OpenRaiser/Intern-Atlas huggingface dataset:
Feature Fusion Use Unsupervised Prior Knowledge to Let Small Object Represent
Venue: arXiv cs.CV 2019
Year: 2019

Fusing low level and high level features is a widely used strategy to provide details that might be missing during convolution and pooling. Different from previous works, we propose a new fusion mechanism called FillIn which takes advantage of prior knowledge described with superpixel segmentation. According to the prior knowledge, the FillIn chooses small region on low level feature map to fill into high level feature map. By using the proposed fusion mechanism, the low level features have equal channels for some tiny region as high level features, which makes the low level features have relatively independent power to decide final semantic label. We demonstrate the effectiveness of our model on PASCAL VOC 2012, it achieves competitive test result based on DeepLabv3+ backbone and visualizations of predictions prove our fusion can let small objects represent and low level features have potential for segmenting small objects.
Geographical and Topology Control based Opportunistic Routing for Ad Hoc Networks
Venue: arXiv cs.NI 2019
Year: 2019

The opportunistic routing has great advantages on improving packet delivery probability between the source node and candidate forwarding set. For improving and reducing energy consumption and network interference, in this paper, we propose an efficient and reliable transmission power control based opportunistic routing (ERTO) for wireless ad hoc networks. In ERTO, the network interference and fading, which are critical to routing performances but not taken into account in previous works, are considered during the prediction of. The, the expected energy consumption, and the relationship between transmission power and node degree are considered to optimize the optimal transmission power and forwarding node degree jointly. For improving routing effectiveness and reducing computational complexity, we introduce the multi-objective optimization and Pareto optimal into ERTO. During the routing process, nodes calculate the optimal transmission power and forwarding node degree according to the properties of the Pareto optimal solution set. Based on these innovations, the energy consumption, the transmission delay, and the throughout have been improved greatly.
Securing Big Data systems, A cybersecurity management discussion
Venue: arXiv cs.CR 2019
Year: 2019

This paper explores the essential areas of cybersecurity management for big data systems. Big data platform stems its complexity from being a collection of interrelated non-standardized systems that interact with each other to process large data-sets. This complexity increases the chances of overlooking critical cybersecurity management practices. The paper discusses various security management for each part of big data systems. This includes security measures, standards, and methodologies. The primary objective is to highlight the essential segments of security management that are expected to be implemented flawlessly in big data systems.
The performance evaluation of Multi-representation in the Deep Learning models for Relation Extraction Task
Venue: arXiv cs.CL 2019
Year: 2019

Single implementing, concatenating, adding or replacing of the representations has yielded significant improvements on many NLP tasks. Mainly in Relation Extraction where static, contextualized and others representations that are capable of explaining word meanings through the linguistic features that these incorporates. In this work addresses the question of how is improved the relation extraction using different types of representations generated by pretrained language representation models. We benchmarked our approach using popular word representation models, replacing and concatenating static, contextualized and others representations of hand-extracted features. The experiments show that representation is a crucial element to choose when DL approach is applied. Word embeddings from Flair and BERT can be well interpreted by a deep learning model for RE task, and replacing static word embeddings with contextualized word representations could lead to significant improvements. While, the hand-created representations requires is time-consuming and not is ensure a improve in combination with others representations.
Kernel-Based Ensemble Learning in Python
Venue: arXiv cs.LG 2019
Year: 2019

We propose a new supervised learning algorithm, for classification and regression problems where two or more preliminary predictors are available. We introduce \texttt{KernelCobra}, a non-linear learning strategy for combining an arbitrary number of initial predictors. \texttt{KernelCobra} builds on the COBRA algorithm introduced by \citet{biau2016cobra}, which combined estimators based on a notion of proximity of predictions on the training data. While the COBRA algorithm used a binary threshold to declare which training data were close and to be used, we generalize this idea by using a kernel to better encapsulate the proximity information. Such a smoothing kernel provides more representative weights to each of the training points which are used to build the aggregate and final predictor, and \texttt{KernelCobra} systematically outperforms the COBRA algorithm. While COBRA is intended for regression, \texttt{KernelCobra} deals with classification and regression. \texttt{KernelCobra} is included as part of the open source Python package \texttt{Pycobra} (0.2.4 and onward), introduced by \citet{guedj2018pycobra}. Numerical experiments assess the performance (in terms of pure prediction and computational complexity) of \texttt{KernelCobra} on real-life and synthetic datasets.
Garbage In, Garbage Out? Do Machine Learning Application Papers in Social Computing Report Where Human-Labeled Training Data Comes From?
Venue: arXiv cs.CY 2019
Year: 2019

