OpenRaiser/Intern-Atlas huggingface dataset:
Convex hull algorithms based on some variational models
Venue: arXiv cs.CV 2019
Year: 2019

Seeking the convex hull of an object is a very fundamental problem arising from various tasks. In this work, we propose two variational convex hull models using level set representation for 2-dimensional data. The first one is an exact model, which can get the convex hull of one or multiple objects. In this model, the convex hull is characterized by the zero sublevel-set of a convex level set function, which is non-positive at every given point. By minimizing the area of the zero sublevel-set, we can find the desired convex hull. The second one is intended to get convex hull of objects with outliers. Instead of requiring all the given points are included, this model penalizes the distance from each given point to the zero sublevel-set. Literature methods are not able to handle outliers. For the solution of these models, we develop efficient numerical schemes using alternating direction method of multipliers. Numerical examples are given to demonstrate the advantages of the proposed methods.
Enhancing Flood Impact Analysis using Interactive Retrieval of Social Media Images
Venue: arXiv cs.IR 2019
Year: 2019

The analysis of natural disasters such as floods in a timely manner often suffers from limited data due to a coarse distribution of sensors or sensor failures. This limitation could be alleviated by leveraging information contained in images of the event posted on social media platforms, so-called "Volunteered Geographic Information (VGI)". To save the analyst from the need to inspect all images posted online manually, we propose to use content-based image retrieval with the possibility of relevance feedback for retrieving only relevant images of the event to be analyzed. To evaluate this approach, we introduce a new dataset of 3,710 flood images, annotated by domain experts regarding their relevance with respect to three tasks (determining the flooded area, inundation depth, water pollution). We compare several image features and relevance feedback methods on that dataset, mixed with 97,085 distractor images, and are able to improve the precision among the top 100 retrieval results from 55% with the baseline retrieval to 87% after 5 rounds of feedback.
Chemical Reactions-Based Microfluidic Transmitter and Receiver for Molecular Communication
Venue: arXiv cs.ET 2019
Year: 2019

The design of communication systems capable of processing and exchanging information through molecules and chemical processes is a rapidly growing interdisciplinary field, which holds the promise to revolutionize how we realize computing and communication devices. While molecular communication (MC) theory has had major developments in recent years, more practical aspects in designing components capable of MC functionalities remain less explored. Motivated by this, we design a microfluidic MC system with a microfluidic MC transmitter and a microfluidic MC receiver based on chemical reactions. Considering existing MC literature on information transmission via molecular pulse modulation, the proposed microfluidic MC transmitter is capable of generating continuously predefined pulse-shaped molecular concentrations upon rectangular triggering signals using chemical reactions inspired by how cells generate pulse-shaped molecular signals in biology. We further design a microfluidic MC receiver capable of demodulating a received signal to a rectangular output signal using a thresholding reaction and an amplifying reaction. Our chemical reactions-based microfluidic molecular communication system is reproducible and well-designed, and more importantly, it overcomes the slow-speed, unreliability, and non-scalability of biological processes in cells. To reveal design insights, we also derive the theoretical signal responses for our designed microfluidic transmitter and receiver, which further facilitate the transmitter design optimization. Our theoretical results are validated via simulations performed through the COMSOL Multiphysics finite element solver. We demonstrate the predefined nature of the generated pulse and the demodulated rectangular signal together with their dependence on design parameters.
Learn to Allocate Resources in Vehicular Networks
Venue: arXiv cs.NI 2019
Year: 2019

