Reviewerly/exHarmony huggingface dataset:
Coordination dynamics of multi-agent interaction in a musical ensemble

Abstract Humans interact with other humans at a variety of timescales and in a variety of social contexts. We exhibit patterns of coordination that may differ depending on whether we are genuinely interacting as part of a coordinated group of individuals vs merely co-existing within the same physical space. Moreover, the local coordination dynamics of an interacting pair of individuals in an otherwise non-interacting group may spread, propagating change in the global coordination dynamics and interaction of an entire crowd. Dynamical systems analyses, such as Recurrence Quantification Analysis (RQA), can shed light on some of the underlying coordination dynamics of multi-agent human interaction. We used RQA to examine the coordination dynamics of a performance of “Welcome to the Imagination World”, composed for wind orchestra. This performance enacts a real-life simulation of the transition from uncoordinated, non-interacting individuals to a coordinated, interacting multi-agent group. Unlike previous studies of social interaction in musical performance which rely on different aspects of video and/or acoustic data recorded from each individual, this project analyzes group-level coordination patterns solely from the group-level acoustic data of an audio recording of the performance. Recurrence and stability measures extracted from the audio recording increased when musicians coordinated as an interacting group. Variability in these measures also increased, indicating that the interacting ensemble of musicians were able to explore a greater variety of behavior than when they performed as non-interacting individuals. As an orchestrated (non-emergent) example of coordination, we believe these analyses provide an indication of approximate expected distributions for recurrence patterns that may be measurable before and after truly emergent coordination.
An Improved Gorilla Troops Optimizer Based on Lens Opposition-Based Learning and Adaptive β-Hill Climbing for Global Optimization

Gorilla troops optimizer (GTO) is a newly developed meta-heuristic algorithm, which is inspired by the collective lifestyle and social intelligence of gorillas.Similar to other metaheuristics, the convergence accuracy and stability of GTO will deteriorate when the optimization problems to be solved become more complex and flexible.To overcome these defects and achieve better performance, this paper proposes an improved gorilla troops optimizer (IGTO).First, Circle chaotic mapping is introduced to initialize the positions of gorillas, which facilitates the population diversity and establishes a good foundation for global search.Then, in order to avoid getting trapped in the local optimum, the lens opposition-based learning mechanism is adopted to expand the search ranges.Besides, a novel local search-based algorithm, namely adaptive β-hill climbing, is amalgamated with GTO to increase the final solution precision.Attributed to three improvements, the exploration and exploitation capabilities of the basic GTO are greatly enhanced.The performance of the proposed algorithm is comprehensively evaluated and analyzed on 19 classical benchmark functions.The numerical and statistical results demonstrate that IGTO can provide better solution quality, local optimum avoidance, and robustness compared with the basic GTO and five other wellknown algorithms.Moreover, the applicability of IGTO is further proved through resolving four engineering design problems and training multilayer perceptron.The experimental results suggest that IGTO exhibits remarkable competitive performance and promising prospects in real-world tasks.
Application of Optimized GA-BPNN Algorithm in English Teaching Quality Evaluation System

The assessment of teaching quality is a very complex and fuzzy nonlinear process, which involves many factors and variables, so the establishment of the mathematical model is complicated, and the traditional evaluation method of teaching quality is no longer fully competent. In order to evaluate teaching quality effectively and accurately, an optimized GA-BPNN algorithm based on genetic algorithm (GA) and backpropagation neural network (BPNN) is proposed. Firstly, an index system of teaching quality evaluation is established, and a questionnaire is designed according to the index system to collect data. Then, an English teaching quality evaluation system is established by optimizing model parameters. The simulation shows that the average evaluation accuracy of the GA-BPNN algorithm is 98.56%, which is 13.23% and 5.85% higher than those of the BPNN model and the optimized BPNN model, respectively. The comparison results show that the GA-BPNN algorithm in teaching quality evaluation can make reasonable and scientific results.
ChannelGAN: Deep Learning-Based Channel Modeling and Generating

The increasing complexity on channel modeling and the cost on collecting plenty of high-quality wireless channel data have become the main bottlenecks of developing deep learning (DL) based wireless communications. In this letter, a DL-based channel modeling and generating approach namely ChannelGAN is proposed. Specifically, the ChannelGAN is designed on a small set of 3rd generation partnerships project (3GPP) link-level multiple-input multiple-output (MIMO) channel. Moreover, two evaluation mechanisms including i) power comparison from the perspective of delay and antenna domain and ii) cross validation are implemented where the power comparison proves the consistency between the modeled fake channel and real channel, and the cross validation verifies the effectiveness and availability of the generated fake channel for supporting related DL-based channel state information (CSI) feedback.
Emergency Remote Teaching and Learning

