Neural Network (LARNN) is a recurrent attention module derived from the Long Short-Term Memory (LSTM) cell and ideas from the consciousness Recurrent Neural Network (RNN). Yes, it LARNNs. The LARNN uses attention on its past cell state values for a limited window size $k$. The formulas are also derived from the Batch Normalized LSTM (BN-LSTM) cell and the Transformer Network for its Multi-Head Attention Mechanism. The Multi-Head Attention Mechanism is used inside the cell such that it can query its own $k$ past values with the attention window. This has the effect of augmenting the rank of the tensor with the attention mechanism, such that the cell can perform complex queries to question its previous inner memories, which should augment the long short-term effect of the memory. With a clever trick, the LARNN cell with attention can be easily used inside a loop on the cell state, just like how any other Recurrent Neural Network (RNN) cell can be looped linearly through time series. This is due to the fact that its state, which is looped upon throughout time steps within time series, stores the inner states in a "first in, first out" queue which contains the $k$ most recent states and on which it is easily possible to add static positional encoding when the queue is represented as a tensor. This neural architecture yields better results than the vanilla LSTM cells. It can obtain results of 91.92% for the test accuracy, compared to the previously attained 91.65% using vanilla LSTM cells. Note that this is not to compare to other research, where up to 93.35% is obtained, but costly using 18 LSTM cells rather than with 2 to 3 cells as analyzed here. Finally, an interesting discovery is made, such that adding activation within the multi-head attention mechanism's linear layers can yield better results in the context researched hereto.
From MVPs to pivots: a hypothesis-driven journey of two software startups
Venue: arXiv cs.SE 2018
Year: 2018

Software startups have emerged as an interesting multiperspective research area. Inspired by Lean Startup, a startup journey can be viewed as a series of experiments that validate a set of business hypotheses an entrepreneurial team make explicitly or inexplicitly about their startup. It is little known about how startups evolve through business hypothesis testing. This study proposes a novel approach to look at the startup evolution as a Minimum Viable Product(MVP) creat- ing process. We identified relationships among business hypotheses and MVPs via ethnography and post-mortem analysis in two software star- tups. We observe that the relationship between hypotheses and MVPs is incomplete and non-linear in these two startups. We also find that entrepreneurs do learn from testing their hypotheses. However, there are hypotheses not tested by MVPs and vice versa, MVPs not related to any business hypothesis. The approach we proposed visualizes the flow of entrepreneurial knowledge across pivots via MVPs.
Constant Arboricity Spectral Sparsifiers
Venue: arXiv cs.DS 2018
Year: 2018

We show that every graph is spectrally similar to the union of a constant number of forests. Moreover, we show that Spielman-Srivastava sparsifiers are the union of O(logn) forests. This result can be used to estimate boundaries of small subsets of vertices in nearly optimal query time.
Confidence and RISC: How Russian papers indexed in the national citation database Russian Index of Science Citation (RISC) characterize universities and research institutes
Venue: arXiv cs.DL 2018
Year: 2018

The paper analyses Russian Index of Science Citation (RISC), a national citation database. We continue our previous study (Moskaleva et al., 2018) and focus on difference between bibliometric indicators calculated on, so to say, "the best" journals, so called RISC Core, and those which take into account all Russian journals available. Such a difference may show focuses of insitutional actors on different document types, publication strategies etc.
Read + Verify: Machine Reading Comprehension with Unanswerable Questions
Venue: arXiv cs.CL 2018
Year: 2018

Machine reading comprehension with unanswerable questions aims to abstain from answering when no answer can be inferred. In addition to extract answers, previous works usually predict an additional "no-answer" probability to detect unanswerable cases. However, they fail to validate the answerability of the question by verifying the legitimacy of the predicted answer. To address this problem, we propose a novel read-then-verify system, which not only utilizes a neural reader to extract candidate answers and produce no-answer probabilities, but also leverages an answer verifier to decide whether the predicted answer is entailed by the input snippets. Moreover, we introduce two auxiliary losses to help the reader better handle answer extraction as well as no-answer detection, and investigate three different architectures for the answer verifier. Our experiments on the SQuAD 2.0 dataset show that our system achieves a score of 74.2 F1 on the test set, achieving state-of-the-art results at the time of submission (Aug. 28th, 2018).
Optimal Distributed Weighted Set Cover Approximation
Venue: arXiv cs.DC 2018
Year: 2018

