Reviewerly/exHarmony huggingface dataset:
Improving teaching practices via virtual reality‐supported simulation‐based learning: Scenario design and the duration of implementation

Abstract Graduate Teaching Assistants (GTAs), or student instructors, are the crucial force in college for undergraduates' learning in Science, Technology, Engineering and Maths (STEM) disciplines. However, professional development of student instructors is often neglected. Providing adequate and appropriate teacher training for student instructors is a critical challenge. When the technology is available, open‐source non‐immersive virtual reality (VR) can be a cost‐efficient and accessible platform for teacher training. Empirical research of designing and implementing VR for the training on teaching knowledge and skills development is inconclusive and thus warranted. In this ex post facto study, we investigated VR‐based teacher training with 33 STEM student instructors to explore the effects on the participants' virtual teaching practices of two design factors: (1) the simulated teaching scenario and (2) the duration of training program implementation. We analysed 7604 event logs from the recordings of their virtual teaching sessions. The results of ordinal logistic regression analyses showed two factors contributed to higher odds of appropriate teaching actions. The first is the simulated scenarios that induced a more dynamic balance of domain‐specific and pedagogical knowledge for decision making in teaching; the second is the teacher training program with a longer duration. Practitioner notes What is already know about this topic Teacher training, especially for college graduate teaching assistants in Science, Technology, Engineering and Maths (STEM) disciplines, is a key for the success of undergraduate students in STEM. Virtual reality (VR)‐supported simulation‐based learning has been found effective for enhancing knowledge and skills development in diverse settings, including when being used for teacher training. What this paper adds A guiding framework for the investigation of scenario design and duration of implementation in VR‐supported teacher training. VR scenarios that encourage more dynamic balance of domain‐specific and pedagogical knowledge for decision making in teaching have higher odds for appropriate teaching acts. A longer duration of program implementation in VR can result in higher odds for appropriate teaching acts. Implications for practice We should carefully consider appropriate scenario designs in VR to enhance dynamic decision making and interactivity in simulation‐based teaching practices for teacher training. We encourage extended duration of VR teacher training programs to facilitate teachers' observant, autonomous and attentive VR‐based micro teaching practices.
A Closer Look at the Joint Training of Object Detection and Re-Identification in Multi-Object Tracking

Unifying object detection and re-identification (ReID) into a single network enables faster multi-object tracking (MOT), while this multi-task setting poses challenges for training. In this work, we dissect the joint training of detection and ReID from two dimensions: label assignment and loss function. We find previous works generally overlook them and directly borrow the practices from object detection, inevitably causing inferior performance. Specifically, we identify a qualified label assignment for MOT should: 1) have the assignment cost aware of ReID cost, not just detection cost; 2) provide sufficient positive samples for robust feature learning while avoiding ambiguous positives (i.e., the positives shared by different ground-truth objects). To achieve the above goals, we first propose Identity-aware Label Assignment, which jointly considers the assignment cost of detection and ReID to select positive samples for each instance without ambiguities. Moreover, we advance a novel Discriminative Focal Loss that integrates ReID predictions with Focal Loss to focus the training on the discriminative samples. Finally, we upgrade the strong baseline FairMOT with our techniques and achieve up to 7.0 MOTA / 54.1% IDs improvements on MOT16/17/20 benchmarks under favorable inference speed, which verifies our tailored label assignment and loss function for MOT are superior to those inherited from object detection.
Suspicious Actions Detection System Using Enhanced CNN and Surveillance Video

Suspicious pre- and post-activity detection in crowded places is essential as many suspicious activities may be carried out by culprits. Usually, there will be installations of surveillance cameras. These surveillance cameras capture videos or images later investigated by authorities and post-event such suspicious activity would be detected. This leads to high human intervention to detect suspicious activity. However, there are no systems available to protect valuable things from such suspicious incidents. Nowadays machine learning (ML)- and deep learning (DL)-based pre-incident warning alarm systems could be adapted to monitor suspicious activity. Suspicious activity prediction would be based on human gestures and unusual activity detection. Even though some methods based on ML or DL have been proposed, the need for a highly accurate, highly precise, low-false-positive and low-false-negative prediction system can be enhanced by hybrid or enhanced ML- or DL-based systems. This proposed research work has introduced an enhanced convolutional neural network (ECNN)-based suspicious activity detection system. The experiment was carried out and the results were claimed. The results are analyzed with the Statistical Package for the Social Sciences (SPSS) tool. The results showed that the mean accuracy, mean precision, mean false-positive rate, and mean false-negative rate of suspicious activity detections were 97.050%, 96.743%, 2.957%, and 2.927% respectively. This result was also compared with the convolutional neural network (CNN) algorithm. This research work can be applied to enhance the pre-suspicious activity alert security system to avoid risky situations.
Combined Heat and Power Dispatch Problem Using Comprehensive Learning Wavelet-Mutated Slime Mould Algorithm

