Reviewerly/exHarmony huggingface dataset:
A Key-Points Based Anchor-Free Cervical Cell Detector

Cervical cell detection is crucial to cervical cytology screening at early stage. Currently most cervical cell detection methods use anchor-based pipeline to achieve the localization and classification of cells, e.g. faster R-CNN and YOLOv3. However, the anchors generally need to be pre-defined before training and the detection performance is inevitably sensitive to these pre-defined hyperparameters (e.g. number of anchors, anchor size and aspect ratios). More importantly, these preset anchors fail to conform to the cells with different morphology at inference phase. In this paper, we present a key-points based anchor-free cervical cell detector based on YOLOv3. Compared with the conventional YOLOv3, the proposed method applies a key-points based anchor-free strategy to represent the cells in the initial prediction phase instead of the preset anchors. Therefore, it can generate more desirable cell localization effect through refinement. Furthermore, PAFPN is applied to enhance the feature hierarchy. GIoU loss is also introduced to optimize the small cell localization in addition to focal loss and smooth L1 loss. Experimental results on cervical cytology ROI datasets demonstrate the effectiveness of our method for cervical cell detection and the robustness to different liquid-based preparation styles (i.e. drop-slide, membrane-based and sedimentation).
Provably Secure Lightweight Mutual Authentication and Key Agreement Scheme for Cloud-Based IoT Environments

A paradigm that combines cloud computing and the Internet of Things (IoT) allows for more impressive services to be provided to users while addressing storage and computational resource issues in the IoT environments. This cloud-based IoT environment has been used in various industries, including public services, for quite some time, and has been researched in academia. However, various security issues can arise during the communication between IoT devices and cloud servers, because communication between devices occurs in open channels. Moreover, issues such as theft of a user's IoT device or extraction of key parameters from the user's device in a remote location can arise. Researchers interested in these issues have proposed lightweight mutual authentication key agreement protocols that are safe and suitable for IoT environments. Recently, a lightweight authentication scheme between IoT devices and cloud servers has been presented. However, we found out their scheme had various security vulnerabilities, vulnerable to insider, impersonation, verification table leakage, and privileged insider attacks, and did not provide users with untraceability. To address these flaws, we propose a provably secure lightweight authentication scheme. The proposed scheme uses the user's biometric information and the cloud server's secret key to prevent the exposure of key parameters. Additionally, it ensures low computational costs for providing users with real-time and fast services using only exclusive OR operations and hash functions in the IoT environments. To analyze the safety of the proposed scheme, we use informal security analysis, Burrows-Abadi-Needham (BAN) logic and a Real-or-Random (RoR) model. The analysis results confirm that our scheme is secure against insider attacks, impersonation attacks, stolen verifier attacks, and so on; furthermore, it provides additional security elements. Simultaneously, it has been verified to possess enhanced communication costs, and total bit size has been shortened to 3776 bits, which is improved by almost 6% compared to Wu et al.'s scheme. Therefore, we demonstrate that the proposed scheme is suitable for cloud-based IoT environments.
Digital holographic profilometry with spherical-wave illumination scanning

A novel measurement method named multi-angle spherical-wave scanning digital holographic profilometry (MISDHP) for acquiring the surface of high gradient objects beyond the NA of the setup is proposed.
Two-Stage Progressive Learning for Vehicle Re-Identification in Variable Illumination Conditions

Vehicle matching in variable illumination environments can be challenging due to the heavy dependence of vehicle appearance on lighting conditions. To address this issue, we propose a two-stage progressive learning (TSPL) framework. In the first stage, illumination-aware metric learning is enforced using a two-branch network via two illumination-specific feature spaces, used to explicitly model differences in lighting. In the second stage, discriminative feature learning is introduced to extract distinguishing features from a given vehicle. This process consists of a local feature extraction attention module, a local constraint, and a balanced sampling strategy. During the metric learning phase, the model expresses the union of local features, extracted from the attention module, with illumination-specific global features to form joint vehicle features. As part of the study, we construct a large-scale dataset, termed VERI-DAN (vehicle re-identification across day and night), to address the current lack of vehicle datasets exhibiting variable lighting conditions. This set is composed of 200,004 images from 16,654 vehicles, collected in various natural illumination environments. Validation experiments conducted with the VERI-DAN and Vehicle-1M datasets demonstrated that our proposed methodology effectively improved vehicle re-identification Rank-1 accuracy.
Few-Shot Classification Based on the Edge-Weight Single-Step Memory-Constraint Network

