OpenRaiser/Intern-Atlas huggingface dataset:
On the application of transfer learning in prognostics and health management
Venue: arXiv cs.LG 2020
Year: 2020

Advancements in sensing and computing technologies, the development of human and computer interaction frameworks, big data storage capabilities, and the emergence of cloud storage and could computing have resulted in an abundance of data in the modern industry. This data availability has encouraged researchers and industry practitioners to rely on data-based machine learning, especially deep learning, models for fault diagnostics and prognostics more than ever. These models provide unique advantages, however, their performance is heavily dependent on the training data and how well that data represents the test data. This issue mandates fine-tuning and even training the models from scratch when there is a slight change in operating conditions or equipment. Transfer learning is an approach that can remedy this issue by keeping portions of what is learned from previous training and transferring them to the new application. In this paper, a unified definition for transfer learning and its different types is provided, Prognostics and Health Management (PHM) studies that have used transfer learning are reviewed in detail, and finally, a discussion on transfer learning application considerations and gaps is provided for improving the applicability of transfer learning in PHM.
A Survey on Sensor Technologies for Unmanned Ground Vehicles
Venue: arXiv cs.CV 2020
Year: 2020

Unmanned ground vehicles have a huge development potential in both civilian and military fields, and have become the focus of research in various countries. In addition, high-precision, high-reliability sensors are significant for UGVs' efficient operation. This paper proposes a brief review on sensor technologies for UGVs. Firstly, characteristics of various sensors are introduced. Then the strengths and weaknesses of different sensors as well as their application scenarios are compared. Furthermore, sensor applications in some existing UGVs are summarized. Finally, the hotspots of sensor technologies are forecasted to point the development direction.
Design and Implementation of SMARTHO -- A Network Initiated Handover mechanism in NG-RAN, on P4-based Xilinx NetFPGA switches
Venue: arXiv cs.NI 2020
Year: 2020

This report deals with the design of handover schemes for radio access networks (RAN) in 5G networks, using programmable data plane switches. In 5G networks, the NG-RAN architecture splits the Base Band Unit (BBU) into Central and Distributed Units (CU and DU). This structure has created a mid-haul Network, connecting CUs and DUs. The recent advancements in dataplane programmability can be used to enhance system performance. We show how P4 switches can be used to parse the packets between DU, CU, and Back Haul (Core Network) for potential system improvements. In particular, we consider the scenario of mobile handover. The proposed protocol is called SMARTHO, illustrating a smart handover. Programming Protocol-Independent Packet Processors (P4) is a programming language designed to support specification and programming the forwarding plane behavior of network switches/routers. In SMARTHO, we use P4 Switches to intervene in the handover process for fixed-path mobile users. A resource pre-allocation scheme that reserves resources before the UE reaches a future cell, is proposed. The solution is implemented using a P4-based switch introduced between the CU and the DU. The P4 switch is used to spoof the behavior of User Equipment (UE) and perform the resource allocation in advance. The proposed SMARTHO framework is implemented in the mininet emulation environment and in a reconfigurable hardware environment using NetFPGA-SUME boards. The emulation results show a handover response time improvement of 18% for a tandem of two HOs and 25% for a tandem of three HOs. For testbed implementation, we used NetFPGA-SUME boards as P4 switches. The handover time was measured to be approximately 50~milliseconds in the experiments conducted.
SAXSDOG: open software for real-time azimuthal integration of 2D scattering images
Venue: arXiv cs.OH 2020
Year: 2020

In-situ small- and wide-angle scattering experiments at synchrotrons often result in massive amounts of data within seconds only. Especially during such beamtimes, processing of the acquired data online, so without mentionable delay, is key to obtain feedback on failure or success of the experiment. We thus developed SAXSDOG, a python based environment for real-time azimuthal integration of large-area scattering-images. The software is primarily designed for dedicated data-pipelines: once a scattering image is transferred from the detector onto the storage-unit, it is automatically integrated and pre-evaluated using integral parameters within milliseconds. The control and configuration of the underlying server-based processes is done via a graphical user interface SAXSLEASH, which visualizes the resulting 1D data together with integral classifiers in real time. SAXSDOG further includes a portable 'take-home' version for users that runs on standalone computers, enabling its use in labs or at the preferred workspace.
Lazy Greedy Hypervolume Subset Selection from Large Candidate Solution Sets
Venue: arXiv cs.NE 2020
Year: 2020