In this article, bio-inspired metaheuristic evolutionary algorithm named as a comprehensive learning wavelet-mutated slime mould algorithm is proposed for finding the optimal solution of combined heat and power dispatch problem with non-linear and discontinuous constraints such as valve point loading effect and prohibited operating zones in thermal only units. The proposed algorithm is based on the behavior of slimes and their reproduction stages. The wavelet mutation was introduced to help to adapt individuals to avoid local minimia. The comprehensive learning method is employed in wavelet mutated slime mould to handle the dimensional complexity that arises during the construction of new slime mould positions from parent slimes by learning from the experiences of more than one sources. Proposed hybrid slime mould algorithm which speeds up the searchability of solutions and hence enhances its robustness. Four different tests are performed and the obtained test solutions by the application of the proposed algorithm are compared with other reported solutions in the literature, in terms of generation cost and convergence speed in the CHPED problem. The obtained results are statistically verified on the Wilcoxon test.
Parametric t-stochastic neighbor embedding with quantum neural network

t-stochastic neighbor embedding (t-SNE) is a nonparametric data visualization method in classical machine learning. It maps the data from the high-dimensional space into a low-dimensional space, especially a two-dimensional plane, while maintaining the relationship or similarities between the surrounding points. In t-SNE, the initial position of the low-dimensional data is randomly determined, and the visualization is achieved by moving the low-dimensional data to minimize a cost function. Its variant called parametric t-SNE uses neural networks for this mapping. In this paper, we propose to use quantum neural networks for parametric t-SNE to reflect the characteristics of high-dimensional quantum data on low-dimensional data. We use fidelity-based metrics instead of Euclidean distance in calculating high-dimensional data similarity. To verify our proposed method, we visualize both classical (Iris dataset) and quantum (time-depending Hamiltonian dynamics) data for classification tasks. Since this method allows us to represent a quantum dataset in a higher dimensional Hilbert space by a quantum dataset in a lower dimension while keeping their similarity, the proposed method can also be used to compress quantum data for further quantum machine learning.
Iterative quantum-phase-estimation protocol for shallow circuits

Given ${N}_{\text{tot}}$ applications of a unitary operation, parametrized by an unknown phase, a phase-estimation protocol on a large-scale fault-tolerant quantum system can reduce the standard deviation of an estimate of the phase from scaling as $O[1/\sqrt{{N}_{\text{tot}}}]$ to scaling as $O[1/{N}_{\text{tot}}]$. Owing to the limited resources available to near-term quantum devices, protocols that do not entangle probes have been developed. Their mean absolute error scales as $O[log({N}_{\text{tot}})/{N}_{\text{tot}}]$. Here, we propose a two-step protocol for near-term phase estimation, with an improved error scaling. Our protocol's first step produces several low-standard-deviation estimates of $\ensuremath{\theta}$, within $\ensuremath{\theta}$'s parameter range. The second step iteratively homes in on one of these estimates. Our protocol achieves a mean-absolute-error scaling of $O[\sqrt{log(log{N}_{\text{tot}})}/{N}_{\text{tot}}]$ and a root-mean-square-error scaling of $O[\sqrt{log{N}_{\text{tot}}}/{N}_{\text{tot}}]$. Furthermore, we demonstrate a reduction in the constant scaling factor and the required circuit depths. This allows our protocol to outperform the asymptotically optimal quantum-phase-estimation algorithm for realistic values of ${N}_{\text{tot}}$.
Joint Device Selection and Bandwidth Allocation for Cost-Efficient Federated Learning in Industrial Internet of Things

Along with the deployment of Industrial Internet of Things (IIoT), massive amounts of industrial data have been generated at the network edge, driving the evolution of edge machine learning (ML). But during the ML model training, it may bring privacy leakage by traditional central methods. To address this issue, federated learning (FL) has been proposed as a distributed learning framework for training a global model without uploading raw data to protect data privacy. Since the communication and computing resources are usually limited in IIoT networks, how to reasonably select device and allocate bandwidth is crucial for the FL model training. Therefore, this article proposes a joint edge device selection and bandwidth allocation scheme for FL to minimize the time-averaged cost under the given long-term energy budget and delay constraints in the IIoT system. To tackle with this long-term optimization problem, we construct a virtual energy deficit queue and leverage the Lyapunov optimization theory to transform it into a list of round-wise drift-plus-cost minimization problems first. Then, we design an iterative algorithm to allocate reasonable bandwidth and select appropriate devices to achieve cost minimization while satisfying the energy consumption constraints. Besides, we develop an optimality analysis of the average cost and energy violation for our proposed scheme. Extensive experiments verify that our proposed scheme can achieve superior performance in cost efficiency over other schemes while guaranteeing FL training performance.
Cost-Sensitive Laplacian Logistic Regression for Ship Detention Prediction

