Implementation Of Algorithm Research Articles

Design of a differentiable L-1 norm for pattern recognition and machine learning

In various applications of pattern recognition, feature selection, and machine learning, L-1 norm is used as either an objective function or a regularizer. Mathematically, L-1 norm has unique characteristics that make it attractive in machine learning, feature selection, optimization, and regression. Computationally, however, L-1 norm presents a hurdle as it is non-differentiable, making the process of finding a solution difficult. Existing approach therefore relies on numerical approaches. In this work we designed an L-1 norm that is differentiable and, thus, has an analytical solution. The differentiable L-1 norm removes the absolute sign in the conventional definition and is everywhere differentiable. The new L-1 norm is almost everywhere linear, a desirable feature that is also present in the conventional L-1 norm. The only limitation of the new L-1 norm is that near zero, its behavior is not linear, hence we consider the new L-1 norm quasi-linear. Being differentiable, the new L-1 norm and its quasi-linear variation make them amenable to analytic solutions. Hence, it can facilitate the development and implementation of many algorithms involving L-1 norm. Our tests validate the capability of the new L-1 norm in various applications.

A Target Collision Algorithm of Autonomous Mobile Robot for Two-player Competition

Abstract A variety of robot competitions are held to train and attract students everywhere. This paper studies the algorithm of autonomous robot with two-player competition. The autonomous robots in a two-player competition takes the scoring form of collision and wins first. The impact object is the enemy target. The key algorithms in a two-player competition are target detection, target path decision, and collision target motion control. First, the vehicle target data set was established and enhanced, the lightweight detection algorithm model was screened and trained, the model pruning optimization experiment and the model deployment optimization experiment were carried out, and then the TensorRT framework was used for inter-layer fusion and data quantification processing. After hitting the target path decision algorithm after detecting the target target, it finally integrates the decision in simple and complex situations. The collision target motion control is the scheme of more intelligent and self-adjustable parameters of fuzzy Radial Basis Function (RBF) network Proportional Integral Derivative (PID) controller. Finally, the collision target implementation algorithm is tested in a real environment, and the results show that the task requirements can be met.

A Review of Advanced Sensor Technologies for Aquatic Products Freshness Assessment in Cold Chain Logistics.

The evaluation of the upkeep and freshness of aquatic products within the cold chain is crucial due to their perishable nature, which can significantly impact both quality and safety. Conventional methods for assessing freshness in the cold chain have inherent limitations regarding specificity and accuracy, often requiring substantial time and effort. Recently, advanced sensor technologies have been developed for freshness assessment, enabling real-time and non-invasive monitoring via the detection of volatile organic compounds, biochemical markers, and physical properties. The integration of sensor technologies into cold chain logistics enhances the ability to maintain the quality and safety of aquatic products. This review examines the advancements made in multifunctional sensor devices for the freshness assessment of aquatic products in cold chain logistics, as well as the application of pattern recognition algorithms for identification and classification. It begins by outlining the categories of freshness criteria, followed by an exploration of the development of four key sensor devices: electronic noses, electronic tongues, biosensors, and flexible sensors. Furthermore, the review discusses the implementation of advanced pattern recognition algorithms in sensor devices for freshness detection and evaluation. It highlights the current status and future potential of sensor technologies for aquatic products within the cold chain, while also addressing the significant challenges that remain to be overcome.

Innovative Integration of Computer Network Technology in Modern Educational Systems

A novel educational system has been developed using the Radial Basis Function (RBF) algorithm to address the limitations of traditional classroom environments, which often rely on standardized material and fixed teaching methods. This research assesses recent advancements in interactive intelligent education systems by combining Artificial Intelligence (AI) with interactive teaching methods. A model based on cognitive functions is constructed using the RBF algorithm to personalize instructional approaches and foster a self-directed learning platform. The research methodology involves a comprehensive literature review, developing an Education Intelligent System (EIS) using AI-driven cognitive modelling, and the implementation of the RBF algorithm within a neural network architecture. The system's effectiveness is evaluated through empirical methods, including extensive data analysis and continuous refinement based on student performance feedback. Additionally, a network topology model is designed to enhance the system's adaptability for different roles within the educational framework. The results show significant improvements in instructional effectiveness, learner engagement, and personalized learning experiences. This research demonstrates that AI and interactive technologies can revolutionize conventional educational methods, enhance learner proficiency, and cultivate a more dynamic and engaging learning environment. Future work aims to improve the system's user interface and analyze larger datasets to refine the AI algorithms further.

