OpenRaiser/Intern-Atlas huggingface dataset:
Home Energy Management Systems: Operation and Resilience of Heuristics against Cyberattacks
Venue: arXiv cs.CR 2021
Year: 2021

Internet of Things (IoT) and advanced communication technologies have demonstrated great potential to manage residential energy resources by enabling demand-side management (DSM). Home energy management systems (HEMSs) can automatically control electricity production and usage inside homes using DSM techniques. These HEMSs will wirelessly collect information from hardware installed in the power system and in homes with the objective to intelligently and efficiently optimize electricity usage and minimize costs. However, HEMSs can be vulnerable to cyberattacks that target the electricity pricing model. The cyberattacker manipulates the pricing information collected by a customer's HEMS to misguide its algorithms toward non-optimal solutions. The customer's electricity bill increases, and additional peaks are created without being detected by the system operator. This article introduces demand-response (DR)-based DSM in HEMSs and discusses DR optimization using heuristic algorithms. Moreover, it discusses the possibilities and impacts of cyberattacks, their effectiveness, and the degree of resilience of heuristic algorithms against cyberattacks. This article also opens research questions and shows prospective directions.
A Learned Stereo Depth System for Robotic Manipulation in Homes
Venue: arXiv cs.RO 2021
Year: 2021

We present a passive stereo depth system that produces dense and accurate point clouds optimized for human environments, including dark, textureless, thin, reflective and specular surfaces and objects, at 2560x2048 resolution, with 384 disparities, in 30 ms. The system consists of an algorithm combining learned stereo matching with engineered filtering, a training and data-mixing methodology, and a sensor hardware design. Our architecture is 15x faster than approaches that perform similarly on the Middlebury and Flying Things Stereo Benchmarks. To effectively supervise the training of this model, we combine real data labelled using off-the-shelf depth sensors, as well as a number of different rendered, simulated labeled datasets. We demonstrate the efficacy of our system by presenting a large number of qualitative results in the form of depth maps and point-clouds, experiments validating the metric accuracy of our system and comparisons to other sensors on challenging objects and scenes. We also show the competitiveness of our algorithm compared to state-of-the-art learned models using the Middlebury and FlyingThings datasets.
Performance Improvement of Dimmable OFDM-Visible Light Communication using Subcarrier Index Modulation and Reed Solomon Encoding
Venue: arXiv cs.IT 2021
Year: 2021

In this paper, we propose a new subcarrier index modulation scheme for orthogonal frequency division multiplexing (OFDM), which incorporates the Reed-Solomon (RS) encoding in a visible light communication (VLC) system. In this scheme, the incoming bits are first encoded using the RS encoder, then a set of symbols in the resulting RS codeword are punctured, and the remaining symbols are modulated and mapped onto the OFDM subcarriers. This system is referred to as RS-OFDM-IM. Unlike the traditional subcarrier index modulation (SIM) schemes, the proposed scheme operates based on conveying extra information by inactivating the selected subcarriers, which facilitates simultaneous clipping noise reduction and spectral efficiency enhancement in OFDM-VLC. The bit error rate (BER) and throughput of the proposed technique is theoretically and numerically analyzed. The simulation results show the superiority of the proposed technique as compared to the coded DCO-OFDM without SIM and the classical SIM in OFDM-VLC in a system with practical clipping conditions.
Indoor Navigation Algorithm Based on a Smartphone Inertial Measurement Unit and Map Matching
Venue: arXiv cs.RO 2021
Year: 2021

We propose an indoor navigation algorithm based on pedestrian dead reckoning (PDR) using an inertial measurement unit in a smartphone and map matching. The proposed indoor navigation system is user-friendly and convenient because it requires no additional device except a smartphone and works with a pedestrian in a casual posture who is walking with a smartphone in their hand. Because the performance of the PDR decreases over time, we greatly reduced the position error of the trajectory estimated by PDR using a map matching method with a known indoor map. To verify the proposed indoor navigation algorithm, we conducted an experiment in a real indoor environment using a commercial Android smartphone. The performance of our algorithm was demonstrated through the results of the experiment.
User-Defined Functions for HDF5
Venue: arXiv cs.DC 2021
Year: 2021

Scientific datasets are known for their challenging storage demands and the associated processing pipelines that transform their information. Some of those processing tasks include filtering, cleansing, aggregation, normalization, and data format translation -- all of which generate even more data. In this paper, we present an infrastructure for the HDF5 file format that enables dataset values to be populated on the fly: task-related scripts can be attached into HDF5 files and only execute when the dataset is read by an application. We provide details on the software architecture that supports user-defined functions (UDFs) and how it integrates with hardware accelerators and computational storage. Moreover, we describe the built-in security model that limits the system resources a UDF can access. Last, we present several use cases that show how UDFs can be used to extend scientific datasets in ways that go beyond the original scope of this work.
Online Adaptation of Parameters using GRU-based Neural Network with BO for Accurate Driving Model
Venue: arXiv cs.LG 2021
Year: 2021

