Reviewerly/exHarmony huggingface dataset:
3D Reconstruction from 2D Cerebral Angiograms as a Volumetric Denoising Problem

Accurately capturing the 3D geometry of the brain’s blood vessels is critical in helping neuro-interventionalists to identify and treat neurovascular disorders, such as stroke and aneurysms. Currently, the gold standard for obtaining a 3D representation of angiograms is through the process of 3D rotational angiography, a timely process requiring expensive machinery, which is also associated with high radiation exposure to the patient. In this research, we propose a new technique for reconstructing 3D volumes from 2D angiographic images, thereby reducing harmful X-ray radiation exposure. Our approach involves parameterizing the input data as a back-projected noisy volume from the images, which is then fed into a 3D denoising autoencoder. Through this method, we have achieved clinically relevant reconstructions with varying amounts of 2D projections from 49 patients. Additionally, our 3D denoising autoencoder outperformed previous generative models in biplane reconstruction by 15.51% for intersection over union (IOU) and 3.5% in pixel accuracy due to keeping a semi-accurate input with back projection. This research highlights the significant role of back-projection in achieving relative visual correspondence in the input space to reconstruct 3D volumes from 2D angiograms. This approach has the potential to be deployed in future neurovascular surgery, where 3D volumes of the patient’s brain blood vessels can be visualized with less X-ray radiation exposure time.
Brain Cortical Surface Registration with Anatomical Atlas Constraints

This work presents a novel cortical surface registration framework by using the whole anatomical atlas structures as correspondence constraints, which are extracted as atlas graphs (nodes are the junctions and edges are the intersecting curves of regions). The focus of this work is on the geometric registration category of cortical surfaces, i.e., brains are registered only using structural information without any functional information. We aim to innovate the geometric registration framework by utilizing the prominent anatomical features, atlas, to drive the registration. Intuitively, we convert the 3D cortical surfaces to 2D disks by special geometric mappings, where the curvy atlas regions become straight and convex polygonal regions; then registration is achieved between 2D domains such that curvy constrains become linear constraints and are solvable in linear time. The mappings generated are intrinsic and have theoretic guarantee of existence, uniqueness and optimality in terms of constrained harmonic energy. It differs from the literature geometric approaches using brain curves or point features. To the best of our knowledge, it is the first work of using atlas graph constraints in geometric registration. Our experiments on various brain data sets demonstrate the efficiency and efficacy for brain registration and the practicability of the proposed framework for brain disease classification.
A Fully Pipelined Architecture of Quantum-Classical Interface for Realizing Fault-Tolerant Quantum Computer

We propose a fully pipelined Quantum-Classical interface (QC-IF) architecture for fault-tolerant quantum computing. The current Quantum-Classical interface has been working for superconducting quantum computers with 64 qubits in RIKEN and 16 qubits in QIQB Osaka University. The interface sends/receives a sequence of pulses to/from the qubits and demodulates the acquired ones, in which a batch controller is designed to operate up to 500 milliseconds, longer than the coherence time. For Fault-Tolerant Quantum Computers(FTQC), however, a fully pipelined Quantum-Classical Interface is required to send and receive pulses, collecting syndrome information over time far beyond the coherence time. Here, we present a control plane newly incorporated into the current architecture to handle consecutive sending, receiving, and demodulating pulses. Since the current system has achieved wire-rate operation to retrieve and transmit signals, the control plane for continuous operation naturally extends the system to a fully pipelined architecture.
Towards Transfer Learning for Large-Scale Image Classification Using Annealing-Based Quantum Boltzmann Machines

