OpenRaiser/Intern-Atlas huggingface dataset:
Knowledge Network and a Knowledge Network Example
Venue: arXiv cs.CY 2019
Year: 2019

Knowledge networks can be defined as social networks that enable the transfer of the knowledge, which is defined as the intellectual product formed as a result of the work of human intelligence, to be transferred to any other means of communication. A knowledge network represents a large number of people, resources and relationships between them, to create the highest value, primarily to accumulate and use knowledge through the process of generating and transmitting knowledge. General structure of knowledge networks; it consists of three basic stages: gathering, organizing and disseminating knowledge. The first step, knowledge collection, institutions and organizations to enter the network structure of the knowledge that is present. The organizing phase is the structuring of irregular and unstructured knowledge in the network structure according to certain standards and recording them regularly in the structure. Knowledge dissemination can be expressed as the transfer of organized knowledge in accordance with user knowledge and needs. The purpose of the training knowledge network is to ensure communication between the student, the teacher and the guide, and to store the knowledge that is formed in a course, to enable the system to be recorded in a systematic way, to disseminate it according to the needs of the users and to update the knowledge. A dynamic website and content management system have been developed for the training knowledge network. The training knowledge network has a flexible structure to support web technologies. By making production, management and publication of knowledge within the framework of specific needs, it provides knowledge network for different problems, producing different and tailored solutions. With the example of education knowledge network, a knowledge network that can be developed according to the innovative knowledge network structure is designed.
Blockchain-Based Electronic Voting System for Elections in Turkey
Venue: arXiv cs.CR 2019
Year: 2019

Traditional elections satisfy neither citizens nor political authorities in recent years. They are not fully secure since it is easy to attack votes. It threatens also privacy and transparency of voters. Additionally, it takes too much time to count the votes. This paper proposes a solution using Blockchain to eliminate all the disadvantages of conventional elections. Security and data integrity of votes are absolutely provided theoretically. Voter privacy is another requirement that is ensured in the system. Lastly, the waiting time for results decreased significantly in the proposed Blockchain voting system.
Pushable chromatic number of graphs with degree constraints
Venue: arXiv cs.DM 2019
Year: 2019

Pushable homomorphisms and the pushable chromatic number $χ_p$ of oriented graphs were introduced by Klostermeyer and MacGillivray in 2004. They notably observed that, for any oriented graph $\overrightarrow{G}$, we have $χ_p(\overrightarrow{G}) \leq χ_o(\overrightarrow{G}) \leq 2 χ_p(\overrightarrow{G})$, where $χ_o(\overrightarrow{G})$ denotes the oriented chromatic number of $\overrightarrow{G}$. This stands as first general bounds on $χ_p$. This parameter was further studied in later works.This work is dedicated to the pushable chromatic number of oriented graphs fulfilling particular degree conditions. For all $Δ\geq 29$, we first prove that the maximum value of the pushable chromatic number of an oriented graph with maximum degree $Δ$ lies between $2^{\fracΔ{2}-1}$ and $(Δ-3) \cdot (Δ-1) \cdot 2^{Δ-1} + 2$ which implies an improved bound on the oriented chromatic number of the same family of graphs. For subcubic oriented graphs, that is, when $Δ\leq 3$, we then prove that the maximum value of the pushable chromatic number is~$6$ or~$7$. We also prove that the maximum value of the pushable chromatic number of oriented graphs with maximum average degree less than~$3$ lies between~$5$ and~$6$. The former upper bound of~$7$ also holds as an upper bound on the pushable chromatic number of planar oriented graphs with girth at least~$6$.
Simplified_edition_Multi-robot SLAM Multi-view Target Tracking based on Panoramic Vision in Irregular Environment
Venue: arXiv cs.RO 2019
Year: 2019

In order to improve the precision of multi-robot SLAM multi-view target tracking process, a improved multi-robot SLAM multi-view target tracking algorithm based on panoramic vision in irregular environment was put forward, adding an correction factor to renew the existing Extended Kalman Filter (EKF) model, obtaining new coordinates X and Y after twice iterations. The paper has been accepted by Computing and Visualization in Science and this is a simplified version.
SM-NAS: Structural-to-Modular Neural Architecture Search for Object Detection
Venue: arXiv cs.CV 2019
Year: 2019

