School of Computer Science and Informatics

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3D-MSFC: A 3D multi-scale features compression method for object detection
(Elsevier, 2024-11-17) Li, Zhengxin; Tian, Chongzhen; Yuan, Hui; Lu, Xin; Malekmohamadi, Hossein
As machine vision tasks rapidly evolve, a new concept of compression, namely video coding for machines (VCM), has emerged. However, current VCM methods are only suitable for 2D machine vision tasks. With the popularization of autonomous driving, the demand for 3D machine vision tasks has significantly increased, leading to an explosive growth in LiDAR data that requires efficient transmission. To address this need, we propose a machine vision-based point cloud coding paradigm inspired by VCM. Specifically, we introduce a 3D multi-scale features compression (3D-MSFC) method, tailored for 3D object detection. Experimental results demonstrate that 3D-MSFC achieves less than a 3% degradation in object detection accuracy at a compression ratio of 2796×. Furthermore, its low-profile variant, 3D-MSFC-L, achieves less than a 2% degradation in accuracy at a compression ratio of 463×. The above results indicate that our proposed method can provide an ultra-high compression ratio while ensuring no significant drop in accuracy, greatly reducing the amount of data required for transmission during each detection. This can significantly lower bandwidth consumption and save substantial costs in application scenarios such as smart cities.
Open Access
A coevolutionary algorithm with detection and supervision strategy for constrained multiobjective optimization
(IEEE, 2024-06-19) Feng, Jian; Liu, Shaoning; Yang, Shengxiang; Zheng, Jun; Xiao, Qi
Balancing objectives and constraints is challenging in addressing constrained multiobjective optimization problems (CMOPs). Existing methods may have limitations in handling various CMOPs due to the complex geometries of the Pareto front (PF). And the complexity arises from the constraints that narrow the feasible region. Categorizing problems based on their geometric characteristics facilitates facing this challenge. For this purpose, this article proposes a novel constrained multiobjective optimization framework with detection and supervision phases, called COEA-DAS. The framework categorizes the problems into four types based on the overlap between the obtained approximate unconstrained PF and constrained PF to guide the coevolution of the two populations. In the detection phase, the detection population approaches the unconstrained PF ignoring the constraints. The main population is guided by the detection population to cross infeasible barriers and approximate the constrained PF. In the supervision phase, specialized evolutionary mechanisms are designed for each possible problem type. The detection population maintains evolution to assist the main population in spreading along the constrained PF. Meanwhile, the supervision strategy is conducted to reevaluate the problem types based on the evolutionary state of the populations. This idea of balancing constraints and objectives based on the type of problem provides a novel approach for more effectively addressing the CMOPs. Experimental results indicate that the proposed algorithm performs better or more competitively on 57 benchmark problems and 12 real-world CMOPs compared with eight state-of-the-art algorithms.
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A Hybrid Classification and Identification of Pneumonia Using African Buffalo Optimization and CNN from Chest X-Ray Images
(Tech Science Press, 2023-12-15) Alalwan, Nasser; Taloba, Ahmed I.; Abozeid, Amr; Alzahrani, Ahmed Ibrahim; Al-Bayatti, Ali Hilal
An illness known as pneumonia causes inflammation in the lungs. Since there is so much information available from various X-ray images, diagnosing pneumonia has typically proven challenging. To improve image quality and speed up the diagnosis of pneumonia, numerous approaches have been devised. To date, several methods have been employed to identify pneumonia. The Convolutional Neural Network (CNN) has achieved outstanding success in identifying and diagnosing diseases in the fields of medicine and radiology. However, these methods are complex, inefficient, and imprecise to analyze a big number of datasets. In this paper, a new hybrid method for the automatic classification and identification of Pneumonia from chest X-ray images is proposed. The proposed method (ABO-CNN) utilized the African Buffalo Optimization (ABO) algorithm to enhance CNN performance and accuracy. The Weinmed filter is employed for pre-processing to eliminate unwanted noises from chest X-ray images, followed by feature extraction using the Grey Level Co-Occurrence Matrix (GLCM) approach. Relevant features are then selected from the dataset using the ABO algorithm, and ultimately, high-performance deep learning using the CNN approach is introduced for the classification and identification of Pneumonia. Experimental results on various datasets showed that, when contrasted to other approaches, the ABO-CNN outperforms them all for the classification tasks. The proposed method exhibits superior values like 96.95%, 88%, 86%, and 86% for accuracy, precision, recall, and F1-score, respectively.
