OpenRaiser/Intern-Atlas huggingface dataset:
HighRes-net: Recursive Fusion for Multi-Frame Super-Resolution of Satellite Imagery
Venue: arXiv cs.CV 2020
Year: 2020

Generative deep learning has sparked a new wave of Super-Resolution (SR) algorithms that enhance single images with impressive aesthetic results, albeit with imaginary details. Multi-frame Super-Resolution (MFSR) offers a more grounded approach to the ill-posed problem, by conditioning on multiple low-resolution views. This is important for satellite monitoring of human impact on the planet -- from deforestation, to human rights violations -- that depend on reliable imagery. To this end, we present HighRes-net, the first deep learning approach to MFSR that learns its sub-tasks in an end-to-end fashion: (i) co-registration, (ii) fusion, (iii) up-sampling, and (iv) registration-at-the-loss. Co-registration of low-resolution views is learned implicitly through a reference-frame channel, with no explicit registration mechanism. We learn a global fusion operator that is applied recursively on an arbitrary number of low-resolution pairs. We introduce a registered loss, by learning to align the SR output to a ground-truth through ShiftNet. We show that by learning deep representations of multiple views, we can super-resolve low-resolution signals and enhance Earth Observation data at scale. Our approach recently topped the European Space Agency's MFSR competition on real-world satellite imagery.
Security of HyperLogLog (HLL) Cardinality Estimation: Vulnerabilities and Protection
Venue: arXiv cs.CR 2020
Year: 2020

Count distinct or cardinality estimates are widely used in network monitoring for security. They can be used, for example, to detect the malware spread, network scans, or a denial of service attack. There are many algorithms to estimate cardinality. Among those, HyperLogLog (HLL) has been one of the most widely adopted. HLL is simple, provides good cardinality estimates over a wide range of values, requires a small amount of memory, and allows merging of estimates from different sources. However, as HLL is increasingly used to detect attacks, it can itself become the target of attackers that want to avoid being detected. To the best of our knowledge, the security of HLL has not been studied before. In this letter, we take an initial step in its study by first exposing a vulnerability of HLL that allows an attacker to manipulate its estimate. This shows the importance of designing secure HLL implementations. In the second part of the letter, we propose an efficient protection technique to detect and avoid the HLL manipulation. The results presented strongly suggest that the security of HLL should be further studied given that it is widely adopted in many networking and computing applications.
On Coresets for Support Vector Machines
Venue: arXiv cs.LG 2020
Year: 2020

We present an efficient coreset construction algorithm for large-scale Support Vector Machine (SVM) training in Big Data and streaming applications. A coreset is a small, representative subset of the original data points such that a models trained on the coreset are provably competitive with those trained on the original data set. Since the size of the coreset is generally much smaller than the original set, our preprocess-then-train scheme has potential to lead to significant speedups when training SVM models. We prove lower and upper bounds on the size of the coreset required to obtain small data summaries for the SVM problem. As a corollary, we show that our algorithm can be used to extend the applicability of any off-the-shelf SVM solver to streaming, distributed, and dynamic data settings. We evaluate the performance of our algorithm on real-world and synthetic data sets. Our experimental results reaffirm the favorable theoretical properties of our algorithm and demonstrate its practical effectiveness in accelerating SVM training.
Blind Adversarial Network Perturbations
Venue: arXiv cs.CR 2020
Year: 2020

Deep Neural Networks (DNNs) are commonly used for various traffic analysis problems, such as website fingerprinting and flow correlation, as they outperform traditional (e.g., statistical) techniques by large margins. However, deep neural networks are known to be vulnerable to adversarial examples: adversarial inputs to the model that get labeled incorrectly by the model due to small adversarial perturbations. In this paper, for the first time, we show that an adversary can defeat DNN-based traffic analysis techniques by applying \emph{adversarial perturbations} on the patterns of \emph{live} network traffic.
Demystifying the Performance of HPC Scientific Applications on NVM-based Memory Systems
Venue: arXiv cs.DC 2020
Year: 2020

