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Customer data analysis helps companies to understand customer intentions and behaviors better. This study introduces an analysis of product reviews to help managers adopt a more efficient strategy to extract valuable knowledge and help detect segment of customers that need a special attention and products that need improvement or with the most impact. The used dataset is a set of Amazon reviews divided into multiple categories; each review has a target column called 'overall' that takes a value between 1 and 5 (customer's satisfaction). Based on the 'overall' column, multiple labeling methods have been used and compared to get a binary target variable, positive or negative, that affects a class to a review. This dataset contains more than one million reviews and can give companies great insight into products' quality and customers' retention. This work has materialized by using customer segmentation and competitive learning with self-organizing map (SOM) Model and adopting a new approach to explore the generated network/map, it is based on clustering and map nodes labelling using a majority voting process. The results show that the proposed dual approach combining the prior knowledge, related to supervised learning, and the competitive learning abilities enhances the SOM model's capabilities. [ABSTRACT FROM AUTHOR]

Abstract Non-orthogonal Multiple Access (NOMA) techniques offer potential enhancements in spectral efficiency for 5G and 6G wireless networks, facilitating broader network access. Central to realizing optimal system performance are factors like joint power control, user grouping, and decoding order. This study investigates power control and user grouping to optimize spectral efficiency in NOMA uplink systems, aiming to reduce computational difficulty. While previous research on this integrated optimization has identified several near-optimal solutions, they often come with considerable system and computational overheads. To address this, this study employed an improved Grey Wolf Optimizer (GWO), a nature-inspired metaheuristic optimization method. Although GWO is effective, it can sometimes converge prematurely and might lack diversity. To enhance its performance, this study introduces a new version of GWO, integrating Competitive Learning, Q-learning, and Greedy Selection. Competitive learning adopts agent competition, balancing exploration and exploitation and preserving diversity. Q-learning guides the search based on past experiences, enhancing adaptability and preventing redundant exploration of sub-optimal regions. Greedy selection ensures the retention of the best solutions after each iteration. The synergistic integration of these three components substantially enhances the performance of the standard GWO. This algorithm was used to manage power and user-grouping in NOMA systems, aiming to strengthen system performance while restricting computational demands. The effectiveness of the proposed algorithm was validated through numerical evaluations. Simulated outcomes revealed that when applied to the joint challenge in NOMA uplink systems, it surpasses the spectral efficiency of conventional orthogonal multiple access. Moreover, the proposed approach demonstrated superior performance compared to the standard GWO and other state-of-the-art algorithms, achieving reduced system complexity under identical constraints.

This study aimed to investigate the variation of individual and group competitive learning strategies on technique, kinematic variables in the front crawl stroke swimming, and intelligent physical-motor among swimming level 2 course students. The sample of the study consisted of 16 students, it was selected based on Intentional style of swimming level 2 course students involved in the second semester (2018/2019) who divided into two similar groups; A: control group (8) students learning with the traditional style, B: experimental group (8) students learning with competitive strategy. The following variables have been measured: skill level, frequency and stroke length, and speed of swimming 50m at front crawl stroke as well as intelligent physical-motor. The results of the study showed that the traditional learning method has a positive impact on improving skillfulness and speed of swimming 50 m at front crawl stroke performance. On the other hand, a competitive learning strategy has a significant effect in improving the performance of swimming 50m at front crawl stroke between students. The researcher recommends using a competitive learning strategy better when implementing swimming education programs to enhance technique skills and mechanical performance in front crawl stroke swimming and evaluate the level of intelligent physical-motor to know the right and viable strategy that would be used in swimming learning and use the results of kinetic analysis when evaluating students swimming to give feedback to detect weaknesses in performance to modify them. [ABSTRACT FROM AUTHOR]

Non-orthogonal Multiple Access (NOMA) techniques offer potential enhancements in spectral efficiency for 5G and 6G wireless networks, facilitating broader network access. Central to realizing optimal system performance are factors like joint power control, user grouping, and decoding order. This study investigates power control and user grouping to optimize spectral efficiency in NOMA uplink systems, aiming to reduce computational difficulty. While previous research on this integrated optimization has identified several near-optimal solutions, they often come with considerable system and computational overheads. To address this, this study employed an improved Grey Wolf Optimizer (GWO), a nature-inspired metaheuristic optimization method. Although GWO is effective, it can sometimes converge prematurely and might lack diversity. To enhance its performance, this study introduces a new version of GWO, integrating Competitive Learning, Q-learning, and Greedy Selection. Competitive learning adopts agent competition, balancing exploration and exploitation and preserving diversity. Q-learning guides the search based on past experiences, enhancing adaptability and preventing redundant exploration of sub-optimal regions. Greedy selection ensures the retention of the best solutions after each iteration. The synergistic integration of these three components substantially enhances the performance of the standard GWO. This algorithm was used to manage power and user-grouping in NOMA systems, aiming to strengthen system performance while restricting computational demands. The effectiveness of the proposed algorithm was validated through numerical evaluations. Simulated outcomes revealed that when applied to the joint challenge in NOMA uplink systems, it surpasses the spectral efficiency of conventional orthogonal multiple access. Moreover, the proposed approach demonstrated superior performance compared to the standard GWO and other state-of-the-art algorithms, achieving reduced system complexity under identical constraints. [ABSTRACT FROM AUTHOR]

