OpenRaiser/Intern-Atlas huggingface dataset:
A Hybrid Approach to Privacy-Preserving Federated Learning
Venue: arXiv cs.LG 2019
Year: 2019

Federated learning facilitates the collaborative training of models without the sharing of raw data. However, recent attacks demonstrate that simply maintaining data locality during training processes does not provide sufficient privacy guarantees. Rather, we need a federated learning system capable of preventing inference over both the messages exchanged during training and the final trained model while ensuring the resulting model also has acceptable predictive accuracy. Existing federated learning approaches either use secure multiparty computation (SMC) which is vulnerable to inference or differential privacy which can lead to low accuracy given a large number of parties with relatively small amounts of data each. In this paper, we present an alternative approach that utilizes both differential privacy and SMC to balance these trade-offs. Combining differential privacy with secure multiparty computation enables us to reduce the growth of noise injection as the number of parties increases without sacrificing privacy while maintaining a pre-defined rate of trust. Our system is therefore a scalable approach that protects against inference threats and produces models with high accuracy. Additionally, our system can be used to train a variety of machine learning models, which we validate with experimental results on 3 different machine learning algorithms. Our experiments demonstrate that our approach out-performs state of the art solutions.
Cryptanalysis of a One-Time Code-Based Digital Signature Scheme
Venue: arXiv cs.CR 2019
Year: 2019

We consider a one-time digital signature scheme recently proposed by Persichetti and show that a successful key recovery attack can be mounted with limited complexity. The attack we propose exploits a single signature intercepted by the attacker, and relies on a statistical analysis performed over such a signature, followed by information set decoding. We assess the attack complexity and show that a full recovery of the secret key can be performed with a work factor that is far below the claimed security level. The efficiency of the attack is motivated by the sparsity of the signature, which leads to a significant information leakage about the secret key.
An Unsupervised Approach for Aspect Category Detection Using Soft Cosine Similarity Measure
Venue: arXiv cs.CL 2019
Year: 2019

Aspect category detection is one of the important and challenging subtasks of aspect-based sentiment analysis. Given a set of pre-defined categories, this task aims to detect categories which are indicated implicitly or explicitly in a given review sentence. Supervised machine learning approaches perform well to accomplish this subtask. Note that, the performance of these methods depends on the availability of labeled train data, which is often difficult and costly to obtain. Besides, most of these supervised methods require feature engineering to perform well. In this paper, we propose an unsupervised method to address aspect category detection task without the need for any feature engineering. Our method utilizes clusters of unlabeled reviews and soft cosine similarity measure to accomplish aspect category detection task. Experimental results on SemEval-2014 restaurant dataset shows that proposed unsupervised approach outperforms several baselines by a substantial margin.
Semantically-Aware Attentive Neural Embeddings for Image-based Visual Localization
Venue: arXiv cs.CV 2019
Year: 2019

We present an approach that combines appearance and semantic information for 2D image-based localization (2D-VL) across large perceptual changes and time lags. Compared to appearance features, the semantic layout of a scene is generally more invariant to appearance variations. We use this intuition and propose a novel end-to-end deep attention-based framework that utilizes multimodal cues to generate robust embeddings for 2D-VL. The proposed attention module predicts a shared channel attention and modality-specific spatial attentions to guide the embeddings to focus on more reliable image regions. We evaluate our model against state-of-the-art (SOTA) methods on three challenging localization datasets. We report an average (absolute) improvement of $19\%$ over current SOTA for 2D-VL. Furthermore, we present an extensive study demonstrating the contribution of each component of our model, showing $8$--$15\%$ and $4\%$ improvement from adding semantic information and our proposed attention module. We finally show the predicted attention maps to offer useful insights into our model.
SDNet: Contextualized Attention-based Deep Network for Conversational Question Answering
Venue: arXiv cs.CL 2019
Year: 2019

