Reviewerly/exHarmony huggingface dataset:
Educational innovation: the architecture of digital technologies as a catalyst for change in university teacher training

Abstract The aim of this research was to determine the impact of the architecture of digital technologies in university teacher training as a catalyst for change and educational innovation. Methodologically, it is framed in a quantitative approach with a pre-experimental design and an explanatory level, with a sample of 269 teachers out of a total population of 450 at the university. The results show a significant impact of the architecture of digital technologies as a catalyst for change in university teacher training, with an increase of 22.88% from pre-test to post-test. The statistical significance of the results is supported by a P -value of 0.000, which means that it is less than the established significance level (α = 0.05), leading to the acceptance of hypothesis H1: The architecture of digital technologies as a catalyst for change in university teacher education has an impact. In conclusion, the architecture of digital technologies, consisting of the two academic pillar systems: the academic management system and the virtual learning system, contribute to the development of digital skills in key areas such as digital literacy, communication and collaboration, digital content creation and problem solving in virtual environments.
A Cross-Domain Recommender System for Literary Books Using Multi-Head Self-Attention Interaction and Knowledge Transfer Learning

Existing book recommendation methods often overlook the rich information contained in the comment text, which can limit their effectiveness. Therefore, a cross-domain recommender system for literary books that leverages multi-head self-attention interaction and knowledge transfer learning is proposed. Firstly, the BERT model is employed to obtain word vectors, and CNN is used to extract user and project features. Then, higher-level features are captured through the fusion of multi-head self-attention and addition pooling. Finally, knowledge transfer learning is introduced to conduct joint modeling between different domains by simultaneously extracting domain-specific features and shared features between domains. On the Amazon dataset, the proposed model achieved MAE and MSE of 0.801 and 1.058 in the “movie-book” recommendation task and 0.787 and 0.805 in the “music-book” recommendation task, respectively. This performance is significantly superior to other advanced recommendation models. Moreover, the proposed model also has good universality on the Chinese dataset.
Quantum Correlation Resource Recycling via Sequential Measurements: Theoretical Models and Optical Experiments

Quantum correlation is a key resource for a variety of quantum information processing and communication tasks, the efficient utilization of which has been a longstanding concern, and it is also one of the main challenges in the application of quantum technology. In this review, we focus on the interaction between quantum measurements and quantum correlations by designing appropriate measurement strategies, specifically exploring the trade-off between information gain and disturbance degree in weak measurements to ensure that quantum correlations from the same source can be shared among multiple independent observers. We introduce the basic knowledge and classification of quantum measurements, investigate the weak measurement scenario, and show the theoretical model construction of quantum correlation recycling in the original works. We summarize the theoretical and experimental development process and the latest progress in this field. Finally, we provide an outlook for more quantum resource applications that can profit from the optimization of quantum measurement strategies.
Arabic Emotion Recognition in Low-Resource Settings: A Novel Diverse Model Stacking Ensemble with Self-Training

Emotion recognition is a vital task within Natural Language Processing (NLP) that involves automatically identifying emotions from text. As the need for specialized and nuanced emotion recognition models increases, the challenge of fine-grained emotion recognition with limited labeled data becomes prominent. Moreover, emotion recognition for some languages, such as Arabic, is a challenging task due to the limited availability of labeled data. This scarcity exists in both size and the granularity of emotions. Our research introduces a novel framework for low-resource fine-grained emotion recognition, which uses an iterative process that integrates a stacking ensemble of diverse base models and self-training. The base models employ different learning paradigms, including zero-shot classification, few-shot methods, machine learning algorithms, and transfer learning. Our proposed method eliminates the need for a large labeled dataset to initiate the training process by gradually generating labeled data through iterations. During our experiments, we evaluated the performance of each base model and our proposed method in low-resource scenarios. Our experimental findings indicate our approach outperforms the individual performance of each base model. It also outperforms the state-of-the-art Arabic emotion recognition models in the literature, achieving a weighted average F1-score equal to 83.19% and 72.12% when tested on the AETD and ArPanEmo benchmark datasets, respectively.
Detection of Vulnerabilities in Smart Buildings Using the Shodan Tool

