Reviewerly/exHarmony huggingface dataset:
The Threshold level of Institutional Quality in the Nexus between Financial Development and Environmental Sustainability in Nigeria

Empirics on the relationship between financial development and environmental sustainability remain ambiguous in the literature. The threshold level at which institutional quality facilitates the relationship between financial development and environmental sustainability in respect to the Nigerian economy is still an open question. This study investigates the threshold level of institutional quality in the link between financial development and environmental sustainability in Nigeria from 1986 to 2020. Times series threshold autoregression technique was applied to determine the threshold level of institutional quality. The result of the threshold revealed that 4.32 is the threshold level of institutional quality in Nigeria. Below the threshold level financial development is not stimulated to improve environmental sustainability but above the threshold level institutional quality stimulates financial development to improve environmental sustainability. This study suggests that institutional credibility and transparency should be enhanced beyond the threshold level to effect the needed change in increasing environmental preservation in Nigeria.
Multi-sensor Integration for Land Vehicular Navigation

&lt;p&gt;In this dissertation, a multi-sensor integrated system is developed to provide an accurate positioning solution under open-sky, challenging environments such as downtown areas and GNSS-denied environments such as indoor parking lots. A PPP system is first developed by utilizing a geodetic-grade GNSS receiver. An Improved Robust adaptive Kalman Filter (IRKF) is adopted and used as the estimation filter to compensate for the GNSS measurement outliers and the dynamic error modeling. Centimeter-level horizontal positioning accuracy is achieved under an open sky environment, while decimeter-level horizontal positioning accuracy is achieved under a challenging environment. An IRKF-based PPP/INS integration algorithm is then developed by utilizing a geodetic-grade GNSS receiver and a tactical-grade IMU. The integrated system is assessed through two ground vehicular field trials. The developed integrated system achieves centimeter-level positioning accuracy under open-sky environments and decimeter-level positioning accuracy under simulated GNSS outages. Furthermore, the IRKF-based integrated system achieves attitude accuracy of 0.052º, 0.048º, and 0.165º for pitch, roll, and azimuth angles, respectively. Thereafter, the performance of the dual-frequency (DF) Xiaomi mi 8 smartphone is tested in static and kinematic PPP modes. The smartphone-based PPP solution achieves decimeter-level positioning accuracy in the static mode and meter-level positioning accuracy in kinematic mode. A DF u-blox GNSS receiver and xsens industrial-grade MEMS IMU are further used to develop an ultra-low-cost PPP/INS integrated system. The integrated system achieves sub-meter-level positioning accuracy in both the north and up directions, and meter-level positioning accuracy in the east direction. Additionally, the integrated GNSS PPP/INS system achieves attitude accuracy of about 0.878°, 0.804°, and 2.905° for the pitch, roll, and azimuth angles, respectively. To provide an accurate positioning solution for GNSS-denied environments, a LiDAR odometry (LO)/INS/simulated ultra-wide band (UWB) integrated system is developed. The simulated UWB solution is used as an external frequent update to augment the accuracy of the LO/INS solution. Meter-level horizontal positioning accuracy is achieved through the LO/INS integration with frequent simulated UWB-based updates. &lt;/p&gt;
Efficient Traffic Classification Using Hybrid Deep Learning

&lt;p&gt;Network traffic classification is important for QoS, security, and billing. Using a single machine learning classification model suffers from low prediction and classification accuracy, especially for high dimensional datasets with a high sparsity level. Recently, hybrid-deep learning algorithms have shown high efficiency for traffic forecasting and classification. In this research, we suggested two hybrid models – the Convolutional Neural Network (CNN) combined with the Recurrent Neural Network (RNN) models and the Gated recurrent unit (GRU) combined with Long ShortTerm Memory (LSTM) – to improve traffic classification accuracy in terms of the ratio of correct predictions to total input samples. The hybrid CNN-LSTM and CNN-GRU have achieved an accuracy of up to 99.23% and 93.92%, respectively, for binary classification and 67.16% for multiclass classification. We employed Markov Blanket Algorithm to make the reduced feature set selection, which significantly improved classification accuracy and fscore for multi-class scenarios.&lt;/p&gt;
Optimal path for automated pedestrian detection: image deblurring algorithm based on generative adversarial network

