Reviewerly/exHarmony huggingface dataset:
Creativity and Cognitive Niche Construction in Salvatore Sciarrino’s L’opera per flauto

The emergence of new musical ideas, and new forms of musicality, is described here as the transformation of conceptual spaces in a cognitive niche, a distributed and self-organised set of intergenerational concepts, materials, and actions. According to this approach, there is a direct co-dependence relation, a co-evolutionary causal looping, between new, acceptable (valid) and surprising musical ideas (which define what is creative in a certain context), agents, tools and artifacts (instruments, musical notations, types of equipment, performance space), and conceivable epistemic and pragmatic actions (instrument techniques, dynamics of collaboration, scenic procedures). This co-evolutionary relation defines a conceptual space of musicality. To demonstrate our thesis, we detail the development of extended techniques for transverse flute, a phenomenon strongly dependent on performer-composer collaboration. We focus on Salvatore Sciarrino’s L’opera per flauto (1990), which was developed in an intensive work with the flautist Roberto Fabbriciani, through investigation of sounding possibilities of the instrument. The collaboration resulted in new and surprising sounds for the transverse flute, including the combination of extended techniques, and their notation, largely applied by later repertoire.
Computer aided aircraft design and simulation for air logistics based on ant colony algorithm

This paper tests the lightweight UAV through physical experiments and proves that its reduced mass can still meet the strength requirements; the feasibility of the improved ant colony algorithm in UAV path planning design is demonstrated through simulation of the path. This paper investigates the UAV in terms of both mechanical structure and path planning, which is greatly in line with its application in the field of logistics and transportation, and can provide reference value for its application in the present society as well as in more in-depth research in the future.
Research on the Construction of Knowledge Graph Model of Heterogeneous Data from Multi-Source Computer

This paper proposes a software knowledge graph model with code structure as the core. A two-layer software knowledge graph framework of knowledge extraction and knowledge fusion is established. Firstly, the self-attention mechanism and feedforward neural network are used to obtain the learner representation with time characteristics. Then, the knowledge points are represented deeply according to the higher-order connectivity characteristics of knowledge points and knowledge points in the triplet of knowledge graph and the characteristics of learners. Finally, the interaction probability between learners and knowledge points is calculated and the recommendation is made according to the probability. This framework supports automatic construction of knowledge graph of software project and intelligent question answering of software project based on knowledge graph. The system effectively improves the efficiency of software project understanding and software reuse.
Implementation of the Learner Centered Design method and the personality approach (Case Study: Redesigning The Interface mobile LMS Tel-U)

The Mobile LMS Tel-U is an e-learning platform developed by Telkom University to support the learning process. However, it still requires more demand from Telkom University students. Usability evaluation was conducted on twelve students using the System Usability Scale (SUS), resulting in a score of 46.5. Interviews and observations revealed interface problems on the dashboard, material, grades, and quizzes. This study aims to redesign the mobile LMS Tel-U interface using the Learner-Centered Design method and incorporating a personality approach by categorizing students into introverts and extroverts. Designing based on personality groups acknowledges the differences in interface design preferences and the relationship between personality and e-learning interface design. This approach yields two different interface designs, one for introverted students and one for extroverted students. The LCD method determines student needs in supporting the learning process. The redesigned interface's usability was evaluated using SUS to assess its appropriateness for students' learning needs. The study shows an average increase in usability scores of 80.4. The introverted student group achieved a usability score of 81, while the extroverted student group obtained a score of 80. Thus, the LCD method and personality approach effectively enhance the usability of distance learning applications (e-learning).
A solar tracking system with light-dependent resistors and a stepper motor controlled by a microcontroller

Solar energy is a clean and simple method of electricity generation. This electricity is utilized for various purposes, ranging from modest electrical gadgets and cars to major enterprises, satellites, and space exploration. The title of this project, "Solar tracking system with light-dependent resistors and a stepper motor controlled by a microcontroller" alludes to the project’s purpose, which is to harvest as much solar energy as possible from a solar panel throughout the day using a specific Sun tracking mechanism. To do this, a mechanism was developed that tracks the sun’s position and rotates the solar panel correspondingly so that it receives maximum sunlight at all times of the day. This is accomplished by mounting a stepper motor and a couple of light-dependent resistors (LDRs) on the solar panel, which adjusts its orientation based on the location of the sun. This project has three primary components: the solar panel, the stepper motor, and the LDRs. Microcontroller, sensor, and stepper motor. For this aim, an Arduino UNO microcontroller has been utilized. It accepts an output signal from a sensor as input, processes it, and controls stepper motors based on the supplied program. A stepper motor is attached to the shaft on which a panel is positioned to raise and lower the panel. Since maximal solar rays always strike the surface of the solar panel module, it is possible to obtain maximum power output, increasing its total efficiency as compared to the static panel.
Can Empowering Intelligent Technology Improve Learning Outcomes?