Resource allocation has a direct and profound impact on the performance of vehicle-to-everything (V2X) networks. Considering the dynamic nature of vehicular environments, it is appealing to devise a decentralized strategy to perform effective resource sharing. In this paper, we exploit deep learning to promote coordination among multiple vehicles and propose a hybrid architecture consisting of centralized decision making and distributed resource sharing to maximize the long-term sum rate of all vehicles. To reduce the network signaling overhead, each vehicle uses a deep neural network to compress its own observed information that is thereafter fed back to the centralized decision-making unit, which employs a deep Q-network to allocate resources and then sends the decision results to all vehicles. We further adopt a quantization layer for each vehicle that learns to quantize the continuous feedback. Extensive simulation results demonstrate that the proposed hybrid architecture can achieve near-optimal performance. Meanwhile, there exists an optimal number of continuous feedback and binary feedback, respectively. Besides, this architecture is robust to different feedback intervals, input noise, and feedback noise.
A Simple Recommender Engine for Matching Final-Year Project Student with Supervisor
Venue: arXiv cs.IR 2019
Year: 2019

This paper discusses a simple recommender engine, which can match final year project student based on their interests with potential supervisors. The recommender engine is constructed based on Euclidean distance algorithm. The initial input data for the recommender system is obtained by distributing questionnaire to final year students and recording their response in CSV format. The recommender engine is implemented using Java class and application, and result of the initial tests has shown promises that the project is feasible to be pursued as it has the potential of solving the problem of final year students in finding their potential supervisors.
Real-Time Elderly Healthcare Monitoring Expert System Using Wireless Sensor Network
Venue: arXiv cs.HC 2019
Year: 2019

Elderly chronic diseases are the main cause of death in the world, accounting 60% of all death. Because elderly with chronic diseases at the early stages has no observed symptoms, and then symptoms starts to appear, it is critical to observe the symptoms as early as possible to avoid any complication. This paper presents an expert system for an Elderly Health Care (EHC) at elderly home tailored for the specific needs of Elderly. The proposed EHC aims to develop an integrated and multidisciplinary method to employ communication technologies and information for covering real health needs of elderly people, mainly of people at high risk due to social and geographic isolation in addition to specific chronic diseases. The proposed EHC provides personalized intervention plans covering chronic diseases such as (body temperature (BT), blood pressure (BP), and Heart beat rate (HR)). The processes and architecture of the proposed EHC are based on the server side and three main clients, one for the elderly and another two for the nurse and the physicians whom take care of them. The proposed EHC model is discussed for proving the usefulness and effectiveness of the expert system.
A Distraction Score for Watermarks
Venue: arXiv cs.CV 2019
Year: 2019

In this work we propose a novel technique to quantify how distracting watermarks are on an image. We begin with watermark detection using a two-tower CNN model composed of a binary classification task and a semantic segmentation prediction. With this model, we demonstrate significant improvement in image precision while maintaining per-pixel accuracy, especially for our real-world dataset with sparse positive examples. We fit a nonlinear function to represent detected watermarks by a single score correlated with human perception based on their size, location, and visual obstructiveness. Finally, we validate our method in an image ranking setup, which is the main application of our watermark scoring algorithm.
Natural-Logarithm-Rectified Activation Function in Convolutional Neural Networks
Venue: arXiv cs.LG 2019
Year: 2019

Activation functions play a key role in providing remarkable performance in deep neural networks, and the rectified linear unit (ReLU) is one of the most widely used activation functions. Various new activation functions and improvements on ReLU have been proposed, but each carry performance drawbacks. In this paper, we propose an improved activation function, which we name the natural-logarithm-rectified linear unit (NLReLU). This activation function uses the parametric natural logarithmic transform to improve ReLU and is simply defined as. NLReLU not only retains the sparse activation characteristic of ReLU, but it also alleviates the "dying ReLU" and vanishing gradient problems to some extent. It also reduces the bias shift effect and heteroscedasticity of neuron data distributions among network layers in order to accelerate the learning process. The proposed method was verified across ten convolutional neural networks with different depths for two essential datasets. Experiments illustrate that convolutional neural networks with NLReLU exhibit higher accuracy than those with ReLU, and that NLReLU is comparable to other well-known activation functions. NLReLU provides 0.16% and 2.04% higher classification accuracy on average compared to ReLU when used in shallow convolutional neural networks with the MNIST and CIFAR-10 datasets, respectively. The average accuracy of deep convolutional neural networks with NLReLU is 1.35% higher on average with the CIFAR-10 dataset.
Differentially Private Aggregated Mobility Data Publication Using Moving Characteristics
Venue: arXiv cs.CR 2019
Year: 2019

