OpenRaiser/Intern-Atlas huggingface dataset:
A Survey of Deep Learning Applications to Autonomous Vehicle Control
Venue: arXiv cs.LG 2019
Year: 2019

Designing a controller for autonomous vehicles capable of providing adequate performance in all driving scenarios is challenging due to the highly complex environment and inability to test the system in the wide variety of scenarios which it may encounter after deployment. However, deep learning methods have shown great promise in not only providing excellent performance for complex and non-linear control problems, but also in generalising previously learned rules to new scenarios. For these reasons, the use of deep learning for vehicle control is becoming increasingly popular. Although important advancements have been achieved in this field, these works have not been fully summarised. This paper surveys a wide range of research works reported in the literature which aim to control a vehicle through deep learning methods. Although there exists overlap between control and perception, the focus of this paper is on vehicle control, rather than the wider perception problem which includes tasks such as semantic segmentation and object detection. The paper identifies the strengths and limitations of available deep learning methods through comparative analysis and discusses the research challenges in terms of computation, architecture selection, goal specification, generalisation, verification and validation, as well as safety. Overall, this survey brings timely and topical information to a rapidly evolving field relevant to intelligent transportation systems.
Point2Node: Correlation Learning of Dynamic-Node for Point Cloud Feature Modeling
Venue: arXiv cs.CV 2019
Year: 2019

Fully exploring correlation among points in point clouds is essential for their feature modeling. This paper presents a novel end-to-end graph model, named Point2Node, to represent a given point cloud. Point2Node can dynamically explore correlation among all graph nodes from different levels, and adaptively aggregate the learned features. Specifically, first, to fully explore the spatial correlation among points for enhanced feature description, in a high-dimensional node graph, we dynamically integrate the node's correlation with self, local, and non-local nodes. Second, to more effectively integrate learned features, we design a data-aware gate mechanism to self-adaptively aggregate features at the channel level. Extensive experiments on various point cloud benchmarks demonstrate that our method outperforms the state-of-the-art.
Deep learning predictions of sand dune migration
Venue: arXiv cs.LG 2019
Year: 2019

A dry decade in the Navajo Nation has killed vegetation, dessicated soils, and released once-stable sand into the wind. This sand now covers one-third of the Nation's land, threatening roads, gardens and hundreds of homes. Many arid regions have similar problems: global warming has increased dune movement across farmland in Namibia and Angola, and the southwestern US. Current dune models, unfortunately, do not scale well enough to provide useful forecasts for the $\sim$5\% of land surfaces covered by mobile sand. We test the ability of two deep learning algorithms, a GAN and a CNN, to model the motion of sand dunes. The models are trained on simulated data from community-standard cellular automaton model of sand dunes. Preliminary results show the GAN producing reasonable forward predictions of dune migration at ten million times the speed of the existing model.
Optimize Cash Collection: Use Machine learning to Predicting Invoice Payment
Venue: arXiv cs.LG 2019
Year: 2019

Predicting invoice payment is valuable in multiple industries and supports decision-making processes in most financial workflows. However, the challenge in this realm involves dealing with complex data and the lack of data related to decisions-making processes not registered in the accounts receivable system. This work presents a prototype developed as a solution devised during a partnership with a multinational bank to support collectors in predicting invoices payment. The proposed prototype reached up to 77\% of accuracy, which improved the prioritization of customers and supported the daily work of collectors. With the presented results, one expects to support researchers dealing with the problem of invoice payment prediction to get insights and examples of how to tackle issues present in real data.
Spectral embedding of regularized block models
Venue: arXiv cs.LG 2019
Year: 2019

