Reviewerly/exHarmony huggingface dataset:
The Iowa Health Data Resource (IHDR): an innovative framework for transforming the clinical health data ecosystem

This manuscript will be of interest to most Clinical and Translational Science Awards (CTSA) as they retool for the increasing emphasis on translational science from translational research. This effort is an extension of the EDW4R work that most CTSAs have done to deploy infrastructure and tools for researchers to access clinical data.The Iowa Health Data Resource (IHDR) is a strategic investment made by the University of Iowa to improve access to real-world health data. The goals of IHDR are to improve the speed of translational health research, to boost interdisciplinary collaboration, and to improve literacy about health data. The first objective toward this larger goal was to address gaps in data access, data literacy, lack of computational environments for processing Personal Health Information (PHI) and the lack of processes and expertise for creating transformative datasets.A three-pronged approach was taken to address the objective. The approach involves integration of an intercollegiate team of non-informatics faculty and staff, a data enclave for secure patient data analyses, and novel comprehensive datasets.To date, all five of the health science colleges (dentistry, medicine, nursing, pharmacy, and public health) have had at least one staff and one faculty member complete the two-month experiential learning curriculum. Over the first two years of this project, nine cohorts totaling 36 data liaisons have been trained, including 18 faculty and 18 staff. IHDR data enclave eliminated the need to duplicate computational infrastructure inside the hospital firewall which reduced infrastructure, hardware and human resource costs while leveraging the existing expertise embedded in the university research computing team. The creation of a process to develop and implement transformative datasets has resulted in the creation of seven domain specific datasets to date.The combination of people, process, and technology facilitates collaboration and interdisciplinary research in a secure environment using curated data sets. While other organizations have implemented individual components to address EDW4R operational demands, the IHDR combines multiple resources into a novel, comprehensive ecosystem IHDR enables scientists to use analysis tools with electronic patient data to accelerate time to science.
Microsoft Word Training for Students of the Manggarai State 11Pusat Kegiatan Belajar Masyarakat South Jakarta

Current technological developments require expertise in managing good words for making job applications, school assignments, making learning materials and other needs that are beneficial to society. Pusat Kegiatan Belajar Masyarakat (PKBM) Negeri 11 Manggarai is one of the institutions that has services for information through education, one of which is Pursue Package C. The unavailability of computer laboratories is a limitation in improving skills in using computer applications, cooperation between PKBM Negeri 11 Manggarai and Universitas Budi Luhur The Faculty of Information Technology has jointly provided an increase in the ability to use the Microsoft Word application for PKBM Negeri 11 Manggarai Jakarta students. The training methods used are preparation, implementation and evaluation. The result of the training activity was an increase in the level of understanding of the 16 participants who were present, as many as 13 participants who already understood and could use the Microsoft Word application
How is the effect of Multiple Intelligences on Students’ Self-Confidence and English Daily Exam Scores?

The research aims to understand the impact of multiple intelligences toward students. Confidence and daily exam score of English Subject. This research was carried out at SMP Negeri 2 Abab, Penukal Abab Lematang Ilir Regency. This research used a quantitative approach. Data testing techniques were used in this research. 193 students were given a multiple intelligences test to find out what is their dominant intelligence and then used the data testing techniques to find out whether there was an impact or not toward the students’ confidence and daily exam score in English Subjects. The result of this research showed that (1) the student’s dominant intelligence did not significantly affect the English daily exam score. (2) some of the students’ dominant intelligence affected the students’ confidence in learning the English process, they were verbal intelligence, kinesthetic intelligence, interpersonal intelligence, intrapersonal intelligence, and naturalistic intelligence. So, it can be concluded that the students’ dominant intelligence didn’t have any impact on the student’s exam scores while some of the students’ dominant intelligence had an impact to the student’s self-confidence.
THE EXERCISE OF POLICE POWERS AND ENFORCEMENT OF INTERNATIONAL HUMAN RIGHTS NORMS IN NIGERIA: AN APPRAISAL

