Reviewerly/exHarmony huggingface dataset:
An Effective Motion-Tracking Scheme for Machine-Learning Applications in Noisy Videos

Detecting and tracking objects of interest in videos is a technology that can be used in various applications. For example, identifying cell movements or mutations through videos obtained in real time can be useful information for decision making in the medical field. However, depending on the situation, the quality of the video may be below the expected level, and in this case, it may be difficult to check necessary information. To overcome this problem, we proposed a technique to effectively track objects by modifying the simplest color balance (SCB) technique. An optimal object detection method was devised by mixing the modified SCB algorithm and a binarization technique. We presented a method of displaying object labels on a per-frame basis to track object movements in a video. Detecting objects and tagging labels through this method can be used to generate object motion-based prediction training data for machine learning. That is, based on the generated training data, it is possible to implement an artificial intelligence model for an expert system based on various object motion measurements. As a result, the main object detection accuracy in noisy videos was more than 95%. This method also reduced the tracking loss rate to less than 10%.
An exploratory approach to data driven knowledge creation

Abstract This paper describes a new approach to knowledge creation that is instrumental for the emerging paradigm of data-intensive science. The proposed approach enables the acquisition of new insights from the data by exploiting existing relationships between diverse types of datasets acquired through various modalities. The value of data consistently improves when it can be linked to other data because linking multiple types of datasets allows creating novel data patterns within a scientific data space. These patterns enable the exploratory data analysis , an analysis strategy that emphasizes incremental and adaptive access to the datasets constituting a scientific data space while maintaining an open mind to alternative possibilities of data interconnectivity. A technology, the Linked Open data (LOD), was developed to enable the linking of datasets. We argue that the LOD technology presents several limitations that prevent the full exploitation of this technology to acquire new insights. In this paper, we outline a new approach that enables researchers to dynamically create data patterns in a research data space by exploiting explicit and implicit/hidden relationships between distributed research datasets. This dynamic creation of data patterns enables the exploratory data analysis strategy.
Digital image correlation by natural textures on biological skin

Research of mechanical behavior of biological skin requires accurate deformation measurement. Digital image correlation (DIC) is an optical method with high accuracy and low environment demands. In this paper, DIC is applied to measure the deformation of biological skin by matching the natural texture of biological skin instead of artificial speckle. In order to obtain better calculation results, the speckle-free DIC (SF-DIC) is proposed. This method avoids using the artificial speckle and only rely on the natural textures, in such situation the solutions about the optimal lighting conditions and the loss of accuracy and efficiency are proposed respectively. Two physical experiments are performed to validate and demonstrate the new methodology. The experimental results reveal that the SF-DIC has access to reliable calculation results without artificial speckle. The measurements proposed skip the step of making artificial speckle and may enable potential application in practice not only on the research of biological skin but also on the other materials with natural textures.
Cross-Platform Gait Analysis and Fall Detection Wearable Device

Since the fall was often occurred in elders daily, this paper focused on gait analysis with fall detection to develop a wearable device. To ensure that the mobile application, APP, could be used in different platform of mobile phone, such Android or iOS, the designed wearable device also could be used in cross-platform in mobile phone. Therefore, a cross-platform gait analysis and fall detection wearable device (CPGAFDWD) was proposed. Since CPGAFDWD APP was used in web browser without limiting to platform, it could be used for different platforms of mobile phone. The gait analysis could be detected at home. The fall detection also could be executed in any place immediately. The patients and medical staff all could query the status of rehabilitation in any place and any time via the Internet. The experimental results showed that the correct rate of gait analysis and fall detection could be up to 90% in cross-platform of mobile phone. In the future, CPGAFDWD will be planned to be verified by Institutional Review Board, IRB, for clinical treatment.
Validation of a Probabilistic Prediction Model for Patients with Type 1 Diabetes Using Compositional Data Analysis