Port state control (PSC) is the last line of defense for substandard ships. During a PSC inspection, ship detention is the most severe result if the inspected ship is identified with critical deficiencies. Regarding the development of ship detention prediction models, this paper identifies two challenges: learning from imbalanced data and learning from unlabeled data. The first challenge, imbalanced data, arises from the fact that a minority of inspected ships were detained. The second challenge, unlabeled data, arises from the fact that in practice not all foreign visiting ships receive a formal PSC inspection, leading to a missing data problem. To address these two challenges, this paper adopts two machine learning paradigms: cost-sensitive learning and semi-supervised learning. Accordingly, we expand the traditional logistic regression (LR) model by introducing a cost parameter to consider the different misclassification costs of unbalanced classes and incorporating a graph regularization term to consider unlabeled data. Finally, we conduct extensive computational experiments to verify the superiority of the developed cost-sensitive semi-supervised learning framework in this paper. Computational results show that introducing a cost parameter into LR can improve the classification rate for substandard ships by almost 10%. In addition, the results show that considering unlabeled data in classification models can increase the classification rate for minority and majority classes by 1.33% and 5.93%, respectively.
SumMER: Structural Summarization for RDF/S KGs

Knowledge graphs are becoming more and more prevalent on the web, ranging from small taxonomies, to large knowledge bases containing a vast amount of information. To construct such knowledge graphs either automatically or manually, tools are necessary for their quick exploration and understanding. Semantic summaries have been proposed as a key technology enabling the quick understanding and exploration of large knowledge graphs. Among the methods proposed for generating summaries, structural methods exploit primarily the structure of the graph in order to generate the result summaries. Approaches in the area focus on identifying the most important nodes and usually employ a single centrality measure, capturing a specific perspective on the notion of a node’s importance. Moving from one centrality measure to many however, has the potential to generate a more objective view on nodes’ importance, leading to better summaries. In this paper, we present SumMER, the first structural summarization technique exploiting machine learning techniques for RDF/S KGs. SumMER explores eight centrality measures and then exploits machine learning techniques for optimally selecting the most important nodes. Then those nodes are linked formulating a subgraph out of the original graph. We experimentally show that combining centrality measures with machine learning effectively increases the quality of the generated summaries.
BART-IT: An Efficient Sequence-to-Sequence Model for Italian Text Summarization

The emergence of attention-based architectures has led to significant improvements in the performance of neural sequence-to-sequence models for text summarization. Although these models have proved to be effective in summarizing English-written documents, their portability to other languages is limited thus leaving plenty of room for improvement. In this paper, we present BART-IT, a sequence-to-sequence model, based on the BART architecture that is specifically tailored to the Italian language. The model is pre-trained on a large corpus of Italian-written pieces of text to learn language-specific features and then fine-tuned on several benchmark datasets established for abstractive summarization. The experimental results show that BART-IT outperforms other state-of-the-art models in terms of ROUGE scores in spite of a significantly smaller number of parameters. The use of BART-IT can foster the development of interesting NLP applications for the Italian language. Beyond releasing the model to the research community to foster further research and applications, we also discuss the ethical implications behind the use of abstractive summarization models.
Mixed-Sized Biomedical Image Segmentation Based on U-Net Architectures

Convolutional neural networks (CNNs) are becoming increasingly popular in medical Image Segmentation. Among them, U-Net is a widely used model that can lead to cutting-edge results for 2D biomedical Image Segmentation. However, U-Net performance can be influenced by many factors, such as the size of the training dataset, the performance metrics used, the quality of the images and, in particular, the shape and size of the organ to be segmented. This could entail a loss of robustness of the U-Net-based models. In this paper, the performance of the considered networks is determined by using the publicly available images from the 3D-IRCADb-01 dataset. Different organs with different features are considered. Experimental results show that the U-Net-based segmentation performance decreases when organs with sparse binary masks are considered. The solution proposed in this paper, based on automated zooming of the parts of interest, allows improving the performance of the segmentation model by up to 20% in terms of Dice coefficient metric, when very sparse segmentation images are used, without affecting the cost of the learning process.
Dynamic classifier approximation for unsupervised domain adaptation