We present a time-optimal deterministic distributed algorithm for approximating a minimum weight vertex cover in hypergraphs of rank $f$. This problem is equivalent to the Minimum Weight Set Cover Problem in which the frequency of every element is bounded by $f$. The approximation factor of our algorithm is $(f+ε)$. Let $Δ$ denote the maximum degree in the hypergraph. Our algorithm runs in the CONGEST model and requires $O(\logΔ / \log \log Δ)$ rounds, for constants $ε\in (0,1]$ and $f\in\mathbb N^+$. This is the first distributed algorithm for this problem whose running time does not depend on the vertex weights or the number of vertices. Thus adding another member to the exclusive family of \emph{provably optimal} distributed algorithms. For constant values of $f$ and $ε$, our algorithm improves over the $(f+ε)$-approximation algorithm of \cite{KuhnMW06} whose running time is $O(\log Δ+ \log W)$, where $W$ is the ratio between the largest and smallest vertex weights in the graph.
Confidential Encrypted Data Hiding and Retrieval Using QR Authentication System
Venue: arXiv cs.CR 2018
Year: 2018

Now, security and authenticity of data is a big challenge. To solve this problem, we propose an innovative method to authenticate the digital documents. In this paper, we propose a new method, where the marks obtained by a candidate will also be encoded in QR CodeTM in encrypted form, so that if an intruder tries to change the marks in the mark sheet then he can not do that in the QR CodeTM, because the encryption key is unknown to him. In this method, we encrypt the mark sheet data using the TTJSA encryption algorithm. The encrypted marks are entered inside QR code and that QR code is also printed with the original data of the mark sheet. The marks can then be retrieved from the QR code and can be decrypted using TTJSA decryption algorithm and then it can be verified with marks already there in the mark sheet.
Towards a Theory-Guided Benchmarking Suite for Discrete Black-Box Optimization Heuristics: Profiling $(1+λ)$ EA Variants on OneMax and LeadingOnes
Venue: arXiv cs.NE 2018
Year: 2018

Theoretical and empirical research on evolutionary computation methods complement each other by providing two fundamentally different approaches towards a better understanding of black-box optimization heuristics. In discrete optimization, both streams developed rather independently of each other, but we observe today an increasing interest in reconciling these two sub-branches. In continuous optimization, the COCO (COmparing Continuous Optimisers) benchmarking suite has established itself as an important platform that theoreticians and practitioners use to exchange research ideas and questions. No widely accepted equivalent exists in the research domain of discrete black-box optimization. Marking an important step towards filling this gap, we adjust the COCO software to pseudo-Boolean optimization problems, and obtain from this a benchmarking environment that allows a fine-grained empirical analysis of discrete black-box heuristics. In this documentation we demonstrate how this test bed can be used to profile the performance of evolutionary algorithms. More concretely, we study the optimization behavior of several $(1+λ)$ EA variants on the two benchmark problems OneMax and LeadingOnes. This comparison motivates a refined analysis for the optimization time of the $(1+λ)$ EA on LeadingOnes.
Optimal Coverage and Rate in Downlink Cellular Networks: A SIR Meta-Distribution Based Approach
Venue: arXiv cs.NI 2018
Year: 2018

In this paper, we present a detailed analysis of the coverage and spectral efficiency of a downlink cellular network. Rather than relying on the first order statistics of received signal-to-interference-ratio (SIR) such as coverage probability, we focus on characterizing its meta-distribution. Our analysis is based on the alpha-beta-gamma (ABG) path-loss model which provides us with the flexibility to analyze urban macro (UMa) and urban micro (UMi) deployments. With the help of an analytical framework, we demonstrate that selection of underlying degrees-of-freedom such as BS height for optimization of first order statistics such as coverage probability is not optimal in the network-wide sense. Consequently, the SIR meta-distribution must be employed to select appropriate operational points which will ensure consistent user experiences across the network. Our design framework reveals that the traditional results which advocate lowering of BS heights or even optimal selection of BS height do not yield consistent service experience across users. By employing the developed framework we also demonstrate how available spectral resources in terms of time slots/channel partitions can be optimized by considering the meta-distribution of the SIR.
Optimization of LTE Radio Resource Block Allocation for Maritime Channels
Venue: arXiv cs.NI 2018
Year: 2018