The police have the duty of prevention, investigation and prosecution of crime in the society. In the course of their duties, conflicts arise between the law officers and the civilians. In Nigeria, the police are known for the abuse of legal processes and the right of the citizens often culminating at excessive use of force, unlawful detention and in extreme cases which is rampant extra-judicial killing. This paper gave an exposition of some international legal instruments and standards applicable to law enforcement and how they help curtail abuse of the citizens’ right. The paper found that there is gross abuse of human rights in the course of the duty of the police in Nigeria. It was noted that the police have an obligation to observe, ensure compliance and implementation of these international human rights norms. It was recommended that there should be training and retraining of the police in Nigeria especially in forensic and technologically driven investigation and prevention of crime. Furthermore, corruption in the Nigerian police should be curbed by giving more incentives like salary increase to the police. The paper concluded that implementation of these recommendations will improve police human right observance in Nigeria.
Visualization of Radiation Scattering Indicatrix on a Polydisperse System of Spherical Particles

The article is devoted to solving the direct problem of light scattering on an ensemble of spherical polydisperse particles. The paper presents an algorithm for computer simulation of the optical radiation scattering indicatrix by a particle system based on the theory of G. Mie. The influence of the relative complex refractive index of scattering centers and their size distribution, wavelength and polarization state of incident light on computer models of scattering indicatrix is investigated. The practical significance of the results obtained lies in the fact that computer models of scattering indicatrix are useful in the experimental study of dispersed systems by static light scattering for the optimal choice of installation parameters and experimental conditions and, more importantly, in solving the inverse problem of the static light scattering method to restore the characteristics of nanoparticles from experimental data.
Conditional Matting For Post-Segmentation Refinement Segment Anything Model

Segment Anything Model (SAM) is a model capable of performing object segmentation in images without requiring any additional training. Although the segmentation produced by SAM lacks high precision, this model holds interesting potential for more accurate segmentation tasks. In this study, we propose a Post-Processing method called Conditional Matting 4 (CM4) to enhance high-precision object segmentation, including prominent, occluded, and complex boundary objects in the segmentation results from SAM. The proposed CM4 Post-Processing method incorporates the use of morphological operations, DistilBERT, InSPyReNet, Grounding DINO, and ViTMatte. We combine these methods to improve the object segmentation produced by SAM. Evaluation is conducted using metrics such as IoU, SAD, MAD, Grad, and Conn. The results of this study show that the proposed CM4 Post-Processing method successfully improves object segmentation with a SAD evaluation score of 20.42 (a 27% improvement from the previous study) and an MSE evaluation score of 21.64 (a 45% improvement from the previous study) compared to the previous research on the AIM-500 dataset. The significant improvement in evaluation scores demonstrates the enhanced capability of CM4 in achieving high precision and overcoming the limitations of the initial segmentation produced by SAM. The contribution of this research lies in the development of an effective CM4 Post-Processing method for enhancing object segmentation in images with high precision. This method holds potential for various computer vision applications that require accurate and detailed object segmentation.
Enhancing Augmentation-Based Resnet50 for Car Brand Classification

This research focuses on car classification and the use of the ResNet-50 neural network architecture to improve the accuracy and reliability of car detection systems. Indonesia, as one of the countries with high daily mobility, has a majority of the population using cars as the main mode of transportation. Along with the increasing use of cars in Indonesia, many automotive industries have built factories in this country, so the cars used are either local or imported. The importance of car classification in traffic management is a major concern, and vehicle make and model recognition plays an important role in traffic monitoring. This study uses the Vehicle images dataset which contains high-resolution images of cars taken from the highway with varying viewing angles and frame rates. This data is used to analyze the best- selling car brands and build car classifications based on output or categories that consumers are interested in. Digital image processing methods, machine learning, and artificial neural networks are used in the development of automatic and real-time car detection systems.The ResNet-50 architecture was chosen because of its ability to overcome performance degradation problems and study complex and abstract features from car images. Residual blocks in the ResNet architecture allow a direct flow of information from the input layer to the output layer, overcoming the performance degradation problem common in neural networks. In this paper, we explain the basic concepts of ResNet-50 in car detection and popular techniques such as optimization, augmentation, and learning rate to improve performance and accuracy. in this study, it is proved that ResNet has a fairly high accuracy of 95%, 92% precision, 93% recall, and 92% F1-Score.
SynTwin: A graph-based approach for predicting clinical outcomes using digital twins derived from synthetic patients