Open Access
A New Framework for Enhancing VANETs through Layer 2 DLT Architectures with Multiparty Threshold Key Management and PETs
(MDPI, 2024-09-09) Kiraz, Mehmet Sabir; Al-Bayatti, Ali Hilal; Adarbah, Haitham; Kardas, Suleyman; Al-Bayatti, Hilal M. Y.
This work proposes a new architectural approach to enhance the security, privacy, and scalability of VANETs through threshold key management and Privacy Enhancing Technologies (PETs), such as homomorphic encryption and secure multiparty computation, integrated with Decentralized Ledger Technologies (DLTs). These advanced mechanisms are employed to eliminate centralization and protect the privacy of transferred and processed information in VANETs, thereby addressing privacy concerns. We begin by discussing the weaknesses of existing VANET architectures concerning trust, privacy, and scalability and then introduce a new architectural framework that shifts from centralized to decentralized approaches. This transition applies a decentralized ledger mechanism to ensure correctness, reliability, accuracy, and security against various known attacks. The use of Layer 2 DLTs in our framework enhances key management, trust distribution, and data privacy, offering cost and speed advantages over Layer 1 DLTs, thereby enabling secure vehicle-to-everything (V2X) communication. The proposed framework is superior to other frameworks as it improves decentralized trust management, adopts more efficient PETs, and leverages Layer 2 DLT for scalability. The integration of multiparty threshold key management and homomorphic encryption also enhances data confidentiality and integrity, thus securing against various existing cryptographic attacks. Finally, we discuss potential future developments to improve the security and reliability of VANETs in the next generation of networks, including 5G networks.
Embargo
A new framework of change response for dynamic multi-objective optimization
(Elsevier, 2024-02-16) Hu, Yaru; Zou, Juan; Zheng, Jinhua; Jiang, Shouyong; Yang, Shengxiang
Combining response strategies into multi-objective evolutionary algorithms (MOEAs) for dynamic multi-objective optimization problems (DMOPs) is very popular. However, most of them hardly focus on DMOPs via enhancing the operator’s searching ability of MOEAs. We present a new framework of change response called MOEA/D-HSS. When a change is detected, MOEA/D-HSS updates and assesses saved historical information, computing the intensity of change on the decision variables and the similarity between the current environment and historical ones. Hybrid search strategies (HSS) adaptively adjust the searching range of the population in each generational cycle based on the knowledge above, which has a great chance of discovering new promising regions. HSS is integrated into the variation operator of MOEA based on decomposition (MOEA/D-DE) to enhance its search ability. We take into account that the historical information may be useless references in the later stage of the evolution. Thus, the frequency of HSS usage is gradually decreased in every time interval to balance the population’s convergence and diversity. Experimental results demonstrate that MOEA/S-HSS is very competitive on most benchmark problems compared with other state-of-the-art algorithms.
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A novel inertia moment estimation algorithm collaborated with Active Force Control scheme for wheeled mobile robot control in constrained environments
(Elsevier, 2021-06-21) Ali, Mohammed A.H.; Radzak, Muhammad S.A.; Mailah, Musa; Yusoff, Nukman; Razak, Bushroa Abd; Karim, Mohd Sayuti Ab.; Ameen, Wadea; Jabbar, Waheb A.; Alsewari, AbdulRahman A.; Rassem, Taha H.; Nasser, Abdullah B.; Abdulghafor, Rawad
This paper presents a novel inertia moment estimation algorithm to enable the Active Force Control Scheme for tracking a wheeled mobile robot (WMR) effectively in a specific trajectory within constrained environments such as on roads or in factories. This algorithm, also known as laser simulator logic, has the capability to estimate the inertia moment of the AFC-controller when the robot is moving in a pre-planned path with the presence of noisy measurements. The estimation is accomplished by calculating the membership function based on the experts’ views in any form (symmetric or non-symmetric) with lowly or highly overlapped linguistic variables. A new Proportional-Derivative Active Force Controller (PD-AFC-LS-QC), employing the use of laser simulator logic and quick compensation loop, has been developed in this paper to robustly reject the noise and disturbances. This controller has three feedback control loops, namely, internal, external and quick compensation loops to compensate effectively the disturbances in the constrained environments. A simulation and experimental studies on WMR path control in two kinds of environments; namely, zigzag and highly curved terrains, were conducted to verify the proposed algorithm and controller which was then compared with other existed control schemes. The results of the simulation and experimental works show the capability of the proposed algorithms and the controller to robustly move the WMR in the constrained environments.
