Reviewerly/exHarmony huggingface dataset:
Leveraging Foundation Models for Clinical Text Analysis

Infectious diseases are a significant public health concern globally, and extracting relevant information from scientific literature can facilitate the development of effective prevention and treatment strategies. However, the large amount of clinical data available presents a challenge for information extraction. To address this challenge, this study proposes a natural language processing (NLP) framework that uses a pre-trained transformer model fine-tuned on task-specific data to extract key information related to infectious diseases from free-text clinical data. The proposed framework includes a data layer for preparing datasets from clinical texts, a foundation model layer for entity extraction, and an assessment layer for performance analysis. The results of the evaluation indicate that the proposed method outperforms standard methods, and leveraging prior knowledge through the pre-trained transformer model makes it useful for investigating other infectious diseases in the future.
Optimizing Management Information Systems Based on Efficiency and Quality Management

The purpose of this research is to explore the increase in efficiency and quality of management in optimizing the application of management information systems at MAN 1 Sungai Full. This research uses qualitative research, and data collection techniques through observation, interviews, and documentation. Data analysis techniques used are data reduction, data presentation, and data verification. The results of this study indicate that the management of optimizing the implementation of management information systems at MAN 1 Sungai Penuh collects data containing teacher and staff data to manage important information about teaching staff and staff registered at the school such as biodata, rank, position, address, working status, hours work history, educational history, career history, training history, attendance rate, salary info and others, management information systems related to student services in the form of New Student Admissions data, student biodata, Management of Student Class Promotion (manual or automatic), Graduation Management / Alumni, Printing Student Cards, and Management of Student Discipline, and information systems related to teaching and learning activities such as Information Systems containing Curriculum Management, Scheduling of Teaching Units, Management of Student Academic Grades and reports on Student Study results, and Student Presence in teaching and learning activities.
Big data bioinformatics discoveries: Machine learning approaches, tools, and perspectives

This paper examines the analysis of the different data types within the field of Bioinformatics. The aim is to study and handpick machine learning algorithms that are best suited for handling large datasets and data compendia with its specific features. The paper proposes a specific informed approach for analyzing each bioinformatics data type, combining, and comparing existing knowledge on the use of machine learning algorithms.
Feature Reduction for Interpretability of Neuro-Fuzzy Classifier

The growing use of machine learning in healthcare demands models that not only predict well but clearly explain their reasoning. Neuro-fuzzy classifiers combine neural networks and fuzzy logic for accurate yet interpretable modeling. However, high-dimensional medical data challenges comprehension. This study presents techniques to distill neuro-fuzzy classifiers down to their most essential components for enhanced interpretability. Recursive feature elimination is employed, iteratively removing the least contributory features according to classifier weights. On a breast cancer prognosis task, these methods significantly reduce features to the minimal set necessary for strong accuracy. The pruned neuro-fuzzy classifier demonstrates competitive prediction with increased transparency through an intuitive rule-based structure relying on only the most critical inputs. Feature reduction thus holds promise for optimizing the balance between predictive capacity and comprehensive explanations in medicine. However, challenges remain in scaling these methods to even higher-dimensional data like medical images while retaining accuracy. Future work should explore the effects of different criteria for ranking feature importance beyond classifier weights. Overall, this study provides an initial demonstration of how feature reduction techniques can produce simplified neuro-fuzzy classifiers more amenable to clinical implementation, but continued research is needed to extend these approaches to complex real-world medical data. Distilling machine learning models down to their most essential components will be critical for trust and adoption of artificial intelligence in high-stakes healthcare applications.
A Combination of Active Learning and Deep Learning for Improving Breast Cancer Prediction

In the field of modern medicine, biomedical informatics is crucial for enhancing illness detection, management, and treatment. A strong basis for applications that can enhance people’s health and quality of life has been established thanks to the convergence of rich medical data with potent information technology. The detection and diagnosis of breast cancer is one of the key fields where biomedical data is used and labeled data plays an important role in supervised learning models. However, labeling data can costly and time consuming. In the field of breast cancer, there is a similar problem. So that, an approach will be proposed for automatic data labeling using active learning to improve breast cancer prediction by combining least confidence sampling with fine-tuned MobileNet model called BCPDAL. Experimental research on a number of fine-tuned deep learning models combined with uncertainty sampling methods and with the dataset used as BUSI. The results have shown that proposed approach BCPDAL can achieve 90% accuracy with only 50% of the labeled data samples in the entire original dataset.
A Real-time Crowd Density Level Detection System