The rise of artificial intelligence (AI) globally has driven educational reform. However, the impact of intelligent technology education on student learning is not yet fully understood. Therefore, this article selected 27 domestic and foreign literature for meta-analysis with a total sample size of 2383. Through in-depth analysis of moderating variables, it was found that intelligent technology has a significant improvement on learning outcomes overall. However, there are significant differences due to the different moderating variables. Based on the research results, the article concludes with practical recommendations on how to better utilize intelligent technology in learning.
Evolving Domain Generalization via Latent Structure-Aware Sequential Autoencoder

Domain generalization (DG) refers to the problem of generalizing machine learning systems to out-of-distribution (OOD) data with knowledge learned from several provided source domains. Most prior works confine themselves to stationary and discrete environments to tackle such generalization issue arising from OOD data. However, in practice, many tasks in non-stationary environments (e.g., autonomous-driving car system, sensor measurement) involve more complex and continuously evolving domain drift, emerging new challenges for model deployment. In this paper, we firstly formulate this setting as the problem of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">evolving domain generalization</i> . To deal with the continuously changing domains, we propose MMD-LSAE, a novel framework that learns to capture the evolving patterns among domains for better generalization. Specifically, MMD-LSAE characterizes OOD data in non-stationary environments with two types of distribution shifts: <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">covariate shift</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">concept shift</i> , and employs deep autoencoder modules to infer their dynamics in latent space separately. In these modules, the inferred posterior distributions of latent codes are optimized to align with their corresponding prior distributions via minimizing maximum mean discrepancy (MMD). We theoretically verify that MMD-LSAE has the inherent capability to implicitly facilitate mutual information maximization, which can promote superior representation learning and improved generalization of the model. Furthermore, the experimental results on both synthetic and real-world datasets show that our proposed approach can consistently achieve favorable performance based on the evolving domain generalization setting.
Review: Machine learning in precision pharmacotherapy of type 2 diabetes—A promising future or a glimpse of hope?

Precision pharmacotherapy of diabetes requires judicious selection of the optimal therapeutic agent for individual patients. Artificial intelligence (AI), a swiftly expanding discipline, holds substantial potential to transform current practices in diabetes diagnosis and management. This manuscript provides a comprehensive review of contemporary research investigating drug responses in patient subgroups, stratified via either supervised or unsupervised machine learning approaches. The prevalent algorithmic workflow for investigating drug responses using machine learning involves cohort selection, data processing, predictor selection, development and validation of machine learning methods, subgroup allocation, and subsequent analysis of drug response. Despite the promising feature, current research does not yet provide sufficient evidence to implement machine learning algorithms into routine clinical practice, due to a lack of simplicity, validation, or demonstrated efficacy. Nevertheless, we anticipate that the evolving evidence base will increasingly substantiate the role of machine learning in molding precision pharmacotherapy for diabetes.
Whole Slide Multiple Instance Learning for Predicting Axillary Lymph Node Metastasis

Breast cancer is a major concern for women’s health globally, with axillary lymph node (ALN) metastasis identification being critical for prognosis evaluation and treatment guidance. This paper presents a deep learning (DL) classification pipeline for quantifying clinical information from digital core-needle biopsy (CNB) images, with one step less than existing methods. A publicly available dataset of 1058 patients was used to evaluate the performance of different baseline state-of-the-art (SOTA) DL models in classifying ALN metastatic status based on CNB images. An extensive ablation study of various data augmentation techniques was also conducted. Finally, the manual tumor segmentation and annotation step performed by the pathologists was assessed. Our proposed training scheme outperformed SOTA by 3.73%. Source code is available here .
A Sheaf Theoretic Perspective for Robust Prostate Segmentation

Deep learning based methods have become the most popular approach for prostate segmentation in MRI. However, domain variations due to the complex acquisition process result in textural differences as well as imaging artefacts which significantly affects the robustness of deep learning models for prostate segmentation across multiple sites. We tackle this problem by using multiple MRI sequences to learn a set of low dimensional shape components whose combinatorially large learnt composition is capable of accounting for the entire distribution of segmentation outputs. We draw on the language of cellular sheaf theory to model compositionality driven by local and global topological correctness. In our experiments, our method significantly improves the domain generalisability of anatomical and tumour segmentation of the prostate. Code is available at https://github.com/AinkaranSanthi/A-Sheaf-Theoretic-Perspective-for-Robust-Segmentation.git .
Collaborative Modality Generation and Tissue Segmentation for Early-Developing Macaque Brain MR Images

