Reviewerly/exHarmony huggingface dataset:
Quantum Kernel Alignment with Stochastic Gradient Descent

Quantum support vector machines have the potential to achieve a quantum speedup for solving certain machine learning problems. The key challenge for doing so is finding good quantum kernels for a given data set - a task called kernel alignment. In this paper we study this problem using the Pegasos algorithm, which is an algorithm that uses stochastic gradient descent to solve the support vector machine optimization problem. We extend Pegasos to the quantum case and and demonstrate its effectiveness for kernel alignment. Unlike previous work which performs kernel alignment by training a QSVM within an outer optimization loop, we show that using Pegasos it is possible to simultaneously train the support vector machine and align the kernel. Our experiments show that this approach is capable of aligning quantum feature maps with high accuracy, and outperforms existing quantum kernel alignment techniques. Specifically, we demonstrate that Pegasos is particularly effective for non-stationary data, which is an important challenge in real-world applications.
Investigation of an efficient multi-modal convolutional neural network for multiple sclerosis lesion detection

Abstract In this study, an automated 2D machine learning approach for fast and precise segmentation of MS lesions from multi-modal magnetic resonance images (mmMRI) is presented. The method is based on an U-Net like convolutional neural network (CNN) for automated 2D slice-based-segmentation of brain MRI volumes. The individual modalities are encoded in separate downsampling branches without weight sharing, to leverage the specific features. Skip connections input feature maps to multi-scale feature fusion (MSFF) blocks at every decoder stage of the network. Those are followed by multi-scale feature upsampling (MSFU) blocks which use the information about lesion shape and location. The CNN is evaluated on two publicly available datasets: The ISBI 2015 longitudinal MS lesion segmentation challenge dataset containing 19 subjects and the MICCAI 2016 MSSEG challenge dataset containing 15 subjects from various scanners. The proposed multi-input 2D architecture is among the top performing approaches in the ISBI challenge, to which open-access papers are available, is able to outperform state-of-the-art 3D approaches without additional post-processing, can be adapted to other scanners quickly, is robust against scanner variability and can be deployed for inference even on a standard laptop without a dedicated GPU.
Racism and Hate Speech Detection Using QAHA Based Hybrid Deep Learning Model: LSTM-CNN

Twitter, a predominant platform for instantaneous communication and idea dissemination, is often exploited by cybercriminals for victim harassment through sexism, racism, hate speech, and trolling using pseudonymous accounts. The propagation of racially charged online discourse poses significant threats to the social, political, and cultural fabric of many societies. Monitoring and prompt eradication of such content from social media, a breeding ground for racist ideologies, are imperative. This study introduces an advanced hybrid forecasting model, utilizing convolutional neural networks (CNNs) and long-short-term memory (LSTM) neural networks, for the efficient and accurate detection of racist and hate speech in English on Twitter. Unlabelled tweets, collated via the Twitter API, formed the basis of the initial investigation. Feature vectors were extracted from these tweets using the TF-IDF (Term Frequency-Inverse Document Frequency) feature extraction technique. This research contrasts the proposed model with existing intelligent classification algorithms in supervised learning. The HateMotiv corpus, a publicly available dataset annotated with types of hate crimes and ideological motivations, was employed, emphasizing Twitter as the primary social media context. A novel aspect of this study is the introduction of a revised artificial hummingbird algorithm (AHA), supplemented by quantum-based optimization (QBO). This quantum-based artificial hummingbird algorithm (QAHA) aims to augment exploration capabilities and reveal potential solution spaces. Employing QAHA resulted in a detection accuracy of approximately 98%, compared to 95.97% without its application. The study's principal contribution lies in the significant advancements achieved in the field of racism and hate speech detection in English through the application of hybrid deep learning methodologies.
Similarity-Based Remaining Useful Lifetime Prediction Method Considering Epistemic Uncertainty

Measuring the similarity between two trajectories is fundamental and essential for the similarity-based remaining useful life (RUL) prediction. Most previous methods do not adequately account for the epistemic uncertainty caused by asynchronous sampling, while others have strong assumption constraints, such as limiting the positional deviation of sampling points to a fixed threshold, which biases the results considerably. To address the issue, an uncertain ellipse model based on the uncertain theory is proposed to model the location of sampling points as an observation drawn from an uncertain distribution. Based on this, we propose a novel and effective similarity measure metric for any two degradation trajectories. Then, the Stacked Denoising Autoencoder (SDA) model is proposed for RUL prediction, in which the models can be first trained on the most similar degradation data and then fine-tuned by the target dataset. Experimental results show that the predictive performance of the new method is superior to prior methods based on edit distance on real sequence (EDR), longest common subsequence (LCSS), or dynamic time warping (DTW) and is more robust at different sampling rates.
Inefficiencies of augmented reality for different sexes and grades in Chinese vocational education