The pedestrian detection technology of automated driving is also facing some challenges. Aiming at the problem of specific target deblurring in the image, this research built a pedestrian detection deblurring model in view of Generative adversarial network and multi-scale convolution. First, it designs an image deblurring algorithm in view of Generative adversarial network. Then, on the basis of image deblurring, a pedestrian deblurring algorithm in view of multi-scale convolution is designed to focus on deblurring the pedestrians in the image. The outcomes showcase that the peak signal to noise ratio and structural similarity index of the image deblurring algorithm in view of the Generative adversarial network are the highest, which are 29.7 dB and 0.943 dB respectively, and the operation time is the shortest, which is 0.50 s. The pedestrian deblurring algorithm in view of multi-scale convolution has the highest peak signal-to-noise ratio (PSNR) and structural similarity indicators in the HIDE test set and GoPro dataset, with 29.4 dB and 0.925 dB, 40.45 dB and 0.992 dB, respectively. The resulting restored image is the clearest and possesses the best visual effect. The enlarged part of the face can reveal more detailed information, and it is the closest to a real clear image. The deblurring effect is not limited to the size of the pedestrians in the image. In summary, the model constructed in this study has good application effects in image deblurring and pedestrian detection, and has a certain promoting effect on the development of autonomous driving technology.
Never-Ending Lonely Distancing Dance Graphs

Lonely distancing is the idea that people can crave company at the same time they fear too much of it. Taking inspiration from this, we construct a scenario in which k players on an n × n grid jockey to occupy space next to a single companion while maintaining at least a minimum distance from everyone else. The tension behind these conflicting needs—companionship versus isolation—induces a dynamical system on the grid we model with directed graphs. Depending on n and k, players are likely to redistribute themselves into one or more equilibrium states. In very special cases, no such resolution is possible, and the players enter a cycle of perpetual redistribution in what we call a never-ending lonely distancing dance.
Rapid calibration method for head-mounted eye-tracker

Head-mounted eye-tracking instruments are important devices for sports and human factors research, and their calibration is a more tedious task due to the varied morphology of the human head as well as wear differences. In order to improve the calibration efficiency, we innovatively proposed a Z-shaped moving target instead of a fixed target in our self-research for the calibration of the wearable eye-tracking device, which improved the time needed for calibration from 100 seconds to less than 30 seconds. In addition, in order to reduce the calibration error caused by the user's head movement during the calibration, the world camera image is feature-matched, and the virtual optic target is computed instead of the recognized optic target, thus eliminating the effect of head movement. In the calibration experiments of nine graduate students, the calibration accuracy was less than 20 pixels, and the average calibration time was about 24 seconds.
A Novel Algorithm for Multi-Criteria Ontology Merging through Iterative Update of RDF Graph

Ontology merging is an important task in ontology engineering to date. However, despite the efforts devoted to ontology merging, the incorporation of relevant features of ontologies such as axioms, individuals and annotations in the output ontologies remains challenging. Consequently, existing ontology-merging solutions produce new ontologies that do not include all the relevant semantic features from the candidate ontologies. To address these limitations, this paper proposes a novel algorithm for multi-criteria ontology merging that automatically builds a new ontology from candidate ontologies by iteratively updating an RDF graph in the memory. The proposed algorithm leverages state-of-the-art Natural Language Processing tools as well as a Machine Learning-based framework to assess the similarities and merge various criteria into the resulting output ontology. The key contribution of the proposed algorithm lies in its ability to merge relevant features from the candidate ontologies to build a more accurate, integrated and cohesive output ontology. The proposed algorithm is tested with five ontologies of different computing domains and evaluated in terms of its asymptotic behavior, quality and computational performance. The experimental results indicate that the proposed algorithm produces output ontologies that meet the integrity, accuracy and cohesion quality criteria better than related studies. This performance demonstrates the effectiveness and superior capabilities of the proposed algorithm. Furthermore, the proposed algorithm enables iterative in-memory update and building of the RDF graph of the resulting output ontology, which enhances the processing speed and improves the computational efficiency, making it an ideal solution for big data applications.
Offline Mongolian Handwriting Recognition Based on Data Augmentation and Improved ECA-Net

Writing is an important carrier of cultural inheritance, and the digitization of handwritten texts is an effective means to protect national culture. Compared to Chinese and English handwriting recognition, the research on Mongolian handwriting recognition started relatively late and achieved few results due to the characteristics of the script itself and the lack of corpus. First, according to the characteristics of Mongolian handwritten characters, the random erasing data augmentation algorithm was modified, and a dual data augmentation (DDA) algorithm was proposed by combining the improved algorithm with horizontal wave transformation (HWT) to augment the dataset for training the Mongolian handwriting recognition. Second, the classical CRNN handwriting recognition model was improved. The structure of the encoder and decoder was adjusted according to the characteristics of the Mongolian script, and the attention mechanism was introduced in the feature extraction and decoding stages of the model. An improved handwriting recognition model, named the EGA model, suitable for the features of Mongolian handwriting was suggested. Finally, the effectiveness of the EGA model was verified by a large number of data tests. Experimental results demonstrated that the proposed EGA model improves the recognition accuracy of Mongolian handwriting, and the structural modification of the encoder and coder effectively balances the recognition accuracy and complexity of the model.
Bridging the Cyber–Physical Divide: A Novel Approach for Quantifying and Visualising the Cyber Risk of Physical Assets

