OpenRaiser/Intern-Atlas huggingface dataset:
CoCo: Compact and Optimized Consolidation of Modularized Service Function Chains in NFV
Venue: arXiv cs.NI 2018
Year: 2018

The modularization of Service Function Chains (SFCs) in Network Function Virtualization (NFV) could introduce significant performance overhead and resource efficiency degradation due to introducing frequent packet transfer and consuming much more hardware resources. In response, we exploit the lightweight and individually scalable features of elements in Modularized SFCs (MSFCs) and propose CoCo, a compact and optimized consolidation framework for MSFC in NFV. CoCo addresses the above problems in two ways. First, CoCo Optimized Placer pays attention to the problem of which elements to consolidate and provides a performance-aware placement algorithm to place MSFCs compactly and optimize the global packet transfer cost. Second, CoCo Individual Scaler innovatively introduces a push-aside scaling up strategy to avoid degrading performance and taking up new CPU cores. To support MSFC consolidation, CoCo also provides an automatic runtime scheduler to ensure fairness when elements are consolidated on CPU core. Our evaluation results show that CoCo achieves significant performance improvement and efficient resource utilization.
A framework for mitigating zero-day attacks in IoT
Venue: arXiv cs.NI 2018
Year: 2018

Internet of Things (IoT) aims at providing connectivity between every computing entity. However, this facilitation is also leading to more cyber threats which may exploit the presence of a vulnerability of a period of time. One such vulnerability is the zero-day threat that may lead to zero-day attacks which are detrimental to an enterprise as well as the network security. In this article, a study is presented on the zero-day threats for IoT networks and a context graph-based framework is presented to provide a strategy for mitigating these attacks. The proposed approach uses a distributed diagnosis system for classifying the context at the central service provider as well as at the local user site. Once a potential zero-day attack is identified, a critical data sharing protocol is used to transmit alert messages and reestablish the trust between the network entities and the IoT devices. The results show that the distributed approach is capable of mitigating the zero-day threats efficiently with 33% and 21% improvements in terms of cost of operation and communication overheads, respectively, in comparison with the centralized diagnosis system.
Optimisation of Least Squares Algorithm: A Study of Frame Based Programming Techniques in Horizontal Networks
Venue: arXiv cs.CE 2018
Year: 2018

Least squares estimation, a regression technique based on minimisation of residuals, has been invaluable in bringing the best fit solutions to parameters in science and engineering. However, in dynamic environments such as in Geomatics Engineering, formation of these equations can be very challenging. And these constraints are ported and apparent in most program models, requiring users at ease with the subject matter. This paper reviews the methods of least squares approximation and examines a one-step automated approach, with error analysis, through the instrumentality of frames, object oriented programming.
DyLink2Vec: Effective Feature Representation for Link Prediction in Dynamic Networks
Venue: arXiv cs.SI 2018
Year: 2018

The temporal dynamics of a complex system such as a social network or a communication network can be studied by understanding the patterns of link appearance and disappearance over time. A critical task along this understanding is to predict the link state of the network at a future time given a collection of link states at earlier time points. In existing literature, this task is known as link prediction in dynamic networks. Solving this task is more difficult than its counterpart in static networks because an effective feature representation of node-pair instances for the case of dynamic network is hard to obtain. To overcome this problem, we propose a novel method for metric embedding of node-pair instances of a dynamic network. The proposed method models the metric embedding task as an optimal coding problem where the objective is to minimize the reconstruction error, and it solves this optimization task using a gradient descent method. We validate the effectiveness of the learned feature representation by utilizing it for link prediction in various real-life dynamic networks. Specifically, we show that our proposed link prediction model, which uses the extracted feature representation for the training instances, outperforms several existing methods that use well-known link prediction features.
Deterministic Document Exchange Protocols, and Almost Optimal Binary Codes for Edit Errors
Venue: arXiv cs.DS 2018
Year: 2018

We study two basic problems regarding edit error, i.e. document exchange and error correcting codes for edit errors (insdel codes). For message length $n$ and edit error upper bound $k$, it is known that in both problems the optimal sketch size or the optimal number of redundant bits is $Θ(k \log \frac{n}{k})$. However, known constructions are far from achieving these bounds. We significantly improve previous results on both problems. For document exchange, we give an efficient deterministic protocol with sketch size $O(k\log^2 \frac{n}{k})$. This significantly improves the previous best known deterministic protocol, which has sketch size $O(k^2 + k \log^2 n)$ (Belazzougui15). For binary insdel codes, we obtain the following results: 1. An explicit binary insdel code which encodes an $n$-bit message $x$ against $k$ errors with redundancy $O(k \log^2 \frac{n}{k})$. In particular this implies an explicit family of binary insdel codes that can correct $\varepsilon$ fraction of insertions and deletions with rate $1-O(\varepsilon \log^2 (\frac{1}{\varepsilon}))=1-\widetilde{O}(\varepsilon)$. 2. An explicit binary insdel code which encodes an $n$-bit message $x$ against $k$ errors with redundancy $O(k \log n)$. This is the first explicit construction of binary insdel codes that has optimal redundancy for a wide range of error parameters $k$, and this brings our understanding of binary insdel codes much closer to that of standard binary error correcting codes. In obtaining our results we introduce the notion of \emph{$\varepsilon$-self matching hash functions} and \emph{$\varepsilon$-synchronization hash functions}. We believe our techniques can have further applications in the literature.
Multidimensional Constellations for Uplink SCMA Systems --- A Comparative Study
Venue: arXiv cs.IT 2018
Year: 2018