Smart buildings, integral components of modern urban landscapes, are confronted with diverse vulnerabilities that jeopardize system robustness, cybersecurity, data confidentiality, and the well-being of the occupants. This work aimed to identify and evaluate vulnerabilities specific to smart buildings, introducing an innovative assessment approach leveraging the Shodan tool. The analysis comprised three stages: information collection, result extraction using Shodan, and vulnerability identification, culminating in a comprehensive evaluation. This study pioneers the use of Shodan for smart building vulnerability detection, together with databases and associated nomenclature, to serve as a robust foundational tutorial for future research. The findings yielded a meticulous analysis of primary security risks inherent in building systems, advocating for implementing targeted measures to mitigate potential impacts. Additionally, this study proposes an evaluation methodology encompassing metrics to gauge the effect of vulnerabilities on integrity, availability, and scope. By addressing insecure configurations, deployment inadequacies, and suboptimal cybersecurity practices, this framework fortifies smart buildings against potential threats. This study’s originality lies in its Shodan-centric framework, revolutionizing the approach to smart building applications and vulnerability detection. This research contributes to the field by identifying critical vulnerabilities and proposing effective mitigation strategies, thereby elevating the overall security and safety of interconnected smart spaces.
Using an Artificial-Intelligence-Generated Program for Positive Efficiency in Filmmaking Education: Insights from Experts and Students

In recent years, despite the widespread success of artificial intelligence (AI) across various domains, its full potential in the field of education, particularly in filmmaking education, remains largely untapped. The purpose of this study is to explore the application of AI-generated programs in filmmaking education to address existing shortcomings in curriculum design. We employed a comprehensive approach, starting with an extensive review of existing filmmaking courses and AI-recommended courses. Subsequently, two rounds of in-depth interviews were conducted, involving both experts and students, to gain profound insights. We utilized user journey maps to visualize the participants’ experiences and feedback, complemented by a mixed-methods analysis approach for a comprehensive data assessment. The study revealed that both the experts and the students derived positive benefits from AI-recommended courses. This research not only provides a fresh perspective on the practical applications of AI in filmmaking education but also offers insights for innovation in the field of education. Theoretically, this study establishes a new foundation for the application of AI in education. In practice, it opens up new possibilities for filmmaking education and promotes the development of cutting-edge teaching methods. Despite limitations in sample size and geographical scope, this study underscores the immense potential of AI in filmmaking education. It provides directions for future research to deepen our understanding of AI’s impact on education.
Transforming Higher Education into Integrated Productive Institutions: A Conceptualization Review

This paper seeks a library search and evaluation of previous literature reviews on the subject of a methodological framework for transforming universities into integrated productive institutions. This paper has successfully developed a theoretical framework that is helpful for future research in this area. Besides, this study has provided new insight view on higher education institutions' studies and shifted the development of research from looking at the performance and success factors to a different angle which is identifying factors to improve the higher education institutions' survival. Nevertheless, as this is a conceptual paper, the main contribution is modeling innovative ideas to generate productive higher education institutions. On top of that, the model in this study is self-constructed with reference to the previous literature during and after Covid-19. Therefore, generalization is suitable to be applied. Further, the same study is encouraged to be implemented in different higher educational institutions and using different innovation drives. Therefore, by improving the use of innovation, higher education institutions' survival can be expanded to grow and flourish.
Forecasting Task Optimization for A New Architecture of MEMS Reservoir Computing Using Stiffness Modulation

This paper reports a novel reservoir computing (RC) architecture based on MEMS resonant accelerometer. Our method makes use of stiffness modulation that the input is reflected in stiffness disturbance of nonlinear resonator. We eliminated the discretization of data before injected into the reservoir which is often necessary in conventional delay-based RC, so that the sensor can process natural signal directly which reduces system complexity and power consumption, providing a novel sensing paradigm. The new system integrates sensing and computing in a single device, and we specifically enhanced its forecasting performance because the original non-delay-based MEMS RC we proposed earlier is only suitable for recognition tasks. We carried out nonlinearity tuning for physical reservoir and optimization of the post-processing algorithm. Forecasting task related to chaotic system was successfully done by the measured response used in simulation. The proposed architecture shows a concept of intelligent sensor which can handle a variety of tasks and scenes.
Smart Telescope System with Automatic Tracking

This study details the design and implementation of an optical see-through device combined with a mirror. It presents a novel optical see-through device that merges a high-speed active vision system operating at 500 FPS with other optical devices. The merit of this system, as demonstrated by the naked-eye display through tracking experiments, lies in its automatic telescopic tracking capability and its effectiveness in observing moving objects beyond the field of view of a fixed camera. The system offers innovative wide-range, high-speed target tracking, and observation solutions by leveraging mirror movement, AI recognition, and template matching.
Breast Cancer Risk Estimation Using Patient Health Information and Tactile Sensing System

Developing an accurate estimation of risk in the early stage of breast cancer increases the prognosis of the disease and awareness of the personal risk of having invasive breast cancers in the lifetime. In this paper, we present the breast cancer risk estimation method to compute the relative total risk score using Tactile Sensing System and patient health information. The main breast cancer risk is estimated by the malignancy risk index from the mechanical properties of the mass, which is obtained by tactile images. Then, the risk score is computed by combining the personal breast cancer risk based on the patient's age and race information. The method was applied to 7 human cases and resulted in 86.0% accuracy in detecting malignant tumors.
Visual Force Sensor to Estimate External Force Distributions from Shape Deformation