The concept of a digital twin came from the engineering, industrial, and manufacturing domains to create virtual objects or machines that could inform the design and development of real objects. This idea is appealing for precision medicine where digital twins of patients could help inform healthcare decisions. We have developed a methodology for generating and using digital twins for clinical outcome prediction. We introduce a new approach that combines synthetic data and network science to create digital twins (i.e. SynTwin) for precision medicine. First, our approach starts by estimating the distance between all subjects based on their available features. Second, the distances are used to construct a network with subjects as nodes and edges defining distance less than the percolation threshold. Third, communities or cliques of subjects are defined. Fourth, a large population of synthetic patients are generated using a synthetic data generation algorithm that models the correlation structure of the data to generate new patients. Fifth, digital twins are selected from the synthetic patient population that are within a given distance defining a subject community in the network. Finally, we compare and contrast community-based prediction of clinical endpoints using real subjects, digital twins, or both within and outside of the community. Key to this approach are the digital twins defined using patient similarity that represent hypothetical unobserved patients with patterns similar to nearby real patients as defined by network distance and community structure. We apply our SynTwin approach to predicting mortality in a population-based cancer registry (n=87,674) from the Surveillance, Epidemiology, and End Results (SEER) program from the National Cancer Institute (USA). Our results demonstrate that nearest network neighbor prediction of mortality in this study is significantly improved with digital twins (AUROC=0.864, 95% CI=0.857-0.872) over just using real data alone (AUROC=0.791, 95% CI=0.781-0.800). These results suggest a network-based digital twin strategy using synthetic patients may add value to precision medicine efforts.
MetaVG: A Meta-Learning Framework for Visual Grounding

Visual grounding aims at localizing objects in images using natural language expressions. This task can be challenging when there are significant differences between the distributions of the training and testing sets. Existing methods tend to excessively focus on the training sets, which could lead to overfitting, especially in small-sample scenarios. To address this issue, in this letter, we present a novel meta-learning-based training framework called MetaVG, for visual grounding. Our approach leverages bi-level optimization to adapt quickly to the target task, thereby alleviating the overfitting issue. To train MetaVG effectively, we propose a novel training mechanism called Random Uncorrelated Meta-training (RUM). This mecha- nism proposes to randomly load uncorrelated batches as support and query sets respectively in the data separation process, then utilize bi-level optimization to directly train the model on visual grounding datasets. Comprehensive experiments on four widely used datasets, as well as in small-sample scenarios, validate the efficacy of MetaVG.
Efficiency Analysis of Brute Force and Knuth Morris Pratt Algorithms for Indonesian Keyword Search on KBBI

Keyword search in Indonesian in the Big Indonesian Dictionary (KBBI) is an important problem in the field of information technology, because KBBI is the main reference source in determining the meaning and significance of words in Indonesian. In the keyword search process, there are several algorithms that can be used, one of which is the Brute Force and Knuth Morris Pratt (KMP) algorithms. In this research, we will analyze the efficiency of both algorithms in searching keywords in KBBI. The Brute Force algorithm will compare each character of the keyword with each character in each word in the KBBI. While the KMP algorithm will use a table to match each character in the keyword with each character in each word in the KBBI. The method used for testing the algorithm is to use the KBBI word data set. This test will analyze the running time, memory usage, precision, accuracy, and keyword related of the two algorithms in completing the keyword search in KBBI. The result of this research is a comparison of the complexity and speed of the Knuth Morris Pratt and Brute Force algorithms. The outcomes demonstrate that while searching for keywords in the KBBI, the Brute Force approach is more effective than the KMP algorithm. The Brute Force algorithm has faster execution time, accuracy, and precision compared to the KMP algorithm, the KMP algorithm can be seeded by using less memory than the Brute Force algorithm. So it can be concluded that the Brute Force algorithm is more efficient in searching keywords in the KBBI compared to the KMP algorithm. As a result, the Brute Force algorithm can be utilized as a substitute to quicken the KBBI's keyword search process.
Improving the Current System of Online Sellers by Creating Database System Using MS Access

The Age of Technology, the Digital Age, has been transforming sales trends and processes. As more companies start incorporating technology into their business strategies, the competition gets tougher and more demanding. Using outdated technology has been a problem for many small business owners, as it hurts overall business performance. The researchers have found one seller who would still use an outdated system by manually sending out forms and updating her database, which could cost her potential clients and sales. This paper then aims to solve that problem by analyzing information on that system and determining its waste. The researchers expect an increase in efficiency, as they use MS Access to create a form and a database for the online seller, aside from adding a new feature that will allow customer queries. The researchers have found that implementing the MS Access database in the system of individual online sellers increases their efficiency and a lesser chance of making a mistake.
A 28nm 49.7TOPS/W Sparse Transformer Processor with Random-Projection-Based Speculation, Multi-Stationary Dataflow, and Redundant Partial Product Elimination

