OpenRaiser/Intern-Atlas huggingface dataset:
Automatic classification of trees using a UAV onboard camera and deep learning
Venue: arXiv cs.CV 2018
Year: 2018

Automatic classification of trees using remotely sensed data has been a dream of many scientists and land use managers. Recently, Unmanned aerial vehicles (UAV) has been expected to be an easy-to-use, cost-effective tool for remote sensing of forests, and deep learning has attracted attention for its ability concerning machine vision. In this study, using a commercially available UAV and a publicly available package for deep learning, we constructed a machine vision system for the automatic classification of trees. In our method, we segmented a UAV photography image of forest into individual tree crowns and carried out object-based deep learning. As a result, the system was able to classify 7 tree types at 89.0% accuracy. This performance is notable because we only used basic RGB images from a standard UAV. In contrast, most of previous studies used expensive hardware such as multispectral imagers to improve the performance. This result means that our method has the potential to classify individual trees in a cost-effective manner. This can be a usable tool for many forest researchers and managements.
A rational parametrization of Bézier like curves
Venue: arXiv cs.CG 2018
Year: 2018

In this paper, we construct a family of Bernstein functions using a class of rational parametrization. The new family of rational Bernstein basis on an index $α\in {\left(-\infty \, , \, 0 \right)}\cup {\left(1 \, , \, +\infty\right)}$, and for a given degree $k\in \mathbb{N}^*$, these basis functions are rational with a numerator and a denominator are polynomials of degree k. All of the classical properties as positivity, partition of unity are hold for these rational Bernstein basis and they constitute approximation basis functions for continuous functions spaces. The Bézier curves obtained verify the classical properties and we have the classical computational algorithms like the deCasteljau Algorithm and the algorithm of subdivision with the similar accuracy. Given a degree k and a control polygon points all of these algorithms converge to the same Bézier curve as the classical case. That means the Bézier curve is independent of the index $α$. The classical polynomial Bernstein basis seems a asymptotic case of our new class of rational Bernstein basis.
Automation Of Road Intersections Using Consensus-based Auction Algorithms
Venue: arXiv cs.SY 2018
Year: 2018

This paper investigates a consensus-based auction algorithm in the context of decentralized traffic control. In particular, we study the automation of a road intersection, where a set of vehicles is required to cross without collisions. The crossing order will be negotiated in a decentralized fashion. An on-board model predictive controller (MPC) will compute an optimal trajectory which avoids collisions with higher priority vehicles, thus retaining convex safety constraints. Simulations are then performed in a time-variant traffic environment.
A Reputation Scheme to Discourage Selfish QoS Manipulation in Two-Hop Wireless Relay Networks
Venue: arXiv cs.NI 2018
Year: 2018

In wireless networks, stations can improve their received quality of service (QoS) by handling packets of source flows with higher priority. Additionally, in cooperative relay networks, the relays can handle transit flows with lower priority. We use game theory to model a two-hop relay network where each of the two involved stations can commit such selfish QoS manipulation. We design and evaluate a reputation-based incentive scheme called RISC2WIN, whereby a trusted third party (e.g., an access point) can limit selfish behavior and preserve appropriate QoS for both stations.
Testing Simon's congruence
Venue: arXiv cs.FL 2018
Year: 2018

