Neural Computing and Applications最新文献

Methods for socially-aware robot path planning are increasingly needed as robots and humans increasingly coexist in shared industrial spaces. The practice of clearly separated zones for humans and robots in shop floors is transitioning towards spaces where both humans and robot operate, often collaboratively. To allow for safer and more efficient manufacturing operations in shared workspaces, mobile robot fleet path planning needs to predict human movement. Accounting for the spatiotemporal nature of the problem, the present work introduces a spatiotemporal graph neural network approach that uses graph convolution and gated recurrent units, together with an attention mechanism to capture the spatial and temporal dependencies in the data and predict human occupancy based on past observations. The obtained results indicate that the graph network-based approach is suitable for short-term predictions but the rising uncertainty beyond short-term would limit its applicability. Furthermore, the addition of learnable edge weights, a feature exclusive to graph neural networks, enhances the predictive capabilities of the model. Adding workspace context-specific embeddings to graph nodes has additionally been explored, bringing modest performance improvements. Further research is needed to extend the predictive capabilities beyond the range of scenarios captured through the original training, and towards establishing standardised benchmarks for testing human motion prediction in industrial environments.

Conventional authentication methods, such as passwords and PINs, are vulnerable to multiple threats, from sophisticated hacking attempts to the inherent weaknesses of human memory. This highlights a critical need for a more secure, convenient, and user-friendly approach to authentication. This paper introduces M2auth, a novel multimodal behavioral biometric authentication framework for smartphones. M2auth leverages a combination of multiple authentication modalities, including touch gestures, keystrokes, and accelerometer data, with a focus on capturing high-quality, intervention-free data. To validate the efficacy of M2auth, we conducted a large-scale field study involving 52 participants over two months, collecting data from touch gestures, keystrokes, and smartphone sensors. The resulting dataset, comprising over 5.5 million action points, serves as a valuable resource for behavioral biometric research. Our evaluation involved two fusion scenarios, feature-level fusion and decision-level fusion, that play a pivotal role in elevating authentication performance. These fusion approaches effectively mitigate challenges associated with noise and variability in behavioral data, enhancing the robustness of the system. We found that the decision-level fusion outperforms the feature level, reaching a 99.98% authentication success rate and an EER reduced to 0.84%, highlighting the robustness of M2auth in real-world scenarios.

Personalized recommendation plays a crucial role in Internet platforms, providing users with tailored content based on their user models and enhancing user satisfaction and experience. To address the challenge of information overload, it is essential to analyze user needs comprehensively, considering historical behavior and interests and the user's intentions and profiles. Previous user modeling approaches for personalized recommendations have exhibited certain limitations, relying primarily on historical behavior data to infer user preferences, which results in challenges such as the cold-start problem, incomplete modeling, and limited explanation. Motivated by recent advancements in large language models (LLMs), we present a novel approach to user modeling by embracing generative user modeling using LLMs. We propose generative user modeling with chain-of-thought prompting for personalized recommendation, which utilizes LLMs to generate comprehensive and accurate user models expressed in natural language and then employs these user models to empower LLMs for personalized recommendation. Specifically, we adopt the chain-of-thought prompting method to reason about user attributes, subjective preferences, and intentions, integrating them into a holistic user model. Subsequently, we utilize the generated user models as input to LLMs and design a collection of prompts to align the LLMs with various recommendation tasks, encompassing rating prediction, sequential recommendation, direct recommendation, and explanation generation. Extensive experiments conducted on real-world datasets demonstrate the immense potential of large language models in generating natural language user models, and the adoption of generative user modeling significantly enhances the performance of LLMs across the four recommendation tasks. Our code and dataset can be found at https://github.com/kwyyangfan/GUMRec.

Double-layer coating of dextran and citrate (DC) on the Fe₃O₄ (magnetite) ferrofluids (FFs) has been conducted for biomedical applications such as hyperthermia and magnetic resonance imaging. The magnetic retardance of DC-coated FFs was measured, and the magnetic heating effect was investigated previously. An experiment was conducted using the uniform design method; we enabled the formula to fit with experimental data on retardance through the stepwise regression analysis. Two intelligent search methods, particle swarm optimization (PSO) and simulated annealing (SA), were used to find the maximum retardance. The optimized parametric combinations were decided as [0.0750, 75.7945, 0.3225, 0.6500] and [0.0750, 75.844, 0.323, 0.65], respectively, denoting the Fe₃O₄ concentration, the coating temperature, the mass of citrate and dextran. The corresponding maximum retardances were 119.6576° and 119.6558°. The PSO algorithm was more effective and accessible than the SA algorithm in optimizing retardance. As for the hybrid optimization selected for solving complex problems, such as PSO was used to find a near-global solution, and SA was then used for searching for a global solution, the parameter fine-tuning of SA affects the final result. A hybrid metaheuristic algorithm with the local gradient-based sequential quadratic programming (SQP) algorithm is used to find the global solution because of its effectiveness and convergence speed in research. Overall, we provide some two-level hybrid optimizations for the global exploration of the retardance of DC-coated FFs. The hybrid algorithms, including PSO-SA, PSO-SQP, or SA-SQP, allow us to explore a more accurate global solution with high performance.