There are clear distinctions among the concepts of emergency remote teaching and learning (ERTL), online distance teaching and learning (ODTL), and open and distance learning (ODL). This chapter explores different aspects of changes that happened in the field of higher education during the time of the global crisis. It focuses on how COVID-19 pushes educational institutions and educators to provide emergency remote teaching and learning opportunities by switching from traditional pedagogies to online pedagogies. This chapter also highlights the digital competencies, which educators and learners had to develop because of the necessity. Special attention is then given to different pedagogical aspects of emergency remote teaching and learning and how digital competencies help in developing innovative pedagogical strategies and practices to face the challenges and overcome the barriers to provide support and continue education.
Adversarial Attacks and Defense for CNN Based Power Quality Recognition in Smart Grid

Vulnerability of various machine learning methods to adversarial examples has been recently explored in the literature. Power systems which use these vulnerable methods face a huge threat against adversarial examples. To this end, we first propose a signal-specific method and a universal signal-agnostic method to attack power systems using generated adversarial examples. Second, black-box attacks based on transferable characteristics and the above two methods are also proposed and evaluated. Third, adversarial training is adopted to defend systems against adversarial attacks. Experimental analyses demonstrate that the signal-specific attack method provides less perturbation compared to the FGSM (Fast Gradient Sign Method), and the signal-agnostic attack method can generate perturbations misclassifying most natural signals with high probability. Furthermore, the attack method based on the universal signal-agnostic algorithm has a higher transfer rate of black-box attacks than the attack method based on the signal-specific algorithm. In addition, the results show that the proposed adversarial training improves robustness of power systems to adversarial examples.
Parametric Algorithm for Searching the Minimum Lower Unity of Monotone Boolean Functions in the Process Synthesis of Control Automates

The problem of minimizing Boolean functions is solved based on the search for the minimum lower unit of monotone Boolean functions, an algorithmic scheme is given for solving discrete extremal problems based on a parametric algorithm for finding the minimum lower unit (m.l.u) of monotone Boolean functions. The parameters of the algorithm for finding the minimum lower unit of monotone Boolean functions for solving certain classes of discrete extremal problems are calculated
Immersive virtual reality to train preservice teachers in managing students' challenging behaviours: A pilot study

Abstract This pilot study investigated the effects of immersive virtual reality (VR) training on the classroom management ability of preservice teachers, the addition of real‐time audio feedback as reinforcement and whether preservice teachers could apply their training in an actual classroom setting. We employed the Breaking Bad Behaviours' immersive VR system to design a training situation. Managing students' challenging behaviours was the training target, and 10 preservice teachers were invited to participate (five men and five women; age range, 20–26 years). The results revealed that the immersive VR training experience improved the speed and effectiveness of the participants' management of challenging behaviours in students and enabled the participants to transfer their learning to an actual classroom setting. After the training, the teachers were more confident in halting challenging behaviours by approaching the students, taking appropriate actions and using oral commands. Real‐time audio feedback had a positive but weak influence and gave the participants a feeling of encouragement and affirmation regarding their actions; the influence of real‐time feedback is expected to be stronger if the target of the reinforcement is focused on specific classroom management strategies. Finally, more interactive student avatars should be added to the immersive VR system to enhance classroom management communication, thus making the system a more effective teacher training tool. Practitioner notes What is already known about this topic Virtual reality (VR) provides a high degree of realism that increases the effectiveness of teacher training simulations. Desktop VR technology has been applied in professional development for classroom management; few studies have used immersive VR for classroom management training. What this paper adds The immersive virtual training experience could improve preservice teachers' ability to handle students' challenging behaviours, and the classroom management ability learned through immersive VR can be transferred to a real classroom. Real‐time audio feedback can encourage preservice teachers and provide a feeling of achievement and affirm their actions. Implications for practice and/or policy Immersive VR simulations can be used as a training tool to provide preservice teachers with authentic classroom management practice experience. Through careful design, real‐time audio feedback can be incorporated into the immersive virtual simulation process to reinforce the target behaviours.
Exploring Future Promising Technologies in Hydrogen Fuel Cell Transportation

The purpose of this research was to derive promising technologies for the transport of hydrogen fuel cells, thereby supporting the development of research and development policy and presenting directions for investment. We also provide researchers with information about technology that will lead the technology field in the future. Hydrogen energy, as the core of carbon neutral and green energy, is a major issue in changing the future industrial structure and national competitive advantage. In this study, we derived promising technology at the core of future hydrogen fuel cell transportation using the published US patent and paper databases (DB). We first performed text mining and data preprocessing and then discovered promising technologies through generative topographic mapping analysis. We analyzed both the patent DB and treatise DB in parallel and compared the results. As a result, two promising technologies were derived from the patent DB analysis, and five were derived from the paper DB analysis.
Towards Learning Multi-Domain Crowd Counting