Spectral embedding is a popular technique for the representation of graph data. Several regularization techniques have been proposed to improve the quality of the embedding with respect to downstream tasks like clustering. In this paper, we explain on a simple block model the impact of the complete graph regularization, whereby a constant is added to all entries of the adjacency matrix. Specifically, we show that the regularization forces the spectral embedding to focus on the largest blocks, making the representation less sensitive to noise or outliers. We illustrate these results on both on both synthetic and real data, showing how regularization improves standard clustering scores.
A Survey of Deep Reinforcement Learning in Video Games
Venue: arXiv cs.MA 2019
Year: 2019

Deep reinforcement learning (DRL) has made great achievements since proposed. Generally, DRL agents receive high-dimensional inputs at each step, and make actions according to deep-neural-network-based policies. This learning mechanism updates the policy to maximize the return with an end-to-end method. In this paper, we survey the progress of DRL methods, including value-based, policy gradient, and model-based algorithms, and compare their main techniques and properties. Besides, DRL plays an important role in game artificial intelligence (AI). We also take a review of the achievements of DRL in various video games, including classical Arcade games, first-person perspective games and multi-agent real-time strategy games, from 2D to 3D, and from single-agent to multi-agent. A large number of video game AIs with DRL have achieved super-human performance, while there are still some challenges in this domain. Therefore, we also discuss some key points when applying DRL methods to this field, including exploration-exploitation, sample efficiency, generalization and transfer, multi-agent learning, imperfect information, and delayed spare rewards, as well as some research directions.
Next Priority Concept: A new and generic algorithm computing concepts from complex and heterogeneous data
Venue: arXiv cs.AI 2019
Year: 2019

In this article, we present a new data type agnostic algorithm calculating a concept lattice from heterogeneous and complex data. Our NextPriorityConcept algorithm is first introduced and proved in the binary case as an extension of Bordat's algorithm with the notion of strategies to select only some predecessors of each concept, avoiding the generation of unreasonably large lattices. The algorithm is then extended to any type of data in a generic way. It is inspired from pattern structure theory, where data are locally described by predicates independent of their types, allowing the management of heterogeneous data.
Towards Deterministic Decomposable Circuits for Safe Queries
Venue: arXiv cs.DB 2019
Year: 2019

There exist two approaches for exact probabilistic inference of UCQs on tuple-independent databases. In the extensional approach, query evaluation is performed within a DBMS by exploiting the structure of the query. In the intensional approach, one first builds a representation of the lineage of the query on the database, then computes the probability of the lineage. In this paper we propose a new technique to construct lineage representations as deterministic decomposable circuits in PTIME. The technique can apply to a class of UCQs that has been conjectured to separate the complexity of the two approaches. We test our technique experimentally, and show that it succeeds on all the queries of this class up to a certain size parameter, i.e., over $20$ million queries.
A Distributed Neural Network Architecture for Robust Non-Linear Spatio-Temporal Prediction
Venue: arXiv cs.LG 2019
Year: 2019

We introduce a distributed spatio-temporal artificial neural network architecture (DISTANA). It encodes mesh nodes using recurrent, neural prediction kernels (PKs), while neural transition kernels (TKs) transfer information between neighboring PKs, together modeling and predicting spatio-temporal time series dynamics. As a consequence, DISTANA assumes that generally applicable causes, which may be locally modified, generate the observed data. DISTANA learns in a parallel, spatially distributed manner, scales to large problem spaces, is capable of approximating complex dynamics, and is particularly robust to overfitting when compared to other competitive ANN models. Moreover, it is applicable to heterogeneously structured meshes.
On Sharing Models Instead of Data using Mimic learning for Smart Health Applications
Venue: arXiv cs.LG 2019
Year: 2019