Many machine learning projects for new application areas involve teams of humans who label data for a particular purpose, from hiring crowdworkers to the paper's authors labeling the data themselves. Such a task is quite similar to (or a form of) structured content analysis, which is a longstanding methodology in the social sciences and humanities, with many established best practices. In this paper, we investigate to what extent a sample of machine learning application papers in social computing --- specifically papers from ArXiv and traditional publications performing an ML classification task on Twitter data --- give specific details about whether such best practices were followed. Our team conducted multiple rounds of structured content analysis of each paper, making determinations such as: Does the paper report who the labelers were, what their qualifications were, whether they independently labeled the same items, whether inter-rater reliability metrics were disclosed, what level of training and/or instructions were given to labelers, whether compensation for crowdworkers is disclosed, and if the training data is publicly available. We find a wide divergence in whether such practices were followed and documented. Much of machine learning research and education focuses on what is done once a "gold standard" of training data is available, but we discuss issues around the equally-important aspect of whether such data is reliable in the first place.
Harzer Roller: Linker-Based Instrumentation for Enhanced Embedded Security Testing
Venue: arXiv cs.CR 2019
Year: 2019

Due to the rise of the Internet of Things, there are many new chips and platforms available for hobbyists and industry alike to build smart devices. The SDKs for these new platforms usually include closed-source binaries containing wireless protocol implementations, cryptographic implementations, or other library functions, which are shared among all user code across the platform. Leveraging such a library vulnerability has a high impact on a given platform. However, as these platforms are often shipped ready-to-use, classic debug infrastructure like JTAG is often times not available. In this paper, we present a method, called Harzer Roller, to enhance embedded firmware security testing on resource-constrained devices. With the Harzer Roller, we hook instrumentation code into function call and return. The hooking not only applies to the user application code but to the SDK used to build firmware as well. While we keep the design of the Harzer Rollergenerally architecture independent, we provide an implementation for the ESP8266 Wi-Fi IoT chip based on the xtensa architecture. We show that the Harzer Roller can be leveraged to trace execution flow through libraries without available source code and to detect stack-based buffer-overflows. Additionally, we showcase how the overflow detection can be used to dump debugging information for later analysis. This enables better usage of a variety of software security testing methods like fuzzing of wireless protocol implementations or proof-of-concept attack development.
Incremental ELMVIS for unsupervised learning
Venue: arXiv cs.LG 2019
Year: 2019

An incremental version of the ELMVIS+ method is proposed in this paper. It iteratively selects a few best fitting data samples from a large pool, and adds them to the model. The method keeps high speed of ELMVIS+ while allowing for much larger possible sample pools due to lower memory requirements. The extension is useful for reaching a better local optimum with greedy optimization of ELMVIS, and the data structure can be specified in semi-supervised optimization. The major new application of incremental ELMVIS is not to visualization, but to a general dataset processing. The method is capable of learning dependencies from non-organized unsupervised data -- either reconstructing a shuffled dataset, or learning dependencies in complex high-dimensional space. The results are interesting and promising, although there is space for improvements.
Coupled Network for Robust Pedestrian Detection with Gated Multi-Layer Feature Extraction and Deformable Occlusion Handling
Venue: arXiv cs.CV 2019
Year: 2019

Pedestrian detection methods have been significantly improved with the development of deep convolutional neural networks. Nevertheless, detecting small-scaled pedestrians and occluded pedestrians remains a challenging problem. In this paper, we propose a pedestrian detection method with a couple-network to simultaneously address these two issues. One of the sub-networks, the gated multi-layer feature extraction sub-network, aims to adaptively generate discriminative features for pedestrian candidates in order to robustly detect pedestrians with large variations on scales. The second sub-network targets in handling the occlusion problem of pedestrian detection by using deformable regional RoI-pooling. We investigate two different gate units for the gated sub-network, namely, the channel-wise gate unit and the spatio-wise gate unit, which can enhance the representation ability of the regional convolutional features among the channel dimensions or across the spatial domain, repetitively. Ablation studies have validated the effectiveness of both the proposed gated multi-layer feature extraction sub-network and the deformable occlusion handling sub-network. With the coupled framework, our proposed pedestrian detector achieves state-of-the-art results on the Caltech and the CityPersons pedestrian detection benchmarks.
Dynamic Toolbox for ETRINV
Venue: arXiv cs.CC 2019
Year: 2019

