Reviewerly/exHarmony huggingface dataset:
Intensification of teacher training within a professional retraining programme

There is a contradiction between the need to purposefully develop the teachers’ readiness for innovation in the course of their academic activity and insufficient attention to this problem when creating and implementing professional retraining programs for the teaching staff. There are a number of projects that bring together scientists in the field of organizing the training and retraining of teachers of engineering universities. One of them is the Erasmus+ project “Pedagogical training of engineering teachers (ENTER)”. The purpose of this study is to test the module “Enhancement of Learning Interactivity” as a part of the program “Innovative Pedagogy for Teachers of Engineering Universities” and to intensify student learning. When designing the module “Enhancement of Learning Interactivity”, we took into account the recommendations of the International Society for Engineering Pedagogy (IGIP), the experience of the Estonian Center for Engineering Pedagogy, which focuses on retraining teachers, as well as modern innovative teaching methods, such as: interactive lectures, group work, discussions, design portfolio, case study, etc. In the course of the experiment the researchers used teaching modeling methodology, pedagogical experiment, and participant observation. The results of the experiment have shown that the level of the final works of the respondents from the experimental group is higher compared to those from the control group: more teachers coped with the final tasks of the module and got better understanding of the teaching materials. The authors have come to the conclusion that the most effective organizational teaching forms are interactive lectures, group work, discussion; the recommended methods are portfolio design, case-study, peer-instruction, presentations, feedback, analysis of one’s own course, metacognitive strategies, pauses for reflection; the recommended visualization tools are mind-maps, concept maps of literature reviews, and thesaurus maps.
Research on UAV Dynamic Target Tracking with Multi-sensor Position Feedback

The path planning and control of unmanned aerial vehicles (UAVs) is an important part of autonomous flight. High-precision positioning and navigation is one of the key technologies to achieve adaptive flight, efficient reconnaissance and precise attack. A control method of adaptive target tracking and obstacle avoidance for rotorcraft UAVs is proposed in the paper to solve the problem. According to the characteristics of the nonlinear UAVs' system, the artificial potential field method and backstepping method are used for path planning and flight control respectively. The unscented Kalman filter combined with UWB and INS is used to obtain accurate position and feedback to the artificial potential field to ensure the stability of the system and the accuracy of target tracking. This paper describes the dynamic target simulation of multiple obstacles. It is shown that the satisfactory tracking performance with the new algorithm, and the stability of obstacle avoidance is also improved.
Overview of Complex Intelligent System Reliability Technology

With the wide application of intelligent technology, model theft of face and fingerprint recognition may lead to information leakage, and even in some extreme cases, such as data poisoning and confrontational attacks may lead to loss of life. In this paper, the SMART architecture is introduced to explore the quantification of intelligent system training set, adversarial attack, model accuracy and uncertainty from three dimensions: operating environment, data, and model. The coupling of intelligent module and system leads to the uncertainty of complex and dynamic structure, which leads to procedural faults. This paper focuses on exploring the variable content of improved reliability and puts forward the power point of reliability analysis and verification method according to the fault characteristics of intelligent system. It also provides ideas for in-depth research on the reliability of intelligent systems.
Particle Swarm Optimizer Without Communications Among Particles

Particle Swarm Optimizer (PSO) is a kind of population-based evolutionary optimizer. Many PSO variants have been proposed and most of them require mutual communications among particles for their fitness values to find the best position, hence leading to their effective collaboration. However, some real scenes using swarm robots to perform PSO cannot provide reliable communications during their distributed search. To handle such issues, this work proposes a novel PSO variant without communications among particles, called Communication-free Particle Swarm Optimizer (CfPSO). It employs particles' detection ability instead of direct communications among them to accomplish the needed collaboration. Experimental results show that it obtains higher accurate performance than the standard PSO equipped with full communication ability, which is against human intuition.
Noise-Tolerant Hardware-Aware Pruning for Deep Neural Networks

Existing hardware-aware pruning methods for deep neural networks do not take the uncertain execution environment of low-end hardware into consideration. That makes those methods unreliable, since the hardware environments they used for evaluating the pruned models contain uncertainty and thus the performance values contain noise. To deal with this problem, this paper proposes noise-tolerant hardware-aware pruning, i.e., NT-HP. It uses a population-based idea to iteratively generate pruned models. Each pruned model is sent to realistic low-end hardware for performance evaluations. For the noisy values of performance indicators collected from hardware, a threshold for comparison is set, where only the pruned models with significantly better performances are kept in the next generation. Our experimental results show that with the noise-tolerant technique involved, NT-HP can get better pruned models in the uncertain execution environment of low-end hardware.
A Dual-Strategy Contest Harmony Search Algorithm Based on Population State for the System Reliability-Redundancy Allocation Problems

