Reviewerly/exHarmony huggingface dataset:
A Data-based Analysis of OS-type Relative Clauses in English

The purpose of this study is two-hold: 1) to examine the distributional patterns of OS-type relative clauses (henceforth, OS-types) and 2) to identify the linguistic factors that characterize the extensive use of OS-types in English discourse. The structural patterns and pragmatic tendencies of OS-types are analyzed in the light of the three linguistic aspects of information processing; 1) newness, 2) heavyness, 3) a SVO word order. A tight correlation between newness and OS-types tells us that the use of OS-types might be one of effective constituent orderings that contributes to the manifestation of the principle of end-focus, which leads to the interplay of ‘given-before-new’ in language-use situations. Another relationship between heavy-NP shifts and OS-types suggests that the grammatical structure or constituent ordering of OS-types are optimally matched to English language users’ language parsing capacity. In addition, the linear, or canonical word order of OS-type structural connections explains why new heavy long and complex constituents tend to be directed towards the sentence-final position and OS-types. Thus, a general claim supported by the findings from this study is that the extensive use of OS-types is closely related to the effectiveness of information processing, and thus OS-types are likely to be preferred or selected for the purpose of facilitating English language users’ language production and comprehension.
A Study of Varistor Harmonics and Nonlinear Characteristics According to Degradation

This study aims to investigates the effects of degradation on the harmonics and nonlinear characteristics of Metal Oxide Varistors (MOVs), which are surge protection devices (SPDs) installed to prevent damage to electrical equipment caused by lightning-induced surges. The physical changes to MOVs under accelerated degradation and gradual voltage ramping were analyzed using Megger S1-1568 and Hipot Analyzer 19055-c. Accelerated aging tests were conducted by applying voltages at 10-hour intervals with leakage currents of 3mA, 4.5mA, and 6mA, for a total of 50 hours to simulate different degrees of degradation. As degradation progressed, both odd harmonics and even harmonics were generated. Initially, the Total Harmonic Distortion (%THD) increased due to degradation, but as the degradation intensified, %THD decreased due to the overall increase in leakage current outweighing the increase in even harmonics. Furthermore, the magnitude of the nonlinear coefficient significantly decreased with the progressing degradation, from 72.67 to 21.79 (at 3mA), 19.95 (at 4.5mA), and 2.52 (at 6mA). By continuously monitoring the physical changes, such as the generation of even harmonics and the decrease in the nonlinear coefficient, with the progressing degradation, it is possible to appropriately determine the replacement cycle of SPDs before thermal runaway occurs.
Application of a Neural Network-based Visual Question Answering System in Preschool Language Education

The continuous progress of modern science and technology has led to comprehensive innovations in education, and the use of information technology for teaching has become the mainstream in the current education field. For children’s preschool language education, the application of a visual question answering (VQA) system has gradually become a new development power. This research uses a Recurrent Neural Network and a VGGNet-16 network to extract features from text and images, respectively, and applies a Hierarchical Joint Attention (HJA) model to the whole VQA system. Experiment results demonstrate that the HJA model reaches the target accuracy after 125 iterations, and convergence performance is good. When using the VQAv1 dataset, accuracy can stabilize at 88% after 18 iterations, and when using the VQAv2 dataset, the highest and lowest overall accuracy rates are 77% and 72%, respectively. The three question types (Num, Y/N, and Other) are answered with high accuracy when using the chosen preschool language education database for children, providing accuracy rates of 90%, 94%, and 91%, respectively. This new reference technique offers a new method for maximization of a VQA system, and significantly raises the preschool language education level of the children.
Development of a Hand-operated Wearable Mouse for Virtual Reality

If the movement of the hand is dataized, it can be applied and used in various fields such as operating drones, vehicles, and the prosthetic arms. In this study, hand movements were recognized using electromyography sensors, inertial sensors and pressure sensors and used for mouse control that can be universally applied in various fields. A device is capable of implementing four functions: left-click, right-click, cursor movement, and wheel scroll was designed and developed. The data obtained through the sensors were stabilized through a low-pass filter and then linked to a PC through Bluetooth communication for the mouse functions and accuracy. Accuracy of functions is 96% for left-click, 70% for drag, 76% for right-click, 79% for wheel scroll. Accuracy of right-click is upgraded by changing electromyography sensor to pressure sensor.
ETSim: A Reference Evapotranspiration Estimator and Its Evaluation at the Southern Region of Japan