Transformer models have shown remarkable performance in various fields with significant accuracy improvement compared to traditional artificial intelligence models [1], [2]. However, the high computational and memory complexity of Transformer models limits their deployment on power-constrained edge devices. Dynamic sparse attention (DSA) is a feasible method to improve throughput and energy efficiency by predicting significant attention computation during inference and then skipping insignificant ones [3], [4]. Although a high sparsity ratio has the potential for significant energy savings, realizing high energy efficiency via DSA is challenging due to three factors, as shown in Fig. 1. Firstly, traditional processors apply single-stage sparsity speculation method, which incurs low speculation benefits in DSA due to a dilemma: the sparsity speculation of attention relies on computing the entire QK matrix [3], which already accounts for 30-48.9% of attention block computation. Secondly, DSA introduces dynamic sparse data distribution and matrix sizes for the input/weight/output (IWO), resulting in redundant memory access when computing with traditional single X-stationary dataflow (X stands for I/W/O) [4]–[6]. Each of the IWO matrices can be the ideal stationary object to achieve minimum memory access. Finally, the attention scores computed after DSA speculation usually have similar significance, involving several identical partial products and sums (PP and PS), which have no contribution since adding the same value to each input of softmax does not affect its outputs.
Identifying and Addressing Problems in the Estimation Process: a Case Study Applying Action Research

Literature shows that a large part of software projects exceeds the amount of effort and estimation duration, even though we currently witness an evolution of software project management discipline. Through its best practices, software engineering tries to reduce the flaws in software development. Several techniques and resources have been presented to help to reduce this problem. This paper aims to propose an approach based on action research to improve the estimation process in software development tasks by identifying problems. A case study is carried out to show the effectiveness of our approach. The results show an improvement of 50% accuracy over the baseline estimation process.
Performance Review of Popular Classification Models

&lt;p&gt;In this paper, we examine and compare the performance of three popular models used for classi▯cation: logistic regression, neural networks and XGBoost. To quantify performance using a variety of metrics, we fit and test the models on credit loan data provided to the public by Lending Club. We ▯nd that the XGBoost algorithm is the clear winner as it performs best even with minimal hyperparameter tuning.&lt;/p&gt;
Additivity of states uniquely determined by marginals

The pure states that can be uniquely determined among all (UDA) states by their marginals are essential to efficient quantum state tomography. We generalize the UDA states from the context of pure states to that of arbitrary (whether pure or mixed) states, motivated by the efficient state tomography of low-rank states. The concept of additivity of $k$-UDA states for three different composite types of tensor product applies if the composite state of two $k$-UDA states is still uniquely determined by the $k$-partite marginals for the corresponding type of tensor product. We show that the additivity holds if one of the two initial states is pure and present the conditions under which the additivity holds for two mixed UDA states. One of the three composite types of tensor product is also adopted to construct genuinely multipartite entangled (GME) states. Therefore, it is effective to construct multipartite $k$-UDA states with genuine entanglement by uniting the additivity of $k$-UDA states and the construction of GME states.
Chinese Language Resources: A Comprehensive Compendium

This chapter will present a collective effort to compile a comprehensive repository of accessible Chinese language resources that can be used online, licensed for use, or accessed in published form. The compendium will be presented in three parts according to each language resource’s type of accessibility, which is a direct consequence of the type of relevant information provided for each resource. Within each accessibility type, the resources were then further divided according to the following resource types: integrated resources, corpora, lexical resources, and wordnet/ontology. We believe that this four-way classification system will facilitate intuitive searches. However, this design will make it difficult to search for a resource within the same class due to having to rely on the alphabetic order of the titles of the resources. Lastly, it is important for our readers to bear in mind that such a repository is bound to be incomplete given the scale and distributional nature of the resources and the productivity of new resource construction. We plan to post this compendium online to allow easier access and provide updates in the future.
DEVELOPMENT OF STUDENTS’ RESEARCH COMPETENCE DURING BUSINESS FOREIGN LANGUAGE (ENGLISH) COURSE

The article deals with the concept of “research competence” and approaches to its development in higher education students during Business foreign language (English) course. The forms of work and conditions for the competence formation have been outlined, recommendations for the development of research competence during Business foreign language (English) course have been presented.&#x0D; Keywords: research competenc; business foreign language; creating a favourable environment; pedagogical conditions; forms and methods of teaching; pedagogical diagnostics; systematic approach; performing professional tasks.