Reviewerly/exHarmony huggingface dataset:
Systematic Review of Web-Based Decision Support Systems for Clinical Applications: Enhancing Ontology with Unified Modeling Language and Ontology Web Language

Authors:
raw_author_name: Ahmed Shihab Ahmed
raw_affiliation_string: LR-OASIS, National Engineering School of Tunis, University of Tunis El Manar,Tunis, Tunisia and Department of Basic Sciences, College of Nursing, University of Baghdad, Baghdad, Iraq
raw_author_name: Safa Bhar Layeb
raw_affiliation_string: LR-OASIS, National Engineering School of Tunis, University of Tunis El Manar,Tunis, Tunisia

Abstract: This article demonstrates an ontology-based technique that aids in envisioning the problem domain before software development, presenting an approach to assist decision-making for reliable and optimal healthcare administration. The proposed approach is designed for utilization by a Web-Based Decision Support System (WebDSS). The purpose of the study is to aid readers in comprehending how ontologies in healthcare management platforms can be modeled using UML and OWL. For the conceptualization and the design, service modelling and the semantics of the web language are crucial. We demonstrate the use of the Unified Modeling Language (UML) and the Ontology Web Language (OWL) in the administration of healthcare operations and key elements of the model-base of healthcare administration systems. Highly educated systems with automated assistance will allow decision-makers to more effectively incorporate aspects of sustainability into their decisions. In order to generate semantic ontologies for medical management, the present study proposes an ontology creation technique and a Semantic Web framework called Protégé. Web-based technologies, decision trees, data mining, and computational algorithms are implemented by WebDSS to assess medical resources administration information in real-time. These methods are employed to develop a decision making framework for this study. Time series graphs, email reports, and webpages are used to display the test findings. When healthcare thresholds are surpassed, the system uses text messages as email reports to automatically deliver warnings. Our study's primary contribution is the creation of a useful tool that makes it easier for academics studying medical concerns to locate online sites with information.
Core network traffic prediction based on vertical federated learning and split learning

Authors:
raw_author_name: Pengyu Li
raw_affiliation_string: 6G Research Center, China Telecom Research Institute, Beijing, 102209, China
raw_author_name: Yanxia Xing
raw_affiliation_string: 6G Research Center, China Telecom Research Institute, Beijing, 102209, China
raw_author_name: Lei Feng
raw_affiliation_string: State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing, 100876, China
raw_author_name: Fanqin Zhou
raw_affiliation_string: State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing, 100876, China

Abstract: Wireless traffic prediction is vital for intelligent cellular network operations, such as load-aware resource management and predictive control. Traditional centralized training addresses this but poses issues like excessive data transmission, disregarding delays, and user privacy. Traditional federated learning methods can meet the requirement of jointly training models while protecting the privacy of all parties' data. However, challenges arise when the local data features among participating parties exhibit inconsistency, making the training process difficult to sustain. Our study introduces an innovative framework for wireless traffic prediction based on split learning (SL) and vertical federated learning. Multiple edge clients collaboratively train high-quality prediction models by utilizing diverse traffic data while maintaining the confidentiality of raw data locally. Each participant individually trains dimension-specific prediction models with their respective data, and the outcomes are aggregated through collaboration. A partially global model is formed and shared among clients to address statistical heterogeneity in distributed machine learning. Extensive experiments on real-world datasets demonstrate our method's superiority over current approaches, showcasing its potential for network traffic prediction and accurate forecasting.
Automated visual quality assessment for virtual and augmented reality based digital twins

Authors:
raw_author_name: Ben Roullier
raw_affiliation_string: Bloc Digital, Derby, UK
raw_author_name: Frank McQuade
raw_affiliation_string: Bloc Digital, Derby, UK
raw_author_name: Ashiq Anjum
raw_affiliation_string: Computing & Mathematical Sciences, University of Leicester, Leicester, UK
raw_author_name: Craig Bower
raw_affiliation_string: Computing & Mathematical Sciences, University of Leicester, Leicester, UK
raw_author_name: Lu Liu
raw_affiliation_string: Computing & Mathematical Sciences, University of Leicester, Leicester, UK

