OpenRaiser/Intern-Atlas huggingface dataset:
Energy Harvesting in 5G Networks: Taxonomy, Requirements, Challenges, and Future Directions
Venue: arXiv cs.NI 2019
Year: 2019

Consciousness of energy saving is increasing in fifth-generation (5G) wireless networks due to the high energy consumption issue. Energy harvesting technology is a possible appealing solution for ultimately prolonging the lifetime of devices and networks. Although considerable research efforts have been conducted in the context of using energy harvesting technology in 5G wireless networks, these efforts are in their infancy, and a tutorial on this topic is still lacking. This study aims to discuss the beneficial role of energy harvesting technology in 5G networks. We categorize and classify the literature available on energy harvesting in 5G networks by devising a taxonomy based on energy sources; energy harvesting devices, phases, and models; energy conversion methods, and energy propagation medium. The key requirements for enabling energy harvesting in 5G networks are also outlined. Several core research challenges that remain to be addressed are discussed. Furthermore, future research directions are provided.
Detecting and Characterizing Lateral Phishing at Scale
Venue: arXiv cs.CR 2019
Year: 2019

We present the first large-scale characterization of lateral phishing attacks, based on a dataset of 113 million employee-sent emails from 92 enterprise organizations. In a lateral phishing attack, adversaries leverage a compromised enterprise account to send phishing emails to other users, benefitting from both the implicit trust and the information in the hijacked user's account. We develop a classifier that finds hundreds of real-world lateral phishing emails, while generating under four false positives per every one-million employee-sent emails. Drawing on the attacks we detect, as well as a corpus of user-reported incidents, we quantify the scale of lateral phishing, identify several thematic content and recipient targeting strategies that attackers follow, illuminate two types of sophisticated behaviors that attackers exhibit, and estimate the success rate of these attacks. Collectively, these results expand our mental models of the 'enterprise attacker' and shed light on the current state of enterprise phishing attacks.
A Mutation-based Approach for Assessing Weight Coverage of a Path Planner
Venue: arXiv cs.SE 2019
Year: 2019

Autonomous cars are subjected to several different kind of inputs (other cars, road structure, etc.) and, therefore, testing the car under all possible conditions is impossible. To tackle this problem, scenario-based testing for automated driving defines categories of different scenarios that should be covered. Although this kind of coverage is a necessary condition, it still does not guarantee that any possible behaviour of the autonomous car is tested. In this paper, we consider the path planner of an autonomous car that decides, at each timestep, the short-term path to follow in the next few seconds; such decision is done by using a weighted cost function that considers different aspects (safety, comfort, etc.). In order to assess whether all the possible decisions that can be taken by the path planner are covered by a given test suite T, we propose a mutation-based approach that mutates the weights of the cost function and then checks if at least one scenario of T kills the mutant. Preliminary experiments on a manually designed test suite show that some weights are easier to cover as they consider aspects that more likely occur in a scenario, and that more complicated scenarios (that generate more complex paths) are those that allow to cover more weights.
Machine-Learning Techniques for Detecting Attacks in SDN
Venue: arXiv cs.CR 2019
Year: 2019

With the advent of Software Defined Networks (SDNs), there has been a rapid advancement in the area of cloud computing. It is now scalable, cheaper, and easier to manage. However, SDNs are more prone to security vulnerabilities as compared to legacy systems. Therefore, machine-learning techniques are now deployed in the SDN infrastructure for the detection of malicious traffic. In this paper, we provide a systematic benchmarking analysis of the existing machine-learning techniques for the detection of malicious traffic in SDNs. We identify the limitations in these classical machine-learning based methods, and lay the foundation for a more robust framework. Our experiments are performed on a publicly available dataset of Intrusion Detection Systems (IDSs).
Research Intelligence (CRIS) and the Cloud: A Review
Venue: arXiv cs.DC 2019
Year: 2019