Embargo
A population hierarchical-based evolutionary algorithm for large-scale many-objective optimization
(Elsevier, 2024-10-19) Wang, Shiting; Zheng, Jinhua; Zou, Yingjie; Liu, Yuan; Zou, Juan; Yang, Shengxiang
In large-scale many-objective optimization problems (LMaOPs), the performance of algorithms faces significant challenges as the number of objective functions and decision variables increases. The main challenges in addressing this type of problem are as follows: the large number of decision variables creates an enormous decision space that needs to be explored, leading to slow convergence; and the high-dimensional objective space presents difficulties in selecting dominant individuals within the population. To address this issue, this paper introduces an evolutionary algorithm based on population hierarchy to address LMaOPs. The algorithm employs different strategies for offspring generation at various population levels. Initially, the population is categorized into three levels by fitness value: poorly performing solutions with higher fitness (P_h), better solutions with lower fitness (P_l), and excellent individuals stored in the archive set (P_a). Subsequently, a hierarchical knowledge integration strategy (HKI) guides the evolution of individuals at different levels. Individuals in P_l generate offspring by integrating differential knowledge from P_a and P_h, while individuals in P_h generate offspring by learning prior knowledge from P_a. Finally, using a cluster-based environment selection strategy balances population diversity and convergence. Extensive experiments on LMaOPs with up to 10 objectives and 5000 decision variables validate the algorithm’s effectiveness, demonstrating superior performance.
Open Access
A prediction and weak coevolution-based dynamic constrained multi-objective optimization
(IEEE, 2024-06-24) Gong, Dunwei; Rong, Miao; Hu, Na; Wang, Yan; Pedrycz, Witold; Yang, Shengxiang
Dynamic multi-objective evolutionary algorithms (DMOEAs) have gained great popularity in dealing with dynamic multi-objective optimization problems (DMOPs). However, existing studies have difficulties in tackling DMOPs subject to (dynamic) constraints. In this paper, we propose a prediction and weak coevolutionary multi-objective optimization algorithm (PWDCMO) to handle dynamic constrained multi-objective optimization problems (DCMOPs), where a prediction strategy is employed to forecast potential optimal regions under the new environment, with a weak coevolutionary constrained multi-objective optimization (CCMO) as the optimizer aiming at balancing exploration and convergence. The proposed method is compared with four popular dynamic constrained multi-objective evolutionary algorithms (DCMOEAs) on six test instances from two various test suites with their convergence and the overall performance being discussed. Furthermore, the performance of the proposed prediction strategy is also investigated to observe its impact on the final results. Additionally, the PWDCMO is employed in the optimization of an integrated coal mine energy system (ICMES) to validate the proficiency in addressing real world problems. Experimental results demonstrate the superiority of PWDCMO.
Embargo
A repulsive-distance-based maximum diversity selection algorithm for multimodal multiobjective optimization
(Elsevier, 2024-12-05) Deng, Qi; Liu, Yuan; Yang, Shengxiang; Zou, Juan; Li, Xijun; Xia, Yizhang; Zheng, Jinhua
Multimodal multiobjective optimization problems (MMOPs) are a challenging class of problems. Several advanced evolutionary algorithms have been developed to solve MMOPs, but these algorithms still have some limitations, such as having many parameters, slow convergence speed, and unsatisfactory performance. To solve these problems, we propose a repulsive-distance-based maximum diversity selection algorithm (RMDS) which aims, during environmental selection, to select individuals with the best comprehensive diversity through the repulsive distance. The repulsive distance is the comprehensive Euclidean distance between an individual and selected individuals with the consideration of distribution of individuals in both the decision and objective spaces. RMDS has the following advantages: first, the repulsive distance allows rational selection of well-distributed solutions in the non-parameterized case. Second, the repulsive distance acts both in the decision and objective spaces, so it provides a good balance between the distribution of the solution set in these two spaces. Third, RDMS has a straightforward principle. Experimental results show that RMDS has superior performance incomparison with other well-known multimodal multiobjective evolutionary algorithms on 31 test functions.