Recently, various natural algorithmic problems have been shown to be $\exists \mathbb{R}$-complete. The reduction relied in many cases on the $\exists \mathbb{R}$-completeness of the problem ETR-INV, which served as a useful intermediate problem. Often some strengthening and modification of ETR-INV was required. This lead to a cluttered situation where no paper included all the previous details. Here, we give a streamlined exposition in a self-contained manner. We also explain and prove various universality results regarding ETR-INV. These notes should not be seen as a research paper with new results. However, we do describe some refinements of earlier results which might be useful for future research. We plan to extend and update this exposition as seems fit.
Data Services with Bindaas: RESTful Interfaces for Diverse Data Sources
Venue: arXiv cs.DB 2019
Year: 2019

The diversity of data management systems affords developers the luxury of building systems with heterogeneous systems that address needs that are unique to the data. It allows one to mix-n-match systems that can store, query, update, and process data, based on specific use cases. However, this heterogeneity brings with it the burden of developing custom interfaces for each data management system. Developers are required to build high-performance APIs for data access while adopting best-practices governing security, data privacy, and access control. These include user authentication, data authorization, role-based access control, and audit mechanisms to avoid compromising the security standards mandated by data providers. In this paper, we present Bindaas, a secure, extensible big data middleware that offers uniform access to diverse data sources. By providing a standard RESTful web service interface to the data sources, Bindaas exposes query, update, store, and delete functionality of the data sources as data service APIs, while providing turn-key support for standard operations involving security, access control, and audit-trails. Bindaas consists of optional features, such as query and response modifiers as well as plugins that implement composable and reusable data operations on the data. The research community has deployed Bindaas in various production environments in healthcare. Our evaluations highlight the efficiency of Bindaas in serving concurrent requests to data source instances. We further observe that the overheads caused by Bindaas on the data sources are negligible.
Bridging the Gap between Community and Node Representations: Graph Embedding via Community Detection
Venue: arXiv cs.SI 2019
Year: 2019

Graph embedding has become a key component of many data mining and analysis systems. Current graph embedding approaches either sample a large number of node pairs from a graph to learn node embeddings via stochastic optimization or factorize a high-order proximity/adjacency matrix of the graph via computationally expensive matrix factorization techniques. These approaches typically require significant resources for the learning process and rely on multiple parameters, which limits their applicability in practice. Moreover, most of the existing graph embedding techniques operate effectively in one specific metric space only (e.g., the one produced with cosine similarity), do not preserve higher-order structural features of the input graph and cannot automatically determine a meaningful number of embedding dimensions. Typically, the produced embeddings are not easily interpretable, which complicates further analyses and limits their applicability. To address these issues, we propose DAOR, a highly efficient and parameter-free graph embedding technique producing metric space-robust, compact and interpretable embeddings without any manual tuning. Compared to a dozen state-of-the-art graph embedding algorithms, DAOR yields competitive results on both node classification (which benefits form high-order proximity) and link prediction (which relies on low-order proximity mostly). Unlike existing techniques, however, DAOR does not require any parameter tuning and improves the embeddings generation speed by several orders of magnitude. Our approach has hence the ambition to greatly simplify and speed up data analysis tasks involving graph representation learning.
Surface HOF: Surface Reconstruction from a Single Image Using Higher Order Function Networks
Venue: arXiv cs.CV 2019
Year: 2019

We address the problem of generating a high-resolution surface reconstruction from a single image. Our approach is to learn a Higher Order Function (HOF) which takes an image of an object as input and generates a mapping function. The mapping function takes samples from a canonical domain (e.g. the unit sphere) and maps each sample to a local tangent plane on the 3D reconstruction of the object. Each tangent plane is represented as an origin point and a normal vector at that point. By efficiently learning a continuous mapping function, the surface can be generated at arbitrary resolution in contrast to other methods which generate fixed resolution outputs. We present the Surface HOF in which both the higher order function and the mapping function are represented as neural networks, and train the networks to generate reconstructions of PointNet objects. Experiments show that Surface HOF is more accurate and uses more efficient representations than other state of the art methods for surface reconstruction. Surface HOF is also easier to train: it requires minimal input pre-processing and output post-processing and generates surface representations that are more parameter efficient. Its accuracy and convenience make Surface HOF an appealing method for single image reconstruction.
The Spectral Bias of the Deep Image Prior
Venue: arXiv cs.CV 2019
Year: 2019

