Reviewerly/exHarmony huggingface dataset:
Two-layer neural network on infinite-dimensional data: global optimization guarantee in the mean-field regime <sup>*</sup>

Abstract The analysis of neural network optimization in the mean-field regime is important as the setting allows for feature learning . The existing theory has been developed mainly for neural networks in finite dimensions, i.e. each neuron has a finite-dimensional parameter. However, the setting of infinite-dimensional input naturally arises in machine learning problems such as nonparametric functional data analysis and graph classification. In this paper, we develop a new mean-field analysis of a two-layer neural network in an infinite-dimensional parameter space. We first give a generalization error bound, which shows that the regularized empirical risk minimizer properly generalizes when the data size is sufficiently large, despite the neurons being infinite-dimensional. Next, we present two gradient-based optimization algorithms for infinite-dimensional mean-field networks, by extending the recently developed particle optimization framework to the infinite-dimensional setting. We show that the proposed algorithms converge to the (regularized) global optimal solution, and moreover, their rates of convergence are of polynomial order in the online setting and exponential order in the finite sample setting, respectively. To the best of our knowledge, this is the first quantitative global optimization guarantee of a neural network on infinite-dimensional input and in the presence of feature learning.
Redundant representations help generalization in wide neural networks <sup>*</sup> <sup>,</sup> †

Abstract Deep neural networks (DNNs) defy the classical bias-variance trade-off; adding parameters to a DNN that interpolates its training data will typically improve its generalization performance. Explaining the mechanism behind this ‘benign overfitting’ in deep networks remains an outstanding challenge. Here, we study the last hidden layer representations of various state-of-the-art convolutional neural networks and find that if the last hidden representation is wide enough, its neurons tend to split into groups that carry identical information and differ from each other only by statistically independent noise. The number of these groups increases linearly with the width of the layer, but only if the width is above a critical value. We show that redundant neurons appear only when the training is regularized and the training error is zero.
Reservoir computing decoupling memory–nonlinearity trade-off

Reservoir computing (RC), a variant recurrent neural network, has very compact architecture and ability to efficiently reconstruct nonlinear dynamics by combining both memory capacity and nonlinear transformations. However, in the standard RC framework, there is a trade-off between memory capacity and nonlinear mapping, which limits its ability to handle complex tasks with long-term dependencies. To overcome this limitation, this paper proposes a new RC framework called neural delayed reservoir computing (ND-RC) with a chain structure reservoir that can decouple the memory capacity and nonlinearity, allowing for independent tuning of them, respectively. The proposed ND-RC model offers a promising solution to the memory-nonlinearity trade-off problem in RC and provides a more flexible and effective approach for modeling complex nonlinear systems with long-term dependencies. The proposed ND-RC framework is validated with typical benchmark nonlinear systems and is particularly successful in reconstructing and predicting the Mackey-Glass system with high time delays. The memory-nonlinearity decoupling ability is further confirmed by several standard tests.
Improved Method of Segmentation of Images From Space-Based Optoelectronic Observation Systems Based on Otsu's Algorithm with Global and Adaptive Thresholds

An analysis of optoelectronic image, received from space-based observation system has been carried out. It has been found out, that this image has a complex structure and a unimodal intensity histogram. Otsu method is chosen as to segment the image. This method is easy and relatively quick, when talking of its realization. Methods of global and adaptive thresholding have been researched to perform image segmentation. Results of experimental research of segmentation of optoelectronic image from space-based observation system with use of Otsu method are developed. Ways for future research in this area are defined.
Military vehicles marking detection algorithm on a digital image

Constant complicating of wars due to considerable development of military weapon and moving to new generation of warfare led to a significant change in means of it. More and more armed forces involve AI into solving a wide range of tasks, since modern war produces a lot of data which need to be processed and analyzed.In this work, an effective military vehicles marking detection algorithm was developed in order to make it possible to determine the ownership of military equipment based on its marking. It uses pure image processing algorithms instead of involving neural networks, like in R-CNN model or its derivatives. As a result, the algorithm does not require a huge dataset of marking templates and time for network training and can be applied immediately.The algorithm is not bound to a specific marking pattern and can be applied to any. Also it is designed for detecting military marking without reference to objects. As a result, the algorithm can be easily integrated with other image processing systems, e.g. military vehicles detection.
Position correction of mobile robot by detecting line landmark using Hough line transform