Abstract Nowadays, augmented reality (AR) is becoming more and more prevalent and used in many fields, including education. Previous studies have reported the positive effect of AR to advocate the use of AR in the classroom. However, it is unclear whether such a positive effect can be reported for any student. In this study, we recruited students from a Chinese vocational college. Due to the peculiar conditions in China, students in vocational colleges may have weaker learning abilities and worse academic performance than students in research universities. Furthermore, the sex and grade of students were considered. We conducted a three-stage experiment for the PPT-based group and the AR-based group separately. We taught the students the orthographic projection, a lesson from the Engineering Drawing course, which is widely learned by students who majored in architecture. The students’ academic performances were rated prior to class, right after class, and 1-week after class, through a direct examination at three stages. We found the inefficiency of AR for students in vocational colleges. The interaction effect between sex and grade of students was also reported. Students undergoing AR-based teaching could not perform better or even worse than students undergoing PPT-based teaching. We recommended that the teachers should consider whether to use AR in the classroom based on the characteristics of students.
Enhancing Question Pairs Identification with Ensemble Learning: Integrating Machine Learning and Deep Learning Models

The effectiveness of machine learning (ML) and deep learning (DL) models on the Quora question pairs dataset is investigated in this study. ML models, including AdaBoost, reached 73.44% test accuracy, while ensemble learning approaches enhanced outcomes even further, with the Hard-Voting Ensemble achieving 76.13%. DL models, such as FCN, demonstrated test accuracy of 81% with cross validation. These findings contribute to natural language processing by demonstrating the potential of ensemble learning for ML models and the DL models' detailed pattern-capturing capacity.
EDUKASI PEMILAHAN SAMPAH DENGAN MEDIA INTERAKTIF PADA SISWA KELAS 1 SD NEGERI PENANGGUNGAN

Waste problems are often encountered in the surrounding community. Penanggungan Village is one of the tourist villages located in Trawas District, Mojokerto Regency. Environmental cleanliness is certainly in the spotlight because many tourists visit the village. In minimizing littering, it is necessary to provide early education about waste sorting, especially for elementary school students. Through community service activities, Bina Desa Trawas of MBKM Universitas Pembangunan Nasional "Veteran" Jawa Timur conducted teaching activities for grade 1 students of SD Negeri Penanggungan. Learning activities are carried out using interactive media in the form of board games about waste sorting. In addition to interactive board game media, learning activities also use supporting media such as animated videos.
Exploring the Use of Immersive Technologies to Enhance the Student Experience

This paper reports on the initial phase of a National Forum funded Learning Enhancement Project (LEP), under the Strategic Alignment of Teaching and Learning Enhancement (SATLE) funding stream. The purpose of the LEP is to explore the use of immersive technologies such as Augmented Reality, Virtual Reality and 360o Learning in three key domain areas within one higher education institution in Ireland. While significant research has been conducted into the use of these technologies in training and business contexts, the application and use in higher education is still scarce. Radianti et al. (2020) suggest that while the use of virtual reality in higher education is promising, it is still quite experimental and focuses more on performance and usability than learning-oriented applications. Augmented Reality has been used in areas such as teacher education (Sáez-López, 2020) and EFL (Arkhipova, 2022) but is also largely experimental. Together with Mixed Reality and 360-degree Learning, these technologies have the potential to offer students a more immersive learning experience. Three domain areas are involved in this project: Online Learning; the university library; and the academic discipline of Business Tourism. It is envisaged that based on their expertise and experience team members will trial at least two of the immersive technologies with their students during the academic year 2023-2024. Engaging the students in the research will foster a partnership approach and provide an understanding of how this new approach to learning benefits the students but will also reveal the challenges associated with introducing new technologies.
Deep Learning-based Simulator Sickness Estimation from 3D Motion

This paper presents a novel solution for estimating simulator sickness in HMDs using machine learning and 3D motion data, informed by user-labeled simulator sickness data and user analysis. We conducted a novel VR user study, which decomposed motion data and used an instant dial-based sickness scoring mechanism. We were able to emulate typical VR usage and collect user simulator sickness scores. Our user analysis shows that translation and rotation differently impact user simulator sickness in HMDs. In addition, users’ demographic information and self-assessed simulator sickness susceptibility data are collected and show some indication of potential simulator sickness. Guided by the findings from the user study, we developed a novel deep learning-based solution to better estimate simulator sickness with decomposed 3D motion features and user profile information. The model was trained and tested using the 3D motion dataset with user-labeled simulator sickness and profiles collected from the user study. The results show higher estimation accuracy when using the 3D motion data compared with methods based on optical flow extracted from the recorded video, as well as improved accuracy when decomposing the motion data and incorporating user profile information.
Real-time Retargeting of Deictic Motion to Virtual Avatars for Augmented Reality Telepresence