Implementation of K-Nearest Neighbors Algorithm in Analyzing Public Interest in Shoping at Supermarkets

People's shopping patterns and behaviors continue to develop along with technological advances and lifestyle changes, thus requiring retail business actors, especially supermarkets, to better understand their customers' interests and preferences. In this context, accurate analysis of customer shopping interests is very important to improve customer satisfaction and optimize marketing strategies. One solution that can be implemented to analyze people's shopping interests is the application of the K-Nearest Neighbors algorithm, a simple yet effective nearest neighbor-based classification method for recognizing patterns from existing data. This study aims to apply the K-Nearest Neighbors algorithm to classify people's interest in shopping at supermarkets. This study also evaluates the effectiveness and performance of the algorithm in the context of business decision-making in the retail sector. The research methodology includes collecting data on people's shopping interests, data pre-processing, implementing the K-Nearest Neighbors algorithm, and evaluating model performance using evaluation metrics such as accuracy, precision, recall, and F1-score. The results of this study indicate that the K-Nearest Neighbors algorithm is able to achieve an accuracy of 88%, with precision, recall, and F1-score all reaching 92.86%. These results indicate that the K-Nearest Neighbors model is very effective in classifying people's shopping interests, with a low error rate. The resulting confusion matrix also shows the model's ability to identify customers who are interested in shopping with little prediction error. This study concludes that we can rely on the K-Nearest Neighbors algorithm to analyze people's shopping interests in supermarkets. This model not only shows good performance in classification but also has great potential to be implemented in recommendation systems and customer segmentation in the real world. This study contributes to the development of consumer behavior analysis methods in the retail sector, as well as providing a basis for further research to explore other algorithms or combinations of techniques to improve the accuracy and effectiveness of classification models.

GAP FILLING ALGORITHM FOR MOTION CAPTURE DATA TO CREATE REALISTIC VEHICLE ANIMATION

The dynamic development of the entertainment market entails the need to develop new methods enabling the application of current scientific achievements. Motion capture is one of the cutting-edge technologies that plays a key role in movement and trajectory computer mapping. The use of optical systems allows one to obtain highly precise motion data that is often applied in computer animations. This study aimed to define the research methodology proposed to analyze the movement of remotely controlled cars utilizing developed gap filling algorithm, a part of post-processing, for creating realistic vehicle animation. On a specially prepared model, six various types of movements were recorded, such as: driving straight line forward, driving straight line backwards, driving on a curve to the left, driving on a curve to the right and driving around a roundabout on both sides. These movements were recorded using a VICON passive motion capture system. As a result, three-dimensional models of vehicles were created that were further post-processed, mainly by filling in the gaps in the trajectories. The case study highlighted problems such as missing points at the beginning and end of the recordings. Therefore, algorithm was developed to solve the above-mentioned problem and allowed for obtaining an accurate movement trajectory throughout the entire route. Realistic animations were created from the prepared data. The preliminary studies allowed one for the verification of the research method and implemented algorithm for obtaining animations reflecting accurate movements.

HAETAE on ARMv8

In this work, we present the highly optimized implementation of the HAETAE algorithm, submitted to the second round of the Korean Post-Quantum Cryptography (KpqC) competition and to the first round of NIST’s additional post-quantum standardization for digital signatures on 64-bit ARMv8 embedded processors. To the best of our knowledge, this is the first optimized implementation of the HAETAE algorithm on 64-bit ARMv8 embedded processors. We apply various optimization techniques to enhance the multiplication operations in the HAETAE algorithm. We utilize parallel operation techniques involving vector registers and NEON (Advanced SIMD technology used in ARM processors) instructions of ARMv8 embedded processors. In particular, we achieved the best performance of the HAETAE algorithm on ARMv8 embedded processors by applying all the state-of-the-art NTT (Number Theoretic Transform) implementation techniques. Performance improvements of up to 3.07×, 3.63×, and 9.15× were confirmed for NTT, Inverse-NTT, and pointwise Montgomery operations (Montgomery multiplication used in modular arithmetic), respectively, by applying the state-of-the-art implementation techniques, including the proposed techniques. As a result, we achieved a maximum performance improvement of up to 1.16× for the key generation algorithm, up to 1.14× for the signature algorithm, and up to 1.25× for the verification algorithm.

