OpenRaiser/Intern-Atlas huggingface dataset:
Nonlinear Model Predictive Control for Distributed Motion Planning in Road Intersections Using PANOC
Venue: arXiv cs.SY 2019
Year: 2019

The coordination of highly automated vehicles (or agents) in road intersections is an inherently nonconvex and challenging problem. In this paper, we propose a distributed motion planning scheme under reasonable vehicle-to-vehicle communication requirements. Each agent solves a nonlinear model predictive control problem in real time and transmits its planned trajectory to other agents, which may have conflicting objectives. The problem formulation is augmented with conditional constraints that enable the agents to decide whether to wait at a stopping line, if safe crossing is not possible. The involved nonconvex problems are solved very efficiently using the proximal averaged Newton method for optimal control (PANOC). We demonstrate the efficiency of the proposed approach in a realistic intersection crossing scenario.
Extending Signature-based Intrusion Detection Systems WithBayesian Abductive Reasoning
Venue: arXiv cs.CR 2019
Year: 2019

Evolving cybersecurity threats are a persistent challenge for systemadministrators and security experts as new malwares are continu-ally released. Attackers may look for vulnerabilities in commercialproducts or execute sophisticated reconnaissance campaigns tounderstand a targets network and gather information on securityproducts like firewalls and intrusion detection / prevention systems(network or host-based). Many new attacks tend to be modificationsof existing ones. In such a scenario, rule-based systems fail to detectthe attack, even though there are minor differences in conditions /attributes between rules to identify the new and existing attack. Todetect these differences the IDS must be able to isolate the subset ofconditions that are true and predict the likely conditions (differentfrom the original) that must be observed. In this paper, we proposeaprobabilistic abductive reasoningapproach that augments an exist-ing rule-based IDS (snort [29]) to detect these evolved attacks by (a)Predicting rule conditions that are likely to occur (based on existingrules) and (b) able to generate new snort rules when provided withseed rule (i.e. a starting rule) to reduce the burden on experts toconstantly update them. We demonstrate the effectiveness of theapproach by generating new rules from the snort 2012 rules set andtesting it on the MACCDC 2012 dataset [6].
Automatic Defect Segmentation on Leather with Deep Learning
Venue: arXiv cs.CV 2019
Year: 2019

Leather is a natural and durable material created through a process of tanning of hides and skins of animals. The price of the leather is subjective as it is highly sensitive to its quality and surface defects condition. In the literature, there are very few works investigating on the defects detection for leather using automatic image processing techniques. The manual defect inspection process is essential in an leather production industry to control the quality of the finished products. However, it is tedious, as it is labour intensive, time consuming, causes eye fatigue and often prone to human error. In this paper, a fully automatic defect detection and marking system on a calf leather is proposed. The proposed system consists of a piece of leather, LED light, high resolution camera and a robot arm. Succinctly, a machine vision method is presented to identify the position of the defects on the leather using a deep learning architecture. Then, a series of processes are conducted to predict the defect instances, including elicitation of the leather images with a robot arm, train and test the images using a deep learning architecture and determination of the boundary of the defects using mathematical derivation of the geometry. Note that, all the processes do not involve human intervention, except for the defect ground truths construction stage. The proposed algorithm is capable to exhibit 91.5% segmentation accuracy on the train data and 70.35% on the test data. We also report confusion matrix, F1-score, precision and specificity, sensitivity performance metrics to further verify the effectiveness of the proposed approach.
Co-evolving Tracing and Fault Injection with Box of Pain
Venue: arXiv cs.DC 2019
Year: 2019

Distributed systems are hard to reason about largely because of uncertainty about what may go wrong in a particular execution, and about whether the system will mitigate those faults. Tools that perturb executions can help test whether a system is robust to faults, while tools that observe executions can help better understand their system-wide effects. We present Box of Pain, a tracer and fault injector for unmodified distributed systems that addresses both concerns by interposing at the system call level and dynamically reconstructing the partial order of communication events based on causal relationships. Box of Pain's lightweight approach to tracing and focus on simulating the effects of partial failures on communication rather than the failures themselves sets it apart from other tracing and fault injection systems. We present evidence of the promise of Box of Pain and its approach to lightweight observation and perturbation of distributed systems.
iGen: Dynamic Interaction Inference for Configurable Software
Venue: arXiv cs.SE 2019
Year: 2019

