Reviewerly/exHarmony huggingface dataset:
Towards Scale Consistent Monocular Visual Odometry by Learning from the Virtual World

Monocular visual odometry (VO) has attracted extensive research attention by providing real-time vehicle motion from cost-effective camera images. However, state-of-the-art optimization-based monocular VO methods suffer from the scale inconsistency problem for long-term predictions. Deep learning has recently been introduced to address this issue by leveraging stereo sequences or ground-truth motions in the training dataset. However, it comes at an additional cost for data collection, and such training data may not be available in all datasets. In this work, we propose VRVO, a novel framework for retrieving the absolute scale from virtual data that can be easily obtained from modern simulation environments, whereas in the real domain no stereo or ground-truth data are required in either the training or inference phases. Specifically, we first train a scale-aware disparity network using both monocular real images and stereo virtual data. The virtual-to-real domain gap is bridged by using an adversarial training strategy to map images from both domains into a shared feature space. The resulting scale-consistent disparities are then integrated with a direct VO system by constructing a virtual stereo objective that ensures the scale consistency over long trajectories. Additionally, to address the suboptimality issue caused by the separate optimization backend and the learning process, we further propose a mutual reinforcement pipeline that allows bidirectional information flow between learning and optimization, which boosts the robustness and accuracy of each other. We demonstrate the effectiveness of our framework on the KITTI and vKITTI2 datasets.
Object Memory Transformer for Object Goal Navigation

This paper presents a reinforcement learning method for object goal navigation (ObjNav) where an agent navigates in 3D indoor environments to reach a target object based on long-term observations of objects and scenes. To this end, we propose Object Memory Transformer (OMT) that consists of two key ideas: 1) Object-Scene Memory (OSM) that enables to store long-term scenes and object semantics, and 2) Transformer that attends to salient objects in the sequence of previously observed scenes and objects stored in OSM. This mechanism allows the agent to efficiently navigate in the indoor environment without prior knowledge about the environments, such as topological maps or 3D meshes. To the best of our knowledge, this is the first work that uses a long-term memory of object semantics in a goal-oriented navigation task. Experimental results conducted on the AI2-THOR dataset show that OMT outperforms previous approaches in navigating in unknown environments. In particular, we show that utilizing the long-term object semantics information improves the efficiency of navigation.
ST-RRT*: Asymptotically-Optimal Bidirectional Motion Planning through Space-Time

We present a motion planner for planning through space-time with dynamic obstacles, velocity constraints, and unknown arrival time. Our algorithm, Space-Time RRT*(ST-RRT*), is a probabilistically complete, bidirectional motion planning algorithm, which is asymptotically optimal with respect to the shortest arrival time. We experimentally evaluate ST-RRT* in both abstract (2D disk, 8D disk in cluttered spaces, and on a narrow passage problem), and simulated robotic path planning problems (sequential planning of 8DoF mobile robots, and 7DoF robotic arms). The proposed planner outperforms RRT-Connect and RRT* on both initial solution time, and attained final solution cost. The code for ST-RRT* is available in the Open Motion Planning Library (OMPL).
HFMNet: Hierarchical Feature Mining Network for Low-Light Image Enhancement

Images captured in low-light environments often suffer from issues related to dark illumination and damaged details, which results in poor visibility. To address these problems, existing methods have attempted to enhance the visibility of low-light images using convolutional neural networks (CNNs). However, due to the insufficient consideration of crucial features such as illumination and edge details, most of them yield unnatural illumination and blurry details. In this work, to fully exploit these features, we present a detailed analysis of the illumination and edge features of low-light images, observing that the frequency components of these two features are considerably different. Therefore, we explore the frequency distributions of the feature maps extracted from different layers of a CNN model and try to seek the best representation for the illumination and edge information. Based on this, we present a hierarchical feature mining network (HFMNet) that extracts illumination and edge features in different network layers. Specifically, we build a feature mining attention (FMA) module combined with a hierarchical supervised loss to mine crucial features in appropriate network layer. Since deep hierarchical supervision tends to cause overfitting, we introduce an unpaired adversarial loss for improving the generality of the enhancement model. Through extensive experiments and analysis, we demonstrate the advantages of the proposed network, which achieves state-of-the-art performance in terms of image quality.
A Radio Signal Recognition Approach Based on Complex-Valued CNN and Self-Attention Mechanism