Machine learning models have been applied extensively for reference evapotranspiration estimation (ETo) for replacing empirical models which are data intensive. However, most of the trained and calibrated machine learning models are only capable of performing local estimation. Therefore, the spatial robustness and applicability of these models have remained major research gaps. In light of these, in this investigation, the authors applied the ETSim estimator, an ETo estimating framework based on a machine learning ensemble (developed in Peninsular Malaysia) to the southern region of Japan to assess its spatial robustness and applicability. Two models, namely the local and global model of the ETSim estimator were used to estimate the daily and monthly mean ETo at Ishigaki, Naha and Miyako located on three different islands in southern Japan. The local model could not estimate the daily ETo of the three testing stations, with the reported mean absolute percentage error (MAPE) of more than 100% when meteorological variables other than solar radiation (Rs) were fed as input. On the other hand, the global model performed much better using the input combination with four meteorological variables. The ETSim estimator was also applied to estimate the monthly mean ETo with a coarser temporal resolution. Despite achieving tremendous improvement, the local model was still unable to provide the monthly mean ETo estimation satisfactorily. The global model, however, estimated the monthly mean ETo decently, with the registered MAPE generally below 20%. At the same time, the goodness-of-fit of the global model was maintained above 0.7 for most of the cases. The results suggest that the ETSim estimator framework has suitable components that allow it to be used elsewhere other than at its original location.
Research on face recognition based on deep learning

With the need of application upgrading in the field of computer vision and pattern recognition, dynamic face recognition technology has also been rapidly developed. At present, there are many face recognition processing methods in the field of video surveillance Only in the front end of the device to do simple recognition and tracking, a large number of complex operations, including face eigenvalue comparison and big data processing part are completed in the server side. For users of video surveillance, the method can not display the face recognition and comparison results in the monitoring terminal in real time. Aiming at this demand, this paper proposes a face recognition video transmission scheme based on edge computing to realize video transmission. The synchronization of face recognition effect between the acquisition and the player in the video. In this scheme, the video processing module of the terminal device simultaneously carries out video coding and face recognition to the video data, according to the terminal face. Database, extract and analyze the face recognition results and record the coordinate information in the video, and the synchronous packet mechanism is used to fuse the two, so as to obtain the video data package with both encoded data and face information. Through the verification system test, it is proved that the scheme can solve the synchronization problem of face frame display and tracking in live video, and has high accuracy and real-time performance.
Head-mounted display system based on Metaverse VR Technology

As the main carrier of Metaverse VR technology, the head-mounted display system plays a vital role in leading this technological trend. This paper implements a head-mounted display system based on Metaverse VR technology. It mainly applies the high-resolution LCD screen, microcontroller, and embedded programming technology. The system that collects data from gyroscopes and directional sensors in the head-mounted display terminal to realize the angle of view and scene switching on a developed Android system. Through the handheld terminal based on STM32 microcontroller, the system realizes human-system interaction. It isolates people's vision and hearing from the outside world and guides the user to a sense of being in a virtual environment. Then, the image generated by the small display in the helmet is magnified by the Fresnel lens, and a three-dimensional sense is generated in the mind by obtaining this differential information from the human eyes through the different images of the left and right eyes.
Bottom-up approach to room-temperature quantum systems

We demonstrate a key ingredient in a bottom-up approach to building complex quantum matter using thermal atomic vapors. We isolate and track very slowly moving individual atoms without the aid of laser cooling. Passive filtering enables us to carefully select atoms whose three-dimensional velocity vector has a magnitude below $\overline{v}/20$, where $\overline{v}$ is the mean velocity of the ensemble. Using a photon correlation technique, we can extract the velocity distributions. We can also follow the trajectory of slowly moving single atoms for more than $1\phantom{\rule{4pt}{0ex}}\textmu{}\mathrm{s}$ within a $25\text{\ensuremath{-}}\textmu{}\mathrm{m}$ field of view, with no obvious limit to the tracking ability while simultaneously observing Rabi oscillations of these single emitters. In addition, we measure the third-order correlation function of single thermal atoms. Our results demonstrate the power and scalability of thermal ensembles for utilization in quantum memories, imaging, and other quantum information applications through bottom-up approaches.
Encrypted-SNN: A Privacy-Preserving Method for Converting Artificial Neural Networks to Spiking Neural Networks

The transformation from Artificial Neural Networks (ANNs) to Spiking Neural Networks (SNNs) presents a formidable challenge, particularly in terms of preserving privacy to safeguard sensitive data during the conversion process. In response to these privacy concerns, a novel Encrypted-SNN approach is proposed for the ANN-SNN conversion. By incorporating noise into the gradients of both ANNs and SNNs, privacy protection without compromising network performance can be enhanced. The proposed method is tested using popular datasets including CIFAR10, MNIST, and Fashion MNIST, achieving respective accuracies of 88.1 $$\%$$ , 99.3 $$\%$$ , and 93.0 $$\%$$ respectively. The influence of three distinct privacy budgets ( $$\epsilon $$ = 0.5, 1.0, and 1.6) on the accuracy of the model are also discussed. Experimental results demonstrate that the Encrypted-SNN approach effectively optimizes the balance between privacy and performance. This has practical implications for data privacy protection and contributes to the enhancement of security and privacy within SNNs.
Multi-scale Information Fusion Combined with Residual Attention for Text Detection