Embargo
A synthetic data generation method and evolutionary transformer model for degradation trajectory prediction in Lithium-ion batteries
(Elsevier, 2024-10-22) Jin, Haiyan; Ru, Rui; Cai, Lei; Meng, Jinhao; Wang, Bin; Peng, Jichang; Yang, Shengxiang
Identifying the long-term degradation of lithium-ion batteries in their early usage phase is crucial for the battery management system (BMS) to properly maintain the battery for practical use. Nevertheless, this procedure is challenging due to variations in the production and operating conditions of the battery. In recent years, it has been empirically proven that the data-driven method is a promising solution for handling the prediction of degradation. However, the lack of appropriate data remains the main obstacle that impacts the ultimate performance of the prediction. Furthermore, the prediction is also influenced by the setup of the predictor, which covers the structure of neural networks and their hyperparameters. The challenge of automating this process remains unresolved. In this study, we propose a novel degradation trajectory prediction framework. First, synthetic data is generated via a conditional generative adversarial network (CGAN), providing the characterization of the battery’s degradation at an early stage and utilizing the argument data to alleviate the issue of insufficient data. Second, an evaluation method to evaluate the quality of the synthetic data is also provided. In addition, a selection method is proposed based on the diversity mechanism to further filter out the redundancy of synthetic data. These two sub-processes aim to promote the quality of the synthetic data. Finally, the synthetic data hybrid with real values is used for the training of a transformer model, whose architecture and hyper-parameters are automatically configured via an evolutionary framework. The experimental results show that the proposed method can achieve accurate predictions compared to its rivals, and its best configuration can be automatically configured without hand-crafted efforts.
Open Access
Adapting genetic algorithms for multifunctional landscape decisions: a theoretical case study on wild bees and farmers in the UK
(Wiley, 2024-09-19) Knight, Ellen; Balzter, Heiko; Breeze, Tom; Brettschneider, Julia; Girling, Robbie; Hagen-Zanker, Alex; Image, Mike; Johnson, Colin; Lee, Christopher; Lovett, Andrew; Petrovskii, Sergei; Varah, Alexa; Whelan, Mick; Yang, Shengxiang; Gardner, Emma
1. Spatial modelling approaches to aid land-use decisions which benefit both wildlife and humans are often limited to the comparison of pre-determined landscape scenarios, which may not reflect the true optimum landscape for any end-user. Furthermore, the needs of wildlife are often under-represented when considered alongside human financial interests in these approaches. 2. We develop a method of addressing these gaps using a case-study of wild bees in the UK, an important group whose declines may adversely affect both human economies and surrounding ecosystems. By combining the genetic algorithm NSGA-II with a process-based pollinator model which simulates bee foraging and population dynamics, Poll4pop, we ‘evolve’ a typical UK agricultural landscape to identify optimum land cover configurations for three different guilds of wild bee. These configurations are compared to those resulting from optimisations for farm income alone, as well as optimisations that seek a compromise between bee populations and farm income objectives. 3. We find that the land cover proportions in landscapes optimised for each bee guild reflect their nesting habitat preferences rather than foraging preferences, highlighting a limiting resource within the study landscape. The spatially explicit nature of these optimised landscapes illustrates how improvement for a given target species may be limited by differences between their movement range and the scale of the units being improved. Land cover composition and configuration differ significantly in landscapes optimised for farm income and bee population growth simultaneously and illustrate how human agents are required to compromise much more when the multifaceted nature of biodiversity is recognised and represented by multiple objectives within an optimisation framework. Our methods provide a way to quantify the extent to which real-life landscapes promote or compromise objectives for different landscape end-users. 4. Our investigation suggests that optimisation set-up (decision-unit scales, traditional choice of a single biodiversity metric) can bias outcomes towards human-centric solutions. It also demonstrates the importance of representing the individual requirements of different actors with different landscape-level needs when using genetic algorithms to support biodiversity-inclusive decision-making in multi-functional landscapes.
