OpenRaiser/Intern-Atlas huggingface dataset:
Spanoids - an abstraction of spanning structures, and a barrier for LCCs
Venue: arXiv cs.CC 2018
Year: 2018

We introduce a simple logical inference structure we call a $\textsf{spanoid}$ (generalizing the notion of a matroid), which captures well-studied problems in several areas. These include combinatorial geometry, algebra (arrangements of hypersurfaces and ideals), statistical physics (bootstrap percolation) and coding theory. We initiate a thorough investigation of spanoids, from computational and structural viewpoints, focusing on parameters relevant to the applications areas above and, in particular, to questions regarding Locally Correctable Codes (LCCs). One central parameter we study is the $\textsf{rank}$ of a spanoid, extending the rank of a matroid and related to the dimension of codes. This leads to one main application of our work, establishing the first known barrier to improving the nearly 20-year old bound of Katz-Trevisan (KT) on the dimension of LCCs. On the one hand, we prove that the KT bound (and its more recent refinements) holds for the much more general setting of spanoid rank. On the other hand we show that there exist (random) spanoids whose rank matches these bounds. Thus, to significantly improve the known bounds one must step out of the spanoid framework. Another parameter we explore is the $\textsf{functional rank}$ of a spanoid, which captures the possibility of turning a given spanoid into an actual code. The question of the relationship between rank and functional rank is one of the main questions we raise as it may reveal new avenues for constructing new LCCs (perhaps even matching the KT bound). As a first step, we develop an entropy relaxation of functional rank to create a small constant gap and amplify it by tensoring to construct a spanoid whose functional rank is smaller than rank by a polynomial factor. This is evidence that the entropy method we develop can prove polynomially better bounds than KT-type methods on the dimension of LCCs.
CNN Based Posture-Free Hand Detection
Venue: arXiv cs.CV 2018
Year: 2018

Although many studies suggest high performance hand detection methods, those methods are likely to be overfitting. Fortunately, the Convolution Neural Network (CNN) based approach provides a better way that is less sensitive to translation and hand poses. However the CNN approach is complex and can increase computational time, which at the end reduce its effectiveness on a system where the speed is essential.In this study we propose a shallow CNN network which is fast, and insensitive to translation and hand poses. It is tested on two different domains of hand datasets, and performs in relatively comparable performance and faster than the other state-of-the-art hand CNN-based hand detection method. Our evaluation shows that the proposed shallow CNN network performs at 93.9% accuracy and reaches much faster speed than its competitors.
Generating Ontologies from Templates: A Rule-Based Approach for Capturing Regularity
Venue: arXiv cs.AI 2018
Year: 2018

We present a second-order language that can be used to succinctly specify ontologies in a consistent and transparent manner. This language is based on ontology templates (OTTR), a framework for capturing recurring patterns of axioms in ontological modelling. The language and our results are independent of any specific DL. We define the language and its semantics, including the case of negation-as-failure, investigate reasoning over ontologies specified using our language, and show results about the decidability of useful reasoning tasks about the language itself. We also state and discuss some open problems that we believe to be of interest.
Learning to Train a Binary Neural Network
Venue: arXiv cs.LG 2018
Year: 2018

Convolutional neural networks have achieved astonishing results in different application areas. Various methods which allow us to use these models on mobile and embedded devices have been proposed. Especially binary neural networks seem to be a promising approach for these devices with low computational power. However, understanding binary neural networks and training accurate models for practical applications remains a challenge. In our work, we focus on increasing our understanding of the training process and making it accessible to everyone. We publish our code and models based on BMXNet for everyone to use. Within this framework, we systematically evaluated different network architectures and hyperparameters to provide useful insights on how to train a binary neural network. Further, we present how we improved accuracy by increasing the number of connections in the network.
Fast and Scalable Position-Based Layout Synthesis
Venue: arXiv cs.GR 2018
Year: 2018

The arrangement of objects into a layout can be challenging for non-experts, as is affirmed by the existence of interior design professionals. Recent research into the automation of this task has yielded methods that can synthesize layouts of objects respecting aesthetic and functional constraints that are non-linear and competing. These methods usually adopt a stochastic optimization scheme, which samples from different layout configurations, a process that is slow and inefficient. We introduce an physics-motivated, continuous layout synthesis technique, which results in a significant gain in speed and is readily scalable. We demonstrate our method on a variety of examples and show that it achieves results similar to conventional layout synthesis based on Markov chain Monte Carlo (McMC) state-search, but is faster by at least an order of magnitude and can handle layouts of unprecedented size as well as tightly-packed layouts that can overwhelm McMC.
Dropout Distillation for Efficiently Estimating Model Confidence
Venue: arXiv cs.CV 2018
Year: 2018

