The content of the speech. by Li Jiashen (20552690)

“…The algorithm enhances the traditional Stockwell transform (S-transform) by incorporating a low-pass filtering function and introducing two adjustable parameters. …”

Time domain diagram of speech signal. by Li Jiashen (20552690)

“…The algorithm enhances the traditional Stockwell transform (S-transform) by incorporating a low-pass filtering function and introducing two adjustable parameters. …”

Example of a typical workflow to construct a network using NeKo. by Marco Ruscone (11747264)

10-fold cross-validation evaluation of accuracy of animal tracking algorithms. by Catalin Mitelut (5587493)

“…<p>We labeled 1,000 frames per cohort under both day and night conditions (total 6,000 human labeled frames across three cohorts), and studied the accuracy of the animal tracking algorithm (SLEAP) as a function of the number of frames (<i>x</i>-axis) trained. …”

Comparative analysis of WordNet replacement impact on data distribution. by Enock Niyonkuru (21246879)

Filled contour plot based on histograms of the occurence-frequency of binned values of and as a function of rate ratio . by Paul Kinsler (3975620)

Subject characteristics. by Dominique Vincent-Genod (20552594)

“…<div><p>Among the 32 items of the Motor Function Measure scale, 3 concern the assessment of hand function on a paper-based support. …”

Research data. by Dominique Vincent-Genod (20552594)

“…<div><p>Among the 32 items of the Motor Function Measure scale, 3 concern the assessment of hand function on a paper-based support. …”

Objective function box diagram. by Huichao Guo (14515171)

“…The experimental results show that compared with the traditional particle swarm optimization algorithm, NACFPSO performs well in both convergence speed and scheduling time, with an average convergence speed of 81.17 iterations and an average scheduling time of 200.00 minutes; while the average convergence speed of the particle swarm optimization algorithm is 82.17 iterations and an average scheduling time of 207.49 minutes. …”

LBQANA python code + Merged Gene Expression Dataset from GSE10810, GSE17907, GSE20711, GSE42568, GSE45827, and GSE61304 for Breast Cancer Biomarker Discovery by M RN (9866504)

Dataset of networks used in assessing the Troika algorithm for clique partitioning and community detection by Samin Aref (4683934)

“…Each network is provided in .gml format or .pkl format which can be read into a networkX graph object using standard functions from the networkX library in Python. For accessing other networks used in the study, please refer to the article for references to the primary sources of those network data.…”

Ethics of algorithmic invasiveness in beauty apps: an online experimental survey of public perspectives by Yves Saint James Aquino (12311423)

“…Results show that functionalities with high degree of algorithmic invasiveness, such as AI-generated attractiveness rankings, are viewed as significantly less socially acceptable than less invasive functionalities. …”

Possible graph filter functions. by Edward Antonian (21453161)

“…<div><p>In this paper, we study a class of non-parametric regression models for predicting graph signals as a function of explanatory variables . Recently, Kernel Graph Regression (KGR) and Gaussian Processes over Graph (GPoG) have emerged as promising techniques for this task. …”

The value functions of comparative policies. by Liying Wang (288626)

“…These expressions are determined using the copula function, and an algorithm is designed to construct the corresponding transition probability matrix. …”

Fig 7 - by Susana Conde (5495675)

Results of the replicated simulations. by Susana Conde (5495675)

“…We propose a <i>K</i>-means-type algorithm in which each cluster is defined by a function-on-function regression model, which, inter alia, allows for multiple functional explanatory variables. …”

S1 File - by Mohammad Hassan Tanha (19832141)

“…<div><p>In the proposed protection coordination scheme, the depreciation of the operation time of the entire relay in the primary and backup protection modes for all possible fault locations is considered as the objective function. The limitations of this problem include the equations for calculating the operation time of the relays in both forward and reverse directions, the limitation of the coordination time interval, the limitation of the setting parameters of the proposed relays, the restriction of the size of the reactance that limits the fault current, and the limitation of the standing time of distributed generation per small signal fault. …”

Flowchart of SAOA. by Mohammad Hassan Tanha (19832141)

“…<div><p>In the proposed protection coordination scheme, the depreciation of the operation time of the entire relay in the primary and backup protection modes for all possible fault locations is considered as the objective function. The limitations of this problem include the equations for calculating the operation time of the relays in both forward and reverse directions, the limitation of the coordination time interval, the limitation of the setting parameters of the proposed relays, the restriction of the size of the reactance that limits the fault current, and the limitation of the standing time of distributed generation per small signal fault. …”

Flowchart of overall solution procedure. by Mohammad Hassan Tanha (19832141)

“…<div><p>In the proposed protection coordination scheme, the depreciation of the operation time of the entire relay in the primary and backup protection modes for all possible fault locations is considered as the objective function. The limitations of this problem include the equations for calculating the operation time of the relays in both forward and reverse directions, the limitation of the coordination time interval, the limitation of the setting parameters of the proposed relays, the restriction of the size of the reactance that limits the fault current, and the limitation of the standing time of distributed generation per small signal fault. …”

Summary of the CEC-2017 test functions. by Xingtao Wu (22139242)

“…<div><p>The icing failures of wind turbine blades are critical factors that affect both power generation efficiency and safety. To improve the diagnostic accuracy and speed, an improved weighted kernel extreme learning machine (IWKELM) optimized by multi-strategy adaptive coati optimization algorithm (MACOA) for icing fault diagnosis model is proposed, i.e., MACOA-IWKELM. …”