At present, many improved algorithms have effectively improved the ability to converge to the optimal value. However, there are still difficulties: how to effectively balance the deep development ability of the algorithm in a small range and the extensive exploration ability in the global search field in the iterative process. For this reason, this topic first proposes a population state evaluation indicator, which continuously detects the state and diversity of the population with iteration. On this basis, the dual-strategy competition mechanism is adopted. Through the population state evaluation index, the strategy can select a strategy that is more conducive to rapid convergence according to the changes of the population during the iteration process. Through the advantage competition of the two strategies, the development ability of the algorithm has been steadily and persistently improved. However, there is still the risk of falling into the local optimal dilemma. Therefore, the change of population diversity is considered in the evaluation index of population status in this study. In order to further increase the activity of the global search range, the computational individuals used in the proposed environmental assessment index of population diversity are randomly selected. In general, in the greedy comparison of the two strategies, this study still ensures the consideration and influence of diversity, and effectively balances the ability of algorithm exploration and development.
On the Complete Area Coverage Problem of Painting Robots

Recently, we proposed a novel algorithm for painting problems based on the concepts of minimum spanning-tree based approach and triangle mesh to obtain an O(nlog n) complete area coverage path planning algorithm on polygonal surfaces with minimum length, where n is the number of triangles. In this article, we reduced the time complexity to O(n) by modifying the algorithm. Our proposed method adopts a mobile robot which navigates through an arrangement of areas to be covered without energy and time constraints. In the end, this robot will return to the original starting point. According to the performance analysis, our method is proven to be the fastest algorithm with minimum length to solve the complete area coverage planning for painting robots on polygonal surfaces. In addition, the number of turns has been significantly reduced by 30.26% using the scheme of triangle merge.
An Improved Fire Hawks Optimizer for Function Optimization

Fire hawk Optimizer (FHO) is a relatively new intake in the family of evolutionary algorithms for a distinct type of optimization problem. Initialization of the population plays a significant role in solving classical optimization issues. Incorporating quasi-random sequences such as the sobol, halton, and torus sequences, this study demonstrates novel ways for swarm initiation. The outcomes of our proposed techniques display outstanding performance as compared with the traditional FHO. The exhaustive experimental results conclude that the proposed algorithm remarkably superior to the standard approach. Additionally, the outcomes produced from our proposed work exhibits anticipation that how immensely the proposed approach highly influences the value of cost function, convergence rate, and diversity.
Multiple Unmanned Aerial Vehicles Path Planning Based on Collaborative Differential Evolution

In this paper, we propose a collaborative differential evolution algorithm (CODE) to solve the problem of the multiple unmanned aerial vehicles (multi-UAVs) path planning in the three-dimensional (3D) environment. Because the centralized differential evolution algorithm (DE) solves the multi-UAVs path planning problem, the dimension is too high and the amount of computation is too large, CODE divides the entire population of DE into several groups equally, and each group searches in parallel, calculates the individual cost of each UAVs and establishes an information exchange mechanism to calculate the cooperation cost between UAVs, and outputs the path corresponding to a UAV. Experimental results show that the proposed algorithm is significantly better than other comparative algorithms.
Blockchain Impact on Supply Chain Performance

BlockchainBlockchain has become more relevant in the efficient and secure management of the supply chainSupply chain management, due to the grown importance that companies give to ensuring the transparency and traceability of all the movements made in each of the links of the supply chain. In this article, a study of the blockchainBlockchain practices carried out by different companies from various sectors, come to infer that traceability, security, and cost reduction are the main objectives when companies decide to tackle projects of this nature. Another issue that can be deduced is that companies generally look for partners specialized in this sector to help them implement these projects, thus sharing both the knowledge generated and the development costs of the projects.
Neural Architecture Search Based on Improved Brain Storm Optimization Algorithm

The performance of deep neural networks (DNNs) often depends on the design of their architectures. But designing a DNN with good performance is a difficult and knowledge-intensive process. In this paper, we propose a neural architecture search method based on improved brain storm optimization (BSO) algorithm to efficiently deal with image classification tasks. BSO successfully transposes the human brainstorming process to design of optimization algorithms, which typically uses grouping, substitution, and creation operators to generate as many solutions as possible to approach the global optimization of the problem generation by generation. However, the BSO algorithm using clustering methods for grouping increases the computational burden, so we use the BSO algorithm based on simple grouping methods to solve the optimal architecture of the neural architecture search (NAS). We also redesigned the search space and designed an efficient encoding strategy for each individual.
Asynchronous Federated Learning Framework Based on Dynamic Selective Transmission