To develop, analyze, and evolve today's highly configurable software systems, developers need deep knowledge of a system's configuration options, e.g., how options need to be set to reach certain locations, what configurations to use for testing, etc. Today, acquiring this detailed information requires manual effort that is difficult, expensive, and error prone. In this paper, we propose iGen, a novel, lightweight dynamic analysis technique that automatically discovers a program's \emph{interactions}---expressive logical formulae that give developers rich and detailed information about how a system's configuration option settings map to particular code coverage. iGen employs an iterative algorithm that runs a system under a small set of configurations, capturing coverage data; processes the coverage data to infer potential interactions; and then generates new configurations to further refine interactions in the next iteration. We evaluated iGen on 29 programs spanning five languages; the breadth of this study would be unachievable using prior interaction inference tools. Our results show that iGen finds precise interactions based on a very small fraction of the number of possible configurations. Moreover, iGen's results confirm several earlier hypotheses about typical interaction distributions and structures.
Validation of a recommender system for prompting omitted foods in online dietary assessment surveys
Venue: arXiv cs.CY 2019
Year: 2019

Recall assistance methods are among the key aspects that improve the accuracy of online dietary assessment surveys. These methods still mainly rely on experience of trained interviewers with nutritional background, but data driven approaches could improve cost-efficiency and scalability of automated dietary assessment. We evaluated the effectiveness of a recommender algorithm developed for an online dietary assessment system called Intake24, that automates the multiple-pass 24-hour recall method. The recommender builds a model of eating behavior from recalls collected in past surveys. Based on foods they have already selected, the model is used to remind respondents of associated foods that they may have omitted to report. The performance of prompts generated by the model was compared to that of prompts hand-coded by nutritionists in two dietary studies. The results of our studies demonstrate that the recommender system is able to capture a higher number of foods omitted by respondents of online dietary surveys than prompts hand-coded by nutritionists. However, the considerably lower precision of generated prompts indicates an opportunity for further improvement of the system.
An Empirical Study of Obsolete Answers on Stack Overflow
Venue: arXiv cs.SE 2019
Year: 2019

Stack Overflow accumulates an enormous amount of software engineering knowledge. However, as time passes, certain knowledge in answers may become obsolete. Such obsolete answers, if not identified or documented clearly, may mislead answer seekers and cause unexpected problems (e.g., using an out-dated security protocol). In this paper, we investigate how the knowledge in answers becomes obsolete and identify the characteristics of such obsolete answers. We find that: 1) More than half of the obsolete answers (58.4%) were probably already obsolete when they were first posted. 2) When an obsolete answer is observed, only a small proportion (20.5%) of such answers are ever updated. 3) Answers to questions in certain tags (e.g., node.js, ajax, android, and objective-c) are more likely to become obsolete. Our findings suggest that Stack Overflow should develop mechanisms to encourage the whole community to maintain answers (to avoid obsolete answers) and answer seekers are encouraged to carefully go through all information (e.g., comments) in answer threads.
Amortized Object and Scene Perception for Long-term Robot Manipulation
Venue: arXiv cs.RO 2019
Year: 2019

Mobile robots, performing long-term manipulation activities in human environments, have to perceive a wide variety of objects possessing very different visual characteristics and need to reliably keep track of these throughout the execution of a task. In order to be efficient, robot perception capabilities need to go beyond what is currently perceivable and should be able to answer queries about both current and past scenes. In this paper we investigate a perception system for long-term robot manipulation that keeps track of the changing environment and builds a representation of the perceived world. Specifically we introduce an amortized component that spreads perception tasks throughout the execution cycle. The resulting query driven perception system asynchronously integrates results from logged images into a symbolic and numeric (what we call sub-symbolic) representation that forms the perceptual belief state of the robot.
Nonlinear Moment Matching for the Simulation-Free Reduction of Structural Systems
Venue: arXiv cs.CE 2019
Year: 2019

This paper transfers the concept of moment matching to nonlinear structural systems and further provides a simulation-free reduction scheme for such nonlinear second-order models. After first presenting the steady-state interpretation of linear moment matching, we then extend this reduction concept to the nonlinear second-order case based on Astolfi [2010]. Then, similar simplifications as in Cruz Varona et al. [2019] are proposed to achieve a simulation-free nonlinear moment matching algorithm. A discussion on the simplifications and their limitations is presented, as well as a numerical example which illustrates the efficiency of the algorithm.
Improved Reinforcement Learning with Curriculum
Venue: arXiv cs.LG 2019
Year: 2019