The emergence of high-density byte-addressable non-volatile memory (NVM) is promising to accelerate data- and compute-intensive applications. Current NVM technologies have lower performance than DRAM and, thus, are often paired with DRAM in a heterogeneous main memory. Recently, byte-addressable NVM hardware becomes available. This work provides a timely evaluation of representative HPC applications from the "Seven Dwarfs" on NVM-based main memory. Our results quantify the effectiveness of DRAM-cached-NVM for accelerating HPC applications and enabling large problems beyond the DRAM capacity. On uncached-NVM, HPC applications exhibit three tiers of performance sensitivity, i.e., insensitive, scaled, and bottlenecked. We identify write throttling and concurrency control as the priorities in optimizing applications. We highlight that concurrency change may have a diverging effect on read and write accesses in applications. Based on these findings, we explore two optimization approaches. First, we provide a prediction model that uses datasets from a small set of configurations to estimate performance at various concurrency and data sizes to avoid exhaustive search in the configuration space. Second, we demonstrate that write-aware data placement on uncached-NVM could achieve $2$x performance improvement with a 60% reduction in DRAM usage.
Text-based Question Answering from Information Retrieval and Deep Neural Network Perspectives: A Survey
Venue: arXiv cs.IR 2020
Year: 2020

Text-based Question Answering (QA) is a challenging task which aims at finding short concrete answers for users' questions. This line of research has been widely studied with information retrieval techniques and has received increasing attention in recent years by considering deep neural network approaches. Deep learning approaches, which are the main focus of this paper, provide a powerful technique to learn multiple layers of representations and interaction between questions and texts. In this paper, we provide a comprehensive overview of different models proposed for the QA task, including both traditional information retrieval perspective, and more recent deep neural network perspective. We also introduce well-known datasets for the task and present available results from the literature to have a comparison between different techniques.
Relativity of Depth and Sophistication
Venue: arXiv cs.IT 2020
Year: 2020

Logical depth and sophistication are two quantitative measures of the non-trivial organization of an object. Although apparently different, these measures have been proven equivalent, when the logical depth is renormalized by the busy beaver function. In this article, the measures are relativized to auxiliary information and re-compared to one another. The ability of auxiliary information to solve the halting problem introduces a distortion between the measures. Finally, similar to algorithmic complexity, sophistication and logical depth (renormalized) each offer a relation between their expression of $(x, y)$, $(x)$ and $(y | x)$.
On the Approximability of Weighted Model Integration on DNF Structures
Venue: arXiv cs.AI 2020
Year: 2020

Weighted model counting (WMC) consists of computing the weighted sum of all satisfying assignments of a propositional formula. WMC is well-known to be #P-hard for exact solving, but admits a fully polynomial randomized approximation scheme (FPRAS) when restricted to DNF structures. In this work, we study weighted model integration, a generalization of weighted model counting which involves real variables in addition to propositional variables, and pose the following question: Does weighted model integration on DNF structures admit an FPRAS? Building on classical results from approximate volume computation and approximate weighted model counting, we show that weighted model integration on DNF structures can indeed be approximated for a class of weight functions. Our approximation algorithm is based on three subroutines, each of which can be a weak (i.e., approximate), or a strong (i.e., exact) oracle, and in all cases, comes along with accuracy guarantees. We experimentally verify our approach over randomly generated DNF instances of varying sizes, and show that our algorithm scales to large problem instances, involving up to 1K variables, which are currently out of reach for existing, general-purpose weighted model integration solvers.
SpotTheFake: An Initial Report on a New CNN-Enhanced Platform for Counterfeit Goods Detection
Venue: arXiv cs.CV 2020
Year: 2020