The purpose of this paper is to explore the impact of the cloud technology on current research information systems (CRIS). Based on an overview of published literature and on empirical evidence from surveys, the paper presents main characteristics, delivery models, service levels and general benefits of cloud computing. The second part assesses how the cloud computing challenges the research information management, from three angles: networking, specific benefits, and the ingestion of data in the cloud. The third part describes three aspects of the implementation of current research systems in the clouds, i.e. service models, requirements and potential risks and barriers. The paper concludes with some perspectives for future work. The paper is written for CRIS administrators and users, in order to improve research information management and to contribute to future development and implementation of these systems, but also for scholars and students who want to have detailed knowledge on this topic.
Exploiting BERT for End-to-End Aspect-based Sentiment Analysis
Venue: arXiv cs.CL 2019
Year: 2019

In this paper, we investigate the modeling power of contextualized embeddings from pre-trained language models, e.g. BERT, on the E2E-ABSA task. Specifically, we build a series of simple yet insightful neural baselines to deal with E2E-ABSA. The experimental results show that even with a simple linear classification layer, our BERT-based architecture can outperform state-of-the-art works. Besides, we also standardize the comparative study by consistently utilizing a hold-out validation dataset for model selection, which is largely ignored by previous works. Therefore, our work can serve as a BERT-based benchmark for E2E-ABSA.
Sublinear Algorithms for Gap Edit Distance
Venue: arXiv cs.CC 2019
Year: 2019

The edit distance is a way of quantifying how similar two strings are to one another by counting the minimum number of character insertions, deletions, and substitutions required to transform one string into the other. A simple dynamic programming computes the edit distance between two strings of length $n$ in $O(n^2)$ time, and a more sophisticated algorithm runs in time $O(n+t^2)$ when the edit distance is $t$ [Landau, Myers and Schmidt, SICOMP 1998]. In pursuit of obtaining faster running time, the last couple of decades have seen a flurry of research on approximating edit distance, including polylogarithmic approximation in near-linear time [Andoni, Krauthgamer and Onak, FOCS 2010], and a constant-factor approximation in subquadratic time [Chakrabarty, Das, Goldenberg, Koucký and Saks, FOCS 2018]. We study sublinear-time algorithms for small edit distance, which was investigated extensively because of its numerous applications. Our main result is an algorithm for distinguishing whether the edit distance is at most $t$ or at least $t^2$ (the quadratic gap problem) in time $\tilde{O}(\frac{n}{t}+t^3)$. This time bound is sublinear roughly for all $t$ in $[ω(1), o(n^{1/3})]$, which was not known before. The best previous algorithms solve this problem in sublinear time only for $t=ω(n^{1/3})$ [Andoni and Onak, STOC 2009]. Our algorithm is based on a new approach that adaptively switches between uniform sampling and reading contiguous blocks of the input strings. In contrast, all previous algorithms choose which coordinates to query non-adaptively. Moreover, it can be extended to solve the $t$ vs $t^{2-ε}$ gap problem in time $\tilde{O}(\frac{n}{t^{1-ε}}+t^3)$.
Stealthy Opaque Predicates in Hardware -- Obfuscating Constant Expressions at Negligible Overhead
Venue: arXiv cs.CR 2019
Year: 2019

Opaque predicates are a well-established fundamental building block for software obfuscation. Simplified, an opaque predicate implements an expression that provides constant Boolean output, but appears to have dynamic behavior for static analysis. Even though there has been extensive research regarding opaque predicates in software, techniques for opaque predicates in hardware are barely explored. In this work, we propose a novel technique to instantiate opaque predicates in hardware, such that they (1) are resource-efficient, and (2) are challenging to reverse engineer even with dynamic analysis capabilities. We demonstrate the applicability of opaque predicates in hardware for both, protection of intellectual property and obfuscation of cryptographic hardware Trojans. Our results show that we are able to implement stealthy opaque predicates in hardware with minimal overhead in area and no impact on latency.
Age of Information with Finite Horizon and Partial Updates
Venue: arXiv cs.IT 2019
Year: 2019

A resource-constrained system monitors a source of information by requesting a finite number of updates subject to random transmission delays. An a priori fixed update request policy is shown to minimize a polynomial penalty function of the age of information over arbitrary time horizons. Partial updates, compressed updates with reduced transmission and information content, in the presented model are shown to incur an age penalty independent of the compression. Finite horizons are shown to have better performance in terms of second order statistic relative to infinite horizons.
SummAE: Zero-Shot Abstractive Text Summarization using Length-Agnostic Auto-Encoders
Venue: arXiv cs.CL 2019
Year: 2019