Abstract Mobile robots are used in a laboratory automation system to transport specimens or experiments to their destinations. When moving, the robot has some error positions because of the wheels skid on the floor and the inaccuracy direction of the robot. The robot needs to correct its position and return to the track. By reading the landmarks in the environment, the robot can correct its position. This research proposes to do the position correction of the robot using a camera by reading the orange lines as an artificial landmark on the laboratory environment floor. The camera detects the orange lines as two parallel edges. And by Hough line transform (HLT) it produces Δρ value between line edges. The robot calculates the error position using the Δρ value and the biggest ρ (ρ MAX ) as the distance prediction robot to the line. Compared to the original robot movement on the Webots simulator application, this method can help the robot keep its position on the track. On the goal position, the robot can reduce 90% the error position. This error improvement makes the error position of the robot becomes around 5 mm from the target position.
Classification of Red Blood Cell Abnormality in Thin Blood Smear Images using Convolutional Neural Networks

Abstract One of the most common morphological red blood cell abnormalities encountered during routine thin blood smear microscopy for the detection of malaria parasite is the rouleaux formation, which is the stacking together of red blood cells to form a chain. Rouleaux formation signifies an underlying infection and as such microscopists are mandated to report its presence. A lot of work has been done in automating malaria diagnosis using deep learning, but no model has been developed which is capable of detecting rouleaux formation in malaria infected red blood cells. Thus, this study collected 231 peripheral blood smear (PBS) images of normal red blood cell morphology and 231 PBS images with rouleaux morphology. The images were pre-processed and segmented into equal instances of 3044 coloured images of size 750×750 pixels. Two convolutional neural network (CNN) models were developed and trained to classify the images into normal red blood cell morphology or rouleaux morphology. The CNN models were trained on two different image sizes: 300×300 and 500×500. The first CNN model achieved validation accuracy/loss values of 87.91%/0.8177 and 56.58%/1.4090 when trained on images of sizes 300×300 and 500×500 respectively. In the second CNN model, the CNN layers of the first model were replaced with depthwise separable CNN layers, it was also trained on images of sizes 300×300 and 500×500 achieving validation accuracy/loss values of 90.95%/0.2804 and 87.75%/0.5904 respectively. This study demonstrates the capability of CNN models in detecting red blood cell morphology abnormality in thin smear images at an optimal image size of 300×300.
PEMANFAATAN MEDIA AUDIOVISUAL DALAM PEMBELAJARAN MATEMATIKA KELAS RENDAH DI MADRASAH IBTIDAIYAH

The development of this research was carried out to streamline the learning process and provide an understanding of the importance of using instructional media in learning activities for teachers and low-grade students at Madrasah Ibtidaiyah. Developing research aims to provide awareness of the importance of media in the learning process. This study uses a qualitative research approach by conducting a survey method. Audio-visual media is a container for conveying material or information to students and has the aim of making the learning process more effective by involving students. The results of using audio-visual media are very good and have a positive impact on students.
"Meta-Analysis of Human Capital in the Literature from 2020 to 2023"

The pandemic, being contained and mitigated by distancing and confinement policies, limited the formation of human capital in the face-to-face classroom. Based on this assumption, the objective of this study was to observe the training models of human capital in different scenarios in the COVID-19 era. A meta-analysis of reflective effects was carried out with a sample of abstracts published in the literature from 2020 to 2023, considering international databases and repositories. The heterogeneity hypothesis was contrasted, which was not rejected, but the extension of the study to other samples and risk propensity scenarios is recommended.
Organization of interaction between the pedagogical university and institutions of additional education for children