Abstract: Abstract Virtual and augmented reality digital twins are becoming increasingly prevalent in a number of industries, though the production of digital-twin systems applications is still prohibitively expensive for many smaller organisations. A key step towards reducing the cost of digital twins lies in automating the production of 3D assets, however efforts are complicated by the lack of suitable automated methods for determining the visual quality of these assets. While visual quality assessment has been an active area of research for a number of years, few publications consider this process in the context of asset creation in digital twins. In this work, we introduce an automated decimation procedure using machine learning to assess the visual impact of decimation, a process commonly used in the production of 3D assets which has thus far been underrepresented in the visual assessment literature. Our model combines 108 geometric and perceptual metrics to determine if a 3D object has been unacceptably distorted during decimation. Our model is trained on almost 4, 000 distorted meshes, giving a significantly wider range of applicability than many models in the literature. Our results show a precision of over 97% against a set of test models, and performance tests show our model is capable of performing assessments within 2 minutes on models of up to 25, 000 polygons. Based on these results we believe our model presents both a significant advance in the field of visual quality assessment and an important step towards reducing the cost of virtual and augmented reality-based digital-twins.
Embedding memory-efficient stochastic simulators as quantum trajectories

Authors:
raw_author_name: Thomas J. Elliott
raw_affiliation_string: Complexity Institute, Nanyang Technological University, Singapore 637335, Singapore; Department of Mathematics, Imperial College London, London SW7 2AZ, United Kingdom; Department of Mathematics, University of Manchester, Manchester M13 9PL, United Kingdom
raw_author_name: Mile Gu
raw_affiliation_string: Centre for Quantum Technologies, National University of Singapore, 3 Science Drive 2, Singapore 117543, Singapore; MajuLab, CNRS-UNS-NUS-NTU International Joint Research Unit, UMI No. 3654, Singapore 117543, Singapore; Nanyang Quantum Hub, School of Physical and Mathematical Sciences, Nanyang Technological University, Singapore 637371, Singapore

Abstract: By exploiting the complexity intrinsic to quantum dynamics, quantum technologies promise a host of computational advantages. One such advantage lies in the field of stochastic modeling, where it has been shown that quantum stochastic simulators can operate with a lower memory overhead than their best classical counterparts. This advantage is particularly pronounced for continuous-time stochastic processes; however, the corresponding quantum stochastic simulators heretofore prescribed operate only on a quasi-continuous-time basis and suffer an ever-increasing circuit complexity with increasing temporal resolution. Here, by establishing a correspondence with quantum trajectories (a method for modeling open quantum systems), we show how truly continuous-time quantum stochastic simulators can be embedded in such open quantum systems, bridging this gap and obviating previous constraints. We further show how such an embedding can be made for discrete-time stochastic processes, which manifest as jump-only trajectories, and discuss how viewing the correspondence in the reverse direction provides a means of studying structural complexity in quantum systems themselves.
Characterization and optimized engineering of bosonic quantum interfaces under single-mode operational constraints

Authors:
raw_author_name: Hoi-Kwan Lau
raw_affiliation_string: Department of Physics, Simon Fraser University, Burnaby, British Columbia V5A 1S6, Canada; Quantum Algorithms Institute, Surrey, British Columbia V3T 5X3, Canada

