Reviewerly/exHarmony huggingface dataset:
Adversarial Reinforcement Learning for Steering Cars from Virtual to Real World

A promising method to achieve autonomous driving is reinforcement learning which excels at sequential decision-making and control problems. Training reinforcement learning models in the virtual environment is appealing as it can provide abundant data at a lower cost and avoid unpredictable safety issues. But the visual reality gap between the virtual environment and the real world makes the vision-based model trained with a simulator hard to generalize to the real world. We proposed a domain-adversarial reinforcement learning method to bridge the reality gap in which a domain classifier that requires only unlabeled real-world data is introduced to help the alignment of visual features between two domains. The common semantic representation of images from different domains is learned when the domain classifier fails to classify the source domain of the feature. Thereafter the model generalizes well in the real world without any extra labeled data or processing. We train the proposed model in The Open Racing Car Simulator (TORCS) and validate its effectiveness and superiority on a real-world dataset containing images recorded in real-world driving with steering angle labels.
LR-SARNET: A Lightweight and Robust Network for Multi-scale and Multi-scene SAR Ship Detection

SAR Ship image has the characteristics of complex background, blurred target edge and scale difference, which makes the target detection difficult. This article builds a lightweight and robust network for multi-scale and multi-scene SAR ship detection (LR-SARNET). Firstly, with the goal to minimize the computational complexity of the model, a lightweight backbone feature extraction network (CGNet) is designed to generate sufficiently redundant feature maps with low computational cost. Secondly, a linear feature fusion module (ENECK) is designed to efficiently fuse deep local feature maps. Finally, the extremely efficient spatial pyramid (EESP) is integrated into the target detection head, which expands the receptive field of the network. The experiment on SSDD and HRSID dataset proves that our algorithm has strong robustness and excellent generalization performance.
Attention-Guided Neural Network for Face Mask Detection

With the outbreak of COVID-19 and various influenza diseases, it is necessary to wear masks properly in crowded public places to prevent the spread of the virus. Therefore, detecting mask-wearing efficiently and accurately is essential for people’s physical health and safety. In this paper, we present a novel one-stage mask detection method, named attention-guided neural network (AGNN) that can efficiently detect non-mask-wearing faces in public. Specifically, we started with YOLOv5 as a baseline and integrated the coordinate attention mechanism module into YOLOv5 to guide the holistic model for improving the ability of feature extraction. Furthermore, we explored utilizing the focal loss to solve the problem of class imbalance. The experiment is conducted on the face mask detection dataset of real-life scenes with twenty different categories. Experimental results demonstrate that the proposed AGNN method achieves higher precision and recall than the original YOLOv5 in multi-classification mask detection.
A Optical Flow-Based Fight Behavior Detection Method for Campus Scene

Campuses contain a large number of facilities that must all be monitored to ensure security. However, most of the existing video surveillance needs to be watched by people, and it is impossible to realize the automatic early warning of some dangerous situations. In this paper, a video-based action detection method is proposed for high-frequency student fight on campus, which uses an optical flow algorithm to perform coarse positioning of the area where fight actions may occur and uses the transformer network to identify the action category of the region of interest. In addition, this paper builds a dataset of fight recognition in middle school campuses for model training, validation and testing. The experimental results show that the method proposed in this paper can locate fight actions relatively accurately and provide real-time early warning.
DPFMN: Dual-Path Feature Match Network for RGB-D and RGB-T Salient Object Detection

Feature match is a hot research topic in salient object detection, because the information definition is complex and it is difficult to explore an effective match strategy. In this paper, we propose a Dual-Path Feature Match Network (DPFMN) to enhance the cross-modal and global-local match efficiency. Specifically, in the cross-modal match, we propose the Auxiliary-enhanced Module (AEM) to excavate the auxiliary information. In the global-local match, we propose the Capsule Correlation Module (CCM) to store information hierarchically in the sub-capsules, which can enhance the correlation from global to local features. Also, we design the Guided Fusion Module (GFM) to integrate global-local features in a distributed manner to ensure information integrity. Considering the quality and detail of the saliency map, we introduce the Saliency Reconstruct Module (SRM) for progressive image reconstruction to avoid the unstable reconstruction information caused by too large gradients. The method proves its effectiveness through a fair comparison with 12 RGB-D and 7 RGB-T networks on 8 public datasets.
Managing employee attendance using real-time face recognition