We propose an end-to-end neural model for zero-shot abstractive text summarization of paragraphs, and introduce a benchmark task, ROCSumm, based on ROCStories, a subset for which we collected human summaries. In this task, five-sentence stories (paragraphs) are summarized with one sentence, using human summaries only for evaluation. We show results for extractive and human baselines to demonstrate a large abstractive gap in performance. Our model, SummAE, consists of a denoising auto-encoder that embeds sentences and paragraphs in a common space, from which either can be decoded. Summaries for paragraphs are generated by decoding a sentence from the paragraph representations. We find that traditional sequence-to-sequence auto-encoders fail to produce good summaries and describe how specific architectural choices and pre-training techniques can significantly improve performance, outperforming extractive baselines. The data, training, evaluation code, and best model weights are open-sourced.
An Introduction to Probabilistic Spiking Neural Networks: Probabilistic Models, Learning Rules, and Applications
Venue: arXiv cs.LG 2019
Year: 2019

Spiking neural networks (SNNs) are distributed trainable systems whose computing elements, or neurons, are characterized by internal analog dynamics and by digital and sparse synaptic communications. The sparsity of the synaptic spiking inputs and the corresponding event-driven nature of neural processing can be leveraged by energy-efficient hardware implementations, which can offer significant energy reductions as compared to conventional artificial neural networks (ANNs). The design of training algorithms lags behind the hardware implementations. Most existing training algorithms for SNNs have been designed either for biological plausibility or through conversion from pretrained ANNs via rate encoding. This article provides an introduction to SNNs by focusing on a probabilistic signal processing methodology that enables the direct derivation of learning rules by leveraging the unique time-encoding capabilities of SNNs. We adopt discrete-time probabilistic models for networked spiking neurons and derive supervised and unsupervised learning rules from first principles via variational inference. Examples and open research problems are also provided.
Computing the largest bond of a graph
Venue: arXiv cs.DS 2019
Year: 2019

A bond of a graph $G$ is an inclusion-wise minimal disconnecting set of $G$, i.e., bonds are cut-sets that determine cuts $[S,V\setminus S]$ of $G$ such that $G[S]$ and $G[V\setminus S]$ are both connected. Given $s,t\in V(G)$, an $st$-bond of $G$ is a bond whose removal disconnects $s$ and $t$. Contrasting with the large number of studies related to maximum cuts, there are very few results regarding the largest bond of general graphs. In this paper, we aim to reduce this gap on the complexity of computing the largest bond and the largest $st$-bond of a graph. Although cuts and bonds are similar, we remark that computing the largest bond of a graph tends to be harder than computing its maximum cut. We show that {\sc Largest Bond} remains NP-hard even for planar bipartite graphs, and it does not admit a constant-factor approximation algorithm, unless $P = NP$. We also show that {\sc Largest Bond} and {\sc Largest $st$-Bond} on graphs of clique-width $w$ cannot be solved in time $f(w)\times n^{o(w)}$ unless the Exponential Time Hypothesis fails, but they can be solved in time $f(w)\times n^{O(w)}$. In addition, we show that both problems are fixed-parameter tractable when parameterized by the size of the solution, but they do not admit polynomial kernels unless NP $\subseteq$ coNP/poly.
Subexponential-time algorithms for finding large induced sparse subgraphs
Venue: arXiv cs.CC 2019
Year: 2019