Humans tend to learn complex abstract concepts faster if examples are presented in a structured manner. For instance, when learning how to play a board game, usually one of the first concepts learned is how the game ends, i.e. the actions that lead to a terminal state (win, lose or draw). The advantage of learning end-games first is that once the actions which lead to a terminal state are understood, it becomes possible to incrementally learn the consequences of actions that are further away from a terminal state - we call this an end-game-first curriculum. Currently the state-of-the-art machine learning player for general board games, AlphaZero by Google DeepMind, does not employ a structured training curriculum; instead learning from the entire game at all times. By employing an end-game-first training curriculum to train an AlphaZero inspired player, we empirically show that the rate of learning of an artificial player can be improved during the early stages of training when compared to a player not using a training curriculum.
Averager-copier-voter models for hybrid opinion dynamics in complex networks
Venue: arXiv cs.SY 2019
Year: 2019

A hybrid model for opinion dynamics in complex multi-agent networks is introduced, wherein some continuous-valued agents average neighbors' opinions to update their own, while other discrete-valued agents use stochastic copying and voting protocols. A statistical and graph-theoretic analysis of the model is undertaken, and consensus is shown to be achieved whenever the network matrix is ergodic. Also, the time required for consensus is characterized, in terms of the network's graph and the distribution of agents of different types.
A Machine Learning Framework for Biometric Authentication using Electrocardiogram
Venue: arXiv cs.CR 2019
Year: 2019

This paper introduces a framework for how to appropriately adopt and adjust Machine Learning (ML) techniques used to construct Electrocardiogram (ECG) based biometric authentication schemes. The proposed framework can help investigators and developers on ECG based biometric authentication mechanisms define the boundaries of required datasets and get training data with good quality. To determine the boundaries of datasets, use case analysis is adopted. Based on various application scenarios on ECG based authentication, three distinct use cases (or authentication categories) are developed. With more qualified training data given to corresponding machine learning schemes, the precision on ML-based ECG biometric authentication mechanisms is increased in consequence. ECG time slicing technique with the R-peak anchoring is utilized in this framework to acquire ML training data with good quality. In the proposed framework four new measure metrics are introduced to evaluate the quality of ML training and testing data. In addition, a Matlab toolbox, containing all proposed mechanisms, metrics and sample data with demonstrations using various ML techniques, is developed and made publicly available for further investigation. For developing ML-based ECG biometric authentication, the proposed framework can guide researchers to prepare the proper ML setups and the ML training datasets along with three identified user case scenarios. For researchers adopting ML techniques to design new schemes in other research domains, the proposed framework is still useful for generating ML-based training and testing datasets with good quality and utilizing new measure metrics.
CUTIE: Learning to Understand Documents with Convolutional Universal Text Information Extractor
Venue: arXiv cs.CV 2019
Year: 2019

Extracting key information from documents, such as receipts or invoices, and preserving the interested texts to structured data is crucial in the document-intensive streamline processes of office automation in areas that includes but not limited to accounting, financial, and taxation areas. To avoid designing expert rules for each specific type of document, some published works attempt to tackle the problem by learning a model to explore the semantic context in text sequences based on the Named Entity Recognition (NER) method in the NLP field. In this paper, we propose to harness the effective information from both semantic meaning and spatial distribution of texts in documents. Specifically, our proposed model, Convolutional Universal Text Information Extractor (CUTIE), applies convolutional neural networks on gridded texts where texts are embedded as features with semantical connotations. We further explore the effect of employing different structures of convolutional neural network and propose a fast and portable structure. We demonstrate the effectiveness of the proposed method on a dataset with up to $4,484$ labelled receipts, without any pre-training or post-processing, achieving state of the art performance that is much better than the NER based methods in terms of either speed and accuracy. Experimental results also demonstrate that the proposed CUTIE model being able to achieve good performance with a much smaller amount of training data.
Testing zero-dimensionality of varieties at a point
Venue: arXiv cs.SC 2019
Year: 2019

Effective methods are introduced for testing zero-dimensionality of varieties at a point. The motivation of this paper is to compute and analyze deformations of isolated hypersurface singularities. As an application, methods for computing local dimensions are also described. For the case where a given ideal contains parameters, the proposed algorithms can output in particular a decomposition of a parameter space into strata according to the local dimension at a point of the associated varieties. The key of the proposed algorithms is the use of the notion of comprehensive Gröbner systems.
Cyber-Social Systems: Modeling, Inference, and Optimal Design
Venue: arXiv cs.SY 2019
Year: 2019