The process of the attendance monitoring system is changing due to different trends and technologies. If we review the past data on attendance tracking systems in industries, organisations and many companies use unique identification methodologies, such as RFID, fingerprint identification, eye recognition and facial recognition systems. Among all these strategies, face recognition is the most common and least time-consuming method. The system gives more flexibility and ease to employees and organisations in marking and managing their attendance. It employs the Haar Cascade Classifier for detecting the face and the local binary patterns histograms (LBPH) algorithm to train the classifier. After the successful entry of an employee's record in the system's dataset, his face will be detected by its name, and his attendance will be marked. After a particular time (depending on the organisation), for example, half an hour, the employee receives a confirmation email for the same. The proposed work provides efficient data management at the end of the Admin.
Hype or Help? The Real Benefits of Blockchain

Blockchain is much more than bitcoins and offers benefits for many industries. Since the introduction of blockchain in 2009, the technology has been continuously developed. Nevertheless, discussions about blockchain applications are still heavily influenced by cryptocurrencies and, since 2021, also by so-called non-fungible tokens (NFT). The result: The confusion around blockchain and its potential is equally great.This study provides clarity and drills down to blockchain's benefits in the real economy. Opportunities and challenges are illustrated using three concrete examples:Self-sovereign (electronic) identities to securely identify people, organizations, and objects in the digital space.Product tracking to increase product safety and transparency along supply chains.Consolidation of sensitive (health) data to improve the quality of diagnosis and therapy without compromising data security.Ultimately, blockchain offers a technical basis to securely digitize and automate business processes, collaborate efficiently, and create more robust digital infrastructures. Once achieved, new business areas can open up. Leaders must detach themselves from hype and sweeping criticism to realize this potential. This study aims to be a starting point to do so.
Information Criteria

In thimore, we clarify that AIC, TIC, and WAIC show almost the same performance when assuming regularity. In addition, we introduce WBIC, which corresponds to a generalization of BIC, and clarify that it shows similar performance when assuming regularity. Note that there is the following relationship among the propositions presented in this chapter.
Mutual authenticated key agreement in Wireless Infrastructure-less network by Chaotic Maps based Diffie-Helman Property

Because wireless infrastructure-less networks are dynamic, varied, and scattered, implementing security in them is exceedingly difficult. Authentication is the most crucial prerequisite for security deployment. It is difficult to implement security based on public-key infrastructure with centralized third-party authentication in an environment without infrastructure. We build and test a chaotic map-based technique that handles authentication as one of the key qualities to accomplish security. We allocate the key management responsibility to cluster-heads after dividing the infrastructure-less into several clusters with cluster-heads. The Diffie-Helman property, which is based on Chebyshev polynomials, is used in the proposed work to establish authentication. Our suggested method avoids unnecessary computations like modular exponentiation and elliptical curve scalar multiplications. It also ensures that the secret session-key is only established between the two designated entities and is resistant to a variety of network attacks.
Global Topology of 3D Symmetric Tensor Fields

There have been recent advances in the analysis and visualization of 3D symmetric tensor fields, with a focus on the robust extraction of tensor field topology. However, topological features such as degenerate curves and neutral surfaces do not live in isolation. Instead, they intriguingly interact with each other. In this paper, we introduce the notion of topological graph for 3D symmetric tensor fields to facilitate global topological analysis of such fields. The nodes of the graph include degenerate curves and regions bounded by neutral surfaces in the domain. The edges in the graph denote the adjacency information between the regions and degenerate curves. In addition, we observe that a degenerate curve can be a loop and even a knot and that two degenerate curves (whether in the same region or not) can form a link. We provide a definition and theoretical analysis of individual degenerate curves in order to help understand why knots and links may occur. Moreover, we differentiate between wedges and trisectors, thus making the analysis more detailed about degenerate curves. We incorporate this information into the topological graph. Such a graph can not only reveal the global structure in a 3D symmetric tensor field but also allow two symmetric tensor fields to be compared. We demonstrate our approach by applying it to solid mechanics and material science data sets
Target Location Detection for UAV Manipulator Grasp in Consumer Electronics

The popularity of the field of consumer electronics has become higher and higher, and the target location detection captured by the unmanned aerial vehicle (UAV) manipulator has gradually become the research focus of scholars at home and abroad. YOLOY5s, as one of the most popular target detectors at present, has the problem of insufficient feature expression ability. To solve this problem, this paper designs a target detection scheme based on feature optimization and proposes a new target detector based on feature enhancement and attention mechanism (FEATT), which improves the detection performance of the general target detector. The main work of this paper is as follows: 1) In order to enrich the feature space of the target detector, this paper proposes a CBMA-based center and scale prediction with spatial enhancement (CCSP). The CCSP module makes good use of the connection of diversity and the nonlinearity of training time to enhance the spatial representation of features without adding additional reasoning time. 2) In order to better capture channel information and spatial information in the network and enhance the ability of network feature representation, this paper proposes coordinate attention with Meta-ACON (CATTM) based on channel and spatial information.
cFedDT: Cross-Domain Federated Learning in Digital Twins for Metaverse Consumer Electronic Products