This paper proposes an asynchronous federated learning framework based on dynamic selective transmission to solve the communication efficiency problem in asynchronous federated learning. The framework first dynamically determines the network layer to be transmitted in each round according to the training progress, and then dynamically adjusts the ratio of parameters to be transmitted according to the training progress and model staleness for the selected network layers, which effectively reduces the negative impact of the local model staleness of the client on the performance of global model while significantly reducing the uplink communication cost of the asynchronous federated learning framework. On two datasets, this paper designs experiments to compare the proposed framework with the existing work. Through the analysis of the experimental results, the effectiveness of the proposed framework is verified.
A Modified Gaining-Sharing Knowledge Algorithm Based on Dual-Population and Multi-operators for Unconstrained Optimization

Evolutionary Algorithms (EAs) have a great power to solve complex optimization problems. In recent years, Gaining-Sharing Knowledge algorithm (GSK) is one of the novel nature-inspired algorithms to solve real life optimization problems. This paper introduces a modified Gaining-Sharing Knowledge algorithm based on dual-population framework and multi-operators (mGSK-DPMO). Meanwhile, a hybrid parameter adaptive technique is implemented in order to enhance its performance. The numerical result shows that the presented algorithm (mGSK-DPMO) is found to be highly competitive and significantly superior to other state-of-the-art algorithms including CEC2020's top 3 algorithms in CEC2020 benchmark problems.
Adaptive Artificial Bee Colony Algorithm Considering Colony’s Memory

The artificial bee colony (ABC) algorithm is a swarm intelligence optimization algorithm inspired by the foraging behavior of honey bee colonies, which means to gradually explore and find food-rich nectar sources through the behavioral patterns of three different bees foraging between hives. We propose an adaptive ABC (AABC) algorithm to accelerate the convergence rate by considering a colony's memory in the employed-bee phase of the algorithm, since some bees will select and explore the best nectar source, and limiting the initial search range of the swarm to search for better honey sources faster. This is done by using tent chaotic search and random backward learning in the scout-bee phase to reselect the nectar source and increase the possibility of jumping out of local optimal solution. We conducted 30 numerical experiments to verify the effectiveness of the proposed adaptive ABC algorithm.
PSO with Local Search Using Personal Best Solution for Environments with Small Number of Particles

Particle swarm optimization (PSO) has been actively studied as an effective search method using populations. The basis of search algorithms using particle swarms is efficient convergence from exploiting the ability to move toward the best individual (Gbest) within the current population. Therefore, PSO has the property of quickly converging to the optimal solution, especially for unimodal search problems. For multimodal search problems, there are also reported methods, such as Standard PSO (SPSO), which divides a population into several subgroups, and research examples in which several particles are used to improve the local search capability. However, using a part of the particles in populations for a local search increases the search time in environments with fewer particles because the force toward Gbest of the entire population tends to be relatively weaker than that of the original PSO. To solve this problem, this paper proposes a search method in which all particles toward to Gbest while conducting a local search using their respective past best solutions, instead of dividing some particles into local searches. Using typical benchmark functions and existing algorithms, we show that the proposed method works well even with a small number of particles.
Swarm Intelligence Algorithms and Applications: An Experimental Survey

Swarm Intelligence draws inspiration from the collective intelligent behavior of animals such as birds, fish, and bees. It refers to the collective behavior of decentralized, self-organized systems composed of many simple agents that interact with each other and their environment. As the Swarm Intelligence (SI) field expands, more SI meta-heuristics are incorporated into developing new Artificial Intelligence (AI) tools. This study presents a comprehensive survey of state-of-the-art Swarm Intelligence algorithms and their applications. Seven SI algorithms are described – Artificial Bee Colony Optimization, Bat-inspired algorithm, Flock by Leader algorithm, Grey Wolf Optimizer, Flower Pollination Algorithm, Whale Optimization, and Moth Flame Optimization, with their applications in three diverse fields: Scheduling Problem, Data Science and Robotics, and Route and Layout Optimization. The performance of the algorithms is compared experimentally, and discussions about future directions in Swarm Intelligence research are outlined.
MACT: Multi-agent Collision Avoidance with Continuous Transition Reinforcement Learning via Mixup