Subset selection is a popular topic in recent years and a number of subset selection methods have been proposed. Among those methods, hypervolume subset selection is widely used. Greedy hypervolume subset selection algorithms can achieve good approximations to the optimal subset. However, when the candidate set is large (e.g., an unbounded external archive with a large number of solutions), the algorithm is very time-consuming. In this paper, we propose a new lazy greedy algorithm exploiting the submodular property of the hypervolume indicator. The core idea is to avoid unnecessary hypervolume contribution calculation when finding the solution with the largest contribution. Experimental results show that the proposed algorithm is hundreds of times faster than the original greedy inclusion algorithm and several times faster than the fastest known greedy inclusion algorithm on many test problems.
Sensor-Based Control for Collaborative Robots: Fundamentals, Challenges and Opportunities
Venue: arXiv cs.RO 2020
Year: 2020

The objective of this paper is to present a systematic review of existing sensor-based control methodologies for applications that involve direct interaction between humans and robots, in the form of either physical collaboration or safe coexistence. To this end, we first introduce the basic formulation of the sensor-servo problem, then present the most common approaches: vision-based, touch-based, audio-based, and distance-based control. Afterwards, we discuss and formalize the methods that integrate heterogeneous sensors at the control level. The surveyed body of literature is classified according to the type of sensor, to the way multiple measurements are combined, and to the target objectives and applications. Finally, we discuss open problems, potential applications, and future research directions.
Healing Spaces: Feasibility of a Multisensory Experience for Older Adults with Advanced Dementia and their Caregivers
Venue: arXiv cs.CY 2020
Year: 2020

Healing Spaces proposes a new approach to multisensory interventions that show potential in ameliorating the behavioral and psychological symptoms of advanced dementia in older adults. Using smart technology, the project combines both digital and physical components to transform spaces and create unified, curated sensory experiences that provide meaningful context for interaction, and are easy for caregivers to deliver. A usability study was conducted for the Healing Spaces app followed by a feasibility evaluation of the full experience in a memory care facility recruiting caregivers, and residents in advanced stages of dementia. The feasibility evaluation successfully illuminated strengths as well as areas for improvement for the Healing Spaces experience in a memory care setting with older adults with advanced dementia. Caregivers and facility managers expressed interest in continuing to use Healing Spaces with the residents of the facility. Lessons learned about the technical and logistical implementation of Healing Spaces are discussed, as well as future directions for study design and potential therapeutic value of the experience.
Multi-Sensor Next-Best-View Planning as Matroid-Constrained Submodular Maximization
Venue: arXiv cs.RO 2020
Year: 2020

3D scene models are useful in robotics for tasks such as path planning, object manipulation, and structural inspection. We consider the problem of creating a 3D model using depth images captured by a team of multiple robots. Each robot selects a viewpoint and captures a depth image from it, and the images are fused to update the scene model. The process is repeated until a scene model of desired quality is obtained. Next-best-view planning uses the current scene model to select the next viewpoints. The objective is to select viewpoints so that the images captured using them improve the quality of the scene model the most. In this paper, we address next-best-view planning for multiple depth cameras. We propose a utility function that scores sets of viewpoints and avoids overlap between multiple sensors. We show that multi-sensor next-best-view planning with this utility function is an instance of submodular maximization under a matroid constraint. This allows the planning problem to be solved by a polynomial-time greedy algorithm that yields a solution within a constant factor from the optimal. We evaluate the performance of our planning algorithm in simulated experiments with up to 8 sensors, and in real-world experiments using two robot arms equipped with depth cameras.
Playing Chess with Limited Look Ahead
Venue: arXiv cs.AI 2020
Year: 2020

We have seen numerous machine learning methods tackle the game of chess over the years. However, one common element in these works is the necessity of a finely optimized look ahead algorithm. The particular interest of this research lies with creating a chess engine that is highly capable, but restricted in its look ahead depth. We train a deep neural network to serve as a static evaluation function, which is accompanied by a relatively simple look ahead algorithm. We show that our static evaluation function has encoded some semblance of look ahead knowledge, and is comparable to classical evaluation functions. The strength of our chess engine is assessed by comparing its proposed moves against those proposed by Stockfish. We show that, despite strict restrictions on look ahead depth, our engine recommends moves of equal strength in roughly $83\%$ of our sample positions.
Scalable Role-based Access Control Using The EOS Blockchain
Venue: arXiv cs.CR 2020
Year: 2020