Piecewise testable languages are a subclass of the regular languages. There are many equivalent ways of defining them; Simon's congruence $\sim_k$ is one of the most classical approaches. Two words are $\sim_k$-equivalent if they have the same set of (scattered) subwords of length at most k. A language L is piecewise testable if there exists some k such that L is a union of $\sim_k$-classes. For each equivalence class of $\sim_k$, one can define a canonical representative in shortlex normal form, that is, the minimal word with respect to the lexicographic order among the shortest words in $\sim_k$. We present an algorithm for computing the canonical representative of the $\sim_k$-class of a given word $w \in A^*$ of length n. The running time of our algorithm is in O(|A|n) even if $k \le n$ is part of the input. This is surprising since the number of possible subwords grows exponentially in k. The case $k > n$ is not interesting since then, the equivalence class of w is a singleton. If the alphabet is fixed, the running time of our algorithm is linear in the size of the input word. Moreover, for fixed alphabet, we show that the computation of shortlex normal forms for $\sim_k$ is possible in deterministic logarithmic space. One of the consequences of our algorithm is that one can check with the same complexity whether two words are $\sim_k$-equivalent (with k being part of the input).
Abstract Mining
Venue: arXiv cs.DL 2018
Year: 2018

We have developed an application that will take a "MEDLINE" output from the PubMed database and allows the user to cluster all non-trivial words of the abstracts of the PubMed output. The number of clusters to use can be selected by the user. A specific cluster may be selected, and the PMIDs and dates for all publications in the selected cluster are displayed underneath. See figure 2, where cluster 12 is selected. The application also has an "Abstracts" tab, where the abstracts for the selected cluster can be perused. Here, it is also possible to download a HTML file containing the PMID, date, title, and abstract for each publication in the selected cluster. A third tab is called "Titles", where all the titles for the selected cluster are displayed. Via a "Use Cluster" button, the selected Cluster can itself be clustered. A "Back" button allows the user to return to any previous state. Finally, it is also possible to exclude documents whose abstracts contain certain words (see figure 3). The application will allow researchers to enter general search terms in the PubMed search engine, then use the application to search for publications of special interest within those search terms.
Urban contact structures for epidemic simulations: Correcting biases in data-driven approaches
Venue: arXiv cs.SI 2018
Year: 2018

Epidemics are emergent phenomena depending on the epidemiological characteristics of pathogens and the interaction and movement of people. Public transit systems have provided much important information about the movement of people, but there are also other means of transportation (e.g., bicycle and private car), that are invisible to public transit data. This discrepancy can induce a bias in disease models that leads to mispredictions of epidemic growth (e.g., peak prevalence and time). In our study, we aim to advance and compare the epidemic spreading dynamics using public transit trips, in contrast to more accurate estimates of population movement using mobile phones traces. In our study, we simulate epidemic outbreaks in a cohort of two million mobile phone users. We use a metapopulation model incorporating susceptible-infected-recovered dynamics to analyze and compare different effective contract matrices, constructed by the public transit systems and mobile phones respectively, on the process of epidemics. We find that epidemic outbreaks using public transit trips tend to be underestimated in terms of the epidemic spreading dynamics, reaching epidemic peaks weaker and later. This is rooted in a later introduction of new infectious people into uninfected locations.
Efficient Non-linear Equalization for 1-bit Quantized Cyclic Prefix-Free Massive MIMO Systems
Venue: arXiv cs.IT 2018
Year: 2018

This paper addresses the problem of data detection for a massive Multiple-Input-Multiple-Output (MIMO) base station which utilizes 1-bit Analog-to-Digital Converters (ADCs) for quantizing the uplink signal. The existing literature on quantized massive MIMO systems deals with Cyclic Prefix (CP) transmission over frequency-selective channels. In this paper, we propose a computationally efficient block processing equalizer based on the Expectation Maximization (EM) algorithm in CP-free transmission for 1-bit quantized systems. We investigate the optimal block length and overlapping factor in relation to the Channel Impulse Response (CIR) length based on the Bit Error-Rate (BER) performance metric. As EM is a non-linear algorithm, the optimal estimate is found iteratively depending on the initial starting point of the algorithm. Through numerical simulations we show that initializing the EM-algorithm with a Wiener-Filter (WF) estimate, which takes the underlying quantization into account, achieves superior BER-performance compared to initialization with other starting points.
Low Rank Approximation in the Presence of Outliers
Venue: arXiv cs.DS 2018
Year: 2018