We propose an efficient way to output better calibrated uncertainty scores from neural networks. The Distilled Dropout Network (DDN) makes standard (non-Bayesian) neural networks more introspective by adding a new training loss which prevents them from being overconfident. Our method is more efficient than Bayesian neural networks or model ensembles which, despite providing more reliable uncertainty scores, are more cumbersome to train and slower to test. We evaluate DDN on the the task of image classification on the CIFAR-10 dataset and show that our calibration results are competitive even when compared to 100 Monte Carlo samples from a dropout network while they also increase the classification accuracy. We also propose better calibration within the state of the art Faster R-CNN object detection framework and show, using the COCO dataset, that DDN helps train better calibrated object detectors.
dynamicMF: A Matrix Factorization Approach to Monitor Resource Usage in High Performance Computing Systems
Venue: arXiv cs.DC 2018
Year: 2018

High performance computing (HPC) facilities consist of a large number of interconnected computing units (or nodes) that execute highly complex scientific simulations to support scientific research. Monitoring such facilities, in real-time, is essential to ensure that the system operates at peak efficiency. Such systems are typically monitored using a variety of measurement and log data which capture the state of the various components within the system at regular intervals of time. As modern HPC systems grow in capacity and complexity, the data produced by current resource monitoring tools is at a scale that it is no longer feasible to be visually monitored by analysts. We propose a method that transforms the multi-dimensional output of resource monitoring tools to a low dimensional representation that facilitates the understanding of the behavior of a High Performance Computing (HPC) system. The proposed method automatically extracts the low-dimensional signal in the data which can be used to track the system efficiency and identify performance anomalies. The method models the resource usage data as a three dimensional tensor (capturing resource usage of all compute nodes for difference resources over time). A dynamic matrix factorization algorithm, called dynamicMF, is proposed to extract a low-dimensional temporal signal for each node, which is subsequently fed into an anomaly detector. Results on resource usage data collected from the Lonestar 4 system at the Texas Advanced Computing Center show that the identified anomalies are correlated with actual anomalous events reported in the system log messages.
Definition and evaluation of model-free coordination of electrical vehicle charging with reinforcement learning
Venue: arXiv cs.LG 2018
Year: 2018

Initial DR studies mainly adopt model predictive control and thus require accurate models of the control problem (e.g., a customer behavior model), which are to a large extent uncertain for the EV scenario. Hence, model-free approaches, especially based on reinforcement learning (RL) are an attractive alternative. In this paper, we propose a new Markov decision process (MDP) formulation in the RL framework, to jointly coordinate a set of EV charging stations. State-of-the-art algorithms either focus on a single EV, or perform the control of an aggregate of EVs in multiple steps (e.g., aggregate load decisions in one step, then a step translating the aggregate decision to individual connected EVs). On the contrary, we propose an RL approach to jointly control the whole set of EVs at once. We contribute a new MDP formulation, with a scalable state representation that is independent of the number of EV charging stations. Further, we use a batch reinforcement learning algorithm, i.e., an instance of fitted Q-iteration, to learn the optimal charging policy. We analyze its performance using simulation experiments based on a real-world EV charging data. More specifically, we (i) explore the various settings in training the RL policy (e.g., duration of the period with training data), (ii) compare its performance to an oracle all-knowing benchmark (which provides an upper bound for performance, relying on information that is not available or at least imperfect in practice), (iii) analyze performance over time, over the course of a full year to evaluate possible performance fluctuations (e.g, across different seasons), and (iv) demonstrate the generalization capacity of a learned control policy to larger sets of charging stations.
A Novel and Efficient Data Point Neighborhood Construction Algorithm based on Apollonius Circle
Venue: arXiv cs.CG 2018
Year: 2018

Neighborhood construction models are important in finding connection among the data points, which helps demonstrate interrelations among the information. Hence, employing a new approach to find neighborhood among the data points is a challenging issue. The methods, suggested so far, are not useful for simultaneous analysis of distances and precise examination of the geometric position of the data as well as their geometric relationships. Moreover, most of the suggested algorithms depend on regulating parameters including number of neighborhoods and limitations in fixed regions. The purpose of the proposed algorithm is to detect and offer an applied geometric pattern among the data through data mining. Precise geometric patterns are examined according to the relationships among the data in neighborhood space. These patterns can reveal the behavioural discipline and similarity across the data. It is assumed that there is no prior information about the data sets at hand. The aim of the present research study is to locate the precise neighborhood using Apollonius circle, which can help us identify the neighborhood state of data points. High efficiency of Apollonius structure in assessing local similarities among the observations has opened a new field of the science of geometry in data mining. In order to assess the proposed algorithm, its precision is compared with the state-of-the-art and well-known (k-Nearest Neighbor and epsilon-neighborhood) algorithms.
Omnidirectional Bipedal Walking with Direct Fused Angle Feedback Mechanisms
Venue: arXiv cs.RO 2018
Year: 2018

