OpenRaiser/Intern-Atlas huggingface dataset:
Automated Machine Learning: State-of-The-Art and Open Challenges
Venue: arXiv cs.LG 2019
Year: 2019

With the continuous and vast increase in the amount of data in our digital world, it has been acknowledged that the number of knowledgeable data scientists can not scale to address these challenges. Thus, there was a crucial need for automating the process of building good machine learning models. In the last few years, several techniques and frameworks have been introduced to tackle the challenge of automating the process of Combined Algorithm Selection and Hyper-parameter tuning (CASH) in the machine learning domain. The main aim of these techniques is to reduce the role of the human in the loop and fill the gap for non-expert machine learning users by playing the role of the domain expert. In this paper, we present a comprehensive survey for the state-of-the-art efforts in tackling the CASH problem. In addition, we highlight the research work of automating the other steps of the full complex machine learning pipeline (AutoML) from data understanding till model deployment. Furthermore, we provide comprehensive coverage for the various tools and frameworks that have been introduced in this domain. Finally, we discuss some of the research directions and open challenges that need to be addressed in order to achieve the vision and goals of the AutoML process.
Brain-Network Clustering via Kernel-ARMA Modeling and the Grassmannian
Venue: arXiv cs.LG 2019
Year: 2019

Recent advances in neuroscience and in the technology of functional magnetic resonance imaging (fMRI) and electro-encephalography (EEG) have propelled a growing interest in brain-network clustering via time-series analysis. Notwithstanding, most of the brain-network clustering methods revolve around state clustering and/or node clustering (a.k.a. community detection or topology inference) within states. This work answers first the need of capturing non-linear nodal dependencies by bringing forth a novel feature-extraction mechanism via kernel autoregressive-moving-average modeling. The extracted features are mapped to the Grassmann manifold (Grassmannian), which consists of all linear subspaces of a fixed rank. By virtue of the Riemannian geometry of the Grassmannian, a unifying clustering framework is offered to tackle all possible clustering problems in a network: Cluster multiple states, detect communities within states, and even identify/track subnetwork state sequences. The effectiveness of the proposed approach is underlined by extensive numerical tests on synthetic and real fMRI/EEG data which demonstrate that the advocated learning method compares favorably versus several state-of-the-art clustering schemes.
Lookout for Zombies: Mitigating Flush+Reload Attack on Shared Caches by Monitoring Invalidated Lines
Venue: arXiv cs.CR 2019
Year: 2019

OS-based page sharing is a commonly used optimization in modern systems to reduce memory footprint. Unfortunately, such sharing can cause Flush+Reload cache attacks, whereby a spy periodically flushes a cache line of shared data (using the clflush instruction) and reloads it to infer the access patterns of a victim application. Current proposals to mitigate Flush+Reload attacks are impractical as they either disable page sharing, or require application rewrite, or require OS support, or incur ISA changes. Ideally, we want to tolerate attacks without requiring any OS or ISA support and while incurring negligible performance and storage overheads. This paper makes the key observation that when a cache line is invalidated due to a Flush-Caused Invalidation (FCI), the tag and data of the invalidated line are still resident in the cache and can be used for detecting Flush-based attacks. We call lines invalidated due to FCI as Zombie lines. Our design explicitly marks such lines as Zombies, preserves the Zombie lines in the cache, and uses the hits and misses to Zombie lines to tolerate the attacks. We propose Zombie-Based Mitigation (ZBM), a simple hardware-based design that successfully guards against attacks by simply treating hits on Zombie-lines as misses to avoid any timing leaks to the attacker. We analyze the robustness of ZBM using three spy programs: attacking AES T-Tables, attacking RSA Square-and-Multiply, and Function Watcher (FW), and show that ZBM successfully defends against these attacks. Our solution requires negligible storage (4-bits per cache line), retains OS-based page sharing, requires no OS/ISA changes, and does not incur slowdown for benign applications.
Automation of Construction Quantity Take-Off: Using Building Information Modeling (BIM)
Venue: arXiv cs.CE 2019
Year: 2019