Avatar-mediated augmented reality telepresence aims to enable distant users to collaborate remotely through avatars. When two spaces involved in telepresence are dissimilar, with different object sizes and arrangements, the avatar movement must be adjusted to convey the user’s intention rather than directly following their motion, which poses a significant challenge. In this paper, we propose a novel neural network-based framework for real-time retargeting of users’ deictic motions (pointing at and touching objects) to virtual avatars in dissimilar environments. Our framework translates the user’s deictic motion, acquired from a sparse set of tracking signals, to the virtual avatar’s deictic motion for a corresponding remote object in real-time. One of the main features of our framework is that a single trained network can generate natural deictic motions for various sizes of users. To this end, our network includes two sub-networks: AngleNet and MotionNet. AngleNet maps the angular state of the user’s motion into a latent representation, which is subsequently converted by MotionNet into the avatar’s pose, considering the user’s scale. We validate the effectiveness of our method in terms of deictic intention preservation and movement naturalness through quantitative comparison with alternative approaches. Additionally, we demonstrate the utility of our approach through several AR telepresence scenarios.
Tokamak plasma disruption precursor onset time study based on semi-supervised anomaly detection

Plasma disruption in tokamak experiments is a challenging issue that causes damage to the device. Reliable prediction methods are needed, but the lack of full understanding of plasma disruption limits the effectiveness of physics-driven methods. Data-driven methods based on supervised learning are commonly used, and they rely on labelled training data. However, manual labelling of disruption precursors is a time-consuming and challenging task, as some precursors are difficult to accurately identify. The mainstream labelling methods assume that the precursor onset occurs at a fixed time before disruption, which leads to mislabeled samples and suboptimal prediction performance. In this paper, we present disruption prediction methods based on anomaly detection to address these issues, demonstrating good prediction performance on J-TEXT and EAST. By evaluating precursor onset times using different anomaly detection algorithms, it is found that labelling methods can be improved since the onset times of different shots are not necessarily the same. The study optimizes precursor labelling using the onset times inferred by the anomaly detection predictor and test the optimized labels on supervised learning disruption predictors. The results on J-TEXT and EAST show that the models trained on the optimized labels outperform those trained on fixed onset time labels.
A Trustworthy Internet of Vehicles: The DAO to Safe, Secure, and Collaborative Autonomous Driving

The potential of the Internet of Vehicles (IoV) to reduce on-board system costs in autonomous vehicles through shared intelligence is considerable. However, it still faces significant challenges, including concerns over data breaches and privacy, inefficiencies and limited fault tolerance in centralized management, and the challenge of ensuring data accuracy. This letter marks the inaugural report from a series of IEEE Transactions on Intelligent Vehicles (TIV) Trustworthy IoV Workshops, which aim to address these issues. In these workshops, we explore the concept of a decentralized IoV (DeIoV), underpinned by decentralized autonomous organizations and operations (DAOs), to facilitate trustworthy interactions between vehicles and other entities. The proposed DeIoV is structured in two layers: the local DAO layer and the global DAO layer. This dual-layer architecture distinguishes between real-time and non-real-time decision-making tasks, aiding in their efficient completion. To ensure data security, integrity, and accuracy, we employ blockchain technology and smart contracts, which allow for mutual verification among adjacent members and utilize encryption algorithms. A reputation value-based voting mechanism for decision-making is also introduced, which helps prevent the monopolization of power through token-based systems, a common issue in traditional DAOs. Ultimately, we believe that DeIoV has the potential to create a trustworthy IoV ecosystem, contributing significantly to safe, secure, and collaborative autonomous driving.
Deep learning-based diagnosis of lung cancer using a nationwide respiratory cytology image set: improving accuracy and inter-observer variability.

Deep learning (DL)-based image analysis has recently seen widespread application in digital pathology. Recent studies utilizing DL in cytopathology have shown promising results, however, the development of DL models for respiratory specimens is limited. In this study, we designed a DL model to improve lung cancer diagnosis accuracy using cytological images from the respiratory tract. This retrospective, multicenter study used digital cytology images of respiratory specimens from a quality-controlled national dataset collected from over 200 institutions. The image processing involves generating extended z-stack images to reduce the phase difference of cell clusters, color normalizing, and cropping image patches to 256 × 256 pixels. The accuracy of diagnosing lung cancer in humans from image patches before and after receiving AI assistance was compared. 30,590 image patches (1,273 whole slide images [WSIs]) were divided into 27,362 (1,146 WSIs) for training, 2,928 (126 WSIs) for validation, and 1,272 (1,272 WSIs) for testing. The Densenet121 model, which showed the best performance among six convolutional neural network models, was used for analysis. The results of sensitivity, specificity, and accuracy were 95.9%, 98.2%, and 96.9% respectively, outperforming the average of three experienced pathologists. The accuracy of pathologists after receiving AI assistance improved from 82.9% to 95.9%, and the inter-rater agreement of Fleiss' Kappa value was improved from 0.553 to 0.908. In conclusion, this study demonstrated that a DL model was effective in diagnosing lung cancer in respiratory cytology. By increasing diagnostic accuracy and reducing inter-observer variability, AI has the potential to enhance the diagnostic capabilities of pathologists.
Guilloche Detection for ID Authentication: A Dataset and Baselines