Conversational question answering (CQA) is a novel QA task that requires understanding of dialogue context. Different from traditional single-turn machine reading comprehension (MRC) tasks, CQA includes passage comprehension, coreference resolution, and contextual understanding. In this paper, we propose an innovated contextualized attention-based deep neural network, SDNet, to fuse context into traditional MRC models. Our model leverages both inter-attention and self-attention to comprehend conversation context and extract relevant information from passage. Furthermore, we demonstrated a novel method to integrate the latest BERT contextual model. Empirical results show the effectiveness of our model, which sets the new state of the art result in CoQA leaderboard, outperforming the previous best model by 1.6% F1. Our ensemble model further improves the result by 2.7% F1.
A Scalable Thermal Reservoir Simulator for Giant Models on Parallel Computers
Venue: arXiv cs.CE 2019
Year: 2019

This paper introduces the model, numerical methods, algorithms and parallel implementation of a thermal reservoir simulator that designed for numerical simulations of thermal reservoir with multiple components in three dimensional domain using distributed-memory parallel computers. Its full mathematical model is introduced with correlations for important properties and well modeling. Various well constraints, such as fixed bottom hole pressure, fixed oil, water, gas and liquid rates, constant heat transfer model, convective heat transfer model, heater model (temperature control, rate control, dual rate/temperature control), and subcool (steam trap), are introduced in details, including their mathematical models and methods. Efficient numerical methods and parallel computing technologies are presented. The simulator is designed for giant models with billions or even trillions of grid blocks using hundreds of thousands of CPUs. Numerical experiments show that our results match commercial simulators, which confirms the correctness of our methods and implementations. SAGD simulation with 15106 well pairs is also presented to study the effectiveness of our numerical methods. Scalability testings demonstrate that our simulator can handle giant models with over 200 billion grid blocks using 100,800 CPU cores and the simulator has good scalability.
An Intelligent Safety System for Human-Centered Semi-Autonomous Vehicles
Venue: arXiv cs.CV 2019
Year: 2019

Nowadays, automobile manufacturers make efforts to develop ways to make cars fully safe. Monitoring driver's actions by computer vision techniques to detect driving mistakes in real-time and then planning for autonomous driving to avoid vehicle collisions is one of the most important issues that has been investigated in the machine vision and Intelligent Transportation Systems (ITS). The main goal of this study is to prevent accidents caused by fatigue, drowsiness, and driver distraction. To avoid these incidents, this paper proposes an integrated safety system that continuously monitors the driver's attention and vehicle surroundings, and finally decides whether the actual steering control status is safe or not. For this purpose, we equipped an ordinary car called FARAZ with a vision system consisting of four mounted cameras along with a universal car tool for communicating with surrounding factory-installed sensors and other car systems, and sending commands to actuators. The proposed system leverages a scene understanding pipeline using deep convolutional encoder-decoder networks and a driver state detection pipeline. We have been identifying and assessing domestic capabilities for the development of technologies specifically of the ordinary vehicles in order to manufacture smart cars and eke providing an intelligent system to increase safety and to assist the driver in various conditions/situations.
Hyperbolic intersection graphs and (quasi)-polynomial time
Venue: arXiv cs.CG 2019
Year: 2019

We study unit ball graphs (and, more generally, so-called noisy uniform ball graphs) in $d$-dimensional hyperbolic space, which we denote by $\mathbb{H}^d$. Using a new separator theorem, we show that unit ball graphs in $\mathbb{H}^d$ enjoy similar properties as their Euclidean counterparts, but in one dimension lower: many standard graph problems, such as Independent Set, Dominating Set, Steiner Tree, and Hamiltonian Cycle can be solved in $2^{O(n^{1-1/(d-1)})}$ time for any fixed $d\geq 3$, while the same problems need $2^{O(n^{1-1/d})}$ time in $\mathbb{R}^d$. We also show that these algorithms in $\mathbb{H}^d$ are optimal up to constant factors in the exponent under ETH. This drop in dimension has the largest impact in $\mathbb{H}^2$, where we introduce a new technique to bound the treewidth of noisy uniform disk graphs. The bounds yield quasi-polynomial ($n^{O(\log n)}$) algorithms for all of the studied problems, while in the case of Hamiltonian Cycle and $3$-Coloring we even get polynomial time algorithms. Furthermore, if the underlying noisy disks in $\mathbb{H}^2$ have constant maximum degree, then all studied problems can be solved in polynomial time. This contrasts with the fact that these problems require $2^{Ω(\sqrt{n})}$ time under ETH in constant maximum degree Euclidean unit disk graphs. Finally, we complement our quasi-polynomial algorithm for Independent Set in noisy uniform disk graphs with a matching $n^{Ω(\log n)}$ lower bound under ETH. This shows that the hyperbolic plane is a potential source of NP-intermediate problems.
Relational Cost Analysis for Functional-Imperative Programs
Venue: arXiv cs.PL 2019
Year: 2019

