OpenRaiser/Intern-Atlas huggingface dataset:
DEEVA: A Deep Learning and IoT Based Computer Vision System to Address Safety and Security of Production Sites in Energy Industry
Venue: arXiv cs.CV 2020
Year: 2020

When it comes to addressing the safety/security related needs at different production/construction sites, accurate detection of the presence of workers, vehicles, equipment important and formed an integral part of computer vision-based surveillance systems (CVSS). Traditional CVSS systems focus on the use of different computer vision and pattern recognition algorithms overly reliant on manual extraction of features and small datasets, limiting their usage because of low accuracy, need for expert knowledge and high computational costs. The main objective of this paper is to provide decision makers at sites with a practical yet comprehensive deep learning and IoT based solution to tackle various computer vision related problems such as scene classification, object detection in scenes, semantic segmentation, scene captioning etc. Our overarching goal is to address the central question of What is happening at this site and where is it happening in an automated fashion minimizing the need for human resources dedicated to surveillance. We developed Deep ExxonMobil Eye for Video Analysis (DEEVA) package to handle scene classification, object detection, semantic segmentation and captioning of scenes in a hierarchical approach. The results reveal that transfer learning with the RetinaNet object detector is able to detect the presence of workers, different types of vehicles/construction equipment, safety related objects at a high level of accuracy (above 90%). With the help of deep learning to automatically extract features and IoT technology to automatic capture, transfer and process vast amount of realtime images, this framework is an important step towards the development of intelligent surveillance systems aimed at addressing myriads of open ended problems in the realm of security/safety monitoring, productivity assessments and future decision making.
Rectangular Pyramid Partitioning using Integrated Depth Sensors (RAPPIDS): A Fast Planner for Multicopter Navigation
Venue: arXiv cs.RO 2020
Year: 2020

We present RAPPIDS: a novel collision checking and planning algorithm for multicopters that is capable of quickly finding local collision-free trajectories given a single depth image from an onboard camera. The primary contribution of this work is a new pyramid-based spatial partitioning method that enables rapid collision detection between candidate trajectories and the environment. By leveraging the efficiency of our collision checking method, we shown how a local planning algorithm can be run at high rates on computationally constrained hardware, evaluating thousands of candidate trajectories in milliseconds. The performance of the algorithm is compared to existing collision checking methods in simulation, showing our method to be capable of evaluating orders of magnitude more trajectories per second. Experimental results are presented showing a quadcopter quickly navigating a previously unseen cluttered environment by running the algorithm on an ODROID-XU4 at 30 Hz.
Med7: a transferable clinical natural language processing model for electronic health records
Venue: arXiv cs.CL 2020
Year: 2020

The field of clinical natural language processing has been advanced significantly since the introduction of deep learning models. The self-supervised representation learning and the transfer learning paradigm became the methods of choice in many natural language processing application, in particular in the settings with the dearth of high quality manually annotated data. Electronic health record systems are ubiquitous and the majority of patients' data are now being collected electronically and in particular in the form of free text. Identification of medical concepts and information extraction is a challenging task, yet important ingredient for parsing unstructured data into structured and tabulated format for downstream analytical tasks. In this work we introduced a named-entity recognition model for clinical natural language processing. The model is trained to recognise seven categories: drug names, route, frequency, dosage, strength, form, duration. The model was first self-supervisedly pre-trained by predicting the next word, using a collection of 2 million free-text patients' records from MIMIC-III corpora and then fine-tuned on the named-entity recognition task. The model achieved a lenient (strict) micro-averaged F1 score of 0.957 (0.893) across all seven categories. Additionally, we evaluated the transferability of the developed model using the data from the Intensive Care Unit in the US to secondary care mental health records (CRIS) in the UK. A direct application of the trained NER model to CRIS data resulted in reduced performance of F1=0.762, however after fine-tuning on a small sample from CRIS, the model achieved a reasonable performance of F1=0.944. This demonstrated that despite a close similarity between the data sets and the NER tasks, it is essential to fine-tune on the target domain data in order to achieve more accurate results.
Gastric histopathology image segmentation using a hierarchical conditional random field
Venue: arXiv cs.CV 2020
Year: 2020