In cases of digital enrolment via mobile and online services, identity documents (IDs) verification is critical to efficiently detect forgery and therefore build user trust in the digital world. In this paper, we propose a copy-move public dataset, called FMIDV (forged mobile ID video dataset) containing forged IDs with respect to guilloche patterns. Also, we propose two fraud detection models on guilloche patterns of IDs, which are based on contrastive and adversarial learning. In the sequel, each proposed model manages to read the entire ID and to recognize the guilloche pattern to check its similarity to the pattern of an authentic ID. The objective of the similarity check is to validate its authenticity or its rejection. Experiments are conducted on MIDV and FMIDV datasets to analyze and identify the most proper parameters to achieve higher authentication performance. The code and the dataset are available at https://github.com/malghadi/CheckID.
Challenges In Optimizing Migration Costs From On-Premises To Microsoft Azure

The current paper analyzes the feasibility of a modular web application's migration procedure from on-premises to the cloud. Our focus is on identifying cost savings and options for hosting the application in the cloud. The research specifically examines the impact of architectural decisions records (ADR) on a modular monolith use case, utilizing .NET Core for the backend and Angular for the front end, with Clean Architecture as the design pattern. We investigate different cloud models (IaaS, PaaS, Serverless) considering project management's triple constraints (time, cost, performance). The study demonstrates that a modular monolith can be migrated with varying effort and costs depending on the chosen cloud model and technology stack. We explore optimizations such as resource utilization, licensing fees, and cost reduction through infrastructure reservations. Our findings show that the migration cost can range from a 20% increase with IaaS to approximately 70% cost reduction with the Serverless strategy compared to the on-premises environment, using equivalent resources. We also explore methods to lower expenses for each model, including resource modifications, Linux operating systems, and longer resource reservations. Considering limitations, we propose a two-stage migration strategy: initially lifting and shifting the application to IaaS with cost optimization, and subsequently migrating to PaaS for scalability and simplified resource management in the long term.
The role of texture summary statistics in material recognition from drawings and photographs

Material depictions in artwork are useful tools for revealing image features that support material categorization. For example, artistic recipes for drawing specific materials make explicit the critical information leading to recognizable material properties (Di Cicco, Wjintjes, & Pont, 2020) and investigating the recognizability of material renderings as a function of their visual features supports conclusions about the vocabulary of material perception. Here, we examined how the recognition of materials from photographs and drawings was affected by the application of the Portilla–Simoncelli texture synthesis model. This manipulation allowed us to examine how categorization may be affected differently across materials and image formats when only summary statistic information about appearance was retained. Further, we compared human performance to the categorization accuracy obtained from a pretrained deep convolutional neural network to determine if observers’ performance was reflected in the network. Although we found some similarities between human and network performance for photographic images, the results obtained from drawings differed substantially. Our results demonstrate that texture statistics play a variable role in material categorization across rendering formats and material categories and that the human perception of material drawings is not effectively captured by deep convolutional neural networks trained for object recognition.
Features of the Motivational Sphere in the Personality of the Fire Rescue Service Employee

The article focuses on the features of motivation that the employees at the Fire Rescue Service show. The concepts of «motivation», «motive», «motivational sphere of personality» are considered. The text provides the findings of the research, which was targeted at defining characteristic features of motivational sphere of employees at the Fire Rescue Service. In order to achieve the objectives of the study, the authors used the techniques designed to identify the socio-psychological imperatives of an individual in the sphere of his/her motivation and needs, motivation for success or failure as well as the motives for career choice. The empirical study conducted revealed the features of the motivation sphere in the personality of the Fire and Rescue Service employee in general, and the ones in the personalities of the employees with different lengths of in-service period. The analysis of the research results showed that the majority of the studied employees who have served more than 5 years demonstrated motivation oriented to freedom and result to a greater extent, and to process and altruism to a lesser extent. The employees tend to hope for success rather than for a failure, and when choosing a profession they are driven by internal need for such work. The group of employees who have served less than 5 years have pronounced motivational orientations to process and altruism, orientation to freedom, result and work are less pronounced. Motivational orientation to power has pronounced very little, and orientations to egoism and money are not expressed at all.