Reviewerly/exHarmony huggingface dataset:
Exploring the landscape of STEM education and personnel training: a comprehensive systematic review

This article focuses on the generalization of foreign experiences in implementing STEM education and places particular emphasis on the areas of professional training for future teachers of STEM disciplines. By conducting bibliometric analyses of sources from Scopus (93 sources) and Web of Science (42 sources) scientometric databases, four clusters were identified using VOSviewer: 1) STEM education and training; 2) teacher training and primary education; 3) surveys on STEM education; and 4) e-learning and computational thinking in the training of future teachers of STEM disciplines. The research results demonstrate the importance and relevance of STEM education in various fields, and the leading role of the cluster "STEM education and training" has been determined based on the number and strength of links within the network of keyword links of sources on professional training of future STEM teachers. The discussion of the research findings highlights views on the prospects for STEM development in different countries. It is determined that: a) the professional training and social status of teachers are strategically important links in STEM education; b) teachers of "traditional" STEM disciplines (mathematics and natural sciences) require an increased level of knowledge in the field of information technology, particularly through the integration of programming as a component of STEM education; and c) social virtual reality platforms, hyperrealistic modeling, virtual laboratories, educational robotics, augmented, and mixed reality technologies are recommended to increase the motivation of pupils and students to study STEM disciplines.
Enhancing Small Tabular Clinical Trial Dataset through Hybrid Data Augmentation: Combining SMOTE and WCGAN-GP

This study addressed the challenge of training generative adversarial networks (GANs) on small tabular clinical trial datasets for data augmentation, which are known to pose difficulties in training due to limited sample sizes. To overcome this obstacle, a hybrid approach is proposed, combining the synthetic minority oversampling technique (SMOTE) to initially augment the original data to a more substantial size for improving the subsequent GAN training with a Wasserstein conditional generative adversarial network with gradient penalty (WCGAN-GP), proven for its state-of-art performance and enhanced stability. The ultimate objective of this research was to demonstrate that the quality of synthetic tabular data generated by the final WCGAN-GP model maintains the structural integrity and statistical representation of the original small dataset using this hybrid approach. This focus is particularly relevant for clinical trials, where limited data availability due to privacy concerns and restricted accessibility to subject enrollment pose common challenges. Despite the limitation of data, the findings demonstrate that the hybrid approach successfully generates synthetic data that closely preserved the characteristics of the original small dataset. By harnessing the power of this hybrid approach to generate faithful synthetic data, the potential for enhancing data-driven research in drug clinical trials become evident. This includes enabling a robust analysis on small datasets, supplementing the lack of clinical trial data, facilitating its utility in machine learning tasks, even extending to using the model for anomaly detection to ensure better quality control during clinical trial data collection, all while prioritizing data privacy and implementing strict data protection measures.
Binocular vision measurement for large-scale weakly textured ship hull plates using feature points encoding method

The accurate forming measurement for curved hull plates is vital to ensure the quality of shipbuilding. However, the weak texture, large scale and complex shapes of curved hull plates present challenges for shape inspections. To enrich the texture of plates, checkerboard patterns are combined with the binocular vision system. Moreover, calculating the sub-pixel coordinates of corner points enhances reconstruction accuracy. A novel encoding method for the feature points on the complex shaped plates with repeatable texture is proposed to match the homologous points correctly. Finally, three dimensional coordinates are obtained by triangulation. Experimental results show that the dimensional accuracy is 300 ± 0.636 mm and the average accuracy is ±0.219 mm by registration. Inspection processes are completed within 1 min. This method is more automatic and convenient than contact measurement. Moreover, the efficiency is improved by about 2 times than multi-vision measuring method, enhancing the accuracy and efficiency of measurement for curved hull plates.
Design of a new multi-wing chaotic system and its application in color image encryption

Due to its chaotic carrier function and pseudorandom nature, chaotic systems are frequently applied in the area of secure communication. To render chaotic signals more complicated, a novel multi-wing chaotic system is developed in this study. Initially, a double-wing attractor generated by a novel 3D Lorenz-like chaotic system that comprises two quadratic terms and four linear terms. By inserting a nonlinear feedback controller, the complex system gets transformed into a multi-wing chaotic system. It is noteworthy that the topological shape of the attractor and the number of equilibrium points can both be altered in the particular multi-wing chaotic system. Secondly, the dynamic features of the multi-wing chaotic system are discussed via employing the bifurcation diagram and the Lyapunov exponential spectrum. According to results from simulation, the system displays widely dispersed chaotic characteristics over a large range of parameters. In addition, the complexity analysis findings of chaotic sequences testify that the chaotic sequences have strong complexity. Therefore, the multi-wing chaotic system will have an excellent effect in image encryption. Finally, a scrambling-diffusion encryption scheme is built using the rich chaotic sequences discussed above. The scheme includes two times scrambling of pixel position and once transform of pixel value by the virtue of Galois field (GF). The proposed algorithm has superior efficiency and excellent security according to the results of the feasibility and security analysis. Moreover, the investigation of the multi-wing chaotic system property and its usage in image encryption additionally broadens the applicability of chaos systems and offers some standards for confidential communication.
Annotated dataset creation through large language models for non-english medical NLP

