OpenRaiser/Intern-Atlas huggingface dataset:
Machine Learning in Generation, Detection, and Mitigation of Cyberattacks in Smart Grid: A Survey
Venue: arXiv cs.CR 2020
Year: 2020

Smart grid (SG) is a complex cyber-physical system that utilizes modern cyber and physical equipment to run at an optimal operating point. Cyberattacks are the principal threats confronting the usage and advancement of the state-of-the-art systems. The advancement of SG has added a wide range of technologies, equipment, and tools to make the system more reliable, efficient, and cost-effective. Despite attaining these goals, the threat space for the adversarial attacks has also been expanded because of the extensive implementation of the cyber networks. Due to the promising computational and reasoning capability, machine learning (ML) is being used to exploit and defend the cyberattacks in SG by the attackers and system operators, respectively. In this paper, we perform a comprehensive summary of cyberattacks generation, detection, and mitigation schemes by reviewing state-of-the-art research in the SG domain. Additionally, we have summarized the current research in a structured way using tabular format. We also present the shortcomings of the existing works and possible future research direction based on our investigation.
Towards Hybrid Classical-Quantum Computation Structures in Wirelessly-Networked Systems
Venue: arXiv cs.NI 2020
Year: 2020

With unprecedented increases in traffic load in today's wireless networks, design challenges shift from the wireless network itself to the computational support behind the wireless network. In this vein, there is new interest in quantum-compute approaches because of their potential to substantially speed up processing, and so improve network throughput. However, quantum hardware that actually exists today is much more susceptible to computational errors than silicon-based hardware, due to the physical phenomena of decoherence and noise. This paper explores the boundary between the two types of computation---classical-quantum hybrid processing for optimization problems in wireless systems---envisioning how wireless can simultaneously leverage the benefit of both approaches. We explore the feasibility of a hybrid system with a real hardware prototype using one of the most advanced experimentally available techniques today, reverse quantum annealing. Preliminary results on a low-latency, large MIMO system envisioned in the 5G New Radio roadmap are encouraging, showing approximately 2--10X better performance in terms of processing time than prior published results.
Automatic and Efficient Variability-Aware Lifting of Functional Programs
Venue: arXiv cs.PL 2020
Year: 2020

A software analysis is a computer program that takes some representation of a software product as input and produces some useful information about that product as output. A software product line encompasses \emph{many} software product variants, and thus existing analyses can be applied to each of the product variations individually, but not to the entire product line as a whole. Enumerating all product variants and analyzing them one by one is usually intractable due to the combinatorial explosion of the number of product variants with respect to product line features. Several software analyses (e.g., type checkers, model checkers, data flow analyses) have been redesigned/re-implemented to support variability. This usually requires a lot of time and effort, and the variability-aware version of the analysis might have new errors/bugs that do not exist in the original one. Given an analysis program written in a functional language based on PCF, in this paper we present two approaches to transforming (lifting) it into a semantically equivalent variability-aware analysis. A light-weight approach (referred to as \emph{shallow lifting}) wraps the analysis program into a variability-aware version, exploring all combinations of its input arguments. Deep lifting, on the other hand, is a program rewriting mechanism where the syntactic constructs of the input program are rewritten into their variability-aware counterparts. Compositionally this results in an efficient program semantically equivalent to the input program, modulo variability. We present the correctness criteria for functional program lifting, together with correctness proof sketches of our program transformations. We evaluate our approach on a set of program analyses applied to the BusyBox C-language product line.
Learned Dual-View Reflection Removal
Venue: arXiv cs.CV 2020
Year: 2020

Traditional reflection removal algorithms either use a single image as input, which suffers from intrinsic ambiguities, or use multiple images from a moving camera, which is inconvenient for users. We instead propose a learning-based dereflection algorithm that uses stereo images as input. This is an effective trade-off between the two extremes: the parallax between two views provides cues to remove reflections, and two views are easy to capture due to the adoption of stereo cameras in smartphones. Our model consists of a learning-based reflection-invariant flow model for dual-view registration, and a learned synthesis model for combining aligned image pairs. Because no dataset for dual-view reflection removal exists, we render a synthetic dataset of dual-views with and without reflections for use in training. Our evaluation on an additional real-world dataset of stereo pairs shows that our algorithm outperforms existing single-image and multi-image dereflection approaches.
CAFT: Congestion-Aware Fault-Tolerant Load Balancing for Three-Tier Clos Data Centers
Venue: arXiv cs.NI 2020
Year: 2020