With the rapid development of GPS enabled devices (smartphones) and location-based applications, location privacy is increasingly concerned. Intuitively, it is widely believed that location privacy can be preserved by publishing aggregated mobility data, such as the number of users in an area at some time. However, a recent attack shows that these aggregated mobility data can be exploited to recover individual trajectories. In this paper, we first propose two differentially private basic schemes for aggregated mobility data publication, namely direct perturbation and threshold perturbation, which preserve location privacy of users and especially resist the trajectory recovery attack. Then, we explore the moving characteristics of mobile users, and design an improved scheme named static hybrid perturbation by combining the two basic schemes according to the moving characteristics. Since static hybrid perturbation works only for static data, which are entirely available before publishing, we further adapt the static hybrid perturbation by combining it with linear regression, and yield another improved scheme named dynamic hybrid perturbation. The dynamic hybrid perturbation works also for dynamic data, which are generated on the fly during publication. Privacy analysis shows that the proposed schemes achieve differential privacy. Extensive experiments on both simulated and real datasets demonstrate that all proposed schemes resist the trajectory recovery attack well, and the improved schemes significantly outperform the basic schemes.
Efficient Three-party Computation: An Information-theoretic Approach from Cut-and-Choose
Venue: arXiv cs.CR 2019
Year: 2019

As far as we know, the literature on secure computation from cut-and-choose has focused on achieving computational security against malicious adversaries. It is unclear whether the idea of cut-and-choose can be adapted to secure computation with information-theoretic security. In this work we explore the possibility of using cut-and-choose in information theoretic setting for secure three-party computation (3PC). Previous work on 3PC has mainly focus on the semi-honest case, and is motivated by the observation that real-word deployments of multi-party computation (MPC) seem to involve few parties. We propose a new protocol for information-theoretically secure 3PC tolerating one malicious party with cheating probability $2^{-s}$ using $s$ runs of circuit computation in the cut-and-choose paradigm. The computational cost of our protocol is essentially only a small constant worse than that of state-of-the-art 3PC protocols against a semi-honest corruption, while its communication round is greatly reduced compared to other maliciously secure 3PC protocols in information-theoretic setting.
Avoidable paths in graphs
Venue: arXiv cs.DM 2019
Year: 2019

We prove a recent conjecture of Beisegel et al. that for every positive integer k, every graph containing an induced P_k also contains an avoidable P_k. Avoidability generalises the notion of simpliciality best known in the context of chordal graphs. The conjecture was only established for k in {1,2} (Ohtsuki et al. 1976, and Beisegel et al. 2019, respectively). Our result also implies a result of Chvátal et al. 2002, which assumed cycle restrictions. We provide a constructive and elementary proof, relying on a single trick regarding the induction hypothesis. In the line of previous works, we discuss conditions for multiple avoidable paths to exist.
A Robust Class of Linear Recurrence Sequences
Venue: arXiv cs.FL 2019
Year: 2019

We introduce a subclass of linear recurrence sequences which we call poly-rational sequences because they are denoted by rational expressions closed under sum and product. We show that this class is robust by giving several characterisations: polynomially ambiguous weighted automata, copyless cost-register automata, rational formal series, and linear recurrence sequences whose eigenvalues are roots of rational numbers.
Data-Driven Randomized Learning of Feedforward Neural Networks
Venue: arXiv cs.LG 2019
Year: 2019