Testing self-driving cars in different areas requires surrounding cars with accordingly different driving styles such as aggressive or conservative styles. A method of numerically measuring and differentiating human driving styles to create a virtual driver with a certain driving style is in demand. However, most methods for measuring human driving styles require thresholds or labels to classify the driving styles, and some require additional questionnaires for drivers about their driving attitude. These limitations are not suitable for creating a large virtual testing environment. Driving models (DMs) simulate human driving styles. Calibrating a DM makes the simulated driving behavior closer to human-driving behavior, and enable the simulation of human-driving cars. Conventional DM-calibrating methods do not take into account that the parameters in a DM vary while driving. These "fixed" calibrating methods cannot reflect an actual interactive driving scenario. In this paper, we propose a DM-calibration method for measuring human driving styles to reproduce real car-following behavior more accurately. The method includes 1) an objective entropy weight method for measuring and clustering human driving styles, and 2) online adaption of DM parameters based on deep learning by combining Bayesian optimization (BO) and a gated recurrent unit neural network. We conducted experiments to evaluate the proposed method, and the results indicate that it can be easily used to measure human driver styles. The experiments also showed that we can calibrate a corresponding DM in a virtual testing environment with up to 26% more accuracy than with fixed calibration methods.
Factors influencing Drug Consumption and Prediction Methods
Venue: arXiv cs.CY 2021
Year: 2021

Estimating the needs of healthcare products and inventory management are still challenging issues in hospitals nowadays. Centers are supposed to cope with tight budgets and patient satisfaction at the same time. Some issues can be tackled in advance, especially regarding the prediction of drug consumption needs. This work delves into the literature in order to highlight existing methods of quantifying and estimating the needs for drugs in health facilities. A second objective is to draw up a list of factors that impact drug consumption in particular, factors that are used in these prediction methods. Following this literature review, it appears that six sustainable methods are being used by practitioners around the world, taking into account certain prerequisites and types of data. Thirty-four factors are identified as well and grouped into three categories. These results should participate in setting up new tools for predicting the need of drugs, to facilitate the upstream dimensioning of new pharmaceutical warehouses and to solve some hospital logistics issues.
Regularization Guarantees Generalization in Bayesian Reinforcement Learning through Algorithmic Stability
Venue: arXiv cs.LG 2021
Year: 2021

In the Bayesian reinforcement learning (RL) setting, a prior distribution over the unknown problem parameters -- the rewards and transitions -- is assumed, and a policy that optimizes the (posterior) expected return is sought. A common approximation, which has been recently popularized as meta-RL, is to train the agent on a sample of $N$ problem instances from the prior, with the hope that for large enough $N$, good generalization behavior to an unseen test instance will be obtained. In this work, we study generalization in Bayesian RL under the probably approximately correct (PAC) framework, using the method of algorithmic stability. Our main contribution is showing that by adding regularization, the optimal policy becomes stable in an appropriate sense. Most stability results in the literature build on strong convexity of the regularized loss -- an approach that is not suitable for RL as Markov decision processes (MDPs) are not convex. Instead, building on recent results of fast convergence rates for mirror descent in regularized MDPs, we show that regularized MDPs satisfy a certain quadratic growth criterion, which is sufficient to establish stability. This result, which may be of independent interest, allows us to study the effect of regularization on generalization in the Bayesian RL setting.
Automated Modular Verification for Race-Free Channels with Implicit and Explicit Synchronization
Venue: arXiv cs.PL 2021
Year: 2021

Ensuring the correctness of software for communication centric programs is important but challenging. Previous approaches, based on session types, have been intensively investigated over the past decade. They provide a concise way to express protocol specifications and a lightweight approach for checking their implementation. Current solutions are based on only implicit synchronization, and are based on the less precise types rather than logical formulae. In this paper, we propose a more expressive session logic to capture multiparty protocols. By using two kinds of ordering constraints, namely "happens-before" <HB and "communicates-before" <CB, we show how to ensure from first principle race-freedom over common channels. Our approach refines each specification with both assumptions and proof obligations to ensure compliance to some global protocol. Each specification is then projected for each party and then each channel, to allow cooperative proving through localized automated verification. Our primary goal in automated verification is to ensure race-freedom and communication-safety, but the approach is extensible for deadlock-freedom as well. We shall also describe how modular protocols can be captured and handled by our approach.
Modeling and Measurements for Multi-path Mitigation with Reconfigurable Intelligent Surfaces
Venue: arXiv cs.IT 2021
Year: 2021