Improvement of Lossless Text Compression Methods using a Hybrid Method by the Integration of RLE, LZW and Huffman Coding Algorithms

In the field of computer science, data compression is essential in the process of data transfer because it reduces file size without losing information. Issues that depend on the characteristics of the text can greatly affect the effectiveness of any compression algorithm. Consequently, each traditional compression algorithm has its own strengths and limitations. Therefore, a highly efficient compression method that achieves higher compression ratio is needed. In this paper, based on the sequential implementation of Huffman, RLE and LZW algorithms, a hybrid data compression method has been proposed. The algorithms have been individually evaluated and compared in terms of compression ratio and compressed file size. We conclude that in terms of compression ratio and compressed file size, LZW is better than Huffman and RLE. The results show that the proposed hybrid algorithm achieves the highest average compression ratio is 2.42 and the lowest average compressed file size is 1138.5 or less compared to the individual implementations of the algorithms. Thus, it can be concluded that the proposed hybrid method enhances lossless text compression in terms of compression ratio and file size according to compression metrics.

Modern levels of structural strength assessment and the algorithm of implementation of the methodology of risk analysis of the operation of welded metal structures

Modern levels of structural strength assessment and the algorithm of implementation of the methodology of risk analysis of the operation of welded metal structures

Complexity analysis and numerical implementation of a new interior-point algorithm for semidefinite optimization

We generalize Zhang and Xu's (2011) [22] interior point algorithm for linear optimization to semidefinite optimization problems in order to define a new search direction. The symmetrization of the search direction is based on the full Nesterov-Todd scaling scheme. Moreover, we show that the obtained algorithm solves the studied problem in polynomial time and that the short-step algorithm has the best-known iteration bound, namely O(nlog⁡nε)-iterations. Finally, we report a comparative numerical study to show the efficiency of our proposed algorithm.

Data-Driven Calibration of SWOT’s Systematic Errors: First In-Flight Assessment

The SWOT satellite, carrying the KaRIN first wide-swath onboard altimeter, was launched in December 2022, and has now delivered more than a year of surface water elevation data over the ocean and inland lakes/rivers. These data are affected by systematic errors which constitute the dominant part of the error budget at scales larger than a few thousands of kilometers. Some strategies for their estimation and calibration were explored during the pre-launch studies with performance estimations. Now, based on the real data, we propose in this study to assess the systematic error budget with statistical methods relying on spectral and co-spectral analysis. From this assessment, suggesting very low error levels (below requirements), we propose the implementation of the calibration algorithms at Level-2 and Level-3 with a few minor adjustments justified by the error spectra. The calibrated products are then validated with usual CalVal metrics.

Gamification as an e-learning tool: A Literature Review

The purpose of the article is to highlight the status of gamification of the educational process as an element of e-learning. The use of the game space in the educational process requires compliance with a number of specific conditions, so the task of the study is to determine the organisational and functional characteristics of gamification. The methodology involves the use of the principles of qualitative research, which analyses scientific views (53 sources were analysed) on the use of gamification in the educational process. The systematic literature review is conducted using the PRISMA 2020 flowchart, which allowed for a variable selection of sources relevant to the study of the current problem. The results of the study indicate the following trends in the positioning of gamification tools in the e-learning cluster: auxiliary status of gamification (42%), reinforcing status of the game factor (39%), alternative status of game-based learning (15%), negative feedback on the actualisation of gamification (4%). The literature review confirms the trend of involving a game case in the educational paradigm. At the same time, the role and format of gamification does not have a clear algorithm for implementation and functioning in learning activities, which indicates the need to streamline this cluster of e-learning. The scientific and pedagogical discourse today does not have a clear answer to the general positioning of gamification in the educational system. Institutionally, gamification is enshrined in educational strategies due to global trends in the digitalisation of society, but at the organisational and functional level, there are contradictions about the status of the game space in the structure of e-learning. The study identified two cases of game-based learning platforms in the innovative educational space: an autonomous online game-based learning format (54%) and a game tool as an element of the digital learning environment (46%). The scientific novelty of the study is focused on the correlation of indicators of quantitative increase in the level of gamification and qualitative indicators of the effectiveness of this e-learning case. Prospects for scientific research are focused on developing a strategy for the development of gamification in the educational sphere in general and in the context of e-learning in particular. Conclusion. Thus, gamification has gained positions in the e-learning system and is forming its own niche in the educational paradigm. The rapid digitalisation of the socio-cultural space causes the diversity and disorder of the organisation of the e-learning game case (especially in the context of the educational and pedagogical status), which is expressed in the scientific discourse of the last five years.