Randomized methods of neural network learning suffer from a problem with the generation of random parameters as they are difficult to set optimally to obtain a good projection space. The standard method draws the parameters from a fixed interval which is independent of the data scope and activation function type. This does not lead to good results in the approximation of the strongly nonlinear functions. In this work, a method which adjusts the random parameters, representing the slopes and positions of the sigmoids, to the target function features is proposed. The method randomly selects the input space regions, places the sigmoids in these regions and then adjusts the sigmoid slopes to the local fluctuations of the target function. This brings very good results in the approximation of the complex target functions when compared to the standard fixed interval method and other methods recently proposed in the literature.
Learning Linear Non-Gaussian Causal Models in the Presence of Latent Variables
Venue: arXiv cs.LG 2019
Year: 2019

We consider the problem of learning causal models from observational data generated by linear non-Gaussian acyclic causal models with latent variables. Without considering the effect of latent variables, one usually infers wrong causal relationships among the observed variables. Under faithfulness assumption, we propose a method to check whether there exists a causal path between any two observed variables. From this information, we can obtain the causal order among them. The next question is then whether or not the causal effects can be uniquely identified as well. It can be shown that causal effects among observed variables cannot be identified uniquely even under the assumptions of faithfulness and non-Gaussianity of exogenous noises. However, we will propose an efficient method to identify the set of all possible causal effects that are compatible with the observational data. Furthermore, we present some structural conditions on the causal graph under which we can learn causal effects among observed variables uniquely. We also provide necessary and sufficient graphical conditions for unique identification of the number of variables in the system. Experiments on synthetic data and real-world data show the effectiveness of our proposed algorithm on learning causal models.
Efficient Intrusion Detection on Low-Performance Industrial IoT Edge Node Devices
Venue: arXiv cs.CR 2019
Year: 2019

Communication between sensors, actors and Programmable Logic Controllers (PLCs) in industrial systems moves from two-wire field buses to IP-based protocols such as Modbus/TCP. This increases the attack surface because the IP-based network is often reachable from everywhere within the company. Thus, centralized defenses, e.g. at the perimeter of the network do not offer sufficient protection. Rather, decentralized defenses, where each part of the network protects itself, are needed. Network Intrusion Detection Systems (IDSs) monitor the network and report suspicious activity. They usually run on a single host and are not able to capture all events in the network and they are associated with a great integration effort. To bridge this gap, we introduce a method for intrusion detection that combines distributed agents on Industrial Internet of Things (IIoT) edge devices with a centralized logging. In contrast to existing IDSs, the distributed approach is suitable for industrial low performance microcontrollers. We demonstrate a Proof of Concept (PoC) implementation on a MCU running FreeRTOS with LwIP and show the feasibility of our approach in an IIoT application.
RWR-GAE: Random Walk Regularization for Graph Auto Encoders
Venue: arXiv cs.LG 2019
Year: 2019

Node embeddings have become an ubiquitous technique for representing graph data in a low dimensional space. Graph autoencoders, as one of the widely adapted deep models, have been proposed to learn graph embeddings in an unsupervised way by minimizing the reconstruction error for the graph data. However, its reconstruction loss ignores the distribution of the latent representation, and thus leading to inferior embeddings. To mitigate this problem, we propose a random walk based method to regularize the representations learnt by the encoder. We show that the proposed novel enhancement beats the existing state-of-the-art models by a large margin (upto 7.5\%) for node clustering task, and achieves state-of-the-art accuracy on the link prediction task for three standard datasets, cora, citeseer and pubmed. Code available at https://github.com/MysteryVaibhav/DW-GAE.
Decision making in dynamic and interactive environments based on cognitive hierarchy theory, Bayesian inference, and predictive control
Venue: arXiv cs.AI 2019
Year: 2019

In this paper, we describe an integrated framework for autonomous decision making in a dynamic and interactive environment. We model the interactions between the ego agent and its operating environment as a two-player dynamic game, and integrate cognitive behavioral models, Bayesian inference, and receding-horizon optimal control to define a dynamically-evolving decision strategy for the ego agent. Simulation examples representing autonomous vehicle control in three traffic scenarios where the autonomous ego vehicle interacts with a human-driven vehicle are reported.
Open Traffic Models -- A framework for hybrid simulation of transportation networks
Venue: arXiv cs.MS 2019
Year: 2019