An omnidirectional closed-loop gait based on the direct feedback of orientation deviation estimates is presented in this paper. At the core of the gait is an open-loop central pattern generator. The orientation feedback is derived from a 3D nonlinear attitude estimator, and split into the relevant angular deviations in the sagittal and lateral planes using the concept of fused angles. These angular deviations from expected are used by a number of independent feedback mechanisms, including one that controls timing, to perform stabilising corrective actions. The tuning of the feedback mechanisms is discussed, including an LQR-based approach for tuning the transient sagittal response. The actuator control scheme and robot pose representations in use are also addressed. Experimental results on an igus Humanoid Open Platform demonstrate the core concept of this paper, that if the sensor management and feedback chains are carefully constructed, comparatively simple model-free and robot-agnostic feedback mechanisms can successfully stabilise a generic bipedal gait.
Plane and Planarity Thresholds for Random Geometric Graphs
Venue: arXiv cs.DM 2018
Year: 2018

A random geometric graph, $G(n,r)$, is formed by choosing $n$ points independently and uniformly at random in a unit square; two points are connected by a straight-line edge if they are at Euclidean distance at most $r$. For a given constant $k$, we show that $n^{\frac{-k}{2k-2}}$ is a distance threshold function for $G(n,r)$ to have a connected subgraph on $k$ points. Based on this, we show that $n^{-2/3}$ is a distance threshold for $G(n,r)$ to be plane, and $n^{-5/8}$ is a distance threshold to be planar. We also investigate distance thresholds for $G(n,r)$ to have a non-crossing edge, a clique of a given size, and an independent set of a given size.
FMIT: Feature Model Integration Techniques
Venue: arXiv cs.SE 2018
Year: 2018

Although feature models are widely used in practice, for example, representing variability in software product lines, their integration is still a challenge. Many integration techniques have been proposed, although none of these have proven to be fully effective. Integrating feature models becomes a difficult, costly, error-prone task. Since their transition occurs in a generalized and automated way, the techniques applied to compose the models end up giving rise to a final model, in many cases undesired, without taking into account the specific needs arising from the requirements determined by the analysts and developers. Therefore, this work proposes FMIT, a technique for integrating feature models. The FMIT is based on contemporary model integration strategies to increase the accuracy and quality of the integrated feature model. In this way, it will be possible to identify the degree of similarity between composite feature diagrams, to verify their accuracy, as well as to identify conflicts. In addition, this work proposes the development of a prototype based on the set of strategies, used to take decisions according to the requirements established during the integration of feature models, whether this is semi-automatic or automatic. To evaluate FMIT, experimental studies were conducted with 10 participants, including students and professionals. Participants performed 12 integration scenarios, 6 using the FMIT and 6 manually. The results suggest that FMIT improved accuracy by 43\% of the cases, as well as reduced the effort by 70\% to perform the integrations.
Collaborative Robot Learning from Demonstrations using Hidden Markov Model State Distribution
Venue: arXiv cs.RO 2018
Year: 2018

In robotics, there is need of an interactive and expedite learning method as experience is expensive. Robot Learning from Demonstration (RLfD) enables a robot to learn a policy from demonstrations performed by teacher. RLfD enables a human user to add new capabilities to a robot in an intuitive manner, without explicitly reprogramming it. In this work, we present a novel interactive framework, where a collaborative robot learns skills for trajectory based tasks from demonstrations performed by a human teacher. The robot extracts features from each demonstration called as key-points and learns a model of the demonstrated skill using Hidden Markov Model (HMM). Our experimental results show that the learned model can be used to produce a generalized trajectory based skill.
Game-Theoretic Model and Experimental Investigation of Cyber Wargaming
Venue: arXiv cs.CR 2018
Year: 2018

We demonstrate that game-theoretic calculations serve as a useful tool for assisting cyber wargaming teams in identifying useful strategies. We note a significant similarity between formulating cyber wargaming strategies and the methodology known in military practice as Course of Action (COA) generation. For scenarios in which the attacker must penetrate multiple layers in a defense-in-depth security configuration, an accounting of attacker and defender costs and penetration probabilities provides cost-utility payoff matrices and penetration probability matrices. These can be used as decision tools by both the defender and attacker. Inspection of the matrices allows players to deduce preferred strategies (or COAs) based on game-theoretical equilibrium solutions. The matrices also help in analyzing anticipated effects of potential human-based choices of wargame strategies and counter-strategies. We describe a mathematical game-theoretic formalism and offer detailed analysis of a table-top cyber wargame executed at the US Army Research Laboratory. Our analysis shows how game-theoretical calculations can provide an effective tool for decision-making during cyber wargames.
A theoretical framework for deep locally connected ReLU network
Venue: arXiv cs.LG 2018
Year: 2018