In neuroscience research, automatic segmentation of macaque brain tissues in magnetic resonance imaging (MRI) is crucial for understanding brain structure and function during development and evolution. Acquisition of multimodal information is a key enabler of accurate tissue segmentation, especially in early-developing macaques with extremely low contrast and dynamic myelination. However, many MRI scans of early-developing macaques are acquired only in a single modality. While various generative adversarial networks (GAN) have been developed to impute missing modality data, current solutions treat modality generation and image segmentation as two independent tasks, neglecting their inherent relationship and mutual benefits. To address these issues, this study proposes a novel Collaborative Segmentation-Generation Framework (CSGF) that enables joint missing modality generation and tissue segmentation of macaque brain MR images. Specifically, the CSGF consists of a modality generation module (MGM) and a tissue segmentation module (TSM) that are trained jointly by a cross-module feature sharing (CFS) and transferring generated modality. The training of the MGM under the supervision of the TSM enforces anatomical feature consistency, while the TSM learns multi-modality information related to anatomical structures from both real and synthetic multi-modality MR images. Experiments show that the CSGF outperforms the conventional independent-task mode on an early-developing macaque MRI dataset with 155 scans, achieving superior quality in both missing modality generation and tissue segmentation.
Developing Large Pre-trained Model for Breast Tumor Segmentation from Ultrasound Images

Early detection and diagnosis of breast cancer using ultrasound images are crucial for timely diagnostic decision and treatment in clinical application. However, the similarity between tumors and background and also severe shadow noises in ultrasound images make accurate segmentation of breast tumor challenging. In this paper, we propose a large pre-trained model for breast tumor segmentation, with robust performance when applied to new datasets. Specifically, our model is built upon UNet backbone with deep supervision for each stage of the decoder. Besides using Dice score, we also design discriminator-based loss on each stage of the decoder to penalize the distribution dissimilarity from multi-scales. Our proposed model is validated on a large clinical dataset with more than 10000 cases, and shows significant improvement than other representative models. Besides, we apply our large pretrained model to two public datasets without fine tuning, and obtain extremely good results. This indicates great generalizability of our large pre-trained model, as well as robustness to multi-site data. The code is publicly available at https://github.com/limy-ulab/US-SEG .
Alias-Free Co-modulated Network for Cross-Modality Synthesis and Super-Resolution of MR Images

Cross-modality synthesis (CMS) and super-resolution (SR) have both been extensively studied with learning-based methods, which aim to synthesize desired modality images and reduce slice thickness for magnetic resonance imaging (MRI), respectively. It is also desirable to build a network for simultaneous cross-modality and super-resolution (CMSR) so as to further bridge the gap between clinical scenarios and research studies. However, these works are limited to specific fields. None of them can flexibly adapt to various combinations of resolution and modality, and perform CMS, SR, and CMSR with a single network. Moreover, alias frequencies are often treated carelessly in these works, leading to inferior detail-restoration ability. In this paper, we propose Alias-Free Co-Modulated network (AFCM) to accomplish all the tasks with a single network design. To this end, we propose to perform CMS and SR consistently with co-modulation, which also provides the flexibility to reduce slice thickness to various, non-integer values for SR. Furthermore, the network is redesigned to be alias-free under the Shannon-Nyquist signal processing framework, ensuring efficient suppression of alias frequencies. Experiments on three datasets demonstrate that AFCM outperforms the alternatives in CMS, SR, and CMSR of MR images. Our codes are available at https://github.com/zhiyuns/AFCM .
Topology-Preserving Automatic Labeling of Coronary Arteries via Anatomy-Aware Connection Classifier

Automatic labeling of coronary arteries is an essential task in the practical diagnosis process of cardiovascular diseases. For experienced radiologists, the anatomically predetermined connections are important for labeling the artery segments accurately, while this prior knowledge is barely explored in previous studies. In this paper, we present a new framework called TopoLab which incorporates the anatomical connections into the network design explicitly. Specifically, the strategies of intra-segment feature aggregation and inter-segment feature interaction are introduced for hierarchical segment feature extraction. Moreover, we propose the anatomy-aware connection classifier to enable classification for each connected segment pair, which effectively exploits the prior topology among the arteries with different categories. To validate the effectiveness of our method, we contribute high-quality annotations of artery labeling to the public orCaScore dataset. The experimental results on both the orCaScore dataset and an in-house dataset show that our TopoLab has achieved state-of-the-art performance.
CellGAN: Conditional Cervical Cell Synthesis for Augmenting Cytopathological Image Classification

