Reviewerly/exHarmony huggingface dataset:
Implementing and assessing Seamlessaccess: A publisher’s experience

This essay provides insights into a publisher’s perspective on implementing and evaluating SeamlessAccess, a collaborative initiative that facilitates seamless access to scholarly resources subscribed by libraries. The essay outlines the motivations behind adopting SeamlessAccess, including the surge in federated authentication usage during the pandemic, its adoption by other publishers, and considerations regarding security and privacy. It discusses IEEE’s decision to employ a hybrid approach, combining the advantages of SeamlessAccess with its own solutions to maximize flexibility. The essay also highlights pilot projects focused on incorporating Identity Providers (IDPs) and EZproxy login URLs into the discovery process, along with strategies employed to monitor and assess the implementation of SeamlessAccess. By utilizing data, the essay demonstrates the overall success of the SeamlessAccess implementation project, while acknowledging potential obstacles that publishers and libraries may encounter during integration. Furthermore, the essay emphasizes the importance of increased collaboration between publishers and libraries to fully realize the vision of federated authentication as a genuinely seamless experience for researchers.
Invisible for a few but essential for many: the role of Histotechnologists in the establishment of digital pathology

Digital pathology (DP) is indisputably the future for histopathology laboratories. The process of digital implementation requires deep workflow reorganisation which involves an interdisciplinary team. This transformation may have the greatest impact on the Histotechnologist (HTL) profession. Our review of the literature has clearly revealed that the role of HTLs in the establishment of DP is being unnoticed and guidance is limited. This article aims to bring HTLs from behind-the-scenes into the spotlight. Our objective is to provide them guidance and practical recommendations to successfully contribute to the implementation of a new digital workflow. Furthermore, it also intends to contribute for improvement of study programs, ensuring the role of HTL in DP is addressed as part of graduate and post-graduate education. In our review, we report on the differences encountered between workflow schemes and the limitations observed in this process. The authors propose a digital workflow to achieve its limitless potential, focusing on the HTL’s role. This article explores the novel responsibilities of HTLs during specimen gross dissection, embedding, microtomy, staining, digital scanning, and whole slide image quality control. Furthermore, we highlight the benefits and challenges that DP implementation might bring the HTLs career. HTLs have an important role in the digital workflow: the responsibility of achieving the perfect glass slide.
Generative AI-based Data Completeness Augmentation Algorithm for Data-driven Smart Healthcare

In the decade, artificial intelligence has achieved great popularity and applications in medicine and healthcare. Various AI-based algorithms have shown astonishing performance. However, in various data-driven smart healthcare algorithms, the problem of incomplete dataset remains a huge challenge. In this paper, we propose a data completeness enhancement algorithm based on generative AI (i.e., GenAI-DAA) to solve the problems of the in-sufficient data for model training, the data imbalance, and the biases of the training samples. We first construct the cognitive field of the generative models and effectively understand the state of incomplete cognition in generative models. Secondly, on this basis, we propose a quest algorithm for abnormal samples in the cognitive field based on local outlier factor. By fine-grained value evaluation, abnormal samples are given more refined attention. Finally, integrating the above process through multiple cognitive adjustments, GenAI-DAA gradually improves the cognitive ability. GenAI-DAA can be summarized as “Quest−→Estimate−→Tune-up”. We have conducted extensive experiments to demonstrate the effectiveness of our proposed algorithm, and shown widely applications to some typical data-driven smart healthcare algorithms
Real-Time Detection and Identification of Suspects in Forensic Imagery Using Advanced YOLOv8 Object Recognition Models

Rapid advancements in artificial intelligence, machine learning, deep learning, coupled with easy access to high-capacity processing hardware, expansive organized datasets, and the evolution of artificial intelligence algorithms, have extensively influenced numerous fields. Digital Forensics is one such discipline where the application of artificial intelligence has been significantly amplified in recent years. The analysis of extensive image and video files derived from forensic evidence presents challenges in terms of time efficiency and accuracy. To surmount these challenges, artificial intelligence models can be employed to perform identification and classification processes on these data, thus expediting the resolution of forensic cases with enhanced precision. In the current study, state-of-the-art pre-trained YOLOv8 object recognition models - nano, small, medium, large, and extra-large - were utilized. These models were trained on the Wider-Face dataset with the objective of identifying suspects from images and videos sourced from digital materials in the field of digital forensics. The models achieved mean Average Precision (mAP) values of 97.513%, 98.569%, 98.763%, 98.775%, and 99.032% respectively. The YOLOv8 architecture demonstrated superior performance, outperforming the YOLOv5 architecture by a margin of 7.1% to 8.8%. To aid digital forensic experts in the detection and identification of suspicious individuals, a desktop application capable of real-time image analysis was developed.
Machine Learning Allows for Distinguishing Precancerous and Cancerous Human Epithelial Cervical Cells Using High-Resolution AFM Imaging of Adhesion Maps