Abstract: Controlling the quantum interface between two bosonic modes is essential in countless implementations of quantum information processing. However, full controllability is rarely achieved in most platforms due to specific physical limitations. In this work, we completely characterize the linear two-mode interfaces under the most pessimistic restriction that only single-mode operation is available. When arbitrary Gaussian single-mode operations can be applied to both modes, we find that every interface can be characterized by an invariant transmission strength. Moreover, in the practical situation that squeezing is restricted in one of the modes, we discover two additional quantities, irreducible squeezing and irreducible shearing, that are invariant under the allowable controls. By using this characterization, we develop systematic strategies to engineer an arbitrary linear interface through cascading multiple fixed component interfaces. Without squeezing restriction, our protocol is optimal and requires at most three component interfaces. Under the squeezing constraint, our protocol can be extended to engineer also the additional invariants by using no more than two more rounds of cascade. We also propose the remote squeezing scheme to tackle the squeezing restriction through interfacing with an active auxiliary mode.
AFRNet: Anchor-Free Object Detection Using Roadside LiDAR in Urban Scenes

Authors:
raw_author_name: Luyang Wang
raw_affiliation_string: Beijing Engineering Research Center of Industrial Spectrum Imaging, School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China; Department of Instrument Science and Technology, School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China
raw_author_name: Jinhui Lan
raw_affiliation_string: Beijing Engineering Research Center of Industrial Spectrum Imaging, School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China; Department of Instrument Science and Technology, School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China
raw_author_name: Min Li
raw_affiliation_string: Beijing Engineering Research Center of Industrial Spectrum Imaging, School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China; Department of Instrument Science and Technology, School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China

Abstract: In urban settings, roadside infrastructure LiDAR is a ground-based remote sensing system that collects 3D sparse point clouds for the traffic object detection of vehicles, pedestrians, and cyclists. Current anchor-free algorithms for 3D point cloud object detection based on roadside infrastructure face challenges related to inadequate feature extraction, disregard for spatial information in large 3D scenes, and inaccurate object detection. In this study, we propose AFRNet, a two-stage anchor-free detection network, to address the aforementioned challenges. We propose a 3D feature extraction backbone based on the large sparse kernel convolution (LSKC) feature set abstraction module, and incorporate the CBAM attention mechanism to enhance the large scene feature extraction capability and the representation of the point cloud features, enabling the network to prioritize the object of interest. After completing the first stage of center-based prediction, we propose a refinement method based on attentional feature fusion, where fused features incorporating raw point cloud features, voxel features, BEV features, and key point features are used for the second stage of refinement to complete the detection of 3D objects. To evaluate the performance of our detection algorithms, we conducted experiments using roadside LiDAR data from the urban traffic dataset DAIR-V2X, based on the Beijing High-Level Automated Driving Demonstration Area. The experimental results show that AFRNet has an average of 5.27 percent higher detection accuracy than CenterPoint for traffic objects. Comparative tests further confirm that our method achieves high accuracy in roadside LiDAR object detection.
An Approach Based on Intuitionistic Fuzzy Sets for Considering Stakeholders’ Satisfaction, Dissatisfaction, and Hesitation in Software Features Prioritization

Authors:
raw_author_name: Vassilis C. Gerogiannis
raw_affiliation_string: Department of Digital Systems, University of Thessaly, 41500 Larissa, Greece
raw_author_name: Dimitrios Tzimos
raw_affiliation_string: Department of Digital Systems, University of Thessaly, 41500 Larissa, Greece
raw_author_name: George Kakarontzas
raw_affiliation_string: Department of Digital Systems, University of Thessaly, 41500 Larissa, Greece
raw_author_name: Eftychia Tsoni
raw_affiliation_string: Department of Informatics, Open Hellenic University, 26335 Patras, Greece
raw_author_name: Omiros Iatrellis
raw_affiliation_string: Department of Digital Systems, University of Thessaly, 41500 Larissa, Greece
raw_author_name: Le Hoang Son
raw_affiliation_string: VNU Information Technology Institute, Vietnam National University, Hanoi 03000, Vietnam
raw_author_name: Andreas Kanavos
raw_affiliation_string: Department of Informatics, Ionian University, 49100 Corfu, Greece

