OpenRaiser/Intern-Atlas huggingface dataset:
Primary Tumor Origin Classification of Lung Nodules in Spectral CT using Transfer Learning
Venue: arXiv cs.CV 2020
Year: 2020

Early detection of lung cancer has been proven to decrease mortality significantly. A recent development in computed tomography (CT), spectral CT, can potentially improve diagnostic accuracy, as it yields more information per scan than regular CT. However, the shear workload involved with analyzing a large number of scans drives the need for automated diagnosis methods. Therefore, we propose a detection and classification system for lung nodules in CT scans. Furthermore, we want to observe whether spectral images can increase classifier performance. For the detection of nodules we trained a VGG-like 3D convolutional neural net (CNN). To obtain a primary tumor classifier for our dataset we pre-trained a 3D CNN with similar architecture on nodule malignancies of a large publicly available dataset, the LIDC-IDRI dataset. Subsequently we used this pre-trained network as feature extractor for the nodules in our dataset. The resulting feature vectors were classified into two (benign/malignant) and three (benign/primary lung cancer/metastases) classes using support vector machine (SVM). This classification was performed both on nodule- and scan-level. We obtained state-of-the art performance for detection and malignancy regression on the LIDC-IDRI database. Classification performance on our own dataset was higher for scan- than for nodule-level predictions. For the three-class scan-level classification we obtained an accuracy of 78\%. Spectral features did increase classifier performance, but not significantly. Our work suggests that a pre-trained feature extractor can be used as primary tumor origin classifier for lung nodules, eliminating the need for elaborate fine-tuning of a new network and large datasets. Code is available at \url{https://github.com/tueimage/lung-nodule-msc-2018}.
Classification Confidence Estimation with Test-Time Data-Augmentation
Venue: arXiv cs.CV 2020
Year: 2020

Machine learning plays an increasingly significant role in many aspects of our lives (including medicine, transportation, security, justice and other domains), making the potential consequences of false predictions increasingly devastating. These consequences may be mitigated if we can automatically flag such false predictions and potentially assign them to alternative, more reliable mechanisms, that are possibly more costly and involve human attention. This suggests the task of detecting errors, which we tackle in this paper for the case of visual classification. To this end, we propose a novel approach for classification confidence estimation. We apply a set of semantics-preserving image transformations to the input image, and show how the resulting image sets can be used to estimate confidence in the classifier's prediction. We demonstrate the potential of our approach by extensively evaluating it on a wide variety of classifier architectures and datasets, including ResNext/ImageNet, achieving state of the art performance. This paper constitutes a significant revision of our earlier work in this direction (Bahat & Shakhnarovich, 2018).
Correction of Faulty Background Knowledge based on Condition Aware and Revise Transformer for Question Answering
Venue: arXiv cs.CL 2020
Year: 2020

The study of question answering has received increasing attention in recent years. This work focuses on providing an answer that compatible with both user intent and conditioning information corresponding to the question, such as delivery status and stock information in e-commerce. However, these conditions may be wrong or incomplete in real-world applications. Although existing question answering systems have considered the external information, such as categorical attributes and triples in knowledge base, they all assume that the external information is correct and complete. To alleviate the effect of defective condition values, this paper proposes condition aware and revise Transformer (CAR-Transformer). CAR-Transformer (1) revises each condition value based on the whole conversation and original conditions values, and (2) it encodes the revised conditions and utilizes the conditions embedding to select an answer. Experimental results on a real-world customer service dataset demonstrate that the CAR-Transformer can still select an appropriate reply when conditions corresponding to the question exist wrong or missing values, and substantially outperforms baseline models on automatic and human evaluations. The proposed CAR-Transformer can be extended to other NLP tasks which need to consider conditioning information.
Linear transformations between dominating sets in the TAR-model
Venue: arXiv cs.DM 2020
Year: 2020

Given a graph $G$ and an integer $k$, a token addition and removal ({\sf TAR} for short) reconfiguration sequence between two dominating sets $D_{\sf s}$ and $D_{\sf t}$ of size at most $k$ is a sequence $S= \langle D_0 = D_{\sf s}, D_1 \ldots, D_\ell = D_{\sf t} \rangle$ of dominating sets of $G$ such that any two consecutive dominating sets differ by the addition or deletion of one vertex, and no dominating set has size bigger than $k$. We first improve a result of Haas and Seyffarth, by showing that if $k=Γ(G)+α(G)-1$ (where $Γ(G)$ is the maximum size of a minimal dominating set and $α(G)$ the maximum size of an independent set), then there exists a linear {\sf TAR} reconfiguration sequence between any pair of dominating sets. We then improve these results on several graph classes by showing that the same holds for $K_{\ell}$-minor free graph as long as $k \ge Γ(G)+O(\ell \sqrt{\log \ell})$ and for planar graphs whenever $k \ge Γ(G)+3$. Finally, we show that if $k=Γ(G)+tw(G)+1$, then there also exists a linear transformation between any pair of dominating sets.
Concave Aspects of Submodular Functions
Venue: arXiv cs.DM 2020
Year: 2020