Building information modeling (BIM) is a major upheaval in construction industry. Although BIM advantages in construction management has been proved in many papers reviewed, there are still many limitations that inhibit organizations to use BIM models efficiently. However, advancement of application programming interface (API) in the recent years has facilitated employing BIM in construction industry. This research contributes to the state of practice in construction management by developing API codes to automate estimation of construction. The developed API automatically filters items related to a particular discipline within a particular work area and facilitate systematical quantity take off in different work areas. This allows the planner to define proper 3D work areas and to estimate the required materials and man-hours in different work areas throughout the project. The results of the developed automated approach are compared with hand calculated results as well as the results calculated from the BIM software interface in special cases. Finally, the research has been validated by a case study of an actual petrochemical project.
Clustered Reinforcement Learning
Venue: arXiv cs.LG 2019
Year: 2019

Exploration strategy design is one of the challenging problems in reinforcement learning~(RL), especially when the environment contains a large state space or sparse rewards. During exploration, the agent tries to discover novel areas or high reward~(quality) areas. In most existing methods, the novelty and quality in the neighboring area of the current state are not well utilized to guide the exploration of the agent. To tackle this problem, we propose a novel RL framework, called \underline{c}lustered \underline{r}einforcement \underline{l}earning~(CRL), for efficient exploration in RL. CRL adopts clustering to divide the collected states into several clusters, based on which a bonus reward reflecting both novelty and quality in the neighboring area~(cluster) of the current state is given to the agent. Experiments on a continuous control task and several \emph{Atari 2600} games show that CRL can outperform other state-of-the-art methods to achieve the best performance in most cases.
Introducing languid particle dynamics to a selection of PSO variants
Venue: arXiv cs.NE 2019
Year: 2019

Previous research showed that conditioning a PSO agent's movement based on its personal fitness improvement enhances the standard PSO method. In this article, languid particle dynamics (LPD) technique is used on five adequate and widely used PSO variants. Five unmodified PSO variants were tested against their LPD-implemented counterparts on three search space dimensionalities (10, 20, and 50 dimensions) and 30 test functions of the CEC 2014 benchmark test. In the preliminary phase of the testing four of the five tested PSO variants showed improvement in accuracy. The worst and best-achieving variants from preliminary test went through detailed investigation on 220 and 770 combinations of method parameters, where both variants showed overall gains in accuracy when enhanced with LPD. Finally, the results obtained with best achieving PSO parameters were subject to statistical analysis which showed that the two variants give statistically significant improvements in accuracy for 13-50% of the test functions.
Optimized Deployment of Millimeter Wave Networks for In-venue Regions with Stochastic Users' Orientation
Venue: arXiv cs.NI 2019
Year: 2019

Millimeter wave (mmW) communication is a promising solution for providing high-capacity wireless network access. However, the benefits of mmW are limited by the fact that the channel between a mmW access point and the user equipment can stochastically change due to severe blockage of mmW links by obstacles such as the human body. Thus, one main challenge of mmW network coverage is to enable directional line-of-sight links between access points and mobile devices. In this paper, a novel framework is proposed for optimizing mmW network coverage within hotspots and in-venue regions, while being cognizant of the body blockage of the network's users. In the studied model, the locations of potential access points and users are assumed as predefined parameters while the orientation of the users is assumed to be stochastic. Hence, a joint stochastic access point placement and beam steering problem subjected to stochastic users' body blockage is formulated, under desired network coverage constraints. Then, a greedy algorithm is introduced to find an approximation solution for the joint deployment and assignment problem using a new `size constrained weighted set cover' approach. A closed-form expression for the ratio between the optimal solution and approximate one (resulting from the greedy algorithm) is analytically derived. The proposed algorithm is simulated for three in-venue regions: the meeting room in the Alumni Assembly Hall of Virginia Tech, an airport gate, and one side of a stadium football. Simulation results show that, in order to guarantee network coverage for different in-venue regions, the greedy algorithm uses at most three more access points (APs) compared to the optimal solution. The results also show that, due to the use of the additional APs, the greedy algorithm will yield a network coverage up to 11.7 % better than the optimal, AP-minimizing solution.
Anytime Lane-Level Intersection Estimation Based on Trajectories of Other Traffic Participants
Venue: arXiv cs.CV 2019
Year: 2019