Quantum Transfer Learning (QTL) recently gained popularity as a hybrid quantum-classical approach for image classification tasks by efficiently combining the feature extraction capabilities of large Convolutional Neural Networks with the potential benefits of Quantum Machine Learning (QML). Existing approaches, however, only utilize gate-based Variational Quantum Circuits for the quantum part of these procedures. In this work we present an approach to employ Quantum Annealing (QA) in QTL-based image classification. Specifically, we propose using annealing-based Quantum Boltzmann Machines as part of a hybrid quantum-classical pipeline to learn the classification of real-world, large-scale data such as medical images through supervised training. We demonstrate our approach by applying it to the three-class COVID-CT-MD dataset, a collection of lung Computed Tomography (CT) scan slices. Using Simulated Annealing as a stand-in for actual QA, we compare our method to classical transfer learning, using a neural network of the same order of magnitude, to display its improved classification performance. We find that our approach consistently outperforms its classical baseline in terms of test accuracy and AUC-ROC-Score and needs less training epochs to do this.
Decomposition Algorithm of an Arbitrary Pauli Exponential Through a Quantum Circuit

We review the staircase algorithm to decompose the exponential of a generalized Pauli matrix and we propose two alternative recursive methods which offer more efficient quantum circuits. The first algorithm we propose, defined as the inverted staircase algorithm, is more efficient in comparison to the standard staircase algorithm in the number of one-qubit gates, giving a polynomial improvement of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$n/2$</tex> . For our second algorithm, we introduce fermionic SWAP quantum gates and a systematic way of generalizing these. Such fermionic gates offer a simplification of the number of quantum gates, in particular of CNOT gates, in most quantum circuits. Regarding the staircase algorithm, fermionic quantum gates reduce the number of CNOT gates in roughly <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$n/2$</tex> for a large number of qubits. In the end, we discuss the difference between the probability outcomes of fermionic and non-fermionic gates and show that, in general, due to interference, one cannot substitute fermionic gates through non-fermionic gates without altering the outcome of the circuit.
Efficient Edge Computing Device for Traffic Monitoring Using Deep Learning Detectors Dispositif informatique de bord efficace pour la surveillance du trafic à l’aide de détecteurs d’apprentissage profond

This article presents a smart camera device for traffic monitoring at intersections. The device is based on the Nvidia Jetson Nano, a small form factor, efficient artificial intelligence (AI) computational device that is capable of deep learning inference. The state-of-the-art deep learning detection models were investigated, and the full YOLOv4 was selected for deployment on the edge device. The deployed model and analytics achieved an average frame rate of 7.8 frames/s (fps). A fisheye lens and camera were selected and integrated with the Jetson processing unit. The original YOLOv4 performed less optimally on fisheye-distorted images. Therefore, we applied transfer learning to the YOLOv4 model using data collected from a local intersection. The final models were evaluated in three different use cases detecting different types of road objects, achieving 100% precision and around 90% accuracy when detecting road vehicles in real time. This article demonstrates the feasibility of running large deep learning models for traffic monitoring services, even on resource-restrained AI edge devices.
Technology and administration: the impact of the gdpl on leaders and organizations

The business world is evolving more and more. From globalization, the systems of production, financial transactions, and mass displacements of people turn regional organizations into powers that operate all over the world. The advancement of technology causes a real cyclone (in this research, understood as the main problem), creating what can be understood as "digital disruption", thus generating a justified "state of paranoia", since organizations and sectors also collapse in unprecedented numbers. Thus, the active leader in a traditional corporation should be worried. Technology aligned with management, added to the relevant concepts of the General Data Protection Law or GDPL are presented as potential alternatives for improving the structure, after all, taking responsibility for the care of information in digital media is consolidated as a strategic action. In order to understand these changes and deepen these necessary concepts, the exploratory, applied, bibliographic and documentary methods were used, effective techniques that provided learning and understanding of the topics addressed. As already aligned and conclusive results, it was possible to perceive that leaders and organizations must adapt and follow the conditions that the market demands, and that it is not possible to remain stagnant in the face of this global evolution that directly relates technology, administration and leadership.
Practical and Malicious Multiparty Private Set Intersection for Small Sets