Submodular Functions are a special class of set functions, which generalize several information-theoretic quantities such as entropy and mutual information [1]. Submodular functions have subgradients and subdifferentials [2] and admit polynomial-time algorithms for minimization, both of which are fundamental characteristics of convex functions. Submodular functions also show signs similar to concavity. Submodular function maximization, though NP-hard, admits constant-factor approximation guarantees, and concave functions composed with modular functions are submodular. In this paper, we try to provide a more complete picture of the relationship between submodularity with concavity. We characterize the super-differentials and polyhedra associated with upper bounds and provide optimality conditions for submodular maximization using the-super differentials. This paper is a concise and shorter version of our longer preprint [3].
Can Your Face Detector Do Anti-spoofing? Face Presentation Attack Detection with a Multi-Channel Face Detector
Venue: arXiv cs.CV 2020
Year: 2020

In a typical face recognition pipeline, the task of the face detector is to localize the face region. However, the face detector localizes regions that look like a face, irrespective of the liveliness of the face, which makes the entire system susceptible to presentation attacks. In this work, we try to reformulate the task of the face detector to detect real faces, thus eliminating the threat of presentation attacks. While this task could be challenging with visible spectrum images alone, we leverage the multi-channel information available from off the shelf devices (such as color, depth, and infrared channels) to design a multi-channel face detector. The proposed system can be used as a live-face detector obviating the need for a separate presentation attack detection module, making the system reliable in practice without any additional computational overhead. The main idea is to leverage a single-stage object detection framework, with a joint representation obtained from different channels for the PAD task. We have evaluated our approach in the multi-channel WMCA dataset containing a wide variety of attacks to show the effectiveness of the proposed framework.
I call BS: Fraud Detection in Crowdfunding Campaigns
Venue: arXiv cs.CY 2020
Year: 2020

Donations to charity-based crowdfunding environments have been on the rise in the last few years. Unsurprisingly, deception and fraud in such platforms have also increased, but have not been thoroughly studied to understand what characteristics can expose such behavior and allow its automatic detection and blocking. Indeed, crowdfunding platforms are the only ones typically performing oversight for the campaigns launched in each service. However, they are not properly incentivized to combat fraud among users and the campaigns they launch: on the one hand, a platform's revenue is directly proportional to the number of transactions performed (since the platform charges a fixed amount per donation); on the other hand, if a platform is transparent with respect to how much fraud it has, it may discourage potential donors from participating. In this paper, we take the first step in studying fraud in crowdfunding campaigns. We analyze data collected from different crowdfunding platforms, and annotate 700 campaigns as fraud or not. We compute various textual and image-based features and study their distributions and how they associate with campaign fraud. Using these attributes, we build machine learning classifiers, and show that it is possible to automatically classify such fraudulent behavior with up to 90.14% accuracy and 96.01% AUC, only using features available from the campaign's description at the moment of publication (i.e., with no user or money activity), making our method applicable for real-time operation on a user browser.
A Chain Graph Interpretation of Real-World Neural Networks
Venue: arXiv cs.LG 2020
Year: 2020

The last decade has witnessed a boom of deep learning research and applications achieving state-of-the-art results in various domains. However, most advances have been established empirically, and their theoretical analysis remains lacking. One major issue is that our current interpretation of neural networks (NNs) as function approximators is too generic to support in-depth analysis. In this paper, we remedy this by proposing an alternative interpretation that identifies NNs as chain graphs (CGs) and feed-forward as an approximate inference procedure. The CG interpretation specifies the nature of each NN component within the rich theoretical framework of probabilistic graphical models, while at the same time remains general enough to cover real-world NNs with arbitrary depth, multi-branching and varied activations, as well as common structures including convolution / recurrent layers, residual block and dropout. We demonstrate with concrete examples that the CG interpretation can provide novel theoretical support and insights for various NN techniques, as well as derive new deep learning approaches such as the concept of partially collapsed feed-forward inference. It is thus a promising framework that deepens our understanding of neural networks and provides a coherent theoretical formulation for future deep learning research.
How to Document Computer Networks
Venue: arXiv cs.NI 2020
Year: 2020