Electronic health records (EHR) systems contain vast amounts of medical information about patients. These data can be used to train machine learning models that can predict health status, as well as to help prevent future diseases or disabilities. However, getting patients' medical data to obtain well-trained machine learning models is a challenging task. This is because sharing the patients' medical records is prohibited by law in most countries due to patients privacy concerns. In this paper, we tackle this problem by sharing the models instead of the original sensitive data by using the mimic learning approach. The idea is first to train a model on the original sensitive data, called the teacher model. Then, using this model, we can transfer its knowledge to another model, called the student model, without the need to learn the original data used in training the teacher model. The student model is then shared to the public and can be used to make accurate predictions. To assess the mimic learning approach, we have evaluated our scheme using different medical datasets. The results indicate that the student model mimics the teacher model performance in terms of prediction accuracy without the need to access to the patients' original data records.
Action Logic is Undecidable
Venue: arXiv cs.LO 2019
Year: 2019

Action logic is the algebraic logic (inequational theory) of residuated Kleene lattices. This logic involves Kleene star, axiomatized by an induction scheme. For a stronger system which uses an $ω$-rule instead (infinitary action logic) Buszkowski and Palka (2007) have proved $Π_1^0$-completeness (thus, undecidability). Decidability of action logic itself was an open question, raised by D. Kozen in 1994. In this article, we show that it is undecidable, more precisely, $Σ_1^0$-complete. We also prove the same complexity results for all recursively enumerable logics between action logic and infinitary action logic; for fragments of those only one of the two lattice (additive) connectives; for action logic extended with the law of distributivity.
On the Decentralized Generation of theRSA Moduli in Multi-Party Settings
Venue: arXiv cs.CR 2019
Year: 2019

RSA cryptography is still widely used. Some of its applications (e.g., distributed signature schemes, cryptosystems) do not allow the RSA modulus to be generated by a centralized trusted entity. Instead, the factorization must remain unknown to all the network participants. To this date, the existing algorithms are either computationally expensive, or limited to two-party settings. In this work, we design a decentralized multi-party computation algorithm able to generate efficiently the RSA modulus.
CProp: Adaptive Learning Rate Scaling from Past Gradient Conformity
Venue: arXiv cs.LG 2019
Year: 2019

Most optimizers including stochastic gradient descent (SGD) and its adaptive gradient derivatives face the same problem where an effective learning rate during the training is vastly different. A learning rate scheduling, mostly tuned by hand, is usually employed in practice. In this paper, we propose CProp, a gradient scaling method, which acts as a second-level learning rate adapting throughout the training process based on cues from past gradient conformity. When the past gradients agree on direction, CProp keeps the original learning rate. On the contrary, if the gradients do not agree on direction, CProp scales down the gradient proportionally to its uncertainty. Since it works by scaling, it could apply to any existing optimizer extending its learning rate scheduling capability. We put CProp to a series of tests showing significant gain in training speed on both SGD and adaptive gradient method like Adam. Codes are available at https://github.com/phizaz/cprop .
Parallel optimization of fiber bundle segmentation for massive tractography datasets
Venue: arXiv cs.DS 2019
Year: 2019

We present an optimized algorithm that performs automatic classification of white matter fibers based on a multi-subject bundle atlas. We implemented a parallel algorithm that improves upon its previous version in both execution time and memory usage. Our new version uses the local memory of each processor, which leads to a reduction in execution time. Hence, it allows the analysis of bigger subject and/or atlas datasets. As a result, the segmentation of a subject of 4,145,000 fibers is reduced from about 14 minutes in the previous version to about 6 minutes, yielding an acceleration of 2.34. In addition, the new algorithm reduces the memory consumption of the previous version by a factor of 0.79.
A Bi-Level Cooperative Driving Strategy Allowing Lane Changes
Venue: arXiv cs.MA 2019
Year: 2019