Understanding theoretical properties of deep and locally connected nonlinear network, such as deep convolutional neural network (DCNN), is still a hard problem despite its empirical success. In this paper, we propose a novel theoretical framework for such networks with ReLU nonlinearity. The framework explicitly formulates data distribution, favors disentangled representations and is compatible with common regularization techniques such as Batch Norm. The framework is built upon teacher-student setting, by expanding the student forward/backward propagation onto the teacher's computational graph. The resulting model does not impose unrealistic assumptions (e.g., Gaussian inputs, independence of activation, etc). Our framework could help facilitate theoretical analysis of many practical issues, e.g. overfitting, generalization, disentangled representations in deep networks.
Learning and Planning with a Semantic Model
Venue: arXiv cs.LG 2018
Year: 2018

Building deep reinforcement learning agents that can generalize and adapt to unseen environments remains a fundamental challenge for AI. This paper describes progresses on this challenge in the context of man-made environments, which are visually diverse but contain intrinsic semantic regularities. We propose a hybrid model-based and model-free approach, LEArning and Planning with Semantics (LEAPS), consisting of a multi-target sub-policy that acts on visual inputs, and a Bayesian model over semantic structures. When placed in an unseen environment, the agent plans with the semantic model to make high-level decisions, proposes the next sub-target for the sub-policy to execute, and updates the semantic model based on new observations. We perform experiments in visual navigation tasks using House3D, a 3D environment that contains diverse human-designed indoor scenes with real-world objects. LEAPS outperforms strong baselines that do not explicitly plan using the semantic content.
Comparison of Self-Aware and Organic Computing Systems
Venue: arXiv cs.SE 2018
Year: 2018

With increasing complexity and heterogeneity of computing devices, it has become crucial for system to be autonomous, adaptive to dynamic environment, robust, flexible, and having so called self-*properties. These autonomous systems are called organic computing(OC) systems. OC system was proposed as a solution to tackle complex systems. Design time decisions have been shifted to run time in highly complex and interconnected systems as it is very hard to consider all scenarios and their appropriate actions in advance. Consequently, Self-awareness becomes crucial for these adaptive autonomous systems. To cope with evolving environment and changing user needs, system need to have knowledge about itself and its surroundings. Literature review shows that for autonomous and intelligent systems, researchers are concerned about knowledge acquisition, representation and learning which is necessary for a system to adapt. This paper is written to compare self-awareness and organic computing by discussing their definitions, properties and architecture.
The Rule of Three: Abstractive Text Summarization in Three Bullet Points
Venue: arXiv cs.CL 2018
Year: 2018

Neural network-based approaches have become widespread for abstractive text summarization. Though previously proposed models for abstractive text summarization addressed the problem of repetition of the same contents in the summary, they did not explicitly consider its information structure. One of the reasons these previous models failed to account for information structure in the generated summary is that standard datasets include summaries of variable lengths, resulting in problems in analyzing information flow, specifically, the manner in which the first sentence is related to the following sentences. Therefore, we use a dataset containing summaries with only three bullet points, and propose a neural network-based abstractive summarization model that considers the information structures of the generated summaries. Our experimental results show that the information structure of a summary can be controlled, thus improving the performance of the overall summarization.
A model for system developers to measure the privacy risk of data
Venue: arXiv cs.CR 2018
Year: 2018

In this paper, we propose a model that could be used by system developers to measure the privacy risk perceived by users when they disclose data into software systems. We first derive a model to measure the perceived privacy risk based on existing knowledge and then we test our model through a survey with 151 participants. Our findings revealed that users' perceived privacy risk monotonically increases with data sensitivity and visibility, and monotonically decreases with data relevance to the application. Furthermore, how visible data is in an application by default when the user discloses data had the highest impact on the perceived privacy risk. This model would enable developers to measure the users' perceived privacy risk associated with data items, which would help them to understand how to treat different data within a system design.
Understanding the influence of Individual's Self-efficacy for Information Systems Security Innovation Adoption: A Systematic Literature Review
Venue: arXiv cs.CR 2018
Year: 2018

Information Systems security cannot be fully apprehended if the user lacks the required knowledge and skills to effectively apply the safeguard measures. Knowledge and skills enhance one's self-efficacy. Individual self-efficacy is an important element in ensuring Information Systems safeguard effectiveness. In this research, we explore the role of individual's self-efficacy for Information Systems security adoption. The study uses the method of Systematic Literature Review using 42 extant studies to evaluate individual self- efficacy for Information Systems security innovation adoption. The systematic review findings reveal the appropriateness of the existing empirical investigations on the individual self-efficacy for Information Systems security adoption. Furthermore, the review results confirmed the significance of the relationship between individual self-efficacy and Information Systems security adoption. In addition, the study validates the past administration of the research on this subject in terms of sample size, sample subject and theoretical grounds.