Obtaining text datasets with semantic annotations is an effortful process, yet crucial for supervised training in natural language processing (NLP). In general, developing and applying new NLP pipelines in domain-specific contexts for tasks often requires custom-designed datasets to address NLP tasks in a supervised machine learning fashion. When operating in non-English languages for medical data processing, this exposes several minor and major, interconnected problems such as the lack of task-matching datasets as well as task-specific pre-trained models. In our work, we suggest to leverage pre-trained large language models for training data acquisition in order to retrieve sufficiently large datasets for training smaller and more efficient models for use-case-specific tasks. To demonstrate the effectiveness of your approach, we create a custom dataset that we use to train a medical NER model for German texts, GPTNERMED, yet our method remains language-independent in principle. Our obtained dataset as well as our pre-trained models are publicly available at https://github.com/frankkramer-lab/GPTNERMED.
Use Your Head: Improving Long-Tail Video Recognition

This paper presents an investigation into long-tail video recognition. We demonstrate that, unlike naturally-collected video datasets and existing long-tail image benchmarks, current video benchmarks fall short on multiple long-tailed properties. Most critically, they lack few-shot classes in their tails. In response, we propose new video benchmarks that better assess long-tail recognition, by sampling subsets from two datasets: SSv2 and VideoLT. We then propose a method, Long-Tail Mixed Reconstruction (LMR), which reduces overfitting to instances from few-shot classes by reconstructing them as weighted combinations of samples from head classes. LMR then employs label mixing to learn robust decision boundaries. It achieves state-of-the-art average class accuracy on EPIC-KITCHENS and the proposed SSv2-LT and VideoLT-LT. Benchmarks and code at: github.com/tobyperrett/lmr
Efficient On-Device Training via Gradient Filtering

Despite its importance for federated learning, continuous learning and many other applications, on-device training remains an open problem for EdgeAI. The problem stems from the large number of operations (e.g., floating point multiplications and additions) and memory consumption required during training by the back-propagation algorithm. Consequently, in this paper, we propose a new gradient filtering approach which enables on-device CNN model training. More precisely, our approach creates a special structure with fewer unique elements in the gradient map, thus significantly reducing the computational complexity and memory consumption of back propagation during training. Extensive experiments on image classification and semantic segmentation with multiple CNN models (e.g., MobileNet, DeepLabV3, UPerNet) and devices (e.g., Raspberry Pi and Jetson Nano) demonstrate the effectiveness and wide applicability of our approach. For example, compared to SOTA, we achieve up to 19× speedup and 77.1% memory savings on ImageNet classification with only 0.1% accuracy loss. Finally, our method is easy to implement and deploy; over 20× speedup and 90% energy savings have been observed compared to highly optimized baselines in MKLDNN and CUDNN on NVIDIA Jetson Nano. Consequently, our approach opens up a new direction of research with a huge potential for on-device training. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> Code: https://github.com/SLDGroup/GradientFilter-CVPR23
Hate Speech Detection Using the GPT-2 and Natural Language Processing

Hate speech on social media has increased as people use it to communicate and express their thoughts. Automatic hate speech detection has been studied in several languages. This study investigates the conclusion of the performance evaluation of the machine learning model GPT-2 (Generative Pre-trained Transformation 2) for natural language processing. The proposed model is developed and evaluated using a hate speech dataset. We attained an accuracy of about 97.12 percent, higher than the existing models called biLSTM, CNN, CNN-LSTM, and BERT.
Recommendation Systems for the Metaverse

The metaverse, a virtual world closely intertwined with reality, represents the next-generation form of the Internet and encompasses advanced sociability and interaction. With the rapid growth of users and virtual products within this environment, the importance of recommendation systems becomes paramount. This article presents a comprehensive survey that examines the technologies influencing the development of recommendation systems tailored for the metaverse. Firstly, we identify the key trends and fundamental concepts associated with these systems. Secondly, we delve into the core supporting technologies and application scenarios within metaverse recommendation systems. Lastly, we provide insights into future research directions for this rapidly evolving field. Our survey reveals that while progress has been achieved, further investigation is necessary to optimize the technical layout and content of metaverse recommendation systems.
[Re]Imagining Indigenous Educational Design: A Conceptual Manifesto to Grow Disruptive Indigenous Digital Activists