Sparse code multiple access (SCMA) is a class of non-orthogonal multiple access (NOMA) that is proposed to support uplink machine-type communication services. In an SCMA system, designing multidimensional constellation plays an important role in the performance of the system. Since the behaviour of multidimensional constellations highly depends on the type of the channel, it is crucial to employ a constellation that is suitable for a certain application. In this paper, we first highlight and review the key performance indicators (KPIs) of multidimensional constellations that should be considered in their design process for various channel scenarios. We then provide a survey on the known multidimensional constellations in the context of SCMA systems with their design criteria. The performance of some of those constellations are evaluated for uncoded, high-rate, and low-rate LTE turbo-coded SCMA systems under different channel conditions through extensive simulations. All turbo-coded comparisons are performed for bit-interleaved coded modulation, with a concatenated detection and decoding scheme. Simulation results confirm that multidimensional constellations that satisfy KPIs of a certain channel scenario outperform others. Moreover, the bit error rate performance of uncoded systems, and the performance of the coded systems are coupled to their bit-labeling. The performance of the systems also remarkably depends on the behavior of the multi-user detector at different signal-to-noise ratio regions.
Models for Capturing Temporal Smoothness in Evolving Networks for Learning Latent Representation of Nodes
Venue: arXiv cs.SI 2018
Year: 2018

In a dynamic network, the neighborhood of the vertices evolve across different temporal snapshots of the network. Accurate modeling of this temporal evolution can help solve complex tasks involving real-life social and interaction networks. However, existing models for learning latent representation are inadequate for obtaining the representation vectors of the vertices for different time-stamps of a dynamic network in a meaningful way. In this paper, we propose latent representation learning models for dynamic networks which overcome the above limitation by considering two different kinds of temporal smoothness: (i) retrofitted, and (ii) linear transformation. The retrofitted model tracks the representation vector of a vertex over time, facilitating vertex-based temporal analysis of a network. On the other hand, linear transformation based model provides a smooth transition operator which maps the representation vectors of all vertices from one temporal snapshot to the next (unobserved) snapshot-this facilitates prediction of the state of a network in a future time-stamp. We validate the performance of our proposed models by employing them for solving the temporal link prediction task. Experiments on 9 real-life networks from various domains validate that the proposed models are significantly better than the existing models for predicting the dynamics of an evolving network.
M-PACT: An Open Source Platform for Repeatable Activity Classification Research
Venue: arXiv cs.CV 2018
Year: 2018

There are many hurdles that prevent the replication of existing work which hinders the development of new activity classification models. These hurdles include switching between multiple deep learning libraries and the development of boilerplate experimental pipelines. We present M-PACT to overcome existing issues by removing the need to develop boilerplate code which allows users to quickly prototype action classification models while leveraging existing state-of-the-art (SOTA) models available in the platform. M-PACT is the first to offer four SOTA activity classification models, I3D, C3D, ResNet50+LSTM, and TSN, under a single platform with reproducible competitive results. This platform allows for the generation of models and results over activity recognition datasets through the use of modular code, various preprocessing and neural network layers, and seamless data flow. In this paper, we present the system architecture, detail the functions of various modules, and describe the basic tools to develop a new model in M-PACT.
Area Rate Evaluation based on Spatial Clustering of massive MIMO Channel Measurements
Venue: arXiv cs.IT 2018
Year: 2018

Channel models for massive MIMO are typically based on matrices with complex Gaussian entries, extended by the Kronecker and Weichselberger model. One reason for observing a gap between modeled and actual channel behavior is the absence of spatial consistency in many such models, that is, spatial correlations over an area in the x, y-dimensions are not accounted for, making it difficult to study, e.g., area-throughput measures. In this paper, we propose an algorithm that can distinguish between regions of non-line-of-sight (NLoS) and line-of-sight (LoS) via a rank-metric criterion combined with a spiral search. With a k-means clustering algorithm a throughput per region (i.e., cluster) can be calculated, leading to what we refer to as "area-throughput". For evaluating the proposed orthogonality clustering scheme we use a simple filtered MIMO channel model which is spatially consistent, with known degrees of freedom. Moreover, we employ actual (spatially consistent) area channel measurements based on spatial sampling using a spider antenna and show that the proposed algorithm can be used to estimate the degrees of freedom, and, subsequently, the number of users that maximizes the throughput per square meter.
Heuristic Approaches for Goal Recognition in Incomplete Domain Models
Venue: arXiv cs.AI 2018
Year: 2018