Automatic modulation recognition (AMR) of radio signals is becoming increasingly important due to its key role in wireless communication system management, monitoring, and control. In this paper, we propose an end-to-end AMR framework based on deep learning (DL), named CCNN-Atten. First, the complex-valued convolutional neural network (CCNN) extracts the features of the radio signal, and the feature calibration (FC) module selectively enhances the important features and suppresses irrelevant features. Then, a temporal context capture (TCC) module uses a modified multi-head attention mechanism (MHA) to capture the temporal dependence in the extracted features. The improved MHA mechanism, as a kind of self-attention mechanism, deploys causal convolutions to encode the temporal information of the input features and captures their local temporal relationship. In addition, due to the limited hardware resources in the real scenarios, we also considered a good compromise between recognition accuracy and computational complexity. Experiments were performed with the RadioML2016.10B, RadioML2016.10A, and RadioML2018.01A datasets, demonstrating the ability of the proposed CCNN-Atten to learn more robust features than other state-of-the-art (SOTA) techniques with 1 ~ 10% higher accuracy and a lower computational complexity than the SOTA models. The experimental results also show that CCNN-Atten achieves outstanding performance in dealing with radio signals with a lower sampling rate and small signal observation window.
Associatively Segmenting Semantics and Estimating Height From Monocular Remote-Sensing Imagery

Numerous deep-learning methods have been successfully applied to semantic segmentation and height estimation of remote-sensing imagery. It has also been proved that such framework can be reusable for multiple tasks to reduce computational resource overhead. However, there are still some technical limitations due to the semantic inconsistency between 3-D and 2-D features and strong interference of different objects with similar spectral-spatial properties. Previous works have sought to address these issues through hard parameter sharing or soft parameter sharing schemes. But due to unintentional integration, the specific information transmitted between multiple tasks is not clear or in a lot of redundancy. Furthermore, tuning the weights by hand between classification and regression loss function is challenging. In this paper, a novel multi-task learning method, termed ASSEH, is proposed to associatively segment semantics and estimate height from monocular remote-sensing imagery. First, considering semantic inconsistency across tasks, we design a task-specific distillation (TSD) module containing a set of task-specific gating units for each task at the cost of fewer parameters. The module allows for task-specific features to be tailored from backbone, whilst allowing for task-shared features to be transmitted. Second, we leverage the proposed cross-task propagation (CTP) module to construct and diffuse the local pattern graphlets at the common positions across tasks. Such a high-order recursive method can bridge two tasks explicitly to effectively settle semantic ambiguities caused by similar spectral characteristics with less computational burden and memory requirements. Third, a dynamic weighted geometric mean (DWGeoMean) strategy is introduced to dynamically learn the weights of each task and be more robust to the magnitude of the loss function. Finally, the results on ISPRS Vaihingen and Urban Semantic 3D data set well demonstrate that our ASSEH achieves the state-of-the-art performance.
Reliable and Secure Deep Learning-Based OFDM-DCSK Transceiver Design Without Delivery of Reference Chaotic Sequences

Chaos-based communications can be applied to high-speed vehicular information transmissions thanks to the anti-jamming and anti-interference capabilities of chaotic transmissions. In traditional practical chaotic systems, reference chaotic signals are required to be delivered to remove complex chaotic synchronization circuits. However, the direct transmission of reference signals will degrade the security performances, while interferences and noises imposed on the reference signals due to imperfect channel conditions will deteriorate the reliability performances. In order to enhance the reliability and security performances over vehicular channels such as the railway channel and the channels undergoing fast fadings, in this paper, we propose a deep learning (DL) aided intelligent OFDM-DCSK transceiver. In this design, no reference chaotic signals are delivered, and we propose to utilize the time-delay neural network (TDNN) to learn the chaotic maps, followed by the long short-term memory (LSTM) units to extract and exploit the correlations between chaotic modulated signals, and multiple fully connected layers (FCLs) to estimate the user bit data. With the aid of the constructed deep neural network (DNN), after the offline neural network training, the receiver can recover the transmitted information with lower bit error rate (BER) and enhance security performances. Theoretical performance is then analyzed for the proposed intelligent transceiver. Simulation results validate the proposed design, and demonstrate that the intelligent DL-based OFDM-DCSK system can achieve better BER and security performances over fast fading and railway channels compared with the benchmark systems.
Introduction of Formal Methods in Blockchain Consensus Mechanism and Its Associated Protocols

