Reviewerly/exHarmony huggingface dataset:
Figure 2 from Deep Learning Enables Spatial Mapping of the Mosaic Microenvironment of Myeloma Bone Marrow Trephine Biopsies

&lt;p&gt;Computational methods for bone thickness analysis and cell infiltration patterns: &lt;b&gt;A,&lt;/b&gt; Image analysis to estimate bone thickness (Supplementary Materials and Methods). Using the same BM sample image as &lt;a href="#fig1" target="_blank"&gt;Fig. 1A&lt;/a&gt;, the bone segmentation (ii) is an output of MoSaicNet (Supplementary Materials and Methods), and each bone is displayed in a different color. The color bar shows the pixel intensity of the image in iii and iv. The pixel intensity on the skeleton indicates half of the bone thickness (Supplementary Materials and Methods). &lt;b&gt;B,&lt;/b&gt; Cell infiltration pattern analysis using NND and the null hypothesis of CSR (Supplementary Materials and Methods). Z &lt; −1.96, Z &gt; 1.96, and −1.96 ≤ Z ≤ 1.96 indicate a clustered, dispersed, and random distribution of observed cells, respectively. std, standard deviation; μ, mean NND of CSR.&lt;/p&gt;
Figure 6 from Deep Learning Enables Spatial Mapping of the Mosaic Microenvironment of Myeloma Bone Marrow Trephine Biopsies

&lt;p&gt;&lt;b&gt;A&lt;/b&gt; and &lt;b&gt;B,&lt;/b&gt; Spatial neighborhood of immune and tumor cells (&lt;b&gt;A&lt;/b&gt; and &lt;b&gt;B&lt;/b&gt;) and between MGUS and NDMM (&lt;b&gt;B&lt;/b&gt;). The &lt;i&gt;P&lt;/i&gt;* indicate &lt;i&gt;P&lt;/i&gt; values after multiple testing correction using the BH method. The points represent the mean and the bars are 95% CIs, indicating uncertainty. &lt;b&gt;C,&lt;/b&gt; Sample images showing an increased number of BLIMP1&lt;sup&gt;+&lt;/sup&gt; cells in the neighborhood with CD8&lt;sup&gt;+&lt;/sup&gt; on NDMM samples (NDMM example shown here is the same image as &lt;a href="#fig5" target="_blank"&gt;Fig. 5L&lt;/a&gt;) compared with MGUS samples. &lt;b&gt;D–I,&lt;/b&gt; Clustered or dispersed pattern of immune and tumor cells in BM trephine sample. Box plots showing the difference in NND and Z score between NDMM and posttreatment for CD8&lt;sup&gt;+&lt;/sup&gt; cells (&lt;b&gt;D&lt;/b&gt;), BLIMP1&lt;sup&gt;+&lt;/sup&gt; cells (&lt;b&gt;E&lt;/b&gt;), and FOXP3&lt;sup&gt;−&lt;/sup&gt;CD4&lt;sup&gt;+&lt;/sup&gt; cells (&lt;b&gt;F&lt;/b&gt;). Box plots showing the difference in NND and Z score between NDMM and MGUS for CD8&lt;sup&gt;+&lt;/sup&gt; cells (&lt;b&gt;G&lt;/b&gt;) and BLIMP1&lt;sup&gt;+&lt;/sup&gt; cells (&lt;b&gt;H&lt;/b&gt;), and between male and female for BLIMP1&lt;sup&gt;+&lt;/sup&gt; cells (&lt;b&gt;I&lt;/b&gt;). The unit of NND is μmol/L. The Z score shows the significance of the difference between the NND distribution for a given cell type from a complete spatial random distribution and the observed NND (Supplementary Materials and Methods).&lt;/p&gt;
Advances in Medical Imaging: Using Convolutional Neural Networks for White Blood Cell Identification

White blood cells (WBC) perform a vital function within the immune system by actively protecting the body from a wide range of diseases and foreign substances. Diverse types of WBCs exist, including neutrophils, lymphocytes, eosinophils, and monocytes, each possessing distinct roles within the immune response. Neutrophils are typically the initial immune cells to mobilize in response to infections and inflammation, exhibiting a rapid and robust reaction. Conversely, lymphocytes play a pivotal role in the recognition and targeted elimination of pathogens. Nevertheless, identifying and classifying WBCs poses significant challenges and demands considerable time, even for seasoned medical practitioners. The process of manual classification is frequently characterized by subjectivity and is susceptible to errors, thereby potentially compromising the precision of both diagnosis and treatment. In response to this challenge, scholars have devised deep learning methodologies that can automate the process of WBC classification, thereby enhancing its precision. This study employs a convolutional neural network (CNN) to classify WBCs based on imaging data. The CNN underwent training using a substantial dataset comprising body cell images. This training facilitated the acquisition of discerning characteristics specific to various WBC types, thereby enabling accurate classification. The methodology was evaluated within a simulated environment, yielding encouraging outcomes. The approach that was proposed successfully achieved an average accuracy rate of 98.33% in the classification of WBCs. This outcome serves as evidence of deep learning techniques enhancing the speed and accuracy of WBC classification.
LeBenchmark 2.0: A standardized, replicable and enhanced framework for self-supervised representations of French speech