Private set intersection (PSI) is a pivotal subject in the realm of privacy computation. Numerous research endeavors have concentrated on situations involving vast and imbalanced sets. Nevertheless, there is a scarcity of existing PSI protocols tailored for small sets. Those that exist are either restricted to interactions between two parties or necessitate resource-intensive homomorphic operations. To bring forth practical multiparty private set intersection solutions for small sets, we present two multiparty PSI protocols founded on the principles of Oblivious Key–Value Stores (OKVSs), polynomials, and gabled cuckoo tables. Our security analysis underscores the resilience of these protocols against malicious models and collision attacks. Through experimental evaluations, we establish that, in comparison to related endeavors, our protocols excel in small-set contexts, particularly in low-bandwidth wide area network (WAN) settings.
Speech Disfluencies of Non-Native Speakers of English in TED Talk Scripts

To speak fluently in the target language, English, has remained a challenge for learners, particularly those who are non-native speakers of English. When speaking, therefore, learners should be aware of speech disfluencies. This paper, accordingly, investigates the speech disfluencies of non-native speakers in their presentations. Data were collected from TED Talk scripts and were then analysed and discussed, based on categories of disfluency. The findings of this document analysis reveal five types of disfluency in the data, namely unfilled pause, filled pause, repetition, deletion, and substitution. It is expected that learners of English at university level can benefit from the study results concerning speech disfluencies to improve their speaking proficiency. Keywords: Disfluency; filled pause; non-native speaker; TED talk
Analysing the Impact of Precipitation and Temperature on Cassava and Cocoa Crop Yields in Ondo State

The impact of rainfall and temperature on agricultural crop production, especially cocoa and cassava in Nigeria, has significant implications for yield, poverty, access to food, and the sustainability of farming as a livelihood. This study investigates the influence of temperature and rainfall on cocoa and cassava yield in Ondo State, Nigeria, from 1991 to 2020. The annual crop yield data for cocoa and cassava for the entire 30-year period were sourced from the Ministry of Agriculture, Ondo State. Data for the key climate variables, temperature (maximum and minimum), and rainfall, were obtained from the Nigeria Meteorological Agency (NIMET). Initially, a line graph was employed to visualize trends in rainfall, temperature, cocoa, and cassava. Subsequently, the data was subjected to K-Means clustering analysis, to partition rainfall, maximum and minimum temperature into distinct and meaningful groups, or cluster, based on the similarities between the years – identifying patterns and structures to reveal hidden relationships and groupings among the years. The collected yield datasets were further grouped by the cluster, an analysis of variance (ANOVA) was further performed to test the influence of each weather variables has on cocoa and cassava yield. The trend analysis revealed that Ondo State experienced minimal temperature fluctuations between 1991 and 2020, while rainfall and cassava exhibited distinctive fluctuations. The results confirmed the significant impact of temperature and rainfall on cocoa and cassava yield.
A Bitcoin Address Multi-classification Mechanism Based on Bipartite Graph-Based Maximization Consensus

Bitcoin is widely used as the most classic electronic currency for various electronic services such as exchanges, gambling, marketplaces, and also scams such as high-yield investment projects. Identifying the services operated by a Bitcoin address can help determine the risk level of that address and build an alert model accordingly. Feature engineering can also be used to flesh out labeled addresses and to analyze the current state of Bitcoin in a small way. In this paper, we address the problem of identifying multiple classes of Bitcoin services, and for the poor classification of individual addresses that do not have significant features, we propose a Bitcoin address identification scheme based on joint multi-model prediction using the mapping relationship between addresses and entities. The innovation of the method is to (1) Extract as many valuable features as possible when an address is given to facilitate the multi-class service identification task. (2) Unlike the general supervised model approach, this paper proposes a joint prediction scheme for multiple learners based on address-entity mapping relationships. Specifically, after obtaining the overall features, the address classification and entity clustering tasks are performed separately, and the results are subjected to graph-based maximization consensus. The final result is made to baseline the individual address classification results while satisfying the constraint of having similarly behaving entities as far as possible. By testing and evaluating over 26,000 Bitcoin addresses, our feature extraction method captures more useful features. In addition, the combined multi-learner model obtained results that exceeded the baseline classifier reaching an accuracy of 77.4%.
Gender Difference in Digital Literacy Among Translation Trainees: Self-perceptions and Application Abilities