For the Convolutional Neural Networks (CNNs) applied in the intelligent diagnosis of gastric cancer, existing methods mostly focus on individual characteristics or network frameworks without a policy to depict the integral information. Mainly, Conditional Random Field (CRF), an efficient and stable algorithm for analyzing images containing complicated contents, can characterize spatial relation in images. In this paper, a novel Hierarchical Conditional Random Field (HCRF) based Gastric Histopathology Image Segmentation (GHIS) method is proposed, which can automatically localize abnormal (cancer) regions in gastric histopathology images obtained by an optical microscope to assist histopathologists in medical work. This HCRF model is built up with higher order potentials, including pixel-level and patch-level potentials, and graph-based post-processing is applied to further improve its segmentation performance. Especially, a CNN is trained to build up the pixel-level potentials and another three CNNs are fine-tuned to build up the patch-level potentials for sufficient spatial segmentation information. In the experiment, a hematoxylin and eosin (H&E) stained gastric histopathological dataset with 560 abnormal images are divided into training, validation and test sets with a ratio of 1 : 1 : 2. Finally, segmentation accuracy, recall and specificity of 78.91%, 65.59%, and 81.33% are achieved on the test set. Our HCRF model demonstrates high segmentation performance and shows its effectiveness and future potential in the GHIS field.
Safe Reinforcement Learning for Autonomous Vehicles through Parallel Constrained Policy Optimization
Venue: arXiv cs.LG 2020
Year: 2020

Reinforcement learning (RL) is attracting increasing interests in autonomous driving due to its potential to solve complex classification and control problems. However, existing RL algorithms are rarely applied to real vehicles for two predominant problems: behaviours are unexplainable, and they cannot guarantee safety under new scenarios. This paper presents a safe RL algorithm, called Parallel Constrained Policy Optimization (PCPO), for two autonomous driving tasks. PCPO extends today's common actor-critic architecture to a three-component learning framework, in which three neural networks are used to approximate the policy function, value function and a newly added risk function, respectively. Meanwhile, a trust region constraint is added to allow large update steps without breaking the monotonic improvement condition. To ensure the feasibility of safety constrained problems, synchronized parallel learners are employed to explore different state spaces, which accelerates learning and policy-update. The simulations of two scenarios for autonomous vehicles confirm we can ensure safety while achieving fast learning.
Convo: What does conversational programming need? An exploration of machine learning interface design
Venue: arXiv cs.HC 2020
Year: 2020

Vast improvements in natural language understanding and speech recognition have paved the way for conversational interaction with computers. While conversational agents have often been used for short goal-oriented dialog, we know little about agents for developing computer programs. To explore the utility of natural language for programming, we conducted a study ($n$=45) comparing different input methods to a conversational programming system we developed. Participants completed novice and advanced tasks using voice-based, text-based, and voice-or-text-based systems. We found that users appreciated aspects of each system (e.g., voice-input efficiency, text-input precision) and that novice users were more optimistic about programming using voice-input than advanced users. Our results show that future conversational programming tools should be tailored to users' programming experience and allow users to choose their preferred input mode. To reduce cognitive load, future interfaces can incorporate visualizations and possess custom natural language understanding and speech recognition models for programming.
Framework of Fracture Network Modeling using Conditioned Data with Sequential Gaussian Simulation
Venue: arXiv cs.CE 2020
Year: 2020

The fracture characterization using a geostatistical tool with conditioning data is a computationally efficient tool for subsurface flow and transport applications. The main objective of the paper is to propose a framework of geostatistical method to model the fracture network. In the method, we have chosen neighborhood area to apply the Gaussian Sequential Simulation in order to generate the fracture network in the unknown region. The angle was propagated from the seed where direction is guided by the neighborhood data in simulation regime. Initial seeds can be distributed by Poisson procedure. The method is applied for geological faults from the Central Kazakhstan and for field data from Scotland, UK. The simulation results are compatible with the original fracture network in the flow and transport modeling setting. From the research that has been carried out, it is possible to conclude that the numerical simulation of fracture network is a valuable tool in the subsurface flow and transport applications.
Bounded Regret for Finitely Parameterized Multi-Armed Bandits
Venue: arXiv cs.LG 2020
Year: 2020

We consider the problem of finitely parameterized multi-armed bandits where the model of the underlying stochastic environment can be characterized based on a common unknown parameter. The true parameter is unknown to the learning agent. However, the set of possible parameters, which is finite, is known a priori. We propose an algorithm that is simple and easy to implement, which we call Finitely Parameterized Upper Confidence Bound (FP-UCB) algorithm, which uses the information about the underlying parameter set for faster learning. In particular, we show that the FP-UCB algorithm achieves a bounded regret under some structural condition on the underlying parameter set. We also show that, if the underlying parameter set does not satisfy the necessary structural condition, the FP-UCB algorithm achieves a logarithmic regret, but with a smaller preceding constant compared to the standard UCB algorithm. We also validate the superior performance of the FP-UCB algorithm through extensive numerical simulations.
The Prolog Debugger and Declarative Programming. Examples
Venue: arXiv cs.PL 2020
Year: 2020