This paper studies the cooperative driving of connected and automated vehicles (CAVs) at conflict areas (e.g., non-signalized intersections and ramping regions). Due to safety concerns, most existing studies prohibit lane change since this may cause lateral collisions when coordination is not appropriately performed. However, in many traffic scenarios (e.g., work zones), vehicles must change lanes. To solve this problem, we categorize the potential collision into two kinds and thus establish a bi-level planning problem. The right-of-way of vehicles for the critical conflict zone is considered in the upper-level, and the right-of-way of vehicles during lane changes is then resolved in the lower-level. The solutions of the upper-level problem are represented in tree space, and a near-optimal solution is searched for by combining Monte Carlo Tree Search (MCTS) with some heuristic rules within a very short planning time. The proposed strategy is suitable for not only the shortest delay objective but also other objectives (e.g., energy-saving and passenger comfort). Numerical examples show that the proposed strategy leads to good traffic performance in real-time.
A taxonomy of risk-based testing
Venue: arXiv cs.SE 2019
Year: 2019

Software testing has often to be done under severe pressure due to limited resources and a challenging time schedule facing the demand to assure the fulfillment of the software requirements. In addition, testing should unveil those software defects that harm the mission-critical functions of the software. Risk-based testing uses risk (re-)assessments to steer all phases of the test process in order to optimize testing efforts and limit risks of the software-based system. Due to its importance and high practical relevance several risk-based testing approaches were proposed in academia and industry. This paper presents a taxonomy of risk-based testing providing a framework to understand, categorize, assess, and compare risk-based testing approaches to support their selection and tailoring for specific purposes. The taxonomy is aligned with the consideration of risks in all phases of the test process and consists of the top-level classes risk drivers, risk assessment, and risk-based test process. The taxonomy of risk-based testing has been developed by analyzing the work presented in available publications on risk-based testing. Afterwards, it has been applied to the work on risk-based testing presented in this special section of the International Journal on Software Tools for Technology Transfer.
Barycenters of Natural Images -- Constrained Wasserstein Barycenters for Image Morphing
Venue: arXiv cs.CV 2019
Year: 2019

Image interpolation, or image morphing, refers to a visual transition between two (or more) input images. For such a transition to look visually appealing, its desirable properties are (i) to be smooth; (ii) to apply the minimal required change in the image; and (iii) to seem "real", avoiding unnatural artifacts in each image in the transition. To obtain a smooth and straightforward transition, one may adopt the well-known Wasserstein Barycenter Problem (WBP). While this approach guarantees minimal changes under the Wasserstein metric, the resulting images might seem unnatural. In this work, we propose a novel approach for image morphing that possesses all three desired properties. To this end, we define a constrained variant of the WBP that enforces the intermediate images to satisfy an image prior. We describe an algorithm that solves this problem and demonstrate it using the sparse prior and generative adversarial networks.
Hopping-Proof and Fee-Free Pooled Mining in Blockchain
Venue: arXiv cs.GT 2019
Year: 2019

The pool-hopping attack casts down the expected profits of both the mining pool and honest miners in Blockchain. The mainstream countermeasures, namely PPS (pay-per-share) and PPLNS (pay-per-last-N-share), can hedge pool hopping, but pose a risk to the pool as well as the cost to miners. In this study, we apply the zero-determinant (ZD) theory to design a novel pooled mining which offers an incentive mechanism for motivating non-memorial and memorial evolutionary miners not to switch in pools strategically. In short, our hopping-proof pooled mining has three unique features: 1) fee-free. No fee is charged if the miner does not hop. 2) wide applicability. It can be employed in both prepaid and postpaid mechanisms. 3) fairness. Even the pool can dominate the game with any miner, he has to cooperate when the miner does not hop among pools. The fairness of our scheme makes it have long-term sustainability. To the best of our knowledge, we are the first to propose a hopping-proof pooled mining with the above three natures simultaneously. Both theoretical and experimental analyses demonstrate the effectiveness of our scheme.
On the Coverage and Capacity of Ultra-Dense Networks with Directional Transmissions
Venue: arXiv cs.IT 2019
Year: 2019

