Reviewerly/exHarmony huggingface dataset:
Design and assessment of a virtual reality learning environment for firefighters

The use of virtual reality (VR) in firefighter training is promising because it provides cost-effective, safe environments that arouse similar behavioral responses to real-life scenarios. However, the pedagogical potential of VR and its impact on learning outcomes compared to traditional methods is currently an under-explored area. This research investigates how well VR can support learning compared to traditional methods in the context of training firefighters in combating vegetation fires. A VR learning environment was developed, informed by a “design for learning” framework providing a pedagogical underpinning. A between-subjects experiment was conducted with 40 participants to measure the knowledge transfer of the VR learning environment against the official textbook. In addition, VR's theorized learning benefits of intrinsic motivation, situational interest, and self-efficacy were compared with textbook-based learning. Lastly, the design quality of the learning environment was assessed based on its learning and user experience. We employed a primarily quantitative approach to data collection and analysis, using a combination of knowledge test results and questionnaires, with supporting qualitative data from semi-structured interviews and observation notes to answer our hypotheses. The results found a significant difference between the knowledge transfer of both conditions, with textbook-based learning more effectively transferring factual and conceptual knowledge than VR. No significant difference was found in reported self-efficacy between the two conditions but was found in reported levels of intrinsic motivation and situational interest, which were higher in the VR condition. The design was found to have facilitated a good user and learning experience, assessed via questionnaire responses. During interviews, VR participants reported high levels of satisfaction with the experience, praising the hands-on learning approach and interactivity, while reporting frustration with the lack of knowledge reinforcement and initial difficulties with the controls. A key finding was that presence was found to be negatively associated with knowledge transfer, which we theorize to be caused by the novelty of the realistic VR environment distracting participants from the more familiar lesson content. This research contributes to the body of work related to knowledge transfer within VR in this domain while highlighting key pedagogical and design considerations that can be used to inform future design implementations.
Addressing durability in collaborative organising: Event atmospheres and polyrhythmic affectivity

Collaborative organising is known to burn like a rocket: it thrives on intense passion, relationality and creativity but quickly falls into pieces. This article explores the underestimated role of events and their affective atmospheres to sustain collaborative work. Drawing insights from two ethnographic field studies within an open-source software community and a network of impact entrepreneurs, we introduce the notion of ‘polyrhythmic affectivity’ at the core of polycentric governance. It encapsulates how frictional reverberances between three atmospherically experienced affective intensities – togetherness, dissonance and mutuality – are able to maintain emergent yet enduring order. We argue that the collective motivational force of collaborative organising, can be stabilised through a process of ‘affective commoning’ to sustain collaborative atmospheres as shared creative resources.
Application of simulation and virtual reality to production learning

Purpose This study explores the impact of new technologies, such as simulation and virtual reality, on the pedagogy and learning of engineering students. It aims to compare the effectiveness of these digital tools against traditional teaching methods in enhancing student learning experiences. Design/methodology/approach Utilizing a quantitative research approach, the study involved third-year engineering students from the “Production Management” course at the School of Engineering of Vitoria-Gasteiz. Data were collected through an ad hoc questionnaire and analyzed using SPSS software, focusing on student satisfaction, challenges in adopting new technologies and the evolving roles of students and teachers. Findings The research highlighted several key aspects. Firstly, it identified the need for adapting teaching methods to incorporate new technologies effectively. Secondly, the integration of simulation and virtual reality was found to facilitate a deeper understanding of real-world problems, as students could engage with these issues in a simulated, virtual environment. Finally, the study emphasized the importance of pedagogical approaches that leverage these technologies to increase student involvement and motivation. The results suggest a positive impact of digital tools on the learning process in engineering education. Research limitations/implications The study’s scope was limited to one course within a single institution, suggesting the need for broader research across various disciplines and educational settings. Originality/value This research offers valuable insights into the integration of simulation and virtual reality in engineering education, underscoring their potential to enhance the learning experience and knowledge acquisition among students.
An Algorithm for Measuring the Similarity of Histograms for Texture Image Segmentation

A simple algorithm for measuring the similarity between multi-column histograms is presented. The proposed algorithm is intended for texture segmentation of images using histograms as texture features. The purpose of developing such a specialized algorithm is to more accurately determine the boundaries between neighboring texture segments. The algorithm is specially designed so that to express the similarity value as a percentage. The main peculiarity of the proposed algorithm is that when calculating the similarity value, it considers not only the corresponding histogram columns but also takes into account their neighboring components. Due to this, the algorithm more adequately evaluates the similarity of histograms. The proposed algorithm was implemented as a computer program as an integral part of the image segmentation model. The efficiency of the histogram comparison algorithm is indirectly confirmed by the texture segmentation results of the image segmentation model in image processing experiments.
NnU-Net versus mesh growing algorithm as a tool for the robust and timely segmentation of neurosurgical 3D images in contrast-enhanced T1 MRI scans

