Reviewerly/exHarmony huggingface dataset:
Ego-Motion Estimation With Stereo Cameras Using Efficient 3D–2D Edge Correspondences

This article presents a new edge-based ego-motion estimation method for stereo cameras using 3D–2D edge alignment. To filter out prominent edgelets from Canny edge extractor, a region growing algorithm is developed to remove cluttered edges out of raw edgelets. Edge matching between stereo image pairs is guided by sparse feature correspondence using optical flow. The disparity is then refined using an inverse compositional algorithm, which is far more computationally efficient than searching along the epipolar line. The relative camera motion is solved by 3D–2D edge alignment based on an iterative closest point (ICP) algorithm, minimizing the point-to-tangent distance. A gradient-orientation-based similarity measurement is proposed, and the optimal correspondence is searched between the 3-D edge point projected onto the current image and the extracted edge pixels in the current frame with 1-nearest neighbor. In addition, the inverse depth of edge points in the reference frame is optimized. Experiments show that the proposed method can provide centimeter-level relative pose error (RPE), which has a better performance than the point-based approach efficient perspective-n-point (EPnP) and the edge-based approach direct edge-alignment (DE-A).
Data-Driven Certification of Neural Networks With Random Input Noise

Methods to certify the robustness of neural networks in the presence of input uncertainty are vital in safety-critical settings. Most certification methods in literature are designed for adversarial or worst-case inputs, but researchers have recently shown a need for methods that consider random input noise. In this article, we examine the setting where inputs are subject to random noise coming from an arbitrary probability distribution. We propose a robustness certification method that lower-bounds the probability that network outputs are safe. This bound is cast as a chance-constrained optimization problem, which is then reformulated using input–output samples to make the optimization constraints tractable. We develop sufficient conditions for the resulting optimization to be convex, as well as on the number of samples needed to make the robustness bound hold with overwhelming probability. Case studies on synthetic, MNIST, and CIFAR-10 networks experimentally demonstrate that this method is able to certify robustness against various input noise regimes over larger uncertainty regions than prior state-of-the-art techniques.
A Multifeature-Assisted Road and Vehicle Detection Method Based on Monocular Depth Estimation and Refined U-V Disparity Mapping

Usually, the detection process of traffic objects, such as vehicles, finds the visual input lacks the necessary depth information, so it is difficult to directly and quickly obtain the results. Typically, to separate the objects from the complex background, it is necessary to utilize a complex model or prior knowledge, which can be computationally expensive or simply infeasible. To battle this issue, depth visual information is used in this paper to accurately segment roads and vehicles, so it doesn’t need to use complex models to detect objects in the visual input. First, an unsupervised deep learning-based monocular depth estimation method is used to obtain the stereo disparity map. Then a non-parametric, refined U-V disparity mapping method is used to obtain the road region of interest. Next, this paper uses the road parallel scanning to determine the source and vanishing points and uses the adjacent disparity similarity algorithm to complement and extract the target region to detect roads and vehicles. This algorithm uses multi-feature fusion such as height-width ratio, perspective ratio, and area ratio to accurately segment the target region, and the effectiveness of the proposed method is tested on a public dataset. The experimental results show that the proposed model can accurately and efficiently detect roads and vehicles in a variety of scenarios.
<i>Block-CPS</i>: Blockchain and Non-Cooperative Game-Based Data Pricing Scheme for Car Sharing

This article proposes a blockchain and non-cooperative game theoretic-based secure and optimized data pricing scheme, i.e.,Block-CPS. It aims to secure the data transactions between vehicle owners and customers for rides. It uses the fifth-generation (5G) communication network that offers ultrareliable low-latency communications between vehicle owners and customers. The Interplanetary file system (IPFS) storage protocol used in the proposal reduces the blockchain data storage cost. We then formulated a non-cooperative game-theoretic approach to maximize the profits for vehicle owners and customers. Formulated non-cooperative game is integrated with blockchain to provide security to the Block-CPS. The vulnerability of the developed smart contract is verified and validated using tools like smartcheck and verisol. The performance of Block-CPS is evaluated by comparing it with the traditional approaches using blockchain with 4G and LTE-A networks. The performance evaluation parameters used are system scalability, network latency, data storage cost and its computation, network throughput, profit, communication reliability, and convergence for the optimal payoff between vehicle owners and customers. The performance results shows the Block-CPS outperforms the traditional blockchain-based systems.
Automatic Variable Reduction

