Reviewerly/exHarmony huggingface dataset:
Research and Application of Power Grid Fault Diagnosis and Auxiliary Decision-making System Based on Artificial Intelligence Technology

The ability of power system equipment fault diagnosis and auxiliary decision-making software directly affects the efficiency of decision-making and recovery in power grid faults events. This paper proposes a data-driven and knowledge-driven power grid fault diagnosis and auxiliary decision-making technology. Firstly, based on the fault diagnosis model realized by expert system, Bayesian network and artificial neural network, an artificial intelligence fault diagnosis fusion algorithm is established by using the information fusion strategy to improve the diagnosis accuracy of the existing single fault diagnosis model. Experimental results show that the accuracy of fault recognition of the fusion model is improved by about 20% compared with the existing expert system. Then, realized a fault handling auxiliary decision-making system based on the knowledge graph of emergency plan, which constructed by extracting the knowledge information of emergency plan. This system can effectively diagnose the failure of power system equipment, and give auxiliary decisions according to the relevant emergency plan map, which can effectively improve the recovery efficiency and capability of power system in fault events, and enhance the intelligent operation level of the power system.
Adaptive Techniques for the Online Estimation of Spatial Fields With Mobile Sensors

The regression-based reconstruction of spatial fields by static sensors requires the number of measurements be at least equal to the number of unknown parameters in order to provide a unique solution. Improvements include the use of mobile sensor networks to arrive at higher accuracy estimates. An alternate approach utilizes adaptive techniques to update the parameter estimates online. Such adaptive techniques need persistence of excitation to ensure convergence of the parameter and function estimates. It is shown that the motion of just a single sensor is a necessary condition for persistence of excitation and, subsequently, of function convergence. Having a prescribed sensor guidance is also a sufficient condition for function convergence. First, a Lyapunov-based adaptation and guidance is presented and it is shown that persistence of excitation necessitates the time dependence of the outer product of the regressor vector with itself and which in turn requires the motion of the sensor within the spatial domain. Subsequently, a gradient-based adaptive scheme relaxes the Lyapunov-based sensor guidance to any user-defined guidance as long as the outer product of the associated rank-one regressor vector with itself has a uniformly positive definite integral. The latter adaptation is conducive to the inclusion of platform dynamics resulting in a vehicle control design needed to attain parameter convergence. Extension of the centralized solution to distributed estimation using a network of mobile sensors is also examined. These results are demonstrated with simulations in one and two dimensions.
Identity Documents Image Quality Assessment

In this work, a No-Reference method for assessing quality of ID Card images is developed, using a combination of Face Image Quality Assessment and Text Quality Assessment, motivated by the fact that face and text are two of the main components on an ID Card. For evaluation, a new private dataset was created, consisting of 12,960 Chilean ID Cards, with their corresponding printed facial reference image for face verification. The results obtained with the proposed quality assessment method, show that as more low quality images are discarded, a simultaneous improvement in face and text verification performance metrics is reached.
ReInstancer

Pattern matching for instanceof is widely used with the advantage of conditionally extracting components from objects and with the disadvantage of the compulsory usage of type castings. The feature of pattern matching has been periodically previewed in the latest released version of JDK to avoid redundant type castings and to optimize pattern matching in multi-branch statements. Although pattern matching has many benefits, manual refactoring for instanceof pattern matching is time-consuming and tedious. Furthermore, no existing work can provide sufficient support for such refactoring. To this end, this paper presents a refactoring tool ReInstancer that mitigates and simplifies type castings by converting a multi-branch statement with instanceof pattern matching into a switch statement or expression automatically. ReInstancer is evaluated on 7 real-world applications, with a total of 4404 instanceof expressions. The evaluation results demonstrate that ReInstancer can successfully remove 2060 type castings and refactor 141 multi-branch statements with a total of 1972 instanceof pattern matching, which improves the code quality.
Holistic 3D Body Reconstruction From a Blurred Single Image

Holistic human pose and shape reconstruction receive huge interest since it restores detailed human pose and shape including facial expression and finger-level hand shape. Existing deep 3D holistic human pose and shape reconstruction methods utilize sharp images as their input which leads to inaccurate human mesh when given a blurred image. Although there exist lots of image deblurring methods, a simple cascaded approach could not produce satisfactory results. In this paper, we introduce D2R (Deblurring-to-Reconstruction), a novel joint framework of human motion deblurring and 3D holistic body reconstruction to solve both problems simultaneously. In addition, we generate a new large-scale dataset that contains sharp/blur image pairs and corresponding 3D body pose/shape. We train the proposed joint framework in an alternating scheme to refine each module’s performance by utilizing an additional structure-aware module. Experimental results show that the proposed method achieves outperforming results 3D holistic human reconstruction qualitatively as well as quantitatively while input image is deblurred.
Encoder-Decoder Interactive Image Segmentation with Inception-Atrous-Collation Blocks