Additive manufacturing, particularly laser powder bed fusion (L-PBF), is an emerging method for fabricating complex parts in various industries. However, it faces the persistent challenge of thermal deformation, a significant barrier to its wider application and reliability. Current strategies, while partially effective, do not fully address the intricate thermal dynamics of the process, indicating a clear research gap in optimizing manufacturing techniques for better thermal management. This study focuses on understanding and mitigating thermal deformation in L-PBF using Genetic Algorithms (GAs). The application of GAs as a ‘black-box’ approach is explored to gain insights into the complex physics of L-PBF. A comprehensive investigation into the optimization of island sequencing within L-PBF processes is presented, employing GAs to systematically reduce thermal deformation. Various island sequences in a bilayered block structure are analyzed to assess the effectiveness of GAs in minimizing deformation, including scenarios such as variations in block sizes and interlayer rotation angles. Statistical tools such as silhouette scores and probability density distribution plots are utilized to provide a thorough analysis of deformation patterns and their respective thermal behaviors. The results show GA's remarkable efficiency in enhancing thermal management, achieving a significant reduction in thermal deformation within a range of 12–15% across the examined scenarios. This achievement highlights GA's capability in rapid optimization of scan sequences for better thermal deformation control. The findings enhance the understanding of thermal dynamics in L-PBF and consequently open new avenues for improving the quality and reliability of other metal additive manufacturing processes as well.

The advances in machine learning (ML) tools and techniques have enabled the non-intrusive collection and rapid analysis of massive amounts of data involving athletes in competitive collegiate sports. It has facilitated the development of services that a coach can employ in analyzing these data into actionable insights in designing training schedules and effective strategies for maximizing an athlete’s performance, while minimizing injury risk. Collegiate sports utilize data to get a competitive advantage. While game statistics are publicly available, relying on more than one form of data can help reveal a pattern. We developed a framework that considers various modalities and creates an athletic signature to predict their future performance. Our research involves the study of 42 distinct features that quantify various internal/external stressors the athletes face to characterize and estimate their athletic readiness (in the form of reactive strength index modified—RSImod) using ML algorithms. Our study, conducted over 26 weeks with 17 collegiate women’s basketball athletes, developed a framework that first performed sensitivity analysis using a hybrid approach combining the strengths of various filter-based, wrapper-based, and embedded feature importance techniques to identify the features most significantly impacting athlete readiness. These features were then categorized into four moderating variables (MVs, i.e. factors): sleep, cardiac rhythm, training strain, and travel schedule. Further, we used factor analysis to enhance interpretability and reduce computational complexity. A hybrid boosted-decision-trees-based model designed based on athlete clusters predicted future athletic readiness based on MVs with a mean squared error (MSE) of 0.0102. Partial dependence plots (PDPs) helped qualitatively learn the relationship between the moderating variables and the RSImod score. Athletic signatures, uniquely defining athlete-specific MV patterns, account for intra-individual variability, offering a better statistical basis for predicting game lineup (green/yellow/red card assignment) in combination with model predictions. SHAP (SHapley Additive exPlanations) values suggest the causative MV in order of significance for each prediction, enabling coaches to make informed decisions about training adjustments and athlete well-being, leading to performance improvement. Using the fingerprint mechanism, we created green (within 1 Standard Deviation (SD)), yellow (> 1SD and < 2SD), and red card (> 2SD) zones for athlete readiness assessment. While, this study was conducted on D-I women’s basketball, the modalities apply to several sports, such as soccer, volleyball, football, and ice hockey. This framework allows coaches to understand their athlete dynamics from a strictly data perspective, which helps them strategize their next moves, combined with their personal experience and interactions with the team.

