Reviewerly/exHarmony huggingface dataset:
Personalized Federated Human Activity Recognition through Semi-supervised Learning and Enhanced Representation

Owing to the widespread utilization of activity recognition, numerous deep learning models have been proposed to facilitate recognition in diverse contexts. Federated learning (FL) is an emerging learning paradigm that enables the collaborative learning of a shared model without compromising the data privacy of users. However, the assumption of FL is to rely on the annotated data on clients, which is difficult to acquire the annotations for human activity recognition (HAR) on all clients due to the lack of expertise or resource. Moreover, a general model is not suitable for each person due to data heterogeneity, resulting from the different physical characteristics and various contextual information. To this end, we propose a semi-supervised learning method for personalized federated HAR, in which clients have completely unlabeled data, while the server has a small amount of labeled data contributed by volunteers. Clients conduct unsupervised learning on autoencoders with locally unlabeled data to collaboratively learn a general representation model. The server conducts supervised learning on an activity classifier with labeled data stored on the server. After that, the shared model is personalized using individually pseudo-labeled data on each client side, wherein both confidence and uncertainty are taken into account concurrently, with the aim of achieving a balanced selection for assigning pseudo-labels to samples. We conduct extensive experiments with two different real-world HAR datasets, demonstrating the effectiveness of the proposed methods.
Evaluating Outside the Box: Lessons Learned on eXtended Reality Multi-modal Experiments Beyond the Laboratory

Over time, numerous multimodal eXtended Reality (XR) user studies have been conducted in laboratory environments, with participants fulfilling tasks under the guidance of a researcher. Although generalizable results contributed to increase the maturity of the field, it is also paramount to address the ecological validity of evaluations outside the laboratory. Despite real-world scenarios being clearly challenging, successful in-situ and remote deployment has become realistic to address a broad variety of research questions, thus, expanding participants' sample to more specific target users, considering multi-modal constraints not reflected in controlled laboratory settings and other benefits. In this paper, a set of multimodal XR experiments conducted outside the laboratory are described (e.g., industrial field studies, remote collaborative tasks, longitudinal rehabilitation exercises). Then, a list of lessons learned is reported, illustrating challenges, and opportunities, aiming to increase the level of awareness of the research community and facilitate performing further evaluations.
Online Mongolian Handwriting Recognition Based on Encoder–Decoder Structure with Language Model

Mongolian online handwriting recognition is a complex task due to the script’s intricate characters and extensive vocabulary. This study proposes a novel approach by integrating a pre-trained language model into the sequence-to-sequence(Seq2Seq) + attention mechanisms(AM) model to enhance recognition accuracy. Three fusion models, including former, latter, and complete fusion, are introduced, showing substantial improvements over the baseline model. The complete fusion model, combined with synchronized language model parameters, achieved the best results, significantly reducing character and word error rates. This research presents a promising solution for accurate Mongolian online handwriting recognition, offering practical applications in preserving and utilizing the Mongolian script.
A wearable-based sports health monitoring system using CNN and LSTM with self-attentions

Sports performance and health monitoring are essential for athletes to maintain peak performance and avoid potential injuries. In this paper, we propose a sports health monitoring system that utilizes wearable devices, cloud computing, and deep learning to monitor the health status of sports persons. The system consists of a wearable device that collects various physiological parameters and a cloud server that contains a deep learning model to predict the sportsperson’s health status. The proposed model combines a Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and self-attention mechanisms. The model is trained on a large dataset of sports persons’ physiological data and achieves an accuracy of 93%, specificity of 94%, precision of 95%, and an F1 score of 92%. The sports person can access the cloud server using their mobile phone to receive a report of their health status, which can be used to monitor their performance and make any necessary adjustments to their training or competition schedule.
One label is all you need: Interpretable AI-enhanced histopathology for oncology