Documenting networks is an essential tool for troubleshooting network problems. The documentation details a network's structure and context, serves as a reference and makes network management more effective. Complex network diagrams are hard to document and maintain and are not guaranteed to reflect reality. They contain many superficial icons (e.g., wall, screen and tower). Defining a single coherent network architecture and topology, similar to engineering schematics, has received great interest. We propose a fundamental approach for methodically specifying a network architecture using a diagramming method to conceptualize the network s structure. The method is called a thinging (abstract) machine, through which the network world is viewed as a single unifying element called the thing/machine (thimac), providing the ontology for modeling the network. To test its viability, the thinging-machine-based methodology was applied to an existing computer network to produce a single integrated, diagrammatic representation that incorporates communication, software and hardware. The resultant description shows a viable, coherent depiction that can replace the current methods.
Makespan minimization of Time-Triggered traffic on a TTEthernet network
Venue: arXiv cs.NI 2020
Year: 2020

The reliability of the increasing number of modern applications and systems strongly depends on interconnecting technology. Complex systems which usually need to exchange, among other things, multimedia data together with safety-related information, as in the automotive or avionic industry, for example, make demands on both the high bandwidth and the deterministic behavior of the communication. TTEthernet is a protocol that has been developed to face these requirements while providing the generous bandwidth of Ethernet up to 1\,Gbit/s and enhancing its determinism by the Time-Triggered message transmission which follows the predetermined schedule. Therefore, synthesizing a good schedule which meets all the real-time requirements is essential for the performance of the whole system. In this paper, we study the concept of creating the communication schedules for the Time-Triggered traffic while minimizing its makespan. The aim is to maximize the uninterrupted gap for remaining traffic classes in each integration cycle. The provided scheduling algorithm, based on the Resource-Constrained Project Scheduling Problem formulation and the load balancing heuristic, obtains near-optimal (within 15\% of non-tight lower bound) solutions in 5 minutes even for industrial sized instances. The universality of the provided method allows easily modify or extend the problem statement according to particular industrial demands. Finally, the studied concept of makespan minimization is justified through the concept of scheduling with porosity according to the worst-case delay analysis of Event-Triggered traffic.
Applying Machine Learning Techniques for Caching in Edge Networks: A Comprehensive Survey
Venue: arXiv cs.NI 2020
Year: 2020

Edge networking is a complex and dynamic computing paradigm that aims to push cloud resources closer to the end user improving responsiveness and reducing backhaul traffic. User mobility, preferences, and content popularity are the dominant dynamic features of edge networks. Temporal and social features of content, such as the number of views and likes are leveraged to estimate the popularity of content from a global perspective. However, such estimates should not be mapped to an edge network with particular social and geographic characteristics. In next generation edge networks, i.e., 5G and beyond 5G, machine learning techniques can be applied to predict content popularity based on user preferences, cluster users based on similar content interests, and optimize cache placement and replacement strategies provided a set of constraints and predictions about the state of the network. These applications of machine learning can help identify relevant content for an edge network. This article investigates the application of machine learning techniques for in-network caching in edge networks. We survey recent state-of-the-art literature and formulate a comprehensive taxonomy based on (a) machine learning technique (method, objective, and features), (b) caching strategy (policy, location, and replacement), and (c) edge network (type and delivery strategy). A comparative analysis of the state-of-the-art literature is presented with respect to the parameters identified in the taxonomy. Moreover, we debate research challenges and future directions for optimal caching decisions and the application of machine learning in edge networks.
Predicting Sample Collision with Neural Networks
Venue: arXiv cs.RO 2020
Year: 2020

Many state-of-art robotics applications require fast and efficient motion planning algorithms. Existing motion planning methods become less effective as the dimensionality of the robot and its workspace increases, especially the computational cost of collision detection routines. In this work, we present a framework to address the cost of expensive primitive operations in sampling-based motion planning. This framework determines the validity of a sample robot configuration through a novel combination of a Contractive AutoEncoder (CAE), which captures a occupancy grids representation of the robot's workspace, and a Multilayer Perceptron, which efficiently predicts the collision state of the robot from the CAE and the robot's configuration. We evaluate our framework on multiple planning problems with a variety of robots in 2D and 3D workspaces. The results show that (1) the framework is computationally efficient in all investigated problems, and (2) the framework generalizes well to new workspaces.
On Finite Entailment of Non-Local Queries in Description Logics
Venue: arXiv cs.AI 2020
Year: 2020

We study the problem of finite entailment of ontology-mediated queries. Going beyond local queries, we allow transitive closure over roles. We focus on ontologies formulated in the description logics ALCOI and ALCOQ, extended with transitive closure. For both logics, we show 2EXPTIME upper bounds for finite entailment of unions of conjunctive queries with transitive closure. We also provide a matching lower bound by showing that finite entailment of conjunctive queries with transitive closure in ALC is 2EXPTIME-hard.
From Simple Features to Moving Features and Beyond?
Venue: arXiv cs.CY 2020
Year: 2020

