The traditional particle swarm optimization algorithms have some shortcomings, such as low convergence precision, slow convergence speed, and susceptibility to falling into local optima when solving complex optimization problems. To address these issues, this paper proposes a new particle swarm optimization algorithm that incorporates teamwork. Specifically, we introduce the concept of teamwork, and divide the particles into multiple teams and selecting team leaders . The particles can fully utilize the team’s prompt information to guide the search process. The team leader updates the search direction of its particles through the generation of information factors, thus giving the algorithm better global search capabilities. The position and behavior of the team leader affect the search behavior of other particles, reducing the risk of falling into local optimal solutions. In addition, to further improve the algorithm’s efficiency, we propose adaptive adjustment of information factors and learning factors. This adaptive adjustment mechanism enables the algorithm to adjust parameters flexibly according to the characteristics of the problem and the current search state, thereby accelerating convergence speed and improving convergence precision. To verify the performance of the proposed algorithm, we make an empirical analysis on 27 different test functions, the shortest path problem and the optimal SINR value problem for UAV deployment. The experimental results show that the proposed algorithm has obvious advantages in convergence accuracy and convergence speed. Compared with other algorithms, this algorithm can find a better solution faster and converge to the global optimal solution more stably.

This research WIP paper describes the current workforce needs of math skills. In today's rapidly evolving technological landscape, understanding the mathematical requirements of the engineering workforce is essential for educational institutions and industry stakeholders alike. This study outlines a comprehensive survey-based study aimed at identifying the current and future mathematical needs of engineers in the workforce, while considering the impact of artificial intelligence (AI) on these requirements. The study will involve administering a survey to practicing engineers to gauge their utilization of mathematics in their respective fields. Questions will delve into areas such as the types of mathematical concepts commonly employed, the most complex problems encountered, and any gaps in mathematical education that have been observed. Furthermore, the survey will seek insights into potential training needs to enhance mathematical skills among professionals. One of the primary objectives of this research is to provide valuable insights into the current state and future perspectives of the workforce's mathematical requirements. By analyzing survey responses, the study aims to offer recommendations to educational institutions to align their curricula with the evolving needs of industry. This proactive approach will ensure that future generations of engineers are equipped with the necessary mathematical competencies to thrive in their careers. A key aspect of this study is the differentiation between engineering and engineering technology disciplines, recognizing that mathematical requirements may vary across different sectors. By delineating these distinctions, the research will provide targeted recommendations tailored to the specific needs of each field for two year and four years institutions. Collaboration with national and international organizations, including IEEE, ASEE, STEM Pioneers and other platforms, will ensure a broad and diverse participation pool for the survey. This strategic partnership will facilitate access to a wide audience of professionals, thereby enhancing the validity and reliability of the study outcomes. In conclusion, this survey-based study represents a concerted effort to comprehensively understand and address the mathematical needs of the current workforce, particularly within AI-driven engineering environments. By bridging the gap between academia and industry, the research aims to foster a symbiotic relationship wherein educational institutions adapt their offerings to meet the demands of the evolving job market, heavily influenced by the pervasive integration of AI technologies. With meticulous data analysis and strategic collaboration, the study anticipates yielding actionable insights that will not only shape the future of mathematical education but also contribute significantly to professional development in engineering and engineering technology fields amidst the omnipresent influence of AI.

Face clustering can help to improve the performance of face recognition models with unlabeled data. The existing methods generally aggregate the features within subgraphs based on a threshold or learned cutoff positions. This may reduce the recall of subgraphs and hence degrade the clustering performance. This work proposed an efficient neighborhood-aware subgraph adjustment method that can significantly improve the quality of the subgraphs. More specifically, it consists of two components, i.e. feature enhancement embeddings and enclosed subgraph embeddings of node pairs. The embeddings are combined to predict the linkage probabilities for all node pairs to produce new subgraphs that can be further used for aggregation of GCNs or other clustering methods. The numerical results confirm that it outperforms the SOTA solutions in terms of generalization capability.

Face clustering has been widely studied, due to its broad applications in academia and industry. Regarding researches on clustering, two vital parts are involved: one is the feature representation space of the image, and the other is the clustering algorithm. However, most of the current researches lay more emphasis on the latter and overlook the need for an appropriate feature representation space for clustering. Therefore, we propose a novel clustering framework, to address the problem of insufficiently compact features in clustering. Our method is comprised of a feature topology learning module (GCN-FT) and an auto-search clustering module (GCN-AS), which called GCN-F&A (GCN-FT & GCN-AS). Specifically, GCN-FT adds a self-adaptive learning structure to the traditional GCN to capture the internal correlation of features, so that the module can better aggregate information from itself and its neighbor nodes, and provide more gathered features for clustering. Our GCN-AS consists of two parts, one is ‘1-NN’, and the other is clustering module based on linkage prediction, namely ‘GCN-LP’. 1-NN is an effective pre-clustering method, which can automatically search hyper-parameters required in GCN-LP, thereby improving the scalability of the clustering module. Experiments on two large-scale face benchmarks and one clothing dataset demonstrate that our method significantly outperforms the state-of-the-arts.

In this paper, we propose a new design criterion and a new class of unitary signal constellations for differential space-time modulation for multiple-antenna systems over Rayleigh flat-fading channels with unknown fading coefficients. Extensive simulations show that the new codes have significantly better performance than existing codes. We have compared the performance of our codes with differential detection schemes using orthogonal design, Cayley differential codes, fixed-point-free group codes, and product of groups and for the same bit-error rate, our codes allow smaller signal-to-noise ratio (SNR) by as much as 10 dB. The design of the new codes is accomplished in a systematic way through the optimization of a performance index that closely describes the bit-error rate as a function of the SNR. The new performance index is computationally simple and we have derived analytical expressions for its gradient with respect to constellation parameters. Decoding of the proposed constellations is reduced to a set of one-dimensional closest point problems that we solve using parallel sphere decoder algorithms. This decoding strategy can also improve efficiency of existing codes.