Abstract The ubiquity of digital technologies and the harvesting of individual data has modified global societies, reinforcing systems of oppression. While a contemporary phenomenon, the convergence of technology and human subjugation is historical, with links to the racialised reproduction of settler-colonialism. Australia, a settler-colonial state, has escalated the prioritisation of digital competitiveness with a recent policy directed at stimulating infrastructure and research-industry-government partnerships. Strategically, Indigenous communities remain constrained, where the ubiquity of technology and the escalation of digital competitiveness compounds the socio-economic impacts of continuing colonisation. Offline, Indigenous communities continue to face rigid political constraint that limits online access and denies opportunities for their people to live anchored to ancestral lands, seas, languages, and knowledge systems. Given the rapid advance of digital disruption, the inability to limit racialised socio-technical systems, or compete at parity in cyberspace, suppresses Indigenous digital activism, governance, and entrepreneurialism. This conceptual paper (part manifesto, part vision statement) offers initial thoughts intended to stimulate further research on twenty-first-century Indigenous educational design. Central to future design considerations is the search for pragmatic solutions capable of overcoming the racialised challenges limiting the collective development of digital activists essential for Indigenous nation-building. In [re]imagining an alternate digital educational agenda, Indigenous communities must collectively advance strategies that deliberately shift away from Australian schools toward local community digital learning hubs.
Energy-time entanglement coexisting with fiber-optical communication in the telecom <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>C</mml:mi></mml:math> band

The authors study the coexistence of quantum entanglement with fiber optical communication at the telecom $C$ band. The performance of quantum key distribution based on distributed energy-time entanglement coexisting with classical light is characterized, which paves the way for developing a cost-effective quantum entanglement network compatible with fiber communications.
EQUI-VOCAL: Synthesizing Queries for Compositional Video Events from Limited User Interactions

We introduce EQUI-VOCAL: a new system that automatically synthesizes queries over videos from limited user interactions. The user only provides a handful of positive and negative examples of what they are looking for. EQUI-VOCAL utilizes these initial examples and additional ones collected through active learning to efficiently synthesize complex user queries. Our approach enables users to find events without database expertise, with limited labeling effort, and without declarative specifications or sketches. Core to EQUI-VOCAL's design is the use of spatio-temporal scene graphs in its data model and query language and a novel query synthesis approach that works on large and noisy video data. Our system outperforms two baseline systems---in terms of F1 score, synthesis time, and robustness to noise---and can flexibly synthesize complex queries that the baselines do not support.
SOF-RRT*: An improved path planning algorithm using spatial offset sampling

Recently, sampling-based algorithms have demonstrated advantages in complex and high-dimensional environments. The RRT* algorithm, as the best variant of RRT, provides progressive optimality. It is shown that the RRT* algorithm has a slow convergence speed and a high initial path cost that result in the low efficiency of the algorithm, due to the low probability of adequate sampling caused by the invalid expansion of the tree and redundant sampling. To overcome these limitations, the SOF-RRT* algorithm is proposed to generate better initial solutions with higher stability and faster convergence. The SOF-RRT* algorithm is an improvement on the F-RRT* algorithm. The SOF-RRT* algorithm introduces a spatial probability weight sampling strategy into sampling, which makes the sampling probability higher in the area with a large feasible area. It reduces redundant sampling and increases the effective sampling rate. In the aspect of tree expansion, the strategy of target bias and obstacle tangential bias is introduced to improve the tree expansion efficiency, which makes tree expansion away from obstacles and bias to the target point. The proposed algorithm improves the expansion efficiency of sampling and tree. Finally, the initial path generation and convergence speed are simulated and compared, which proves that the algorithm has been greatly optimized in terms of the number of iterations, path quality, and convergence speed.
Evaluation of classification models in limited data scenarios with application to additive manufacturing