A reconfigurable intelligent surface (RIS) is capable of manipulating electromagnetic waves with its flexibly configurable unit cells, thus is an appealing technology to resist fast fading caused by multi-path in wireless communications. In this paper, a two-path propagation model for RIS-assisted wireless communications is proposed by considering both the direct path from the transmitter to the receiver and the assisted path provided by the RIS. The proposed propagation model unveils that the phase shifts of RISs can be optimized by appropriate configuration for multi-path fading mitigation. In particular, four types of RISs with different configuration capabilities are introduced and their performances on improving received signal power in virtue of the assisted path to resist fast fading are compared through extensive simulation results. In addition, an RIS operating at 35 GHz is used for experimental measurement. The experimental results verify that an RIS has the ability to combat fast fading and thus improves the receiving performance, which may lay a foundation for further researches.
Learnable Triangulation for Deep Learning-based 3D Reconstruction of Objects of Arbitrary Topology from Single RGB Images
Venue: arXiv cs.CV 2021
Year: 2021

We propose a novel deep reinforcement learning-based approach for 3D object reconstruction from monocular images. Prior works that use mesh representations are template based. Thus, they are limited to the reconstruction of objects that have the same topology as the template. Methods that use volumetric grids as intermediate representations are computationally expensive, which limits their application in real-time scenarios. In this paper, we propose a novel end-to-end method that reconstructs 3D objects of arbitrary topology from a monocular image. It is composed of of (1) a Vertex Generation Network (VGN), which predicts the initial 3D locations of the object's vertices from an input RGB image, (2) a differentiable triangulation layer, which learns in a non-supervised manner, using a novel reinforcement learning algorithm, the best triangulation of the object's vertices, and finally, (3) a hierarchical mesh refinement network that uses graph convolutions to refine the initial mesh. Our key contribution is the learnable triangulation process, which recovers in an unsupervised manner the topology of the input shape. Our experiments on ShapeNet and Pix3D benchmarks show that the proposed method outperforms the state-of-the-art in terms of visual quality, reconstruction accuracy, and computational time.
How to find a good image-text embedding for remote sensing visual question answering?
Venue: arXiv cs.CV 2021
Year: 2021

Visual question answering (VQA) has recently been introduced to remote sensing to make information extraction from overhead imagery more accessible to everyone. VQA considers a question (in natural language, therefore easy to formulate) about an image and aims at providing an answer through a model based on computer vision and natural language processing methods. As such, a VQA model needs to jointly consider visual and textual features, which is frequently done through a fusion step. In this work, we study three different fusion methodologies in the context of VQA for remote sensing and analyse the gains in accuracy with respect to the model complexity. Our findings indicate that more complex fusion mechanisms yield an improved performance, yet that seeking a trade-of between model complexity and performance is worthwhile in practice.
Training dataset generation for bridge game registration
Venue: arXiv cs.CV 2021
Year: 2021

This paper presents a method for automatic generation of a training dataset for a deep convolutional neural network used for playing card detection. The solution allows to skip the time-consuming processes of manual image collecting and labelling recognised objects. The YOLOv4 network trained on the generated dataset achieved an efficiency of 99.8% in the cards detection task. The proposed method is a part of a project that aims to automate the process of broadcasting duplicate bridge competitions using a vision system and neural networks.
Tackling Inter-Class Similarity and Intra-Class Variance for Microscopic Image-based Classification
Venue: arXiv cs.CV 2021
Year: 2021

Automatic classification of aquatic microorganisms is based on the morphological features extracted from individual images. The current works on their classification do not consider the inter-class similarity and intra-class variance that causes misclassification. We are particularly interested in the case where variance within a class occurs due to discrete visual changes in microscopic images. In this paper, we propose to account for it by partitioning the classes with high variance based on the visual features. Our algorithm automatically decides the optimal number of sub-classes to be created and consider each of them as a separate class for training. This way, the network learns finer-grained visual features. Our experiments on two databases of freshwater benthic diatoms and marine plankton show that our method can outperform the state-of-the-art approaches for classification of these aquatic microorganisms.
RSDet++: Point-based Modulated Loss for More Accurate Rotated Object Detection
Venue: arXiv cs.CV 2021
Year: 2021

We classify the discontinuity of loss in both five-param and eight-param rotated object detection methods as rotation sensitivity error (RSE) which will result in performance degeneration. We introduce a novel modulated rotation loss to alleviate the problem and propose a rotation sensitivity detection network (RSDet) which is consists of an eight-param single-stage rotated object detector and the modulated rotation loss. Our proposed RSDet has several advantages: 1) it reformulates the rotated object detection problem as predicting the corners of objects while most previous methods employ a five-para-based regression method with different measurement units. 2) modulated rotation loss achieves consistent improvement on both five-param and eight-param rotated object detection methods by solving the discontinuity of loss. To further improve the accuracy of our method on objects smaller than 10 pixels, we introduce a novel RSDet++ which is consists of a point-based anchor-free rotated object detector and a modulated rotation loss. Extensive experiments demonstrate the effectiveness of both RSDet and RSDet++, which achieve competitive results on rotated object detection in the challenging benchmarks DOTA1.0, DOTA1.5, and DOTA2.0. We hope the proposed method can provide a new perspective for designing algorithms to solve rotated object detection and pay more attention to tiny objects. The codes and models are available at: https://github.com/yangxue0827/RotationDetection.
Distributed Estimation of Sparse Inverse Covariances
Venue: arXiv cs.LG 2021
Year: 2021