Open Access
Advancements in brain tumor identification: Integrating synthetic GANs with federated-CNNs in medical imaging analysis
(Elsevier, 2024-07-03) Alalwan, Nasser; Alwadin, Ayed; Alzahrani, Ahmed Ibrahim; Al-Bayatti, Ali Hilal; Abozeid, Amr; El-Aziz, Rasha M. Abd
Brain tumors are a significant health concern worldwide, necessitating accurate and timely diagnosis for effective treatment planning and management. However, conventional methods for brain tumor identification through medical imaging analysis often face challenges related to accuracy, efficiency, and privacy concerns. Current approaches may struggle with limited datasets, privacy regulations hindering data sharing, and the need for specialized expertise in interpreting medical images. Accurately identifying brain tumors is pivotal in diagnosis, treatment planning, and patient prognosis. This research proposes a novel approach for advancing brain tumor identification by integrating Synthetic Generative Adversarial Networks with federated convolutional neural Networks in medical imaging analysis. Federated-CNNs are a type of neural network architecture designed for federated learning scenarios. In federated learning, model training occurs locally on data distributed across multiple devices or institutions without exchanging raw data. Federated CNNs allow collaborative model training across these distributed datasets by aggregating local model updates rather than exchanging raw data. This approach ensures that sensitive data remain localized within each participating institution, thus addressing privacy concerns in medical imaging analysis. Our methodology harnesses the power of GANs to generate synthetic brain MRI images, addressing data scarcity issues commonly encountered in medical imaging datasets. These synthetic images are then utilized in conjunction with Federated-CNNs, enabling cooperative model training between many healthcare institutions while maintaining the anonymity and privacy of data. Moreover, integrating Federated CNNs ensures that sensitive medical imaging data remain localized within participating institutions, addressing data privacy concerns and fostering collaboration among medical professionals. The research advances medical imaging analysis by introducing a novel methodology that leverages existing technologies to improve brain tumor identification accuracy. Specifically, the feature extraction phase using DenseNet121, implemented in MATLAB, achieves an outstanding accuracy of 99.82 % and outperforms Various existing methods, including Inception-V3, ResNet-18, and GoogleNet, demonstrating the efficacy of our approach in capturing discriminative features from medical imaging data. This high accuracy underscores the potential of our methodology to enhance diagnostic accuracy and clinical decision-making in neurology and oncology. The research offers a promising avenue for further exploration and innovation in medical imaging analysis, with significant implications for improving patient outcomes and advancing healthcare practices.
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Africa, ChatGPT, and Generative AI Systems: Ethical Benefits, Concerns, and the Need for Governance
(MDPI, 2024-06-02) Wakunuma, Kutoma; Eke, Damian
This paper examines the impact and implications of ChatGPT and other generative AI technologies within the African context while looking at the ethical benefits and concerns that are particularly pertinent to the continent. Through a robust analysis of ChatGPT and other generative AI systems using established approaches for analysing the ethics of emerging technologies, this paper provides unique ethical benefits and concerns for these systems in the African context. This analysis combined approaches such as anticipatory technology ethics (ATE), ethical impact assessment (EIA), and ethical issues of emerging ICT applications with AI (ETICA) with specific issues from the literature. The findings show that ChatGPT and other generative AI systems raise unique ethical concerns such as bias, intergenerational justice, exploitation of labour and cultural diversity in Africa but also have significant ethical benefits. These ethical concerns and benefits are considered crucial in shaping the design and deployment of ChatGPT and similar technologies responsibly. It further explores the potential applications of ChatGPT in critical domain areas such as education, agriculture, and healthcare, thereby demonstrating the transformative possibilities that these technologies can have on Africa. This paper underscores the critical role of AI governance as Africa increasingly adopts ChatGPT and similar AI systems. It argues that a comprehensive understanding of AI governance is essential not only for maximising the benefits of generative AI systems but also for facilitating a global dialogue. This dialogue aims to foster shared knowledge and insights between the Global North and the Global South, which is important for the development and creation of inclusive and equitable AI policies and practices that can be beneficial for all regions.