Recently, deep learning-based crowd counting methods have achieved promising performance on test data with the same distribution as training set, while performance degradation usually occurs when testing on other or unseen domains. Due to the variations in scene contexts, crowd densities and head scales, it is a very challenging issue to tackle multi-domain crowd counting using one deep model. In this work, we propose a domain-guided channel attention network (DCANet) towards learning multi-domain crowd counting. In particular, our DCANet consists of feature extraction module, channel attention-guided multi-dilation (CAMD) module and density map prediction module. Given a testing image from a certain domain, channel attention is adopted to guide the extraction of domain-specific feature representation, and thus our DCANet can adaptively handle images from multiple domains. We further propose two domain-guided learning strategies, i.e., dataset-level domain kernel (DDK) supervision and image-level domain kernel (IDK) supervision, by which channel attention in CAMD can be explicitly optimized to emphasize the channels corresponding to the domain of an input image. Furthermore, IDK can be adaptively updated when training DCANet, thereby improving the generalization ability to unseen scenes. Experimental results on benchmark datasets show that our DCANet performs favorably for handling multi-domain datasets using one single model. Moreover, our IDK training strategy can be applied to boost state-of-the-art methods on single domain dataset.
NAP: Neural architecture search with pruning

There has been continuously increasing attention attracted by Neural Architecture Search (NAS). Due to its computational efficiency, gradient-based NAS methods like DARTS have become the most popular framework for NAS tasks. Nevertheless, as the search iterates, the derived model in previous NAS frameworks becomes dominated by skip-connects, causing the performance downfall. In this work, we present a novel approach to alleviate this issue, named Neural Architecture search with Pruning (NAP). Unlike prior differentiable architecture search works, our approach draws the idea from network pruning. We first train an over-parameterized network, including all candidate operations. Then we propose a criterion to prune the network. Based on a newly designed relaxation of architecture representation, NAP can derive the most potent model by removing trivial and redundant edges from the whole network topology. Experiments show the effectiveness of our proposed approach. Specifically, the model searched by NAP achieves state-of-the-art performances (2.48% test error) on CIFAR-10. We transfer the model to ImageNet and obtains a 25.1% test error with only 5.0 M parameters, which is on par with modern NAS methods.
Using Deep Learning for Collecting Data about Museum Visitor Behavior

Nowadays, technology makes it possible to admire objects and artworks exhibited all over the world remotely. We have been able to appreciate this convenience even more in the last period, in which the pandemic has forced us into our homes for a long time. However, visiting art sites in person remains a truly unique experience. Even during on-site visits, technology can help make them much more satisfactory, by assisting visitors during the fruition of cultural and artistic resources. To this aim, it is necessary to monitor the active user for acquiring information about their behavior. We, therefore, need systems able to monitor and analyze visitor behavior. The literature proposes several techniques for the timing and tracking of museum visitors. In this article, we propose a novel approach to indoor tracking that can represent a promising and non-expensive solution for some of the critical issues that remain. In particular, the system we propose relies on low-cost equipment (i.e., simple badges and off-the-shelf RGB cameras) and harnesses one of the most recent deep neural networks (i.e., Faster R-CNN) for detecting specific objects in an image or a video sequence with high accuracy. An experimental evaluation performed in a real scenario, namely, the “Exhibition of Fake Art” at Roma Tre University, allowed us to test our system on site. The collected data has proven to be accurate and helpful for gathering insightful information on visitor behavior.
Fuzzy-based metaheuristic algorithm for optimization of fuzzy controller: fault-tolerant control application

Purpose Fuzzy-based metaheuristic algorithm is used to optimize the fuzzy controllers for the nonlinear level control system subject to uncertainty specially in the main actuator that has lost effectiveness (LOE). To optimize the fuzzy controller, type-1 harmonic search (HS) and interval type-2 (HS) will be used. Design/methodology/approach The type-1 and type-2 fuzzy-based HS algorithms are designed for optimization of fuzzy controllers for Fault-Tolerant Control (FTC) applications, and this research proposes a fuzzy-based HS metaheuristic method. The performance of a fuzzy logic-based HS algorithm applied to a nonlinear two-tank level control process with a main actuator that has lost effectiveness (LOE) and also the same controller will be tested on DC motor angular position control with and without noise. Findings The key contribution of this work is the discovery of the best approach for generating an optimal vector of values for the fuzzy controller's membership function optimization. This is done in order to improve the controller's performance, bringing the process value of the two-tank level control process closer to the target process value (set point). It is worth noting that the type-1 fuzzy controller that has been optimized is an interval type-2 fuzzy system, which can handle more uncertainty than a type-1 fuzzy system. Originality/value The type-1 and type-2 fuzzy-based HS algorithms are designed for optimization of fuzzy controllers for FTC applications, and this research proposes a fuzzy-based HS metaheuristic method. The performance of a fuzzy logic-based HS algorithm applied to a nonlinear two-tank level control process with a main actuator that has LOE will be tested on DC motor angular position control with noise. Two nonlinear uncertain processes are used to demonstrate the effectiveness of the proposed control scheme.
SII-Net: Spatial Information Integration Network for Small Target Detection in SAR Images