Driven by deep learning and neural networks, text detection technology has made further developments. Due to the complexity and diversity of scene text, detecting text of arbitrary shapes has become a challenging task. Previous segmentation-based text detection methods can hardly solve the problem of missed detection in complexity scene text detection. In this paper, we propose a text detection model that combines residual attention with a multi-scale information fusion structure to effectively capture text information in natural scenes and avoid text omission. Specifically, the multi-scale information fusion structure extracts text features from different levels to achieve better text localisation and facilitate the fusion of text information. At the same time, residual attention is combined with features from high-resolution images to enhance the contextual information of the text and avoid text omission. Finally, text instances are obtained by a binarisation method. The proposed model is very helpful for text detection in complex scenes. Experiments conducted on three public benchmark datasets show that the model achieves state-of-the-art performance.
Emotion Extraction from Likert-Scale Questionnaires

Sentiment analysis tasks are used in various domains, including education. Likert-scale questionnaires are often used to gain insights into the respondents’ views in various contexts. However, these questionnaires can allow for more information than they are designed for. This research paper explores an emotion classification technique for extracting emotional information from likert-scale questionnaires. A case study is presented in which a tailored questionnaire was employed to gather students’ opinions on school-related matters, such as learning importance, academic performance and family and peer involvement and support. The students (n = 845) answered the questionnaire using a scale from totally disagree to totally agree. Through this questionnaire-based approach, data on students’ emotions was collected.
Do optimism and subjective well-being help in coping with COVID-19?

The SARS-CoV-2 pandemic has affected the whole world, bringing uncertainty and changes in people's lives, including in their emotional aspects. The main aim of this study was to understand, in the light of Positive Psychology, which are the roles of positive and adaptive internal resources for coping with the situation, highlighting the optimism and subjective well-being constructs as auxiliaries in this process. For that a cross-sectional study with a correlational design was carried out. The survey was conducted via internet, with a sample of 433 people, using a sociodemographic questionnaire, the Satisfaction with Life Scale, Affects Scale and LOT-R. Statistical regression analyses, correlations, t-test and ANOVA were performed. As a result, it was confirmed that optimism and the components of well-being are significantly related, with a strong magnitude and that there is a predictive power of optimism with the components of well-being, and family income on positive affect and satisfaction with life. COVID-19 coping variables showed data variance in relation to the components of subjective well-being and optimism.
Efficient Model for Probabilistic Web Resources Under Uncertainty

Individuals, organizations, and connected objects produce and publish vast data on the web. These data are then combined into mashups to produce new valuable data. However, combining data from different sources may lead to data uncertainty because these sources contain heterogeneous, contradictory, or incomplete information. In expert and intelligent systems, the word “uncertainty” is related to working with inaccurate data, imprecise and incomplete information, and unreliability of results that can lead to irrational decisions. An expert and intelligent system can consider all the uncertain information, and manage web services and web resources based on the best possible answer rather than on the quality of the exact answer to manage such problems. This paper proposes a new model to define, compute, and interpret uncertain web resources in the context of classical hypertext navigation and evaluation of data queries. The experimental study as well as the analysis of the results, which we carried out on different corpora, confirmed the usefulness of our approach. It provides the effect of the treatment of uncertainty on the execution time and the importance of calculating the confidence level of the response returned to the user. The analysis of the IT execution time shows that the time required for the composition of the services by our approach is negligible, as compared to that of the other approach studied. The impact of the variation in the number of nodes of type “mux” and type “ind” were also evaluated. Our algorithm checks all possibilities in polynomial time and can adapt to many possibilities of multiplexing values.
Globular Cluster Detection in M33 Using Multiple Views Representation Learning

Globular clusters (GC) are crucial for understanding galaxy formation and evolution. However, identifying them in large imagery datasets is a time-consuming task. This prompts the development of an automated GC detection algorithm. Although GC detection is fundamentally an object detection problem, the state-of-the-art object detection algorithms are unable to produce accurate results. Motivated by how GCs are identified by astronomers, we propose a deep neural network that fuses multiple views of raw imaging data and learns a better representation of the input image. The proposed network is then combined with YOLO object detection algorithm resulting in YOLO for Globular Cluster detection (YOLO-GC) model. Experimental results based on a real catalog of GCs in the M33 Galaxy showed that the proposed multi-view representation learning technique helps improve detection performance.
A Grammatical Evolution Based Automated Configuration of an Ensemble Differential Evolution Algorithm