Self-supervised learning (SSL) is at the origin of unprecedented improvements in many different domains including computer vision and natural language processing. Speech processing drastically benefitted from SSL as most of the current domain-related tasks are now being approached with pre-trained models. This work introduces LeBenchmark 2.0 an open-source framework for assessing and building SSL-equipped French speech technologies. It includes documented, large-scale and heterogeneous corpora with up to 14,000 h of heterogeneous speech, ten pre-trained SSL wav2vec 2.0 models containing from 26 million to one billion learnable parameters shared with the community, and an evaluation protocol made of six downstream tasks to complement existing benchmarks. LeBenchmark 2.0 also presents unique perspectives on pre-trained SSL models for speech with the investigation of frozen versus fine-tuned downstream models, task-agnostic versus task-specific pre-trained models as well as a discussion on the carbon footprint of large-scale model training. Overall, the newly introduced models trained on 14,000 h of French speech outperform multilingual and previous LeBenchmark SSL models across the benchmark but also required up to four times more energy for pre-training.
Comparing translation accuracy in Belt and Road Malaysia children’s literature: Malay and Chinese native speakers vs ChatGPT

The study investigates the translation processes of human and artificial intelligence translators in comparison. Human translators consist of a Chinese native speaker and belt and road translators. Different versions of artificial intelligence translators comprise ChatGPT 3.5 and ChatGPT 4.0. The research methodology employed is a keyword detection technique. One human translator and one translator powered by artificial intelligence achieved the highest scores in keyword detection, according to the results. Human translators continue to be indispensable in the field of translation, particularly in the translation of literary works. However, the research group is optimistic that artificial intelligence will soon be able to resolve this issue.
Simulation of a physical reservoir made of a Ag<sub>2</sub>S islands network

Abstract Recently, a physical reservoir operation utilizing atomic switch technologies was demonstrated. Atomic switch operates by controlling the formation and annihilation of a metal filament between two electrodes using solid-state electrochemical reactions. In this study, we simulated the operation of an atomic switch-based reservoir by arranging modeled atomic switches in a network. The aim of this study is to confirm that nonlinear transformation and short-term memory in a reservoir operation observed in the experiment can be realized by the integration of atomic switches showing nonvolatile bipolar operation. We incorporated these characteristics by making a simple operating model of a single atomic switch, which successfully reproduced major characteristics of the experimental results of a reservoir operation.
Zip Codes with High Pediatric Asthma Hospital Encounters are Areas with Increased Social Needs – Lessons From Geo-Mapping Technology

Social determinants of health (SDoH) factors are associated with increased asthma risk. We aimed to utilize Geo-mapping techniques to identify hotspot zip codes in Detroit with the most pediatric hospital encounters for asthma and to explore the associated SDoH factors within these areas.
Gesture-Based Interactions: Integrating Accelerometer and Gyroscope Sensors in the Use of Mobile Apps

This study investigates the feasibility and functionality of accelerometer and gyroscope sensors for gesture-based interactions in mobile app user experience. The core of this innovative approach lies in introducing a dynamic and intuitive user interaction model with the device sensors. The Android app developed for this purpose has been created for its use in controlled experiments. Methodologically, it was created as a stand-alone tool to both capture quantitative (time, automatically captured) and qualitative (behavior, collected with post-task questionnaires) variables. The app’s setting features a set of modules with two levels each (randomized presentation applied, minimizing potential learning effects), allowing users to interact with both sensor-based and traditional touch-based scenarios. Preliminary results with 22 participants reveal that tasks involving sensor-based interactions tend to take longer to complete when compared to the traditional ones. Remarkably, many participants rated sensor-based interactions as a better option than touch-based interactions, as seen in the post-task questionnaires. This apparent discrepancy between objective completion times and subjective user perceptions requires a future in-depth exploration of factors influencing user experiences, including potential learning curves, cognitive load, and task complexity. This study contributes to the evolving landscape of mobile app user experience, emphasizing the benefits of considering the integration of device sensors (and gesture-based interactions) in common mobile usage.
Digitalization-growth nexus: some empirical evidence from four regions with a Muslim majority