Crowd density level evaluates the number of people gathering in a particular area or scene, typically based on videos generated from real-time closed-circuit television (CCTV) cameras. The goal of estimating the levels of a crowd is to provide accurate and reliable information about the crowd size and density, which can be helpful in various situations. Furthermore, detecting and analyzing crowds in real-time is a crucial task for many applications such as monitoring large crowds in public areas, detecting any suspicious activities or potential threats; managing crowds in public places to prevent overcrowding and ensure the safety of individuals; monitoring the flow of people in an event to optimize the use of security officers and facilities. In this paper, we present a deep learning-based architecture and system for detecting levels of crowd density from CCTV cameras. In particular, the system can automatically generate alert information when identifying high-density camera areas, enabling security staff to take corrective measures to manage the crowd and prevent overcrowding before it happens. The proposed system provides a reliable approach for real-time crowd detection, which can be integrated into existing surveillance systems.
Certified, total serialisers with an application to Huffman encoding

The other day, I was assembling lecture material for a course on Agda. Pursuing an application-driven approach, I was looking for correctness proofs of popular algorithms. One of my all-time favourites is Huffman data compression (Huffman, 1952). Even though it is probably safe to assume that you are familiar with this algorithmic gem, a brief reminder of the essential idea may not be amiss.
Improved Approach for Mapping Anthropological Facial Features Based on A Convolutional Neural Network

Our facial anthropological and actions are non-verbal communication tools that account for 93% of emotions in human communication, 55% of which are facial expressions and gestures. Facial emotions can be easily analyzed from facial images, and computers can interact with people the way people interact with each other. For this reason, automatic classification and analysis of facial emotions is currently an interesting and challenging problem that has great implications for society. In this work, the random forest method combined with a neural network was proposed to improve the classification of emotions by building feature maps. Training and testing models were built to perform the experiments, which achieved very good accuracy in the range of 98.5% to 99.7% for the two datasets.
DCDynUnet: Deep Supervision Attention Context for Brain Segmentation

Brain segmentation is considered a gold standard in the analysis of brain diseases. However, this task requires much time, effort, and highly professional skills. Many research studies applying artificial intelligence and deep learning have been developed to assist doctors in brain segmentation to solve this task automatically. In this paper, our primary goal is to propose a deep learning model using attention and deep supervision mechanisms to improve the performance of brain segmentation. We will use an attention block in the deep supervision mechanism proposed by us. This attention block helps to identify standard features between the steps in the decoder stage and the output features of the network. This helps to enhance these standard features and achieve good performance. In addition, we also conducted ablation studies to evaluate the effectiveness of the proposed fusion method. Experimental results have shown that our performance is quite good, with the highest results being 96.06% of CSF, 93.36% of WM, and 91.94% of GM of the iSeg2019 dataset.
Sustainability by Design: Innovative Ways of Revolutionizing Production Practices for a Better Tomorrow

The incorporation of virtual design, augmented reality (AR), and immersive 3D modelling has resulted in a significant and extensive impact on several aspects of society and culture. The aforementioned technologies have not only brought about significant changes in various sectors such as education, entertainment, healthcare, and real estate, but they have also fundamentally transformed the way in which we engage in communication, interaction, and our overall perception of the surrounding world. Within the field of education, the utilisation of virtual design and immersive technology has expanded the possibilities for learning, presenting opportunities for interactive, experiential, and dynamic educational encounters. The use of various resources, such as 3D anatomy lectures and virtual historical excursions, has significantly augmented our capacity to comprehend intricate issues and facilitated a more captivating methodology towards education. The field of healthcare and medicine has experienced significant advancements as a result of these technologies, which have facilitated enhanced surgical planning, medical training, and therapeutic interventions. The field of telemedicine has seen a significant transformation with the integration of augmented reality (AR), resulting in enhanced accessibility and convenience of healthcare services, particularly in geographically isolated or underserved regions. The influence of these technologies has been extended to the preservation of cultural heritage, facilitating the digitization and conservation of heritage places and artefacts, so ensuring their accessibility for future generations to study and appreciate. The democratisation of creativity and empowerment has facilitated the artistic expression and utilisation of 3D models and augmented reality (AR) applications by individuals from various backgrounds. it is crucial to acknowledge ethical and societal concerns as we adopt these revolutionary technologies. The issues of privacy, addiction, misinformation, and the ethical utilisation of data are matters that necessitate continuous focus and resolution.
Development of neural network-based video preprocessing method to increase the VMAF score relative to source video using distillation

