Reviewerly/exHarmony huggingface dataset:
FIT: Frequency-Based Image Translation for Domain Adaptive Object Detection

Domain adaptive object detection (DAOD) aims to adapt the detector from a labelled source domain to an unlabelled target domain. In recent years, DAOD has attracted massive attention since it can alleviate performance degradation due to the large shift of data distributions in the wild. To align distributions between domains, adversarial learning is widely used in existing DAOD methods. However, the decision boundary for the adversarial domain discriminator may be inaccurate, causing the model biased towards the source domain. To alleviate this bias, we propose a novel Frequency-based Image Translation (FIT) framework for DAOD. First, by keeping domain-invariant frequency components and swapping domain-specific ones, we conduct image translation to reduce domain shift at the input level. Second, hierarchical adversarial feature learning is utilized to further mitigate the domain gap at the feature level. Finally, we design a joint loss to train the entire network in an end-to-end manner without extra training to obtain translated images. Extensive experiments on three challenging DAOD benchmarks demonstrate the effectiveness of our method.
Online learning compensation control of an electro-hydraulic shaking table using Echo State Networks

Electro-hydraulic shaking table (EHST) is widely used to simulate earthquake excitation in structural seismic tests. However, the nonlinear characteristics of the EHST, such as oil flow, friction, and dead zone, often lead to the distortion of seismic wave acceleration tracking and even cause the failure of the test. This paper describes the first application of Echo-State Networks (ESNs) to achieve high-precision seismic acceleration tracking of the EHST. The nonlinear control ability of the conventional three-variable control (TVC) algorithm in the EHST is discussed. An online learning compensation control framework using ESNs for "Transfer System" (The "Transfer System" denotes the nonlinear EHST controlled by TVC) is proposed. The least mean square (LMS) algorithm based on filtered-X is used to train the "ESN-Controller" online, and the initial transients of the "ESN-Controller" are reduced by using an estimation model of the actual "Transfer System" and a conditional switch. The offline iterative control (OIC) and nonlinear signal-based control (NSBC) are taken as the baseline algorithms of the proposed online learning compensation control. Simulation results show that the proposed online learning compensation control achieved excellent control with Pearson correlation coefficients of reference and responses above 99.6%, whereas the OIC and NSBC provided insufficient control. Moreover, the proposed online learning compensation control can also reduce the control error caused by the non-optimal TVC gains in the linear part of the EHST. This work throws new light on the study of the online learning control algorithm of the EHST.
Privacy-Preserving Federated Learning for Pneumonia Diagnosis

Early diagnosis of diseases has become the major focus of researchers today. Machine Learning (ML) and Deep Learning (DL) algorithms have provided a much-needed boost to this field to make early diagnosis possible. With the help of sufficient data and computation, DL algorithms can be developed to detect diseases with high precision. Even with this uplift, gathering high-quality and quantity data for medical studies is very difficult. Although implementing Federated Learning (FL) solves data availability issues, its high variance makes it incompatible with Medical Diagnosis. In this paper, we present an FL approach for diagnosing pneumonia in patients that is easy to use and provides state-of-the-art results. Before training, a combination of pre-processing steps is performed that significantly increase the performance of the FL architecture.
TurboMGNN: Improving Concurrent GNN Training Tasks on GPU With Fine-Grained Kernel Fusion

<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Graph Neural Networks</i> (GNN) have evolved as powerful models for graph representation learning. Many works have been proposed to support GNN training efficiently on GPU. However, these works only focus on a single GNN training task such as operator optimization, task scheduling, and programming model. Concurrent GNN training, which is needed in the applications such as neural network structure search, has not been explored yet. This work aims to improve the training efficiency of the concurrent GNN training tasks on GPU by developing fine-grained methods to fuse the kernels from different tasks. Specifically, we propose a fine-grained <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Sparse Matrix Multiplication</i> (SpMM) based kernel fusion method to eliminate redundant accesses to graph data. In order to increase the fusion opportunity and reduce the synchronization cost, we further propose a novel technique to enable the fusion of the kernels in forward and backward propagation. Finally, in order to reduce the resource contention caused by the increased number of concurrent, heterogeneous GNN training tasks, we propose an adaptive strategy to group the tasks and match their operators according to resource contention. We have conducted extensive experiments, including kernel- and model-level benchmarks. The results show that the proposed methods can achieve up to 2.6X performance speedup.
Weakly-Interactive-Mixed Learning: Less Labelling Cost for Better Medical Image Segmentation