Purpose- The purpose of this study is to investigate whether regional variations exist in the development of the digital economy and how they impact growth in the four global regions where Muslims predominate. Methodology- The study employs empirical examination of 48 countries with Muslim majority divided in four regions (Middle East and North Africa region, Europe and Eurasia region, South Asia, East Asia region and the Pacific and Africa region) between 2000 to 2021. The study employs Kao Residual Cointegration Test, and the Long-run Valuation of Fully Modified Ordinary Least Squares (FMOLS/DOLS-Dynamic). A certain number of specific variables in the econometric model will be employed to measure the level of digitization on economic growth, such as: Digital economy infrastructure proxied by Individuals using the internet, Digital economic openness proxied by ICT product exports and Digital technology competitiveness proxied by Research and Development as share of GDP. Findings- The analysis reveals that despite the fact that the digital economy made a positive contribution to economic growth in both Sub-Saharan Africa and Europe and Euroasia, the impact on these regions is less than that on the Middle East and North Africa, South Asia, East Asia, and the Pacific countries due to the underdeveloped infrastructure of the digital economy The least effect of digitalization on growth was found in Sub-Saharan African countries with low incomes.. Conclusion- Based upon the analysis, it may be concluded that digitalization can considerably boost economic growth, but its benefits may differ depending on how developed a nation is. There is a clear geographical imbalance in the development of the digital economy across 48 nations with a majority of Muslims .To increase overall GDP growth, those countries need to look into policies that will help increase ICT and the digital economy use. Keywords: Digital economy, economic growth, ICT, the Muslim Word, panel cointegration model. JEL Codes: O40, O47, E22.
USER MANAGEMENT SYSTEM USING ADMIN PANEL

This project aims at creating a simple user management system that is required by every website where multiple users can login. This let new user registration , login &amp; logout for every user. A User Management System implemented with JavaScript and PHP is a vital component of web applications that facilitates user registration, authentication, authorization, and profile management. This abstract outlines the key features and components of such a system, focusing on the synergy between JavaScript and PHP to deliver a secure, interactive, and efficient user experience. The system allows users to create accounts, log in securely, and manage their profiles while administrators can oversee and administer user accounts.Robust security measures, including data validation and protection against common web vulnerabilities, ensure the integrity of user data and the application's overall security. The system empowers web applications to efficiently manage and serve a diverse user base, ensuring a seamless and secure user experience.
Usability of blood morphology in diagnostics of ovarian cancer

Introduction: It has been reported that the levels of neutrophil to lymphocyte ratio (NLR) and platelet to lymphocyte ratio (PLR) might be correlated with ovarian cancer (OC) staging and useful in predicting OC histotype. Information on these parameters is usually obtained during routine diagnostics.
Improve exploration in deep reinforcement learning for UAV path planning using state and action entropy

Abstract Despite being a widely adopted development framework for unmanned aerial vehicle (UAV), deep reinforcement learning is often considered sample inefficient. Particularly, UAV struggles to fully explore the state and action space in environments with sparse rewards. While some exploration algorithms have been proposed to overcome the challenge of sparse rewards, they are not specifically tailored for UAV platform. Consequently, applying those algorithms to UAV path planning may lead to problems such as unstable training processes and neglect of action space comprehension, possibly causing negative impacts on the path planning results. To address the problem of sparse rewards in UAV path planning, we propose an information-theoretic exploration algorithm, Entropy Explorer (EE) , specifically for UAV platform. The proposed EE generates intrinsic rewards based on state entropy and action entropy to compensate for the scarcity of extrinsic rewards. To further improve sampling efficiency, a framework integrating EE and Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithms is proposed. Finally, the TD3-EE algorithm is tested in AirSim and compared against benchmarking algorithms. The simulation outcomes manifest that TD3-EE effectively stimulates the UAV to comprehensively explore both state and action spaces, thereby attaining superior performance compared to the benchmark algorithms in the realm of path planning.
Techniques Toward Quantum Computing System Scaling

It is expected that a useful quantum computing (QC) system would require millions of qubits within one system. The state-of-the-arts QC systems contain hundreds of qubits, which is still several orders of magnitude away from the ultimate goal. To scale the QC system, namely, to increase the number of qubits and QC circuit layers, and also to improve the performance such as fidelity, robustness, etc., we need to develop scalable techniques including high-density qubit devices, ultra-low power qubit interfacing circuits, 3D interconnection and packaging etc. This article will review the recent reported device, circuit and system-level techniques that contribute to the QC system scaling and may ultimately lead to a fully functional quantum computer.
How to Support ML End-User Programmers through a Conversational Agent