Production data centers operate under various workload sizes ranging from latency-sensitive mice flows to long-lived elephant flows. However, the predominant load balancing scheme in data center networks, equal-cost multi-path (ECMP), is agnostic to path conditions and performs poorly in asymmetric topologies, resulting in low throughput and high latencies. In this paper, we propose CAFT, a distributed congestion-aware fault-tolerant load balancing protocol for 3-tier data center networks. It first collects, in real time, the complete congestion information of two subsets from the set of all possible paths between any two hosts. Then, the best path congestion information from each subset is carried across the switches, during the Transport Control Protocol (TCP) connection process, to make path selection decision. Having two candidate paths improve the robustness of CAFT to asymmetries caused by link failures. Large-scale ns-3 simulations show that CAFT outperforms Expeditus in mean flow completion time (FCT) and network throughput for both symmetric and asymmetric scenarios.
LiRaNet: End-to-End Trajectory Prediction using Spatio-Temporal Radar Fusion
Venue: arXiv cs.CV 2020
Year: 2020

In this paper, we present LiRaNet, a novel end-to-end trajectory prediction method which utilizes radar sensor information along with widely used lidar and high definition (HD) maps. Automotive radar provides rich, complementary information, allowing for longer range vehicle detection as well as instantaneous radial velocity measurements. However, there are factors that make the fusion of lidar and radar information challenging, such as the relatively low angular resolution of radar measurements, their sparsity and the lack of exact time synchronization with lidar. To overcome these challenges, we propose an efficient spatio-temporal radar feature extraction scheme which achieves state-of-the-art performance on multiple large-scale datasets.Further, by incorporating radar information, we show a 52% reduction in prediction error for objects with high acceleration and a 16% reduction in prediction error for objects at longer range.
Extreme-SAX: Extreme Points Based Symbolic Representation for Time Series Classification
Venue: arXiv cs.LG 2020
Year: 2020

Time series classification is an important problem in data mining with several applications in different domains. Because time series data are usually high dimensional, dimensionality reduction techniques have been proposed as an efficient approach to lower their dimensionality. One of the most popular dimensionality reduction techniques of time series data is the Symbolic Aggregate Approximation (SAX), which is inspired by algorithms from text mining and bioinformatics. SAX is simple and efficient because it uses precomputed distances. The disadvantage of SAX is its inability to accurately represent important points in the time series. In this paper we present Extreme-SAX (E-SAX), which uses only the extreme points of each segment to represent the time series. E-SAX has exactly the same simplicity and efficiency of the original SAX, yet it gives better results in time series classification than the original SAX, as we show in extensive experiments on a variety of time series datasets.
Exploiting Multi-Path for Safeguarding mmWave Communications Against Randomly Located Eavesdroppers
Venue: arXiv cs.IT 2020
Year: 2020

Communication in the millimeter-wave (mmWave) band has recently been proposed to enable giga-bit-per-second data rates for next generation wireless systems. Physical layer security techniques have emerged as a simple and yet effective way to safeguard these systems against eavesdropping attacks. These techniques make use of the large antenna arrays available in mmWave systems to provide an array gain at the target receiver and degrade the signal quality at the eavesdropper. Despite their effectiveness, majority of these techniques are based on line-of-sight communication links between the transmitter and the receiver, and may fail in the presence of blockages or non-line-of-sight links. This paper builds upon previous work and extends physical layer security to the non-line-of-sight communication case and randomly located eavesdroppers. Specifically, the large dimensional antenna arrays in mmWave systems and the intrinsic characteristics of wireless channel are exploited to induce noiselike signals that jam eavesdroppers with sensitive receivers. Numerical results show that the proposed techniques provide higher secrecy rate when compared to conventional array and physical layer techniques based on line-of-sight links.
A recursion theoretic foundation of computation over real numbers
Venue: arXiv cs.LO 2020
Year: 2020

We define a class of computable functions over real numbers using functional schemes similar to the class of primitive and partial recursive functions defined by Gödel and Kleene. We show that this class of functions can also be characterized by master-slave machines, which are Turing machine like devices. The proof of the characterization gives a normal form theorem in the style of Kleene. Furthermore, this characterization is a natural combination of two most influential theories of computation over real numbers, namely, the type-two theory of effectivity (TTE) (see, for example, Weihrauch) and the Blum-Shub-Smale model of computation (BSS). Under this notion of computability, the recursive (or computable) subsets of real numbers are exactly effective $Δ^0_2$ sets.
Combining Weak Distributive Laws: Application to Up-To Techniques
Venue: arXiv cs.LO 2020
Year: 2020

The coalgebraic modelling of alternating automata and of probabilistic automata has long been obstructed by the absence of distributive laws of the powerset monad over itself, respectively of the powerset monad over the finite distribution monad. This can be fixed using the framework of weak distributive laws. We extend this framework to the case when one of the monads is only a functor. We provide abstract compositionality results, a generalized determinization procedure, and systematic soundness of up-to techniques. Along the way, we apply these results to alternating automata as a motivating example. Another example is given by probabilistic automata, for which our results yield soundness of bisimulation up-to convex hull.
CAPTION: Correction by Analyses, POS-Tagging and Interpretation of Objects using only Nouns
Venue: arXiv cs.CV 2020
Year: 2020