Estimating and understanding the current scene is an inevitable capability of automated vehicles. Usually, maps are used as prior for interpreting sensor measurements in order to drive safely and comfortably. Only few approaches take into account that maps might be outdated and lead to wrong assumptions on the environment. This work estimates a lane-level intersection topology without any map prior by observing the trajectories of other traffic participants. We are able to deliver both a coarse lane-level topology as well as the lane course inside and outside of the intersection using Markov chain Monte Carlo sampling. The model is neither limited to a number of lanes or arms nor to the topology of the intersection. We present our results on an evaluation set of 1000 simulated intersections and achieve 99.9% accuracy on the topology estimation that takes only 36ms, when utilizing tracked object detections. The precise lane course on these intersections is estimated with an error of 15cm on average after 140ms. Our approach shows a similar level of precision on 14 real-world intersections with 18cm average deviation on simple intersections and 27cm for more complex scenarios. Here the estimation takes only 113ms in total.
Boosting Operational DNN Testing Efficiency through Conditioning
Venue: arXiv cs.SE 2019
Year: 2019

With the increasing adoption of Deep Neural Network (DNN) models as integral parts of software systems, efficient operational testing of DNNs is much in demand to ensure these models' actual performance in field conditions. A challenge is that the testing often needs to produce precise results with a very limited budget for labeling data collected in field. Viewing software testing as a practice of reliability estimation through statistical sampling, we re-interpret the idea behind conventional structural coverages as conditioning for variance reduction. With this insight we propose an efficient DNN testing method based on the conditioning on the representation learned by the DNN model under testing. The representation is defined by the probability distribution of the output of neurons in the last hidden layer of the model. To sample from this high dimensional distribution in which the operational data are sparsely distributed, we design an algorithm leveraging cross entropy minimization. Experiments with various DNN models and datasets were conducted to evaluate the general efficiency of the approach. The results show that, compared with simple random sampling, this approach requires only about a half of labeled inputs to achieve the same level of precision.
Multidimensional Outlier Detection in Temporal Interaction Networks: An Application to Political Communication on Twitter
Venue: arXiv cs.SI 2019
Year: 2019

In social network Twitter, users can interact with each other and spread information via retweets. These millions of interactions may result in media events whose influence goes beyond Twitter framework. In this paper, we thoroughly explore interactions to provide a better understanding of the emergence of certain trends. First, we consider an interaction on Twitter to be a triplet $(s,a,t)$ meaning that user $s$, called the spreader, has retweeted a tweet of user $a$, called the author, at time $t$. We model this set of interactions as a data cube with three dimensions: spreaders, authors and time. Then, we provide a method which builds different contexts, where a context is a set of features characterizing the circumstances of an event. Finally, these contexts allow us to find relevant unexpected behaviors, according to several dimensions and various perspectives: a user during a given hour which is abnormal compared to its usual behavior, a relationship between two users which is abnormal compared to all other relationships, \textit{etc.} We apply our method to a set of retweets related to the 2017 French presidential election and show that one can build interesting insights regarding political organization on Twitter.
Combining Reinforcement Learning and Configuration Checking for Maximum k-plex Problem
Venue: arXiv cs.AI 2019
Year: 2019