We propose a modular encoder-decoder model framework, NEED (NAS-Enhanced Encoder-Decoder), for interactive image segmentation. As part of our solution, we introduce a general-purpose convolutional module, Inception-Atrous-Collation (IAC) blocks. IAC structures collate multiple kernel and dilation sizes with residual information from previous network layers to generate information-rich image features. Our network additionally incorporates “context-aware” skip connections, which serve to amplify the influence of the user-provided interactive features throughout the model. With this general structure in place, we optimize our final network architecture using a Neural Architecture Search (NAS) procedure. The resultant model is a lightweight network that outperforms state-of-the-art algorithms with respect to both overall segmentation accuracy (IOU) and fine-grain boundary segmentation accuracy (bIOU) - a metric we introduce. We provide experimental results across several standard datasets, in addition to ablation studies to validate the effectiveness of our algorithm.
3D scene reconstruction from monocular spherical video with motion parallax

In this paper, we describe a method to capture nearly entirely spherical (360 degree) depth information using two adjacent frames from a single spherical video with motion parallax. After illustrating a spherical depth information retrieval using two spherical cameras, we demonstrate monocular spherical stereo by using stabilized firstperson video footage. Experiments demonstrated that the depth information was retrieved on up to 97% of the entire sphere in solid angle. At a speed of 30 km/h, we were able to estimate the depth of an object located over 30 m from the camera. We also reconstructed the 3D structures (point cloud) using the obtained depth data and confirmed the structures can be clearly observed. We can apply this method to 3D structure retrieval of surrounding environments such as 1) previsualization, location hunting/planning of a film, 2) real scene/computer graphics synthesis and 3) motion capture. Thanks to its simplicity, this method can be applied to various videos. As there is no pre-condition other than to be a 360 video with motion parallax, we can use any 360 videos including those on the Internet to reconstruct the surrounding environments. The cameras can be lightweight enough to be mounted on a drone. We also demonstrated such applications.
Target-Driven Autonomous Robot Exploration in Mappless Indoor Environments Through Deep Reinforcement Learning

In this paper, we present a deep reinforcement learning (DRL)-based autonomous end-to-end system for wheeled robots in an unmapped environment. Potential Waypoints (PWPs) are obtained along the way towards the global target for possible better navigation directions. Based on the available data, we use a novel heuristics function to evaluate and select the optimal waypoint. Following the waypoints, the robot is guided towards the global goal. A local navigation system based on DRL is developed to generate the motion policy that guide the robot move between waypoints and towards global goal. The Proximal Policy Optimization algorithm and long short-term memory form the basic foundation of the DRL network. A special reward system is created to steer the robot away from dynamic impediments and to maintain a smooth trajectory. a long short-term memory architecture is used to alleviate the local optimum problem and help avoid obstacles out of the current range of sensors. Experiments demonstrate that the proposed method, which does not rely on a map or prior knowledge in complicated static as well as dynamic situations, has an advantage over similar exploration methods.
The Application of Deep Convolution Neural Network in Volleyball Video Behavior Recognition

The purpose is to solve the problems of solid subjectivity in the traditional manual analysis of volleyball game videos and the traditional Human Behavior Recognition (HBR) algorithm, such as excessive calculation, high hardware conditions, and poor long-stream video modeling ability. Firstly, this paper expounds on the relevant theories. Secondly, a fusion Convolutional Neural Network (CNN)-based HBR model is implemented by combining two-stream CNN (TSCNN), Three-Dimensional (3D) CNN, and Long Short-Term Memory (LSTM) network. Notably, the LSTM has an excellent long-term Dynamic Information Extraction (DIE) ability. Finally, the public dataset is selected to verify the model’s volleyball-game-video-oriented HBR performance. The experimental results corroborate that: (1) The parameters of the proposed fusion-CNN-based HBR model are determined as follows: the number of video segments is three, the average method is used for feature fusion, and then the HBR accuracy is the highest when the fusion ratio of spatial feature map and temporal feature map is 4:6, and the learning rate is 0.0014. (2) The average HBR accuracy of the proposed fusion-CNN-based HBR model on three different datasets is 4%, 2.7%, and 3% higher than that of other popular networks, respectively. The improvement effect of the model is remarkable, and it is suitable for the study of Human Behavior Analysis (HBA) in volleyball match videos. Finally, the proposed HBR model can provide more accurate results for volleyball video-based HBR, which is significant in promoting the rapid development of volleyball sports.
Detection and Identification of Cervical Cancer on Elephant Herding Optimization on Convolutional Neural Network