Let $\mathcal{C}$ and $\mathcal{D}$ be hereditary graph classes. Consider the following problem: given a graph $G\in\mathcal{D}$, find a largest, in terms of the number of vertices, induced subgraph of $G$ that belongs to $\mathcal{C}$. We prove that it can be solved in $2^{o(n)}$ time, where $n$ is the number of vertices of $G$, if the following conditions are satisfied: * the graphs in $\mathcal{C}$ are sparse, i.e., they have linearly many edges in terms of the number of vertices; * the graphs in $\mathcal{D}$ admit balanced separators of size governed by their density, e.g., $\mathcal{O}(Δ)$ or $\mathcal{O}(\sqrt{m})$, where $Δ$ and $m$ denote the maximum degree and the number of edges, respectively; and * the considered problem admits a single-exponential fixed-parameter algorithm when parameterized by the treewidth of the input graph. This leads, for example, to the following corollaries for specific classes $\mathcal{C}$ and $\mathcal{D}$: * a largest induced forest in a $P_t$-free graph can be found in $2^{\tilde{\mathcal{O}}(n^{2/3})}$ time, for every fixed $t$; and * a largest induced planar graph in a string graph can be found in $2^{\tilde{\mathcal{O}}(n^{3/4})}$ time.
Optimistic Value Iteration
Venue: arXiv cs.LO 2019
Year: 2019

Markov decision processes are widely used for planning and verification in settings that combine controllable or adversarial choices with probabilistic behaviour. The standard analysis algorithm, value iteration, only provides a lower bound on unbounded probabilities or reward values. Two "sound" variations, which also deliver an upper bound, have recently appeared. In this paper, we present optimistic value iteration, a new sound approach that leverages value iteration's ability to usually deliver tight lower bounds: we obtain a lower bound via standard value iteration, use the result to "guess" an upper bound, and prove the latter's correctness. Optimistic value iteration is easy to implement, does not require extra precomputations or a priori state space transformations, and works for computing reachability probabilities as well as expected rewards. It is also fast, as we show via an extensive experimental evaluation using our publicly available implementation within the Modest Toolset.
Intrusion detection systems using classical machine learning techniques versus integrated unsupervised feature learning and deep neural network
Venue: arXiv cs.CR 2019
Year: 2019

Security analysts and administrators face a lot of challenges to detect and prevent network intrusions in their organizations, and to prevent network breaches, detecting the breach on time is crucial. Challenges arise while detecting unforeseen attacks. This work includes a performance comparison of classical machine learning approaches that require vast feature engineering, versus integrated unsupervised feature learning and deep neural networks on the NSL-KDD dataset. Various trials of experiments were run to identify suitable hyper-parameters and network configurations of machine learning models. The DNN using 15 features extracted using Principal Component analysis was the most effective modeling method. The further analysis using the Software Defined Networking features also presented a good accuracy using Deep Neural network.
Path and Ancestor Queries on Trees with Multidimensional Weight Vectors
Venue: arXiv cs.DS 2019
Year: 2019

We consider an ordinal tree $T$ on $n$ nodes, with each node assigned a $d$-dimensional weight vector $\pnt{w} \in \{1,2,\ldots,n\}^d,$ where $d \in \mathbb{N}$ is a constant. We study path queries as generalizations of well-known {\textit{orthogonal range queries}}, with one of the dimensions being tree topology rather than a linear order. Since in our definitions $d$ only represents the number of dimensions of the weight vector without taking the tree topology into account, a path query in a tree with $d$-dimensional weight vectors generalize the corresponding $(d+1)$-dimensional orthogonal range query. We solve {\textit{ancestor dominance reporting}} problem as a direct generalization of dominance reporting problem, %in time $Ø((\lg^{d-1} n)/(\lg\lg n)^{d-2}+k)$ in time $Ø(\lg^{d-1}{n}+k)$ %and space of $Ø(n(\lg n)^{d-1}/(\lg \lg n)^{d-2})$ words, and space of $Ø(n\lg^{d-2}n)$ words, where $k$ is the size of the output, for $d \geq 2.$ We also achieve a tradeoff of $Ø(n\lg^{d-2+\eps}{n})$ words of space, with query time of $Ø((\lg^{d-1} n)/(\lg\lg n)^{d-2}+k),$ for the same problem, when $d \geq 3.$ We solve {\textit{path successor problem}} in $Ø(n\lg^{d-1}{n})$ words of space and time $Ø(\lg^{d-1+\eps}{n})$ for $d \geq 1$ and an arbitrary constant $\eps > 0.$ We propose a solution to {\textit{path counting problem}}, with $Ø(n(\lg{n}/\lg\lg{n})^{d-1})$ words of space and $Ø((\lg{n}/\lg\lg{n})^{d})$ query time, for $d \geq 1.$ Finally, we solve {\textit{path reporting problem}} in $Ø(n\lg^{d-1+\eps}{n})$ words of space and $Ø((\lg^{d-1}{n})/(\lg\lg{n})^{d-2}+k)$ query time, for $d \geq 2.$ These results match or nearly match the best tradeoffs of the respective range queries. We are also the first to solve path successor even for $d = 1$.
Accelerating Data Loading in Deep Neural Network Training
Venue: arXiv cs.LG 2019
Year: 2019