Critical infrastructures and their physical assets are under increasing threat of cyber-attacks as technological integration creates cyber–physical systems (CPSs). This has led to an urgent need to better understand which physical assets in these systems are most at risk, but this requires crossing the divide between cyber and physical risk assessments. However, existing cyber-security methods generally focus solely on the vulnerabilities and security of the cyber network and efforts to quantify the impacts of these cyber vulnerabilities on physical assets are generally limited to the consideration of individual attacks, rather than system-wide risk assessments. Similarly, risk assessments of physical infrastructure systems generally ignore potential impacts due to cyber-attacks. To overcome this cyber–physical divide in risk assessment, we introduce a novel approach for assessing risk across this divide. The proposed approach assesses the cyber risk of physical assets as a function of the vulnerabilities of their connected cyber components, and the contribution of cyber components to this risk. The approach is demonstrated with a case study of the C-Town water distribution system. The results indicate that the approach shows a modified prioritisation of risk compared to that obtained using conventional cyber or physical assessments, highlighting the importance of considering the connection between cyber and physical components in risk assessments of critical infrastructure and their physical assets.
Survey on Utilizing Deep Learning Algorithms to Improve Face Detection in Videos with Challenging Conditions

Face detection in videos is a fundamental task with significant practical applications in various domains, including security, surveillance, and human-computer interaction. However, it becomes increasingly challenging when dealing with videos that contain various adverse conditions, such as low lighting, occlusions, pose variations, and scale changes. Traditional face detection methods often struggle to provide accurate and reliable results under these challenging conditions. This research explores the application of deep learning algorithms to enhance the performance of face detection in videos under challenging conditions. To validate the effectivenessof our approach, we conduct extensive experiments on benchmark datasets with various challenging conditions. The experimental results demonstrate that our proposed method outperforms state-of-the-art techniques in terms of accuracy, robustness, and computational efficiency. Additionally, we provide an in-depth analysis of the model’s performance under different challenging scenarios, highlighting its ability to handle occlusions, pose variations, and low- resolution frames effectively. Keywords: Face Detection , Deep Learning , Video Analysis, Challenging Conditions , Low lighting , Occlusion .
Comprehensive Study on Cloud Computing Services and their Implications for Businesses and Industries

A review of the literature on cloud computing is conducted in this work. With its advantages for many kinds of enterprises, cloud computing is one of the computer industry's fastest-growing technologies. Since cloud computing entered the marketplace, service providers have developed a variety of amenities and made them accessible to potential clients. This paper presents the service model for cloud computing. In particular, Platform as a Service (PaaS), Software as a Service (SaaS), and Infrastructure as a Service (IaaS). The study used a mixed research methodology. It entails compiling and assessing both quantitative and qualitative data from the identical literature review and combining the findings of previous, relevant studies. Results introduce readers to the difficulties and issues related to cloud computing while highlighting the trends in cloud computing both now and in the future. The literature study indicated that the use of technology is projected to increase in the future and shows promise. Researchers to solve the issues and difficulties associated with cloud computing, including privacy and security threats, mobile cloud computing, and cloud computing governance. Keywords—Cloud Computing Services, Cloud providers.
Continuing Professional Development for Physical Education Teachers in China: Basis for Teaching Enhancement Program