Cervical cancer is the leading cause of cancer death in women. Early diagnosis and treatment of this tumor can reduce its complications. The purpose of this article is to diagnose and classify cervical cancer using an Elephant Herd Optimization on the basis of Convolutional Neural Network (EHCNN). First, Image database of open-source system is collected. The images are sent to a central filter to remove unwanted noise. This proposed study helps in prediction and classification of cervical cancer at early stages to avoid complications. The EHCNN algorithm's weight for classifying normal, abnormal, and the type of aberrant cells was enhanced as part of this classification. This proposed classification method is used to improve the application of CNN design parameters and is selected with the help of EHO. The project shows that the images can be identified using deep learning techniques and they may have some relevance to diagnose early cervical cancer. The impact of the proposed technical results is evaluated and used for performance and image quality analysis in terms of accuracy metrics.
Data-Free Network Compression via Parametric Non-uniform Mixed Precision Quantization

Deep Neural Networks (DNNs) usually have a large number of parameters and consume a huge volume of storage space, which limits the application of DNNs on memory-constrained devices. Network quantization is an appealing way to compress DNNs. However, most of existing quantization methods require the training dataset and a fine-tuning procedure to preserve the quality of a full-precision model. These are unavailable for the confidential scenarios due to personal privacy and security problems. Focusing on this issue, we propose a novel data-free method for network compression called PNMQ, which employs the Parametric Non-uniform Mixed precision Quantization to generate a quantized network. During the compression stage, the optimal parametric non-uniform quantization grid is calculated directly for each layer to minimize the quantization error. User can directly specify the required compression ratio of a network, which is used by the PNMQ algorithm to select bitwidths of layers. This method does not require any model retraining or expensive calculations, which allows efficient implementations for network compression on edge devices. Extensive experiments have been conducted on various computer vision tasks and the results demonstrate that PNMQ achieves better performance than other state-of-the-art methods of network compression.
How Quantum Computing-Friendly Multispectral Data can be?

Quantum computers consisting of hundreds of noisy qubits are already available and can run specific quantum algorithms although a large-scale fully error-corrected quantum computer is decades away. It is important to study their application to real-life computational problems. One such problem is Land Use and Land Cover classification of Earth Observation data set collected from the earth observation satellite mission using quantum machine learning methods. In this work, we compare the performance of the classical neural network on the re-labeled dataset of the Copernicus Sentinel-2 mission, when the model has access to Projected Quantum Kernel features. We show that classical neural net-work training accuracy increases drastically when the model has access to Projected Quantum Kernel features. This study shows the potential for quantum machine learning methods to Earth Observation data and provides key evidence for further investigation.
Optimization of a Fuzzy Classifier for Obtaining the Blood Pressure Levels Using the Ant Lion Optimizer

These days, we live in a difficult situation due to the recent pandemic. For this reason, we have the opportunity of utilizing soft computing techniques that help in medical diagnosis. In this paper, the optimization of three proposed classifiers with type-1 fuzzy logic is presented using the Ant Lion Optimizer (ALO). The experimentation was based on the Framingham dataset obtaining good results in the classification of blood pressure levels. The results demonstrate that the optimized fuzzy classifier obtains betters results compared with non optimized, and the utilized algorithm helps in optimizing type-1 fuzzy classifiers.
Bangladeshi Paper Currency Recognition Using Lightweight CNN Architectures

Paper note currency is the most frequently used way of completing a transaction globally, which applies to most developing countries such as Bangladesh. Hence, it is essential to recognize any currency notes within seconds to save time for an uninterrupted transaction. This paper aims to identify different banknotes of Bangladesh using the avant-garde convolutional neural network (CNN) models with transfer learning by utilizing the dataset at hand and updating and augmenting the dataset. To achieve a robust, lightweight, and efficient model for this research problem, we have used various augmentation techniques on a current publicly available dataset and applied custom hyperparameter tuning to various pre-trained CNN models to attain a maximum accuracy of 99.97%.
PoseBERT: A Generic Transformer Module for Temporal 3D Human Modeling

