OpenRaiser/Intern-Atlas huggingface dataset:
Super-Resolution-based Snake Model -- An Unsupervised Method for Large-Scale Building Extraction using Airborne LiDAR Data and Optical Image
Venue: arXiv cs.CV 2020
Year: 2020

Automatic extraction of buildings in urban and residential scenes has become a subject of growing interest in the domain of photogrammetry and remote sensing, particularly since mid-1990s. Active contour model, colloquially known as snake model, has been studied to extract buildings from aerial and satellite imagery. However, this task is still very challenging due to the complexity of building size, shape, and its surrounding environment. This complexity leads to a major obstacle for carrying out a reliable large-scale building extraction, since the involved prior information and assumptions on building such as shape, size, and color cannot be generalized over large areas. This paper presents an efficient snake model to overcome such challenge, called Super-Resolution-based Snake Model (SRSM). The SRSM operates on high-resolution LiDAR-based elevation images -- called z-images -- generated by a super-resolution process applied to LiDAR data. The involved balloon force model is also improved to shrink or inflate adaptively, instead of inflating the snake continuously. This method is applicable for a large scale such as city scale and even larger, while having a high level of automation and not requiring any prior knowledge nor training data from the urban scenes (hence unsupervised). It achieves high overall accuracy when tested on various datasets. For instance, the proposed SRSM yields an average area-based Quality of 86.57% and object-based Quality of 81.60% on the ISPRS Vaihingen benchmark datasets. Compared to other methods using this benchmark dataset, this level of accuracy is highly desirable even for a supervised method. Similarly desirable outcomes are obtained when carrying out the proposed SRSM on the whole City of Quebec (total area of 656 km2), yielding an area-based Quality of 62.37% and an object-based Quality of 63.21%.
Proposal of Automatic FPGA Offloading for Applications Loop Statements
Venue: arXiv cs.DC 2020
Year: 2020

In recent years, with the prediction of Moore's law slowing down, utilization of hardware other than CPU such as FPGA which is energy effective is increasing. However, when using heterogeneous hardware other than CPUs, barriers of technical skills such as OpenCL are high. Based on that, I have proposed environment adaptive software that enables automatic conversion, configuration, and high-performance operation of once written code, according to the hardware to be placed. Partly of the offloading to the GPU was automated previously. In this paper, I propose and evaluate an automatic extraction method of appropriate offload target loop statements of source code as the first step of offloading to FPGA. I evaluate the effectiveness of the proposed method using existing applications.
How context impacts on media choice
Venue: arXiv cs.SI 2020
Year: 2020

The relevance of mobile working is steadily increasing. Based on new mobile devices (e.g. smartphones) and their innovative functionalities, an increasing amount of data is being made available ubiquitously. As a result, the growing diffusion of smartphones offers new potential for enterprises. Current mobile devices and related mobile networks have reached a high level of maturity. Thus, the organizational aspects of mobile work have become a focal point of interest for enterprises as well as for academics. This research article addresses the question: How does context influence the choice of communication channels of mobile knowledge workers? An explorative research approach is used to collect and analyse 418 communication incidents, which were initiated by mobile knowledge workers. The results indicate that (1) the context (e.g. travelling on trains) influences the usage of communication channels and (2) smartphones enable the usage of communication channels (e.g. email) in certain contexts.
Are You A Risk Taker? Adversarial Learning of Asymmetric Cross-Domain Alignment for Risk Tolerance Prediction
Venue: arXiv cs.LG 2020
Year: 2020

Most current studies on survey analysis and risk tolerance modelling lack professional knowledge and domain-specific models. Given the effectiveness of generative adversarial learning in cross-domain information, we design an Asymmetric cross-Domain Generative Adversarial Network (ADGAN) for domain scale inequality. ADGAN utilizes the information-sufficient domain to provide extra information to improve the representation learning on the information-insufficient domain via domain alignment. We provide data analysis and user model on two data sources: Consumer Consumption Information and Survey Information. We further test ADGAN on a real-world dataset with view embedding structures and show ADGAN can better deal with the class imbalance and unqualified data space than state-of-the-art, demonstrating the effectiveness of leveraging asymmetrical domain information.
BiFNet: Bidirectional Fusion Network for Road Segmentation
Venue: arXiv cs.CV 2020
Year: 2020