Autonomous collision avoidance is a critical component in various multi-robot applications. While deep reinforcement learning (RL) has demonstrated success in some robotic control tasks, it remains challenging to apply it to real-world multi-agent collision tasks due to poor sample efficiency. The limited amount of transition data available and its strong correlation with multi-agent task characteristics have restricted recent research in this area. In this paper, we propose Multi-agent Collision Avoidance with Continuous Transition Reinforcement Learning via Mixup (MACT) to address these challenges. MACT generates new continuous transitions for training by linearly interpolating consecutive transitions. To ensure the authenticity of constructed transitions, we develop a discriminator that automatically adjusts the mixup parameters. Our proposed approach is evaluated through simulations and real-world swarm robots consisting of E-pucks, demonstrating its practical application. Our learned policies outperform existing collision avoidance methods in terms of safety and efficiency.
Distributed Ledger Technology in Industry 4.0: An Implementation

Blockchain, also known as distributed ledger technology, is a type of transformational technology that is currently regarded as one of the most important instruments of the well-knownIndustry 4.0 Industry 4.0. Blockchain'sBlockchain many properties, including smart contractsSmart Contracts, decentralization, transparency, traceability, data immutability, and data protection, combined with a consensus structure, make it appropriate for application in today's fast-paced global businesses. As a result, businesses should evaluate and compare the value of traditional supply chains with new Blockchain-based systems that add characteristics like transparency to the picture. The purpose of this article is to use a literature review to demonstrate the benefits of BlockchainBlockchain in supply chain management, highlighting key features such as sustainability, decentralization, data immutability, and the usage of smart contractsSmart Contracts. It also seeks to offer experts with positive consequences so that suitable measures may be taken to deploy this technology.
Multi-label Adversarial Defense Scheme Based on Negative Correlation Ensemble

Adversarial examples have become an important issue in the field of deep learning security. There have been many studies on adversarial example attack and defense algorithms for single-label classification models. However, in the real world, multi-label classification models are also widely used. There are only a few studies on adversarial example attack and defense algorithms for multi-label classification models. In this paper, we propose a negative correlation ensemble defense scheme against multi-label adversarial examples (ML-NCEn). The fundamental principle of ML-NCEn is to make the gradient directions and magnitudes of member models negatively correlated with those of other members, respectively, in the positive and negative label sets. Experimental results show that ML-NCEn has good adversarial robustness.
Analysis of the Impact of Mathematics Courses on Professional Courses in Science and Engineering Majors

In order to explore the influence of mathematics courses on professional courses, we studied the course scores of students majoring in four science and engineering majors. Firstly, we counted the distribution of students' grades in professional courses under each grade band of mathematics courses. Then we identified the correlation coefficient between mathematics courses and professional courses and explored the degree of influence of every course in liberal studies courses on professional courses with canonical correlation analysis. As a result, we found that mathematics courses have a strong correlation with professional courses and this correlation cannot be affected by external factors such as grade level. In addition, mathematics courses have a greater correlation with professional courses compared with other liberal studies courses .
GEFWA: Gradient-Enhanced Fireworks Algorithm for Optimizing Convolutional Neural Networks

The efficacy of evolutionary and swarm intelligence-based black-box optimization algorithms in machine learning has increased their usage, but concerns have been raised about their low sample efficiency owing to their reliance on sampling. Consequently, improving the sample efficiency of conventional black-box optimization algorithms while retaining their strengths is crucial. To this end, we propose a new algorithm called Gradient Enhanced Fireworks Algorithm (GEFWA) that incorporates first-order gradient information into the population-based fireworks algorithm (FWA). We enhance the explosion operator with the gradient-enhanced explosion (GEE) and take advantage of attraction-based cooperation (ABC) for firework collaboration. Experimental results illustrate that GEFWA outperforms traditional first-order stochastic gradient descent-based optimization methods such as Adm and SGD when it comes to optimizing convolutional neural networks. These results demonstrate the potential of integrating gradient information into the FWA framework for addressing large-scale machine learning problems.
Dual-Populatuion Differential Evolution L-NTADE for ICSI-OC’2023 Competition

In this study the performance of a dual-population differential evolution algorithm L-NTADE (Linear population size reduction Newest and Top Adaptive Differential Evolution) is evaluated on the set of benchmark functions of the ICSI-OC'2023 competition. The original L-NTADE algorithm is modified by repairing crossover rate values in parameter adaptation, allowing more precise tuning. The performed computational experiments on the ICSI-OC'2022 and ICSI-OC'2023 have shown that L-NTADE is superior compared to one of the top methods from previous year competition.