OpenRaiser/Intern-Atlas huggingface dataset:
Asynchronous Deep Model Reference Adaptive Control
Venue: arXiv cs.RO 2020
Year: 2020

In this paper, we present Asynchronous implementation of Deep Neural Network-based Model Reference Adaptive Control (DMRAC). We evaluate this new neuro-adaptive control architecture through flight tests on a small quadcopter. We demonstrate that a single DMRAC controller can handle significant nonlinearities due to severe system faults and deliberate wind disturbances while executing high-bandwidth attitude control. We also show that the architecture has long-term learning abilities across different flight regimes, and can generalize to fly different flight trajectories than those on which it was trained. These results demonstrating the efficacy of this architecture for high bandwidth closed-loop attitude control of unstable and nonlinear robots operating in adverse situations. To achieve these results, we designed a software+communication architecture to ensure online real-time inference of the deep network on a high-bandwidth computation-limited platform. We expect that this architecture will benefit other deep learning in the closed-loop experiments on robots.
Towards Usability Guidelines for the Design of Effective Arabic Websites: Design Practices and Lessons Focusing on Font and Image usage
Venue: arXiv cs.HC 2020
Year: 2020

The Arabic websites constitute 1% of the web content with more than 225 million viewers and 41% Internet penetration. However, there is a lack of design guidelines related to the selection and use of appropriate font type and size and images in Arabic websites. Both text and images are vital multimedia components of websites and thereby were selected for investigation in this study. The herein paper performed an indepth inspection of font and image design practices within 73 most visited Arabic websites in Saudi Arabia according to Alexa Internet ranking in the first quarter of 2019. Our exhaustive analysis showed discrepancies between the international design recommendations and the actual design of Arabic websites. There was a considerable variation and inconsistency in using font types and sizes between and within the Arabic websites. Arabic Droid Kufi was used mostly for styling page titles and navigation menus, whilst Tahoma was used for styling paragraphs. The font size of the Arabic text ranged from 12 to 16 pixels, which may lead to poor readability. Images were used heavily in the Arabic websites causing prolonged site loading times. Moreover, the images strongly reflected the dimensions of the Saudi culture, especially collectivism and masculinity. Current Arabic web design practices are compared against the findings from past studies about international designs and lessons aiming at ameliorating the Arabic web design are inferred.
Optimization of Virtual Networks
Venue: arXiv cs.NI 2020
Year: 2020

We introduce a general and comprehensive model for the design and optimization of Virtual Networks, and for the related concept of Network Slicing. The model is flexible, so that by adjusting some of its elements, it can accommodate many different specific cases of importance. Yet, surprisingly, it still allows efficient optimization, in the sense that the global optimum can be well approximated by efficient algorithms.
Mining Functionally Related Genes with Semi-Supervised Learning
Venue: arXiv cs.LG 2020
Year: 2020

The study of biological processes can greatly benefit from tools that automatically predict gene functions or directly cluster genes based on shared functionality. Existing data mining methods predict protein functionality by exploiting data obtained from high-throughput experiments or meta-scale information from public databases. Most existing prediction tools are targeted at predicting protein functions that are described in the gene ontology (GO). However, in many cases biologists wish to discover functionally related genes for which GO terms are inadequate. In this paper, we introduce a rich set of features and use them in conjunction with semisupervised learning approaches in order to expand an initial set of seed genes to a larger cluster of functionally related genes. Among all the semi-supervised methods that were evaluated, the framework of learning with positive and unlabeled examples (LPU) is shown to be especially appropriate for mining functionally related genes. When evaluated on experimentally validated benchmark data, the LPU approaches1 significantly outperform a standard supervised learning algorithm as well as an established state-of-the-art method. Given an initial set of seed genes, our best performing approach could be used to mine functionally related genes in a wide range of organisms.
Towards Keypoint Guided Self-Supervised Depth Estimation
Venue: arXiv cs.CV 2020
Year: 2020

This paper proposes to use keypoints as a self-supervision clue for learning depth map estimation from a collection of input images. As ground truth depth from real images is difficult to obtain, there are many unsupervised and self-supervised approaches to depth estimation that have been proposed. Most of these unsupervised approaches use depth map and ego-motion estimations to reproject the pixels from the current image into the adjacent image from the image collection. Depth and ego-motion estimations are evaluated based on pixel intensity differences between the correspondent original and reprojected pixels. Instead of reprojecting the individual pixels, we propose to first select image keypoints in both images and then reproject and compare the correspondent keypoints of the two images. The keypoints should describe the distinctive image features well. By learning a deep model with and without the keypoint extraction technique, we show that using the keypoints improve the depth estimation learning. We also propose some future directions for keypoint-guided learning of structure-from-motion problems.
Smart Time-Multiplexing of Quads Solves the Multicamera Interference Problem
Venue: arXiv cs.CV 2020
Year: 2020