This paper presents a novel framework that enables the generation of unbiased estimates for test loss using fewer labeled samples, effectively evaluating the predictive performance of classification models in data-limited applications. The framework's key innovation lies in developing an adaptive sampling distribution that iteratively identifies influential testing samples based on interactions between learner and evaluator agents. Notably, the adaptive distribution dynamically adjusts the evaluator agent's supervisory role by prioritizing inputs with discrepancies between the agents and considering the evaluator's uncertainty. Comprehensive experimental analyses on synthetic data and two sparse data sets from material extrusion additive manufacturing problems validate the framework's superiority over uniform and fixed sampling distributions. First, the proposed framework provides unbiased estimates of the test loss across various data sets, sampling ratios, and evaluator models. Second, the introduced adaptive sampling distribution significantly reduces the standard deviation of the test loss estimator compared to uniform sampling, achieving a 50% reduction for a 10% sampling ratio in the filament selection benchmark. Third, the framework demonstrates its efficacy in model selection to determine the optimal number of hidden units with a reduced number of test samples. Overall, this work offers a promising framework for evaluating classification models in applications where acquiring labeled data is time-consuming and resource-intensive, including materials science and engineering.
Unsupervised underwater image enhancement via content-style representation disentanglement

The absorption and scattering properties of the water medium cause various types of distortion in underwater images, which seriously affects the accuracy and effectiveness of subsequent processing. The application of supervised learning algorithms in underwater image enhancement is limited by the difficulty of obtaining a large number of underwater paired images in practical applications. As a solution, we propose an unsupervised representation disentanglement based underwater image enhancement method (URD-UIE). URD-UIE disentangles content information (e.g., texture, semantics) and style information (e.g., chromatic aberration, blur, noise, and clarity) from underwater images and then employs the disentangled information to generate the target distortion-free image. Our proposed method URD-UIE adopts an unsupervised cycle-consistent adversarial translation architecture and combines multiple loss functions to impose specific constraints on the output results of each module to ensure the structural consistency of underwater images before and after enhancement. The experimental results demonstrate that the URD-UIE technique effectively enhances the quality of underwater images when training with unpaired data, resulting in a significant improvement in the performance of the standard model for underwater object detection and semantic segmentation.
Feature reconstruction graph convolutional network for skeleton-based action recognition

Skeleton-based action recognition is an important task in computer vision. Recently, graph convolutional networks (GCNs) have been successfully applied to this task and achieved remarkable results. However, there are still some non-negligible limitations with existing GCN-based methods. First, the artificial predefined skeleton partition lacks the joint modeling for different types of edges. Second, most GCN models use interleaved deployment of spatial-only and temporal-only modules to achieve feature learning, which makes them ineffective in capturing spatiotemporal co-occurrence from action sequences. To tackle the above issues, we propose a novel feature reconstruction graph convolutional network (FR-GCN) for skeleton-based action recognition. The proposed FR-GC combines coarse-grained temporal and spatial features to reconstruct fine-grained spatiotemporal features, realizing simultaneous learning of temporal and spatial representations in a single module and significantly improving the capability of the model for spatiotemporal feature extraction. We also propose a topology partition enhancement module to achieve adaptive complementation among different types of edges. Moreover, an efficient multi-scale dual-domain temporal convolution is used to complete further temporal modeling. Compared with state-of-the-art methods, the proposed FR-GCN achieves competitive results on both NTU RGB+D 60 dataset and NTU RGB+D 120 dataset. Especially under the cross-subject benchmarks of the two datasets, the proposed FR-GCN achieves new state-of-the-art performance.
Intelligent Localization and Deep Human Activity Recognition through IoT Devices

Ubiquitous computing has been a green research area that has managed to attract and sustain the attention of researchers for some time now. As ubiquitous computing applications, human activity recognition and localization have also been popularly worked on. These applications are used in healthcare monitoring, behavior analysis, personal safety, and entertainment. A robust model has been proposed in this article that works over IoT data extracted from smartphone and smartwatch sensors to recognize the activities performed by the user and, in the meantime, classify the location at which the human performed that particular activity. The system starts by denoising the input signal using a second-order Butterworth filter and then uses a hamming window to divide the signal into small data chunks. Multiple stacked windows are generated using three windows per stack, which, in turn, prove helpful in producing more reliable features. The stacked data are then transferred to two parallel feature extraction blocks, i.e., human activity recognition and human localization. The respective features are extracted for both modules that reinforce the system's accuracy. A recursive feature elimination is applied to the features of both categories independently to select the most informative ones among them. After the feature selection, a genetic algorithm is used to generate ten different generations of each feature vector for data augmentation purposes, which directly impacts the system's performance. Finally, a deep neural decision forest is trained for classifying the activity and the subject's location while working on both of these attributes in parallel. For the evaluation and testing of the proposed system, two openly accessible benchmark datasets, the ExtraSensory dataset and the Sussex-Huawei Locomotion dataset, were used. The system outperformed the available state-of-the-art systems by recognizing human activities with an accuracy of 88.25% and classifying the location with an accuracy of 90.63% over the ExtraSensory dataset, while, for the Sussex-Huawei Locomotion dataset, the respective results were 96.00% and 90.50% accurate.