A variable reduction strategy (VRS) is an effective method to accelerate the optimization process of evolutionary algorithms (EAs) by simplifying the corresponding optimization problems. Unfortunately, the VRS is manually realized in a trial-and-error manner currently. To boost the efficiency of VRS and enable a more extensive application, we propose a variable reduction optimization problem (VROP) to represent a decision space with the smallest sets of variables. Thereafter, a heuristic rule-based automatic variable reduction algorithm (AVRA) is designed to address the VROP. AVRA sequentially reduces variables by considering several sophisticated designed heuristic rules, which search for a variable that can be utilized to represent as many variables as possible and that can be represented by the smallest set of variables possible. With AVRA, the decision variables of an optimization problem can be automatically grouped into reduced variables and core variables, where core variables can represent reduced variables and the entire decision space. During the optimization process, we only need to search the core variables to optimize the problem. Therefore, the dimensionality of the decision space can be reduced by AVRA, subsequently, simplifying the complexity of the problem and improving the search efficiency of EAs. To testify the effectiveness of AVRA, we blend AVRA with several promising EAs to solve two types of challenging problems: 1) continuous equality-constrained optimization problems and 2) nonlinear equations systems. Extensive experiments verify that an EA with AVRA outperforms the standard EA.
Exposing the many biases in machine learning

In recent years, there have been numerous articles highlighting issues with bias in machine learning algorithms underpinning the use of AI in decision making. Specifically, algorithms trained on historical real-world observations. However, less is written about the many ways bias can be introduced into the machine learning process. This article outlines 12 different types of bias that can occur during the data science process, from capture through curation to analysis and application.
Research on General Model of Intelligence Level for Smart Home

As an important vertical application field of the Internet of things, smart home has been widely used after continuous commercialization exploration with the joint support of technology innovation and market demand. With the development of Internet of things, big data and artificial intelligence technology, the overall intelligence level of smart home has been significantly improved. How to evaluate the intelligence level of smart home system has become a hot issue in the industry, but there are few studies on the intelligence level evaluation of smart home system in the academic community. This paper proposes the human-system collaborative relationship model of the smart home system, and divides the smart home&#x2019;s intelligence level based on this model, and establishes an evaluation method for the smart home system to better guide the smart development of the smart home industry and enhance consumers' awareness of smart home. At the end of this paper, the next step work is also prospected, and an experimental environment for the evaluation of the intelligence level of the smart home system is built to verify the intelligence level and its evaluation method.
Face-Mask Recognition and Detection Using Deep Learning

A methodical approach to curb the spread of this menacing disease, COVID-19 must be taken. 'CO' answer for corona, 'VI' denote virus, and 'D' represent disease. The absence of highly effective medicines and scarcity of vaccination make this disease more lethal and vicious; this makes it important for us to find a provisional yet efficacious way to cushion ourselves and that one love. Wearing masks can act like that cushion, it’s truly a camouflage, acting as a Non-Pharmaceutical Intervention (NPI) proceeding that could be easily implemented without much capital investment. This thesis evaluates an efficient way of face mask detection that can be used by private or government authorities as a tool against COVID-19. This research aims to build a light weighted and user-friendly model that can be easily used in static or real-time face mask detection. This study of face mask detection is made possible using Deep Learning, Convolutional neural network algorithm, and MobileNetV2, a python open-source image-processing and classification model. Steps involved in designing and implementing the model are collecting and accessing the dataset, data processing and encoding, testing, and training data, accuracy prediction, and model implementation in a real-time project that can instantly detect and provide the desired output. The model can make 98% accurate production in real-time, distinguishing between individuals with a mask or without ma masks. Our proposed face mask detection model outperforms existing models in terms of accuracy and easy usability.
A Venona Style Attack to Determine Block Size, Language, and Attacking Ciphers

The One Time Pad (OTP) is regarded as the only “perfectly secure cipher.” It was used extensively during World War II, and during the Cold War by the USSR. The OTP was rendered vulnerable when the USSR started to recycle and reuse the keys that were already used for encryption. The USA exploited this vulnerability during the Cold War with the USSR, which lasted well into the 1980s. The exploit used by the USA was code-named as the ”Venona Attack.” Nevertheless, there was little to no declassified information about the technique used in the Venona Attack. In this paper, we propose an algorithm that may potentially be the crux of the Venona-Attack, and we establish the conditions that would facilitate a Venona-style exploit. The proposed exploit from this paper can be used to attack serial ciphers and any cipher that reuses keys. To demonstrate our algorithm, we present results from our preliminary test corpora to establish the limitations of the attack testing with block sizes of 1 and 3, along with countermeasures that can be employed to counteract a Venona-style attack.
Variational convolutional neural networks classifiers