Ship detection based on synthetic aperture radar (SAR) images has made a breakthrough in recent years. However, small ships, which may be regarded as speckle noise, pose enormous challenges to the accurate detection of SAR images. In order to enhance the detection performance of small ships in SAR images, a novel detection method named a spatial information integration network (SII-Net) is proposed in this paper. First, a channel-location attention mechanism (CLAM) module which extracts position information along with two spatial directions is proposed to enhance the detection ability of the backbone network. Second, a high-level features enhancement module (HLEM) is customized to reduce the loss of small target location information in high-level features via using multiple pooling layers. Third, in the feature fusion stage, a refined branch is presented to distinguish the location information between the target and the surrounding region by highlighting the feature representation of the target. The public datasets LS-SSDD-v1.0, SSDD and SAR-Ship-Dataset are used to conduct ship detection tests. Extensive experiments show that the SII-Net outperforms state-of-the-art small target detectors and achieves the highest detection accuracy, especially when the target size is less than 30 pixels by 30 pixels.
Cooperation of unmanned systems for agricultural applications: A case study in a vineyard

Fully-autonomous vehicles, both aerial and ground, could provide great benefits in the Agriculture 4.0 framework when operating within cooperative architectures, thanks to their ability to tackle difficult tasks, particularly within complex irregular and unstructured scenarios such as vineyards on sloped terrains. A decentralised multi-phase approach has been proposed as an alternative to more common cooperative schemes. When perennial crops are considered, it is advantageous to build a simplified geometrical (and georeferenced) crops model, which can be identified by using 3D point clouds acquired during a-priori explorative missions by unmanned aerial vehicles. This model can be used to plan the tasks to be performed within the crops by the in-field aerial and ground drones. In this companion paper, the proposed strategy is applied to a specific case study involving a vineyard on a sloped terrain, located in the Barolo region in Piedmont, Italy. Ad-hoc technologies and guidance, navigation and control algorithms were designed and implemented. The main objectives were to improve the autonomous driving capabilities of the drones involved and to automate the process of retrieving low-complexity maps from the data collected with preliminary remote sensing missions to make them available for the autonomous navigation by a quadrotor and an unmanned 4-wheel steering ground vehicle within the vine rows. Preliminary results highlight the benefits achievable by exploiting the tailored technologies selected and applied to improve each of the analysed mission phases.
Privacy-Aware Factorization-Based Hybrid Recommendation Method for Healthcare Services

With the advancements of the Health 2.0 technology, large-scale healthcare services are available online. Recommender systems for healthcare services have emerged for decision assistance. Most existing collaborative recommendation algorithms only mine global interactions while failing to capture the local different information of users or items. Besides, privacy concern is another significant problem to be considered in recommender systems for healthcare services. In this article, a privacy-aware factorization-based hybrid method is proposed for healthcare service recommendations. For better modeling of user preferences and service features, multiview embeddings of users and healthcare services are learned. Besides, we address the privacy problem by integrating local differential privacy and locality-sensitive hashing techniques into the recommendation model for privacy-aware neighbor searching. The final prediction is made based on a stochastic gradient descent learning-based hybrid collaborative model. Experiments demonstrate the effectiveness of the proposed method in both recommendation performance and privacy concerns.
Towards comprehensive approaches for the rating prediction phase in memory-based collaborative filtering recommender systems

Recommender systems play an indispensable role in today’s online businesses. In these systems, memory-based (neighborhood-based) collaborative filtering is an important strategy to predict items as expected by users. It consists of two phases: computing the preference similarity between each pair of users in the offline phase and predicting the rating of an active user for a target item in the online phase by aggregating ratings of his/her neighbors for the target item. Previous studies on memory-based collaborative filtering have heavily concentrated on proposing methods for the computation of user preference similarity. To further improve the performance of memory-based collaborative filtering, this paper is aimed at the rating prediction phase. By optimizing a proposed objective function, the method we used in the rating prediction phase helps more accurately estimate the weight between the active user and each of his/her neighbors. The experimental results show that the proposed method outperforms others, especially in the case of a small and medium number of selected neighbors.