We consider the problem of principal component analysis (PCA) in the presence of outliers. Given a matrix $A$ ($d \times n$) and parameters $k, m$, the goal is to remove a set of at most $m$ columns of $A$ (known as outliers), so as to minimize the rank-$k$ approximation error of the remaining matrix. While much of the work on this problem has focused on recovery of the rank-$k$ subspace under assumptions on the inliers and outliers, we focus on the approximation problem above. Our main result shows that sampling-based methods developed in the outlier-free case give non-trivial guarantees even in the presence of outliers. Using this insight, we develop a simple algorithm that has bi-criteria guarantees. Further, unlike similar formulations for clustering, we show that bi-criteria guarantees are unavoidable for the problem, under appropriate complexity assumptions.
Data-Driven Methods for Solving Algebra Word Problems
Venue: arXiv cs.AI 2018
Year: 2018

We explore contemporary, data-driven techniques for solving math word problems over recent large-scale datasets. We show that well-tuned neural equation classifiers can outperform more sophisticated models such as sequence to sequence and self-attention across these datasets. Our error analysis indicates that, while fully data driven models show some promise, semantic and world knowledge is necessary for further advances.
Novel Prediction Techniques Based on Clusterwise Linear Regression
Venue: arXiv cs.LG 2018
Year: 2018

In this paper we explore different regression models based on Clusterwise Linear Regression (CLR). CLR aims to find the partition of the data into $k$ clusters, such that linear regressions fitted to each of the clusters minimize overall mean squared error on the whole data. The main obstacle preventing to use found regression models for prediction on the unseen test points is the absence of a reasonable way to obtain CLR cluster labels when the values of target variable are unknown. In this paper we propose two novel approaches on how to solve this problem. The first approach, predictive CLR builds a separate classification model to predict test CLR labels. The second approach, constrained CLR utilizes a set of user-specified constraints that enforce certain points to go to the same clusters. Assuming the constraint values are known for the test points, they can be directly used to assign CLR labels. We evaluate these two approaches on three UCI ML datasets as well as on a large corpus of health insurance claims. We show that both of the proposed algorithms significantly improve over the known CLR-based regression methods. Moreover, predictive CLR consistently outperforms linear regression and random forest, and shows comparable performance to support vector regression on UCI ML datasets. The constrained CLR approach achieves the best performance on the health insurance dataset, while enjoying only $\approx 20$ times increased computational time over linear regression.
Precise Box Score: Extract More Information from Datasets to Improve the Performance of Face Detection
Venue: arXiv cs.CV 2018
Year: 2018

For the training of face detection network based on R-CNN framework, anchors are assigned to be positive samples if intersection-over-unions (IoUs) with ground-truth are higher than the first threshold(such as 0.7); and to be negative samples if their IoUs are lower than the second threshold(such as 0.3). And the face detection model is trained by the above labels. However, anchors with IoU between first threshold and second threshold are not used. We propose a novel training strategy, Precise Box Score(PBS), to train object detection models. The proposed training strategy uses the anchors with IoUs between the first and second threshold, which can consistently improve the performance of face detection. Our proposed training strategy extracts more information from datasets, making better utilization of existing datasets. What's more, we also introduce a simple but effective model compression method(SEMCM), which can boost the performance of face detectors further. Experimental results show that the performance of face detection network can consistently be improved based on our proposed scheme.
Hybrid Point Cloud Attribute Compression Using Slice-based Layered Structure and Block-based Intra Prediction
Venue: arXiv cs.MM 2018
Year: 2018