The state-of-the-art object detection method is complicated with various modules such as backbone, feature fusion neck, RPN and RCNN head, where each module may have different designs and structures. How to leverage the computational cost and accuracy trade-off for the structural combination as well as the modular selection of multiple modules? Neural architecture search (NAS) has shown great potential in finding an optimal solution. Existing NAS works for object detection only focus on searching better design of a single module such as backbone or feature fusion neck, while neglecting the balance of the whole system. In this paper, we present a two-stage coarse-to-fine searching strategy named Structural-to-Modular NAS (SM-NAS) for searching a GPU-friendly design of both an efficient combination of modules and better modular-level architecture for object detection. Specifically, Structural-level searching stage first aims to find an efficient combination of different modules; Modular-level searching stage then evolves each specific module and pushes the Pareto front forward to a faster task-specific network. We consider a multi-objective search where the search space covers many popular designs of detection methods. We directly search a detection backbone without pre-trained models or any proxy task by exploring a fast training from scratch strategy. The resulting architectures dominate state-of-the-art object detection systems in both inference time and accuracy and demonstrate the effectiveness on multiple detection datasets, e.g. halving the inference time with additional 1% mAP improvement compared to FPN and reaching 46% mAP with the similar inference time of MaskRCNN.
Attack Agnostic Statistical Method for Adversarial Detection
Venue: arXiv cs.LG 2019
Year: 2019

Deep Learning based AI systems have shown great promise in various domains such as vision, audio, autonomous systems (vehicles, drones), etc. Recent research on neural networks has shown the susceptibility of deep networks to adversarial attacks - a technique of adding small perturbations to the inputs which can fool a deep network into misclassifying them. Developing defenses against such adversarial attacks is an active research area, with some approaches proposing robust models that are immune to such adversaries, while other techniques attempt to detect such adversarial inputs. In this paper, we present a novel statistical approach for adversarial detection in image classification. Our approach is based on constructing a per-class feature distribution and detecting adversaries based on comparison of features of a test image with the feature distribution of its class. For this purpose, we make use of various statistical distances such as ED (Energy Distance), MMD (Maximum Mean Discrepancy) for adversarial detection, and analyze the performance of each metric. We experimentally show that our approach achieves good adversarial detection performance on MNIST and CIFAR-10 datasets irrespective of the attack method, sample size and the degree of adversarial perturbation.
Machine: The New Art Connoisseur
Venue: arXiv cs.AI 2019
Year: 2019

The process of identifying and understanding art styles to discover artistic influences is essential to the study of art history. Traditionally, trained experts review fine details of the works and compare them to other known works. To automate and scale this task, we use several state-of-the-art CNN architectures to explore how a machine may help perceive and quantify art styles. This study explores: (1) How accurately can a machine classify art styles? (2) What may be the underlying relationships among different styles and artists? To help answer the first question, our best-performing model using Inception V3 achieves a 9-class classification accuracy of 88.35%, which outperforms the model in Elgammal et al.'s study by more than 20 percent. Visualizations using Grad-CAM heat maps confirm that the model correctly focuses on the characteristic parts of paintings. To help address the second question, we conduct network analysis on the influences among styles and artists by extracting 512 features from the best-performing classification model. Through 2D and 3D T-SNE visualizations, we observe clear chronological patterns of development and separation among the art styles. The network analysis also appears to show anticipated artist level connections from an art historical perspective. This technique appears to help identify some previously unknown linkages that may shed light upon new directions for further exploration by art historians. We hope that humans and machines working in concert may bring new opportunities to the field.
FlipIn: A Game-Theoretic Cyber Insurance Framework for Incentive-Compatible Cyber Risk Management of Internet of Things
Venue: arXiv cs.CR 2019
Year: 2019

Internet of Things (IoT) is highly vulnerable to emerging Advanced Persistent Threats (APTs) that are often operated by well-resourced adversaries. Achieving perfect security for IoT networks is often cost-prohibitive if not impossible. Cyber insurance is a valuable mechanism to mitigate cyber risks for IoT systems. In this work, we propose a bi-level game-theoretic framework called FlipIn to design incentive-compatible and welfare-maximizing cyber insurance contracts. The framework captures the strategic interactions among APT attackers, IoT defenders, and cyber insurance insurers, and incorporates influence networks to assess the systemic cyber risks of interconnected IoT devices. The FlipIn framework formulates a game over networks within a principal-agent problem of moral-hazard type to design a cyber risk-aware insurance contract. We completely characterize the equilibrium solutions of the bi-level games for a network of distributed defenders and a semi-homogeneous centralized defender and show that the optimal insurance contracts cover half of the defenders' losses. Our framework predicts the risk compensation of defenders and the Peltzman effect of insurance. We study a centralized security management scenario and its decentralized counterpart, and leverage numerical experiments to show that network connectivity plays an important role in the security of the IoT devices and the insurability of both distributed and centralized defenders.
Technical report: supervised training of convolutional spiking neural networks with PyTorch
Venue: arXiv cs.NE 2019
Year: 2019

