Reviewerly/exHarmony huggingface dataset:
Broken Stick Model for Irregular Longitudinal Data

Many longitudinal studies collect data that have irregular observation times, often requiring the application of linear mixed models with time-varying outcomes. This paper presents an alternative that splits the quantitative analysis into two steps. The first step converts irregularly observed data into a set of repeated measures through the broken stick model. The second step estimates the parameters of scientific interest from the repeated measurements at the subject level. The broken stick model approximates each subject's trajectory by a series of connected straight lines. The breakpoints, specified by the user, divide the time axis into consecutive intervals common to all subjects. Specification of the model requires just three variables: time, measurement and subject. The model is a special case of the linear mixed model, with time as a linear B-spline and subject as the grouping factor. The main assumptions are: Subjects are exchangeable, trajectories between consecutive breakpoints are straight, random effects follow a multivariate normal distribution, and unobserved data are missing at random. The R package brokenstick v2.5.0 offers tools to calculate, predict, impute and visualize broken stick estimates. The package supports two optimization methods, including options to constrain the variance-covariance matrix of the random effects. We demonstrate six applications of the model: Detection of critical periods, estimation of the time-to-time correlations, profile analysis, curve interpolation, multiple imputation and personalized prediction of future outcomes by curve matching.
GRAPHIC DESIGN IN THE PROFESSIONAL TRAINING OF FUTURE SPECIALISTS

The attendance of a slowdown in the level of experiences and skills formed in grads from the actual practical expert context leads to the necessity to revise and transform the scholarly complex. Modern professional preparation of future specialists is characterized by the renewal of the methodological material and innovative technology and software tools, advanced technologies, methods, and forms of education. An essential element of educational innovations is the revision of professional competencies and a change in their content component. In this paper, on the example of training future specialists, the intensification of educational and methodological support and the formation of a graphic vector to develop acknowledged competencies are considered. A unique role is given to the development of visual competence as a response to the current pace of improvement of software and hardware for vocational training, the introduction of new information and communication training tools, and the professional sphere's needs in highly qualified specialists. The article examines the features of the use of traditional graphic tools and computer technologies at different stages of the project activities of future specialists. The analysis of thought processes accompanying tutorial project activities is given. The advantages of using computer technologies in the educational process at the final stages of design are shown.
Agent bankers and customer victimization in Ado City, Nigeria

Criminal victimization against agent bankers and their customers has recently been on the increase, and the multiplier effect is a major socio-economic problem that could cause a downturn in the economy. This study examines the exposure of agent bankers and their customers to criminal victimization in Ado city, Nigeria. Specifically, the study explores the forms of criminal victimization against agent bankers and their customers, examines the consequences of criminal victimization, looks into the cases of agent bankers and customer victimization, and determines the preventive measures adopted by agent bankers and their customers against victimization. Lifestyle-routine activity theory was deployed as the conceptual framework. An exploratory research design and snowball and purposive sampling with key informant and in-depth interviews were used to interview 12 victimized and non-victimized agent bankers and their customers. Interviews were conducted in Fayose market, King market, Bisi market, and Irona market. Findings reveal that agents’ bankers and their customers were being targeted by criminals who use non-violent tactics. The eagerness of the agent bankers to transact business brings a measure of carelessness to their business dealings. This carelessness and lack of security measures exposes them to motivated offenders. Victimization through fake alert, fraudulent transfer, withdrawal under false pretense, fraudulent alteration, intentional criminal patronage, fake identity, and urgent withdrawal under duress represent the themes found. It is imperative for agent bankers and their customers to set up security measures that could protect them against being victimized.
Resilient Supply Chains to Improve the Integrity of Accounting Data in Financial Institutions Worldwide Using Blockchain Technology

Accounting information systems (AIS) gather, store, and analyze data in providing information to business leaders. Information technology resources and a computer-based accounting system are often used to monitor accounting activities in an accounting information system. Supply chain management strategies, planning, and implementation are increasingly dependent on the expertise of accountants with globalization. The accountant's job is to assist the supply chain design, development, and implementation group. Top management commitment, the kind of accounting information systems used, and input controls are all factors that affect accounting information systems' data quality. They can trace any form of theft or misappropriation using the blockchain (BC), which maintains asset transfers. In hopes of avoiding fraud, agreements loaded with economics and finance principles might be used to govern corporate operations. Using internet of things (IoT) data, banks can better understand their customers' business demands and value chain.
Learning a Practical SDR-to-HDRTV Up-conversion using New Dataset and Degradation Models