Automatic examination of thin-prep cytologic test (TCT) slides can assist pathologists in finding cervical abnormality for accurate and efficient cancer screening. Current solutions mostly need to localize suspicious cells and classify abnormality based on local patches, concerning the fact that whole slide images of TCT are extremely large. It thus requires many annotations of normal and abnormal cervical cells, to supervise the training of the patch-level classifier for promising performance. In this paper, we propose CellGAN to synthesize cytopathological images of various cervical cell types for augmenting patch-level cell classification. Built upon a lightweight backbone, CellGAN is equipped with a non-linear class mapping network to effectively incorporate cell type information into image generation. We also propose the Skip-layer Global Context module to model the complex spatial relationship of the cells, and attain high fidelity of the synthesized images through adversarial learning. Our experiments demonstrate that CellGAN can produce visually plausible TCT cytopathological images for different cell types. We also validate the effectiveness of using CellGAN to greatly augment patch-level cell classification performance. Our code and model checkpoint are available at https://github.com/ZhenrongShen/CellGAN .
PERANCANGAN SISTEM INFORMASI LAYANAN GADAI EMAS MENGGUNAKAN MICROSOFT VISUAL STUDIO PADA BANK SYARIAH INDONESIA KC CIMAHI

Bank Syariah Indonesia is a bank that emerged from a merger between PT Bank BRI Syariah Tbk, PT Bank Syariah Mandiri, and PT Bank BNI Syariah. The consolidation between the three Islamic banks resulted in more complete services, greater coverage, and more optimal funding capacity. One of the services from BSI is the Gold Pawn Service, which is a lending product based on gold as collateral as a practical way to obtain cash. The purpose of the research that designed the Gold Pawn Service information system was to facilitate recording and reporting of all activities in the Gold Pawn Service. Technology in designing the Gold Pawn Service information system using Microsoft Visual Studio with Microsoft Access as database. This research resulted in the design of a Gold Pawn Service financing simulation information system that features news information and knowledge about calculations and explanations of gold pawn service financing products at Bank Syariah Indonesia at Cimahi Branch Office.
The Huff curve–ElGamal graphic public key cryptosystem

A new graph for the cryptographic application is presented in this work. This graph is defined using the Huff curve over a prime field. The Huffs curve graph (HCg) considered as an alternative graph of the Edwards curve graph (EdCG).The HCg is used to give another modified Version Public-key cryptosystem ElGamal (EPKC) the proposed HC-ElGamal graphic cryptosystem enhances the secure communication in compare with original EPKC.
Blockchain Technology for Tourism Post COVID-19

During the pandemic, the tourism industry was one of the most severely impacted sectors. As vaccines are now widely available, each government is working to develop a system that can generate a digital vaccine certificate and PCR lab test result to verify that a person has been fully vaccinated or has a negative PCR test result, in order to allow them to enter business premises, travel overseas or cross state borders. However, the use of centralised systems in the development of the digital COVID-19 pass system results in a number of challenges, including the system’s high susceptibility to failures, sluggish and inefficient information transmission, and vulnerability. The goal of this research is to offer a new digital COVID-19 pass based on the proposed “SmartHealthCard” blockchain technology. SmartHealthCard is a decentralised application (dApp) encrypting and hashing user data and safely storing it in a distributed database. Privacy preservation, GDPR compliance, self-sovereignty, KYC compliance and data integrity are featured. This initiative has the potential to benefit the public, healthcare professionals, service providers and the government. SmartHealthCard enables quick verification of tamper-proof COVID-19 tests/vaccines, aiding in COVID-19 transmission control while respecting the user’s right to privacy.
Solving a new NP-Complete problem that resembles image pattern recognition using deep learning

In computer science, non-deterministic polynomialtime&#x0D; (NP) denotes the set of problems for which a&#x0D; solution might or might not be found quickly but can&#x0D; be checked quickly. We also call the set of problems&#x0D; in NP that are at least as hard as any other NP problem&#x0D; non-deterministic polynomial-time complete (NPcomplete).&#x0D; Although not known if they can be solved&#x0D; quickly, NP-complete problems can be converted&#x0D; into one another quickly. A prominent and practical&#x0D; example is the protein folding problem, which is a&#x0D; problem of determining the shape and function of&#x0D; proteins. In this paper, we discovered and provided&#x0D; a mathematical proof of a new NP-complete problem,&#x0D; that we named 3-Grid Pattern Decision Problem (3-&#x0D; GPDP). 3-GPDP is a decision problem that asks if a&#x0D; specific kind of pattern exists or not on a grid/matrix&#x0D; of numbers consisting of zeros and ones, much like&#x0D; image recognition. We hypothesized that a neural net&#x0D; would be able to solve 3-GPDP problems with high&#x0D; accuracy and low loss when trained. In support of&#x0D; our hypothesis, our experiment resulted in a neural&#x0D; net that performed with 84% accuracy and 0.14 loss&#x0D; without underfitting or overfitting. Moreover, there&#x0D; was also an increase in accuracy and decrease in&#x0D; loss with more training data. As a result, since neural&#x0D; networks are effective at solving 3-GPDP problems,&#x0D; which are NP-complete, then we can convert important&#x0D; NP-complete problems into 3-GPDP problems, solve&#x0D; the 3-GPDP problems using neural networks, and&#x0D; convert the solution back to the solution of the initial&#x0D; problem, quickly.