Artificial Intelligence (AI)-enhanced histopathology presents unprecedented opportunities to benefit oncology through interpretable methods that require only one overall label per hematoxylin and eosin (H&E) slide with no tissue-level annotations. We present a structured review of these methods organized by their degree of verifiability and by commonly recurring application areas in oncological characterization. First, we discuss morphological markers (tumor presence/absence, metastases, subtypes, grades) in which AI-identified regions of interest (ROIs) within whole slide images (WSIs) verifiably overlap with pathologist-identified ROIs. Second, we discuss molecular markers (gene expression, molecular subtyping) that are not verified via H&E but rather based on overlap with positive regions on adjacent tissue. Third, we discuss genetic markers (mutations, mutational burden, microsatellite instability, chromosomal instability) that current technologies cannot verify if AI methods spatially resolve specific genetic alterations. Fourth, we discuss the direct prediction of survival to which AI-identified histopathological features quantitatively correlate but are nonetheless not mechanistically verifiable. Finally, we discuss in detail several opportunities and challenges for these one-label-per-slide methods within oncology. Opportunities include reducing the cost of research and clinical care, reducing the workload of clinicians, personalized medicine, and unlocking the full potential of histopathology through new imaging-based biomarkers. Current challenges include explainability and interpretability, validation via adjacent tissue sections, reproducibility, data availability, computational needs, data requirements, domain adaptability, external validation, dataset imbalances, and finally commercialization and clinical potential. Ultimately, the relative ease and minimum upfront cost with which relevant data can be collected in addition to the plethora of available AI methods for outcome-driven analysis will surmount these current limitations and achieve the innumerable opportunities associated with AI-driven histopathology for the benefit of oncology.
ATB-Net: attention-based network for facial expression recognition

Recent years, the related research on facial expression recognition is increasingly attracting people’s attention. As an important branch of artificial intelligence, facial expression recognition has a significant role in human-computer interaction and other related fields. However, due to the high similarity between different expressions and the small interclass differences, it is still a daunting task for researchers to obtain a robust and accurate expression recognition model. Considering the characteristics of human expressions, a new convolution mechanism is proposed, which we call the attention convolution module. We also apply the convolution module to the field of expression recognition and propose a new expression recognition network (ATB-NET). The experimental results show that our method can focus on relatively important regions in the face, besides, compared with previous work, it also has a significant improvement on multiple public data sets (such as FER2013, CK+).
Blockchain as a driver for transformations in the public sector

Blockchain architecture, originally designed for Bitcoin, has revolutionized finance through decentralized transactions and secured data management.It has been utilized to maintain private citizen records, allowing data owners to grant access via the blockchain for direct communication.Despite its potential, this technology remains relatively unexplored by both citizens and the public sector.By carrying out a thorough literature review, this article aims to shed light on this field.The research focus encompasses two key elements: (1) analyzing blockchain dimensions and (2) exploring its transformative impact on the public sector.The methodology involves an extensive meta-analysis of existing research on blockchain's analytical aspects and its role in reshaping public administration.Additionally, a questionnaire is administered to Information Technologies (IT) experts in public services, comparing their perceptions with established scientific studies.The research's core findings address various analysis dimensions, including regulatory risks, data management challenges, privacy concerns, and technological limitations.On the transformation front, organizations adopting blockchain technology anticipate enhanced networked services, fortified data security, operational efficiency, informed decision-making, and novel public services.The potential of blockchain to drive innovative services and safeguard data is widely acknowledged, yet organizations with blockchain are cautiously optimistic about its practical implications compared to those without.
On the Importance of Attention and Augmentations for Hypothesis Transfer in Domain Adaptation and Generalization

Unsupervised domain adaptation (UDA) aims to mitigate the performance drop due to the distribution shift between the training and testing datasets. UDA methods have achieved performance gains for models trained on a source domain with labeled data to a target domain with only unlabeled data. The standard feature extraction method in domain adaptation has been convolutional neural networks (CNNs). Recently, attention-based transformer models have emerged as effective alternatives for computer vision tasks. In this paper, we benchmark three attention-based architectures, specifically vision transformer (ViT), shifted window transformer (SWIN), and dual attention vision transformer (DAViT), against convolutional architectures ResNet, HRNet and attention-based ConvNext, to assess the performance of different backbones for domain generalization and adaptation. We incorporate these backbone architectures as feature extractors in the source hypothesis transfer (SHOT) framework for UDA. SHOT leverages the knowledge learned in the source domain to align the image features of unlabeled target data in the absence of source domain data, using self-supervised deep feature clustering and self-training. We analyze the generalization and adaptation performance of these models on standard UDA datasets and aerial UDA datasets. In addition, we modernize the training procedure commonly seen in UDA tasks by adding image augmentation techniques to help models generate richer features. Our results show that ConvNext and SWIN offer the best performance, indicating that the attention mechanism is very beneficial for domain generalization and adaptation with both transformer and convolutional architectures. Our ablation study shows that our modernized training recipe, within the SHOT framework, significantly boosts performance on aerial datasets.
Red Teaming Generative AI/NLP, the BB84 Quantum Cryptography Protocol and the NIST-Approved Quantum-Resistant Cryptographic Algorithms