Relational cost analysis aims at formally establishing bounds on the difference in the evaluation costs of two programs. As a particular case, one can also use relational cost analysis to establish bounds on the difference in the evaluation cost of the same program on two different inputs. One way to perform relational cost analysis is to use a relational type-and-effect system that supports reasoning about relations between two executions of two programs. Building on this basic idea, we present a type-and-effect system, called ARel, for reasoning about the relative cost of array-manipulating, higher-order functional-imperative programs. The key ingredient of our approach is a new lightweight type refinement discipline that we use to track relations (differences) between two arrays. This discipline combined with Hoare-style triples built into the types allows us to express and establish precise relative costs of several interesting programs which imperatively update their data.
PDE-Net 2.0: Learning PDEs from Data with A Numeric-Symbolic Hybrid Deep Network
Venue: arXiv cs.LG 2019
Year: 2019

Partial differential equations (PDEs) are commonly derived based on empirical observations. However, recent advances of technology enable us to collect and store massive amount of data, which offers new opportunities for data-driven discovery of PDEs. In this paper, we propose a new deep neural network, called PDE-Net 2.0, to discover (time-dependent) PDEs from observed dynamic data with minor prior knowledge on the underlying mechanism that drives the dynamics. The design of PDE-Net 2.0 is based on our earlier work \cite{Long2018PDE} where the original version of PDE-Net was proposed. PDE-Net 2.0 is a combination of numerical approximation of differential operators by convolutions and a symbolic multi-layer neural network for model recovery. Comparing with existing approaches, PDE-Net 2.0 has the most flexibility and expressive power by learning both differential operators and the nonlinear response function of the underlying PDE model. Numerical experiments show that the PDE-Net 2.0 has the potential to uncover the hidden PDE of the observed dynamics, and predict the dynamical behavior for a relatively long time, even in a noisy environment.
Fast Algorithms for Rank-1 Bimatrix Games
Venue: arXiv cs.GT 2019
Year: 2019

The rank of a bimatrix game is the matrix rank of the sum of the two payoff matrices. This paper comprehensively analyzes games of rank one, and shows the following: (1) For a game of rank r, the set of its Nash equilibria is the intersection of a generically one-dimensional set of equilibria of parameterized games of rank r-1 with a hyperplane. (2) One equilibrium of a rank-1 game can be found in polynomial time. (3) All equilibria of a rank-1 game can be found by following a piecewise linear path. In contrast, such a path-following method finds only one equilibrium of a bimatrix game. (4) The number of equilibria of a rank-1 game may be exponential. (5) There is a homeomorphism between the space of bimatrix games and their equilibrium correspondence that preserves rank. It is a variation of the homeomorphism used for the concept of strategic stability of an equilibrium component.
An Exchange Mechanism to Coordinate Flexibility in Residential Energy Cooperatives
Venue: arXiv cs.MA 2019
Year: 2019