Time-of-flight (ToF) cameras are becoming increasingly popular for 3D imaging. Their optimal usage has been studied from the several aspects. One of the open research problems is the possibility of a multicamera interference problem when two or more ToF cameras are operating simultaneously. In this work we present an efficient method to synchronize multiple operating ToF cameras. Our method is based on the time-division multiplexing, but unlike traditional time multiplexing, it does not decrease the effective camera frame rate. Additionally, for unsynchronized cameras, we provide a robust method to extract from their corresponding video streams, frames which are not subject to multicamera interference problem. We demonstrate our approach through a series of experiments and with a different level of support available for triggering, ranging from a hardware triggering to purely random software triggering.
Evaluating the Performance of Twitter-based Exploit Detectors
Venue: arXiv cs.CR 2020
Year: 2020

Patch prioritization is a crucial aspect of information systems security, and knowledge of which vulnerabilities were exploited in the wild is a powerful tool to help systems administrators accomplish this task. The analysis of social media for this specific application can enhance the results and bring more agility by collecting data from online discussions and applying machine learning techniques to detect real-world exploits. In this paper, we use a technique that combines Twitter data with public database information to classify vulnerabilities as exploited or not-exploited. We analyze the behavior of different classifying algorithms, investigate the influence of different antivirus data as ground truth, and experiment with various time window sizes. Our findings suggest that using a Light Gradient Boosting Machine (LightGBM) can benefit the results, and for most cases, the statistics related to a tweet and the users who tweeted are more meaningful than the text tweeted. We also demonstrate the importance of using ground-truth data from security companies not mentioned in previous works.
PubSqueezer: A Text-Mining Web Tool to Transform Unstructured Documents into Structured Data
Venue: arXiv cs.IR 2020
Year: 2020

The amount of scientific papers published every day is daunting and constantly increasing. Keeping up with literature represents a challenge. If one wants to start exploring new topics it is hard to have a big picture without reading lots of articles. Furthermore, as one reads through literature, making mental connections is crucial to ask new questions which might lead to discoveries. In this work, I present a web tool which uses a Text Mining strategy to transform large collections of unstructured biomedical articles into structured data. Generated results give a quick overview on complex topics which can possibly suggest not explicitly reported information. In particular, I show two Data Science analyses. First, I present a literature based rare diseases network build using this tool in the hope that it will help clarify some aspects of these less popular pathologies. Secondly, I show how a literature based analysis conducted with PubSqueezer results allows to describe known facts about SARS-CoV-2. In one sentence, data generated with PubSqueezer make it easy to use scientific literate in any computational analysis such as machine learning, natural language processing etc. Availability: http://www.pubsqueezer.com
Affinity LCFCN: Learning to Segment Fish with Weak Supervision
Venue: arXiv cs.CV 2020
Year: 2020

Aquaculture industries rely on the availability of accurate fish body measurements, e.g., length, width and mass. Manual methods that rely on physical tools like rulers are time and labour intensive. Leading automatic approaches rely on fully-supervised segmentation models to acquire these measurements but these require collecting per-pixel labels -- also time consuming and laborious: i.e., it can take up to two minutes per fish to generate accurate segmentation labels, almost always requiring at least some manual intervention. We propose an automatic segmentation model efficiently trained on images labeled with only point-level supervision, where each fish is annotated with a single click. This labeling process requires significantly less manual intervention, averaging roughly one second per fish. Our approach uses a fully convolutional neural network with one branch that outputs per-pixel scores and another that outputs an affinity matrix. We aggregate these two outputs using a random walk to obtain the final, refined per-pixel segmentation output. We train the entire model end-to-end with an LCFCN loss, resulting in our A-LCFCN method. We validate our model on the DeepFish dataset, which contains many fish habitats from the north-eastern Australian region. Our experimental results confirm that A-LCFCN outperforms a fully-supervised segmentation model at fixed annotation budget. Moreover, we show that A-LCFCN achieves better segmentation results than LCFCN and a standard baseline. We have released the code at \url{https://github.com/IssamLaradji/affinity_lcfcn}.
ULSD: Unified Line Segment Detection across Pinhole, Fisheye, and Spherical Cameras
Venue: arXiv cs.CV 2020
Year: 2020