Convolutional neural networks have been shown to extract features better than traditional algorithms in the fields such as image classification, object detection, and speech recognition. In parallel, a variational quantum algorithm incorporating parameterized quantum circuits has higher performance on near-term quantum processors. In this paper, we propose a classification algorithm called variational convolutional neural networks (VCNN), allowing for efficient training and implementation on near-term quantum devices. The VCNN algorithm combines the multi-scale entanglement renormalization ansatz. We deploy the VCNN algorithm on the TensorFlow Quantum platform with the numerical simulator backends using the MNIST and Fashion MNIST datasets. Experimental results show that the average accuracy of VCNN on classification tasks can reach up to 96.41%. Our algorithm has higher learning accuracy and fewer training epochs than quantum neural network algorithms. Moreover, we conclude that circuit-based models have excellent resilience to noise by numerical simulations.
Gaussian kernel based gene selection in a single cell gene decision space

Information system is a database that shows relationships between objects and attributes. A real-valued information system is an information system whose information values are real numbers. A real-valued information system with decision attributes is referred to as a real-valued decision information system. If objects, conditional attributes and information values in a real-valued decision information system are cells, genes and gene expression values, respectively, then this information system is said to be a gene decision space. In a gene decision space, people are faced with gene expression data . If gene expression data in a gene decision space changes to single cell RNA-seq data, then this space is referred to as a single cell gene decision space. This paper explores gene selection in a single cell gene decision space in terms of Gaussian kernel . In the first place, the distance between two cells in each subspace of a single cell gene decision space is constructed. Next, the fuzzy T cos -equivalence relation on the cell set is obtained in terms of Gaussian kernel. After that, measures of uncertainty for a single cell gene decision space are investigated. Lastly, gene selection algorithms in a single cell gene decision space are presented in terms of the proposed information entropy and information granularity . The presented algorithms are testified in several publicly open single cell RNA-seq data sets. Experimental results reveal that the presented algorithms can select appropriate genes related to classification , and significantly improve classification performances.
Multivehicle Motion Planning With Posture Constraints in Real World

This article addresses the posture constraints problem in multivehicle motion planning for specific applications such as ground exploration tasks. Unlike most of the related work in motion planning, this article investigates more practical applications in the real world for nonholonomic unmanned ground vehicles (UGVs). In this case, a strategy of diversion is designed to optimize the smoothness of motion. Considering the problem of the posture constraints, a postured collision avoidance algorithm is proposed for the motion planning of the multiple nonholonomic UGVs. Two simulation experiments were conducted to verify the effectiveness and analyze the quantitative performance of the proposed method. Then, the practicability of the proposed algorithm was verified with an experiment in a natural environment.
Autonomous Reinforcement Control of Visual Underwater Vehicles: Real-Time Experiments Using Computer Vision

Swift decision-making based on visual environment perception is crucial for autonomous control of visual underwater vehicles (VUVs) during underwater missions. However, learning perception and decision models individually might result in weak robustness of overall control system as the mismatched state extraction and control decision making are asynchronous. As a remedy, we will introduce in this paper an end-to-end monocular autonomous reinforcement control (MARC) framework for autonomous control of VUVs, which is performed in two cascaded procedures, i.e., 1) perception, where a geometric network (GeoNet) is designed based on a convolutional encoder-decoder network to generate depth maps from input environmental videos; 2) decision, where with depth maps as input, a reinforcement control network (CtrlNet) integrates a convolutional neural network into a deep deterministic policy gradient network and outputs action decisions, which are refined by reinforcement learning algorithm for obstacle-avoiding based autonomous control. Numerical and experimental results demonstrate that the proposed MARC exhibits high-quality depth prediction and is capable of conducting obstacle-avoiding navigation and autonomous control of VUVs with high accuracy and strong robustness.
Weakly supervised moment localization with natural language based on semantic reconstruction