Role-based access control (RBAC) policies represent the rights of subjects in terms of roles to access resources. This research proposes a scalable, flexible and auditable RBAC system using the EOS blockchain platform to meet the security requirements of organizations. The EOS blockchain platform for developing smart contract and decentralized applications (DAPPs) aims to address the scalability problem found in existing blockchain platforms. This smart contract platform aims to eliminate transaction fees while conducting millions of transactions per second. In our proposed approach, the EOS blockchain transparently stores RBAC policies. Administrative roles control access to resources at a higher level according to the way organisations perform operations. An organisation creates roles, role hierarchies and constraints to regulate user actions. Therefore, once an RBAC framework is established, the administrative user (issuer) only needs to grant and revoke roles to support changes in the organisational structure. Our proposed blockchain-based RBAC supports delegation capabilities using gaseless transactions which makes it adoptable and appealing in a large number of application scenarios. Our proposed solution is application-agnostic and well-suited for diverse use cases. Existing state-of-the art security frameworks are not suitable due to the difficulty of scale, higher cost and single point of failure. Consequently, organisations demand a scalable, cost-effective and lightweight access control solution which can better protect their privacy as well. A proof of concept implementation is developed based on the EOS blockchain. Our experimental results and analysis clearly show that our EOS blockchain-based RBAC outperforms existing blockchain platforms in terms of cost, latency, block generation time, contract execution time and throughput.
Neuro-Symbolic Generative Art: A Preliminary Study
Venue: arXiv cs.AI 2020
Year: 2020

There are two classes of generative art approaches: neural, where a deep model is trained to generate samples from a data distribution, and symbolic or algorithmic, where an artist designs the primary parameters and an autonomous system generates samples within these constraints. In this work, we propose a new hybrid genre: neuro-symbolic generative art. As a preliminary study, we train a generative deep neural network on samples from the symbolic approach. We demonstrate through human studies that subjects find the final artifacts and the creation process using our neuro-symbolic approach to be more creative than the symbolic approach 61% and 82% of the time respectively.
Characterizing Online Vandalism: A Rational Choice Perspective
Venue: arXiv cs.SI 2020
Year: 2020

What factors influence the decision to vandalize? Although the harm is clear, the benefit to the vandal is less clear. In many cases, the thing being damaged may itself be something the vandal uses or enjoys. Vandalism holds communicative value: perhaps to the vandal themselves, to some audience at whom the vandalism is aimed, and to the general public. Viewing vandals as rational community participants despite their antinormative behavior offers the possibility of engaging with or countering their choices in novel ways. Rational choice theory (RCT) as applied in value expectancy theory (VET) offers a strategy for characterizing behaviors in a framework of rational choices, and begins with the supposition that subject to some weighting of personal preferences and constraints, individuals maximize their own utility by committing acts of vandalism. This study applies the framework of RCT and VET to gain insight into vandals' preferences and constraints. Using a mixed-methods analysis of Wikipedia, I combine social computing and criminological perspectives on vandalism to propose an ontology of vandalism for online content communities. I use this ontology to categorize 141 instances of vandalism and find that the character of vandalistic acts varies by vandals' relative identifiability, policy history with Wikipedia, and the effort required to vandalize.
A Case for Lifetime Reliability-Aware Neuromorphic Computing
Venue: arXiv cs.NE 2020
Year: 2020

Neuromorphic computing with non-volatile memory (NVM) can significantly improve performance and lower energy consumption of machine learning tasks implemented using spike-based computations and bio-inspired learning algorithms. High voltages required to operate certain NVMs such as phase-change memory (PCM) can accelerate aging in a neuron's CMOS circuit, thereby reducing the lifetime of neuromorphic hardware. In this work, we evaluate the long-term, i.e., lifetime reliability impact of executing state-of-the-art machine learning tasks on a neuromorphic hardware, considering failure models such as negative bias temperature instability (NBTI) and time-dependent dielectric breakdown (TDDB). Based on such formulation, we show the reliability-performance trade-off obtained due to periodic relaxation of neuromorphic circuits, i.e., a stop-and-go style of neuromorphic computing.
Sentiment Analysis on Customer Responses
Venue: arXiv cs.CL 2020
Year: 2020

Sentiment analysis is one of the fastest spreading research areas in computer science, making it challenging to keep track of all the activities in the area. We present a customer feedback reviews on product, where we utilize opinion mining, text mining and sentiments, which has affected the surrounded world by changing their opinion on a specific product. Data used in this study are online product reviews collected from Amazon.com. We performed a comparative sentiment analysis of retrieved reviews. This research paper provides you with sentimental analysis of various smart phone opinions on smart phones dividing them Positive, Negative and Neutral Behaviour.
A Systematic Evaluation of Object Detection Networks for Scientific Plots
Venue: arXiv cs.CV 2020
Year: 2020