Multi-sensor fusion-based road segmentation plays an important role in the intelligent driving system since it provides a drivable area. The existing mainstream fusion method is mainly to feature fusion in the image space domain which causes the perspective compression of the road and damages the performance of the distant road. Considering the bird's eye views(BEV) of the LiDAR remains the space structure in horizontal plane, this paper proposes a bidirectional fusion network(BiFNet) to fuse the image and BEV of the point cloud. The network consists of two modules: 1) Dense space transformation module, which solves the mutual conversion between camera image space and BEV space. 2) Context-based feature fusion module, which fuses the different sensors information based on the scenes from corresponding features.This method has achieved competitive results on KITTI dataset.
Three Modern Roles for Logic in AI
Venue: arXiv cs.AI 2020
Year: 2020

We consider three modern roles for logic in artificial intelligence, which are based on the theory of tractable Boolean circuits: (1) logic as a basis for computation, (2) logic for learning from a combination of data and knowledge, and (3) logic for reasoning about the behavior of machine learning systems.
Underwater image enhancement with Image Colorfulness Measure
Venue: arXiv cs.CV 2020
Year: 2020

Due to the absorption and scattering effects of the water, underwater images tend to suffer from many severe problems, such as low contrast, grayed out colors and blurring content. To improve the visual quality of underwater images, we proposed a novel enhancement model, which is a trainable end-to-end neural model. Two parts constitute the overall model. The first one is a non-parameter layer for the preliminary color correction, then the second part is consisted of parametric layers for a self-adaptive refinement, namely the channel-wise linear shift. For better details, contrast and colorfulness, this enhancement network is jointly optimized by the pixel-level and characteristiclevel training criteria. Through extensive experiments on natural underwater scenes, we show that the proposed method can get high quality enhancement results.
Data Model, Collection and Evaluation Framework for Local Energy Systems
Venue: arXiv cs.DC 2020
Year: 2020

Distributed ledgers are a new type of database technology that allows open access to data stored across distributed, decentralised, publicly maintained infrastructures. Current implementations of the such ledgers expect competition between participants, are often energy hungry, poor in maintaining the natural structure of data and suffer from scalability constraints. The aim of my research work is to develop a distributed ledger-based middleware for data modelling and collection on household energy generation and use, while addressing scalability and energy inefficiency concerns of the ledger for this particular application domain. The energy data collected and made available through this middleware will be used for digital energy service delivery (e.g., automated peer to peer energy trading, topological estimations, etc.). The middleware also provides a platform for a consumer focused digital energy service delivery, as well as service model evaluation. The model evaluation will enable the prospective service users to evaluate the suitability of the given service for their needs before making a decision of service subscription.
Honesty Based Democratic Scheme to Improve Community Cooperation for IoT Based Vehicular Delay Tolerant Networks
Venue: arXiv cs.DC 2020
Year: 2020

Many Internet of things (IoT) applications have been developed and implemented on unreliable wireless networks like the Delay tolerant network (DTN), however, efficient data transfer in DTN is still an important issue for the IoT applications. One of the application areas of DTN is Vehicular Delay Tolerant Network (VDTN) where the network faces communication disruption due to lack of end-to-end relay route. It is challenging as some of the nodes show selfish behavior to preserve their resources like memory, and energy level and become non-cooperative. In this article, an Honesty based Democratic Scheme (HBDS) is introduced where vehicles with higher honesty level are elected as heads -- during the process. Vehicles involved in the process would maximize their rewards (reputation) through active participation in the network activities whereas nodes with non-cooperative selfish behavior are punished. The honesty level of the heads is analyzed using Vickrey, Clarke, and Groves (VCG) model. The mathematical model and algorithms developed in the proposed HBDS technique are simulated using the VDTNSim framework to evaluate their efficiency. The performance results show that the proposed scheme dominates current schemes in terms of packet delivery probability, packet delivery delay, number of packets drop, and overhead ratio.
Automatically Characterizing Targeted Information Operations Through Biases Present in Discourse on Twitter
Venue: arXiv cs.CY 2020
Year: 2020