Driven by the advancement of Information and communication Technology (ICT), ability to apply state-of-the-art technology in translation has become a requirement for professional translators. In the context of translation education, the emphasis of digital literacy as a core competence for trainees were agreed-upon by stakeholders and curriculum developers. Accordingly, the development and inclusion of translation technology courses in a translation training program have pervasively reshaped translation pedagogy in a digital era. However, the investigation on trainees’ level of digital literacy, especially from a gender perspective, was rarely practiced in existing literature. The present study embarked on filling the lacunae in our understanding of digital literacy by inquiring into their self-perceived abilities of general ICT and translation-specific technologies. Adopting a mixed-method research design, the research triangulated the findings from a survey of self-perceived level of digital literacy answered by 218 respondents (n=218) and focus group discussions with 28 participants (n=28). The findings rejected the stereotypical view that male students were endowed with greater potential in learning technology-centered knowledge. The gender differences in digital literacy among translation trainees were limited to only a few aspects: creativity in using modern technologies, involvement in online communities, and critical thinking, etc. The research would contribute to the expansion of our knowledge in defining translation learners’ digital literacy and understanding the individual difference of trainees in a program.
Professional Ethics, Challenges and Opportunities for Blockchain Technology in Healthcare Sector: A Systematic Review

Background:: The healthcare sector faces numerous issues, such as insurance fraud, electronic medical record management, interoperability, insecure dissemination of information, etc. The novel Blockchain technology holds tremendous potential to transform the healthcare sector by addressing these rising challenges in the industry. It provides a secure platform for storing, disseminating, and retrieving sensitive patient data and health records while preserving the ethical principles of the healthcare sector. Method:: In this study, we systematically reviewed the literature on blockchain technology in healthcare using PRISMA and highlighted how blockchain technology might promote innovation and deliver major improvements in the healthcare sector. Result:: Our goal is to examine the present status of this discipline, focusing on limits and potential advances. Queries were used to gather Scopus, PubMed, SpringerLink, IEEE Xplore, and Web of Science publications that met the criteria for the selection of papers. Conclusion:: This article, thus, analyses the potential for blockchain in the healthcare industry and outlines blockchain-based products in healthcare. Our study enhances and complements prior healthcare blockchain research.
ASSESSING THE IMPACT OF HIGHER EDUCATION TEACHERS' DIGITAL COMPETENCES ON CREATING INNOVATIVE STRATEGIES FOR EVALUATING STUDENT LEARNING OUTCOMES

ASSESSING THE IMPACT OF HIGHER EDUCATION TEACHERS' DIGITAL COMPETENCES ON CREATING INNOVATIVE STRATEGIES FOR EVALUATING STUDENT LEARNING OUTCOMES
PICTURE BOOK AS AN INCENTIVE FOR THE DEVELOPMENT OF CREATIVITY IN EARLY AND PRESCHOOL EDUCATION

Appears in: ICERI2023 Proceedings Publication year: 2023Pages: 8452-8456ISBN: 978-84-09-55942-8ISSN: 2340-1095doi: 10.21125/iceri.2023.2156Conference name: 16th annual International Conference of Education, Research and InnovationDates: 13-15 November, 2023Location: Seville, Spain
COLLABORATIVE EXPERIENCES AMONG TEACHERS ORIENTED TOWARDS THE PEDAGOGICAL INTEGRATION OF DIGITAL TECHNOLOGIES: A SYSTEMATIC LITERATURE REVIEW

COLLABORATIVE EXPERIENCES AMONG TEACHERS ORIENTED TOWARDS THE PEDAGOGICAL INTEGRATION OF DIGITAL TECHNOLOGIES: A SYSTEMATIC LITERATURE REVIEW
DIGITAL COMPETENCE AND SELF-DETERMINATION: NEW FRONTIERS FOR ONLIFE EDUCATION FROM AN INCLUSIVE PERSPECTIVE