Recently, it has been shown that spiking neural networks (SNNs) can be trained efficiently, in a supervised manner, using backpropagation through time. Indeed, the most commonly used spiking neuron model, the leaky integrate-and-fire neuron, obeys a differential equation which can be approximated using discrete time steps, leading to a recurrent relation for the potential. The firing threshold causes optimization issues, but they can be overcome using a surrogate gradient. Here, we extend previous approaches in two ways. Firstly, we show that the approach can be used to train convolutional layers. Convolutions can be done in space, time (which simulates conduction delays), or both. Secondly, we include fast horizontal connections à la Denève: when a neuron N fires, we subtract to the potentials of all the neurons with the same receptive the dot product between their weight vectors and the one of neuron N. As Denève et al. showed, this is useful to represent a dynamic multidimensional analog signal in a population of spiking neurons. Here we demonstrate that, in addition, such connections also allow implementing a multidimensional send-on-delta coding scheme. We validate our approach on one speech classification benchmarks: the Google speech command dataset. We managed to reach nearly state-of-the-art accuracy (94%) while maintaining low firing rates (about 5Hz). Our code is based on PyTorch and is available in open source at http://github.com/romainzimmer/s2net
Learnable Pooling in Graph Convolution Networks for Brain Surface Analysis
Venue: arXiv cs.CV 2019
Year: 2019

Brain surface analysis is essential to neuroscience, however, the complex geometry of the brain cortex hinders computational methods for this task. The difficulty arises from a discrepancy between 3D imaging data, which is represented in Euclidean space, and the non-Euclidean geometry of the highly-convoluted brain surface. Recent advances in machine learning have enabled the use of neural networks for non-Euclidean spaces. These facilitate the learning of surface data, yet pooling strategies often remain constrained to a single fixed-graph. This paper proposes a new learnable graph pooling method for processing multiple surface-valued data to output subject-based information. The proposed method innovates by learning an intrinsic aggregation of graph nodes based on graph spectral embedding. We illustrate the advantages of our approach with in-depth experiments on two large-scale benchmark datasets. The flexibility of the pooling strategy is evaluated on four different prediction tasks, namely, subject-sex classification, regression of cortical region sizes, classification of Alzheimer's disease stages, and brain age regression. Our experiments demonstrate the superiority of our learnable pooling approach compared to other pooling techniques for graph convolution networks, with results improving the state-of-the-art in brain surface analysis.
A Transfer Learning Method for Goal Recognition Exploiting Cross-Domain Spatial Features
Venue: arXiv cs.LG 2019
Year: 2019

The ability to infer the intentions of others, predict their goals, and deduce their plans are critical features for intelligent agents. For a long time, several approaches investigated the use of symbolic representations and inferences with limited success, principally because it is difficult to capture the cognitive knowledge behind human decisions explicitly. The trend, nowadays, is increasingly focusing on learning to infer intentions directly from data, using deep learning in particular. We are now observing interesting applications of intent classification in natural language processing, visual activity recognition, and emerging approaches in other domains. This paper discusses a novel approach combining few-shot and transfer learning with cross-domain features, to learn to infer the intent of an agent navigating in physical environments, executing arbitrary long sequences of actions to achieve their goals. Experiments in synthetic environments demonstrate improved performance in terms of learning from few samples and generalizing to unseen configurations, compared to a deep-learning baseline approach.
Privately Learning Thresholds: Closing the Exponential Gap
Venue: arXiv cs.DS 2019
Year: 2019

We study the sample complexity of learning threshold functions under the constraint of differential privacy. It is assumed that each labeled example in the training data is the information of one individual and we would like to come up with a generalizing hypothesis $h$ while guaranteeing differential privacy for the individuals. Intuitively, this means that any single labeled example in the training data should not have a significant effect on the choice of the hypothesis. This problem has received much attention recently; unlike the non-private case, where the sample complexity is independent of the domain size and just depends on the desired accuracy and confidence, for private learning the sample complexity must depend on the domain size $X$ (even for approximate differential privacy). Alon et al. (STOC 2019) showed a lower bound of $Ω(\log^*|X|)$ on the sample complexity and Bun et al. (FOCS 2015) presented an approximate-private learner with sample complexity $\tilde{O}\left(2^{\log^*|X|}\right)$. In this work we reduce this gap significantly, almost settling the sample complexity. We first present a new upper bound (algorithm) of $\tilde{O}\left(\left(\log^*|X|\right)^2\right)$ on the sample complexity and then present an improved version with sample complexity $\tilde{O}\left(\left(\log^*|X|\right)^{1.5}\right)$. Our algorithm is constructed for the related interior point problem, where the goal is to find a point between the largest and smallest input elements. It is based on selecting an input-dependent hash function and using it to embed the database into a domain whose size is reduced logarithmically; this results in a new database, an interior point of which can be used to generate an interior point in the original database in a differentially private manner.
Can Taxes Improve Congestion on All Networks?
Venue: arXiv cs.GT 2019
Year: 2019