This paper considers the problem of automatically characterizing overall attitudes and biases that may be associated with emerging information operations via artificial intelligence. Accurate analysis of these emerging topics usually requires laborious, manual analysis by experts to annotate millions of tweets to identify biases in new topics. We introduce extensions of the Word Embedding Association Test from Caliskan et al. to a new domain (Caliskan, 2017). Our practical and unsupervised method is used to quantify biases promoted in information operations. We validate our method using known information operation-related tweets from Twitter's Transparency Report. We perform a case study on the COVID-19 pandemic to evaluate our method's performance on non-labeled Twitter data, demonstrating its usability in emerging domains.
Model-Predictive Control via Cross-Entropy and Gradient-Based Optimization
Venue: arXiv cs.LG 2020
Year: 2020

Recent works in high-dimensional model-predictive control and model-based reinforcement learning with learned dynamics and reward models have resorted to population-based optimization methods, such as the Cross-Entropy Method (CEM), for planning a sequence of actions. To decide on an action to take, CEM conducts a search for the action sequence with the highest return according to the dynamics model and reward. Action sequences are typically randomly sampled from an unconditional Gaussian distribution and evaluated on the environment. This distribution is iteratively updated towards action sequences with higher returns. However, this planning method can be very inefficient, especially for high-dimensional action spaces. An alternative line of approaches optimize action sequences directly via gradient descent, but are prone to local optima. We propose a method to solve this planning problem by interleaving CEM and gradient descent steps in optimizing the action sequence. Our experiments show faster convergence of the proposed hybrid approach, even for high-dimensional action spaces, avoidance of local minima, and better or equal performance to CEM. Code accompanying the paper is available here https://github.com/homangab/gradcem.
Prism Removes Consensus Bottleneck for Smart Contracts
Venue: arXiv cs.DC 2020
Year: 2020

The performance of existing permissionless smart contract platforms such as Ethereum is limited by the consensus layer. Prism is a new proof-of-work consensus protocol that provably achieves throughput and latency up to physical limits while retaining the strong guarantees of the longest chain protocol. This paper reports experimental results from implementations of two smart contract virtual machines, EVM and MoveVM, on top of Prism and demonstrates that the consensus bottleneck has been removed. Code can be found at https://github.com/wgr523/prism-smart-contracts.
Network Coding-based Routing and Spectrum Allocation in Elastic Optical Networks for Enhanced Physical Layer Security
Venue: arXiv cs.NI 2020
Year: 2020

In this work, an eavesdropping-aware routing and spectrum allocation approach is proposed utilizing network coding (NC) in elastic optical networks (EONs). To provide physical layer security in EONs and secure the confidential connections against eavesdropping attacks using NC, the signals of the confidential connections are combined (XOR-ed) with other signals at different nodes in their path, while transmitted through the network. The combination of signals through NC significantly increases the security of confidential connections, since an eavesdropper must access all individual signals, traversing different links, in order to decrypt the combined signal. A novel heuristic approach is proposed, that solves the combined network coding and routing and spectrum allocation (NC-RSA) problem, that also takes into account additional NC constraints that are required in order to consider a confidential connection as secure. Different routing and spectrum allocation strategies are proposed, aiming to maximize the level of security provided for the confidential demands, followed by an extensive performance evaluation of each approach in terms of the level of security provided, as well as the spectrum utilization and blocking probability, under different network parameters. Performance results demonstrate that the proposed approaches can provide efficient solutions in terms of network performance, while providing the level of security required for each demand.
Dataset for anomalies detection in 3D printing
Venue: arXiv cs.OH 2020
Year: 2020