Abstract: This paper introduces a semi-automated approach for the prioritization of software features in medium- to large-sized software projects, considering stakeholders’ satisfaction and dissatisfaction as key criteria for the incorporation of candidate features. Our research acknowledges an inherent asymmetry in stakeholders’ evaluations, between the satisfaction from offering certain features and the dissatisfaction from not offering the same features. Even with systematic, ordinal scale-based prioritization techniques, involved stakeholders may exhibit hesitation and uncertainty in their assessments. Our approach aims to address these challenges by employing the Binary Search Tree prioritization method and leveraging the mathematical framework of Intuitionistic Fuzzy Sets to quantify the uncertainty of stakeholders when expressing assessments on the value of software features. Stakeholders’ rankings, considering satisfaction and dissatisfaction as features prioritization criteria, are mapped into Intuitionistic Fuzzy Numbers, and objective weights are automatically computed. Rankings associated with less hesitation are considered more valuable to determine the final features’ priorities than those rankings with more hesitation, reflecting lower indeterminacy or lack of knowledge from stakeholders. We validate our proposed approach with a case study, illustrating its application, and conduct a comparative analysis with existing software requirements prioritization methods.
Domain Adaptive Channel Pruning

Authors:
raw_author_name: Chao Zhang
raw_affiliation_string: Beijing Institute of Space Long March Vehicle, Nandahongmen Fengtai District, Beijing 100076, China
raw_author_name: Ling Gao
raw_affiliation_string: China Southern Power Grid, Shenzhen Luo Hu Power Supply Bureau, Shenzhen 518000, China
raw_author_name: Yufei Guo
raw_affiliation_string: Intelligent Science & Technology Academy of CASIC, Beijing 100854, China
raw_author_name: Jinyang Guo
raw_affiliation_string: State Key Laboratory of Software Development Environment, Institute of Artificial Intelligence, Beihang University, Beijing 100191, China

Abstract: Domain adaptation is an effective approach to improve the generalization ability of deep learning methods, which makes a deep model more stable and robust. However, these methods often suffer from a deployment problem when deep models are deployed on different types of edge devices. In this work, we propose a new channel pruning method called Domain Adaptive Channel Pruning (DACP), which is specifically designed for the unsupervised domain adaptation task, where there is considerable data distribution mismatch between the source and the target domains. We prune the channels and adjust the weights in a layer-by-layer fashion. In contrast to the existing layer-by-layer channel pruning approaches that only consider how to reconstruct the features from the next layer, our approach aims to minimize both classification error and domain distribution mismatch. Furthermore, we propose a simple but effective approach to utilize the unlabeled data in the target domain. Our comprehensive experiments on two benchmark datasets demonstrate that our newly proposed DACP method outperforms the existing channel pruning approaches under the unsupervised domain adaptation setting.
HBMD-Net: Feature Fusion Based Breast Cancer Classification with Class Imbalance Resolution

Authors:
raw_author_name: Barsha Abhisheka
raw_affiliation_string: Computer Science and Engineering, NIT Silchar, Silchar, 788010, Assam, India
raw_author_name: Saroj Kr. Biswas
raw_affiliation_string: Computer Science and Engineering, NIT Silchar, Silchar, 788010, Assam, India
raw_author_name: Biswajit Purkayastha
raw_affiliation_string: Computer Science and Engineering, NIT Silchar, Silchar, 788010, Assam, India