Common medical image segmentation tasks require large training datasets with pixel-level annotations which are very expensive and time-consuming to prepare. To overcome such limitation and achieve the desired segmentation accuracy, a novel Weakly-Interactive-Mixed Learning (WIML) framework is proposed by efficiently using weak labels. On one hand, utilize weak labels to reduce annotation time for high-quality strong labels by designing a Weakly-Interactive Annotation (WIA) part of the WIML which prudently introduces interactive learning into the weakly-supervised segmentation strategy. On the other hand, utilize weak labels and very few strong labels to achieve desired segmentation accuracy by designing a Mixed-Supervised Learning (MSL) part of the WIML which can boost the segmentation accuracy by providing strong prior knowledge during training. Besides, a multi-task Full-Parameter-Sharing Network (FPSNet) is proposed to better implement this framework. Specifically, to further reduce annotation time, attention modules (scSE) are integrated into FPSNet to improve the class activation map (CAM) performance for the first time. To further improve segmentation accuracy, a Full-Parameter-Sharing (FPS) strategy is designed in FPSNet to alleviate the overfitting of the segmentation task supervised by very few strong labels. The proposed method is validated on the BraTS 2019 and LiTS 2017 datasets, and experiments demonstrate that the proposed method WIML-FPSNet outperforms several state-of-the-art segmentation methods with minimal annotation efforts.
High-speed modulation in a waveguide magneto-optical switch with impedance-matching electrode

A magneto-optical switch responding to signal with 200 ps rise time was demonstrated. The switch uses current-induced magnetic field to modulate the magneto-optical effect. Impedance-matching electrodes were designed to apply high-frequency current and accommodate the high-speed switching. A static magnetic field generated by a permanent magnet was applied orthogonal to the current-induced ones and acts as a torque and helps the magnetic moment reverse its direction which assist the high-speed magnetization reversal.
RaKShA: A Trusted Explainable LSTM Model to Classify Fraud Patterns on Credit Card Transactions

Credit card (CC) fraud has been a persistent problem and has affected financial organizations. Traditional machine learning (ML) algorithms are ineffective owing to the increased attack space, and techniques such as long short-term memory (LSTM) have shown promising results in detecting CC fraud patterns. However, owing to the black box nature of the LSTM model, the decision-making process could be improved. Thus, in this paper, we propose a scheme, RaKShA, which presents explainable artificial intelligence (XAI) to help understand and interpret the behavior of black box models. XAI is formally used to interpret these black box models; however, we used XAI to extract essential features from the CC fraud dataset, consequently improving the performance of the LSTM model. The XAI was integrated with LSTM to form an explainable LSTM (X-LSTM) model. The proposed approach takes preprocessed data and feeds it to the XAI model, which computes the variable importance plot for the dataset, which simplifies the feature selection. Then, the data are presented to the LSTM model, and the output classification is stored in a smart contract (SC), ensuring no tampering with the results. The final data are stored on the blockchain (BC), which forms trusted and chronological ledger entries. We have considered two open-source CC datasets. We obtain an accuracy of 99.8% with our proposed X-LSTM model over 50 epochs compared to 85% without XAI (simple LSTM model). We present the gas fee requirements, IPFS bandwidth, and the fraud detection contract specification in blockchain metrics. The proposed results indicate the practical viability of our scheme in real-financial CC spending and lending setups.
Intelligent Fault Identification for Industrial Internet of Things via Prototype-Guided Partial Domain Adaptation With Momentum Weight

Partial domain adaptation (PDA) for fault identification has been widely researched to help construct self-monitoring systems in the era of the Industrial Internet of Things (IIoT). However, the existing PDA fault identification methods neglect the influence of uncertainty of the target domain on the identification performance. To solve this problem, this work developed a prototype-guided PDA method with momentum weight for fault diagnosis. Specifically, to reduce the risk of ruling out the outlier by the output of a classifier or a discriminator, a classwise selectively source weighting strategy that follows the number of the target pseudo labels is proposed. The target instances’ pseudo labels, which are obtained by calculating the distance between the target instance and the source prototypes, are irrelevant to the classifier and discriminator. Furthermore, the momentum algorithm, by which the historical weights information could be retained, is employed in the source weights calculation procedure to alleviate the fluctuation and more closely to the global optimal. Experiments demonstrated the effectiveness and superiority of the developed method.
An improved pedestrian detection algorithm based on YOLOv3

For the detection needs of small target size and obscured targets in the pedestrian detection human task in complex scenes and the shortcomings of existing deep learning models, this paper proposes an improved pedestrian detection algorithm based on YOLOv3. First, the three-scale detection layer is increased to a four-scale detection layer to improve the accuracy of small target recognition through the retention of high-resolution feature map position information; second, add Spatial Pyramid Pooling layer to improve the generalization ability of the model; finally, the Convolutional Block Attention Module attention mechanism module is added to reinforce the important feature information and ignore irrelevant information.
The Role of Hungarian Traffic Rules Education and Examination System – a Quality Function Deployment Approach