Open Access
An empirical-informed model for the early degradation trajectory prediction of lithium-ion battery
(IEEE, 2024-04-04) Meng, Jinhao; Cai, Lei; Yang, Shengxiang; Li, Junxin; Zhou, Feifan; Peng, Jichang; Song, Zhengxiang
Early prediction of the lithium-ion (Li-ion) battery degradation trajectory is of great importance to arrange the maintenance of battery energy storage systems (BESSs). Although extensive data driven methods have achieved a super good performance in state of health (SOH) and remaining useful life (RUL) prediction, the nonlinear characteristics of the Li-ion battery degradation trajectory still prevent an accurate prediction once very limited cycling data known in advance. To solve this issue, this paper proposes an empirical-informed model for the degradation trajectory prediction with only few data from the Li-ion battery's early cycling stage, which integrates the experience based knowledge to train the data driven model. A novel experience based model is proposed to describe the battery degradation curve, which further guides the training procedure of the long-short term memory (LSTM) network. In addition, XGBoost is selected to use a perceptually important point (PIP) based feature for providing the reference capacities. In this way, the proposed method can implement an end-to-end early prediction of the battery trajectory using only partial charging voltage as the input. The performance of the proposed method is verified on three datasets.
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Assessment of Dataset Scalability for Classification of Black Sigatoka in Banana Crops Using UAV-Based Multispectral Images and Deep Learning Techniques
(MDPI, 2024-09-19) Gongora, Mario Augusto; Linero-Ramos, R.; Parra-Rodríguez, C.; Espinosa-Valdez, A.; Gómez-Rojas, J.
This paper presents an evaluation of different convolutional neural network (CNN) architectures using false-colour images obtained by multispectral sensors on drones for the detection of Black Sigatoka in banana crops. The objective is to use drones to improve the accuracy and efficiency of Black Sigatoka detection to reduce its impact on banana production and improve the sustainable management of banana crops, one of the most produced, traded, and important fruits for food security consumed worldwide. This study aims to improve the precision and accuracy in analysing the images and detecting the presence of the disease using deep learning algorithms. Moreover, we are using drones, multispectral images, and different CNNs, supported by transfer learning, to enhance and scale up the current approach using RGB images obtained by conventional cameras and even smartphone cameras, available in open datasets. The innovation of this study, compared to existing technologies for disease detection in crops, lies in the advantages offered by using drones for image acquisition of crops, in this case, constructing and testing our own datasets, which allows us to save time and resources in the identification of crop diseases in a highly scalable manner. The CNNs used are a type of artificial neural network widely utilised for machine training; they contain several specialised layers interconnected with each other in which the initial layers can detect lines and curves, and gradually become specialised until reaching deeper layers that recognise complex shapes. We use multispectral sensors to create false-colour images around the red colour spectra to distinguish infected leaves. Relevant results of this study include the construction of a dataset with 505 original drone images. By subdividing and converting them into false-colour images using the UAV’s multispectral sensors, we obtained 2706 objects of diseased leaves, 3102 objects of healthy leaves, and an additional 1192 objects of non-leaves to train classification algorithms. Additionally, 3640 labels of Black Sigatoka were generated by phytopathology experts, ideal for training algorithms to detect this disease in banana crops. In classification, we achieved a performance of 86.5% using false-colour images with red, red edge, and near-infrared composition through MobileNetV2 for three classes (healthy leaves, diseased leaves, and non-leaf extras). We obtained better results in identifying Black Sigatoka disease in banana crops using the classification approach with MobileNetV2 as well as our own datasets.
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Autonomous sensory Meridian response as a physically felt signature of positive and negative emotions
(Frontiers, 2024-03-01) Leung, Wai Lam; Romano, Daniela M.
Introduction: Current research on Autonomous Sensory Meridian Response (ASMR) assumes that ASMR is always accompanied by contentment, and it is distinct from frisson due to positive emotions. Thus, research investigations tend to limit their scope to solely focusing on the sensation of relaxation that ASMR induces. This study explores whether it is possible to have a different emotional experience and still perceive ASMR, testing the theory of ASMR as an amplifier of pre-existing emotion instead of a determination of positive affect. Methods: The emotional arousal and valence, and mood changes of 180 ASMR-capable and incapable individuals were analysed using questionnaires after altering the affective interpretation associated with auditory ASMR (tapping) with visual priming to examine whether the primed emotion (fearful, relaxing, or neutral) could be amplified. Results: It was found that an ASMR response occurred in all priming conditions, including the fear priming group. No significant difference was found in the emotional outcome or mood of the neutral and relaxing priming groups. Upon comparison with ASMR-incapable individuals, both the relaxing and neutral priming groups demonstrated the same affect, but greater potent for ASMR-capable. Individuals who appraised ASMR after visual fear priming demonstrated a significant decrease in positive emotional valence and increased arousal. Conclusion: The findings suggest that ASMR occurs in both positive and negative emotional situations, suppressing contentment induction if ASMR stimuli are interpreted negatively and amplifying contentment when interpreted positively. While more research is needed, the results highlight that ASMR and frisson might describe the same phenomenon, both a physically felt signature of emotion. Therapeutic usage of ASMR should carefully select appropriate stimuli that emphasise contentment to avoid potential health risks associated with negative emotions until a further understanding of ASMR’s affective parameters has been established.