In pursuit of life-long learning, the teaching profession requires continuing education for institutional promotion, salary increase, and administrative position. Supportive academic institutions increase their teacher’s professional learning in the 21st century to increase their students’ further education and work as well. China consistently invested in their teachers’ professional development but empirical evidence on these programs’ effectivity is limited. Specifically, there is a demand for more quality professional development (PD) opportunities for PE teachers focused on the following reasons: (a) the education standards movement, (b) professional organizations, and (c) a call for research on teaching. Apparently, in China, most of them have not recognized their leading role and undervalued their educational career. Thus, this study revisited the impact of the PD programs in China focusing focus its lens on the continuing professional development (CPD) of physical education teachers to address the urgent call to improve the PE-CPD. Guided by the global Continuing Professional Development (CPD) framework, this study investigated the continuing professional development provided to physical education teachers in China and the results of this investigation would be the basis for their teaching enhancement program. The findings revealed that most of the PE teachers in China were male, middle aged with relative bachelor’s degree and permanent position, and developing teachers with 1-10 years teaching experience. They partly considered their involvement in skill development opportunities and interaction and collaboration, but highly considered their involvement in personal growth and reflection in their continuing professional development. Their level of involvement in continuing professional development were significantly different considering their age, sex, educational attainment, and years of teaching. Moreover, the found professional development about classroom management very effective and impactful but teaching practices about ICT skills has minimal effectiveness. However, there is a significant difference in the evaluation of PE teachers on the effectivity of the professional development when they are grouped according to their age, educational attainment, and years of teaching. Nevertheless, the significant relationship found between the their involvement in professional development (PD) and evaluation on the effectivity of the professional development became the basis for the strategic factors to be integrated in the teaching enhancement program for the CPD of PE teachers in the new normal.
Smart Classroom in the Era of Education Informatization 2.0

The advent of the era of education informatization 2.0 has brought challenges and opportunities to the educational technology work of colleges and universities. As an important part of education informatization, smart classroom has an important theoretical framework and technical support system. This paper analyzes the characteristics of the era of education informatization 2.0, discusses the concept and importance of smart classrooms, and compares them with traditional classrooms. At the same time, through innovative practices and case studies, the innovative achievements and effect evaluation of educational technology work in colleges and universities are demonstrated. In addition, this paper also introduces the technical support system of the smart classroom, including the functions and architecture of the smart education platform, and the application of big data, artificial intelligence, cloud computing, and mobile learning Xi in the smart classroom. Finally, this paper looks forward to the future-oriented development trend of educational technology in colleges and universities, and puts forward suggestions for the development of smart classrooms and the future prospects of educational technology work in colleges and universities.
How reliable is the scholarly literature?

A few years ago, I came across a cartoon that seemed to capture a particular aspect of scholarly journal publishing quite well: The academic journal publishing system sure feels all too often a bit like a sinking boat.
Sentiment Analysis of Code-Mixed Text: A Comprehensive Review

Sentiment Analysis is the task of identifying and extracting the opinion expressed in a text to determine the writer&amp;#39;s perception of an entity. Due to globalization, people often mix two or more languages and use phonetic typing and lexical borrowing in web communication. This concept is known as code-mixing. Although extracting the opinion of text written in monolingual languages is simple and straightforward, Sentiment Analysis of code-mixed text is challenging. Classifiers fail within the context of the code-mixed text as text may consist of creative writing, spelling variations, grammatical errors, and different word orders. Hence, SA of code-mixed text is an interesting, challenging, and popular research area. This paper presents the state-of-the-art in Sentiment Analysis of code-mixed text by discussing each concept in detail. The paper also discusses the focused areas, techniques used, limitations, and performances of the studies related to code-mixing.
Deep-learning-based head pose estimation from a single RGB image and its application to medical CROM measurement

Abstract For human beings, neck movement will be degraded due to aging, trauma, musculoskeletal disorders, or degenerative diseases. Cervical range of motion (CROM) measurement is one of the popular quantitative neck examinations. Despite radiography is considered as the gold standard, it suffers from invasiveness, radiation exposure, and expensiveness. Recently, vision-based methods have been applied for CROM measurement but achieve large errors and require depth camera. On the other hand, deep neural networks provide good performances on head pose estimation (HPE) from a single image, thus promising for medical CROM measurement. We propose to use CNN networks to extract pyramidal or multi-level image features, which are passed to cross-level attention modules for feature fusion and then to a modified ASPP module and a multi-bin classification/regression module for spatial-channel attention and Euler angle conversion/prediction, respectively. The proposed technique was evaluated on public datasets, such as 300W_LP, AFLW2000, and BIWI, to verify its superior performances (with mean MAE = 3.50°, 3.40°, and 2.31° for different experimental protocols) than state-of-the-art methods. Our pre-trained model was also evaluated with our own collected dataset from hospital for CROM measurement. It also achieved the lowest MAE of 4.58° among other methods and conformed with a medical standard of 5 degrees except the pitch angle (which has a MAE of 5.70°, larger than the standard and the yaw (MAE = 3.60°) and roll angles (MAE = 4.44°)). In general, HPE technique is feasible for CROM measurement and shows its advantages of speed, non-invasiveness, free of anatomical landmark and low cost of operation.