Previously, the analysis of atomic force microscopy (AFM) images allowed us to distinguish normal from cancerous/precancerous human epithelial cervical cells using only the fractal dimension parameter. High-resolution maps of adhesion between the AFM probe and the cell surface were used in that study. However, the separation of cancerous and precancerous cells was rather poor (the area under the curve (AUC) was only 0.79, whereas the accuracy, sensitivity, and specificity were 74%, 58%, and 84%, respectively). At the same time, the separation between premalignant and malignant cells is the most significant from a clinical point of view. Here, we show that the introduction of machine learning methods for the analysis of adhesion maps allows us to distinguish precancerous and cancerous cervical cells with rather good precision (AUC, accuracy, sensitivity, and specificity are 0.93, 83%, 92%, and 78%, respectively). Substantial improvement in sensitivity is significant because of the unmet need in clinical practice to improve the screening of cervical cancer (a relatively low specificity can be compensated by combining this approach with other currently existing screening methods). The random forest decision tree algorithm was utilized in this study. The analysis was carried out using the data of six precancerous primary cell lines and six cancerous primary cell lines, each derived from different humans. The robustness of the classification was verified using K-fold cross-validation (K = 500). The results are statistically significant at p &lt; 0.0001. Statistical significance was determined using the random shuffle method as a control.
Multiattention Mechanism 3D Object Detection Algorithm Based on RGB and LiDAR Fusion for Intelligent Driving

This paper proposes a multimodal fusion 3D target detection algorithm based on the attention mechanism to improve the performance of 3D target detection. The algorithm utilizes point cloud data and information from the camera. For image feature extraction, the ResNet50 + FPN architecture extracts features at four levels. Point cloud feature extraction employs the voxel method and FCN to extract point and voxel features. The fusion of image and point cloud features is achieved through regional point fusion and voxel fusion methods. After information fusion, the Coordinate and SimAM attention mechanisms extract fusion features at a deep level. The algorithm’s performance is evaluated using the DAIR-V2X dataset. The results show that compared to the Part-A2 algorithm; the proposed algorithm improves the mAP value by 7.9% in the BEV view and 7.8% in the 3D view at IOU = 0.5 (cars) and IOU = 0.25 (pedestrians and cyclists). At IOU = 0.7 (cars) and IOU = 0.5 (pedestrians and cyclists), the mAP value of the SECOND algorithm is improved by 5.4% in the BEV view and 4.3% in the 3D view, compared to other comparison algorithms.
Constraints on Optimising Encoder-Only Transformers for Modelling Sign Language with Human Pose Estimation Keypoint Data

Supervised deep learning models can be optimised by applying regularisation techniques to reduce overfitting, which can prove difficult when fine tuning the associated hyperparameters. Not all hyperparameters are equal, and understanding the effect each hyperparameter and regularisation technique has on the performance of a given model is of paramount importance in research. We present the first comprehensive, large-scale ablation study for an encoder-only transformer to model sign language using the improved Word-level American Sign Language dataset (WLASL-alt) and human pose estimation keypoint data, with a view to put constraints on the potential to optimise the task. We measure the impact a range of model parameter regularisation and data augmentation techniques have on sign classification accuracy. We demonstrate that within the quoted uncertainties, other than ℓ2 parameter regularisation, none of the regularisation techniques we employ have an appreciable positive impact on performance, which we find to be in contradiction to results reported by other similar, albeit smaller scale, studies. We also demonstrate that the model architecture is bounded by the small dataset size for this task over finding an appropriate set of model parameter regularisation and common or basic dataset augmentation techniques. Furthermore, using the base model configuration, we report a new maximum top-1 classification accuracy of 84% on 100 signs, thereby improving on the previous benchmark result for this model architecture and dataset.
Engineering the future of <scp>3D</scp> pathology

Abstract In recent years, technological advances in tissue preparation, high‐throughput volumetric microscopy, and computational infrastructure have enabled rapid developments in nondestructive 3D pathology, in which high‐resolution histologic datasets are obtained from thick tissue specimens, such as whole biopsies, without the need for physical sectioning onto glass slides. While 3D pathology generates massive datasets that are attractive for automated computational analysis, there is also a desire to use 3D pathology to improve the visual assessment of tissue histology. In this perspective, we discuss and provide examples of potential advantages of 3D pathology for the visual assessment of clinical specimens and the challenges of dealing with large 3D datasets (of individual or multiple specimens) that pathologists have not been trained to interpret. We discuss the need for artificial intelligence triaging algorithms and explainable analysis methods to assist pathologists or other domain experts in the interpretation of these novel, often complex, large datasets.
Machine Learning Approach for Facial Image Detection System