Few-shot classification algorithms have gradually emerged in recent years, and many breakthroughs have been made in the research of migration networks, metric spaces, and data enhancement. However, the few-shot classification algorithm based on Graph Neural Network is still being explored. In this paper, an edge-weight single-step memory-constraint network is proposed based on mining hidden features and optimizing the attention mechanism. According to the hidden distribution characteristics of edge-weight data, a new graph structure is designed, where node features are fused and updated to realize feature enrichment and full utilization of limited sample data. In addition, based on the convolution block attention mechanism, different integration methods of channel attention and spatial attention are proposed to help the model extract more meaningful features from samples through feature attention. The ablation experiments and comparative analysis of each training mode are carried out on standard datasets. The experimental results obtained prove the rationality and innovation of the proposed method.
Machine learning aided classification of SHG microscopy images of esophageal squamous cell carcinoma progression and high-grade dysplasia

Computational analyses such as textural analysis play an important role for the effective study of pathological images, it enables faster analysis and allows image classifications based on many features.
Personalized Federated Learning for Institutional Prediction Model using Electronic Health Records: A Covariate Adjustment Approach

Federated learning (FL) has attracted attention as a technology that allows multiple medical institutions to collaborate on AI without disclosing each other's patient data. However, FL has the challenge of being unable to robustly learn when the data of participating clients is non-independently and non-identically distributed (Non-IID). Personalized Federated Learning (PFL), which constructs a personalized model for each client, has been proposed as a solution to this problem. However, conventional PFL methods do not ensure the interpretability of personalization, specifically, the identification of which data samples are contributed to each personalized learning, which is important for AI in medical applications. In this study, we propose a novel PFL framework, Federated Adjustment of Covariate (FedCov), which acquires a propensity score model representing the covariate shift among clients through prior FL, then learns a final model by weighting the contribution of each training sample to PFL based on the estimated propensity score. This approach enables both the learning of personalized models through covariate adjustment and the visualization of the contribution of each client to PFL. FedCov was evaluated in the prediction of in-hospital mortality across 50 hospitals in the eICU Collaborative Research Database, achieving an ROC-AUC of 0.750. This result outperformed the AUCs in the 0.720-0.735 range achieved by conventional FL methods and was closest to the AUC of 0.754 achieved by centralized learning.Clinical Relevance- This study demonstrates the feasibility of providing sophisticated and personalized AI-driven clinical decision support to any medical institution through personalized federated learning.
Open Questions About the Visualization of Sociodemographic Data

This paper collects a set of open research questions on how to visu-alize sociodemographic data. Sociodemographic data is a common part of datasets related to people, including institutional censuses, health data systems, and human-resources files. This data is sensitive, and its collection, sharing, and analysis require careful consideration. For instance, the European Union, through the General Data Pro-tection Regulation (GDPR), protects the collection and processing of any personal data, including sexual orientation, ethnicity, and religion. Data visualization of sociodemographic data can reinforce stereotypes, marginalize groups, and lead to biased decision-making. It is, therefore, critical that these visualizations are created based on good, equitable design principles. In this paper, we discuss and provide a set of open research questions around the visualization of sociodemographic data. Our work contributes to an ongoing reflection on representing data about people and highlights some important future research directions for the VIS community. A version of this paper and its figures are available online at osf.io/a2u9c.
Design and Assembly of a Prototype Equipment for Measuring the Refractive Power of Progressive Ophthalmic Lenses

The design and assembly of a prototype device to measure the refractive power of progressive ophthalmic lenses is presented. We highlight its capabilities and expected applications in ophthalmology and the optical industry.
Multiresolution Self-Supervised Feature Integration via Attention Multiple Instance Learning for Histopathology Analysis

Digital histopathology image analysis of tumor tissue sections has seen great research interest for automating standard diagnostic tasks, but also for developing novel prognostic biomarkers. However, research has mainly been focused on developing uniresolution models, capturing either high-resolution cellular features or low-resolution tissue architectural features. In addition, in the patch-based weakly-supervised training of deep learning models, the features which represent the intratumoral heterogeneity are lost. In this study, we propose a multiresolution attention-based multiple instance learning framework that can capture cellular and contextual features from the whole tissue for predicting patient-level outcomes. Several basic mathematical operations were examined for integrating multiresolution features, i.e. addition, mean, multiplication and concatenation. The proposed multiplication-based multiresolution model performed the best (AUC=0.864), while all multiresolution models outperformed the uniresolution baseline models (AUC=0.669, 0.713) for breast-cancer grading. (Implementation: https://github.com/tsikup/multiresolution-clam).
Bridging the Divide: Promoting Serendipitous Discovery of Opposing Viewpoints with Visual Analytics in Social Media