In recent years, domain adaptation (DA) method has been proposed to solve the problem of domain shift between the training set and test set. However, most feature-based unsupervised domain adaptation methods have a weakness of strict requirement on the transformation matrix and they are not integrated with classifier. Therefore, this paper proposes a novel dynamic classifier approximation (DCA) method for unsupervised domain adaptation. Specifically, the proposed DCA, which combines classifier method to relax the requirements of transformation matrix, learns domain invariant features from structure and data level. 1) At the structure level, the low-rank representation, sparse representation and manifold learning are combined to preserve the structure information of data in the original feature space. 2) At the data level, distribution alignments are adopted to minimize the distribution difference of the source and target domains in the common subspace, which is helpful to reduce the negative impact caused by domain shift. In addition, for learning the common subspace more flexibly, a dynamic classifier is introduced to reduce the strict requirements on projection matrix. This paper carries out a large number of experiments on four data sets, and the experimental results show that the proposed method is superior to many of the latest domain adaptation methods, which can indicate its effectiveness.
Identification of defects in composite laminates by comparison of mode shapes from electronic speckle pattern interferometry

A novel technique for identifying defects in carbon fibre reinforced plates has been developed. Firstly, modal analysis by impact excitation was performed to obtain the first five resonant frequencies for three defect-free and three defective specimens with in-plane fibre waviness. Then amplitude-fluctuation electronic speckle pattern interferometry (AF-ESPI) was used to obtain the mode shapes at these frequencies. The contours of the nodal regions visible in the mode shapes were extracted using a specially-developed algorithm employing density-based spatial clustering of applications with noise (DBSCAN). Fourier descriptors, that are invariant to rotations, translations, and scaling, were used to decompose the contours to reduce data dimensionality and make comparisons. The differences in contours between the two sets of specimens showed that the 4th and 5th mode shapes can be used for identifying the presence of the waviness defects. This technique for nodal region comparison was found to greatly simplify the comparison of fringe patterns for the purpose of damage assessment and could potentially be used as part of validation procedures.
Height aware understanding of remote sensing images based on cross-task interaction

3D scene perception that provides semantic interpretation for each point has a wide range of practical scenarios. However, existing methods are generally implemented based on 3D point cloud. In this work, we investigate the inverse projection problem from 2D plane image to 3D scene perception and propose a multi-task model to jointly predict height information and semantic categories. Taking into account the commonalities and differences between the two tasks, we have carefully designed the multi-task model to explicitly strengthen the establishment of cross-task correlations. Specifically, the calibration refinement attention (CRA) module is proposed before the classifier heads, incorporating the beneficial information while filtering the inconsistent characteristics among the two tasks. Besides, a spatial structure enhanced (SSE) module is introduced to integrate the spatial structure information into the output features of CRA module through skip-connection. After that, the neighboring pixel affinity (NPA) loss and the soft weighted ordinal (SWO) classification loss for the two tasks are introduced to optimize the direction of the task gradients. At the same time, to validate the effectiveness of the proposed method, a novel metric named height constrained semantic accuracy (HCSA) is proposed to consider the accuracy of semantic segmentation and height estimation jointly. Extensive experiments on Vaihingen, Potsdam, and DFC2019 demonstrate that the proposed method achieves generalization and competitive performance.
An adaptive machine learning algorithm for the resource-constrained classification problem

Resource-constrained classification tasks are common in real-world applications such as allocating tests for disease diagnosis, hiring decisions when filling a limited number of positions, and defect detection in manufacturing settings under a limited inspection budget. Typical classification algorithms treat the learning process and the resource constraints as two separate and sequential tasks. We develop an adaptive learning approach that considers resource constraints and learning jointly by iteratively fine-tuning misclassification costs. Via a structured experimental study using a publicly available data set, we evaluate a decision tree classifier that utilizes the proposed approach. The adaptive learning approach performs significantly better than alternative approaches, especially for difficult classification problems in which the performance of common approaches may be unsatisfactory. The suggested approach reaches similar classification decisions for different costs, thus it may be useful when misclassification costs are not known precisely or are costly to achieve. We envision the suggested learning approach as an important addition to the repertoire of techniques for handling resource-constrained classification problems.
Soft-label guided non-negative matrix factorization for unsupervised feature selection

Unsupervised feature selection (UFS) aims to select the most representative features from the original data, which can efficiently reduce the influence of redundancy, outliers and noises. Over the past decades, various UFS algorithms have been proposed. However, these methods often do not consider the necessity of sparsity or ignore the fuzziness of the data. To tackle these shortcomings, in this paper, a novel soft-label guided non-negative matrix factorization (SLNMF) method is proposed. Specifically, both the convex NMF and ℓ2,1−norm regularization are introduced to ensure the sparsity of the feature selection matrix. Furthermore, the soft-label matrix based on local distance is used to supervise the feature selection, and a linear regression is developed to find the correlation between the low-dimensional representation and the soft-label space. Finally, extensive experiments on several benchmark datasets are conducted. The results show that the proposed method is advanced over several state-of-the-art UFS methods.