Point cloud compression is a key enabler for the emerging applications of immersive visual communication, autonomous driving and smart cities, etc. In this paper, we propose a hybrid point cloud attribute compression scheme built on an original layered data structure. First, a slice-partition scheme and geometry-adaptive k dimensional-tree (kd-tree) method are devised to generate the four-layer structure. Second, we introduce an efficient block-based intra prediction scheme containing a DC prediction mode and several angular modes, in order to exploit the spatial correlation between adjacent points. Third, an adaptive transform scheme based on Graph Fourier Transform (GFT) is Lagrangian optimized to achieve better transform efficiency. The Lagrange multiplier is off-line derived based on the statistics of color attribute coding. Last but not least, multiple reordering scan modes are dedicated to improve coding efficiency for entropy coding. In intra-frame compression of point cloud color attributes, results demonstrate that our method performs better than the state-of-the-art region-adaptive hierarchical transform (RAHT) system, and on average a 29.37$\%$ BD-rate gain is achieved. Comparing with the test model for category 1 (TMC1) anchor's coding results, which were recently published by MPEG-3DG group on 121st meeting, a 16.37$\%$ BD-rate gain is obtained.
Performance Analysis of Sensing-Based Semi-Persistent Scheduling in C-V2X Networks
Venue: arXiv cs.NI 2018
Year: 2018

The 3rd Generation Partnership Project released the cellular vehicular-to-everything (C-V2X) specifications as part of the LTE framework in Release 14. C-V2X is the alternative to dedicated short range communications and both are specifically designed for V2X control signaling. C-V2X extends the device-to-device specifications by adding two more modes of operation targeting the vehicular environment in coverage and out of coverage of LTE base stations. Vehicle-to-vehicle communications (V2V) is established with Mode 4, where the devices schedule their transmissions in a distributed way employing sensing-based semi-persistent scheduling (SPS). Research is needed to assess the performance of SPS, especially in congested radio environments. This paper presents the first open-source C-V2X simulator that enables such research. The simulator is implemented in ns-3. We analyze the effect of the Mode 4 resource pool configuration and some of the key SPS parameters on the scheduling performance and find that the resource reservation interval significantly influences packet data rate performance, whereas resource reselection probability has little effect in dense vehicular highway scenarios. Our results show that proper configuration of scheduling parameters can significantly improve performance. We conclude that research on congestion control mechanisms is needed to further enhance the SPS performance for many practical use cases.
Handling Uncertainty in Social Lending Credit Risk Prediction with a Choquet Fuzzy Integral Model
Venue: arXiv cs.CE 2018
Year: 2018

As one of the main business models in the financial technology field, peer-to-peer (P2P) lending has disrupted traditional financial services by providing an online platform for lending money that has remarkably reduced financial costs. However, the inherent uncertainty in P2P loans can result in huge financial losses for P2P platforms. Therefore, accurate risk prediction is critical to the success of P2P lending platforms. Indeed, even a small improvement in credit risk prediction would be of benefit to P2P lending platforms. This paper proposes an innovative credit risk prediction framework that fuses base classifiers based on a Choquet fuzzy integral. Choquet integral fusion improves creditworthiness evaluations by synthesizing the prediction results of multiple classifiers and finding the largest consistency between outcomes among conflicting and consistent results. The proposed model was validated through experimental analysis on a real- world dataset from a well-known P2P lending marketplace. The empirical results indicate that the combination of multiple classifiers based on fuzzy Choquet integrals outperforms the best base classifiers used in credit risk prediction to date. In addition, the proposed methodology is superior to some conventional combination techniques.
KRust: A Formal Executable Semantics of Rust
Venue: arXiv cs.PL 2018
Year: 2018

Rust is a new and promising high-level system programming language. It provides both memory safety and thread safety through its novel mechanisms such as ownership, moves and borrows. Ownership system ensures that at any point there is only one owner of any given resource. The ownership of a resource can be moved or borrowed according to the lifetimes. The ownership system establishes a clear lifetime for each value and hence does not necessarily need garbage collection. These novel features bring Rust high performance, fine low-level control of C and C++, and unnecessity in garbage collection, which differ Rust from other existing prevalent languages. For formal analysis of Rust programs and helping programmers learn its new mechanisms and features, a formal semantics of Rust is desired and useful as a fundament for developing related tools. In this paper, we present a formal executable operational semantics of a realistic subset of Rust, called KRust. The semantics is defined in K, a rewriting-based executable semantic framework for programming languages. The executable semantics yields automatically a formal interpreter and verification tools for Rust programs. KRust has been thoroughly validated by testing with hundreds of tests, including the official Rust test suite.
A Bimodal Learning Approach to Assist Multi-sensory Effects Synchronization
Venue: arXiv cs.AI 2018
Year: 2018