Open Access
Benchmark for CEC 2024 Competition on Multiparty Multiobjective Optimization
(2024-02) Luo, Wenjian; Xu, Peilan; Yang, Shengxiang; Shi, Yuhui
The competition focuses on Multiparty Multiobjective Optimization Problems (MPMOPs), where multiple decision makers have conflicting objectives, as seen in applications like UAV path planning. Despite their importance, MPMOPs remain understudied in comparison to conventional multiobjective optimization. The competition aims to address this gap by encouraging researchers to explore tailored modeling approaches. The test suite comprises two parts: problems with common Pareto optimal solutions and Biparty Multiobjective UAV Path Planning (BPMO-UAVPP) problems with unknown solutions. Optimization algorithms for the first part are evaluated using Multiparty Inverted Generational Distance (MPIGD), and the second part is evaluated using Multiparty Hypervolume (MPHV) metrics. The average algorithm ranking across all problems serves as a performance benchmark.
Open Access
Benchmark Functions for CEC 2022 Competition on Seeking Multiple Optima in Dynamic Environments
(2022-01) Luo, Wenjian; Lin, Xin; Li, Changhe; Yang, Shengxiang; Shi, Yuhui
Dynamic and multimodal features are two important properties and widely existed in many real-world optimization problems. The former illustrates that the objectives and/or constraints of the problems change over time, while the latter means there is more than one optimal solution (sometimes including the accepted local solutions) in each environment. The dynamic multimodal optimization problems (DMMOPs) have both of these characteristics, which have been studied in the field of evolu tionary computation and swarm intelligence for years, and attract more and more attention. Solving such problems requires optimization algorithms to simultaneously track multiple optima in the changing environments. So that the decision makers can pick out one optimal solution in each environment according to their experiences and preferences, or quickly turn to other solutions when the current one cannot work well. This is very helpful for the decision makers, especially when facing changing environments. In this competition, a test suit about DMMOPs is given, which models the real-world applications. Specifically, this test suit adopts 8 multimodal functions and 8 change modes to construct 24 typical dynamic multimodal optimization problems. Meanwhile, the metric is also given to measure the algorithm performance, which considers the average number of optimal solutions found in all environments. This competition will be very helpful to promote the development of dynamic multimodal optimization algorithms.
Open Access
Benchmark Problems for CEC2023 Competition on Dynamic Constrained Multiobjective Optimization
(2022-12) Gui, Yinan; Chen, Guoyu; Yue, Caitong; Liang, Jing; Wang, Yong; Yang, Shengxiang
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Comparative Analysis of Imputation Methods for Enhancing Predictive Accuracy in Data Models
(Society of Visual Informatics, 2024-09-25) Zamri, Nurul Aqilah; Jaya, M. Izham; Irawati, Indrarini Dyah; Rassem, Taha H.; Rasyidah; Kasim, Shahreen
The presence of missing values within datasets can introduce a detrimental bias, significantly impeding the predictive algorithm's ability to discern patterns and accurately execute prediction. This paper aims to elucidate the intricacies of data imputation methods, providing a more profound understanding of prevalent imputation methods, including list-wise deletion (IGN), mean imputation (AVG), K-Nearest Neighbors (KNN), MissForest (MF), and Predictive Mean Matching (PMM). The dataset employed in this study consists of financial data about S&P 500 companies in the Compustat North America database. The training and validation dataset encompasses 1973 instances, consisting of data during the fourth quarter of 2009, the first quarter of 2010, and the third quarter of 2014. Within this set, 457 missing values were identified and imputed. The test dataset comprises 197 randomly selected instances from the fourth quarter of 2014, equivalent to ten percent of the total instances in the training dataset. The evaluation findings prominently position the dataset derived from MF imputation as the leading performer among all the imputed datasets. The insights derived from this study are intended to assist practitioners in making informed choices when selecting the most suitable data imputation method, particularly in the context of predictive modeling tasks.

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