The Maximum k-plex Problem is an important combinatorial optimization problem with increasingly wide applications. Due to its exponential time complexity, many heuristic methods have been proposed which can return a good-quality solution in a reasonable time. However, most of the heuristic algorithms are memoryless and unable to utilize the experience during the search. Inspired by the multi-armed bandit (MAB) problem in reinforcement learning (RL), we propose a novel perturbation mechanism named BLP, which can learn online to select a good vertex for perturbation when getting stuck in local optima. To our best of knowledge, this is the first attempt to combine local search with RL for the maximum $ k $-plex problem. Besides, we also propose a novel strategy, named Dynamic-threshold Configuration Checking (DTCC), which extends the original Configuration Checking (CC) strategy from two aspects. Based on the BLP and DTCC, we develop a local search algorithm named BDCC and improve it by a hyperheuristic strategy. The experimental result shows that our algorithms dominate on the standard DIMACS and BHOSLIB benchmarks and achieve state-of-the-art performance on massive graphs.
New Features in the Second Version of the Cadabra Computer Algebra System
Venue: arXiv cs.SC 2019
Year: 2019

In certain scientific domains, there is a need for tensor operations. To facilitate tensor computations,computer algebra systems are employed. In our research, we have been using Cadabra as the main computer algebra system for several years. Recently, an operable second version of this software was released. In this version, a number of improvements were made that can be regarded as revolutionary ones. The most significant improvements are the implementation of component computations and the change in the ideology of the Cadabra's software mechanism as compared to the first version. This paper provides a brief overview of the key improvements in the Cadabra system.
Improving Robustness Without Sacrificing Accuracy with Patch Gaussian Augmentation
Venue: arXiv cs.LG 2019
Year: 2019

Deploying machine learning systems in the real world requires both high accuracy on clean data and robustness to naturally occurring corruptions. While architectural advances have led to improved accuracy, building robust models remains challenging. Prior work has argued that there is an inherent trade-off between robustness and accuracy, which is exemplified by standard data augment techniques such as Cutout, which improves clean accuracy but not robustness, and additive Gaussian noise, which improves robustness but hurts accuracy. To overcome this trade-off, we introduce Patch Gaussian, a simple augmentation scheme that adds noise to randomly selected patches in an input image. Models trained with Patch Gaussian achieve state of the art on the CIFAR-10 and ImageNetCommon Corruptions benchmarks while also improving accuracy on clean data. We find that this augmentation leads to reduced sensitivity to high frequency noise(similar to Gaussian) while retaining the ability to take advantage of relevant high frequency information in the image (similar to Cutout). Finally, we show that Patch Gaussian can be used in conjunction with other regularization methods and data augmentation policies such as AutoAugment, and improves performance on the COCO object detection benchmark.
How a Single Paper Affects the Impact Factor: Implications for Scholarly Publishing
Venue: arXiv cs.DL 2019
Year: 2019

Because the Impact Factor (IF) is an average quantity and most journals are small, IFs are volatile. We study how a single paper affects the IF using data from 11639 journals in the 2017 Journal Citation Reports. We define as volatility the IF gain (or loss) caused by a single paper, and this is inversely proportional to journal size. We find high volatilities for hundreds of journals annually due to their top-cited paper: whether it is a highly-cited paper in a small journal, or a moderately (or even low) cited paper in a small and low-cited journal. For example, 1218 journals had their most cited paper boost their IF by more than 20%, while for 231 journals the boost exceeded 50%. We find that small journals are rewarded much more than large journals for publishing a highly-cited paper, and are also penalized more for publishing a low-cited paper, especially if they have a high IF. This produces a strong incentive for prestigious, high-IF journals to stay small, to remain competitive in IF rankings. We discuss the implications for breakthrough papers to appear in prestigious journals. We also question the practice of ranking journals by IF given this uneven reward mechanism.
Iterative Self-Learning: Semi-Supervised Improvement to Dataset Volumes and Model Accuracy
Venue: arXiv cs.CV 2019
Year: 2019

A novel semi-supervised learning technique is introduced based on a simple iterative learning cycle together with learned thresholding techniques and an ensemble decision support system. State-of-the-art model performance and increased training data volume are demonstrated, through the use of unlabelled data when training deeply learned classification models. Evaluation of the proposed approach is performed on commonly used datasets when evaluating semi-supervised learning techniques as well as a number of more challenging image classification datasets (CIFAR-100 and a 200 class subset of ImageNet).
Globally-Aware Multiple Instance Classifier for Breast Cancer Screening
Venue: arXiv cs.LG 2019
Year: 2019