Energy cooperatives (ECs) such as residential and industrial microgrids have the potential to mitigate increasing fluctuations in renewable electricity generation, but only if their joint response is coordinated. However, the coordination and control of independently operated flexible resources (e.g., storage, demand response) imposes critical challenges arising from the heterogeneity of the resources, conflict of interests, and impact on the grid. Correspondingly, overcoming these challenges with a general and fair yet efficient exchange mechanism that coordinates these distributed resources will accommodate renewable fluctuations on a local level, thereby supporting the energy transition. In this paper, we introduce such an exchange mechanism. It incorporates a payment structure that encourages prosumers to participate in the exchange by increasing their utility above baseline alternatives. The allocation from the proposed mechanism increases the system efficiency (utilitarian social welfare) and distributes profits more fairly (measured by Nash social welfare) than individual flexibility activation. A case study analyzing the mechanism performance and resulting payments in numerical experiments over real demand and generation profiles of the Pecan Street dataset elucidates the efficacy to promote cooperation between co-located flexibilities in residential cooperatives through local exchange.
Spatial-Temporal Digital Image Correlation: A Unified Framework
Venue: arXiv cs.CV 2019
Year: 2019

A comprehensive and systematic framework for easily extending and implementing the subset-based spatial-temporal digital image correlation (DIC) algorithm is presented. The framework decouples the three main factors (i.e. shape function, correlation criterion, and optimization algorithm) involved in algorithm implementation of DIC and represents different algorithms in a uniform form. One can freely choose and combine the three factors to meet his own need, or freely add more parameters to extract analytic results. Subpixel translation and a simulated image series with different velocity characters are analyzed using different algorithms based on the proposed framework, confirming the merit of noise suppression and velocity compatibility. An application of mitigating air disturbance due to heat haze using spatial-temporal DIC is given to demonstrate the applicability of the framework.
Application of the Fast Multipole Fully Coupled Poroelastic Displacement Discontinuity Method to Hydraulic Fracturing Problems
Venue: arXiv cs.NA 2019
Year: 2019

In this study, a fast multipole method (FMM) is used to decrease the computational time of a fully-coupled poroelastic hydraulic fracture model with a controllable effect on its accuracy. The hydraulic fracture model is based on the poroelastic formulation of the displacement discontinuity method (DDM) which is a special formulation of the boundary element method (BEM). DDM is a powerful and efficient method for problems involving fractures. However, this method becomes slow as the number of temporal, or spatial elements increases, or necessary details such as poroelasticity, that makes the solution history-dependent, are added to the model. FMM is a technique to expedite matrix-vector multiplications within a controllable error without forming the matrix explicitly. Fully-coupled poroelastic formulation of DDM involves the multiplication of a dense matrix with a vector in several places. A crucial modification to DDM is suggested in two places in the algorithm to leverage the speed efficiency of FMM for carrying out these multiplications. The first modification is in the time-marching scheme, which accounts for the solution of previous time steps to compute the current time step. The second modification is in the generalized minimal residual method (GMRES) to iteratively solve for the problem unknowns. Several examples are provided to show the efficiency of the proposed approach in problems with large degrees of freedom (in time and space). Examples include hydraulic fracturing of a horizontal well and randomly distributed pressurized fractures at different orientations with respect to horizontal stresses. The results are compared to the conventional DDM in terms of computational processing time and accuracy. Accordingly, the proposed algorithm may be used for fracture propagation studies while substantially reducing the processing time with a controllable error.
DeepTract: A Probabilistic Deep Learning Framework for White Matter Fiber Tractography
Venue: arXiv cs.CV 2019
Year: 2019

We present DeepTract, a deep-learning framework for estimating white matter fibers orientation and streamline tractography. We adopt a data-driven approach for fiber reconstruction from diffusion weighted images (DWI), which does not assume a specific diffusion model. We use a recurrent neural network for mapping sequences of DWI values into probabilistic fiber orientation distributions. Based on these estimations, our model facilitates both deterministic and probabilistic streamline tractography. We quantitatively evaluate our method using the Tractometer tool, demonstrating competitive performance with state-of-the art classical and machine learning based tractography algorithms. We further present qualitative results of bundle-specific probabilistic tractography obtained using our method. The code is publicly available at: https://github.com/itaybenou/DeepTract.git.
Efficient Dispersion of Mobile Robots on Arbitrary Graphs and Grids
Venue: arXiv cs.DC 2019
Year: 2019