Mobility data science lacks common data structures and analytical functions. This position paper assesses the current status and open issues towards a universal API for mobility data science. In particular, we look at standardization efforts revolving around the OGC Moving Features standard which, so far, has not attracted much attention within the mobility data science community. We discuss the hurdles any universal API for movement data has to overcome and propose key steps of a roadmap that would provide the foundation for the development of this API.
Ontology-guided Semantic Composition for Zero-Shot Learning
Venue: arXiv cs.AI 2020
Year: 2020

Zero-shot learning (ZSL) is a popular research problem that aims at predicting for those classes that have never appeared in the training stage by utilizing the inter-class relationship with some side information. In this study, we propose to model the compositional and expressive semantics of class labels by an OWL (Web Ontology Language) ontology, and further develop a new ZSL framework with ontology embedding. The effectiveness has been verified by some primary experiments on animal image classification and visual question answering.
Interdependence in active mobility adoption: Joint modelling and motivational spill-over in walking, cycling and bike-sharing
Venue: arXiv cs.CY 2020
Year: 2020

Active mobility offers an array of physical, emotional, and social wellbeing benefits. However, with the proliferation of the sharing economy, new nonmotorized means of transport are entering the fold, complementing some existing mobility options while competing with others. The purpose of this research study is to investigate the adoption of three active travel modes; namely walking, cycling and bikesharing, in a joint modeling framework. The analysis is based on an adaptation of the stages of change framework, which originates from the health behavior sciences. Multivariate ordered probit modeling drawing on U.S. survey data provides well-needed insights into individuals preparedness to adopt multiple active modes as a function of personal, neighborhood and psychosocial factors. The research suggests three important findings. 1) The joint model structure confirms interdependence among different active mobility choices. The strongest complementarity is found for walking and cycling adoption. 2) Each mode has a distinctive adoption path with either three or four separate stages. We discuss the implications of derived stage-thresholds and plot adoption contours for selected scenarios. 3) Psychological and neighborhood variables generate more coupling among active modes than individual and household factors. Specifically, identifying strongly with active mobility aspirations, experiences with multimodal travel, possessing better navigational skills, along with supportive local community norms are the factors that appear to drive the joint adoption decisions. This study contributes to the understanding of how decisions within the same functional domain are related and help to design policies that promote active mobility by identifying positive spillovers and joint determinants.
Tomographic Auto-Encoder: Unsupervised Bayesian Recovery of Corrupted Data
Venue: arXiv cs.LG 2020
Year: 2020

We propose a new probabilistic method for unsupervised recovery of corrupted data. Given a large ensemble of degraded samples, our method recovers accurate posteriors of clean values, allowing the exploration of the manifold of possible reconstructed data and hence characterising the underlying uncertainty. In this setting, direct application of classical variational methods often gives rise to collapsed densities that do not adequately explore the solution space. Instead, we derive our novel reduced entropy condition approximate inference method that results in rich posteriors. We test our model in a data recovery task under the common setting of missing values and noise, demonstrating superior performance to existing variational methods for imputation and de-noising with different real data sets. We further show higher classification accuracy after imputation, proving the advantage of propagating uncertainty to downstream tasks with our model.
Mining Documentation to Extract Hyperparameter Schemas
Venue: arXiv cs.LG 2020
Year: 2020

AI automation tools need machine-readable hyperparameter schemas to define their search spaces. At the same time, AI libraries often come with good human-readable documentation. While such documentation contains most of the necessary information, it is unfortunately not ready to consume by tools. This paper describes how to automatically mine Python docstrings in AI libraries to extract JSON Schemas for their hyperparameters. We evaluate our approach on 119 transformers and estimators from three different libraries and find that it is effective at extracting machine-readable schemas. Our vision is to reduce the burden to manually create and maintain such schemas for AI automation tools and broaden the reach of automation to larger libraries and richer schemas.
QPSO-CD: Quantum-behaved Particle Swarm Optimization Algorithm with Cauchy Distribution
Venue: arXiv cs.NE 2020
Year: 2020

Motivated by particle swarm optimization (PSO) and quantum computing theory, we have presented a quantum variant of PSO (QPSO) mutated with Cauchy operator and natural selection mechanism (QPSO-CD) from evolutionary computations. The performance of proposed hybrid quantum-behaved particle swarm optimization with Cauchy distribution (QPSO-CD) is investigated and compared with its counterparts based on a set of benchmark problems. Moreover, QPSO-CD is employed in well-studied constrained engineering problems to investigate its applicability. Further, the correctness and time complexity of QPSO-CD are analysed and compared with the classical PSO. It has been proven that QPSO-CD handles such real-life problems efficiently and can attain superior solutions in most of the problems. The experimental results showed that QPSO associated with Cauchy distribution and natural selection strategy outperforms other variants in the context of stability and convergence.