This paper introduces a new approach to hybrid traffic modeling, along with its implementation in software. The software allows modelers to assign traffic models to individual links in a network. Each model implements a series of methods, refered to as the modeling interface. These methods are used by the program to exchange information between adjacent models. Traffic controllers are implemented in a similar manner. The paper outlines the important components of the method: the network description, the description of demands, and the modeling and control interfaces. We include tests demonstrating the propagation of congestion between pairs of macroscpoic, mesoscopic, and microscopic models. Open Traffic Models is an open source implementation of these concepts, and is available at https://github.com/ggomes/otm-sim.
Douglas-Quaid -- Open Source Image Matching Library
Venue: arXiv cs.CR 2019
Year: 2019

Security analysts need to classify, search and correlate numerous images. Automatic classification tools improve the efficiency of such tasks. However, no open-source and turnkey library was found able to reach this goal. The present paper introduces an Open-Source modular library for the specific cases of visual correlation and Image Matching named Douglas-Quaid. The design of the library, chosen tradeoffs, encountered challenges, envisioned solutions as well as quality and speed results are presented in this paper. We also explore researches directions and future potential developments of the library. Our claim is that even partial automation of screenshots classification would reduce the burden on security teams and that Douglas-Quaid is a step forward in this direction.
An overlapping-free leaf segmentation method for plant point clouds
Venue: arXiv cs.CV 2019
Year: 2019

Automatic leaf segmentation, as well as identification and classification methods that built upon it, are able to provide immediate monitoring for plant growth status to guarantee the output. Although 3D plant point clouds contain abundant phenotypic features, plant leaves are usually distributed in clusters and are sometimes seriously overlapped in the canopy. Therefore, it is still a big challenge to automatically segment each individual leaf from a highly crowded plant canopy in 3D for plant phenotyping purposes. In this work, we propose an overlapping-free individual leaf segmentation method for plant point clouds using the 3D filtering and facet region growing. In order to separate leaves with different overlapping situations, we develop a new 3D joint filtering operator, which integrates a Radius-based Outlier Filter (RBOF) and a Surface Boundary Filter (SBF) to help to separate occluded leaves. By introducing the facet over-segmentation and facet-based region growing, the noise in segmentation is suppressed and labeled leaf centers can expand to their whole leaves, respectively. Our method can work on point clouds generated from three types of 3D imaging platforms, and also suitable for different kinds of plant species. In experiments, it obtains a point-level cover rate of 97% for Epipremnum aureum, 99% for Monstera deliciosa, 99% for Calathea makoyana, and 87% for Hedera nepalensis sample plants. At the leaf level, our method reaches an average Recall at 100.00%, a Precision at 99.33%, and an average F-measure at 99.66%, respectively. The proposed method can also facilitate the automatic traits estimation of each single leaf (such as the leaf area, length, and width), which has potential to become a highly effective tool for plant research and agricultural engineering.
Automatic Fashion Knowledge Extraction from Social Media
Venue: arXiv cs.IR 2019
Year: 2019

Fashion knowledge plays a pivotal role in helping people in their dressing. In this paper, we present a novel system to automatically harvest fashion knowledge from social media. It unifies three tasks of occasion, person and clothing discovery from multiple modalities of images, texts and metadata. A contextualized fashion concept learning model is applied to leverage the rich contextual information for improving the fashion concept learning performance. At the same time, to counter the label noise within training data, we employ a weak label modeling method to further boost the performance. We build a website to demonstrate the quality of fashion knowledge extracted by our system.
Link Crossing Number is NP-hard
Venue: arXiv cs.CG 2019
Year: 2019

We show that determining the crossing number of a link is NP-hard. For some weaker notions of link equivalence, we also show NP-completeness.