Copyright of Wool Textile Journal is the property of National Wool Textile Science & Technology Information Center and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The increasing interest in Learning Experience Design (LXD) has consolidated this field as a new way to transform educational practices. Thanks to its interdisciplinary nature which is mainly rooted in the close relation between human-computer interaction, user experience design and the learning sciences, LXD is a field that demands frameworks to design and develop products that can grow into services, to create interactive learning environments able to provide improvement-driven analytics and to guarantee a significant and satisfactory experience, designed to achieve learning outcomes. Innovative Village serious video game (IVVG) is a service-oriented product within an entrepreneurship and innovation system of platforms developed as an abilities-focused learning environment, and that builds a case study for LXD. This research aims to contribute to the consolidation of the emergent field of Learning Experience Design by providing a case around the Entrepreneurship and Innovation area from EAFIT University in Medellin, a learning system that comprises several service-oriented products; by being one of the products that constitute this system, and as a serious video game, Innovative Village has proven to be a key player in facilitating the learning outcomes and the knowledge integration that stem from the learning environment of the Interactive Design program, where the students' learning experiences take place. First, related theoretical concepts and historical data will be analyzed to provide background information, then the case study will be addressed focusing on the materials, methods, and results. The study shows that the video game encourages collaborative behavior between players, as perceived by a significant proportion of participants. The research establishes a link between this perception and the role of creaticides in the game. Learning Experience Design (LXD) is about creating products that link the learning process with key competences. The game "Innovative Village" exemplifies this approach and provides insights into design, use, and required competencies. It also presents a framework for designing user-centered learning experiences that incorporate assessments to enhance the learning process. This framework is applicable from the early stages and can be tested in real learning environments. [ABSTRACT FROM AUTHOR]

Self-organizing Map (SOM) neural network is a prominent algorithm in unsupervised machine learning, which is widely used for data clustering, high-dimensional visualization, and feature extraction. However, the hardware implementation of SOM is limited by the von Neumann bottleneck. Herein, a SOM neural network is implemented by the combination of 3D NAND flash memory arrays and in-memory Euclidean distance (ED) calculation. The weights in the SOM network are mapped to the conductance of the 3D NAND differential pair. It is experimentally demonstrated that the differential pair in 3D NAND flash array possesses superior characteristics for neuromorphic computing during increasing and decreasing synaptic weight. Using the 3D NAND-based SOM, a competitive learning neural network is established and used for the unsupervised classification of a set of Gaussian distribution data points. The experimental results illustrate the excellent performance and efficiency of the proposed architecture, highlighting the potential of 3D NAND-based in-memory computing for artificial intelligence applications.

Heating, ventilation and air conditioning (HVAC) systems account for approximately 50% of the total energy consumption in buildings. Advanced control and optimal operation, seen as key technologies in reducing the energy consumption of HVAC systems, indispensably rely on an accurate prediction of the building's heating/cooling load. Therefore, the goal of this research is to develop a model capable of making such accurate predictions. To streamline the process, this study employs sensitivity and correlation analysis for feature selection, thereby eliminating redundant parameters, and addressing distortion problems caused by multicollinearity among input parameters. Four model identification methods including multivariate polynomial regression (MPR), support vector regression (SVR), multilayer perceptron (MLP), and extreme gradient boosting (XGBoost) are implemented in parallel to extract value from diverse building datasets. These models are trained and selected autonomously based on statistical performance criteria. The prediction models were deployed in a nearly zero-energy office building, and the impacts of feature selection, training set size, and real-world uncertainty factors were analyzed and compared. The results showed that feature selection considerably improved prediction accuracy while reducing model dimensionality. The research also recognized that prediction accuracy during model deployment can be influenced significantly by factors like personnel mobility during holidays and weather forecast uncertainties. Additionally, for nearly zero-energy buildings, the thermal inertia of the building itself can considerably impact prediction accuracy in certain scenarios. [ABSTRACT FROM AUTHOR]