Machine Learning (ML) is increasingly gaining significance for enduser programmer (EUP) applications. However, machine learning end-user programmers (ML-EUPs) without the right background face a daunting learning curve and a heightened risk of mistakes and flaws in their models. In this work, we designed a conversational agent named "Newton" as an expert to support ML-EUPs. Newton's design was shaped by a comprehensive review of existing literature, from which we identified six primary challenges faced by ML-EUPs and five strategies to assist them. To evaluate the efficacy of Newton's design, we conducted a Wizard of Oz within-subjects study with 12 ML-EUPs. Our findings indicate that Newton effectively assisted ML-EUPs, addressing the challenges highlighted in the literature. We also proposed six design guidelines for future conversational agents, which can help other EUP applications and software engineering activities.
AIGC-Assisted Instructional Design for the Self-Regulated Learning Course on "Implementing 2D Animation for the Ziyun Temple Stories"

The instructional design effectively utilizes generative AI to assist in the self-regulated learning course "2D Animation Implementation of Chiayi Temple Stories". It adopts 2D animation and the PPVIO teaching model, leveraging generative AI to create 2D animation scripts and 2D animations. This enhances and sharpens students' storytelling, 2D animation production, and the ability to use generative AI. It also promotes social care, communication, collaboration, and practical application skills. Within the domain of animation graphics, tools like Midjourney and other image-generating AIs are used, with animations completed using the Cartoon Animator 5 software. In this teaching design, teachers play a guiding and mentoring role, leading students to ask more precise prompts to make good use of ChatGPT, and guiding students to overcome the setbacks of self-regulated learning. With the assistance of AI generation tools, students can not only achieve cross-disciplinary learning objectives but, more importantly, reduce the time needed to learn drawing tools. This allows students, even those not specializing in IT, to enhance their digital capabilities in a shorter period. Two suggestions are put forth. Firstly, in the creative section, although the use of AI generation tools is convenient, once technical barriers are removed, the educational content should elevate the level of students' professional creation, such as emphasizing artistic styles. Secondly, regarding self-regulated learning, self-regulated learning should not be pursued for its own sake, nor should learning be for the mere act of learning. It's crucial to stress self-monitoring, self-regulation, and self-discipline to truly grasp the essence of self-regulated learning.
A Comparative Analysis of Feature Selection Algorithms in Cross Domain Sentiment Classification

Background: Cross-domain Sentiment Classification is a well-researched field in sentiment analysis. The biggest challenge in CDSC arises from the differences in domains and features, which cause a decrease in model performance when applying source domain features to predict sentiment in the target domain. To address this challenge, several feature selection methods can be employed to identify the most relevant features for training and testing in CDSC. Method: The primary objective of this study is to perform a comparative analysis of different feature selection methods on the various CDSC tasks. In this study, statistical test-based feature selection methods using 18 classifiers for the CDSC task has been implemented. The impact of these feature selection methods on Amazon product reviews, specifically those in the DVD, Electronics, Kitchen, and TV domains, has been compared. Total 12x18 experiments were conducted for each feature selection method by varying source and target domain pairs from the Amazon product reviews dataset and by using 18 classifiers. Performance evaluation measures are accuracy and f-score. Results: From the experiments, it has been inferred that the CSDC task depends on various factors for a good performance, from the right domain selection to the right feature selection method. We have concluded that the best training dataset is Electronics as it gives more precise results while testing in either domain selected for our study. Conclusion: Cross-domain sentiment analysis is a dynamic and interdisciplinary field that offers valuable insights for understanding how sentiment varies across different domains.
Knowledge Graph Embedding via Triplet Component Interactions

Abstract In knowledge graph embedding, multidimensional representations of entities and relations are learned in vector space. Although distance-based graph embedding methods have shown promise in link prediction, they neglect context information among the triplet components, i.e., the head_entity, relation, and tail_entity, limiting their ability to describe multivariate relation patterns and mapping properties. Such context information denotes the entity structural association inside the same triplet and implies the correlation between entities that are not directly connected. In this work, we propose a novel knowledge graph embedding model that explicitly considers context information in graph e mbedding via t riplet c omponent i nteractions (TCIE). To build connections between components and incorporate contextual information, entities and relations are represented as vectors comprised of two specialized parts, enabling comprehensive interaction. By simultaneously interacting with one-hop related head and tail entities, TCIE strengthens the connections between distant entities and enables contextual information to be transmitted across the knowledge graph. Mathematical proofs and experiments are performed to analyse the modelling ability of TCIE in knowledge graph embedding. TCIE shows a strong capacity for modelling four relation patterns (i.e., symmetry, antisymmetry, inverse, and composition) and four mapping properties (i.e., one-to-one, one-to-many, many-to-one, and many-to-many). The experimental evaluation of ogbl-wikikg2, ogbl-biokg, FB15k, and FB15k-237 shows that TCIE achieves state-of-the-art results in link prediction.