Learning the relationships between various entities from time-series data is essential in many applications. Gaussian graphical models have been studied to infer these relationships. However, existing algorithms process data in a batch at a central location, limiting their applications in scenarios where data is gathered by different agents. In this paper, we propose a distributed sparse inverse covariance algorithm to learn the network structure (i.e., dependencies among observed entities) in real-time from data collected by distributed agents. Our approach is built on an online graphical alternating minimization algorithm, augmented with a consensus term that allows agents to learn the desired structure cooperatively. We allow the system designer to select the number of communication rounds and optimization steps per data point. We characterize the rate of convergence of our algorithm and provide simulations on synthetic datasets.
From images in the wild to video-informed image classification
Venue: arXiv cs.CV 2021
Year: 2021

Image classifiers work effectively when applied on structured images, yet they often fail when applied on images with very high visual complexity. This paper describes experiments applying state-of-the-art object classifiers toward a unique set of images in the wild with high visual complexity collected on the island of Bali. The text describes differences between actual images in the wild and images from Imagenet, and then discusses a novel approach combining informational cues particular to video with an ensemble of imperfect classifiers in order to improve classification results on video sourced images of plants in the wild.
Slicing functional aspects out of legacy applications
Venue: arXiv cs.SE 2021
Year: 2021

Aspect-oriented software development builds upon object-oriented development by offering additional abstraction boundaries that help us separate different types of requirements into so-called aspects. Given a legacy OO application, it pays to identify existing aspects to help understand the structure of the application and potentially to extract those aspects into separate software modules that can be maintained and reused individually. We present an approach to extract functional aspects using program slicing. We first characterize what we mean by functional aspect, and explore the extent to which program slicing techniques could improve over existing aspect mining and feature location methods for such aspects. We then describe the results of applying our approach to two medium-sized open source programs. Our preliminary results show that program slicing can yield well-defined functional aspects when the appropriate slicing criteria are used. Finally, we explore the effect of design quality on the candidate functional aspects.
GERNERMED -- An Open German Medical NER Model
Venue: arXiv cs.CL 2021
Year: 2021

The current state of adoption of well-structured electronic health records and integration of digital methods for storing medical patient data in structured formats can often considered as inferior compared to the use of traditional, unstructured text based patient data documentation. Data mining in the field of medical data analysis often needs to rely solely on processing of unstructured data to retrieve relevant data. In natural language processing (NLP), statistical models have been shown successful in various tasks like part-of-speech tagging, relation extraction (RE) and named entity recognition (NER). In this work, we present GERNERMED, the first open, neural NLP model for NER tasks dedicated to detect medical entity types in German text data. Here, we avoid the conflicting goals of protection of sensitive patient data from training data extraction and the publication of the statistical model weights by training our model on a custom dataset that was translated from publicly available datasets in foreign language by a pretrained neural machine translation model. The sample code and the statistical model is available at: https://github.com/frankkramer-lab/GERNERMED
Accurate Remaining Useful Life Prediction with Uncertainty Quantification: a Deep Learning and Nonstationary Gaussian Process Approach
Venue: arXiv cs.LG 2021
Year: 2021

Remaining useful life (RUL) refers to the expected remaining lifespan of a component or system. Accurate RUL prediction is critical for prognostic and health management and for maintenance planning. In this work, we address three prevalent challenges in data-driven RUL prediction, namely the handling of high dimensional input features, the robustness to noise in sensor data and prognostic datasets, and the capturing of the time-dependency between system degradation and RUL prediction. We devise a highly accurate RUL prediction model with uncertainty quantification, which integrates and leverages the advantages of deep learning and nonstationary Gaussian process regression (DL-NSGPR). We examine and benchmark our model against other advanced data-driven RUL prediction models using the turbofan engine dataset from the NASA prognostic repository. Our computational experiments show that the DL-NSGPR predictions are highly accurate with root mean square error 1.7 to 6.2 times smaller than those of competing RUL models. Furthermore, the results demonstrate that RUL uncertainty bounds with the proposed DL-NSGPR are both valid and significantly tighter than other stochastic RUL prediction models. We unpack and discuss the reasons for this excellent performance of the DL-NSGPR.