The increasing interest in Learning Experience Design (LXD) has consolidated this field as a new way to transform educational practices. Thanks to its interdisciplinary nature which is mainly rooted in the close relation between human–computer interaction, user experience design and the learning sciences, LXD is a field that demands frameworks to design and develop products that can grow into services, to create interactive learning environments able to provide improvement-driven analytics and to guarantee a significant and satisfactory experience, designed to achieve learning outcomes. Innovative Village serious video game (IVVG) is a service-oriented product within an entrepreneurship and innovation system of platforms developed as an abilities-focused learning environment, and that builds a case study for LXD. This research aims to contribute to the consolidation of the emergent field of Learning Experience Design by providing a case around the Entrepreneurship and Innovation area from EAFIT University in Medellin, a learning system that comprises several service-oriented products; by being one of the products that constitute this system, and as a serious video game, Innovative Village has proven to be a key player in facilitating the learning outcomes and the knowledge integration that stem from the learning environment of the Interactive Design program, where the students’ learning experiences take place. First, related theoretical concepts and historical data will be analyzed to provide background information, then the case study will be addressed focusing on the materials, methods, and results. The study shows that the video game encourages collaborative behavior between players, as perceived by a significant proportion of participants. The research establishes a link between this perception and the role of creaticides in the game. Learning Experience Design (LXD) is about creating products that link the learning process with key competences. The game “Innovative Village” exemplifies this approach and provides insights into design, use, and required competencies. It also presents a framework for designing user-centered learning experiences that incorporate assessments to enhance the learning process. This framework is applicable from the early stages and can be tested in real learning environments.

In the realm of computational problem-solving, the search for efficient algorithms tailored for real-world engineering challenges and software requirement prioritization is relentless. This paper introduces the Multi-Learning-Based Reptile Search Algorithm (MLBRSA), a novel approach that synergistically integrates Q-learning, competitive learning, and adaptive learning techniques. The essence of multi-learning lies in harnessing the strengths of these individual learning paradigms to foster a more robust and versatile search mechanism. Q-learning brings the advantage of reinforcement learning, enabling the algorithm to make informed decisions based on past experiences. On the other hand, competitive learning introduces an element of competition, ensuring that the best solutions are continually evolving and adapting. Lastly, adaptive learning ensures the algorithm remains flexible, adjusting the traditional Reptile Search Algorithm (RSA) parameters. The application of the MLBRSA to numerical benchmarks and a few real-world engineering problems demonstrates its ability to find optimal solutions in complex problem spaces. Furthermore, when applied to the complicated task of software requirement prioritization, MLBRSA showcases its capability to rank requirements effectively, ensuring that critical software functionalities are addressed promptly. Based on the results obtained, the MLBRSA stands as evidence of the potential of multi-learning, offering a promising solution to engineering and software-centric challenges. Its adaptability, competitiveness, and experience-driven approach make it a valuable tool for researchers and practitioners. [ABSTRACT FROM AUTHOR]

The present paper aims to propose a new learning and interpretation method called "impartial competitive learning", meaning that all participants in a competition should be winners. Due to its importance, the impartiality is forced to be realised even by increasing the corresponding cost in terms of the strength of weights. For the first approximation, three types of impartial competition can be developed: componential, computational, and collective competition. In the componential competition, every weight should have an equal chance on average to win the competition. In the computational competition, all computational procedures should have an equal chance to be applied sequentially in learning. In collective computing for interpretation, all network configurations, obtained by learning, have an equal chance to participate in a process of interpretation, representing one of the most idealised forms of impartiality. The method was applied to a well-known second-language-learning data set. The intuitive conclusion, stressed in the specific science, could not be extracted by the conventional natural language processing methods, because they can deal only with word frequency. The present method tried to extract a main feature beyond the word frequency by competing connection weights and computational procedures impartially, followed by collective and impartial competition for interpretation. [ABSTRACT FROM AUTHOR]

Competitive‐learning‐based spiking neural networks are capable of rapid, highly accurate pattern recognition with minimal data through denoising mechanisms provide by adaptive interneuron inhibition. However, hardware implementations of such networks are currently area‐inefficient due to the high device count require to execute dual excitatory‐inhibitory synapses. To mitigate this, n‐/p‐ reconfigurable tungsten diselenide memtransistors is introduced that can execute excitatory and inhibitory synapses in a highly compact bio‐inspired feature extractor hardware architecture. The reconfigurability is realized through a dual mode memory device with a flash‐memory‐like floating‐gate for n‐/p‐ programing and a memristor‐like selenium vacancy‐based resistive switching that varies in memristive output with majority carrier modulation. Through a device‐system codesign, an effective 27% device count reduction in the peripheral circuits is achieved , which ameliorates circuit component congestion and circuit complexity. Compared to the prevalent winner‐takes‐all approach, the proposed machine learning with adaptive interneuron inhibition achieves high‐accuracy convergence with up to five times smaller training dataset. This accelerated learning can potentially enable edge‐artificial intelligence (AI) processors capable of ultra‐low‐energy training with limited data. [ABSTRACT FROM AUTHOR]