Abstract Purpose This study evaluates the nnU-Net for segmenting brain, skin, tumors, and ventricles in contrast-enhanced T1 (T1CE) images, benchmarking it against an established mesh growing algorithm (MGA). Methods We used 67 retrospectively collected annotated single-center T1CE brain scans for training models for brain, skin, tumor, and ventricle segmentation. An additional 32 scans from two centers were used test performance compared to that of the MGA. The performance was measured using the Dice-Sørensen coefficient (DSC), intersection over union (IoU), 95th percentile Hausdorff distance (HD95), and average symmetric surface distance (ASSD) metrics, with time to segment also compared. Results The nnU-Net models significantly outperformed the MGA ( p &lt; 0.0125) with a median brain segmentation DSC of 0.971 [95CI: 0.945–0.979], skin: 0.997 [95CI: 0.984–0.999], tumor: 0.926 [95CI: 0.508–0.968], and ventricles: 0.910 [95CI: 0.812–0.968]. Compared to the MGA’s median DSC for brain: 0.936 [95CI: 0.890, 0.958], skin: 0.991 [95CI: 0.964, 0.996], tumor: 0.723 [95CI: 0.000–0.926], and ventricles: 0.856 [95CI: 0.216–0.916]. NnU-Net performance between centers did not significantly differ except for the skin segmentations Additionally, the nnU-Net models were faster (mean: 1139 s [95CI: 685.0–1616]) than the MGA (mean: 2851 s [95CI: 1482–6246]). Conclusions The nnU-Net is a fast, reliable tool for creating automatic deep learning-based segmentation pipelines, reducing the need for extensive manual tuning and iteration. The models are able to achieve this performance despite a modestly sized training set. The ability to create high-quality segmentations in a short timespan can prove invaluable in neurosurgical settings.
Enhancing Data Use Ontology (DUO) for health-data sharing by extending it with ODRL and DPV

The Global Alliance for Genomics and Health is an international consortium that is developing the Data Use Ontology (DUO) as a standard providing machine-readable codes for automation in data discovery and responsible sharing of genomics data. DUO concepts, which are encoded using OWL, only contain the textual descriptions of the conditions for data use they represent, and do not specify the intended permissions, prohibitions, and obligations explicitly – which limits their usefulness. We present an exploration of how the Open Digital Rights Language (ODRL) can be used to explicitly represent the information inherent in DUO concepts to create policies that are then used to represent conditions under which datasets are available for use, conditions in requests to use them, and to generate agreements based on a compatibility matching between the two. We also address a current limitation of DUO regarding specifying information relevant to privacy and data protection law by using the Data Privacy Vocabulary (DPV) which supports expressing legal concepts in a jurisdiction-agnostic manner as well as for specific laws like the GDPR. Our work supports the existing socio-technical governance processes involving use of DUO by providing a complementary rather than replacement approach. To support this and improve DUO, we provide a description of how our system can be deployed with a proof of concept demonstration that uses ODRL rules for all DUO concepts, and uses them to generate agreements through matching of requests to data offers. All resources described in this article are available at: https://w3id.org/duodrl/repo.
Optimizing convolutional neural networks for IoT devices: performance and energy efficiency of quantization techniques

Abstract This document addresses some inherent problems in Machine Learning (ML), such as the high computational and energy costs associated with their implementation on IoT devices. It aims to study and analyze the performance and efficiency of quantization as an optimization method, as well as the possibility of training ML models directly on an IoT device. Quantization involves reducing the precision of model weights and activations while still maintaining acceptable levels of accuracy. Using representative networks for facial recognition developed with TensorFlow and TensorRT, Post-Training Quantization and Quantization-Aware Training are employed to reduce computational load and improve energy efficiency. The computational experience was conducted on a general-purpose computer featuring an Intel i7-1260P processor and an NVIDIA RTX 3080 graphics card used as an accelerator. Additionally, a NVIDIA Jetson AGX Orin was used as an example of an IoT device. We analyze the feasibility of training on an IoT device, the impact of quantization optimization on knowledge transfer-trained models and evaluate the differences between Post-Training Quantization and Quantization-Aware Training in such networks on different devices. Furthermore, the performance and efficiency of NVIDIA’s inference accelerator (Deep Learning Accelerator - DLA, in its 2.0 version) available at the Jetson Orin architecture are studied. We concluded that the Jetson device is capable of performing training on its own. The IoT device can achieve inference performance similar to that of the more powerful processor, thanks to the optimization process, with better energy efficiency. Post-Training Quantization has shown better performance, while Quantization-Aware Training has demonstrated higher energy efficiency. However, since the accelerator cannot execute certain layers of the models, the use of DLA worsens both the performance and efficiency results.
Content and structure based attention for graph node classification