We ask if it is possible to positively influence social behavior with no risk of unintentionally incentivizing pathological behavior. In network routing problems, if network traffic is composed of many individual agents, it is known that self-interested behavior among the agents can lead to suboptimal network congestion. We study situations in which a system planner charges monetary tolls for the use of network links in an effort to incentivize efficient routing choices by the users, but in which the users' sensitivity to tolls is heterogeneous and unknown. We seek locally-computed tolls that are guaranteed not to incentivize worse network routing than in the un-influenced case. Our main result is to show that if networks are sufficiently complex and populations sufficiently diverse, perverse incentives cannot be systematically avoided: any taxation mechanism that improves outcomes on one network must necessarily degrade them on another. Nonetheless, for the simple class of parallel networks, non-perverse taxes do exist; we fully characterize all such taxation mechanisms, showing that they are a generalized version of traditional marginal-cost tolls.
Joint Modeling of Local and Global Temporal Dynamics for Multivariate Time Series Forecasting with Missing Values
Venue: arXiv cs.LG 2019
Year: 2019

Multivariate time series (MTS) forecasting is widely used in various domains, such as meteorology and traffic. Due to limitations on data collection, transmission, and storage, real-world MTS data usually contains missing values, making it infeasible to apply existing MTS forecasting models such as linear regression and recurrent neural networks. Though many efforts have been devoted to this problem, most of them solely rely on local dependencies for imputing missing values, which ignores global temporal dynamics. Local dependencies/patterns would become less useful when the missing ratio is high, or the data have consecutive missing values; while exploring global patterns can alleviate such problems. Thus, jointly modeling local and global temporal dynamics is very promising for MTS forecasting with missing values. However, work in this direction is rather limited. Therefore, we study a novel problem of MTS forecasting with missing values by jointly exploring local and global temporal dynamics. We propose a new framework LGnet, which leverages memory network to explore global patterns given estimations from local perspectives. We further introduce adversarial training to enhance the modeling of global temporal distribution. Experimental results on real-world datasets show the effectiveness of LGnet for MTS forecasting with missing values and its robustness under various missing ratios.
A Measurement of Social Capital in an Open Source Software Project
Venue: arXiv cs.SI 2019
Year: 2019

The paper provides an understanding of social capital in organizations that are open membership multi-agent systems with an emphasis in our formulation on the dynamic network of social interaction that, in part, elucidate evolving structures and impromptu topologies of networks. This paper, therefore, models an open source project as an organizational network. It provides definitions of social capital for this organizational network and formulation of the mechanism to optimize the social capital for achieving its goal that is optimized productivity. A case study of an open source Apache-Hadoop project is considered and empirically evaluated. An analysis of how social capital can be created within this type of organizations and driven to a measurement for its value is provided. Finally, a verification on whether the social capital of the organizational network is proportional towards optimizing their productivity is considered.
Weighted Laplacian and Its Theoretical Applications
Venue: arXiv cs.LG 2019
Year: 2019

In this paper, we develop a novel weighted Laplacian method, which is partially inspired by the theory of graph Laplacian, to study recent popular graph problems, such as multilevel graph partitioning and balanced minimum cut problem, in a more convenient manner. Since the weighted Laplacian strategy inherits the virtues of spectral methods, graph algorithms designed using weighted Laplacian will necessarily possess more robust theoretical guarantees for algorithmic performances, comparing with those existing algorithms that are heuristically proposed. In order to illustrate its powerful utility both in theory and in practice, we also present two effective applications of our weighted Laplacian method to multilevel graph partitioning and balanced minimum cut problem, respectively. By means of variational methods and theory of partial differential equations (PDEs), we have established the equivalence relations among the weighted cut problem, balanced minimum cut problem and the initial clustering problem that arises in the middle stage of graph partitioning algorithms under a multilevel structure. These equivalence relations can indeed provide solid theoretical support for algorithms based on our proposed weighted Laplacian strategy. Moreover, from the perspective of the application to the balanced minimum cut problem, weighted Laplacian can make it possible for research of numerical solutions of PDEs to be a powerful tool for the algorithmic study of graph problems. Experimental results also indicate that the algorithm embedded with our strategy indeed outperforms other existing graph algorithms, especially in terms of accuracy, thus verifying the efficacy of the proposed weighted Laplacian.