This paper examines the traffic rules education and examination system in Hungary, by using the Quality Function Deployment (QFD) method, as a new approach towards this complex topic.The education and examination of traffic rules are necessary for the stakeholders, but they have slightly different goals and objectives.This system has two separate stakeholders, the citizens and the authority, with their own set of goals, objectives, desires and ideas, about this system.The QFD reveals the connections between these layers.The paper analyses statistical data regarding road safety and presents the QFD model of both stakeholders and their inter-connections.The results of this work can be used to redesign education and examination methods, during the application of digitalized egovernment solutions and as a general approach to match individual and public interests.
A Three-Layer Attentional Framework Based on Similar Users for Dual-Target Cross-Domain Recommendation

To address the long-standing data sparsity problem in recommender systems (RSs), cross-domain recommendation (CDR) has been proposed. Traditional CDR leverages the relatively richer information from a richer domain to improve recommendation performance in a sparser domain, which is also called single-target CDR. In recent years, dual-target CDR has been proposed to improve recommendation performance in both domains simultaneously. The existing dual-target CDR methods are based on common users between domains, where they extract the embeddings of common users and then transfer the embeddings to the two target domains to improve recommendation performance. However, in real life, the proportion of common users between domains is usually very small, which makes it hard to generate representative and high-quality user embeddings, and thus, limits the performance of the existing methods in real applications. To address this problem, in this paper, we propose a Three-Layer Attentional Framework based on Similar Users, called TASU. In addition to common users, TASU leverages information from similar users to improve the quality of user embeddings. By a three-layer attentional framework, TASU can generate more representative and high-quality user embeddings to improve recommendation performance in both domains. Extensive experiments conducted on three real-world datasets demonstrate that TASU significantly outperforms the state-of-the-art approaches.
Computational Notebooks as Co-Design Tools: Engaging Young Adults Living with Diabetes, Family Carers, and Clinicians with Machine Learning Models

Engaging end user groups with machine learning (ML) models can help align the design of predictive systems with people's needs and expectations. We present a co-design study investigating the benefits and challenges of using computational notebooks to inform ML models with end user groups. We used a computational notebook to engage young adults, carers, and clinicians with an example ML model that predicted health risk in diabetes care. Through co-design workshops and retrospective interviews, we found that participants particularly valued using the interactive data visualisations of the computational notebook to scaffold multidisciplinary learning, anticipate benefits and harms of the example ML model, and create fictional feature importance plots to highlight care needs. Participants also reported challenges, from running code cells to managing information asymmetries and power imbalances. We discuss the potential of leveraging computational notebooks as interactive co-design tools to meet end user needs early in ML model lifecycles.
Large Baseline Optical Imaging Assisted by Single Photons and Linear Quantum Optics

In this Letter, we show that by combining quantum metrology and networking tools, it is possible to extend the baseline of an interferometric optical telescope and thus improve diffraction-limited imaging of point source positions. The quantum interferometer is based on single-photon sources, linear optical circuits, and efficient photon number counters. Surprisingly, with thermal (stellar) sources of low photon number per mode and high transmission losses across the baseline, the detected photon probability distribution still retains a large amount of Fisher information about the source position, allowing for a significant improvement in the resolution of positioning point sources, on the order of 10 μas. Our proposal can be implemented with current technology. In particular, our proposal does not require experimental optical quantum memories.
Multi-Label Classification Based on Associations

Associative classification (AC) has been shown to outperform other methods of single-label classification for over 20 years. In order to create rules that are both more precise and simpler to grasp, AC combines the rules of mining associations with the task of classification. However, the current state of knowledge and the views of various specialists indicate that the issue of multi-label classification (MLC) cannot be solved by any AC method. Since this is the case, adapting or using an AC algorithm to manage multi-label datasets is one of the most pressing issues. To solve the MLC issue, this research proposes modifying the classification based on associations (msCBA) method by extending its capabilities to consider more than one class label in the consequent of its rules and modifying its rules order procedure to fit the nature of the multi-label dataset. The proposed algorithm outperforms several other MLC algorithms from various learning techniques across a variety of performance measuresand using six datasets with different domains. The main findings of this research are the significance of utilizing the local dependencies among labels compared to global dependencies, and the important rule of AC in solving the problem of MLC.
Combined approach for assessing metal(loid)s leaching, mobility and accumulation in a specific near-neutral (pH) environment of a former Cu-smelting area in the Old Copper Basin, Poland