Recent advances in reinforcement learning with social agents have allowed such models to achieve human-level performance on certain interaction tasks. However, most interactive scenarios do not have performance alone as an end-goal; instead, the social impact of these agents when interacting with humans is as important and largely unexplored. In this regard, this work proposes a novel reinforcement learning mechanism based on the social impact of rivalry behavior. Our proposed model aggregates objective and social perception mechanisms to derive a rivalry score that is used to modulate the learning of artificial agents. To investigate our proposed model, we design an interactive game scenario, using the Chef's Hat Card Game, and examine how the rivalry modulation changes the agent's playing style, and how this impacts the experience of human players on the game. Our results show that humans can detect specific social characteristics when playing against rival agents when compared to common agents, which affects directly the performance of the human players in subsequent games. We conclude our work by discussing how the different social and objective features that compose the artificial rivalry score contribute to our results. [ABSTRACT FROM AUTHOR]

Heating, ventilation and air conditioning (HVAC) systems account for approximately 50% of the total energy consumption in buildings. Advanced control and optimal operation, seen as key technologies in reducing the energy consumption of HVAC systems, indispensably rely on an accurate prediction of the building’s heating/cooling load. Therefore, the goal of this research is to develop a model capable of making such accurate predictions. To streamline the process, this study employs sensitivity and correlation analysis for feature selection, thereby eliminating redundant parameters, and addressing distortion problems caused by multicollinearity among input parameters. Four model identification methods including multivariate polynomial regression (MPR), support vector regression (SVR), multilayer perceptron (MLP), and extreme gradient boosting (XGBoost) are implemented in parallel to extract value from diverse building datasets. These models are trained and selected autonomously based on statistical performance criteria. The prediction models were deployed in a nearly zero-energy office building, and the impacts of feature selection, training set size, and real-world uncertainty factors were analyzed and compared. The results showed that feature selection considerably improved prediction accuracy while reducing model dimensionality. The research also recognized that prediction accuracy during model deployment can be influenced significantly by factors like personnel mobility during holidays and weather forecast uncertainties. Additionally, for nearly zero-energy buildings, the thermal inertia of the building itself can considerably impact prediction accuracy in certain scenarios.

Copyright of Journal of Signal Processing is the property of Journal of Signal Processing and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Early diagnosis of epilepsy is crucial for patient survival and well-being, making it essential to develop effective methods for early disease detection based on health parameters. This paper introduces a novel approach to enhance electroencephalogram (EEG)--based epileptic seizure detection, presenting a robust Takagi-Sugeno-Kang (TSK) fuzzy classifier. Our key contribution lies in the integration of advanced techniques, specifically, possibility-based clustering with competitive learning (PC-CL), into the classifier. This not only refines fuzzy rule parameters with unprecedented precision but also imparts resilience against noise, elevating the interpretability of the model. The distinctive amalgamation of PC-CL ensures a more nuanced and accurate representation of complex EEG patterns associated with epileptic seizures. Additionally, a hybrid approach combining a twin support vector machine with a fuzzy system (TSVM-PC-CL-TSK) is proposed for epilepsy classification. Results from experiments show that the proposed classifier performs more efficiently than previous, achieving an impressive 99.41 % accuracy rate in identifying abnormal EEG signals associated with epileptic seizures. The research showcases the significant potential of this innovative approach in epilepsy research and clinical applications, offering improved epileptic seizure detection and elevated patient care standards. [ABSTRACT FROM AUTHOR]

In the realm of computational problem-solving, the search for efficient algorithms tailored for real-world engineering challenges and software requirement prioritization is relentless. This paper introduces the Multi-Learning-Based Reptile Search Algorithm (MLBRSA), a novel approach that synergistically integrates Q-learning, competitive learning, and adaptive learning techniques. The essence of multi-learning lies in harnessing the strengths of these individual learning paradigms to foster a more robust and versatile search mechanism. Q-learning brings the advantage of reinforcement learning, enabling the algorithm to make informed decisions based on past experiences. On the other hand, competitive learning introduces an element of competition, ensuring that the best solutions are continually evolving and adapting. Lastly, adaptive learning ensures the algorithm remains flexible, adjusting the traditional Reptile Search Algorithm (RSA) parameters. The application of the MLBRSA to numerical benchmarks and a few real-world engineering problems demonstrates its ability to find optimal solutions in complex problem spaces. Furthermore, when applied to the complicated task of software requirement prioritization, MLBRSA showcases its capability to rank requirements effectively, ensuring that critical software functionalities are addressed promptly. Based on the results obtained, the MLBRSA stands as evidence of the potential of multi-learning, offering a promising solution to engineering and software-centric challenges. Its adaptability, competitiveness, and experience-driven approach make it a valuable tool for researchers and practitioners.