Implementation of resource-efficient fetal echocardiography detection algorithms in edge computing.

Recent breakthroughs in medical AI have proven the effectiveness of deep learning in fetal echocardiography. However, the limited processing power of edge devices hinders real-time clinical application. We aim to pioneer the future of intelligent echocardiography equipment by enabling real-time recognition and tracking in fetal echocardiography, ultimately assisting medical professionals in their practice. Our study presents the YOLOv5s_emn (Extremely Mini Network) Series, a collection of resource-efficient algorithms for fetal echocardiography detection. Built on the YOLOv5s architecture, these models, through backbone substitution, pruning, and inference optimization, while maintaining high accuracy, the models achieve a significant reduction in size and number of parameters, amounting to only 5%-19% of YOLOv5s. Tested on the NVIDIA Jetson Nano, the YOLOv5s_emn Series demonstrated superior inference speed, being 52.8-125.0 milliseconds per frame(ms/f) faster than YOLOv5s, showcasing their potential for efficient real-time detection in embedded systems.

COFFEE: consensus single cell-type specific inference for gene regulatory networks.

The inference of gene regulatory networks (GRNs) is crucial to understanding the regulatory mechanisms that govern biological processes. GRNs may be represented as edges in a graph, and hence, it have been inferred computationally for scRNA-seq data. A wisdom of crowds approach to integrate edges from several GRNs to create one composite GRN has demonstrated improved performance when compared with individual algorithm implementations on bulk RNA-seq and microarray data. In an effort to extend this approach to scRNA-seq data, we present COFFEE (COnsensus single cell-type speciFic inFerence for gEnE regulatory networks), a Borda voting-based consensus algorithm that integrates information from 10 established GRN inference methods. We conclude that COFFEE has improved performance across synthetic, curated, and experimental datasets when compared with baseline methods. Additionally, we show that a modified version of COFFEE can be leveraged to improve performance on newer cell-type specific GRN inference methods. Overall, our results demonstrate that consensus-based methods with pertinent modifications continue to be valuable for GRN inference at the single cell level. While COFFEE is benchmarked on 10 algorithms, it is a flexible strategy that can incorporate any set of GRN inference algorithms according to user preference. A Python implementation of COFFEE may be found on GitHub: https://github.com/lodimk2/coffee.

A Subsampling Strategy for AIC-based Model Averaging with Generalized Linear Models

Subsampling is an effective approach to address computational challenges associated with massive datasets. However, existing subsampling methods do not consider model uncertainty. In this paper, we investigate the subsampling technique for the Akaike information criterion (AIC) and extend the subsampling method to the smoothed AIC model-averaging framework in the context of generalized linear models. By correcting the asymptotic bias of the maximized subsample objective function used to approximate the Kullback–Leibler divergence, we derive the form of the AIC based on the subsample. We then provide a subsampling strategy for the smoothed AIC model-averaging estimator and study the corresponding asymptotic properties of the loss and the resulting estimator. A practically implementable algorithm is developed, and its performance is evaluated through numerical experiments on both real and simulated datasets.

Incorporation of computational routines in a microservice architecture in AgDataBox platform

Agriculture has been undergoing a digital process that aims to apply digital technologies to make the sector more productive, profitable, and environmentally responsible. This trend has been adopted since applying precision agriculture (PA) techniques and, more recently, with digital agriculture (DA). DA aims to use all available information and knowledge to enable the automation of sustainable processes in agriculture, applying data analysis methods and techniques by specific software and platforms to collect and transform data into meaningful information for agriculture. Platform AgDataBox (ADB) offers tools to allow agriculture specialists to obtain, process, and visualize data for the correct decision-making. However, its structure needed to be readjusted to new software architecture to allow the aggregation of new functionalities and expand the ADB platform. This study aimed to develop a web microservices architecture (ADB-MSA) to incorporate the required functionalities to create thematic maps (TMs) and delineate management zones (MZs). ADB-MSA provided eight microservices, six of which (statistics, spatial, interpolation, clustering, rectification, and lime/nutrient recommendation) execute procedures based on JavaScript, R, and Python programming languages. At the same time, the other two are used to store data. In the case study, the procedures to create TMs and delineate MZs were performed with data from one commercial area. Thus, the services provided in the architecture meet the steps of creating TMs and delineating MZs, as MZs for fertilizer application were generated and evaluated according to phosphorus and potassium requirements. ADB-MSA allows the development of several new client applications (web, mobile, desktop, and embedded systems) to promote solutions in agriculture, streamlining processes, as it abstracts the implementation and execution complexity of available algorithms.