The counterfeit goods trade represents nowadays more than 3.3% of the whole world trade and thus it's a problem that needs now more than ever a lot of attention and a reliable solution that would reduce the negative impact it has over the modern society. This paper presents the design and early stage development of a novel counterfeit goods detection platform that makes use of the outstsanding learning capabilities of the classical VGG16 convolutional model trained through the process of "transfer learning" and a multi-stage fake detection procedure that proved to be not only reliable but also very robust in the experiments we have conducted so far using an image dataset of various goods which we gathered ourselves.
Four-valued monitorability of $ω$-regular languages
Venue: arXiv cs.FL 2020
Year: 2020

Runtime Verification (RV) is a lightweight formal technique in which program or system execution is monitored and analyzed, to check whether certain properties are satisfied or violated after a finite number of steps. The use of RV has led to interest in deciding whether a property is monitorable: whether it is always possible for the satisfaction or violation of the property to be determined after a finite future continuation. However, classical two-valued monitorability suffers from two inherent limitations. First, a property can only be evaluated as monitorable or non-monitorable; no information is available regarding whether only one verdict (satisfaction or violation) can be detected. Second, monitorability is defined at the language-level and does not tell us whether satisfaction or violation can be detected starting from the current monitor state during system execution. To address these limitations, this paper proposes a new notion of four-valued monitorability for $ω$-languages and applies it at the state-level. Four-valued monitorability is more informative than two-valued monitorability as a property can be evaluated as a four-valued result, denoting that only satisfaction, only violation, or both are active for a monitorable property. We can also compute state-level weak monitorability, i.e., whether satisfaction or violation can be detected starting from a given state in a monitor, which enables state-level optimizations of monitoring algorithms. Based on a new six-valued semantics, we propose procedures for computing four-valued monitorability of $ω$-regular languages, both at the language-level and at the state-level. We have developed a new tool that implements the proposed procedure for computing monitorability of LTL formulas.
Overlaid oriented Voronoi diagrams and the 1-Steiner tree problem
Venue: arXiv cs.CG 2020
Year: 2020

Overlaid oriented Voronoi diagrams (OOVDs) are known to provide useful data for the construction of optimal Euclidean $1$-Steiner trees. The theoretical time complexity of construction methods exploiting the OOVD is $O(n^2)$, but a computational study has never been performed, and robust constructions for OOVDs have not previously been implemented. In this paper, we outline a numerically stable implementation for constructing OOVDs using tools from the Computational Geometry Algorithms Library (CGAL), and test its performance on random point sets. We then study the effect that the OOVD data has in reducing the complexity of $1$-Steiner tree construction when compared to a naive approach. The number of iterations of the main loop of the 1-Steiner algorithm is directly determined by the number of faces in the OOVD, and this appears to be linear for the random inputs we tested. We also discuss methods for processing the OOVD data that lead to a reduction in construction time by roughly a factor of 12.
Computing Covers under Substring Consistent Equivalence Relations
Venue: arXiv cs.DS 2020
Year: 2020

Covers are a kind of quasiperiodicity in strings. A string $C$ is a cover of another string $T$ if any position of $T$ is inside some occurrence of $C$ in $T$. The shortest and longest cover arrays of $T$ have the lengths of the shortest and longest covers of each prefix of $T$, respectively. The literature has proposed linear-time algorithms computing longest and shortest cover arrays taking border arrays as input. An equivalence relation $\approx$ over strings is called a substring consistent equivalence relation (SCER) iff $X \approx Y$ implies (1) $|X| = |Y|$ and (2) $X[i:j] \approx Y[i:j]$ for all $1 \le i \le j \le |X|$. In this paper, we generalize the notion of covers for SCERs and prove that existing algorithms to compute the shortest cover array and the longest cover array of a string $T$ under the identity relation will work for any SCERs taking the accordingly generalized border arrays.
Residual Continual Learning
Venue: arXiv cs.LG 2020
Year: 2020