In mulsemedia applications, traditional media content (text, image, audio, video, etc.) can be related to media objects that target other human senses (e.g., smell, haptics, taste). Such applications aim at bridging the virtual and real worlds through sensors and actuators. Actuators are responsible for the execution of sensory effects (e.g., wind, heat, light), which produce sensory stimulations on the users. In these applications sensory stimulation must happen in a timely manner regarding the other traditional media content being presented. For example, at the moment in which an explosion is presented in the audiovisual content, it may be adequate to activate actuators that produce heat and light. It is common to use some declarative multimedia authoring language to relate the timestamp in which each media object is to be presented to the execution of some sensory effect. One problem in this setting is that the synchronization of media objects and sensory effects is done manually by the author(s) of the application, a process which is time-consuming and error prone. In this paper, we present a bimodal neural network architecture to assist the synchronization task in mulsemedia applications. Our approach is based on the idea that audio and video signals can be used simultaneously to identify the timestamps in which some sensory effect should be executed. Our learning architecture combines audio and video signals for the prediction of scene components. For evaluation purposes, we construct a dataset based on Google's AudioSet. We provide experiments to validate our bimodal architecture. Our results show that the bimodal approach produces better results when compared to several variants of unimodal architectures.
A Unified Framework for Domain Adaptation using Metric Learning on Manifolds
Venue: arXiv cs.LG 2018
Year: 2018

We present a novel framework for domain adaptation, whereby both geometric and statistical differences between a labeled source domain and unlabeled target domain can be integrated by exploiting the curved Riemannian geometry of statistical manifolds. Our approach is based on formulating transfer from source to target as a problem of geometric mean metric learning on manifolds. Specifically, we exploit the curved Riemannian manifold geometry of symmetric positive definite (SPD) covariance matrices. We exploit a simple but important observation that as the space of covariance matrices is both a Riemannian space as well as a homogeneous space, the shortest path geodesic between two covariances on the manifold can be computed analytically. Statistics on the SPD matrix manifold, such as the geometric mean of two matrices can be reduced to solving the well-known Riccati equation. We show how the Ricatti-based solution can be constrained to not only reduce the statistical differences between the source and target domains, such as aligning second order covariances and minimizing the maximum mean discrepancy, but also the underlying geometry of the source and target domains using diffusions on the underlying source and target manifolds. A key strength of our proposed approach is that it enables integrating multiple sources of variation between source and target in a unified way, by reducing the combined objective function to a nested set of Ricatti equations where the solution can be represented by a cascaded series of geometric mean computations. In addition to showing the theoretical optimality of our solution, we present detailed experiments using standard transfer learning testbeds from computer vision comparing our proposed algorithms to past work in domain adaptation, showing improved results over a large variety of previous methods.
Business Model of a Botnet
Venue: arXiv cs.CY 2018
Year: 2018

Botnets continue to be an active threat against firms or companies and individuals worldwide. Previous research regarding botnets has unveiled information on how the system and their stakeholders operate, but an insight on the economic structure that supports these stakeholders is lacking. The objective of this research is to analyse the business model and determine the revenue stream of a botnet owner. We also study the botnet life-cycle and determine the costs associated with it on the basis of four case studies. We conclude that building a full scale cyber army from scratch is very expensive where as acquiring a previously developed botnet requires a little cost. We find that initial setup and monthly costs were minimal compared to total revenue.