We often hear in mass media regularly statements of the specialists in the field of education that characterizes the situation with the staffing of the industry with the phrase: "There is no one to work." Indeed, there are still a number of problems in this area that need to be addressed. The Concept of the Development of Additional Education for Children until 2030 explicitly indicates the insufficient staffing of organizations of additional education and the low level of involvement of educational organizations of higher education in the implementation of additional general education programs. Updating the content of students' training, as well as the success of their professional socialization, is largely determined by the effectiveness of the functioning of the system of relations between an educational institution of higher education and employers. Within the framework of such cooperation, pedagogical universities offer programs for teachers of institutions aimed at improving their professional level. In turn, these educational institutions provide students with opportunities for practical training, which helps them to better understand professional realities. The article substantiates the relevance of the problem of building a system of interaction between institutions of higher pedagogical and children's additional education. The integration of pedagogical universities and children's educational institutions of additional training is a key factor for the development of the modern education system. It contributes to the formation of a unified professional community that can effectively solve current problems in the field of pedagogy and education. The scientific novelty of the study consists in the fact that on the basis of the concretization of scientific ideas and generalization of successful practices, the main categories of the interaction of a pedagogical university with a regional system of additional education for children are determined; The practical significance of the research lies in the possibility of using the proposed algorithm for building joint activities of a pedagogical university with educational organizations implementing additional educational programs in the design and implementation of basic professional educational programs, the development of educational, industrial and other types of practices that provide solutions to the tasks of training student teachers.
Assessing the adoption, implementation and effective utilization of education technology and innovation in education sector in Malawi - (A case study of Mpherembe CDSS)

The integration of education technology and innovation into the education sector is increasingly becoming a global imperative, with the potential to transform teaching and learning practices. In Malawi, like many other developing nations, the adoption, implementation, and effective utilization of education technology remain critical challenges. This case study focuses on Mpherembe (CDSS) in Malawi to provide a detailed assessment of the status of education technology adoption and its impact on teaching and learning. The need for educational technology has risen in many schools in Malawi, especially during the pandemic's arrival of COVID-19, cholera outbreaks, and Cyclone Freddy, bringing attention among teachers, students, and policymakers. The study was centered at Mpherembe CDSS, where a sample of 20 students and teachers was selected using a probability sampling technique. The data was collected using a questionnaire and analyzed using SPSS. The analyzed data was presented in the form of bar graphs. The study uncovered that there was a moderate extent of adoption, implementation, and effective utilization of educational technology in Malawi. Furthermore, the study revealed that there are several significant benefits of educational technology in Malawi. The main ones include reducing transportation and accommodation costs and accommodating both work and learning simultaneously. Despite the benefits, educational technology faces several challenges that hinder its adoption, implementation, and utilization in Malawi. These challenges include poor network infrastructure, high costs, and limited access to educational technology.
Learning Method Recommendation Based on VARK Model Using Certainty Factor Algorithm

In lecture activities, students are required to master several courses that have been determined based on their respective majors. In the learning process, students often have difficulty understanding lecture material. One factor is the mismatch between how students learn and the type of learning style of each student. It is important for each student to know their respective learning styles so that in the learning process can understand the material to the fullest. One way to find out the type of student learning style is with VARK modalities (Visual, Auditory, Read/Write, and Kinaesthetic). The VARK model classifies learning style types into four types. Everyone must have all four types of learning styles, but there must be one of the most dominant. By knowing the type of learning style, students can determine how to learn according to the type of learning style. This recommendation system is implemented using Certainty Factor algorithms involving the expertise of a psychologist in it, this system is built in the website platform. The system achieves an accuracy of 94.52%, so it is good enough to provide recommendations on how to learn properly for users.
Reasoning arithmetic word problems entailing implicit relations based on the chain-of-thought model

&lt;abstract&gt;&lt;p&gt;Solving arithmetic word problems ($ AWPs $) that involve deep implicit relations can be quite challenging. However, the paper proposed two approaches to tackle this issue. The first approach used the modifier-to-matrix ($ MTM $) model to extract noun modification components from the problem text. Specifically, a missing entity recovery ($ MER $) model translated explicit expressions into a node dependency graph ($ NDG $). The nodes on the graph then recursively acquired connections from the knowledge base through the $ MER $ model until the goal was achieved with a known quantity. The solving engine then selected the appropriate knowledge as the prompt. The second approach proposed in the paper was a comprehensive one that combined explicit and implicit knowledge to enhance reasoning abilities. The experimental results of the dataset demonstrate that the proposed algorithm is superior to the baseline algorithms in solving $ AWPs $ that require deep implicit relations.&lt;/p&gt;&lt;/abstract&gt;
Regulatory Compliance at Optimal Cost with Minimum Exposure to Risk