We propose a novel continual learning method called Residual Continual Learning (ResCL). Our method can prevent the catastrophic forgetting phenomenon in sequential learning of multiple tasks, without any source task information except the original network. ResCL reparameterizes network parameters by linearly combining each layer of the original network and a fine-tuned network; therefore, the size of the network does not increase at all. To apply the proposed method to general convolutional neural networks, the effects of batch normalization layers are also considered. By utilizing residual-learning-like reparameterization and a special weight decay loss, the trade-off between source and target performance is effectively controlled. The proposed method exhibits state-of-the-art performance in various continual learning scenarios.
CQ-VQA: Visual Question Answering on Categorized Questions
Venue: arXiv cs.CV 2020
Year: 2020

This paper proposes CQ-VQA, a novel 2-level hierarchical but end-to-end model to solve the task of visual question answering (VQA). The first level of CQ-VQA, referred to as question categorizer (QC), classifies questions to reduce the potential answer search space. The QC uses attended and fused features of the input question and image. The second level, referred to as answer predictor (AP), comprises of a set of distinct classifiers corresponding to each question category. Depending on the question category predicted by QC, only one of the classifiers of AP remains active. The loss functions of QC and AP are aggregated together to make it an end-to-end model. The proposed model (CQ-VQA) is evaluated on the TDIUC dataset and is benchmarked against state-of-the-art approaches. Results indicate competitive or better performance of CQ-VQA.
Data and Model Dependencies of Membership Inference Attack
Venue: arXiv cs.LG 2020
Year: 2020

Machine learning (ML) models have been shown to be vulnerable to Membership Inference Attacks (MIA), which infer the membership of a given data point in the target dataset by observing the prediction output of the ML model. While the key factors for the success of MIA have not yet been fully understood, existing defense mechanisms such as using L2 regularization \cite{10shokri2017membership} and dropout layers \cite{salem2018ml} take only the model's overfitting property into consideration. In this paper, we provide an empirical analysis of the impact of both the data and ML model properties on the vulnerability of ML techniques to MIA. Our results reveal the relationship between MIA accuracy and properties of the dataset and training model in use. In particular, we show that the size of shadow dataset, the class and feature balance and the entropy of the target dataset, the configurations and fairness of the training model are the most influential factors. Based on those experimental findings, we conclude that along with model overfitting, multiple properties jointly contribute to MIA success instead of any single property. Building on our experimental findings, we propose using those data and model properties as regularizers to protect ML models against MIA. Our results show that the proposed defense mechanisms can reduce the MIA accuracy by up to 25\% without sacrificing the ML model prediction utility.
Semantics of negative sequential patterns
Venue: arXiv cs.AI 2020
Year: 2020

In the field of pattern mining, a negative sequential pattern is specified by means of a sequence consisting of events to occur and of other events, called negative events, to be absent. For instance, containment of the pattern $\langle a\ \neg b\ c\rangle$ arises with an occurrence of a and a subsequent occurrence of c but no occurrence of b in between. This article is to shed light on the ambiguity of such a seemingly intuitive notation and we identify eight possible semantics for the containment relation between a pattern and a sequence. These semantics are illustrated and formally studied, in particular we propose dominance and equivalence relations between them. Also we prove that support is anti-monotonic for some of these semantics. Some of the results are discussed with the aim of developing algorithms to extract efficiently frequent negative patterns.
Citation Recommendation: Approaches and Datasets
Venue: arXiv cs.IR 2020
Year: 2020

Citation recommendation describes the task of recommending citations for a given text. Due to the overload of published scientific works in recent years on the one hand, and the need to cite the most appropriate publications when writing scientific texts on the other hand, citation recommendation has emerged as an important research topic. In recent years, several approaches and evaluation data sets have been presented. However, to the best of our knowledge, no literature survey has been conducted explicitly on citation recommendation. In this article, we give a thorough introduction into automatic citation recommendation research. We then present an overview of the approaches and data sets for citation recommendation and identify differences and commonalities using various dimensions. Last but not least, we shed light on the evaluation methods, and outline general challenges in the evaluation and how to meet them. We restrict ourselves to citation recommendation for scientific publications, as this document type has been studied the most in this area. However, many of the observations and discussions included in this survey are also applicable to other types of text, such as news articles and encyclopedic articles.
RapidLayout: Fast Hard Block Placement of FPGA-optimized Systolic Arrays using Evolutionary Algorithms
Venue: arXiv cs.AR 2020
Year: 2020