This paper contains examples for a companion paper "The Prolog Debugger and Declarative Programming", which discusses (in)adequacy of the Prolog debugger for declarative programming. Logic programming is a declarative programming paradigm. Programming language Prolog makes logic programming possible, at least to a substantial extent. However the Prolog debugger works solely in terms of the operational semantics. So it is incompatible with declarative programming. The companion paper tries to find methods of using it from the declarative point of view. Here we provide examples of applying them.
SoK: A Survey of Open-Source Threat Emulators
Venue: arXiv cs.CR 2020
Year: 2020

Threat emulators are tools or sets of scripts that emulate cyber attacks or malicious behavior. They can be used to create and launch single procedure attacks and multi-step attacks; the resulting attacks may be known or unknown cyber attacks. The motivation for using threat emulators varies and includes the need to perform automated security audits in organizations or reduce the size of red teams in order to lower pen testing costs; or the desire to create baseline tests for security tools under development or supply pen testers with another tool in their arsenal. In this paper, we review and compare various open-source threat emulators. We focus on tactics and techniques from the MITRE ATT&CK Enterprise matrix and determine whether they can be performed and tested with the emulators. We develop a comprehensive methodology for our qualitative and quantitative comparison of threat emulators with respect to general features, such as prerequisites, attack definition, cleanup, and more. Finally, we discuss the circumstances in which one threat emulator is preferred over another. This survey can help security teams, security developers, and product deployment teams examine their network environment or products with the most suitable threat emulator. Using the guidelines provided, a team can select the threat emulator that best meets their needs without evaluating all of them.
Are You an Introvert or Extrovert? Accurate Classification With Only Ten Predictors
Venue: arXiv cs.LG 2020
Year: 2020

This paper investigates how accurately the prediction of being an introvert vs. extrovert can be made with less than ten predictors. The study is based on a previous data collection of 7161 respondents of a survey on 91 personality and 3 demographic items. The results show that it is possible to effectively reduce the size of this measurement instrument from 94 to 10 features with a performance loss of only 1%, achieving an accuracy of 73.81% on unseen data. Class imbalance correction methods like SMOTE or ADASYN showed considerable improvement on the validation set but only minor performance improvement on the testing set.
A Metric for Evaluating Neural Input Representation in Supervised Learning Networks
Venue: arXiv cs.NE 2020
Year: 2020

Supervised learning has long been attributed to several feed-forward neural circuits within the brain, with attention being paid to the cerebellar granular layer. The focus of this study is to evaluate the input activity representation of these feed-forward neural networks. The activity of cerebellar granule cells is conveyed by parallel fibers and translated into Purkinje cell activity; the sole output of the cerebellar cortex. The learning process at this parallel-fiber-to-Purkinje-cell connection makes each Purkinje cell sensitive to a set of specific cerebellar states, determined by the granule-cell activity during a certain time window. A Purkinje cell becomes sensitive to each neural input state and, consequently, the network operates as a function able to generate a desired output for each provided input by means of supervised learning. However, not all sets of Purkinje cell responses can be assigned to any set of input states due to the network's own limitations (inherent to the network neurobiological substrate), that is, not all input-output mapping can be learned. A limiting factor is the representation of the input states through granule-cell activity. The quality of this representation will determine the capacity of the network to learn a varied set of outputs. In this study we present an algorithm for evaluating quantitatively the level of compatibility/interference amongst a set of given cerebellar states according to their representation (granule-cell activation patterns) without the need for actually conducting simulations and network training. The algorithm input consists of a real-number matrix that codifies the activity level of every considered granule-cell in each state. The capability of this representation to generate a varied set of outputs is evaluated geometrically, thus resulting in a real number that assesses the goodness of the representation
Marketplace for AI Models
Venue: arXiv cs.CY 2020
Year: 2020