Topsoils, soil profiles, water, and stream sediments as well as slags and rocks were analyzed to assess the extent and severity of environmental pollution resulting from historical Cu mining and smelting in the vicinity of Leszczyna, Old Copper Basin, SW Poland. Numerous tailings, smelting wastes, and Cu-rich rocks were disposed in the study area, causing long-term leaching and accumulation of various metal(loid)s. Surrounding the anthropogenically impacted area, two types of geochemical background are distinguished, one of which is associated with rocks and soils that are naturally enriched in metal(loid)s and the second one is free of them. The presence of significant anthropogenic changes combined with natural enrichment creates a highly complex environmental situation with regard to different sources of metal(loid)s. Additionally, the ore-hosting carbonate rocks are responsible for specific near-neutral (pH) conditions. With a focus on numerous elements (Cu, Zn, Pb, Ni, As, Cd, Co, Ba, and Cr) we studied spatial and vertical metal(loid)s distributions in soils to determine elements fate under near-neutral conditions. We applied the Synthetic Precipitation Leaching Procedure (SPLP) to evaluate the leaching potential of geogenic and anthropogenic materials and the Acid Neutralization Potential (ANP) test to measure the impact of slags on the pH of local soils. The EDTA extraction indicated high (bio)availability of Cu, Cd, Pb, and Zn, and suggested that metals from Cu-rich rocks are substantially more mobile than these of metallurgical origin. Our data indicate significant metal(loid)s mobility from surface-deposited wastes, especially into soils and stream sediments. The study proves that in the near-neutral conditions wastes are mainly subjected to short-time, rainfall-associated leaching, which is responsible for mobilization of labile metal(loid)s fractions (especially Cu, Pb, Zn, and Cd).
An Active Multi-Object Ultrafast Tracking System with CNN-Based Hybrid Object Detection

This study proposes a visual tracking system that can detect and track multiple fast-moving appearance-varying targets simultaneously with 500 fps image processing. The system comprises a high-speed camera and a pan-tilt galvanometer system, which can rapidly generate large-scale high-definition images of the wide monitored area. We developed a CNN-based hybrid tracking algorithm that can robustly track multiple high-speed moving objects simultaneously. Experimental results demonstrate that our system can track up to three moving objects with velocities lower than 30 m per second simultaneously within an 8-m range. The effectiveness of our system was demonstrated through several experiments conducted on simultaneous zoom shooting of multiple moving objects (persons and bottles) in a natural outdoor scene. Moreover, our system demonstrates high robustness to target loss and crossing situations.
Gamification Elements in the Process of Ukrainian Language Training of Pedagogy Specialists

The article studies the problem of using gamification elements in the process of training future preschool and primary school teachers on the example of the methodology of teaching Ukrainian Language for Professional Purposes. The necessity of improving the methodology of teaching the language to students of different age groups while taking into account the main educational trends and public demand has been substantiated. According to the goal, the essence of the "gamification" concept has been defined regarding the modern education sector. The peculiarities of using gamification, which have a great impact on the effectiveness of teaching students at pedagogical institutions of higher education, have been determined.&#x0D; It is emphasized that the growth of the number of game means in the methodology of teaching Ukrainian Language for Professional Purposes is not always directly proportional to the increase of success. Since gamification is impossible without using information and communications technology, consideration of the factor of appropriate technological support is necessary. The age factor manifests itself rather in the information and technological skills and abilities of certain age groups than the effectiveness of completed tasks on the condition of unhindered mastery of the technology. In the process of gamification, the element of novelty has a significant impact, as well as the temporal and motivational dependence of the interest of the performers on the duration or algorithmization of the tasks. The psychological factor is manifested through gender, which substantiates the inefficiency of using gamification with elements of competition or open ranking of results for female audience. In the context of training pedagogy specialists, the process of gamification during the study of a certain discipline has a dual purpose: increasing the motivation and therefore the effectiveness of training, formation of necessary competencies of future teachers; mastering completed gamified tasks as possible ways and means of teaching the Ukrainian language in future professional activity.&#x0D; Based on the analysis of the information space of the problem, a system of tasks with gamification elements has been developed and adapted to the content of the Ukrainian Language for Professional Purposes academic discipline.
Theory-driven design of AIED systems for enhanced interaction and problem-solving

Successful AI learning environments are driven by effective designs for learning that incorporate what we know about how learners learn best. Theory-driven designs about the cognitive, metacognitive, affective, motivational, and behavioral components of learning need to be considered jointly in designing effective AI interfaces for learning and instruction. We provide examples of AI interfaces that embody specific theories. We discuss the benefits of AI-supported interfaces along with AI developments that enable new functionalities and modalities of AIED systems. We conclude with suggestions for future interface considerations.
Computer Vision and Machine Learning for Intelligent Sensing Systems

Intelligent sensing systems have been fueled to make sense of visual sensory data to handle complex and difficult real-world sense-making challenges due to the rapid growth of computer vision and machine learning technologies [...].