Recent approaches to goal recognition have progressively relaxed the assumptions about the amount and correctness of domain knowledge and available observations, yielding accurate and efficient algorithms. These approaches, however, assume completeness and correctness of the domain theory against which their algorithms match observations: this is too strong for most real-world domains. In this paper, we develop goal recognition techniques that are capable of recognizing goals using \textit{incomplete} (and possibly incorrect) domain theories. We show the efficiency and accuracy of our approaches empirically against a large dataset of goal and plan recognition problems with incomplete domains.
Are Abstracts Enough for Hypothesis Generation?
Venue: arXiv cs.IR 2018
Year: 2018

The potential for automatic hypothesis generation (HG) systems to improve research productivity keeps pace with the growing set of publicly available scientific information. But as data becomes easier to acquire, we must understand the effect different textual data sources have on our resulting hypotheses. Are abstracts enough for HG, or does it need full-text papers? How many papers does an HG system need to make valuable predictions? How sensitive is a general-purpose HG system to hyperparameter values or input quality? What effect does corpus size and document length have on HG results? To answer these questions we train multiple versions of knowledge network-based HG system, Moliere, on varying corpora in order to compare challenges and trade offs in terms of result quality and computational requirements. Moliere generalizes main principles of similar knowledge network-based HG systems and reinforces them with topic modeling components. The corpora include the abstract and full-text versions of PubMed Central, as well as iterative halves of MEDLINE, which allows us to compare the effect document length and count has on the results. We find that, quantitatively, corpora with a higher median document length result in marginally higher quality results, yet require substantially longer to process. However, qualitatively, full-length papers introduce a significant number of intruder terms to the resulting topics, which decreases human interpretability. Additionally, we find that the effect of document length is greater than that of document count, even if both sets contain only paper abstracts. Reproducibility: Our code and data are available at github.com/jsybran/moliere, and bit.ly/2GxghpM respectively.
PMC text mining subset in BioC: 2.3 million full text articles and growing
Venue: arXiv cs.DL 2018
Year: 2018

Interest in full text mining biomedical research articles is growing. NCBI provides the PMC Open Access and Author Manuscript sets of articles which are available for text mining. We have made all of these articles available in BioC, an XML and JSON format which is convenient for sharing text, annotations, and relations. These articles are available both via ftp for bulk download and via a Web API for updates or more focused collection. Availability: https://www.ncbi.nlm.nih.gov/research/bionlp/APIs/BioC-PMC/
Parallel Complexity Analysis with Temporal Session Types
Venue: arXiv cs.PL 2018
Year: 2018

We study the problem of parametric parallel complexity analysis of concurrent, message-passing programs. To make the analysis local and compositional, it is based on a conservative extension of binary session types, which structure the type and direction of communication between processes and stand in a Curry-Howard correspondence with intuitionistic linear logic. The main innovation is to enrich session types with the temporal modalities next ($\bigcirc A$), always ($\Box A$), and eventually ($\Diamond A$), to additionally prescribe the timing of the exchanged messages in a way that is precise yet flexible. The resulting temporal session types uniformly express properties such as the message rate of a stream, the latency of a pipeline, the response time of a concurrent queue, or the span of a fork/join parallel program. The analysis is parametric in the cost model and the presentation focuses on communication cost as a concrete example. The soundness of the analysis is established by proofs of progress and type preservation using a timed multiset rewriting semantics. Representative examples illustrate the scope and usability of the approach.
Optimal Multiphase Investment Strategies for Influencing Opinions in a Social Network
Venue: arXiv cs.SI 2018
Year: 2018

We study the problem of optimally investing in nodes of a social network in a competitive setting, where two camps aim to maximize adoption of their opinions by the population. In particular, we consider the possibility of campaigning in multiple phases, where the final opinion of a node in a phase acts as its initial biased opinion for the following phase. Using an extension of the popular DeGroot-Friedkin model, we formulate the utility functions of the camps, and show that they involve what can be interpreted as multiphase Katz centrality. Focusing on two phases, we analytically derive Nash equilibrium investment strategies, and the extent of loss that a camp would incur if it acted myopically. Our simulation study affirms that nodes attributing higher weightage to initial biases necessitate higher investment in the first phase, so as to influence these biases for the terminal phase. We then study the setting in which a camp's influence on a node depends on its initial bias. For single camp, we present a polynomial time algorithm for determining an optimal way to split the budget between the two phases. For competing camps, we show the existence of Nash equilibria under reasonable assumptions, and that they can be computed in polynomial time.
Normal Image Manipulation for Bas-relief Generation with Hybrid Styles
Venue: arXiv cs.GR 2018
Year: 2018