Line segment detection is essential for high-level tasks in computer vision and robotics. Currently, most stateof-the-art (SOTA) methods are dedicated to detecting straight line segments in undistorted pinhole images, thus distortions on fisheye or spherical images may largely degenerate their performance. Targeting at the unified line segment detection (ULSD) for both distorted and undistorted images, we propose to represent line segments with the Bezier curve model. Then the line segment detection is tackled by the Bezier curve regression with an end-to-end network, which is model-free and without any undistortion preprocessing. Experimental results on the pinhole, fisheye, and spherical image datasets validate the superiority of the proposed ULSD to the SOTA methods both in accuracy and efficiency (40.6fps for pinhole images). The source code is available at https://github.com/lh9171338/Unified-LineSegment-Detection.
Advances and Open Problems in Backscatter Networking
Venue: arXiv cs.NI 2020
Year: 2020

Despite significant research in backscatter communication over the past decade, key technical open problems remain under-explored. Here, we first systematically lay out the design space for backscatter networking and identify applications that make backscatter an attractive communication primitive. We then identify 10 research problems that remain to be solved in backscatter networking. These open problems span across the network stack to include circuits, embedded systems, physical layer, MAC and network protocols as well as applications. We believe that addressing these problems can help deliver on backscatter's promise of low-power ubiquitous connectivity.
Hi-UCD: A Large-scale Dataset for Urban Semantic Change Detection in Remote Sensing Imagery
Venue: arXiv cs.CV 2020
Year: 2020

With the acceleration of the urban expansion, urban change detection (UCD), as a significant and effective approach, can provide the change information with respect to geospatial objects for dynamical urban analysis. However, existing datasets suffer from three bottlenecks: (1) lack of high spatial resolution images; (2) lack of semantic annotation; (3) lack of long-range multi-temporal images. In this paper, we propose a large scale benchmark dataset, termed Hi-UCD. This dataset uses aerial images with a spatial resolution of 0.1 m provided by the Estonia Land Board, including three-time phases, and semantically annotated with nine classes of land cover to obtain the direction of ground objects change. It can be used for detecting and analyzing refined urban changes. We benchmark our dataset using some classic methods in binary and multi-class change detection. Experimental results show that Hi-UCD is challenging yet useful. We hope the Hi-UCD can become a strong benchmark accelerating future research.
Learning Behavior Trees with Genetic Programming in Unpredictable Environments
Venue: arXiv cs.RO 2020
Year: 2020

Modern industrial applications require robots to be able to operate in unpredictable environments, and programs to be created with a minimal effort, as there may be frequent changes to the task. In this paper, we show that genetic programming can be effectively used to learn the structure of a behavior tree (BT) to solve a robotic task in an unpredictable environment. Moreover, we propose to use a simple simulator for the learning and demonstrate that the learned BTs can solve the same task in a realistic simulator, reaching convergence without the need for task specific heuristics. The learned solution is tolerant to faults, making our method appealing for real robotic applications.
The ApposCorpus: A new multilingual, multi-domain dataset for factual appositive generation
Venue: arXiv cs.CL 2020
Year: 2020

News articles, image captions, product reviews and many other texts mention people and organizations whose name recognition could vary for different audiences. In such cases, background information about the named entities could be provided in the form of an appositive noun phrase, either written by a human or generated automatically. We expand on the previous work in appositive generation with a new, more realistic, end-to-end definition of the task, instantiated by a dataset that spans four languages (English, Spanish, German and Polish), two entity types (person and organization) and two domains (Wikipedia and News). We carry out an extensive analysis of the data and the task, pointing to the various modeling challenges it poses. The results we obtain with standard language generation methods show that the task is indeed non-trivial, and leaves plenty of room for improvement.
Learning to Respond with Your Favorite Stickers: A Framework of Unifying Multi-Modality and User Preference in Multi-Turn Dialog
Venue: arXiv cs.CL 2020
Year: 2020

Stickers with vivid and engaging expressions are becoming increasingly popular in online messaging apps, and some works are dedicated to automatically select sticker response by matching the stickers image with previous utterances. However, existing methods usually focus on measuring the matching degree between the dialog context and sticker image, which ignores the user preference of using stickers. Hence, in this paper, we propose to recommend an appropriate sticker to user based on multi-turn dialog context and sticker using history of user. Two main challenges are confronted in this task. One is to model the sticker preference of user based on the previous sticker selection history. Another challenge is to jointly fuse the user preference and the matching between dialog context and candidate sticker into final prediction making. To tackle these challenges, we propose a \emph{Preference Enhanced Sticker Response Selector} (PESRS) model. Specifically, PESRS first employs a convolutional based sticker image encoder and a self-attention based multi-turn dialog encoder to obtain the representation of stickers and utterances. Next, deep interaction network is proposed to conduct deep matching between the sticker and each utterance. Then, we model the user preference by using the recently selected stickers as input, and use a key-value memory network to store the preference representation. PESRS then learns the short-term and long-term dependency between all interaction results by a fusion network, and dynamically fuse the user preference representation into the final sticker selection prediction. Extensive experiments conducted on a large-scale real-world dialog dataset show that our model achieves the state-of-the-art performance for all commonly-used metrics. Experiments also verify the effectiveness of each component of PESRS.
A Precedence-Driven Approach for Concurrent Model Synchronization Scenarios using Triple Graph Grammars
Venue: arXiv cs.SE 2020
Year: 2020