Nowadays, Internet of Things plays a significant role in many domains. Especially, Industry 4.0 is making a great usage of concepts like smart sensors and big data analysis. IoT devices are commonly used to monitor industry machines and detect anomalies in their work. In this paper we present and describe a set of data streams coming from working 3D printer. Among others, it contains accelerometer data of printer head, intrusion power and temperatures of the printer elements. In order to gain data we lead to several printing malfunctions applied to the 3D model. Resulting dataset can therefore be used for anomalies detection research.
Preprocessing Vertex-Deletion Problems: Characterizing Graph Properties by Low-Rank Adjacencies
Venue: arXiv cs.DS 2020
Year: 2020

We consider the $Π$-free Deletion problem parameterized by the size of a vertex cover, for a range of graph properties $Π$. Given an input graph $G$, this problem asks whether there is a subset of at most $k$ vertices whose removal ensures the resulting graph does not contain a graph from $Π$ as induced subgraph. Many vertex-deletion problems such as Perfect Deletion, Wheel-free Deletion, and Interval Deletion fit into this framework. We introduce the concept of characterizing a graph property $Π$ by low-rank adjacencies, and use it as the cornerstone of a general kernelization theorem for $Π$-Free Deletion parameterized by the size of a vertex cover. The resulting framework captures problems such as AT-Free Deletion, Wheel-free Deletion, and Interval Deletion. Moreover, our new framework shows that the vertex-deletion problem to perfect graphs has a polynomial kernel when parameterized by vertex cover, thereby resolving an open question by Fomin et al. [JCSS 2014]. Our main technical contribution shows how linear-algebraic dependence of suitably defined vectors over $\mathbb{F}_2$ implies graph-theoretic statements about the presence of forbidden induced subgraphs.
Extended Abstract of Performance Analysis and Prediction of Model Transformation
Venue: arXiv cs.SE 2020
Year: 2020

In the software development process, model transformation is increasingly assimilated. However, systems being developed with model transformation sometimes grow in size and become complex. Meanwhile, the performance of model transformation tends to decrease. Hence, performance is an important quality of model transformation. According to current research model transformation performance focuses on optimising the engines internally. However, there exists no research activities to support transformation engineer to identify performance bottleneck in the transformation rules and hence, to predict the overall performance. In this paper we vision our aim at providing an approach of monitoring and profiling to identify the root cause of performance issues in the transformation rules and to predict the performance of model transformation. This will enable software engineers to systematically identify performance issues as well as predict the performance of model transformation.
Minimizing Total Interference in Asymmetric Sensor Networks
Venue: arXiv cs.CG 2020
Year: 2020

The problem of computing a connected network with minimum interference is a fundamental problem in wireless sensor networks. Several models of interference have been studied in the literature. The most common model is the receiver-centric, in which the interference of a node $p$ is defined as the number of other nodes whose transmission range covers $p$. In this paper, we study the problem of assigning a transmission range to each sensor, such that the resulting network is strongly connected and the total interference of the network is minimized. For the one-dimensional case, we show how to solve the problem optimally in $O(n^3)$ time. For the two-dimensional case, we show that the problem is NP-complete and give a polynomial-time 2-approximation algorithm for the problem.
Planar Bichromatic Bottleneck Spanning Trees
Venue: arXiv cs.CG 2020
Year: 2020

Given a set $P$ of $n$ red and blue points in the plane, a \emph{planar bichromatic spanning tree} of $P$ is a spanning tree of $P$, such that each edge connects between a red and a blue point, and no two edges intersect. In the bottleneck planar bichromatic spanning tree problem, the goal is to find a planar bichromatic spanning tree $T$, such that the length of the longest edge in $T$ is minimized. In this paper, we show that this problem is NP-hard for points in general position. Moreover, we present a polynomial-time $(8\sqrt{2})$-approximation algorithm, by showing that any bichromatic spanning tree of bottleneck $λ$ can be converted to a planar bichromatic spanning tree of bottleneck at most $8\sqrt{2}λ$.
Junk News Bubbles: Modelling the Rise and Fall of Attention in Online Arenas
Venue: arXiv cs.SI 2020
Year: 2020