Training state-of-the-art models for human pose estimation in videos requires datasets with annotations that are really hard and expensive to obtain. Although transformers have been recently utilized for body pose sequence modeling, related methods rely on pseudo-ground truth to augment the currently limited training data available for learning such models. In this paper, we introduce PoseBERT, a transformer module that is fully trained on 3D Motion Capture (MoCap) data via masked modeling. It is simple, generic and versatile, as it can be plugged on top of any image-based model to transform it in a video-based model leveraging temporal information. We showcase variants of PoseBERT with different inputs varying from 3D skeleton keypoints to rotations of a 3D parametric model for either the full body (SMPL) or just the hands (MANO). Since PoseBERT training is task agnostic, the model can be applied to several tasks such as pose refinement, future pose prediction or motion completion without finetuning. Our experimental results validate that adding PoseBERT on top of various state-of-the-art pose estimation methods consistently improves their performances, while its low computational cost allows us to use it in a real-time demo for smoothly animating a robotic hand via a webcam. Test code and models are available at https://github.com/naver/posebert.
Self-supervised Depth Estimation from Spectral Consistency and Novel View Synthesis

Single image depth estimation is a critical issue for robot vision, augmented reality, and many other applications when an image sequence is not available. Self-supervised single image depth estimation models target at predicting accurate disparity map just from one single image without ground truth supervision or stereo image pair during real applications. Compared with direct single image depth estimation, single image stereo algorithm can generate the depth from different camera perspectives. In this paper, we propose a novel architecture to infer accurate disparity by leveraging both spectral-consistency based learning model and view-prediction based stereo reconstruction algorithm. Direct spectral-consistency based method can avoid false positive matching in smooth regions. Single image stereo can preserve more distinct boundaries from another camera perspective. By learning confidence maps and designing a fusion strategy, the two disparities from the two approaches are able to be effectively fused to produce the refined disparity. Extensive experiments and ablations indicate that our method exploits both advantages of spectral consistency and view prediction, especially in constraining object boundaries and correcting wrong predicting regions.
Towards cryptographically-authenticated in-memory data structures

Modern processors include high-performance cryptographic functionalities such as Intel's AES- NI and ARM's Pointer Authentication that allow programs to efficiently authenticate data held by the program. Pointer Authentication is already used to protect return addresses in recent Apple devices, but as yet these structures have seen little use for the protection of general program data. In this paper, we show how cryptographically-authenticated data structures can be used to protect against attacks based on memory corruption, and show how they can be efficiently realized using widely available hardware-assisted cryptographic mechanisms. We present realizations of secure stacks and queues with minimal overall performance overhead (3.4%-6.4% slowdown of the OpenCV core performance tests), and provide proofs of correctness.
Quantitative Analysis of Object Detectors for Autonomous Driving and Autonomous Parking

State-of-the-art (SOTA) object detectors are generally evaluated on object detection challenge datasets. However, automotive domain-specific quantitative analysis of detectors is limited and often incomplete. Moreover, evaluation of detectors is mainly focused on average precision (AP) metric, ignoring AP’s limitation such as insensitive to shape of the precision-recall curve. These issues are addressed in this paper by evaluating the most popular SOTA detectors on autonomous driving and autonomous parking datasets. AP weakness is addressed by evaluating detectors using the Localization-Recall-Precision (LRP) metric, which also provides a detailed understanding of a detector. Motivated from LRP, this paper also presents the optimal AP (oAP) metric for the fair comparison of detectors, which was ignored until now. The proposed oAP metric can be easily used with any object detector. In addition, a complete object detection pipeline starting from data collection to deployment on an embedded device is demonstrated in this work. Experimental results are presented graphically to analyze detector from a different perspective.
Fuzzy Model Development for a Continuous Dynamic Black-Box System

The paper describes a method of a black-box fuzzy model design based only on the system measured input/output quantity without necessity of any preliminary knowledge about the modeled system order and without any access to system state variables. The method is based on the use of continuous linear dynamic filters of the first order, which in practice are easily feasible and are always stable. The proposed methodology is applied in the fuzzy model design for a three-phase induction motor which represents a higher order nonlinear system. Simulation measurements in MATLAB confirmed the accuracy, effectiveness and good dynamic properties of the proposed fuzzy model and also its applicability to continuous dynamic systems with as little previous knowledge as possible. The aim of the article is to show that the automated fuzzy modeling techniques can be used also to obtain an accurate fuzzy model of a low complexity based on a set of series of measurements.
Gaussian-Beta Filters With Unknown Probability of Measurement Loss

Data loss is ubiquitous in practical engineering applications due to communication delay or congestion.Data loss rate is a key metric to evaluate the reliability of state estimation.To jointly estimate system state and data loss rate, we propose a class of Gaussian-Beta filters for linear and moderate nonlinear Gaussian state-space models with unknown probability of measurement loss.In the filters, the arrival of the measurement at each time is formulated as a binary random variable, which is determined by the classical threshold technology.In addition, the hidden state and the unknown probability of measurement loss are modeled as a product of Gaussian and Beta distributions, and the form remains unchanged through recursive operations.Simulation results verify the effectiveness of the proposed Gaussian-Beta filters compared with the existing filtering algorithms.