As the size of data is increasing exponentially, its security is a major concern, emerging technology like blockchain is used to provide security.Since the inception of blockchain, it has been adopted by researchers and industry, however, it gained enormous attention after cryptocurrency.It can be defined as a means of storing information in such a way that modification and hacking the system is difficult or impossible.A blockchain is a decentralized ledger that is digital and public, consisting of records of transactions called blocks.A consensus technology assures that all nodes agree on a unique sequence for appending blocks.A comprehensive examination of these algorithms will aid in understanding how and why each blockchain operates in the manner that it does.In this study, we addressed extensively used consensus techniques in the blockchain and the importance of consensus protocol in blockchain technology.The underlying consensus algorithm is a critical component of every blockchain system which determine the performance and security of the system.Ensuring the correctness of consensus protocols is uttermost important to create trust in the blockchain-based systems and formal methods are the way to create that trust and develop correct and verified systems.Formal modeling is a method of writing a system mathematically and examining the correctness and verifying the developed system.This study analyzed the importance of consensus mechanisms and how formal methods are helping to develop a correct blockchain-based system.This paper is a rigorous study on the current scenario of the application of formal methods in the consensus mechanism of blockchain for their verification.
Revisiting Convolutional Sparse Coding for Image Denoising: From a Multi-Scale Perspective

Recently, convolutional sparse coding (CSC) has shown great success in many image processing tasks, such as image super-resolution and image separation. However, it performs poorly in image denoising task. In this letter, we provide a new insight for CSC denoising by revisiting the CSC from a multi-scale perspective. We propose a multi-scale CSC model for image denoising. By unrolling the multi-scale solution into a learnable network, we obtain an interpretable lightweight multiscale network, namely MCSCNet. Experimental results show that the proposed MCSCNet significantly advances the denoising performance, with an average PSNR improvement of 0.32 dB over the state-of-the-art (SOTA) CSC based method. In addition, our MCSCNet is on par with many SOTA deep learning based methods, with less network parameters and lower FLOPs. The ablation study also validates the effectiveness of the multi-scale CSC mechanism.
Introduction to Data Mining and Knowledge Discovery

Data mining is a process of discovering some necessary hidden patterns from a large chunk of data that can be stored in multiple heterogeneous resources. It has an enormous use to make strategic decisions by business executives after analyzing the hidden truth of data. Data mining one of the steps in the knowledge-creation process. A data mining system consists of a data warehouse, a database server, a data mining engine, a pattern analysis module, and a graphical user interface. Data mining techniques include mining the frequent patterns and association learning rules with analysis, sequence analysis. Data mining technique is applicable on the top of various kinds of intelligent data storage systems such as data warehouses. It provides some analysis processes to make some useful strategic decisions. There are various issues and challenges faced by a data mining system in large databases. It provides a great place to work for data researchers and developers. Data mining is the process of classification, which can be executed based on the examination of training data (i.e., objects whose class label is predefined). With the help of an expert set of previous class objects with known class labels, it can find a model that can predict a class object with an unknown class label. These classification models can be classified into a variety of categories, including nearest neighbor, neural network, and others. Bayesian model, decision tree, neural network Random forest, decision trees Support vector machine, random forest SVM (support vector machine), for example. By analyzing the most common class among k closest samples, the K-Nearest Neighbor (KNN) technique aids in predicting of the class object with the unknown class label. It's an easy-to-use strategy that yields a solid classification result from any distribution. The Naive Bayes theory helps to perform the classification. It is one of the fastest classification algorithms, capable of efficiently handling real-world discrete data.
Breaking up data-enabled design: expanding and scaling up for the clinical context

Abstract Data-enabled design (DED) is a promising new methodology for designing with users from within their own context in an iterative and hands-on fashion. However, the agile and flexible qualities of the methodology do not directly translate to every context. In this article, we reflect on the design process of an intelligent ecosystem, called ORBIT, and a proposed evaluative study planned with it. This was part of a DED project in collaboration with a medical hospital to study the post-operative behavior in the (remote) context of bariatric patients. The design and preparation of this project and the process towards an eventual study rejection from the medical ethical committee (METC) provide rich insights into (1) what it means to conduct DED research in a clinical context, and (2) where the boundaries of the method might lie in this specific application area. We highlight insights from carefully designing the substantial infrastructure for the study, and how different aspects of DED translated less easily to the clinical context. We analyze the proposed study setup through the lenses of several modifications we made to DED and further reflect on how to expand and scale up the methodology and adapt the process for the clinical context.
Simulation model of the car service inventory management system: input parameters

The paper describes the problem of car service inventory management and the need for operational management of their quantity. The purpose and use of simulation modeling for solving the problem of order management are indicated. A model of inventory management at car service enterprises is constructed using simulation methods. The components of the simulation model are determined, which include controlled managed parameters, controlled unmanaged parameters, uncontrolled parameters and output indicators. For each component a characteristic is given. The input parameters of simulation modeling are presented, including the volumes of orders for car service repair parts. The input parameters used for the simulation are based on actual car dealership orders. Based on the data obtained, calculations of the arithmetic mean, standard deviation, median, coefficient of variation, asymmetry and kurtosis are performed. A distribution histogram is constructed for individual parameters. The results of the study reveal an exponential distribution of the volume of orders.
ViMDH: Visible-Imperceptible Medical Data Hiding for Internet of Medical Things