Evolutionary algorithms can outperform conventional placement algorithms such as simulated annealing, analytical placement as well as manual placement on metrics such as runtime, wirelength, pipelining cost, and clock frequency when mapping FPGA hard block intensive designs such as systolic arrays on Xilinx UltraScale+ FPGAs. For certain hard-block intensive, systolic array accelerator designs, the commercial-grade Xilinx Vivado CAD tool is unable to provide a legal routing solution without tedious manual placement constraints. Instead, we formulate an automatic FPGA placement algorithm for these hard blocks as a multi-objective optimization problem that targets wirelength squared and maximum bounding box size metrics. We build an end-to-end placement and routing flow called RapidLayout using the Xilinx RapidWright framework. RapidLayout runs 5-6$\times$ faster than Vivado with manual constraints and eliminates the weeks-long effort to generate placement constraints manually for the hard blocks. We also perform automated post-placement pipelining of the long wires inside each convolution block to target 650MHz URAM-limited operation. RapidLayout outperforms (1) the simulated annealer in VPR by 33% in runtime, 1.9-2.4$\times$ in wirelength, and 3-4$\times$ in bounding box size, while also (2) beating the analytical placer UTPlaceF by 9.3$\times$ in runtime, 1.8-2.2$\times$ in wirelength, and 2-2.7$\times$ in bounding box size. We employ transfer learning from a base FPGA device to speed-up placement optimization for similar FPGA devices in the UltraScale+ family by 11-14$\times$ than learning the placements from scratch.
An Experimental Study of ILP Formulations for the Longest Induced Path Problem
Venue: arXiv cs.DS 2020
Year: 2020

Given a graph $G=(V,E)$, the longest induced path problem asks for a maximum cardinality node subset $W\subseteq V$ such that the graph induced by $W$ is a path. It is a long established problem with applications, e.g., in network analysis. We propose novel integer linear programming (ILP) formulations for the problem and discuss efficient implementations thereof. Comparing them with known formulations from literature, we prove that they are beneficial in theory, yielding stronger relaxations. Moreover, our experiments show their practical superiority.
Block-Approximated Exponential Random Graphs
Venue: arXiv cs.SI 2020
Year: 2020

An important challenge in the field of exponential random graphs (ERGs) is the fitting of non-trivial ERGs on large graphs. By utilizing fast matrix block-approximation techniques, we propose an approximative framework to such non-trivial ERGs that result in dyadic independence (i.e., edge independent) distributions, while being able to meaningfully model both local information of the graph (e.g., degrees) as well as global information (e.g., clustering coefficient, assortativity, etc.) if desired. This allows one to efficiently generate random networks with similar properties as an observed network, and the models can be used for several downstream tasks such as link prediction. Our methods are scalable to sparse graphs consisting of millions of nodes. Empirical evaluation demonstrates competitiveness in terms of both speed and accuracy with state-of-the-art methods -- which are typically based on embedding the graph into some low-dimensional space -- for link prediction, showcasing the potential of a more direct and interpretable probabalistic model for this task.
Energy-Efficiency Routing algorithms in Wireless Sensor Networks: a Survey
Venue: arXiv cs.NI 2020
Year: 2020

A Wireless Sensor Network (WSN) is a collection of tiny nodes that have low energy levels and have become an essential component of the modern communication infrastructure and very important in industry and academia. Energy is crucial in WSN, and thus the design of WSN in the research community is based on energy efficiency, and node energy consumption is a great challenge to enhance WSN's lifetime. It may be costly or even impossible to charge or replace consumed batteries because of the difficult environment. Many energy efficiency methods are introduced in this article to decrease energy consumption, improve network performance and increase network lifetime.