Enterprises are fast evolving into a complex system of systems that need to operate in a dynamic uncertain world characterized by unprecedented changes and partial information. With businesses getting increasingly regulated, new regulations are introduced and existing regulations modified to plug any holes. Enterprises need to adapt to these changes in regulatory regime in an increasingly shortening time window. Current practice of meeting this need relying solely on human expertise spanning law, business domain and IT systems is turning out to be inadequate. Incorrect adaptive response not only results in heavy fine but also leads to reputational damage. Recent advances in NLP technology augmented with proven results from modelling and software engineering offer a pragmatic line of attack. This chapter will motivate the need, survey the state of the art, propose a line of attack and discuss how it has fared so far across multiple domains and what more seems required, thus highlighting open research challenges.
Democratized Hyper-automated Software Development

Modern enterprises are evolving into a complex system of systems that need to deliver stated goals while operating in a dynamic and uncertain environment. Given the ever-increasing pervasiveness of software, enterprises are relying heavily on software systems to address a variety of adaptive needs at strategy, process and system levels. The demand-supply situation for trained software developers is already skewed, and the skew is likely to increase further with time. Recent advances in AI techniques in general and Generative AI in particular may lead to a promising solution wherein Subject Matter Experts are empowered to play a greater and more direct role in software development. This chapter motivates the need, proposes a pragmatic line of attack and discusses technology enablers to support this line of attack.
The AI-Enabled Enterprise

Modern enterprises are evolving into complex system of systems that need to deliver stated goals while operating in a dynamic and uncertain environment. Current practice of arriving at suitable adaptive response at strategy, process and software system levels is turning out to be ineffective. To meet this critical need, a new set of capabilities need to be developed for addressing critical concerns such as decision-making in the face of uncertainty, staying compliant at optimal cost with minimal exposure to risk, dynamic adaption of software and processes, low-code/no-code approaches to accelerate software development, leveraging structured/semi-structured/unstructured information and coordinated continuous digital transformation. This chapter motivates the need for an AI-Enabled Enterprise and briefly discusses how the above concerns can be addressed.
Type-3 Fuzzy Theory

This chapter presents the type-3 terminology, including type-3 sets, fuzzy logicFuzzy Logic and fuzzy systems. These concepts are necessary to fully understand the methods and applications that are presented in the following chapters.
Review of Type-3 Fuzzy Control

We describe in this chapterType-3 Fuzzy Control an overview of theFuzzy Control existing papers in the area of type-3 fuzzy control to provide an idea of the state in which this area is at the moment. We utilized the Scopus database as a basis for elaborating this review. We discuss number of papers, number of citations and other statistics, that will offer to the reader an overview of the advancements made in the type-3 fuzzy area.
Approach for Type-3 Fuzzy Control

This chapterType-3 Fuzzy Control outlines Interval Type-3 Fuzzy LogicFuzzy Logic Systems (IT3FLSs) applied to control with the objective of analyzing their performance. Two cases are outlined in which IT3FLSs allows to design effective controllers when compared to other methods. The vertical slice construct is utilized to estimate the outputs of IT3FLSs for control applications. Results with type-2 are outlined and a comparative study with IT3FLS is presented and analyzed.
Conclusions of Interval Type-3 Fuzzy Control

In this chapter the basic terminology and methods for designing and developing type-3 fuzzy setsFuzzy Sets, membership functionsMembership Functions, inference and fuzzy controllers have been outlined in this book (Castillo et al. in Interval Type-3 Fuzzy Systems: Theory and Design, Springer, Cham, Switzerland, 2022). Also, possible future trends about type-3 fuzzy controlType-3 Fuzzy Control are outlined. The design of fuzzy controllers is a challenging endeavor, and this is particularly true for type-2 fuzzy controllersType-2 Fuzzy Controllers.
Privacy-Preserving and Verifiable Outsourcing Linear Inference Computing Framework

In machine learning (ML), the massive data processing and dense computations based on matrices make outsourced inference computation a growing trend. The unreliability of cloud platforms makes privacy protection and inference correctness increasingly important in outsourced computations. Unfortunately, current works cannot provide an effective verification mechanism and privacy protection for outsourcing linear computing simultaneously. To address the issue, in the service architectures with malicious behaviors (such as curiosity, dishonesty, and collusion), we propose privacy-preserving and verifiable outsourcing inference computing (PPVLC) for the most fundamental linear computations in ML. PPVLC uses secret sharing and blinding techniques to protect privacy and achieve secure computation of matrix linear computation. Meanwhile, bilinear mapping based on matrix digest is utilized to verify computation correctness, ensuring the trustworthiness of the service. Security analysis and experiments demonstrate the reliability of our scheme and service efficiency.