Covariance matrices of spatially-correlated wireless channels in millimeter wave (mmWave) vehicular networks can be employed to design environment-aware beamforming codebooks. Such covariance matrices can be represented over non-Euclidean (i.e., curved surfaces) manifolds, thanks to their symmetric positive definite (SPD) structures. In this paper, we propose three learning models for channel covariance estimation over Riemannian manifolds. First, we propose an unsupervised Riemannian K-means clustering approach, where Log-Euclidean metric (LEM) is utilized as a distance metric among channel covariance matrices over the Riemannian manifold. Second, we propose a Riemannian Competitive Learning (RCL) model, which is an online clustering solution that is intended to reduce the learning time of the offline K-means models. Third, we apply a Riemannian Dictionary Learning (RDL) model that leverages the sparsity properties of mmWave channels, while estimating their channel covariance matrices. Simulation results show that the proposed Riemannian K-means, online RCL and RDL models can achieve 41%, 30% and 44% higher data rate, respectively, than their Euclidean-based counterparts. Furthermore, they require few training samples and hence fast construction of codebook design in dynamic environments, which leads to low latency communication.

In this paper, we propose a novel algorithm based on Zidan's quantum computing model for remotely controlling the direction of a quantumcontrolledmobile robot equippedwith n-movements. The proposed algorithm is based on the measurement of concurrence value for the different movements of the robot. Consider a faraway robot that moves in the deep space (e.g., moves toward a galaxy), and it is required to control the direction of this robot from a ground station by some person Alice. She sends an unknown qubit α |0〉+ß |1〉 via the teleportation protocol to the robot. Then, the proposed algorithm decodes the received unknown qubit into an angle θ, that determines the motion direction of the robot, based on the concurrence value. The proposed algorithm has been tested for four and eight movements. Two simulators have been tested; IBM Quantum composer and IBM's system, The two simulators achieved the same result approximately. The motion of any part of the robot is considered, if it has a pre-existing sensor system and a locomotive system. We can use this technique in many places like in space robots (16 directions). The results show that the proposed technique can be easily used for a huge number of movements. However, increasing the number of movements of the robot will increase the number of qubits. [ABSTRACT FROM AUTHOR]

Applying conventional autoencoders for textual data often results in learning trivial and redundant representations due to high text dimensionality, sparsity, and following power-law word distribution. To address these challenges, we propose two novel autoencoders, SCAT (Second Chance Autoencoder for Text) and SSCAT (Similarity-based SCAT). Our autoencoders utilize competitive learning among the k winner neurons in the bottleneck layer, which become specialized in recognizing specific patterns, leading to learning more semantically meaningful representations of textual data. In addition, the SSCAT model presents a novel competition based on a similarity measurement to eliminate redundant features. Our experiments prove that SCAT and SSCAT achieve high performance on several tasks, including classification, topic modeling, and document visualization, compared to LDA, k-Sparse, KATE, ProdLDA, NVCTM and ZeroShotTM.The experiments were conducted using the 20 Newsgroups, Wiki10+, and Reuters datasets. [ABSTRACT FROM AUTHOR]

Person detection has attracted great attention in the computer vision area and is an imperative element in human-centric computer vision. Although the predictive performances of person detection networks have been improved dramatically, they are vulnerable to adversarial patch attacks. Changing the pixels in a restricted region can easily fool the person detection network in safety-critical applications such as autonomous driving and security systems. Despite the necessity of countering adversarial patch attacks, very few efforts have been dedicated to defending person detection against adversarial patch attack. In this paper, we propose a novel defense strategy that defends against an adversarial patch attack by optimizing a defensive frame for person detection. The defensive frame alleviates the effect of the adversarial patch while maintaining person detection performance with clean person. The proposed defensive frame in the person detection is generated with a competitive learning algorithm which makes an iterative competition between detection threatening module and detection shielding module in person detection. Comprehensive experimental results demonstrate that the proposed method effectively defends person detection against adversarial patch attacks. [ABSTRACT FROM AUTHOR]