The goal of cross-modal moment localization is to find the temporal moment in the untrimmed video that semantically corresponds to the natural language query. The majority of current approaches learn the cross-modal moment localization models from fine-grained temporal annotations in the video, which are extremely time-consuming and labor-intensive to obtain. In this paper, we offer a novel framework for weakly supervised cross-modal moment localization that incorporates a proposal generation module and a semantic reconstruction module. The proposal generation module uses a two-dimensional temporal feature map to model cross-modal video representations and can encode the moment-by-moment temporal relationships of moment candidates. The semantic reconstruction module, which is based on the generated proposals, assesses a proposal's capacity to restore the text query and provides weak supervision for network training. Besides, a punishment loss is also proposed to further eliminate the effect of the invalid area. Extensive experimental results show that the proposed method achieves state-of-the-art performance, demonstrating its effectiveness for weakly supervised moment localization with natural language. • A new framework is proposed for weakly supervised cross-modal moment localization. • A multi-task loss is designed for the weakly supervised optimizing of the network. • The proposal generation module could exploit moment-wise temporal relationships. • Extensive experimental results demonstrate the effectiveness of our method.
Small Object Detection Method Based on Weighted Feature Fusion and CSMA Attention Module

Small object detection is one of the challenging tasks in computer vision. Most of the existing small object detection models cannot fully extract the characteristics of small objects within an image, due to the small coverage area, low resolution and unclear detailed information of small objects in the image; hence, the effect of these models is not ideal. To solve this problem, a simple and efficient reinforce feature pyramid network R-FPN is proposed for the YOLOv5 algorithm. The learnable weight is introduced to show the importance of different input features, make full use of the useful information of different feature layers and strengthen the extraction of small object features. At the same time, a channel space mixed attention CSMA module is proposed to extract the detailed information of small objects combined with spaces and channels, suppress other useless information and further improve the accuracy of small object detection. The experimental results show that the proposed method improves the average accuracy AP, AP50 and AR100 of the original algorithm by 2.11%, 2.86% and 1.94%, respectively, and the detection effect is better than the existing small object detection algorithms, which proves the effectiveness of the proposed method.
Active gamma-ray well logging pattern localization with reinforcement learning

Gamma-ray well-log depth matching is one of the essential tasks in the well-logging data processing. Up until now, welllog curves analysis and pattern hand-picking matching require intensive human-involved labour. Removing this contamination is challenging due to the complex nature of the patterns that correspond with the unknown strata. Besides, signal corruption such as blur, shift, and noise also obstructs the development of the automated pattern localization method. We propose a data-driven offline reinforcement learning (RL) framework to overcome those challenges. This system can learn the human attention-inspired active signal pattern localizing strategy. This framework leverages logged historical human labelled data to learn the process of human vision focus for logging patterns. Following top-down reasoning, this agent learns to deform a bounding window using simple transformation actions to determine the target signal segments' location The experiments on augmented field Gamma-ray welllog dataset show the promising localization capability of reinforcement learning.
NewSQL Database Management System Compiler Errors: Effectiveness and Usefulness

Modern database management is often faced with a high number of concurrent end-users, and the need for database distribution to ensure fault tolerance and high throughput.To flexibly address these challenges, many modern database management systems (DBMS) provide highly automated and effortless, i.e., highly usable database distribution, deployment, and maintenance.However, the usability considerations are yet to extend from the aforementioned DBMS features to query language compilers.In this study, based on participant answers (N ¼ 157), we compare the error message qualities of four modern DBMSs (CockroachDB, SingleStore, NuoDB, and VoltDB) using one objective and three subjective metrics.Our results show that some of the DBMSs provide the users with more useful error messages, even though many of these error messages violate even the most basic usability guidelines.These results (i) are applicable in further developing the usability aspects of query language compilers, (ii) provide a timely effort of bridging the gap between human-computer interaction and query language compilers, and (iii) offer suggestions on teaching novices, who require emphasized support in query formulation.
Steepest entropy ascent solution for a continuous-time quantum walker

We consider the steepest entropy ascent (SEA) ansatz to describe the nonlinear thermodynamic evolution of a quantum system. Recently this principle has been dubbed the fourth law of thermodynamics [Beretta, Phil. Trans. R. Soc. A 378, 20190168 (2020)10.1098/rsta.2019.0168]. A unique global equilibrium state exists in this context, and any other state is driven by the maximum entropy generation principle towards this equilibrium. We study the SEA evolution of a continuous-time quantum walker (CTQW) on a cycle graph with N nodes. SEA solutions are difficult to find analytically. We provide an approximate scheme to find a general single-particle evolution equation governed by the SEA principle, whose solution produces dissipation dynamics. We call this scheme the fixed Lagrange's multiplier (FLM) method. In the Bloch sphere representation, we find trajectories traced out by the Bloch vector within the sphere itself. We have discussed these trajectories under various initial conditions for the case of a qubit. A similar dissipative motion is also observed in the case of CTQW, where probability amplitudes have been used to characterize decoherence. Our FLM scheme shows good agreement with numerical results. As we report, in CTQW, a strong delocalization exists for low system relaxation time.