Determination of hydrostatic pressure on the plane surface of an arbitrary non-simmetrical form by the three-command method K123

Competitive struggle in the conditions of wide access to unlimited amounts of information and computerization of all branches of production and services imposes strict requirements on specialized knowledge, practical skills and general mathematical culture of future engineers. The online program of modern multivariate engineering calculation of the problem of determining the force of hydrostatic pressure on an element of a flat asymmetric surface with a curvilinear face is presented - https://www.k123.org.ua/jeh5.html. Algorithms are implemented according to the author's method of three commands K123 (c) Yu.D. Kopanytsia (hereinafter the K123 method). A client-server solution based on CGI technology with a web form for inputting source data has been implemented. The results of the online calculation were performed according to the new analytical dependencies obtained on the basis of the author's method K123. In parallel, the numerical algorithms of the author's method of three K123 commands and the conclusions of the corresponding calculations in tabular form were implemented. Realized generation of graphics by the server program based on the coordinates of the points obtained by iterative calculations according to the algorithms of the numerical implementation of the K123 method. The relative error of iterative calculations and the estimation of the accuracy of the result are automatically determined based on the proposed exact analytical dependencies.

MonoVisual3DFilter: 3D tomatoes’ localisation with monocular cameras using histogram filters

Abstract Performing tasks in agriculture, such as fruit monitoring or harvesting, requires perceiving the objects’ spatial position. RGB-D cameras are limited under open-field environments due to lightning interferences. So, in this study, we state to answer the research question: “How can we use and control monocular sensors to perceive objects’ position in the 3D task space?” Towards this aim, we approached histogram filters (Bayesian discrete filters) to estimate the position of tomatoes in the tomato plant through the algorithm MonoVisual3DFilter. Two kernel filters were studied: the square kernel and the Gaussian kernel. The implemented algorithm was essayed in simulation, with and without Gaussian noise and random noise, and in a testbed at laboratory conditions. The algorithm reported a mean absolute error lower than 10 mm in simulation and 20 mm in the testbed at laboratory conditions with an assessing distance of about 0.5 m. So, the results are viable for real environments and should be improved at closer distances.

Algorithms for generating small random samples

AbstractWe present algorithms for generating small random samples without replacement. We consider two cases. We present an algorithm for sampling a pair of distinct integers, and an algorithm for sampling a triple of distinct integers. The worst‐case runtime of both algorithms is constant, while the worst‐case runtimes of common algorithms for the general case of sampling elements from a set of increase with . Java implementations of both algorithms are included in the open source library .

Deregulation and Shattering of Chromosomal Segments Containing Multiple Oncogenic Targets in the Pathogenesis of Diffuse Large B Cell Lymphoma, Not Otherwise Specified (DLBCL, NOS)

Diffuse large B cell lymphoma, not otherwise specified (DLBCL, NOS) is the most common type of non-Hodgkin lymphoma (NHL). Significant efforts have been focused on utilizing advanced genomic technologies to further subclassify DLBCL, NOS into clinically relevant subtypes. These efforts have led to the implementation of novel algorithms to support optimal risk-oriented therapy and improvement in the overall survival of DLBCL patients. The pathogenesis of DLBCL at the molecular level indicates copy number variation (CNV) as one of the major forms of genetic alterations in the somatic mutational landscape. Random deregulation that results in complex breaks of chromosomes and restructuring of shattered chromosomal segments is called chromothripsis. Gene expression changes influenced by chromothripsis have been reported in cancer and congenital diseases. This chaotic phenomenon results in complex CNV, gene fusions, and amplification and loss of tumor suppressor genes. We present herein a summary of the most clinically relevant genomic aberrations, with particular focus on copy number aberrations in a case that highlights DLBCL, NOS arising from relapsed Hodgkin lymphoma. The focus of our study was to understand the relationship between the clinical, morphological, and genomic abnormalities in DLBCL, NOS through multiple techniques for therapeutic considerations.