The mobile robot dispersion problem on graphs asks $k\leq n$ robots placed initially arbitrarily on the nodes of an $n$-node anonymous graph to reposition autonomously to reach a configuration in which each robot is on a distinct node of the graph. This problem is of significant interest due to its relationship to other fundamental robot coordination problems, such as exploration, scattering, load balancing, and relocation of self-driven electric cars (robots) to recharge stations (nodes). In this paper, we provide two novel deterministic algorithms for dispersion, one for arbitrary graphs and another for grid graphs, in a synchronous setting where all robots perform their actions in every time step. Our algorithm for arbitrary graphs has $O(\min(m,kΔ) \cdot \log k)$ steps runtime using $O(\log n)$ bits of memory at each robot, where $m$ is the number of edges and $Δ$ is the maximum degree of the graph. This is an exponential improvement over the $O(mk)$ steps best previously known algorithm. In particular, the runtime of our algorithm is optimal (up to a $O(\log k)$ factor) in constant-degree arbitrary graphs. Our algorithm for grid graphs has $O(\min(k,\sqrt{n}))$ steps runtime using $Θ(\log k)$ bits at each robot. This is the first algorithm for dispersion in grid graphs. Moreover, this algorithm is optimal for both memory and time when $k=Ω(n)$.
Attention Please: Consider Mockito when Evaluating Newly Proposed Automated Program Repair Techniques
Venue: arXiv cs.SE 2019
Year: 2019

Automated program repair (APR) has attracted widespread attention in recent years with substantial techniques being proposed. Meanwhile, a number of benchmarks have been established for evaluating the performances of APR techniques, among which Defects4J is one of the most wildly used benchmark. However, bugs in Mockito, a project augmented in a later-version of Defects4J, do not receive much attention by recent researches. In this paper, we aim at investigating the necessity of considering Mockito bugs when evaluating APR techniques. Our findings show that: 1) Mockito bugs are not more complex for repairing compared with bugs from other projects; 2) the bugs repaired by the state-of-the-art tools share the same repair patterns compared with those patterns required to repair Mockito bugs; however, 3) the state-of-the-art tools perform poorly on Mockito bugs (Nopol can only correctly fix one bug while SimFix and CapGen cannot fix any bug in Mockito even if all the buggy locations have been exposed). We conclude from these results that existing APR techniques may be overfitting to their evaluated subjects and we should consider Mockito, or even more bugs from other projects, when evaluating newly proposed APR techniques. We further find out a unique repair action required to repair Mockito bugs named external package addition. Importing the external packages from the test code associated with the source code is feasible for enlarging the search space and this action can be augmented with existing repair actions to advance existing techniques.
Impact of Data Normalization on Deep Neural Network for Time Series Forecasting
Venue: arXiv cs.LG 2019
Year: 2019

For the last few years it has been observed that the Deep Neural Networks (DNNs) has achieved an excellent success in image classification, speech recognition. But DNNs are suffer great deal of challenges for time series forecasting because most of the time series data are nonlinear in nature and highly dynamic in behaviour. The time series forecasting has a great impact on our socio-economic environment. Hence, to deal with these challenges its need to be redefined the DNN model and keeping this in mind, data pre-processing, network architecture and network parameters are need to be consider before feeding the data into DNN models. Data normalization is the basic data pre-processing technique form which learning is to be done. The effectiveness of time series forecasting is heavily depend on the data normalization technique. In this paper, different normalization methods are used on time series data before feeding the data into the DNN model and we try to find out the impact of each normalization technique on DNN to forecast the time series. Here the Deep Recurrent Neural Network (DRNN) is used to predict the closing index of Bombay Stock Exchange (BSE) and New York Stock Exchange (NYSE) by using BSE and NYSE time series data.
A Trivial Yet Optimal Solution to Vertex Fault Tolerant Spanners
Venue: arXiv cs.DS 2019
Year: 2019

We give a short and easy upper bound on the worst-case size of fault tolerant spanners, which improves on all prior work and is fully optimal at least in the setting of vertex faults.