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A New Oversampling Method Based on Triangulation of Sample Space

Coping with imbalanced data is a challenging task in practical classification problems. One of effective methods to solve imbalanced problems is to oversample the minority class. SMOTE is a classical oversampling method. However, it exhibits two disadvantages, namely, a linear generation and overgeneralization. In this article, an improved synthetic minority oversampling technique (SMOTE) method, FE-SMOTE, is proposed based on the idea of the method of finite elements. FE-SMOTE not only overcomes the above two disadvantages of SMOTE but also can generate samples that are more in line with the density distribution of the original minority class than those generated by the existing SMOTE variants. The originality of the proposed method stems from constructing a simplex for every minority sample and then triangulating it to expand the region of synthetic samples from lines to space. A new definition of the relative size for triangular elements not only helps determine the number of synthetic samples but also weakens the adverse impact of outliers. Generated samples by FE-SMOTE can effectively reflect the local potential distribution structure arising around every minority sample. Compared with 16 commonly studied oversampling methods, FE-SMOTE produces promising results quantified in terms of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$G$</tex-math> </inline-formula> -mean, AUC, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$F$</tex-math> </inline-formula> -measure, and accuracy on 22 benchmark imbalanced datasets and the big dataset MNIST.
Accurate real-time obstacle detection of coal mine driverless electric locomotive based on ODEL-YOLOv5s

Abstract The accurate identification and real-time detection of obstacles have been considered the premise to ensure the safe operation of coal mine driverless electric locomotives. The harsh coal mine roadway environment leads to low detection accuracy of obstacles based on traditional detection methods such as LiDAR and machine learning, and these traditional obstacle detection methods lead to slower detection speeds due to excessive computational reasoning. To address the above-mentioned problems, we propose a deep learning-based ODEL-YOLOv5s detection model based on the conventional YOLOv5s. In this work, several data augmentation methods are introduced to increase the diversity of obstacle features in the dataset images. An attention mechanism is introduced to the neck of the model to improve the focus of the model on obstacle features. The three-scale prediction of the model is increased to a four-scale prediction to improve the detection ability of the model for small obstacles. We also optimize the localization loss function and non-maximum suppression method of the model to improve the regression accuracy and reduce the redundancy of the prediction boxes. The experimental results show that the mean average precision (mAP) of the proposed ODEL-YOLOv5s model is increased from 95.2 to 98.9% compared to the conventional YOLOv5s, the average precision of small obstacle rock is increased from 89.2 to 97.9%, the detection speed of the model is 60.2 FPS, and it has better detection performance compared with other detection models, which can provide technical support for obstacle identification and real-time detection of coal mine driverless electric locomotives.
Three-Dimensional Sonification for Image-Guided Surgery

In neuronavigation and image-guided surgery, the clinician’s surgical instrument is tracked in relation to the patient’s anatomy. This way, they can be guided towards pre-planned target locations, like positions for burr holes, incision points for biopsy needles, or the center of a tumor for thermal ablation. In this paper, I present the initial results of an experiment that simulates a surgical scenario using a skull phantom. After 50 minutes of explanation and training, 24 participants were guided towards target locations by auditory, visual, and audiovisual clues. So far, the results show that all methods successfully guide the participants to the targets. The trajectory lengths, the needed time to find the targets as well as the precision seem to lie in a similar order of magnitude. When guided by sonification, trajectories tend to be longest and axis-by- axis-motions can be observed. Visual guidance seems to suffer from the missing depth of pseudo-3D graphics on a 2D screen. Audiovisual guidance leads to more micro-motions near the targets, compared to purely visual guidance. Further analysis will quantify the effectiveness of sonification as a three-dimensional guidance tool for surgery.
Improving learning outcomes: The effectiveness of e-book reading literacy based in learning management psychology

The research aims to improve learning outcomes of Management Psychology Courses use E-book teaching material reading literacy based for the student of the Education Administration Study Program, Faculty of Education, Universitas Negeri Padang, Indonesia. The research used 2 x 2 factorial design experimental methods with ANOVA. The research obtained the highest score 96.6 for experimental class, and 93.3 for control class. The students learning outcomes in the experimental class have an average 81.93 and average 71.75 for control class. The result of t-test for both of class, tcount was 5.11 and ttable was 1.66. It implied there was a significant difference about students learning outcomes. The learning outcomes of student that used E-book teaching material was higher than students that used printed teaching materials. The students who have high reading literacy and used E-book teaching material, the student learning outcomes was higher than the student who have high reading literacy but used printed materials. And the students who have low reading literacy and used E-book teaching material, the learning outcomes was higher than the students who have low reading literacy but used printed teaching material.
1-D INTERFEROMETER IN 3-D SPACE AND RADON TRANSFORM