In this study, we describe the behavior of LTE over the sea and investigate the problem of radio resource block allocation in such SINR limited maritime channels. For simulations of such sea environment, we considered a network scenario of Bosphorus Strait in Istanbul, Turkey with different number of ships ferrying between two ports at a given time. After exploiting the network characteristics, we formulated and solved the radio resource allocation problem by max-min integer linear programming method. The radio resource allocation fairness in terms of Jain's fairness index was computed and it was compared with round robin and opportunistic methods. Results show that the max-min optimization method performs better than the opportunistic and round robin methods. This result in turn reflects that the max-min optimization method gives us the high minimum best throughput as compared to other two methods considering different ship density scenarios in the sea. Also, it was observed that as the number of ships begin to increase in the sea, the max-min method performs significantly better with good fairness as compared to the other two methods.
Supremacy by Accelerated Warfare through the Comprehension Barrier and Beyond: Reaching the Zero Domain and Cyberspace Singularity
Venue: arXiv cs.CR 2018
Year: 2018

It is questionable and even unlikely that cyber supremacy could be reached by overwhelming capabilities manifested by stacking more technical capacity and adding attack vectors. The alternative is to use time as the vehicle to supremacy by accelerating the velocity of the engagements beyond the speed at which the enemy can target, and precisely execute and comprehend the events unfolding. The space created beyond the adversary's comprehension is called the Zero Domain. Military traditionally sees the battle space as land, sea, air, space, and cyber domains. When fighting the battle beyond the adversary's comprehension, no traditional warfighting domain serves as a battle space; it is not a vacuum nor an unclaimed terra nullius, but instead the Zero Domain. In the Zero Domain, cyberspace superiority surfaces as the outfall of the accelerated time and a digital space-separated singularity that benefit the more-rapid actor. The Zero Domain has a time space that is accessible only by the rapid actor and a digital landscape that is not accessible by the slower actor due to the execution velocity in enhanced accelerated warfare. Velocity achieves cyber Anti AccessArea Denial A2AD, which can be achieved without active initial interchanges by accelerating the execution and cyber ability in a solitaire state. During this process, any adversarial probing engagements only affect the actor on the approach to the Comprehension Barrier and once arrived in the Zero Domain a complete state of A2AD is present.
Maximising Throughput in a Complex Coal Export System
Venue: arXiv cs.CE 2018
Year: 2018

The Port of Newcastle features three coal export terminals, operating primarily in cargo assembly mode, that share a rail network on their inbound side, and a channel on their outbound side. Maximising throughput at a single coal terminal, taking into account its layout, its equipment, and its operating policies, is already challenging, but maximising throughput of the Hunter Valley coal export system as a whole requires that terminals and inbound and outbound shared resources be considered simultaneously. Existing approaches to do so either lack realism or are too computationally demanding to be useful as an everyday planning tool. We present a parallel genetic algorithm to optimise the integrated system. The algorithm models activities in continuous time, can handle practical planning horizons efficiently, and generates solutions that match or improve solutions obtained with the state-of-the-art solvers, whilst vastly outperforming them both in memory usage and running time.
Learning to Compose over Tree Structures via POS Tags
Venue: arXiv cs.CL 2018
Year: 2018

Recursive Neural Network (RecNN), a type of models which compose words or phrases recursively over syntactic tree structures, has been proven to have superior ability to obtain sentence representation for a variety of NLP tasks. However, RecNN is born with a thorny problem that a shared compositional function for each node of trees can't capture the complex semantic compositionality so that the expressive power of model is limited. In this paper, in order to address this problem, we propose Tag-Guided HyperRecNN/TreeLSTM (TG-HRecNN/TreeLSTM), which introduces hypernetwork into RecNNs to take as inputs Part-of-Speech (POS) tags of word/phrase and generate the semantic composition parameters dynamically. Experimental results on five datasets for two typical NLP tasks show proposed models both obtain significant improvement compared with RecNN and TreeLSTM consistently. Our TG-HTreeLSTM outperforms all existing RecNN-based models and achieves or is competitive with state-of-the-art on four sentence classification benchmarks. The effectiveness of our models is also demonstrated by qualitative analysis.