The "deep image prior" proposed by Ulyanov et al. is an intriguing property of neural nets: a convolutional encoder-decoder network can be used as a prior for natural images. The network architecture implicitly introduces a bias; If we train the model to map white noise to a corrupted image, this bias guides the model to fit the true image before fitting the corrupted regions. This paper explores why the deep image prior helps in denoising natural images. We present a novel method to analyze trajectories generated by the deep image prior optimization and demonstrate: (i) convolution layers of the an encoder-decoder decouple the frequency components of the image, learning each at different rates (ii) the model fits lower frequencies first, making early stopping behave as a low pass filter. The experiments study an extension of Cheng et al which showed that at initialization, the deep image prior is equivalent to a stationary Gaussian process.
Subgraph Classification, Clustering and Centrality for a Degree Asymmetric Twitter Based Graph Case Study: Suicidality
Venue: arXiv cs.SI 2019
Year: 2019

We present some initial results from a case study in social media data harvesting and visualization utilizing the tools and analytical features of NodeXL applied to a degree asymmetric vertex graph set. We consider twitter graphs harvested for topics related to suicidal ideation, suicide attempts, self-harm and bullycide. While the twitter-sphere only captures a small and age biased sample of communications it is a readily available public database for a wealth of rich topics yielding a large sample set. All these topics gave rise to highly asymmetric vertex degree graphs and all shared the same general topological features. We find a strong preference for in degree vertex information transfer with a 4:25 out degree to in degree vertex ratio with a power law distribution. Overall there is a low global clustering coefficient average of 0.038 and a graph clustering density of 0.00034 for Clauset-Newman-Moore grouping with a maximum geodesic distance of 6. Eigenvector centrality does not give any large central impact vertices and betweenness centrality shows many bridging vertices indicating a sparse community structure. Parts of speech sentiment scores show a strong asymmetry of predominant negative scores for almost all word and word pairs with salience greater than one. We used an Hoaxy analysis to check for deliberate misinformation on these topics by a Twitter-Bot.
Architectural Invariants and Correctness of IoT-based Systems
Venue: arXiv cs.SE 2019
Year: 2019

Internet of Things applications impact more and more industrial areas such as smart manufacturing, smart health monitoring and home automation; physical objects or devices equipped with sensors and actuators are interconnected and then controlled with software applications. Ensuring the correct construction, the well-functioning and the reliability of these applications constitute important issues for some of these applications which can be critical in case of dysfunction. We propose on the basis of the formal model of their common architectural properties, a generic framework for the formal modelling of IoT-based applications, the rigorous analysis of their consistency properties, their rigorous construction and evolution. Specific properties can be gradually added and checked. The proposed framework is then implemented and experimented using Event-B. We exploit the observation that the main requirements of the IoT-based physical architectures and control software are common to all IoT-based applications; this leaded us to the definition of the generic formal model together with invariant architectural and consistency properties. The proposed approach is generic, extensible, and can be profitably adapted to more general hybrid or cyber-physical systems. Our current implementation is independent of the formal model, it can be achieved in various other formal analysis environments.
Classification des S{é}ries Temporelles Incertaines par Transformation Shapelet
Venue: arXiv cs.LG 2019
Year: 2019

Time serie classification is used in a diverse range of domain such as meteorology, medicine and physics. It aims to classify chronological data. Many accurate approaches have been built during the last decade and shapelet transformation is one of them. However, none of these approaches does take data uncertainty into account. Using uncertainty propagation techiniques, we propose a new dissimilarity measure based on euclidean distance. We also show how to use this new measure to adapt shapelet transformation to uncertain time series classification. An experimental assessment of our contribution is done on some state of the art datasets.
Graphlets in Multiplex Networks
Venue: arXiv cs.SI 2019
Year: 2019

We develop graphlet analysis for multiplex networks and discuss how this analysis can be extended to multilayer and multilevel networks as well as to graphs with node and/or link categorical attributes. The analysis has been adapted for two typical examples of multiplexes: economic trade data represented as a 957-plex network and 75 social networks each represented as a 12-plex network. We show that wedges (open triads) occur more often in economic trade networks than in social networks, indicating the tendency of a country to produce/trade of a product in local structure of triads which are not closed. Moreover, our analysis provides evidence that the countries with small diversity tend to form correlated triangles. Wedges also appear in the social networks, however the dominant graphlets in social networks are triangles (closed triads). If a multiplex structure indicates a strong tie, the graphlet analysis provides another evidence for the concepts of strong/weak ties and structural holes. In contrast to Granovetter's seminal work on the strength of weak ties, in which it has been documented that the wedges with only strong ties are absent, here we show that for the analyzed 75 social networks, the wedges with only strong ties are not only present but also significantly correlated.