We introduce a normal-based bas-relief generation and stylization method which is motivated by the recent advancement in this topic. Creating bas-relief from normal images has successfully facilitated bas-relief modeling in image space. However, the use of normal images in previous work is often restricted to certain type of operations only. This paper is intended to extend normal-based methods and construct bas-reliefs from normal images in a versatile way. Our method can not only generate a new normal image by combining various frequencies of existing normal images and details transferring, but also build bas-reliefs from a single RGB image and its edge-based sketch image. In addition, we introduce an auxiliary function to represent a smooth base surface and generate a layered global shape. To integrate above considerations into our framework, we formulate the bas- relief generation as a variational problem which can be solved by a screened Poisson equation. Some advantages of our method are that it expands the bas-relief shape space and generates diversified styles of results, and that it is capable of transferring details from one region to other regions. Our method is easy to implement, and produces good-quality bas-relief models. We experiment our method on a range of normal images and it compares favorably to other popular classic and state-of-the-art methods.
Feature Propagation on Graph: A New Perspective to Graph Representation Learning
Venue: arXiv cs.SI 2018
Year: 2018

We study feature propagation on graph, an inference process involved in graph representation learning tasks. It's to spread the features over the whole graph to the $t$-th orders, thus to expand the end's features. The process has been successfully adopted in graph embedding or graph neural networks, however few works studied the convergence of feature propagation. Without convergence guarantees, it may lead to unexpected numerical overflows and task failures. In this paper, we first define the concept of feature propagation on graph formally, and then study its convergence conditions to equilibrium states. We further link feature propagation to several established approaches such as node2vec and structure2vec. In the end of this paper, we extend existing approaches from represent nodes to edges (edge2vec) and demonstrate its applications on fraud transaction detection in real world scenario. Experiments show that it is quite competitive.
A Support Tensor Train Machine
Venue: arXiv cs.LG 2018
Year: 2018

There has been growing interest in extending traditional vector-based machine learning techniques to their tensor forms. An example is the support tensor machine (STM) that utilizes a rank-one tensor to capture the data structure, thereby alleviating the overfitting and curse of dimensionality problems in the conventional support vector machine (SVM). However, the expressive power of a rank-one tensor is restrictive for many real-world data. To overcome this limitation, we introduce a support tensor train machine (STTM) by replacing the rank-one tensor in an STM with a tensor train. Experiments validate and confirm the superiority of an STTM over the SVM and STM.
VC-Dimension Based Generalization Bounds for Relational Learning
Venue: arXiv cs.LG 2018
Year: 2018

In many applications of relational learning, the available data can be seen as a sample from a larger relational structure (e.g. we may be given a small fragment from some social network). In this paper we are particularly concerned with scenarios in which we can assume that (i) the domain elements appearing in the given sample have been uniformly sampled without replacement from the (unknown) full domain and (ii) the sample is complete for these domain elements (i.e. it is the full substructure induced by these elements). Within this setting, we study bounds on the error of sufficient statistics of relational models that are estimated on the available data. As our main result, we prove a bound based on a variant of the Vapnik-Chervonenkis dimension which is suitable for relational data.
Investigating Backtranslation in Neural Machine Translation
Venue: arXiv cs.CL 2018
Year: 2018

A prerequisite for training corpus-based machine translation (MT) systems -- either Statistical MT (SMT) or Neural MT (NMT) -- is the availability of high-quality parallel data. This is arguably more important today than ever before, as NMT has been shown in many studies to outperform SMT, but mostly when large parallel corpora are available; in cases where data is limited, SMT can still outperform NMT. Recently researchers have shown that back-translating monolingual data can be used to create synthetic parallel corpora, which in turn can be used in combination with authentic parallel data to train a high-quality NMT system. Given that large collections of new parallel text become available only quite rarely, backtranslation has become the norm when building state-of-the-art NMT systems, especially in resource-poor scenarios. However, we assert that there are many unknown factors regarding the actual effects of back-translated data on the translation capabilities of an NMT model. Accordingly, in this work we investigate how using back-translated data as a training corpus -- both as a separate standalone dataset as well as combined with human-generated parallel data -- affects the performance of an NMT model. We use incrementally larger amounts of back-translated data to train a range of NMT systems for German-to-English, and analyse the resulting translation performance.