With the development of Consumer Electronic Products (CEP) in the Computer, Communication, and Consumer electronics (3C) industry, research over high speed, high precision, and reliability CEP has been an urgent need. Due to the high manufacturing requirements, it has been challenging to product, transmit, assembly, or test the CEP. Metaverse with Virtual Reality (VR)/Augmented Reality (AR) technologies connect reality and virtual, which provides a more convenient manufacturing platform for CEP, thus bringing up the concept of Metaverse Consumer Electronic Products (MCEP). Digital Twins (DT) has effectively provided an emulated software replica to create the Metaverse. DT for MCEP (DTM) facilitates the implementation of more advanced applications, especially Artificial Intelligence (AI) applications. An essential component in DTM AI applications is the labeled dataset, but such carefully labeled data are usually inaccessible since labeling is costly. In addition, the unlabeled data are usually stored locally, referred to as Federated Learning (FL). Previous works adopted Domain Adaptation to construct a pseudo-labeled dataset. However, those approaches have rising potential in accuracy, convergence speed, and communication efficiency. In this paper, we propose cFedDT, a cross-domain FL algorithm, to construct a pseudo-labeled dataset in DTM. cFedDT contains three modules. Ensembled Knowledge for Cross Distillation distills the knowledge from the source models to the target model to improve the target accuracy. Adaptive Batch adjustment for Stable Convergence gradually adjusts the number of batches on each client to speed up the converging process. Magnitude Pruning for Efficient Communication reduces the parameters that require communication to speed up the training. Our experimental results illustrate the priority of cFedDT. Compared with other state-of-the-art algorithms, cFedDT can increase the target accuracy by 9.8% or reduce up to 99.97% of the communication with a target accuracy loss of less than 2.7%.
Visual Analytics for Understanding Draco's Knowledge Base

Draco has been developed as an automated visualization recommendation system formalizing design knowledge as logical constraints in ASP (Answer-Set Programming). With an increasing set of constraints and incorporated design knowledge, even visualization experts lose overview in Draco and struggle to retrace the automated recommendation decisions made by the system. Our paper proposes an Visual Analytics (VA) approach to visualize and analyze Draco's constraints. Our VA approach is supposed to enable visualization experts to accomplish identified tasks regarding the knowledge base and support them in better understanding Draco. We extend the existing data extraction strategy of Draco with a data processing architecture capable of extracting features of interest from the knowledge base. A revised version of the ASP grammar provides the basis for this data processing strategy. The resulting incorporated and shared features of the constraints are then visualized using a hypergraph structure inside the radial-arranged constraints of the elaborated visualization. The hierarchical categories of the constraints are indicated by arcs surrounding the constraints. Our approach is supposed to enable visualization experts to interactively explore the design rules' violations based on highlighting respective constraints or recommendations. A qualitative and quantitative evaluation of the prototype confirms the prototype's effectiveness and value in acquiring insights into Draco's recommendation process and design constraints.
OldVisOnline: Curating a Dataset of Historical Visualizations

With the increasing adoption of digitization, more and more historical visualizations created hundreds of years ago are accessible in digital libraries online. It provides a unique opportunity for visualization and history research. Meanwhile, there is no large-scale digital collection dedicated to historical visualizations. The visualizations are scattered in various collections, which hinders retrieval. In this study, we curate the first large-scale dataset dedicated to historical visualizations. Our dataset comprises 13K historical visualization images with corresponding processed metadata from seven digital libraries. In curating the dataset, we propose a workflow to scrape and process heterogeneous metadata. We develop a semi-automatic labeling approach to distinguish visualizations from other artifacts. Our dataset can be accessed with OldVisOnline, a system we have built to browse and label historical visualizations. We discuss our vision of usage scenarios and research opportunities with our dataset, such as textual criticism for historical visualizations. Drawing upon our experience, we summarize recommendations for future efforts to improve our dataset.
Scalable Evidential <i>K</i>-Nearest Neighbor Classification on Big Data