Recently, Deep Learning (DL) methods have shown an excellent performance in image captioning and visual question answering. However, despite their performance, DL methods do not learn the semantics of the words that are being used to describe a scene, making it difficult to spot incorrect words used in captions or to interchange words that have similar meanings. This work proposes a combination of DL methods for object detection and natural language processing to validate image's captions. We test our method in the FOIL-COCO data set, since it provides correct and incorrect captions for various images using only objects represented in the MS-COCO image data set. Results show that our method has a good overall performance, in some cases similar to the human performance.
Linear Classifier Combination via Multiple Potential Functions
Venue: arXiv cs.LG 2020
Year: 2020

A vital aspect of the classification based model construction process is the calibration of the scoring function. One of the weaknesses of the calibration process is that it does not take into account the information about the relative positions of the recognized objects in the feature space. To alleviate this limitation, in this paper, we propose a novel concept of calculating a scoring function based on the distance of the object from the decision boundary and its distance to the class centroid. An important property is that the proposed score function has the same nature for all linear base classifiers, which means that outputs of these classifiers are equally represented and have the same meaning. The proposed approach is compared with other ensemble algorithms and experiments on multiple Keel datasets demonstrate the effectiveness of our method. To discuss the results of our experiments, we use multiple classification performance measures and statistical analysis.
Stochastic Analysis of Satellite Broadband by Mega-Constellations with Inclined LEOs
Venue: arXiv cs.NI 2020
Year: 2020

As emerging massive constellations are intended to provide seamless connectivity for remote areas using hundreds of small low Earth orbit (LEO) satellites, new methodologies have great importance to study the performance of these networks. In this paper, we derive both downlink and uplink analytical expressions for coverage probability and data rate of an inclined LEO constellation under general fading, regardless of exact satellites' positions. Our solution involves two phases as we, first, abstract the network into a uniformly distributed network. Secondly, we obtain a new parameter, effective number of satellites, for every user's latitude which compensates for the performance mismatch between the actual and uniform constellations. In addition to exact derivation of the network performance metrics, this study provides insight into selecting the constellation parameters, e.g., the total number of satellites, altitude, and inclination angle.
Time Matters: Time-Aware LSTMs for Predictive Business Process Monitoring
Venue: arXiv cs.LG 2020
Year: 2020

Predictive business process monitoring (PBPM) aims to predict future process behavior during ongoing process executions based on event log data. Especially, techniques for the next activity and timestamp prediction can help to improve the performance of operational business processes. Recently, many PBPM solutions based on deep learning were proposed by researchers. Due to the sequential nature of event log data, a common choice is to apply recurrent neural networks with long short-term memory (LSTM) cells. We argue, that the elapsed time between events is informative. However, current PBPM techniques mainly use 'vanilla' LSTM cells and hand-crafted time-related control flow features. To better model the time dependencies between events, we propose a new PBPM technique based on time-aware LSTM (T-LSTM) cells. T-LSTM cells incorporate the elapsed time between consecutive events inherently to adjust the cell memory. Furthermore, we introduce cost-sensitive learning to account for the common class imbalance in event logs. Our experiments on publicly available benchmark event logs indicate the effectiveness of the introduced techniques.
Virtual Smartphone: High Fidelity Interaction with Proxy Objects in Virtual Reality
Venue: arXiv cs.HC 2020
Year: 2020

This workshop paper presents two proxy objects for high fidelity interaction in virtual reality (VR): a paper map and a smartphone. We showcase how our virtual paper map can increase interactivity and orientation, while our virtual smartphone extends the use of a proxy object, as it allows for actual touch input on a real phone leading to an almost infinite set of possible (inter-)actions (e.g. snapping pictures in the virtual world). Observations showed that participants were very precise in holding and interacting with both the paper map and the smartphone even though they did not see their hands in VR. The interaction in general was very intuitive which was mostly attributed to the realistic size of the virtual objects. Using our findings we discuss the trade off between adaptivity and high fidelity of proxy objects in VR.
Efficient Estimation of Graph Trussness
Venue: arXiv cs.DS 2020
Year: 2020