Glycemia assessment in people with type 1 diabetes (T1D) has focused on the time spent in different glucose ranges. As this time reflects the relative contributions to the finite duration of a day, it should be treated as compositional data (CoDa) that can be applied to T1D data. Previous works presented a tool for the individual categorization of days and proposed a probabilistic transition model between categories, although validation has hitherto not been presented. In this study, we consider data from eight real adult patients with T1D obtained from continuous glucose monitoring (CGM) sensors and introduce a methodology based on compositional methods to validate the previously presented probability transition model. We conducted 5-fold cross-validation, with both the training and validation data being CoDa vectors, which requires developing new performance metrics. We design new accuracy and precision measures based on statistical error calculations. The results show that the precision for the entire model is higher than 95% in all patients. The use of a probabilistic transition model can help doctors and patients in diabetes treatment management and decision-making. Although the proposed method was tested with CoDa applied to T1D data obtained from CGM, the newly developed accuracy and precision measures apply to any other data or validation based on CoDa.
A Spider Monkey Optimization Based on Beta-Hill Climbing Optimizer for Unmanned Combat Aerial Vehicle (UCAV)

Unmanned Combat Aerial Vehicle (UCAV) path planning is a challenging optimization problem that seeks the optimal or near-optimal flight path for military operations. The problem is further complicated by the need to operate in a complex battlefield environment with minimal military risk and fewer constraints. To address these challenges, highly sophisticated control methods are required, and Swarm Intelligence (SI) algorithms have proven to be one of the most effective approaches. In this context, a study has been conducted to improve the existing Spider Monkey Optimization (SMO) algorithm by integrating a new explorative local search algorithm called Beta-Hill Climbing Optimizer (BHC) into the three main phases of SMO. The result is a novel SMO variant called SMOBHC, which offers improved performance in terms of intensification, exploration, avoiding local minima, and convergence speed. Specifically, BHC is integrated into the main SMO algorithmic structure for three purposes: to improve the new Spider Monkey solution generated in the SMO Local Leader Phase (LLP), to enhance the new Spider Monkey solution produced in the SMO Global Leader Phase (GLP), and to update the positions of all Local Leader members of each local group under a specific condition in the SMO Local Leader Decision (LLD) phase. To demonstrate the effectiveness of the proposed algorithm, SMOBHC is applied to UCAV path planning in 2D space on three different complex battlefields with ten, thirty, and twenty randomly distributed threats under various conditions. Experimental results show that SMOBHC outperforms the original SMO algorithm and a large set of twenty-six powerful and recent evolutionary algorithms. The proposed method shows better results in terms of the best, worst, mean, and standard deviation outcomes obtained from twenty independent runs on small-scale (D = 30), medium-scale (D = 60), and large-scale (D = 90) battlefields. Statistically, SMOBHC performs better on the three battlefields, except in the case of SMO, where there is no significant difference between them. Overall, the proposed SMO variant significantly improves the obstacle avoidance capability of the SMO algorithm and enhances the stability of the final results. The study provides an effective approach to UCAV path planning that can be useful in military operations with complex battlefield environments.
Machine Translation

This chapter generally introduces the technology of computer translation, or machine translation (MT), also called automatic translation. The definition of machine translation is given first; then its history is introduced, various approaches and evaluation methods are described, and finally the applications are summarized.
Pre-Trained Joint Model for Intent Classification and Slot Filling with Semantic Feature Fusion

The comprehension of spoken language is a crucial aspect of dialogue systems, encompassing two fundamental tasks: intent classification and slot filling. Currently, the joint modeling approach for these two tasks has emerged as the dominant method in spoken language understanding modeling. However, the existing joint models have limitations in terms of their relevancy and utilization of contextual semantic features between the multiple tasks. To address these limitations, a joint model based on BERT and semantic fusion (JMBSF) is proposed. The model employs pre-trained BERT to extract semantic features and utilizes semantic fusion to associate and integrate this information. The results of experiments on two benchmark datasets, ATIS and Snips, in spoken language comprehension demonstrate that the proposed JMBSF model attains 98.80% and 99.71% intent classification accuracy, 98.25% and 97.24% slot-filling F1-score, and 93.40% and 93.57% sentence accuracy, respectively. These results reveal a significant improvement compared to other joint models. Furthermore, comprehensive ablation studies affirm the effectiveness of each component in the design of JMBSF.
Student Teachers’ Classroom Impact during Their Practicum in the Times of the Pandemic