Are existing object detection methods adequate for detecting text and visual elements in scientific plots which are arguably different than the objects found in natural images? To answer this question, we train and compare the accuracy of various SOTA object detection networks on the PlotQA dataset. At the standard IOU setting of 0.5, most networks perform well with mAP scores greater than 80% in detecting the relatively simple objects in plots. However, the performance drops drastically when evaluated at a stricter IOU of 0.9 with the best model giving a mAP of 35.70%. Note that such a stricter evaluation is essential when dealing with scientific plots where even minor localisation errors can lead to large errors in downstream numerical inferences. Given this poor performance, we propose minor modifications to existing models by combining ideas from different object detection networks. While this significantly improves the performance, there are still 2 main issues: (i) performance on text objects which are essential for reasoning is very poor, and (ii) inference time is unacceptably large considering the simplicity of plots. To solve this open problem, we make a series of contributions: (a) an efficient region proposal method based on Laplacian edge detectors, (b) a feature representation of region proposals that includes neighbouring information, (c) a linking component to join multiple region proposals for detecting longer textual objects, and (d) a custom loss function that combines a smooth L1-loss with an IOU-based loss. Combining these ideas, our final model is very accurate at extreme IOU values achieving a mAP of 93.44%@0.9 IOU. Simultaneously, our model is very efficient with an inference time 16x lesser than the current models, including one-stage detectors. With these contributions, we enable further exploration on the automated reasoning of plots.
EmotionGIF-Yankee: A Sentiment Classifier with Robust Model Based Ensemble Methods
Venue: arXiv cs.CL 2020
Year: 2020

This paper provides a method to classify sentiment with robust model based ensemble methods. We preprocess tweet data to enhance coverage of tokenizer. To reduce domain bias, we first train tweet dataset for pre-trained language model. Besides, each classifier has its strengths and weakness, we leverage different types of models with ensemble methods: average and power weighted sum. From the experiments, we show that our approach has achieved positive effect for sentiment classification. Our system reached third place among 26 teams from the evaluation in SocialNLP 2020 EmotionGIF competition.
TilinGNN: Learning to Tile with Self-Supervised Graph Neural Network
Venue: arXiv cs.CV 2020
Year: 2020

We introduce the first neural optimization framework to solve a classical instance of the tiling problem. Namely, we seek a non-periodic tiling of an arbitrary 2D shape using one or more types of tiles: the tiles maximally fill the shape's interior without overlaps or holes. To start, we reformulate tiling as a graph problem by modeling candidate tile locations in the target shape as graph nodes and connectivity between tile locations as edges. Further, we build a graph convolutional neural network, coined TilinGNN, to progressively propagate and aggregate features over graph edges and predict tile placements. TilinGNN is trained by maximizing the tiling coverage on target shapes, while avoiding overlaps and holes between the tiles. Importantly, our network is self-supervised, as we articulate these criteria as loss terms defined on the network outputs, without the need of ground-truth tiling solutions. After training, the runtime of TilinGNN is roughly linear to the number of candidate tile locations, significantly outperforming traditional combinatorial search. We conducted various experiments on a variety of shapes to showcase the speed and versatility of TilinGNN. We also present comparisons to alternative methods and manual solutions, robustness analysis, and ablation studies to demonstrate the quality of our approach.
BeeTrace: A Unified Platform for Secure Contact Tracing that Breaks Data Silos
Venue: arXiv cs.CR 2020
Year: 2020

Contact tracing is an important method to control the spread of an infectious disease such as COVID-19. However, existing contact tracing methods alone cannot provide sufficient coverage and do not successfully address privacy concerns of the participating entities. Current solutions do not utilize the huge volume of data stored in business databases and individual digital devices. This information is typically stored in data silos and cannot be used due to regulations in place. To successfully unlock the potential of contact tracing, we need to consider both data utilization from multiple sources and the privacy of the participating parties. To this end, we propose BeeTrace, a unified platform that breaks data silos and deploys state-of-the-art cryptographic protocols to guarantee privacy goals.
EvilCoder: Automated Bug Insertion
Venue: arXiv cs.CR 2020
Year: 2020

The art of finding software vulnerabilities has been covered extensively in the literature and there is a huge body of work on this topic. In contrast, the intentional insertion of exploitable, security-critical bugs has received little (public) attention yet. Wanting more bugs seems to be counterproductive at first sight, but the comprehensive evaluation of bug-finding techniques suffers from a lack of ground truth and the scarcity of bugs. In this paper, we propose EvilCoder, a system to automatically find potentially vulnerable source code locations and modify the source code to be actually vulnerable. More specifically, we leverage automated program analysis techniques to find sensitive sinks which match typical bug patterns (e.g., a sensitive API function with a preceding sanity check), and try to find data-flow connections to user-controlled sources. We then transform the source code such that exploitation becomes possible, for example by removing or modifying input sanitization or other types of security checks. Our tool is designed to randomly pick vulnerable locations and possible modifications, such that it can generate numerous different vulnerabilities on the same software corpus. We evaluated our tool on several open-source projects such as for example libpng and vsftpd, where we found between 22 and 158 unique connected source-sink pairs per project. This translates to hundreds of potentially vulnerable data-flow paths and hundreds of bugs we can insert. We hope to support future bug-finding techniques by supplying freshly generated, bug-ridden test corpora so that such techniques can (finally) be evaluated and compared in a comprehensive and statistically meaningful way.