This research proposes a new data-driven sensing technology to estimate forces based on visual information on the shape deformation of objects. Utilizing a camera and machine learning technology, this method aims to simplify force measurement in everyday environments. The unique points of the proposed method lie in estimating not only the force using visual information on shape deformation, but also the force distribution applied to multiple points by directly dealing with the shape information. First, the principle of the method was verified using a dataset with ideal shape deformation created by a finite element model (FEM). Then the accuracy was verified using a dataset with realistic shape deformation produced from an experimental chair. The experimental results show that the force estimation error was approximately 6% in the setting where the shape deformation was obtained using FEM. In the real-world setting where the shape deformation was obtained using an RGB-D camera, the force estimation error was about 19%. The proposed method can be applied to deformable commodities and is expected to simplify force sensing in daily living spaces. This may accelerate the construction of smart home environments and facilitate services based on improved understanding of everyday environments.
Multispectral Pedestrian Detection with Visible and Far-infrared Images Under Drifting Ambient Light and Temperature

The contrast between pedestrians and the back-ground in visible (RGB) images greatly depends on the ambient light, while in far-infrared (FIR) images, it depends on the ambient temperature. Although existing open datasets for RGB-FIR combined images consider ambient light differences between daytime and nighttime, these datasets do not take into account temperature variations between hot and cold conditions. In this paper, we prepare new datasets that include both environmental differences, and we evaluate them using a multispectral detection system. Our results show that RGB-FIR detection achieves higher accuracy with an average precision (AP) compared with RGB detection when ambient light changes. Furthermore, the results indicate that RGB-FIR detection outperforms FIR detection when ambient temperature changes. Finally, the result of the cross-validate experiment shows that the RGB-FIR model trained in intermediate temperature conditions decreases by 48.3 points and 24.7 points under the hot and cold conditions even though it achieved a high AP: 83.0 % under the training condition. The results suggest all environmental variations should be included when we train the RGB-FIR model for pedestrian detection.
Measurement of Fluid Flow Velocity by Using Infrared and Visual Cameras: Comparison and Evaluation of Optical Flow Estimation Algorithms

Due to the rising importance of energy conservation and emissions reduction, gas leak detection has become critical. Optical gas imaging (OGI) is widely used in this field. For the quantification of gas emissions, the images captured by OGI cameras can be analyzed by optical flow estimation algorithms to achieve gas velocity estimation. Three different algorithms have been applied in this field, namely Brox, Farnebäck, and Flownet2. This study investigates the performance of fluid flow velocity measurements by using three optical flow estimation algorithms. Meanwhile, the algorithms are also compared under different flow-generating substances that are visible under visual and mid-wave infrared OGI cameras. Due to the higher frame rate and resolution, the results of the three different algorithms from the visual data set prove better than those from the infrared data set. In addition, the effect of different parameters on flow velocity measurements is analyzed. The Brox and Farnebäck methods are sensitive to parameter choices. In contrast, the deep learning based Flownet2 algorithm exhibits greater robustness and is less affected by speed or frame rate variations.
The Heterogeneity-Intensified and Heterogeneity Ratio-Stratified Bootstrap (HiS- and HeRS-Boot) Oversampling to Boost a Detector Performance

We investigated two variations of the previously proposed heterogeneity-stratified bootstrap (HSBoot) oversampling method, namely the improved Heterogeneity-Stratified (HiS-) and Heterogeneity Ratio-Stratified (HeRS-) Boot, for balancing a data set by assigning higher resampling probabilities to sample points in less homogeneous regions. Our study focused on two detection cases: spoiled food and allergen. Results demonstrate the effectiveness and generalizability of our method across different sensors, highlighting its potential for real-world applications and positive impact on daily life.
Sensor Fusion by Spatial Encoding for Autonomous Driving

Sensor fusion is critical to perception systems for task domains such as autonomous driving and robotics. Recently, the Transformer integrated with CNN has demonstrated high performance in sensor fusion for various perception tasks. In this work, we introduce a method for fusing data from camera and LiDAR. By employing Transformer modules at multiple resolutions, proposed method effectively combines local and global contextual relationships. The performance of the proposed method is validated by extensive experiments with two adversarial benchmarks with lengthy routes and high-density traffics. The proposed method outperforms previous approaches with the most challenging benchmarks, achieving significantly higher driving and infraction scores. Compared with TransFuser, it achieves 8% and 19% improvement in driving scores for the Longest6 and Town05 Long benchmarks, respectively.
TinyTracker: Ultra-Fast and Ultra-Low-Power Edge Vision In-Sensor for Gaze Estimation