This article proposes a novel semi-supervised competitive learning (SSCL) algorithm for vocal pattern classifications in Parkinson's disease (PD). The acoustic parameters of voice records were grouped into the families of jitter, shimmer, harmonic-to-noise, frequency, and nonlinear measures, respectively. The linear correlations were computed within each acoustic parameter family. According to the correlation matrix results, the jitter, shimmer, and harmonic-to-noise parameters presented as highly correlated in terms of Pearson's correlation coefficients. Then, the principal component analysis (PCA) technique was implemented to eliminate the redundant dimensions of the acoustic parameters for each family. The Mann–Whitney–Wilcoxon hypothesis test was used to evaluate the significant difference of the PCA-projected features between the healthy subjects and PD patients. Eight dominant PCA-projected features were selected based on the eigenvalue threshold criterion and the statistical significance level (p < 0.05) of the hypothesis test. The SSCL algorithm proposed in this paper included the procedures of the competitive prototype seed selection, K-means optimization, and the nearest neighbor classifications. The pattern classification experimental results showed that the proposed SSCL method can provide the excellent diagnostic performances in terms of accuracy (0.838), recall (0.825), specificity (0.85), precision (0.846), F-score (0.835), Matthews correlation coefficient (0.675), area under the receiver operating characteristic curve (0.939), and Kappa coefficient (0.675), which were consistently better than those results of conventional KNN or SVM classifiers. [ABSTRACT FROM AUTHOR]

This study aimed at investigating the comparative effects of cooperative and competitive learning on English as Foreign Language (EFL) learners’ speaking ability and self-confidence. Moreover, EFL learners’ attitude towards cooperative and competitive learning procedures were explored. To these ends 90 learners were selected on the basis of a sample PET results and were assigned into three 30-learner cooperative, competitive and control groups. The speaking section of a sample PET test and Academic Confidence Scale were administered to the three groups before the treatment. As the treatment, the first experimental group members were engaged in cooperative learning, the second experimental group members were engaged in competitive learning, and the control group received regular teacher-fronted instruction. At the end of the treatment, the speaking section of another sample PET and Academic Confidence Scale were re-administered as the posttests.10 EFL learners were randomly selected from each experimental group and seated for a semi-structured interview. The analyses revealed that while both cooperative and competitive learning procedures had significant within-group effects on the learners' speaking ability, the impact on academic self-confidence was significant only for cooperative learning procedure. Moreover, analyses verified that the impact of cooperative learning on speaking ability and self-confidence of EFL learners was statistically superior to the other procedures. Content analysis of the interview data showed that the majority of EFL learners believed that cooperative learning helps them develop their own ideas in greater depth, enhance their participation and creativity in speaking, improve self-confidence and reduce anxiety.

In a competitive game scenario, a set of agents have to learn decisions that maximize their goals and minimize their adversaries' goals at the same time. Besides dealing with the increased dynamics of the scenarios due to the opponents' actions, they usually have to understand how to overcome the opponent's strategies. Most of the common solutions, usually based on continual learning or centralized multi-agent experiences, however, do not allow the development of personalized strategies to face individual opponents. In this paper, we propose a novel model composed of three neural layers that learn a representation of a competitive game, learn how to map the strategy of specific opponents, and how to disrupt them. The entire model is trained online, using a composed loss based on a contrastive optimization, to learn competitive and multiplayer games. We evaluate our model on a pokemon duel scenario and the four-player competitive Chef's Hat card game. Our experiments demonstrate that our model achieves better performance when playing against offline, online, and competitive-specific models, in particular when playing against the same opponent multiple times. We also present a discussion on the impact of our model, in particular on how well it deals with on specific strategy learning for each of the two scenarios. [ABSTRACT FROM AUTHOR]

Due to the current high availability of omics, data-driven biology has greatly expanded, and several papers have reviewed state-of-the-art technologies. Nowadays, two main types of investigation are available for a multi-omics dataset: extraction of relevant features for a meaningful biological interpretation and clustering of the samples. In the latter case, a few reviews refer to some outdated or no longer available methods, whereas others lack the description of relevant clustering metrics to compare the main approaches. This work provides a general overview of the major techniques in this area, divided into four groups: graph, dimensionality reduction, statistical and neural-based. Besides, eight tools have been tested both on a synthetic and a real biological dataset. An extensive performance comparison has been provided using four clustering evaluation scores: Peak Signal-to-Noise Ratio (PSNR), Davies-Bouldin(DB) index, Silhouette value and the harmonic mean of cluster purity and efficiency. The best results were obtained by using the dimensionality reduction, either explicitly or implicitly, as in the neural architecture. [ABSTRACT FROM AUTHOR]