DIGITAL COMPETENCE AND SELF-DETERMINATION: NEW FRONTIERS FOR ONLIFE EDUCATION FROM AN INCLUSIVE PERSPECTIVE
TOWARDS A DIGITALLY COMPETENT TEACHER WITH DIG2PHY: ESTABLISHING A DIGITAL SPACE TO DESIGN TANGIBLE EDUCATIONAL RESOURCES

TOWARDS A DIGITALLY COMPETENT TEACHER WITH DIG2PHY: ESTABLISHING A DIGITAL SPACE TO DESIGN TANGIBLE EDUCATIONAL RESOURCES
THE IMPORTANCE OF ATTACHMENT THEORIES IN EARLY CHILDHOOD LANGUAGE EDUCATION

Appears in: ICERI2023 Proceedings Publication year: 2023Pages: 3343-3350ISBN: 978-84-09-55942-8ISSN: 2340-1095doi: 10.21125/iceri.2023.0872Conference name: 16th annual International Conference of Education, Research and InnovationDates: 13-15 November, 2023Location: Seville, Spain
Inovasi Ecollabo8 pada Sampah Plastik untuk Kebutuhan Interior

Plastic waste is one of the biggest causes of environmental pollution, especially soil and sea pollution. Proper management is needed to reduce the impact of plastic waste on the environment. Meanwhile, in Indonesia, waste management has not been able to reduce the impact of plastic waste by recycling and turning it into something more valuable. Ecollabo8 impact of plastic waste itself. It is necessary to process plastic waste that can be reused and increase the value of recycled products. Ecollabo8 is a community that aims to recycle plastic into interior materials, such as furniture, wall coverings, or materials for interior aesthetic elements. The purpose of this research is to provide information and references related to the processing of plastic waste from various categories so that it can become an interior material innovation by Ecollabo8. The research method used is the netnographic method by observing the activities and culture of Ecollabo8 through social media and online documentation.
Local Differential Privacy-Based Data-Sharing Scheme for Smart Utilities

The manufacturing sector is a vital component of most economies, which leads to a large number of cyberattacks on organisations, whereas disruption in operation may lead to significant economic consequences. Adversaries aim to disrupt the production processes of manufacturing companies, gain financial advantages, and steal intellectual property by getting unauthorised access to sensitive data. Access to sensitive data helps organisations to enhance the production and management processes. However, majority of the existing data-sharing mechanisms are either susceptible to different cyber-attacks or heavy in terms of computation overhead. In this paper, a privacy-preserving data-sharing scheme for smart utilities is proposed. First, a customer’s privacy adjustment mechanism is proposed to make sure that end-users have control over their privacy, which is required by the latest government regulations, such as the General Data Protection Regulation. Secondly, a local differential privacy-based mechanism is proposed to ensure privacy of the end-users by hiding real data based on the end-user preferences. The proposed scheme may be applied for different industrial control systems, whereas in this study, it is validated for energy utility use case consisting of smart intelligent devices. The results show that the proposed scheme may guarantee the required level of privacy with an expected relative error in utility.
A New Benchmark for Consumer Visual Tracking and Apparent Demographic Estimation from RGB and Thermal Images

Visual tracking and attribute estimation related to age or gender information of multiple person entities in a scene are mature research topics with the advent of deep learning techniques. However, when it comes to indoor images such as video sequences of retail consumers, data are not always adequate or accurate enough to essentially train effective models for consumer detection and tracking under various adverse factors. This in turn affects the quality of recognizing age or gender for those detected instances. In this work, we introduce two novel datasets: Consumers comprises 145 video sequences compliant to personal information regulations as far as facial images are concerned and BID is a set of cropped body images from each sequence that can be used for numerous computer vision tasks. We also propose an end-to-end framework which comprises CNNs as object detectors, LSTMs for motion forecasting of the tracklet association component in a sequence, along with a multi-attribute classification model for apparent demographic estimation of the detected outputs, aiming to capture useful metadata of consumer product preferences. Obtained results on tracking and age/gender prediction are promising with respect to reference systems while they indicate the proposed model’s potential for practical consumer metadata extraction.