While social media promises open access to information, prior works suggest that it also plays a role as a catalyst for the social divide, which is often attributed to a shift towards algorithmic content curation based on users' digital footprints. To combat this issue, methods that support serendipity have received attention in recent years that aim to provide information beyond a user's viewpoint or preference. However, the utility of systems that promote serendipity in raising awareness of opposing viewpoints remains underexplored, especially in the political context. To that end, we conducted a study where we asked 14 participants to explore tweets about two politically charged topics - gun control and immigration - using an interaction-driven visual analytics tool that visualizes users' exploration patterns and provides serendipitous suggestions from opposing viewpoints. We found that as participants explored the tweets, they gradually became aware of opposing viewpoints and identified information they had not considered before which helped them gain knowledge about arguments from all sides. We also found while people were keen to use technology that promotes serendipity to cover more topical information, they do not necessarily trust the information found on social media. We hope that our work will motivate future researchers to investigate serendipitous aspects in visualizations to promote a more holistic exploration of various viewpoints.
A Two-stage Diagnostic Framework for Post-ablation Treatment Response Assessment in Patients with Hepatocellular Carcinoma

Contrast-enhanced ultrasound (CEUS) video plays an important role in post-ablation treatment response assessment in patients with hepatocellular carcinoma (HCC). However, the assessment of treatment response using CEUS video is challenging due to issues such as high inter-frame data repeatability, small ablation area and poor imaging quality of CEUS video. To address these issues, we propose a two-stage diagnostic framework for post-ablation treatment response assessment in patients with HCC using CEUS video. The first stage is a location stage, which is used to locate the ablation area. At this stage, we propose a Yolov5-SFT to improve the location results of the ablation area and a similarity comparison module (SCM) to reduce data repeatability. The second stage is an assessment stage, which is used for the evaluation of postoperative efficacy. At this stage, we design an EfficientNet-SK to improve assessment accuracy. The Experimental results on the self-collected data show that the proposed framework outperforms other selected algorithms, and can effectively assist doctors in the assessment of post-ablation treatment response.
Deep Learning for Polycystic Kidney Disease (PKD): Utilizing Neural Networks for Accurate and Early Detection through Gene Expression Analysis

With PKD, an under-researched area in the field of nephrology, potentially leading to fatal complications in patients due to the formation of cysts, early detection of the disease is crucial for effective management. However, the various patient-specific factors that play a role in the diagnosis make it an intricate puzzle for clinicians to solve, leading to possible kidney failure. Therefore, in this study we aim to utilize a deep learning-based approach for early disease detection through gene expression analysis. The devised neural network is able to achieve accurate and robust prediction results for possible PKD in kidneys, thereby improving patient outcomes. Furthermore, by conducting a gene ontology analysis, we were able to predict the top gene processes and functions that PKD may affect.
A Web Application for Data Exchange Blockchain Platform

The rapid advancement of blockchain technology and services based on blockchain applications inevitably faces the challenge of big data. To support the information and communication of smart crop production, the solution can be found in the implementation of blockchain technologies to provide services related to the acquisition, storage, processing and analysis of big data. The purpose of this paper is to present a web application for smart crop production data exchange platform integrated with Antelope blockchain and Interplanetary File System. The web application is designed using open-source solutions, enabling users to authenticate via a blockchain user account and corresponding private key, or via the standard blockchain wallet Anchor. The exchange of files and data, as well as the transfer of tokens between users, are facilitated through smart contracts. These contracts enable the tracking of chronological statements of completed transactions and provide an activity log overview of all file uploads, downloads, and edits. Future development phases include tests to evaluate the performance, speed, scalability and network security of the platform. The ultimate goal is for the web application to become a decentralized application (dApp).
Exploring Public Opinions Toward the Use of Generative Artificial Intelligence Chatbot in Higher Education: An Insight from Topic Modelling and Sentiment Analysis

The Generative Artificial Intelligence chatbots (GAI chatbots) have emerged as promising tools in various domains, including higher education, so this study aims to investigate the role of Bard, a newly developed GAI chatbot, in higher education. English tweets were collected from Twitter's free streaming Application Programming Interface (API). The Latent Dirichlet Allocation (LDA) algorithm was applied to extract latent topics from the tweets. User sentiments were extracted using the NRC Affect Intensity Lexicon and SentiStrength tools. This study explored the benefits, challenges, and future implications of integrating GAI chatbots in higher education. The findings shed light on the potential power of such tools, exemplified by Bard, in enhancing the learning process and providing support to students throughout their educational journey.
Empowering People with Intellectual and Developmental Disabilities through Cognitively Accessible Visualizations