Face detection systems are based on the assumption that each individual has a unique face structure and that computerized face matching is possible using facial symmetry. Face recognition technology has been employed for security purposes in many organizations and businesses throughout the world. This research examines the classifications in machine learning approaches using feature extraction for the facial image detection system. Due to its high level of accuracy and speed, the Viola-Jones method is utilized for facial detection using the MUCT database. The LDA feature extraction method is applied as an input to three algorithms of machine learning approaches, which are the J48, OneR, and JRip classifiers. The experiment’s result indicates that the J48 classifier with LDA achieves the highest performance with 96.0001% accuracy.
Potential Analysis of Surface Water Balance in the Water District of the Kali Lamong Watershed, Indonesia

This research intends to evaluate the water availability condition and the development in the future in the water district of Kali Lamong Watershed, so the quantitative study that integrates the supply and demand at every water district is integrated from upstream until downstream, is necessary to be carried out. The methodology consists of comprehensive quantitative analysis to the water resources including the supply and demand in the water districts that have been carried out. Missed from the uncertainty that is big enough related with the development in the future, the water management nowadays and in the future can be seen from the analysis result of water balance potency that is carried out in each water district. Based on the analysis result of water balance that is carried out, there is obtained that the water balance in each water district gives the information about water surplus and deficit. Water deficit in the future is happened in the water district of Lamong Hulu, Sabeng, and Kalitemu, meanwhile the water surplus is happened in the water district of Lamong Tengah, Ngrembeng, and Lamong Hilir. The period of water deficit in the water district is very short by the small dimension. In 2100, the water deficit of Lamong Hulu water district will be about -1.024 m 3 /s, in the Sabeng water district - about -2.99 m 3 /s, and in the Kalitemu water district - about -2.128 m 3 /s. Keywords : watershed, water balance, water district, Kali Lamong. https://doi.org/10.55463/issn.1674-2974.50.9.7
Multi-Label Classification With High-Rank and High-Order Label Correlations

Exploiting label correlations is important to multi-label classification. Previous methods capture the high-order label correlations mainly by transforming the label matrix to a latent label space with low-rank matrix factorization. However, the label matrix is generally a full-rank or approximate full-rank matrix, making the low-rank factorization inappropriate. Besides, in the latent space, the label correlations will become implicit. To this end, we propose a simple yet effective method to depict the high-order label correlations explicitly, and at the same time maintain the high-rank of the label matrix. Moreover, we estimate the label correlations and infer model parameters simultaneously via the local geometric structure of the input to achieve mutual enhancement. Comparative studies over twelve benchmark data sets validate the effectiveness of the proposed algorithm in multi-label classification. The exploited high-order label correlations are consistent with common sense empirically. <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Our code is publicly available at <uri>https://github.com/Chongjie-Si/HOMI</uri>.</b>
AERO: Automotive Ethernet Real-Time Observer for Anomaly Detection in In-Vehicle Networks

Automotive Ethernet enables high-bandwidth in-vehicle networking, facilitating the transmission of sensor data among electronic control units. However, the increasing connectivity and potential vulnerability inheritance in connected and autonomous vehicles expose them to security risks. To address this challenge, an intrusion detection system (IDS) capable of analyzing automotive Ethernet traffic and detecting anomalies is essential. In thisarticle, we propose automotive Ethernet real-time observer (AERO), an unsupervised network IDS designed to protect in-vehicle networks. AERO consists of three components: a feature extractor that constructs three multimodal features, a neural network for processing the extracted features, and an online anomaly detector that calculates outlier scores in real time. We evaluate the performance of AERO using the TOW-IDS automotive Ethernet intrusion dataset. The experimental results demonstrate that AERO achieves high detection performance across five different attack types and is highly applicable to automotive-grade devices for real-time anomaly detection.
ENCFIS: An Exclusionary Neural Complex Fuzzy Inference System for Robust Regression Learning

Robust learning, an emerging research topic in recent years, is a promising branch of advanced artificial intelligence. Robust learning models target mainly noisy and rough datasets, predominantly in situations where noises and outliers are hard to remove. In this paper, the concept of robust learning is combined with complex fuzzy theory for the first time, proposing a novel neuro-fuzzy system ENCFIS with extensive adaptability to numerical regression problems, with or without noise. Simulation results indicate that such architecture has excellent performance on a dataset with massive (45%) label noises and on a distorted time series dataset (25% corrupted). Additionally, experimental results on a metallurgy dataset also show that the approximation performance of ENCFIS is not compromised for the increase in robustness, making it an ideal candidate for general industrial scenarios with weak noise but difficult data characteristics.
THE INFLUENCE OF LEARNING METHODS AND LEARNING INTEREST ON IPS LEARNING OUTCOMES CLASS VI STUDENTS OF STATE ELEMENTARY SCHOOL 053993 LUBUK KERTANG