The multidimensional knapsack problem (MKP) is a well-known combinatorial optimization problem with many real-life applications. In this article, a memetic algorithm based on probability learning (MA/PL) is proposed to solve MKP. The main highlights of this article are two-fold: 1) problem-dependent heuristics for MKP and 2) a novel framework of MA/PL. For the problem-dependent heuristics, we first propose two kinds of logarithmic utility functions (LUFs) based on the special structure of MKP, in which the profit value and weight vector of each item are considered simultaneously. Then, LUFs are applied to effectively guide the repair operator for infeasible solutions and the local search operator. For the framework of MA/PL, we propose two problem-dependent probability distributions to extract the special knowledge of MKP, that is, the marginal probability distribution (MPD) of each item and the joint probability distribution (JPD) of two conjoint items. Next, learning rules for MPD and JPD, which borrow ideas from competitive learning and binary Markov chain, are proposed. Thereafter, we generate MA/PL’s offspring by integrating MPD and JPD, such that the univariate probability information of each item as well as the dependency of conjoint items can be sufficiently used. Results of experiments on 179 benchmark instances and a real-life case study demonstrate the effectiveness and practical values of the proposed MKP. [ABSTRACT FROM AUTHOR]

The study opted to: 1) Investigate differences between cooperative and competitive learning modes in the extent to which they affect English language achievement; 2) Find gender, intra-gender, and inter-gender differences in English language achievement within and across the cooperative, competitive, and control group learning conditions; and 3) Study the relationship between students’ motivation to learn the English language and English language achievement. The cooperative and competitive learning groups were used as treatment groups while the control group was the individualized learning group. An English language test was administered to 120 secondary school grade nine students. The 12-item mini-Attitude/Motivation Test Battery (Mini-AMTB) was administered to the students. Different parametric tests were used in the pre-test and post-test data analysis. Post-test data analysis results revealed that cooperative learners significantly outperformed both competitive learners and the control group, but the control group significantly outperformed competitive learners. The gender difference in English language achievement was not significant. The cooperative learning (CL) mode was favored by both male and female students but more favored by males. The five aggregate measures of the mini-AMTB (Integrativeness, Motivational Intensity, Attitudes towards the Learning Situation, Instrumental Orientation, and Parental Encouragement) produced significant positive correlations with English language achievement; however, Language Anxiety negatively and significantly correlated with English language achievement. The CL mode, with its effective CL technique, was recommended to be researched and applied by trained teachers to improve student achievement. Its implications for teacher training were also given. Treatment of second language motivation was recommended as an important issue in second language learning.

In this paper, we propose a novel method of cluster analysis called unsupervised functional link artificial neural networks (UFLANNs), which inherit the best characteristics of functional link artificial neural networks and self-organizing feature maps (SOFMs). UFLANNs adopt three types of basis functions such as Chebyshev, Legendre orthogonal polynomials, and power series for mapping the input data into a new feature space with higher dimensions, where the objects are clustered based on the principle of competitive learning of SOFMs. The effectiveness of this algorithm has been tested with various artificial and real-life datasets including remote sensing images. A thorough comparison with other popular clustering algorithms shows that the proposed method is promising in revealing clusters from many complex datasets.

Sensor ontologies formally model the core concepts in the sensor domain and their relationships, which facilitates the trusted communication and collaboration of Artificial Intelligence of Things (AIoT). However, due to the subjectivity of the ontology building process, sensor ontologies might be defined by different terms, leading to the problem of heterogeneity. In order to integrate the knowledge of two heterogeneous sensor ontologies, it is necessary to determine the correspondence between two heterogeneous concepts, which is the so-called ontology matching. Recently, more and more neural networks have been considered as an effective approach to address the ontology heterogeneity problem, but they require a large number of manually labelled training samples to train the network, which poses an open challenge. In order to improve the quality of the sensor ontology alignment, an unsupervised neural network model is proposed in this work. It first models the ontology matching problem as a binary classification problem, and then uses a competitive learning strategy to efficiently cluster the ontologies to be matched, which does not require the labelled training samples. The experiment utilizes the benchmark track provided by the Ontology Alignment Evaluation Initiative (OAEI) and multiple real sensor ontology alignment tasks to test our proposal’s performance. The experimental results show that the proposed approach is able to determine higher quality alignment results compared to other matching strategies under different domain knowledge such as bibliographic and real sensor ontologies.

Inspired by competitive rules of the nature, competitive learning contributes to the specialization of the human brain and the general creativity of mankind. However, the construction of hardware competitive learning neural network still faces great challenges due to the lack of an accurate distance computation method and a self‐adaptive in situ training scheme. Herein, a fully memristive Euclidean distance (ED) engine based on analog multiply‐accumulate operation in a 32 × 32 TiN/TaO x /HfO x /TiN 1T1R array is demonstrated. The dual‐layer devices perform multilevel modulation under the target‐aware programming method with excellent read linearity in a dynamic range of 10–100 μS. The ED calculation is verified experimentally on a test board with an O(1) temporal complexity. Furthermore, in situ training and offline inference schemes for competitive learning, based on the ED engine, are developed and the simulated results show comparable success rates with those obtained by the CPU‐based software. Compared with a state‐of‐the‐art RTX6000 GPU (0.5 TOPS W−1), the energy efficiency of competitive learning models on ED engines can yield 100× improvements by utilizing optimized memristive devices.