Data loading can dominate deep neural network training time on large-scale systems. We present a comprehensive study on accelerating data loading performance in large-scale distributed training. We first identify performance and scalability issues in current data loading implementations. We then propose optimizations that utilize CPU resources to the data loader design. We use an analytical model to characterize the impact of data loading on the overall training time and establish the performance trend as we scale up distributed training. Our model suggests that I/O rate limits the scalability of distributed training, which inspires us to design a locality-aware data loading method. By utilizing software caches, our method can drastically reduce the data loading communication volume in comparison with the original data loading implementation. Finally, we evaluate the proposed optimizations with various experiments. We achieved more than 30x speedup in data loading using 256 nodes with 1,024 learners.
Automatic Group Cohesiveness Detection With Multi-modal Features
Venue: arXiv cs.CV 2019
Year: 2019

Group cohesiveness is a compelling and often studied composition in group dynamics and group performance. The enormous number of web images of groups of people can be used to develop an effective method to detect group cohesiveness. This paper introduces an automatic group cohesiveness prediction method for the 7th Emotion Recognition in the Wild (EmotiW 2019) Grand Challenge in the category of Group-based Cohesion Prediction. The task is to predict the cohesive level for a group of people in images. To tackle this problem, a hybrid network including regression models which are separately trained on face features, skeleton features, and scene features is proposed. Predicted regression values, corresponding to each feature, are fused for the final cohesive intensity. Experimental results demonstrate that the proposed hybrid network is effective and makes promising improvements. A mean squared error (MSE) of 0.444 is achieved on the testing sets which outperforms the baseline MSE of 0.5.
Learning Optimal Solutions for Extremely Fast AC Optimal Power Flow
Venue: arXiv cs.LG 2019
Year: 2019

In this paper, we develop an online method that leverages machine learning to obtain feasible solutions to the AC optimal power flow (OPF) problem with negligible optimality gaps on extremely fast timescales (e.g., milliseconds), bypassing solving an AC OPF altogether. This is motivated by the fact that as the power grid experiences increasing amounts of renewable power generation, controllable loads, and other inverter-interfaced devices, faster system dynamics and quicker fluctuations in the power supply are likely to occur. Currently, grid operators typically solve AC OPF every 15 minutes to determine economic generator settings while ensuring grid constraints are satisfied. Due to the computational challenges with solving this nonconvex problem, many efforts have focused on linearizing or approximating the problem in order to solve the AC OPF on faster timescales. However, many of these approximations can be fairly poor representations of the actual system state and still require solving an optimization problem, which can be time consuming for large networks. In this work, we leverage historical data to learn a mapping between the system loading and optimal generation values, enabling us to find near-optimal and feasible AC OPF solutions on extremely fast timescales without actually solving an optimization problem.
Weakly supervised segmentation from extreme points
Venue: arXiv cs.CV 2019
Year: 2019

Annotation of medical images has been a major bottleneck for the development of accurate and robust machine learning models. Annotation is costly and time-consuming and typically requires expert knowledge, especially in the medical domain. Here, we propose to use minimal user interaction in the form of extreme point clicks in order to train a segmentation model that can, in turn, be used to speed up the annotation of medical images. We use extreme points in each dimension of a 3D medical image to constrain an initial segmentation based on the random walker algorithm. This segmentation is then used as a weak supervisory signal to train a fully convolutional network that can segment the organ of interest based on the provided user clicks. We show that the network's predictions can be refined through several iterations of training and prediction using the same weakly annotated data. Ultimately, our method has the potential to speed up the generation process of new training datasets for the development of new machine learning and deep learning-based models for, but not exclusively, medical image analysis.