Abstract: Breast cancer, a widespread global disease, represents a significant threat to women's health and lives, ranking as one of the most vulnerable malignant tumors they face. Many researchers have proposed their computer-aided diagnosis systems for classifying breast cancer. The majority of these approaches primarily utilize deep learning (DL) methods, which are not entirely reliable. These approaches overlook the crucial necessity of incorporating both local and global information for precise tumor detection, despite the fact that the subtle nuances are crucial for precise breast cancer classification. In addition, there are a limited number of publicly available breast cancer datasets, and the ones that are available tend to be imbalanced in nature. Therefore, this paper presents the hybrid breast mass detection-network (HBMD-Net) to address two critical challenges: class imbalance and the need to recognize that relying solely on either global or local features falls short in achieving precise tumor classification. To overcome the problem of class imbalance, HBMD-Net incorporates the borderline synthetic minority over-sampling technique (BSMOTE). Simultaneously, it employs a feature fusion approach, combining features by utilizing ResNet50 to extract deep features that provide global information, while handcrafted features are derived using histogram orientation gradient (HOG), that provide local information. In addition, an ROI segmentation has been implemented to avoid misclassifications. This integrated strategy substantially enhances breast cancer classification performance. Moreover, the proposed method integrates the block matching and 3D (BM3D) denoising filter to effectively eliminate multiplicative noise that has enhanced the performance of the system. The evaluation of the proposed HBMD-Net encompasses two breast ultrasound (BUS) datasets, namely BUSI and UDIAT. The proposed model has demonstrated a satisfactory performance, achieving accuracies of 99.14% and 94.49% respectively.
Random Fourier Surrogate for simulation calibration

Authors:
raw_author_name: LS Souza
raw_affiliation_string: National Institute of Advanced Industrial Science and Technology (AIST), Japan
raw_author_name: B Batalo
raw_affiliation_string: National Institute of Advanced Industrial Science and Technology (AIST), Japan
raw_author_name: K Yamazaki
raw_affiliation_string: National Institute of Advanced Industrial Science and Technology (AIST), Japan

Abstract: Abstract In this paper we propose Random Fourier Surrogate (RFS) as a method for simulation calibration. Computer simulations are actively used in various fields to model and analyze complex real-world phenomena, enabling practitioners to explore situations inaccessible by other means. To create a simulation that accurately reflects the desired real-world scenario requires calibrating the simulation parameters, a non-trivial process that requires domain expertise and iterative trial-and-error. The model-bridge paradigm has been proposed to automatize this process, where a more computationally efficient surrogate model is used in lieu of the real simulation, and a bridge model is used to map the noninterpretable surrogate parameters to calibrated simulation parameters, which are physically interpretable. Recently an efficient solution called tangent slope-intercept (TSI) descriptors has been proposed, where tangent lines of a principal curve are used to represent the simulation in low dimensionality. However, we find that it has two key problems: it is prone to overfitting and is sensitive to initialization. We address these issues using Random Fourier Features to construct a surrogate model, thereby reducing calibration time by avoiding complex computations, while retaining high accuracy. We evaluate the effectiveness of our method with different experiments on synthetic signal simulations and physical simulations of turbulent flow dynamics.
Effect on a QKD Chain Going Through a Generic Pauli Channel Together with a Correction Based on the 3-qubit Code

Authors:
raw_author_name: Francisco Delgado
raw_affiliation_string: Tecnologico de Monterrey, School of Engineering and Sciences, Carr. a Lago de Guadalupe km. 3.5, Atizapán, Estado de México, CP. 52926, México.

Abstract: Abstract Quantum cryptography is an emergent area in quantum technologies to set secure communication. BB84 protocol is a procedure to exchange a secure key to encode communication in the form of a chain of quantum bits. In this work, we model the non-ideal transmission of such a key through a parametric family of noisy channels: Pauli channels (including Bit-flipping and Dephasing noises). Assuming that Quantum Key Distribution is performed under such protocol, we analyse the fidelity of the transmission and the probability distribution for the imperfect outcomes. The analysis also includes the application of the quantum error correction algorithm: the 3-qubit code. Finally, we add the presence of an eavesdropper getting his conditional success rate to become unnoticed. The agreement in the basis selection between the transmitter and receiver is assumed as under a tentative reconciliation procedure. Outcomes show just positive effects on the transmission between sender and receiver, but a mild improvement for the success probability for the eavesdropper.