Graph-structured data is ubiquitous in real-world applications, such as social networks, citation networks, and communication networks. Graph neural network (GNN) is the key to process them. In recent years, graph attention networks (GATs) have been proposed for node classification and achieved encouraging performance. It focuses on the content associated on nodes to evaluate the attention weights, and the rich structure information in the graph is almost ignored. Therefore, we propose a multi-head attention mechanism to fully employ node content and graph structure information. The core idea is to introduce the interactions in the topological structure into the existing GATs. This method can more accurately estimate the attention weights among nodes, thereby improving the convergence of GATs. Second, the mechanism is lightweight and efficient, requires no training to learn, can accurately analyze higher-order structural information, and can be strongly interpreted through heatmaps. We name the proposed model content- and structure-based graph attention network (CSGAT). Furthermore, our proposed model achieves state-of-the-art performance on a number of datasets in node classification. The code and data are available at https://github.com/CroakerShark/CSGAT.
Cooperative target estimation of strap-down missiles considering random measurement delay

Aiming at the problem of cooperative target estimation for multiple strap-down missiles, this paper is concerned with developing a distributed cooperative estimation fusion algorithm considering the random transmission delay of observation data. As a stepping stone, the random measurement delay system with multiple strap-down missiles is transformed into a delay-free system with correlation noise in finite time intervals using the random observation delay model for strap-down missiles. Then, the distributed fusion technique without feedback is proposed based on the design of the best local filter for each strap-down sampling subsystem; To further enhance the robustness and stability of the cooperative target estimation system, the distributed fusion algorithm with feedback is suggested. Simulation in various cases and comparison studies are conducted to verify the effectiveness and robustness of the proposed scheme.
Monitoring and analysis of physical activity and health conditions based on smart wearable devices

The rapid growth of the Internet of Things (IoT) brings sweeping changes in various industries. Healthcare industries have become a prime example where the Internet of Healthcare Things (IoHT) is making significant progress, particularly in how we approach real-time patient care. Traditional systems for monitoring older people and people with special needs are frequently expensive, require a large workforce, and fall short of providing real-time data. This paper introduces the “3-Tier Health Care Architecture,” an integrated approach to mitigating these issues. This architecture capitalizes on IoHT technologies and is constructed around three principal tiers: Sensor, Fog, and Cloud. The Sensor Tier employs Health Metrics Acquisition Units (HMAUs) fitted with an nRF5340 Development Kit, capturing an extensive range of health-related metrics via wearable sensors. These metrics are then relayed to the Local Processing Units (LPUs) in Fog Tier, which operates on Raspberry Pi Zero 2 W microprocessors for the initial data processing before forwarding to the cloud. The Cloud Tier uses a hybrid CNN-LSTM Machine Learning (ML) model to perform Real-Time Healthcare Monitoring (RTHM) status assessments and includes an Early Warning System for immediate alert issuance. The proposed architecture is resilient, scalable, and efficient, serving as a fortified and all-encompassing solution for RTHM. This enables quick medical interventions, thus elevating healthcare quality and potentially life-saving.
Design and Practice of Japanese Interactive Teaching Systems in Colleges and Universities Under the Background of Big Data

Relying on the background of big data, this paper introduces the blended teaching model into the secondary vocational Japanese oral classroom and explores whether the teaching model is conducive to the improvement of the secondary vocational Japanese oral learning effect and teaching effect. In order to make this research more scientific and effective, this paper refers to a large number of literature materials. First, the purpose, content, and methods of this research are clarified; secondly, the relevant literature is sorted out and summarized. The SPOC blended teaching design was carried out, and teaching experiments were carried out accordingly. Finally, the data collected during the experiment were compared and analyzed, and the research results were verified with objective data. Research has shown that the use of a Japanese language teaching system enhances students' learning experience, promotes effective use of time, and improves overall learning outcomes.
Process control of chemical dynamic system based on multi-strategy mayfly optimization algorithm

Dynamic optimization problems exist widely in chemical industry, and its operational variables change with the evolution of both space and time. Therefore, dynamic optimization problems have important research significance and challenges. To solve this problem, a multi-strategy mayfly optimization algorithm (MMOA) combined with control variable parameterization method(CVP) is proposed in this paper. MMOA introduces three improvements on the basis of the original algorithm, namely, circle chaos crossover strategy, center wandering strategy and boundary correction strategy. The hybrid strategy can better balance the exploration and exploitation ability of the algorithm. Based on MATLAB simulation environment, MMOA was evaluated. The experimental results show that MMOA has excellent performance in solving precision, convergence speed and stability for the benchmark function. For the six classical chemical dynamic optimization problems, MMOA obtained the performance indexes of 0.61071, 0.4776, 0.57486, 0.73768, 0.11861 and 0.13307, respectively. Compared with the data in the previous literature, MMOA can obtain more accurate control trajectory and better performance indicators. It provides an effective way to solve the dynamic optimization problem.
Teaching the asynchronous general physics course in a crisis context: a case study at Igor Sikorsky Kyiv Polytechnic Institute