Observations in three-dimensional space using an interferometer generally require a corresponding rank of the base vector system of the interferometric system. The paper considers one of the ways to solve such a problem using a 1D interferometer with a moving antenna. Due to its movement, it is possible to synthesize an interferometer of a higher rank, similar to aperture synthesis. Moving the moving antenna in the plane allows formation of a 2D interferometer. The obtained analytical expressions show that such an interferometer performs the Radon transform of the angular structure of the spatial image when observing sources far beyond the size of the interferometer base. In the quasi-monochromatic approximation, a comparison is made between the van Cittert-Zernike theorem, which is widely used in such problems, and the considered transformation. It is shown that the obtained conclusions agree well with each other, while the Radon transform better meets the problem and is free from a number of limitations. Key words : 1D and 2D interferometers, aperture synthesis, Radon transform, mutual coherence function, angular intensity distribution.
Convolutional Neural Network

Convolutional neural network is one of the most important networks in deep learning. Different from common artificial neural network, the main characteristic of convolutional neural network is the convolution operation. It has made remarkable achievements in computer vision and natural language processing. Moreover, convolutional neural network has received extensive attention from industry and academia. This chapter first introduces the convolution operation of the convolutional neural network. Then performance evaluation metrics are introduced. Based on two typical convolutional neural network, transfer learning is demonstrated to use trained convolutional neural network to solve new computer vision problems. Finally, the state-of-the-art research progress of artificial neural network is provided.
The Contribution of Journalistic Knowledge to Students’ Skills in Writing News: A Study of Correlational Analysis

Journalistic knowledge serves as a robust underpinning upon which learners can refine and enhance their writing abilities, specifically in the domain of news composition. The multifaceted landscape of journalism incorporates a medley of elements that collectively contribute to the cultivation of effective writing skills. The mastery of these facets plays a pivotal role in augmenting students' proficiency in articulating their ideas cogently, engaging their audience adeptly, and ensuring the meticulous conveyance of information. Against this backdrop, the present study endeavors to explore the substantive role played by journalistic knowledge in improving students' skills in writing news. To attain the research aim, the researchers applied a correlational study to find out the correlation between journalistic knowledge and students’ skills in writing news. The participants of the study consisted of 51 students of Indonesian language and literary education at the University of PGRI Semarang. The sample was taken using a simple random sampling technique. The data were collected using tests. Based on the data analysis, there is a correlation between journalistic knowledge and students’ skills in writing news. It means that the components of journalistic knowledge such as understanding the fundamental news values such as timeliness, prominence, proximity, conflict, and human interest, have contributed to helping students in writing news.
ZETAR: Modeling and Computational Design of Strategic and Adaptive Compliance Policies

Compliance management plays an important role in mitigating insider threats. Incentive design is a proactive and noninvasive approach to achieving compliance by aligning an insider’s incentive with the defender’s security objective, which motivates (rather than commands) an insider to act in the organization’s interests. Controlling insiders’ incentives for population-level compliance is challenging because they are neither precisely known nor directly controllable. To this end, we develop ZEro-Trust Audit with strategic Recommendation (ZETAR), a zero-trust audit and recommendation framework, to provide a quantitative approach to model insiders’ incentives and design customized recommendation policies to improve their compliance. We formulate primal and dual convex programs to compute the optimal bespoke recommendation policies. We create the theoretical underpinning for understanding trust, compliance, and satisfaction, which leads to scoring mechanisms of how compliant and persuadable an insider is. After classifying insiders as malicious, self-interested, or amenable based on their incentive misalignment levels with the defender, we establish bespoke information disclosure principles for these insiders of different incentive categories. We identify the policy separability principle and the set convexity, which enable finite-step algorithms to efficiently learn the completely trustworthy (CT) policy set when insiders’ incentives are unknown. Finally, we present a case study to corroborate the design. Our results show that ZETAR can well adapt to insiders with different risk and compliance attitudes and significantly improve compliance. Moreover, trustworthy recommendations can provably promote cyber hygiene and insiders’ satisfaction.