Deep learning models designed for visual classification tasks on natural images have become prevalent in medical image analysis. However, medical images differ from typical natural images in many ways, such as significantly higher resolutions and smaller regions of interest. Moreover, both the global structure and local details play important roles in medical image analysis tasks. To address these unique properties of medical images, we propose a neural network that is able to classify breast cancer lesions utilizing information from both a global saliency map and multiple local patches. The proposed model outperforms the ResNet-based baseline and achieves radiologist-level performance in the interpretation of screening mammography. Although our model is trained only with image-level labels, it is able to generate pixel-level saliency maps that provide localization of possible malignant findings.
Recognizing American Sign Language Manual Signs from RGB-D Videos
Venue: arXiv cs.CV 2019
Year: 2019

In this paper, we propose a 3D Convolutional Neural Network (3DCNN) based multi-stream framework to recognize American Sign Language (ASL) manual signs (consisting of movements of the hands, as well as non-manual face movements in some cases) in real-time from RGB-D videos, by fusing multimodality features including hand gestures, facial expressions, and body poses from multi-channels (RGB, depth, motion, and skeleton joints). To learn the overall temporal dynamics in a video, a proxy video is generated by selecting a subset of frames for each video which are then used to train the proposed 3DCNN model. We collect a new ASL dataset, ASL-100-RGBD, which contains 42 RGB-D videos captured by a Microsoft Kinect V2 camera, each of 100 ASL manual signs, including RGB channel, depth maps, skeleton joints, face features, and HDface. The dataset is fully annotated for each semantic region (i.e. the time duration of each word that the human signer performs). Our proposed method achieves 92.88 accuracy for recognizing 100 ASL words in our newly collected ASL-100-RGBD dataset. The effectiveness of our framework for recognizing hand gestures from RGB-D videos is further demonstrated on the Chalearn IsoGD dataset and achieves 76 accuracy which is 5.51 higher than the state-of-the-art work in terms of average fusion by using only 5 channels instead of 12 channels in the previous work.
Mixed Strategy Game Model Against Data Poisoning Attacks
Venue: arXiv cs.LG 2019
Year: 2019

In this paper we use game theory to model poisoning attack scenarios. We prove the non-existence of pure strategy Nash Equilibrium in the attacker and defender game. We then propose a mixed extension of our game model and an algorithm to approximate the Nash Equilibrium strategy for the defender. We then demonstrate the effectiveness of the mixed defence strategy generated by the algorithm, in an experiment.
Smart Contract Design Meets State Machine Synthesis: Case Studies
Venue: arXiv cs.SE 2019
Year: 2019

Modern blockchain systems support creation of smart contracts -- stateful programs hosted and executed on a blockchain. Smart contracts hold and transfer significant amounts of digital currency which makes them an attractive target for security attacks. It has been shown that many contracts deployed to public ledgers contain security vulnerabilities. Moreover, the design of blockchain systems does not allow the code of the smart contract to be changed after it has been deployed to the system. Therefore, it is important to guarantee the correctness of smart contracts prior to their deployment. Formal verification is widely used to check smart contracts for correctness with respect to given specification. In this work we consider program synthesis techniques in which the specification is used to generate correct-by-construction programs. We focus on one of the special cases of program synthesis where programs are modeled with finite state machines (FSMs). We show how FSM synthesis can be applied to the problem of automatic smart contract generation. Several case studies of smart contracts are outlined: crowdfunding platform, blinded auction and a license contract. For each case study we specify the corresponding smart contract with a set of formulas in linear temporal logic (LTL) and use this specification together with test scenarios to synthesize a FSM model for that contract. These models are later used to generate executable Solidity code which can be directly used in a blockchain system.