A $k$-truss is an edge-induced subgraph $H$ such that each of its edges belongs to at least $k-2$ triangles of $H$. This notion has been introduced around ten years ago in social network analysis and security, as a form of cohesive subgraph that is rich of triangles and less stringent than the clique. The \emph{trussness} of a graph is the maximum $k$ such that a $k$-truss exists. The problem of computing $k$-trusses has been largely investigated from the practical and engineering point of view. On the other hand, the theoretical side of the problem has received much less attention, despite presenting interesting challenges. The existing methods share a common design, based on iteratively removing the edge with smallest support, where the support of an edge is the number of triangles containing it. The aim of this paper is studying algorithmic aspects of graph trussness. While it is possible to show that the time complexity of computing exactly the graph trussness and that of counting/listing all triangles is inherently the same, we provide efficient algorithms for estimating its value, under suitable conditions, with significantly lower complexity than the exact approach. In particular, we provide a $(1 \pm ε)$-approximation algorithm that is asymptotically faster than the exact approach, on graphs which contain $ω(m \, polylog(n))$ triangles, and has the same running time on graphs that do not. For the latter case, we also show that it is impossible to obtain an approximation algorithm with faster running time than the one of the exact approach when the number of triangles is $O(m)$, unless well known conjectures on triangle-freeness and Boolean matrix multiplication are false.
An Empirical Study of DNNs Robustification Inefficacy in Protecting Visual Recommenders
Venue: arXiv cs.IR 2020
Year: 2020

Visual-based recommender systems (VRSs) enhance recommendation performance by integrating users' feedback with the visual features of product images extracted from a deep neural network (DNN). Recently, human-imperceptible images perturbations, defined \textit{adversarial attacks}, have been demonstrated to alter the VRSs recommendation performance, e.g., pushing/nuking category of products. However, since adversarial training techniques have proven to successfully robustify DNNs in preserving classification accuracy, to the best of our knowledge, two important questions have not been investigated yet: 1) How well can these defensive mechanisms protect the VRSs performance? 2) What are the reasons behind ineffective/effective defenses? To answer these questions, we define a set of defense and attack settings, as well as recommender models, to empirically investigate the efficacy of defensive mechanisms. The results indicate alarming risks in protecting a VRS through the DNN robustification. Our experiments shed light on the importance of visual features in very effective attack scenarios. Given the financial impact of VRSs on many companies, we believe this work might rise the need to investigate how to successfully protect visual-based recommenders. Source code and data are available at https://anonymous.4open.science/r/868f87ca-c8a4-41ba-9af9-20c41de33029/.
Differentiable Weighted Finite-State Transducers
Venue: arXiv cs.LG 2020
Year: 2020

We introduce a framework for automatic differentiation with weighted finite-state transducers (WFSTs) allowing them to be used dynamically at training time. Through the separation of graphs from operations on graphs, this framework enables the exploration of new structured loss functions which in turn eases the encoding of prior knowledge into learning algorithms. We show how the framework can combine pruning and back-off in transition models with various sequence-level loss functions. We also show how to learn over the latent decomposition of phrases into word pieces. Finally, to demonstrate that WFSTs can be used in the interior of a deep neural network, we propose a convolutional WFST layer which maps lower-level representations to higher-level representations and can be used as a drop-in replacement for a traditional convolution. We validate these algorithms with experiments in handwriting recognition and speech recognition.
Deep Convolutional Transform Learning -- Extended version
Venue: arXiv cs.LG 2020
Year: 2020

This work introduces a new unsupervised representation learning technique called Deep Convolutional Transform Learning (DCTL). By stacking convolutional transforms, our approach is able to learn a set of independent kernels at different layers. The features extracted in an unsupervised manner can then be used to perform machine learning tasks, such as classification and clustering. The learning technique relies on a well-sounded alternating proximal minimization scheme with established convergence guarantees. Our experimental results show that the proposed DCTL technique outperforms its shallow version CTL, on several benchmark datasets.
How Far Are We From an Optimal, Adaptive DE?
Venue: arXiv cs.NE 2020
Year: 2020

We consider how an (almost) optimal parameter adaptation process for an adaptive DE might behave, and compare the behavior and performance of this approximately optimal process to that of existing, adaptive mechanisms for DE. An optimal parameter adaptation process is an useful notion for analyzing the parameter adaptation methods in adaptive DE as well as other adaptive evolutionary algorithms, but it cannot be known generally. Thus, we propose a Greedy Approximate Oracle method (GAO) which approximates an optimal parameter adaptation process. We compare the behavior of GAODE, a DE algorithm with GAO, to typical adaptive DEs on six benchmark functions and the BBOB benchmarks, and show that GAO can be used to (1) explore how much room for improvement there is in the performance of the adaptive DEs, and (2) obtain hints for developing future, effective parameter adaptation methods for adaptive DEs.
Attention-Based Clustering: Learning a Kernel from Context
Venue: arXiv cs.LG 2020
Year: 2020

In machine learning, no data point stands alone. We believe that context is an underappreciated concept in many machine learning methods. We propose Attention-Based Clustering (ABC), a neural architecture based on the attention mechanism, which is designed to learn latent representations that adapt to context within an input set, and which is inherently agnostic to input sizes and number of clusters. By learning a similarity kernel, our method directly combines with any out-of-the-box kernel-based clustering approach. We present competitive results for clustering Omniglot characters and include analytical evidence of the effectiveness of an attention-based approach for clustering.