We investigate the performance of a downlink ultra-dense network (UDN) with directional transmissions via stochastic geometry. Considering the dual-slope path loss model and sectored beamforming pattern, we derive the expressions and asymptotic characteristics of the coverage probability and constrained area spectrum efficiency (ASE). Several special scenarios, namely the physically feasible path loss model and adjustable beam pattern, are also analyzed. Although signal-to-interference-plus-noise ratio collapsing still exists when the path loss exponent in the near-field is no larger than 2, using strategies like beam pattern adaption, can avoid the decrease of the coverage probability and constrained ASE even when the base station density approaches infinity.
An Uncertainty- and Collusion-Proof Voting Consensus Mechanism in Blockchain
Venue: arXiv cs.GT 2019
Year: 2019

Though voting-based consensus algorithms in Blockchain outperform proof-based ones in energy- and transaction-efficiency, they are prone to incur wrong elections and bribery elections. The former originates from the uncertainties of candidates' capability and availability; and the latter comes from the egoism of voters and candidates. Hence, in this paper, we propose an uncertainty- and collusion-proof voting consensus mechanism, including the selection pressure-based voting consensus algorithm and the trustworthiness evaluation algorithm. The first algorithm can decrease the side effects of candidates' uncertainties, lowering wrong elections while trading off the balance between efficiency and fairness in electing miners. The second algorithm adopts an incentive compatible scoring rule to evaluate the trustworthiness of voting, motivating voters to report true beliefs on candidates by making egoism in consistent with altruism so as to avoid bribery elections. A salient feature of our work is theoretically analyzing the proposed voting consensus mechanism by the large deviation theory. Our analysis provides not only the voting failure rate of a candidate but also its decay speed, based on which the concepts of {\it the effective selection valve} and {\it the effective expectation of merit} are introduced to help the system designer to determine the optimal voting standard and guide a candidate to behave in an optimal way for lowering the voting failure rate.
Extending Multi-Object Tracking systems to better exploit appearance and 3D information
Venue: arXiv cs.CV 2019
Year: 2019

Tracking multiple objects in real time is essential for a variety of real-world applications, with self-driving industry being at the foremost. This work involves exploiting temporally varying appearance and motion information for tracking. Siamese networks have recently become highly successful at appearance based single object tracking and Recurrent Neural Networks have started dominating both motion and appearance based tracking. Our work focuses on combining Siamese networks and RNNs to exploit appearance and motion information respectively to build a joint system capable of real time multi-object tracking. We further explore heuristics based constraints for tracking in the Birds Eye View Space for efficiently exploiting 3D information as a constrained optimization problem for track prediction.
Learning to Answer Ambiguous Questions with Knowledge Graph
Venue: arXiv cs.AI 2019
Year: 2019

In the task of factoid question answering over knowledge base, many questions have more than one plausible interpretation. Previous works on SimpleQuestions assume only one interpretation as the ground truth for each question, so they lack the ability to answer ambiguous questions correctly. In this paper, we present a new way to utilize the dataset that takes into account the existence of ambiguous questions. Then we introduce a simple and effective model which combines local knowledge subgraph with attention mechanism. Our experimental results show that our approach achieves outstanding performance in this task.
Unity in Diversity: Learning Distributed Heterogeneous Sentence Representation for Extractive Summarization
Venue: arXiv cs.CL 2019
Year: 2019

Automated multi-document extractive text summarization is a widely studied research problem in the field of natural language understanding. Such extractive mechanisms compute in some form the worthiness of a sentence to be included into the summary. While the conventional approaches rely on human crafted document-independent features to generate a summary, we develop a data-driven novel summary system called HNet, which exploits the various semantic and compositional aspects latent in a sentence to capture document independent features. The network learns sentence representation in a way that, salient sentences are closer in the vector space than non-salient sentences. This semantic and compositional feature vector is then concatenated with the document-dependent features for sentence ranking. Experiments on the DUC benchmark datasets (DUC-2001, DUC-2002 and DUC-2004) indicate that our model shows significant performance gain of around 1.5-2 points in terms of ROUGE score compared with the state-of-the-art baselines.