Abstract The imposition of martial law and power outages in Ukraine made the first semester of the 2022/2023 academic year at Igor Sikorsky Kyiv Polytechnic Institute a challenge for lecturers and students, especially in learning the general physics course. An asynchronous learning mode became the only possible form in such a situation, so the problem of increasing the efficiency of conducting a lecture, problem sessions, and laboratory work in general physics arose. This study aims to describe the implementation of the discipline of general physics (part 1) promoted in an asynchronous mode by lecturers of the Department of General Physics and Modeling of Physical Processes. We verified that in crisis conditions, it is essential to: (i) provide students with all the necessary material on Moodle course and YouTube channel, allowing them to learn the theoretical material, solve the homework problems, and analyse the data from laboratory work, (ii) give feedback and consultations to students via Zoom/Telegram chat upon request, (iii) give the possibility to perform the laboratory work both in the virtual regime and as YouTube demonstration. The results of the survey of 82 students who studied the general physics course taking into account the recommendations mentioned above revealed that the majority of the surveyed students were completely/to a greater extent satisfied with the level of organisation of asynchronous learning, as well as the level of knowledge obtained, which undoubtedly indicates the effectiveness and efficiency of the proposed approach.
Chinese-Vietnamese cross-lingual event retrieval method based on knowledge distillation

Cross-lingual event retrieval is an information retrieval task aimed at cross-lingual event retrieval among multiple languages to find text or documents related to a specific event. Specific to Chinese-Vietnamese cross-language event retrieval, it involves using Chinese as a query to retrieve Vietnamese documents related to the query event. The critical issue is how to efficiently align query and document representations with limited resources. Existing cross-language pre-training models are trained on large-scale multilingual corpora, but their training goals do not include explicit language alignment tasks. Due to the uneven distribution of training corpora between different languages, these models have The problem of language bias. Therefore, this linguistic bias is also inherited in cross-lingual retrieval based on these models. To solve this problem, this paper proposes a Chinese-Vietnamese cross-lingual event retrieval method based on knowledge distillation. This approach enables the model to learn good query-document matching features from monolingual retrieval by transferring knowledge from high-resource to low-resource languages. By enhancing the alignment between queries and documents in different languages in a shared semantic space, the method improves the performance of Chinese-Vietnamese cross-lingual event retrieval.
Abnormal-trajectory detection method based on sub-trajectory classification and outlier-factor acquisition

Abnormal-trajectory detection can be used to detect fraudulent behavior of taxi drivers transporting passengers. Aiming at the problem that existing methods do not fully consider abnormal fragments of trajectories, this paper proposes an abnormal-trajectory detection method based on sub-trajectory classification and outlier-factor acquisition, which effectively detects abnormal sub-trajectories and further detects abnormal trajectories. First, each trajectory is reconstructed using the turning angles and is divided into multiple sub-trajectories according to the turning angle threshold and trajectory point original acceleration. The sub-trajectories are then classified according to the extracted directional features. Finally, the multivariate distances between angular adjacent segments are calculated to obtain the outlier factor, and abnormal sub-trajectories are detected. The sum of the lengths of the abnormal sub-trajectories is used to calculate the outlier score and identify abnormal trajectories. Based on experimental results using real trajectory datasets, it has been found that the proposed method performs better at detecting abnormal trajectories than other similar methods.
Context and Multi-Features-Based Vulnerability Detection: A Vulnerability Detection Frame Based on Context Slicing and Multi-Features

With the increasing use of open-source libraries and secondary development, software projects face security vulnerabilities. Existing studies on source code vulnerability detection rely on natural language processing techniques, but they overlook the intricate dependencies in programming languages. To address this, we propose a framework called Context and Multi-Features-based Vulnerability Detection (CMFVD). CMFVD integrates source code graphs and textual sequences, using a novel slicing method called Context Slicing to capture contextual information. The framework combines graph convolutional networks (GCNs) and bidirectional gated recurrent units (BGRUs) with attention mechanisms to extract local semantic and syntactic information. Experimental results on Software Assurance Reference Datasets (SARDs) demonstrate CMFVD’s effectiveness, achieving the highest F1-score of 0.986 and outperforming other models. CMFVD offers a promising approach to identifying and rectifying security flaws in large-scale codebases.