Over the recent years, volume of medical images and related digital records, called electronic medical records, generated, shared, and stored by different intelligent devices, sensors, and Internet of medical things networks, to name a few, has drastically increased. Such records are shared by cloud providers for storage and further processing. However, an increasingly serious concern is the illegal copying, modification, and forgery of medical records. This article presents a visible and imperceptible medical data hiding technique, namely ViMDH, which can prevent to intellectual property theft of medical records. The carrier image is visibly marked with logo mark, which is suitable for owner identification and avoid illegal duplication, and then an imperceptible data hiding based on nonsubsampled shearlet transform (NSST), redundant discrete wavelet transform (RDWT), and multiresolution singular value decomposition is introduced. Finally, key-based encryption scheme designed by RDWT-RSVD ensure the security of the watermarking system. Under the experimental evaluation, our ViMDH is not only visible and imperceptible, but also has a satisfactory advantage in robustness and security compared with the traditional watermarking schemes.
Sequential pattern mining on chemical bonding database in the bioinformatics field

In recent decades, the bioinformatics, biology and biotechnology plays a major role in accumulation of numerous biomedical data, which offers an in-depth analysis. On other hand, with the advent of data mining research leads to the development of scalable and efficient models for mining some interesting data pattern in case of small or large databases. It is hence essential to bridge bioinformatics with data mining models for successful implementation of finding the meaning pattern in case of biological data. In this paper, we present a comprehensive survey on various data mining models that helps in analyzing the chemical bonding and its structured index in case of sequential pattern mining.
ConvLSTMAE: A Spatiotemporal Parallel Autoencoders for Automatic Modulation Classification

Automatic modulation classification (AMC) is the key technique in both military and civilian wireless communication. However, the performance is unsatisfactory, even several deep learning-based methods are involved. Targeting its low accuracy at low SNR, high computational cost and label overdependence, we propose a novel AMC framework, where the autoencoder (AE) serves as the backbone and Convolution-AE and LSTM-AE are combined in a parallel way as temporal and spatial feature extractors. The comparisons with serval algorithms on the radioML2016.10a show that our proposed network can achieve higher classification accuracy at low SNR with a low cost. In addition, it suits the semi-supervised scenario since the dependence on labels is loosen.
Recursive Quadratic Filtering for Linear Discrete Non-Gaussian Systems Over Time-Correlated Fading Channels

In this paper, the recursive quadratic filtering issue is addressed for a class of linear discrete-time systems with non-Gaussian noises over time-correlated fading channels. The time-correlated fading channel, whose fading coefficient is modeled by a dynamic process subject to non-Gaussian random disturbance, is adopted to better characterize the time-correlation nature of the communication channel. By resorting to the state/measurement augmentation approach, the underlying system is converted into an augmented one with respect to the aggregation of the original vectors and the second-order Kronecker powers. Accordingly, the focus of this paper is on the design of a recursive quadratic filtering algorithm in the minimum-variance framework. To be more specific, an upper bound is first ensured on the filtering error covariance by solving certain matrix difference equations, and such an upper bound is then minimized by choosing the proper gain parameters. Moreover, sufficient conditions are obtained to guarantee the mean-square boundedness of the filtering error. Finally, some numerical simulations are provided to illustrate the correctness and validity of our developed quadratic filtering algorithm.
Unsupervised Single-Scene Semantic Segmentation for Earth Observation

Earth observation data has huge potential to enrich our knowledge about our planet. An important step in many Earth observation tasks is semantic segmentation. Generally, a large number of pixelwise labeled images are required to train deep models for supervised semantic segmentation. On the contrary, strong inter-sensor and geographic variations impede the availability of annotated training data in Earth observation. In practice, most Earth observation tasks use only the target scene without assuming availability of any additional scene, labeled or unlabeled. Keeping in mind such constraints, we propose a semantic segmentation method that learns to segment from a single scene, without using any annotation. Earth observation scenes are generally larger than those encountered in typical computer vision datasets. Exploiting this, the proposed method samples smaller unlabeled patches from the scene. For each patch an alternate view is generated by simple transformations, e.g., addition of noise. Both views are then processed through a two-stream network and weights are iteratively refined using deep clustering, spatial consistency, and contrastive learning in the pixel space. The proposed model automatically segregates the major classes present in the scene and produces the segmentation map. Extensive experiments on four Earth observation datasets collected by different sensors show the effectiveness of the proposed method. Implementation is available at https://gitlab.lrz.de/ai4eo/cd/-/tree/main/unsupContrastiveSemanticSeg.