In media industry, the demand of SDR-to-HDRTV up-conversion arises when users possess HDR-WCG (high dynamic range-wide color gamut) TVs while most off-the-shelf footage is still in SDR (standard dynamic range). The research community has started tackling this low-level vision task by learning-based approaches. When applied to real SDR, yet, current methods tend to produce dim and desaturated result, making nearly no improvement on viewing experience. Different from other network-oriented methods, we attribute such deficiency to training set (HDR-SDR pair). Consequently, we propose new HDRTV dataset (dubbed HDRTV4K) and new HDR-to-SDR degradation models. Then, it's used to train a luminance-segmented network (LSN) consisting of a global mapping trunk, and two Transformer branches on bright and dark luminance range. We also update assessment criteria by tailored metrics and subjective experiment. Finally, ablation studies are conducted to prove the effectiveness. Our work is available at: https://github.com/AndreGuo/HDRTVDM.
Fault-tolerant quantum algorithm for dual-threshold image segmentation

Abstract The intrinsic high parallelism and entanglement characteristics of quantum computing have made quantum image processing techniques a focus of great interest. One of the most widely used techniques in image processing is segmentation, which in one of their most basic forms can be carried out using thresholding algorithms. In this paper, a fault-tolerant quantum dual-threshold algorithm has been proposed. This algorithm has been built using only Clifford+ T gates for compatibility with error detection and correction codes. Because fault-tolerant implementation of T gates has a much higher cost than other quantum gates, our focus has been on reducing the number of these gates. This has allowed adding noise tolerance, computational cost reduction, and fault tolerance to the state-of-the-art dual-threshold segmentation circuits. Since the dual-threshold image segmentation involves the comparison operation, as part of this work we have implemented two full comparator circuits. These circuits optimize the metrics T -count and T -depth with respect to the best circuit comparators currently available in the literature.
Harnessing Deep Learning for Efficient and Responsible AI Code Assistants: A Comprehensive Study of Methods, Evaluation, and Human Interaction

The increasing complexity of software development has led to a growing need for intelligent code assistance tools that can aid developers in various tasks, such as code generation, translation, and repair. Deep learning has emerged as a promising approach to addressing this need. This paper presents a comprehensive study of deep learning methods, evaluation metrics, and human interaction techniques for AI code assistants, aiming to provide a solid foundation for researchers and practitioners in the field. We discuss the importance of responsible AI in the context of code assistance, focusing on fairness, security, robustness, and privacy. Furthermore, we highlight the significance of open science practices in promoting reproducibility and transparency in the development of deep learning models for code. Finally, we outline potential future research directions in the area of deep learning for code assistance.
Uncalibrated Adaptive Visual Servoing of Robotic Manipulators with Uncertainties in Kinematics and Dynamics

In the study, we propose a novel adaptive visual servoing control scheme for robotic manipulators with kinematic and dynamic uncertainties, where the camera used is uncalibrated, which implies that its intrinsic and extrinsic parameters are unavailable for measurement. For our scheme, a depth-independent composite Jacobian matrix is constructed to make visual parameters and robotic physical parameters appear linearly in a parametrized uniform form so that an adaptive algorithm can be developed to estimate their values. With the raised adaptive algorithm, the potential singularity of the Jacobian matrix can be well circumvented by updating estimated parameters in an appropriate tiny range of actual values. With our scheme, the asymptotic convergence of the image tracking error to zero is established successfully, in addition to the signal boundedness of the closed-loop system. The effectiveness of the proposed scheme is confirmed by simulation results based on a 6-DOF PUMA manipulator.
A note on national leadership and technology in moderating finance-growth nexus

This paper seeks to determine the factors energising economic growth using balanced panel data between 1997 and 2017 from 44 developing economies. The heterogeneous panel data estimator, namely the pool mean group (PMG) estimator is utilised to analyse the data. We find national leadership, telecommunication technology and finance significantly impact growth. Additionally, the effects of finance and telecommunication on growth are both contingents upon exemplary leadership.
The use of measurement frameworks to explore the qualities of test items