Data has transformative potential to empower people with Intellec-tual and Developmental Disabilities (IDD). However, conventional data visualizations often rely on complex cognitive processes, and existing approaches for day-to-day analysis scenarios fail to consider neurodivergent capabilities, creating barriers for people with IDD to access data and leading to even further marginalization. We argue that visualizations could be an equalizer for people with IDD to participate in data-driven conversations. Drawing on preliminary research findings and our experiences working with people with IDD and their data, we introduce and expand on the concept of cognitively accessible visualizations, unpack its meaning and roles in increasing IDD individuals' access to data, and discuss two immediate research objectives. Specifically, we argue that cognitively accessible visualizations should support people with IDD in personal data storytelling for effective self-advocacy and self-expression, and balance novelty and familiarity in data design to accommodate cognitive diversity and promote inclusivity.
Big Data Processing for Bulgarian Healthcare - Smart Cards and Some Simulating Decisions

This publication presents a review of the application of large databases (Big Data) in medicine. The aim of the work is to provide some positive guidelines for the optimization of health care and improvement of patient care in Bulgaria. Therefore, the article discusses designed applications that can find their place in various fields of medicine. Our proposed optimization is the introduction of electronic chip cards. The development assumes that each patient will be provided with a processor card carrying information about basic data related to the condition and its personalization and possible access to this data when necessary. Thus, the proposed solution provides an opportunity to upgrade (for the future) and build an information system with a connection to the health fund. In addition, an early-stage project is described to integrate data from personal portable devices with the electronic health record, thereby providing information to medical personnel about the individual's health status. Portable personal devices and other non-medical equipment generate a huge, almost continuous flow of data that can be useful in making decisions regarding the treatment of patients with various health conditions. The applications described are a continuation of the team's previous developments.
Modeling and Correcting Pincushion Distortion in Wide-angle Projection Images of Diffractive Optical Elements

This paper presented a model of pincushion distortion in wide-angle projection images of diffractive optical elements (DOE). Correction of the distortion was verified using a Huygens-Fresnel principle and the fabricated DOEs showed the results correctly.
Enhancing Privacy Preservation in Vehicular Trust Management Systems through Blockchain Technology

The advent of the Internet of Vehicles (IoV) has led to a significant surge in data generation from vehicles, prompting the innovative utilization of data transactions within the IoV environment. However, due to the inherent trustless nature of data transactions in IoV, concerns have arisen regarding the lack of trust between involved parties and the potential compromise of user privacy. To address these issues, blockchain technology emerges as a suitable decentralized distributed storage and security management solution, offering transparency and security in data transactions. In this study, we leverage blockchain to integrate with the IoV, devising a robust trust management framework, and devising a privacy protection scheme to safeguard user privacy concerns. Additionally, we employ the performance evaluation process algebra (PEPA) method for system modeling and performance analysis to assess the efficacy of our proposed solution. Empirical findings demonstrate that our approach effectively enhances the performance of data transactions within the IoV while ensuring that the privacy of users remains intact.
Energy-Efficient Integrated Photonics for Future Optical Interconnects and Neuromorphic Computing

The purpose of this paper is to introduce some of the technological breakthroughs in heterogeneous III-V/Si phase tuners which can be used for both ultra-energy efficient data transmission and photonic neuromorphic computing.
Educational Applications of Big Data and Learning Analytics in Personalized E-Learning

There is plenty of research on the use of Big educational data, learning analytics, semantics-based knowledge modeling, and other innovative technologies within an educational environment for improving learning and tutoring. Each of these research fields is very wide and complex, and different researchers usually concentrate their work only on one or two of them and on the related subfields. The authors believe that high-quality personalized learning requires the integration of all these technologies into one intelligent educational system. Hence, the paper proposes an integrated model for combining big data learning analytics, and intelligent technologies for personalized learning and discusses the role of these technologies in achieving a more efficient learning process and interoperability between e-learning systems.
Smart Attendance System - CountMe

This article discusses the development of a system for facilitating the registration of student attendance in lectures, exercises, and laboratory sessions - CountMe, which has been tested in the “Java Programming” courses at the Faculty of Mathematics and Informatics at Plovdiv University. The system implements a quick way to register attendance by shifting the time-consuming manual tasks from the instructor to the students. This is made possible through the widespread use of mobile smart devices, which, using QR codes, reduce the registration process to a few seconds. The system also provides attendance statistics for individual events as well as for groups of events, making it a convenient tool for instructors. The architecture and implementation of the system are briefly outline.