Designing/configuring ensemble Differential Evolution (DE) algorithms with complementary search characteristics is a complex problem requiring both in-depth understanding of the constituent algorithm’s dynamics and tacit knowledge. This paper proposes a Grammatical Evolution (GE) based automated configuration of a recent ensemble DE algorithm - Improved Multi-population Ensemble Differential Evolution (IMPEDE). A Backus Naur Form grammar, with nine ensemble and DE parameters, has been designed to represent all possible IMPEDE configurations. The proposed approach has been employed to evolve IMPEDE configurations that solve CEC’17 benchmark optimization problems. The evolved configurations have been validated on CEC’14 suite and a real-world optimization problem - economic load dispatch (ELD) problem - from CEC’11 suite. The simulation experiments demonstrate that the proposed approach is capable of evolving IMPEDE configurations that exhibit statistically superior or comparable performance against the manual configuration of IMPEDE as well as against other prominent ensemble DE algorithms.
Universal Detection and Source Attribution of Diffusion Model Generated Images with High Generalization and Robustness

The proliferation of synthetic media over the internet is posing a significant social threat. Recent advancements in Diffusion Models (DM) have made it easier to create astonishingly photo-realistic synthetic media with high stability and control. Moreover, applications like DALLE-2, powered by DM and Large Language Models (LLM), permit visual content generation from natural language description, enabling opportunities for everyone to generate visual media. Hence, there is an immediate need to identify synthetic images and attribute them to their source architectures. In this work, we propose a synthetic image detector as universal detector and a source model attributor based on a popular transfer-learning model ResNet-50 and compare the results with other popular models, including Visual Geometry Group (VGG) 16, XceptionNet and InceptionNet. The proposed universal detector attains over 96% accuracy, with a source attribution, accuracy over 93% for detection of Diffusion Model generated images. The model also succeeds in achieving significant generalization and robustness capabilities under different training-testing configurations, as proven by our experiments.
DeFlare-Net: Flare Detection and Removal Network

In this paper, we propose DeFlare-Net to detect, and remove flares. Typically, flares in hand-held devices are inherent due to internal reflection of light and forward scattering of lens material. The distortions due to flares limit the applications in the field of computer vision. Research challenges towards detection and removal of flare persist due to multiple occurrences of flare with varying intensities. The performance of existing flare removal methods are sensitive to the assumption of underlying physics and geometry, leading to artefacts in the deflared image. The current approaches for deflaring involve elimination of light-source implicitly, whilst removal of flare from the image leading to loss of information. Towards this, we propose DeFlare-Net for detection, and removal of flares, while retaining light-source. In this framework, we include Light Source Detection (LSD) module for detection of light-source, and Flare Removal Network (FRN) to remove the flares. Unlike state-of-the-art methods, we propose a novel loss function and call it as DeFlare loss $$L_{DeFlare}$$ . The loss $$L_{DeFlare}$$ includes flare loss $$L_{flare}$$ , light-source loss $$L_{ls} $$ , and reconstruction loss $$L_{recon}$$ towards removal of flare. We demonstrate the results of proposed methodology on benchmark datasets in comparison with SOTA techniques using appropriate quantitative metrics.
Cyberbully: Aggressive Tweets, Bully and Bully Target Profiling from Multilingual Indian Tweets

The present work proposes an end-to-end solution to identify a potential bully and bully-targets from multilingual aggressive tweets in Indian Twitter-o-sphere. The proposed work uses two LSTM based classifiers in pipeline to detect the tweet’s language and aggressiveness. The model was trained with over 150,000 tweets of Hindi, English, Bengali, and Hinglish languages. F1 scores achieved for English, Hindi, Bengali, and Hinglish are 0.73, 0.83, 0.69, and 0.91, respectively. The paper further reported the patterns identified for several different attributes such as followers count, friends count, frequencies of tweets, and percentage of aggressive tweets of such potential bully and target users.
Membership Adjusted Superpixel Based Fuzzy C-Means for White Blood Cell Segmentation

Fuzzy C-means (FCM) is a well-known clustering technique that is efficiently used for image segmentation. However, the performance of the FCM degrades for noisy images and slow convergence due to the repeated calculation of the distance among pixels and cluster centers. Therefore, this study develops a Membership Adjusted superpixel-based Fuzzy C-Means (MASFCM) to overcome both issues. The proposed MASFCM utilizes superpixel image as input to make the clustering method fast and noise robust. Membership scaling based on triangle inequality has been employed to speed up performance by avoiding unnecessary distance calculations. Lastly, the final membership matrix has been filtered using morphological reconstruction to enhance the robustness of the MASFRFCM. Furthermore, the proposed MASFCM has been efficiently applied to segment the White Blood Cell (WBC) from pathology images. The visual and numerical results clearly demonstrate that the proposed MASFCM produces promising outcomes compared to other tested state-of-the-art clustering techniques over clean as well as noisy pathology images.