The <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">K</i> -Nearest Neighbor (K-NN) algorithm has garnered widespread utilization in real-world scenarios, due to its exceptional interpretability that other classification algorithms may not have. The evidential K-NN (EK-NN) algorithm builds upon the same nearest neighbor search procedure as K-NN, and provides more informative classification outcomes. However, EK-NN is not practical for big data because it is computationally complex. First, the search for <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">K</i> nearest neighbors of test samples from <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$n$</tex-math></inline-formula> training samples requires <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$O(n^{2})$</tex-math></inline-formula> operations. Additionally, estimating parameters involves performing complicated matrix calculations that increase in scale as the dataset becomes larger. To address these issues, we propose two scalable EK-NN classifiers, Global Exact EK-NN and Local Approximate EK-NN, under the distributed Spark framework. Along with the Local Approximate EK-NN, a new distributed gradient descent algorithm is developed to learn parameters. Data parallelism is used to reduce negative impacts caused by data distribution differences. Experimental results show that Our algorithms are able to achieve state-of-the-art scaling efficiency and accuracy on large datasets with more than 10 million samples.
Integrating Hyperparameter Search into Model-Free AutoML with Context-Free Grammars

Automated Machine Learning (AutoML) has become increasingly popular in recent years due to its ability to reduce the amount of time and expertise required to design and develop machine learning systems. This is very important for the practice of machine learning, as it allows building strong baselines quickly, improving the efficiency of the data scientists, and reducing the time to production. However, despite the advantages of AutoML, it faces several challenges, such as defining the solutions space and exploring it efficiently. Recently, some approaches have been shown to be able to do it using tree-based search algorithms and context-free grammars. In particular, GramML presents a model-free reinforcement learning approach that leverages pipeline configuration grammars and operates using Monte Carlo tree search. However, one of the limitations of GramML is that it uses default hyperparameters, limiting the search problem to finding optimal pipeline structures for the available data preprocessors and models. In this work, we propose an extension to GramML that supports larger search spaces including hyperparameter search. We evaluated the approach using an OpenML benchmark and found significant improvements compared to other state-of-the-art techniques.
Hyper-box Classification Model Using Mathematical Programming

Classification constitutes focal topic of study within the machine learning research community. Interpretable machine learning algorithms have been gaining ground against black box models because people want to understand the decision-making process. Mathematical programming based classifiers have received attention because they can compete with state-of-the-art algorithms in terms of accuracy and interpretability. This work introduces a single-level hyper-box classification approach, which is formulated mathematically as Mixed Integer Linear Programming model. Its objective is to identify the patterns of the dataset using a hyper-box representation. Hyper-boxes enclose as many samples of the corresponding class as possible. At the same time, they are not allowed to overlap with hyper-boxes of different class. The interpretability of the approach stems from the fact that IF-THEN rules can easily be generated. Towards the evaluation of the performance of the proposed method, its prediction accuracy is compared to other state-of-the-art interpretable approaches in a number of real-world datasets. The results provide evidence that the algorithm can compare favourably against well-known counterparts.
Hardware-Assisted Static and Runtime Attestation for Cloud Deployments

This paper is devoted to the problems of static and runtime integrity for cloud deployments. Existing remote attestation solutions for cloud infrastructure do not cover static and dynamic attestation as a whole. They evaluate either the static or dynamic part, not considering the rest. We address this gap by proposing a runtime attestation process based on hardware CET technology, as an enhancement to static attestation enabled by SGX. We show how hardware-assisted protection for control-flow-related attacks can enhance virtual deployment security with minimal tradeoff. Our solution does not significantly increase the processing time. Moreover, a processing time can even be reduced when this mechanism is used as a default protection method against control-flow related attacks.
Cost Function Unrolling in Unsupervised Optical Flow

Steepest descent algorithms, which are commonly used in deep learning, use the gradient as the descent direction, either as-is or after a direction shift using preconditioning. In many scenarios calculating the gradient is numerically hard due to complex or non-differentiable cost functions, specifically next to singular points. This has been commonly overcome by increased DNN model sizes and complexity. In this work we propose a novel mechanism we refer to as Cost Unrolling, for improving the ability of a given DNN model to solve a complex cost function, without modifying its architecture or increasing computational complexity. We focus on the derivation of the Total Variation (TV) smoothness constraint commonly used in unsupervised cost functions. We introduce an iterative differentiable alternative to the TV smoothness constraint, which is demonstrated to produce more stable gradients during training, enable faster convergence and improve the predictions of a given DNN model. We test our method in several tasks, including image denoising and unsupervised optical flow. Replacing the TV smoothness constraint with our loss during DNN training, we report improved results in all tested scenarios. Specifically, our method improves flows predicted at occluded regions, a crucial task by itself, resulting in sharper motion boundaries.