&lt;span lang="EN-US"&gt;Psychometricians and researchers have grown increasingly concern about students’ performance. It is noteworthy from observation in the selected university of education in Nigeria that many of these students do not attend lectures for general courses particularly EDU 222 (tests and measurement) due to overcrowded lecture halls, the classroom climate factor, and other latent factors, such as exam conditions, test anxiety among others. Consequently therefore, students’ performance in EDU 222 does not significantly correlate with these challenges. The characteristics of the item parameter of the Tests and Measurement achievement test at the selected University of Education in Ogun State, Nigeria, were therefore examined in this study using measurement theories. The study employed an ex-post facto research of non-experimental design. Purposive sampling was used to select 6,203 second-year undergraduates offering EDU 222 for the 2018/2019 academic year. To analysis students’ responses to 30 multiple-choice questions on tests and measurement, factor analysis, item calibration, an independent sample t-test with a significance threshold of 0.05 were utilized with empirical reliability of 0.64. The results showed the test items satisfied the three-parameter logistics model and uni-dimensionality assumption. The findings also showed that the item response theory (IRT) eliminated 7 items due to guessing, while the classical test theory (CTT) eliminated 13 items. The test is prone to guessing, thus, helps pupils do better on the exam. To establish parameter estimates during test development, higher education institutions (HEIs) should employ the use of IRT.&lt;/span&gt;
Error Analysis Prompting Enables Human-Like Translation Evaluation in Large Language Models: A Case Study on ChatGPT

Generative large language models (LLMs), e.g., ChatGPT, have demonstrated remarkable proficiency across several NLP tasks, such as machine translation, text summarization. Recent research (Kocmi and Federmann, 2023) has shown that utilizing LLMs for assessing the quality of machine translation (MT) achieves state-of-the-art performance at the system level but \textit{performs poorly at the segment level}. To further improve the performance of LLMs on MT quality assessment, we investigate several prompting designs, and propose a new prompting method called \textbf{\texttt{Error Analysis Prompting}} (EAPrompt) by combining Chain-of-Thoughts (Wei et al., 2022) and Error Analysis (Lu et al., 2023). This technique emulates the commonly accepted human evaluation framework - Multidimensional Quality Metrics (MQM, Freitag et al. (2021)) and \textit{produces explainable and reliable MT evaluations at both the system and segment level}. Experimental Results from the WMT22 metrics shared task validate the effectiveness of EAPrompt on various LLMs, with different structures. Further analysis confirms that EAPrompt effectively distinguishes major errors from minor ones, while also sharing a similar distribution of the number of errors with MQM. These findings highlight the potential of EAPrompt as a human-like evaluator prompting technique for MT evaluation.
Quasi-Newton Methods for Topology Optimization Using a Level-Set Method

The ability to efficiently solve topology optimization problems is of great importance for many practical applications. Hence, there is a demand for efficient solution algorithms. In this paper, we propose novel quasi-Newton methods for solving PDE-constrained topology optimization problems. Our approach is based on and extends the popular solution algorithm of Amstutz and Andr\"a (A new algorithm for topology optimization using a level-set method, Journal of Computational Physics, 216, 2006). To do so, we introduce a new perspective on the commonly used evolution equation for the level-set method, which allows us to derive our quasi-Newton methods for topology optimization. We investigate the performance of the proposed methods numerically for the following examples: Inverse topology optimization problems constrained by linear and semilinear elliptic Poisson problems, compliance minimization in linear elasticity, and the optimization of fluids in Navier-Stokes flow, where we compare them to current state-of-the-art methods. Our results show that the proposed solution algorithms significantly outperform the other considered methods: They require substantially less iterations to find a optimizer while demanding only slightly more resources per iteration. This shows that our proposed methods are highly attractive solution methods in the field of topology optimization.
Term Spotting: A Quick-and-dirty Method for Extracting Typological Features of Language from Grammatical Descriptions

Starting from a large collection of digitized raw-text descriptions of languages of the world, we address the problem of extracting information of interest to linguists from these. We describe a general technique to extract properties of the described languages associated with a specific term. The technique is simple to implement, simple to explain, requires no training data or annotation, and requires no manual tuning of thresholds. The results are evaluated on a large gold standard database on classifiers with accuracy results that match or supersede human inter-coder agreement on similar tasks. Although accuracy is competitive, the method may still be enhanced by a more rigorous probabilistic background theory and usage of extant NLP tools for morphological variants, collocations and vector-space semantics.
An Automatic Breast Tumor Detection and Classification including Automatic Tumor Volume Estimation Using Deep Learning Technique