Artificial intelligence shows promise for solving many practical societal problems in areas such as healthcare and transportation. However, the current mechanisms for AI model diffusion such as Github code repositories, academic project webpages, and commercial AI marketplaces have some limitations; for example, a lack of monetization methods, model traceability, and model auditabilty. In this work, we sketch guidelines for a new AI diffusion method based on a decentralized online marketplace. We consider the technical, economic, and regulatory aspects of such a marketplace including a discussion of solutions for problems in these areas. Finally, we include a comparative analysis of several current AI marketplaces that are already available or in development. We find that most of these marketplaces are centralized commercial marketplaces with relatively few models.
Robotic Grasping through Combined Image-Based Grasp Proposal and 3D Reconstruction
Venue: arXiv cs.RO 2020
Year: 2020

We present a novel approach to robotic grasp planning using both a learned grasp proposal network and a learned 3D shape reconstruction network. Our system generates 6-DOF grasps from a single RGB-D image of the target object, which is provided as input to both networks. By using the geometric reconstruction to refine the the candidate grasp produced by the grasp proposal network, our system is able to accurately grasp both known and unknown objects, even when the grasp location on the object is not visible in the input image. This paper presents the network architectures, training procedures, and grasp refinement method that comprise our system. Experiments demonstrate the efficacy of our system at grasping both known and unknown objects (91% success rate in a physical robot environment, 84% success rate in a simulated environment). We additionally perform ablation studies that show the benefits of combining a learned grasp proposal with geometric reconstruction for grasping, and also show that our system outperforms several baselines in a grasping task.
Laser Induced Speckle as a Foundation for Physical Security and Optical Computing
Venue: arXiv cs.ET 2020
Year: 2020

We present a photonic system that exploits the speckle generated by the interaction of a laser source and a semitransparent scattering medium, in our case a large-core optical fiber, as a physical root of trust for cryptographic applications, while the same configuration can act as a high-rate machine learning paradigm.
Artificial Intelligence, Chaos, Prediction and Understanding in Science
Venue: arXiv cs.GL 2020
Year: 2020

Machine learning and deep learning techniques are contributing much to the advancement of science. Their powerful predictive capabilities appear in numerous disciplines, including chaotic dynamics, but they miss understanding. The main thesis here is that prediction and understanding are two very different and important ideas that should guide us about the progress of science. Furthermore, it is emphasized the important role played by that nonlinear dynamical systems for the process of understanding. The path of the future of science will be marked by a constructive dialogue between big data and big theory, without which we cannot understand.
Semantic sensor fusion: from camera to sparse lidar information
Venue: arXiv cs.RO 2020
Year: 2020

To navigate through urban roads, an automated vehicle must be able to perceive and recognize objects in a three-dimensional environment. A high-level contextual understanding of the surroundings is necessary to plan and execute accurate driving maneuvers. This paper presents an approach to fuse different sensory information, Light Detection and Ranging (lidar) scans and camera images. The output of a convolutional neural network (CNN) is used as classifier to obtain the labels of the environment. The transference of semantic information between the labelled image and the lidar point cloud is performed in four steps: initially, we use heuristic methods to associate probabilities to all the semantic classes contained in the labelled images. Then, the lidar points are corrected to compensate for the vehicle's motion given the difference between the timestamps of each lidar scan and camera image. In a third step, we calculate the pixel coordinate for the corresponding camera image. In the last step we perform the transfer of semantic information from the heuristic probability images to the lidar frame, while removing the lidar information that is not visible to the camera. We tested our approach in the Usyd Dataset \cite{usyd_dataset}, obtaining qualitative and quantitative results that demonstrate the validity of our probabilistic sensory fusion approach.
GarmentGAN: Photo-realistic Adversarial Fashion Transfer
Venue: arXiv cs.CV 2020
Year: 2020

The garment transfer problem comprises two tasks: learning to separate a person's body (pose, shape, color) from their clothing (garment type, shape, style) and then generating new images of the wearer dressed in arbitrary garments. We present GarmentGAN, a new algorithm that performs image-based garment transfer through generative adversarial methods. The GarmentGAN framework allows users to virtually try-on items before purchase and generalizes to various apparel types. GarmentGAN requires as input only two images, namely, a picture of the target fashion item and an image containing the customer. The output is a synthetic image wherein the customer is wearing the target apparel. In order to make the generated image look photo-realistic, we employ the use of novel generative adversarial techniques. GarmentGAN improves on existing methods in the realism of generated imagery and solves various problems related to self-occlusions. Our proposed model incorporates additional information during training, utilizing both segmentation maps and body key-point information. We show qualitative and quantitative comparisons to several other networks to demonstrate the effectiveness of this technique.