In this work, we consider the problem of creating a video preprocessing method that improves video’s quality score measured by the Video Multimethod Assessment Fusion (VMAF) metric. The paper describes a neural network method for automatic preprocessing of input video, operating in real time. Preprocessing is carried out by a deep neural network based on U-Net architecture. In the course of network training, a trained VMAF approximation is used. The paper describes ways of improving the quality of the final method, namely, adding neural network compression, using SSIM in the loss function, and filtering the training set. The final version of the method increases the VMAF score of the original video by an average of 18% after preprocessing. The developed method demonstrates the flaws of the VMAF quality assessment method that can be used by developers of video processing algorithms to improve the ratings of their methods during automatic comparison carried out using VMAF quality assessment method.
Eye-tracking Devices for Virtual and Augmented Reality Metaverse Environments and Their Compatibility With the European Union General Data Protection Regulation

Even though the Metaverse from science fiction is not a reality yet, it is possible to take a glimpse into how it might look like. Several companies and platforms are currently working and developing their versions of how the Metaverse will look in a not-so-distant future. However, the current vision of the Metaverse does not only encompass software. A great deal of companies is complementing their Metaverse projects with Virtual and Augmented Reality devices such as headsets and glasses.In this line, one of the last technological advancements in virtual and augmented reality devices included the introduction of eye-tracking technology. However, when new and additional kinds of data are processed, emerging risks for data protection might arise. While there is an already incipient stream of scholarship that analyses the risks that eye-tracking devices might entail for privacy, such literature mostly focuses on the technical side. However, no scholarship, up to this moment, has examined such devices from a legal perspective and particularly from a data protection lens.This paper will, therefore, discuss the compatibility of eye-tracking devices for virtual and augmented reality environments with the European Union General Data Protection Regulation (GDPR). Being the GDPR considered a worldwide role model in terms of fundamental rights protection, the compatibility of such devices with one of the most severe data protection regimes will be put to the hardest test. The paper will do so by analyzing the state of the art of the technology, its use in headsets and glasses for virtual and augmented reality Metaverse environments, and the potential risks that such use might entail for data protection. After that, such risks will be confronted with the relevant applicable provisions of the GDPR. Finally, the paper will issue policy recommendations regarding the need for more interdisciplinary research on privacy-enhancing techniques to solve the privacy utility conundrum; careful monitoring of access to data by third parties, including data security and minimization requirements; more guidance from supranational Data Protection Authorities and more attention when designing privacy policies, especially for children.
An Overview of the Markets in Crypto-Assets Regulation (MiCAR).

The EU has enacted its first, structured and complete legislation on crypto-assets, substantiated in the Markets in Crypto Assets Regulation. With this Regulation, which constitutes a new, large pillar of the Union's financial legislation, the EU finds itself in a unique position, also on a geopolitical level, as it now constitutes the sole multi-state geographic area endowed with a homogeneous and uniform discipline (as directly applicable in the Member States) on markets in crypto-assets. This paper wishes to provide an overall illustration of the structure of the MiCA Regulation and its rational, highlighting some its major interpretative issues, including taxonomy, definitions, and interplay with National legislations, also with the aim of stimulating further research.
Analyzing the Obstacles and Crucial Aspects of Implementing Agile Scrum in Southeast Asian IT Companies

Agile Scrum methodology has emerged as a transformative approach in the software development landscape, fostering collaboration, flexibility, and rapid delivery. However, the implementation of Agile Scrum in Southeast Asian IT companies presents unique challenges rooted in cultural, organizational, and contextual factors. This research endeavors to explore these challenges comprehensively, shedding light on the intricate dynamics of Agile Scrum adoption in Southeast Asia. Through an in-depth analysis of case studies, interviews, and relevant literature, this paper not only identifies hurdles but also proposes strategic solutions and best practices tailored to the Southeast Asian context, aiming to facilitate successful Agile Scrum implementation in the region.
Study of machine transliteration for cross language retrieval

The World Wide Web search is the most vital and elementary way to get knowledge and access the information. Over the globe the information retrieval is implemented using the regional language whereas literature is available in regional as well as in foreign languages. The lexicon transliteration from source to target language was difficult without considering the context of the original script; it leads to ambiguous transliteration. This study focuses on the survey of different research papers in order to get an idea of how the machine transliteration has evolved from its very start and what are various models, approaches, methods and technologies being built for transliteration which will be efficiently used for the information retrieval purposes.
Earthquake Emulation for Environmental Sensing in Terrestrial Telecom Networks

There is a huge interest in exploiting the extensive telecom fiber infrastructure for early warning of environmental disasters. We propose a method to generate synthetic earthquakes events to test their detection in terrestrial telecommunication networks.
All-Optical Reconfigurable Low-Threshold Nonlinear Activation Functions for High-Precision Neural Network