This study aims to develop automatic breast tumor detection and classification including automatic tumor volume estimation using deep learning techniques based on computerized analysis of breast ultrasound images. When the skill levels of the radiologists and image quality are important to detect and diagnose the tumor using handheld ultrasound, the ability of this approach tends to assist the radiologist's decision for breast cancer diagnosis.Breast ultrasound images were provided by the Department of Radiology of Thammasat University and Queen Sirikit Center of Breast Cancer of Thailand. The dataset consists of 655 images including 445 benign and 210 malignant. Several data augmentation methods including blur, flip vertical, flip horizontal, and noise have been applied to increase the training and testing dataset. The tumor detection, localization, and classification were performed by drawing the appropriate bounding box around it using YOLO7 architecture based on deep learning techniques. Then, the automatic tumor volume estimation was performed using a simple pixel per metric technique.The model demonstrated excellent tumor detection performance with a confidence score of 0.95. In addition, the model yielded satisfactory predictions on the test sets, with a lesion classification accuracy of 95.07%, a sensitivity of 94.97%, a specificity of 95.24%, a PPV of 97.42%, and an NPV of 90.91%.An automatic breast tumor detection and classification including automatic tumor volume estimation using deep learning technique yielded satisfactory predictions in distinguishing benign from malignant breast lesions. In addition, automatic tumor volume estimation was performed. Our approach could be integrated into the conventional breast ultrasound machine to assist the radiologist's decision for breast cancer diagnosis.
STRATEGIES TO OVERCOME FOREIGN LANGUAGE SPEAKING ANXIETY IN INDONESIAN EFL LEARNING CONTEXTS

During the taxing L2 communicative enterprises undergone by Indonesian EFL learners, language teachers are heavily instigated to erect a more critical standpoint in picking up the most appropriate speaking learning approaches, strategies, and activities working best for the entire learners. This document analysis study was run to profoundly investigate 20 previous foreign language-speaking anxiety literature concerning the contributing factors and probable strategies that can potentially alleviate this psychological phenomenon. Two alleviating foreign language speaking anxiety strategies were proposed as follows: (1) The internalization of more innovative L2 speaking learning activities and (2) The existence of more enjoyable L2 speaking learning circumstances. At the end of the subsection, the researcher expectantly hoped prospective Indonesian EFL speaking learning experts, practitioners, policy-makers, and educationalists to promote a more positive speaking classroom atmosphere, constant speaking learning guidance, and various contextual speaking learning materials to progressively minimize learners’ foreign language speaking anxiety as well as foster their L2 communicative competencies into a greater level.
Predicting Students Performance Using Supervised Machine Learning Based on Imbalanced Dataset and Wrapper Feature Selection

For learning environments like schools and colleges, predicting the performance of students is one of the most crucial topics since it aids in the creation of practical systems that, among other things, promote academic performance and prevent dropouts. The decision-makers and stakeholders in educational institutions always seek tools that help in predicting the number of failed courses for the students. These tools can help in finding and investigating the factors that led to this failure. In this paper, many supervised machine learning algorithms will investigate finding and exploring the optimal algorithm for predicting the number of failed courses of students. An imbalanced dataset will be handled with Synthetic Minority Oversampling TEchinque (SMOTE) to get an equal representation of the final class. Two feature selection approaches will be implemented to find the best approach that produces a highly accurate prediction. Wrapper with Particle Swarm Optimization (SPO) will be applied to find the optimal subset of features, and Info Gain with ranker to get the most correlated individual features to the final class. Many supervised algorithms will be implemented such as (Naïve Bayes, Random Forest, Random Tree, C4.5, LMT, Logistic, and Sequential Minimal Optimization algorithm (SMO)). The findings show that the wrapper filter with SPO-based SMOTE outperforms the Info-Gain filter with SMOTE and improves the performance of the algorithms. Random Forest outperforms the other supervised machine learning algorithms with (85.6%) in TP average rate and Recall, and (96.7%) in ROC curve.
A Survey on variants of DoS attacks: Issues and defense mechanisms

Wireless-based networks encompass assisted human life in distinct domains like Agriculture, health, infrastructure, climate, transportation and defense. In particular, the wireless sensor networks have been addressed in this article. Though it has many advancements, providing secure data transmission needs a major focus on these platforms. The disruptions or interference is one of the vulnerable security problems in Wireless Sensor Networks. Inside the network, an intruder can encroach and interrupt the whole transmission process. The involvement of an intruder in the device must be remembered in the network. WSN encompasses a wide geographical region and focus on rectification of issues in protocols for routing, interoperability, connectivity, and defense plays a major role. Hence, this article addresses about routing protocols of WSN, applications, recent network security issues and their variants of defense mechanisms. The conclusion depicts the classification of network security issues, requirements, and descriptive defense mechanisms for secure data transmission.
EC-KitY: Evolutionary computation tool kit in Python with seamless machine learning integration

<h2>Abstract</h2> EC-KitY is a comprehensive Python library for doing evolutionary computation (EC), licensed under the BSD 3-Clause License, and compatible with scikit-learn. Designed with modern software engineering and machine learning integration in mind, EC-KitY can support all popular EC paradigms, including genetic algorithms, genetic programming, coevolution, evolutionary multi-objective optimization, and more. This paper provides an overview of the package, including the ease of setting up an EC experiment, the architecture, the main features, and a comparison with other libraries.