This study aims to: (1) analyze the differences in students' social studies learning outcomes taught using the PAIKEM method compared to the Picture and Picture method; (2) Analyzing the differences in social studies learning outcomes of students who have high interest in learning compared to students who have low interest in learning; (3) Knowing the interaction between learning methods and students' interest in learning in influencing students' social studies learning outcomes. The sample in this study were 40 students of the State Elementary School Class 053993 Lubuk Kertang in the 2017/2018 academic year. Collecting data in this study through a questionnaire of interest in learning and social studies learning outcomes test. Hypothesis testing is done by using the Two Way Anova test. The results showed that: (1) Social studies learning outcomes of students taught by the PAIKEM method were higher than those of the Picture and Picture method (Fcount = 15,681 and sig. 0.000 &gt; 0.05); (2) Social studies learning outcomes of students who have high interest in learning are higher than students who have low interest in learning (Fcount = 4.395 and sig. 0.043 &gt; 0.05); and (3) There is an interaction between learning methods and interest in learning in influencing students' social studies learning outcomes (Fcount = 7.696 and sig. 0.009 &gt; 0.05).
Do Labour and Political Will Affect Agricultural Output?

Agriculture has been at the forefront in employment creation and revenue generation in Africa until there was a shift from the sector to the non-farm or service industry which threatens the economic sustenance despite the growing population of youth in the region. The neglect of the agricultural sector resulted from labour migration and lack of political will in sub-Saharan Africa (SSA), which has severe implications on agricultural output. This study, however, investigated the nexus among labour, political will and agricultural production in SSA from 1998 – 2018 using the dynamic System – GMM estimation technique. The study found employment in the agricultural sector, agricultural raw material, exchange rate, political will, and agricultural material and exchange rate interaction significantly influenced agricultural output in SSA. Therefore, it is recommended that for SSA future to be sustained, the governments should discourage labour migration from the agricultural sector through government supports to boost employment and poverty reduction.
Methodological support of the pedagogical content of the managerial activity of the head of the internal Affairs body of the Russian Federation

The article defines the importance of the pedagogical aspect of the managerial activity of the head of the internal affairs body of the Russian Federation. The author focuses on the fact that for the effective implementation by the head of the internal affairs bodies of pedagogical functions that implement the pedagogical content of management activities, he should form pedagogical competence. The pedagogical competence of the head presupposes mastering the methodology of managerial activity, for which it is necessary to provide methodological support for the pedagogical content of the managerial activity of the head of the internal affairs body of the Russian Federation. Special attention is paid to pedagogical stimulation, presented as the process of applying external stimuli that interact with the motives and internal attitudes of the individual. The author presents the results of an empirical study of the motivation of official and professional activities, types of incentives and incentive measures, which substantiates the need to create a methodology for the application of pedagogical functions in the management activities of the head of the internal affairs body at any level. 350 employees of the internal affairs bodies took part in the study: 170 people – managers of various levels, 180 people - subordinates. The normalized sample from among the heads of the Department of Internal Affairs was distributed as follows: male managers – 54%, female – 46%. Age from 28 years to 51 years. The category of subordinate employees is represented by: male employees – 44% and female employees – 56%. The age of the interviewed employees ranges from 23 to 54 years. In conclusion, a reasoned conclusion is made about the importance and necessity of mastering the methodology of providing the pedagogical content of managerial activities by the heads of internal affairs bodies.
Clinical Dialogue Transcription Error Correction with Self-supervision

A clinical dialogue is a conversation between a clinician and a patient to share medical information, which is critical in clinical decision-making. The reliance on manual note-taking is highly inefficient and leads to transcription errors when digitising notes. Speech-to-text applications designed using Automatic Speech Recognition (ASR) can potentially overcome these errors using post-ASR error correction. Pre-trained language models are increasingly used in this area. However, the performance suffers from the lack of domain-specific vocabulary and the mismatch between error correction and pre-training objectives. This research explores these challenges in gastrointestinal specialism by introducing self-supervision strategies to fine-tune pre-trained language models for clinical dialogue error correction. We show that our mask-filling objective specialised for the medical domain (med-mask-filling) outperforms the best performing commercial ASR system by 10.27%.