A novel technique is proposed for maintaining the diversity of sample features and modeling imbalanced datasets in multi-channel time-varying signal classification. The RBF-LVQPNN consists of a time-varying signal input layer, an RBF process neuron hidden layer, an LVQ competition layer, a pattern layer, and a classifier. Dynamic clustering was used to divide samples in each pattern class into several pattern subclasses with similar features. Typical signal samples in the pattern subclasses are then used as the kernel centers for RBFPNs, to achieve embedding of the diversity signal class features. The output of the RBFPN layer was used as input to the LVQN. In the competition layer, the 'winning' neurons were selected to represent a pattern subclass. In the process of reorganizing pattern subclasses into pattern classes, subclass boundaries were combined into irregular class boundaries to reduce the overlap of decision spaces for different pattern classes. The RBF-LVQNN could improve the memory ability of typical signal features and discriminability of signals, realize structural and data constraints of the model, and improve the modeling property of imbalanced datasets. In this paper, the properties of the RBF-LVQPNN are analyzed and a comprehensive training algorithm is established. Experimental validation was performed with classification diagnoses from seven types of cardiovascular diseases based on 12-lead ECG signals. Results demonstrated that the proposed technique significantly improved both classification accuracy and generalizability comparing with other methods in the experiment. [ABSTRACT FROM AUTHOR]

Self-organizing growth neural network (SOGNN) is an unsupervised clustering algorithm based on competitive learning, which can extract the distribution information and topology information of input data. However, the current SOGNN lacks effective and stable judgment indicators for the scale of network node growth, thereby failing to effectively evaluate and control the reasonable size of the output space. To this end, this paper proposes a judgment index, i.e., expected quantization error stability (EQES), to objectively judge the approximation degree of the output space to the input space. Based on the Growing Neural Gas (GNG) algorithm, an improved GNG algorithm, called GNG-EQES, is proposed, which introduces the EQES criterion to enable the GNG algorithm to generate an appropriate number of output network nodes autonomously without pre-determining the size of the output network. This not only improves the accuracy of feature extraction of the SOGNN, but also improves its adaptive ability and expands its application scope. The experiments in continuous input space and discrete input space have verified the validity and feasibility of the method proposed, and the method is applied to the construction of mobile robot environment topology map.

This article proposes a novel semi-supervised competitive learning (SSCL) algorithm for vocal pattern classifications in Parkinson’s disease (PD). The acoustic parameters of voice records were grouped into the families of jitter, shimmer, harmonic-to-noise, frequency, and nonlinear measures, respectively. The linear correlations were computed within each acoustic parameter family. According to the correlation matrix results, the jitter, shimmer, and harmonic-to-noise parameters presented as highly correlated in terms of Pearson’s correlation coefficients. Then, the principal component analysis (PCA) technique was implemented to eliminate the redundant dimensions of the acoustic parameters for each family. The Mann–Whitney–Wilcoxon hypothesis test was used to evaluate the significant difference of the PCA-projected features between the healthy subjects and PD patients. Eight dominant PCA-projected features were selected based on the eigenvalue threshold criterion and the statistical significance level (p < 0.05) of the hypothesis test. The SSCL algorithm proposed in this paper included the procedures of the competitive prototype seed selection, K-means optimization, and the nearest neighbor classifications. The pattern classification experimental results showed that the proposed SSCL method can provide the excellent diagnostic performances in terms of accuracy (0.838), recall (0.825), specificity (0.85), precision (0.846), F-score (0.835), Matthews correlation coefficient (0.675), area under the receiver operating characteristic curve (0.939), and Kappa coefficient (0.675), which were consistently better than those results of conventional KNN or SVM classifiers.

In this paper we present a Competitive Rate-Based Algorithm (CRBA) that approximates operation of a Competitive Spiking Neural Network (CSNN). CRBA is based on modeling of the competition between neurons during a sample presentation, which can be reduced to ranking of the neurons based on a dot product operation and the use of a discrete Expectation Maximization algorithm; the latter is equivalent to the spike time-dependent plasticity rule. CRBA's performance is compared with that of CSNN on the MNIST and Fashion-MNIST datasets. The results show that CRBA performs on par with CSNN, while using three orders of magnitude less computational time. Importantly, we show that the weights and firing thresholds learned by CRBA can be used to initialize CSNN's parameters that results in its much more efficient operation.