All-optical reconfigurable and ultra-low-threshold nonlinear activation functions are experimentally demonstrated based on a high-Q microring resonator (MRR) assisted Mach-Zehnder interferometers (MZIs). We propose a heterogeneously integrated all-optical high-precision neural network for low power consumption.
Lithography-free Integrated Photonics

We report an unprecedented lithography-free paradigm for an integrated photonic processor, which creates exceptional field programmability and functionality from a global perspective, enabling accurate multiply–accumulate operations and in-situ training of optical neural networks.
Inverse-Design of High-Dimensional Quantum Optical Circuits in a Complex Medium

We program high-dimensional linear optical circuits within a commercial multi-mode fibre, turning it into a generalised multi-outcome measurement device, which allows us to both transport and certify high-dimensional entanglement within the transmission channel itself.
Use of a Hybrid Opto-electronic Correlator for High-Speed Pattern Recognition in Fundamental Science Applications

Current electronics-based approaches used by research facilities such as CERN and LIGO are sub-optimal for signal recognition in large-scale data streams. We demonstrate the Hybrid Opto-electronic correlator as a potential alternative for fast data processing.
Neural nonlinearity using a surface normal photodetector for diffractive optical neural networks

We propose a neural nonlinearity using a surface-normal photodetector with a response time of 5.7 µ s and an energy efficiency of &lt; 10 nW/pixel. This device offers a significant improvement in free-space diffractive optical neural networks.
Observer-Based Stabilization for Discrete Interval Type-2 Fuzzy Models With Semi-Markov Parameters

The issue of observer-based stabilization is investigated for discrete interval type-2 fuzzy (IT2F) models with semi-Markov parameters. Different from the traditional T-S fuzzy model, the nonlinear plant suffering from uncertain parameters is denoted by the IT2F model. Allowing for the fact that state information is difficult to obtain in a complex environment, an observer-based measurement is considered to estimate the system state. Based on the semi-Markov kernel, IT2F, and multiple Lyapunov functions dependent on the elapsed time of the current mode, the stability criterion is constructed for the corresponding nonlinear model with the help of additional matrix variables. Furthermore, numerically checkable conditions about a fuzzy observer-based feedback controller are formulated to realize the σ-error mean-square stability of the underlying augmented system. Finally, the superiority of the control algorithm is verified by simulating the tunnel circuit model.
Quantum-noise-limited optical neural networks using few photons per neuron activation

We pushed the optical energy consumption of optical neural networks to a new regime. Despite dominant quantum noise, we experimentally achieved accurate image classification using 0.008 photons/MAC, demonstrating deterministic machine-learning tasks with ultra-low-power stochastic systems.
Encryptocurrency as Property

Globally, cryptocurrencies have caused a stir regarding their legal and economic implications. The associated concerns are with regard to the nature and application of cryptocurrency as legal tender, or a virtual currency, or a means of digital bartering. The concerns extend to whether, and how, cryptocurrency can be regarded as a type of property. Despite the prevailing economic value of cryptocurrency, the legal system - at least in Roman-Dutch law systems - has not yet created appropriate legal accommodations for the property rights of cryptocurrency holders.This article interrogates the concept of property under Roman-Dutch law and weaves the golden thread to its most modern conceptualization. Subsequently, it discusses the nature of cryptocurrency and the technology underlying it. It analyses the implications of its nature in the context of the Roman-Dutch law regarding property. In analyzing the legal nature of cryptocurrency as property, the article explores analogical symmetries and asymmetries between cryptocurrencies and recent developments in intangible property (i.e. shares, funds credited to a bank account, electricity, and a participatory interest in a collective investment scheme). Ultimately, the article journeys to an inescapable conclusion that legislative recognition is required in order to appropriately protect the rights of cryptocurrency holders.
Peramalan Data Pengukuran Pergeseran Tanah Jalur Kereta Api Menggunakan Metode Fuzzy Time Series

Unstable land conditions on the railway line cause land shift. Land movements that occur suddenly in large volumes can cause landslides easily. This situation supports the development of detecting ground shifts by forecasting land shift data. The land shift data is in the form of the rheostat output voltage value which has been measured against the land. Forecasting data using fuzzy time series method. The research was conducted in front of the PPI Laboratory with an implementation time of 1 month in June 2020. Mean Absolute Percentage Error (MAPE) and Squared Root of Mean Squared Error (RMSE) are methods used to calculate the percentage error value in a time series model. From the research that has been done, the average MAPE values for rheostat 1 and 2 data forecasting results were 22.49% and 25.74%, respectively. The RMSE values for rheostat 1 and 2 data forecasting results were 5.597% and 4.587%. So, that the data forecast has a fairly good predictive accuracy value.