Concurrent model synchronization is the task of restoring consistency between two correlated models after they have been changed concurrently and independently. To determine whether such concurrent model changes conflict with each other and to resolve these conflicts taking domain- or user-specific preferences into account is highly challenging. In this paper, we present a framework for concurrent model synchronization algorithms based on Triple Graph Grammars (TGGs). TGGs specify the consistency of correlated models using grammar rules; these rules can be used to derive different consistency restoration operations. Using TGGs, we infer a causal dependency relation for model elements that enables us to detect conflicts non-invasively. Different kinds of conflicts are detected first and resolved by the subsequent conflict resolution process. Users configure the overall synchronization process by orchestrating the application of consistency restoration fragments according to several conflict resolution strategies to achieve individual synchronization goals. As proof of concept, we have implemented this framework in the model transformation tool eMoflon. Our initial evaluation shows that the runtime of our presented approach scales with the size of model changes and conflicts, rather than model size.
Analyzing the Productivity of GitHub Teams based on Formation Phase Activity
Venue: arXiv cs.SE 2020
Year: 2020

Our goal is to understand the characteristics of high-performing teams on GitHub. Towards this end, we collect data from software repositories and evaluate teams by examining differences in productivity. Our study focuses on the team formation phase, the first six months after repository creation. To better understand team activity, we clustered repositories based on the proportion of their work activities and discovered three work styles in teams: toilers, communicators, and collaborators. Based on our results, we contend that early activities in software development repositories on GitHub establish coordination processes that enable effective collaborations over time.
DRAST -- A Deep Learning and AST Based Approach for Bug Localization
Venue: arXiv cs.SE 2020
Year: 2020

Context: Given a bug report and source code of the project, bug localization can help developers to focus on fixing probable buggy files rather than searching the entire source code repository. While existing research uses information retrieval (IR) and/or combination of machine learning (ML) or deep learning (DL) approaches, they focus primarily on benchmark Java projects, and also motivate the need for multi-language bug localization approach. Objective: To create a novel bug localization approach that leverages the syntactic structure of source code, bug report information and which can support multi-language projects along with a new dataset of C projects. Method: The proposed DRAST approach represents source code as code vectors by using its high-level AST and combines rVSM, an IR technique with ML/DL models such as Random Forest and Deep Neural Network regressor to rank the list of buggy files. We also use features such as textual similarity using IR techniques, lexical mismatch using DNNs, and history of the project using the metadata of BugC dataset. Results: We tested DRAST on seven projects from the BugC dataset, which consists of 2462 bug reports from 21 open-source C projects. The results show that DRAST can locate correct buggy files 90% of the time from top 1, 5, and 10 suggested files with MAP and MRR scores of above 90% for the randomly selected seven projects. We also tested DRAST on Tomcat and AspectJ, projects from benchmark dataset with better results at accuracy@1, MAP and MRR when compared with state-of-the-art. Conclusions: This paper presents a novel bug localization approach that works on C and Java projects and a bug localization C dataset along with a novel source code representation. The results for C projects using DRAST are promising and could motivate researchers/practitioners to focus on developing and creating multi-language bug localization approaches.
Look Before You Leap: Trusted User Interfaces for the Immersive Web
Venue: arXiv cs.HC 2020
Year: 2020

Part of what makes the web successful is that anyone can publish content and browsers maintain certain safety guarantees. For example, it's safe to travel between links and make other trust decisions on the web because users can always identify the location they are at. If we want virtual and augmented reality to be successful, we need that same safety. On the traditional, two-dimensional (2D) web, this user interface (UI) is provided by the browser bars and borders (also known as the chrome). However, the immersive, three-dimensional (3D) web has no concept of a browser chrome, preventing routine user inspection of URLs. In this paper, we discuss the unique challenges that fully immersive head-worn computing devices provide to this model, evaluate three different strategies for trusted immersive UI, and make specific recommendations to increase user safety and reduce the risks of spoofing.
Study of Automatic GPU Offloading Method from Various Language Applications
Venue: arXiv cs.DC 2020
Year: 2020

In recent years, utilization of heterogeneous hardware other than small core CPU such as GPU, FPGA or many core CPU is increasing. However, when using heterogeneous hardware, barriers of technical skills such as CUDA 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. However, the source language for offloading was mainly C/C++ language applications currently, and there was no research for common offloading for various language applications. In this paper, I study a common method for automatically offloading for various language applications not only in C language but also in Python and Java.