Intelligent edge vision tasks encounter the critical challenge of ensuring power and latency efficiency due to the typically heavy computational load they impose on edge platforms.This work leverages one of the first "Artificial Intelligence (AI) in sensor" vision platforms, IMX500 by Sony, to achieve ultrafast and ultra-low-power end-to-end edge vision applications.We evaluate the IMX500 and compare it to other edge platforms, such as the Google Coral Dev Micro and Sony Spresense, by exploring gaze estimation as a case study.We propose TinyTracker, a highly efficient, fully quantized model for 2D gaze estimation designed to maximize the performance of the edge vision systems considered in this study.TinyTracker achieves a 41x size reduction (∼ 600Kb) compared to iTracker [1] without significant loss in gaze estimation accuracy (maximum of 0.16 cm when fully quantized).TinyTracker's deployment on the Sony IMX500 vision sensor results in end-to-end latency of around 19ms.The camera takes around 17.9ms to read, process and transmit the pixels to the accelerator.The inference time of the network is 0.86ms with an additional 0.24 ms for retrieving the results from the sensor.The overall energy consumption of the end-to-end system is 4.9 mJ, including 0.06 mJ for inference.The end-to-end study shows that IMX500 is 1.7x faster than Coral Micro (19ms vs 34.4ms) and 7x more power efficient (4.9mJ VS 34.2mJ).
A Comparison of Sensing Technologies on a Room-Exit Detection System

This article describes four prototypes of a low-cost automatic system dedicated to room-exit detection in nursing homes. All implementations utilize BLE-beacon to identify the person of interest and alarm the caregiver about room-exit but differ in sensing technologies applied to detect motion direction. We experimentally evaluated the technologies in the same environmental conditions and compared the results in terms of accuracy, response time and implementation cost.
On Orientation Distribution Representations with Reliable Uncertainties in Inertial Motion Tracking

Inertial sensor-based human movement capturing systems typically estimate the limb or sensor orientation using a relatively high-frequency stream (usually at least 60Hz) of measurements. However, for many motion analysis tasks, only a so-called segment of a motion, consisting of lower frequencies is analysed and classified. This is typically done via learning a task-specific model that identifies the movement segment, e.g. an action or a gesture. Once movement segment identification models are learned, applying them to obtain low-frequency estimation depends on the accuracy of the uncertainty estimates of movement segment models. To connect both, we start with investigating the accuracy of the distributional representations of orientations using Probabilistic Movement Primitives (ProMP) and Variational Inference (VI) based approximate distributions (e.g., Sparse Gaussian Processes (SGPs)). These techniques are commonly employed to represent movement segment distributions and concisely represent uncertainties. Furthermore, we address challenges associated with orientation regression, based on a case study of human movement data.
Enhancing Lightweight Neural Networks for Small Object Detection in IoT Applications

Advances in lightweight neural networks have revolutionized computer vision in a broad range of Internet of Things (IoT) applications, encompassing remote monitoring and process automation. However, the detection of small objects, which is crucial for many of these applications, remains an underexplored area in current computer vision research, particularly for embedded devices. To address this gap, the paper proposes a novel adaptive tiling method that can be used on top of any existing object detector including the popular Faster Objects More Objects (FOMO) network for object detection on microcontrollers. Our experimental results show that the proposed tiling method can boost the F1-score by up to 225% while reducing the average object count error by up to 76%. Furthermore, the findings of this work suggest that using a soft F1 loss over the popular binary cross-entropy loss can significantly reduce the negative impact of imbalanced data. Finally, we validate our approach by conducting experiments on the Sony Spresense microcontroller, showcasing the proposed method's ability to strike a balance between detection performance, low latency, and minimal memory consumption.
Bayesian Optimisation of Existing Object Detection Methods for New Contexts

Pre-trained object detectors exhibit strong variations in performance when applied in different contexts, e.g., daytime vs. nighttime performance, up-close vs. long range detection. These variations limit the usefulness of pre-trained detectors for safety critical applications like autonomous driving, which require consistent decision making in every context. Retraining for all contexts is often impossible or prohibitively expensive due to the need for large amounts of labels in each context. Instead, we propose a probabilistic calibration layer which takes these context dependencies into account to translate the detection score produced by a pre-trained detector into a conditional probability of presence. As a proof of concept, we demonstrate that reinterpreting the confidence scores of three commonly used detectors based on the estimated distance to the supposed object yields an improvement in average precision of pedestrian detection of up to 3% on the NuScenes dataset.