Wireless Sensor Networks (WSNs) are essential for collecting and transmitting data in modern applications that rely on data, where effective network connectivity and coverage are crucial. The optimal placement of router nodes within WSNs is a fundamental challenge that significantly impacts network performance and reliability. Researchers have explored various approaches using metaheuristic algorithms to address these challenges and optimize WSN performance. This paper introduces a new hybrid algorithm, CFL-PSO, based on combining an enhanced Fick’s Law algorithm with comprehensive learning and Particle Swarm Optimization (PSO). CFL-PSO exploits the strengths of these techniques to strike a balance between network connectivity and coverage, ultimately enhancing the overall performance of WSNs. We evaluate the performance of CFL-PSO by benchmarking it against nine established algorithms, including the conventional Fick’s law algorithm (FLA), Sine Cosine Algorithm (SCA), Multi-Verse Optimizer (MVO), Salp Swarm Optimization (SSO), War Strategy Optimization (WSO), Harris Hawk Optimization (HHO), African Vultures Optimization Algorithm (AVOA), Capuchin Search Algorithm (CapSA), Tunicate Swarm Algorithm (TSA), and PSO. The algorithm’s performance is extensively evaluated using 23 benchmark functions to assess its effectiveness in handling various optimization scenarios. Additionally, its performance on WSN router node placement is compared against the other methods, demonstrating its competitiveness in achieving optimal solutions. These analyses reveal that CFL-PSO outperforms the other algorithms in terms of network connectivity, client coverage, and convergence speed. To further validate CFL-PSO’s effectiveness, experimental studies were conducted using different numbers of clients, routers, deployment areas, and transmission ranges. The findings affirm the effectiveness of CFL-PSO as it consistently delivers favorable optimization results when compared to existing methods, highlighting its potential for enhancing WMN performance. Specifically, CFL-PSO achieves up to a 66.5% improvement in network connectivity, a 16.56% improvement in coverage, and a 21.4% improvement in the objective function value when compared to the standard FLA.

The objective of this research is to develop some novel operational laws based of T-norm and T-conorm and then using these operational laws to develop several Einstein operators for aggregating the different complex intuitionistic fuzzy numbers (CIFNs) by considering the dependency between the pairs of its membership degrees. In the existing studies of fuzzy and its extensions, the uncertainties present in the data are handled with the help of degrees of membership that are the subset of real numbers, which may also loss some valuable data and hence consequently affect the decision results. A modification to these, complex intuitionistic fuzzy set handles the uncertainties with the degree whose ranges are extended from real subset to the complex subset with unit disk and hence handle the two-dimensional information in a single set. Thus, motivated by this and this paper we present some novel methods such as complex intuitionistic fuzzy Einstein weighted geometric aggregation (CIFEWGA) operator, complex intuitionistic fuzzy Einstein ordered weighted geometric aggregation (CIFEOWGA) operator, complex intuitionistic fuzzy Einstein hybrid geometric aggregation (CIFEHGA) operator, induced complex intuitionistic fuzzy Einstein ordered weighted geometric aggregation (I-CIFEOWGA) operator and induced complex intuitionistic fuzzy Einstein hybrid geometric aggregation (I-CIFEHGA) operator. We present some of their desirable properties such as idempotency, boundedness and monotonicity. Furthermore, based on these methods a multi-attribute group decision-making problem developed under complex intuitionistic fuzzy set environment. An illustrative example related to the selection of the best alternative is considered to show the effectiveness, importance and efficiency of the novel approach.

Cervical cancer is a type of cancer in which abnormal cell growth occurs on the surface lining of the cervix. In this study, we propose a novel residual deep convolutional generative adversarial network (RES_DCGAN) for data augmentation and ResNet50V2 self-attention method to classify cervical cells, to improve the generalizability and performance of the model. The proposed method involves adding residual blocks in the generator of the DCGAN to enhance data flow and generate higher-quality images. Subsequently, a self-attention mechanism is incorporated at the top of the pre-trained models to allow the model to focus more on significant features of the input data. To evaluate our approach, we utilized the Pomeranian and SIPaKMeD cervical cell imaging datasets. The results demonstrate superior performance, achieving an accuracy of 98% with Xception and 96.4% with ResNet50V2 on the Pomeranian dataset. Additionally, DenseNet121 with self-attention achieved accuracies of 92% and 95% in multiclass and binary classification, respectively, using the SIPaKMeD dataset. In conclusion, our RES_DCGAN-based data augmentation and pre-trained with self-attention model yields a promising result in the classification of cervical cancer cells.

Cyberbullying is a serious issue in online social media platforms (OSMP), which requires effective detection and intervention systems. Multimodal emotion recognition (MER) technology can help prevent cyberbullying by analyzing emotions from textual messages, vision, facial expressions, tone of voice, and physiological signals. However, existing machine learning-based MER models have limitations in accuracy and generalization. Deep learning (DL) methods have achieved remarkable successes in various tasks and have been applied to learn high-level emotional features for MER. This paper provides a systematic review of the recent research on DL-based MER for cyberbullying detection (MERCD). We first introduce the concept of cyberbullying and the general framework of MERCD, as well as the commonly used multimodal emotion datasets. Then, we overview the principles and advancements of representative DL techniques. Next, we focus on the research progress of two key steps in MERCD: emotion feature extraction from speech, vision, and text modalities; and multimodal information fusion strategies. Finally, we discuss the challenges and opportunities in designing a cyberbullying prediction model and suggest possible directions in the MERCD area for future research.