In this paper, we present a type of media disorder which we call "`junk news bubbles" and which derives from the effort invested by online platforms and their users to identify and share contents with rising popularity. Such emphasis on trending matters, we claim, can have two detrimental effects on public debates: first, it shortens the amount of time available to discuss each matter; second it increases the ephemeral concentration of media attention. We provide a formal description of the dynamic of junk news bubbles, through a mathematical exploration the famous "public arenas model" developed by Hilgartner and Bosk in 1988. Our objective is to describe the dynamics of the junk news bubbles as precisely as possible to facilitate its further investigation with empirical data.
Space Debris Ontology for ADR Capture Methods Selection
Venue: arXiv cs.AI 2020
Year: 2020

Studies have concluded that active debris removal (ADR) of the existing in-orbit mass is necessary. However, the quest for an optimal solution does not have a unique answer and the available data often lacks coherence. To improve this situation, modern knowledge representation techniques, that have been shaping the World Wide Web, medicine and pharmacy, should be employed. Prior efforts in the domain of space debris have only focused onto space situational awareness, neglecting ADR. To bridge this gap we present a domain-ontology of intact derelict objects, i.e. payloads and rocket bodies, for ADR capture methods selection. The ontology is defined on a minimal set of physical, dynamical and statistical parameters of a target object. The practicality and validity of the ontology are demonstrated by applying it onto a database of 30 representative objects, built by combining structured and unstructured data from publicly available sources. The analysis of results proves the ontology capable of inferring the most suited ADR capture methods for considered objects. Furthermore, it confirms its ability to handle the input data from different sources transparently, minimizing user input. The developed ontology provides an initial step towards a more comprehensive knowledge representation framework meant to improve data management and knowledge discovery in the domain of space debris. Furthermore, it provides a tool that should make the initial planning of future ADR missions simpler yet more systematic.
MER-GCN: Micro Expression Recognition Based on Relation Modeling with Graph Convolutional Network
Venue: arXiv cs.CV 2020
Year: 2020

Micro-Expression (ME) is the spontaneous, involuntary movement of a face that can reveal the true feeling. Recently, increasing researches have paid attention to this field combing deep learning techniques. Action units (AUs) are the fundamental actions reflecting the facial muscle movements and AU detection has been adopted by many researches to classify facial expressions. However, the time-consuming annotation process makes it difficult to correlate the combinations of AUs to specific emotion classes. Inspired by the nodes relationship building Graph Convolutional Networks (GCN), we propose an end-to-end AU-oriented graph classification network, namely MER-GCN, which uses 3D ConvNets to extract AU features and applies GCN layers to discover the dependency laying between AU nodes for ME categorization. To our best knowledge, this work is the first end-to-end architecture for Micro-Expression Recognition (MER) using AUs based GCN. The experimental results show that our approach outperforms CNN-based MER networks.
Tree Echo State Autoencoders with Grammars
Venue: arXiv cs.NE 2020
Year: 2020

Tree data occurs in many forms, such as computer programs, chemical molecules, or natural language. Unfortunately, the non-vectorial and discrete nature of trees makes it challenging to construct functions with tree-formed output, complicating tasks such as optimization or time series prediction. Autoencoders address this challenge by mapping trees to a vectorial latent space, where tasks are easier to solve, and then mapping the solution back to a tree structure. However, existing autoencoding approaches for tree data fail to take the specific grammatical structure of tree domains into account and rely on deep learning, thus requiring large training datasets and long training times. In this paper, we propose tree echo state autoencoders (TES-AE), which are guided by a tree grammar and can be trained within seconds by virtue of reservoir computing. In our evaluation on three datasets, we demonstrate that our proposed approach is not only much faster than a state-of-the-art deep learning autoencoding approach (D-VAE) but also has less autoencoding error if little data and time is given.