This paper models the cyber-social system as a cyber-network of agents monitoring states of individuals in a social network. The state of each individual is represented by a social node and the interactions among individuals are represented by a social link. In the cyber-network each node represents an agent and the links represent information sharing among agents. Agents make an observation of social states and perform distributed inference. In this direction, the contribution of this work is threefold: (i) A novel distributed inference protocol is proposed that makes no assumption on the rank of the underlying social system. This is significant as most protocols in the literature only work on full-rank systems. (ii) A novel agent classification is developed, where it is shown that connectivity requirement on the cyber-network differs for each type. This is particularly important in finding the minimal number of observations and minimal connectivity of the cyber-network as the next contribution. (iii) The cost-optimal design of cyber-network constraint with distributed observability is addressed. This problem is subdivided into sensing cost optimization and networking cost optimization where both are claimed to be NP-hard. We solve both problems for certain types of social networks and find polynomial-order solutions.
Deep Representation with ReLU Neural Networks
Venue: arXiv cs.LG 2019
Year: 2019

We consider deep feedforward neural networks with rectified linear units from a signal processing perspective. In this view, such representations mark the transition from using a single (data-driven) linear representation to utilizing a large collection of affine linear representations tailored to particular regions of the signal space. This paper provides a precise description of the individual affine linear representations and corresponding domain regions that the (data-driven) neural network associates to each signal of the input space. In particular, we describe atomic decompositions of the representations and, based on estimating their Lipschitz regularity, suggest some conditions that can stabilize learning independent of the network depth. Such an analysis may promote further theoretical insight from both the signal processing and machine learning communities.
GÉANT Software Maturity Model
Venue: arXiv cs.SE 2019
Year: 2019

GÉANT project is an example of a large organization with around 30 software projects and around 20 software development teams. Software development teams consist of many skilled associates coming from all members National Research and Education Networks. Three main issues that are common for all these software development teams and their members are: geographical distribution, scattered manpower percentage, and parallel involvement in other high priority projects in their native organizations. This paper presents a novel software maturity model that is designed specifically for GÉANT software development teams and aims to address the described issues.
Color Refinement, Homomorphisms, and Hypergraphs
Venue: arXiv cs.DM 2019
Year: 2019

Recent results show that the structural similarity of graphs can be characterized by counting homomorphisms to them: the Tree Theorem states that the well-known color-refinement algorithm does not distinguish two graphs G and H if and only if, for every tree T, the number of homomorphisms Hom(T,G) from T to G is equal to the corresponding number Hom(T,H) from T to H (Dell, Grohe, Rattan 2018). We show how this approach transfers to hypergraphs by introducing a generalization of color refinement. We prove that it does not distinguish two hypergraphs G and H if and only if, for every connected Berge-acyclic hypergraph B, we have Hom(B,G) = Hom(B,H). To this end, we show how homomorphisms of hypergraphs and of a colored variant of their incidence graphs are related to each other. This reduces the above statement to one about vertex-colored graphs.
Cross-Subject Transfer Learning in Human Activity Recognition Systems using Generative Adversarial Networks
Venue: arXiv cs.LG 2019
Year: 2019

Application of intelligent systems especially in smart homes and health-related topics has been drawing more attention in the last decades. Training Human Activity Recognition (HAR) models -- as a major module -- requires a fair amount of labeled data. Despite training with large datasets, most of the existing models will face a dramatic performance drop when they are tested against unseen data from new users. Moreover, recording enough data for each new user is unviable due to the limitations and challenges of working with human users. Transfer learning techniques aim to transfer the knowledge which has been learned from the source domain (subject) to the target domain in order to decrease the models' performance loss in the target domain. This paper presents a novel method of adversarial knowledge transfer named SA-GAN stands for Subject Adaptor GAN which utilizes Generative Adversarial Network framework to perform cross-subject transfer learning in the domain of wearable sensor-based Human Activity Recognition. SA-GAN outperformed other state-of-the-art methods in more than 66% of experiments and showed the second best performance in the remaining 25% of experiments. In some cases, it reached up to 90% of the accuracy which can be obtained by supervised training over the same domain data.
BootKeeper: Validating Software Integrity Properties on Boot Firmware Images
Venue: arXiv cs.CR 2019
Year: 2019

Boot firmware, like UEFI-compliant firmware, has been the target of numerous attacks, giving the attacker control over the entire system while being undetected. The measured boot mechanism of a computer platform ensures its integrity by using cryptographic measurements to detect such attacks. This is typically performed by relying on a Trusted Platform Module (TPM). Recent work, however, shows that vendors do not respect the specifications that have been devised to ensure the integrity of the firmware's loading process. As a result, attackers may bypass such measurement mechanisms and successfully load a modified firmware image while remaining unnoticed. In this paper we introduce BootKeeper, a static analysis approach verifying a set of key security properties on boot firmware images before deployment, to ensure the integrity of the measured boot process. We evaluate BootKeeper against several attacks on common boot firmware implementations and demonstrate its applicability.