The COVID-19 pandemic impacted societal structures worldwide. In the educational realm, the forced closure, and subsequent reopening of school settings disrupted the personal and professional lives of administrators, teachers, parents, and students. Two groups of Spanish stakeholders affected by the return to face-to-face instruction during the pandemic were the University of Extremadura’s student teachers and their mentors during the practicum, as student teaching is known in Spain. This study investigated 28 student teachers’ and 26 mentors’ responses to a questionnaire enquiring on the main challenges resulting from the pandemic, as well as student teachers’ contributions to the classroom during this difficult time. Cualitative software was used to analyze participants’ responses allowed us to identify four main themes: attitudes; classroom management issues; adaptations and restrictions; and academic–experiential modifications. Overall, the findings revealed student teachers’ and mentors’ positive opinions about their joint work experience and about student teachers’ logistical and technological support.
Intrinsically Interpretable Gaussian Mixture Model

Understanding the reasoning behind a predictive model’s decision is an important and longstanding problem driven by ethical and legal considerations. Most recent research has focused on the interpretability of supervised models, whereas unsupervised learning has received less attention. However, the majority of the focus was on interpreting the whole model in a manner that undermined accuracy or model assumptions, while local interpretation received much less attention. Therefore, we propose an intrinsic interpretation for the Gaussian mixture model that provides both global insight and local interpretations. We employed the Bhattacharyya coefficient to measure the overlap and divergence across clusters to provide a global interpretation in terms of the differences and similarities between the clusters. By analyzing the GMM exponent with the Garthwaite–Kock corr-max transformation, the local interpretation is provided in terms of the relative contribution of each feature to the overall distance. Experimental results obtained on three datasets show that the proposed interpretation method outperforms the post hoc model-agnostic LIME in determining the feature contribution to the cluster assignment.
Evaluating the Privacy and Utility of Time-Series Data Perturbation Algorithms

Data collected from sensor-rich systems may reveal user-related patterns that represent private information. Sensitive patterns from time-series data can be protected using diverse perturbation methods; however, choosing the method that provides the desired privacy and utility level is challenging. This paper proposes a new procedure for evaluating the utility and privacy of perturbation techniques and an algorithm for comparing perturbation methods. The contribution is significant for those involved in protecting time-series data collected from various sensors as the approach is sensor-type-independent, algorithm-independent, and data-independent. The analysis of the impact of data integrity attacks on the perturbed data follows the methodology. Experimental results obtained using actual data collected from a VW Passat vehicle via the OBD-II port demonstrate the applicability of the approach to measuring the utility and privacy of perturbation algorithms. Moreover, important benefits have been identified: the proposed approach measures both privacy and utility, various distortion and perturbation methods can be compared (no matter how different), and an evaluation of the impact of data integrity attacks on perturbed data is possible.
A Coverless Audio Steganography Based on Generative Adversarial Networks

Traditional audio steganography by cover modification causes changes to the cover features during the embedding of a secret, which is easy to detect with emerging neural-network steganalysis tools. To address the problem, this paper proposes a coverless audio-steganography model to conceal a secret audio. In this method, the stego-audio is directly synthesized by our model, which is based on the WaveGAN framework. An extractor is meticulously designed to reconstruct the secret audio, and it contains resolution blocks to learn the different resolution features. The method does not perform any modification to an existing or generated cover, and as far as we know, this is the first directly generated stego-audio. The experimental results also show that it is difficult for the current steganalysis methods to detect the existence of a secret in the stego-audio generated by our method because there is no cover audio. The MOS metric indicates that the generated stego-audio has high audio quality. The steganography capacity can be measured from two perspectives, one is that it can reach 50% of the stego-audio from the simple size perspective, the other is that 22–37 bits can be hidden in a two-second stego-audio from the semantic. In addition, we prove using spectrum diagrams in different forms that the extractor can reconstruct the secret audio successfully on hearing, which guarantees complete semantic transmission. Finally, the experiment of noise impacts on the stego-audio transmission shows that the extractor can still completely reconstruct the semantics of the secret audios, which indicates that the proposed method has good robustness.
Style, content, and the success of ideas

Abstract From marketers and consumers to leaders and health officials, everyone wants to increase their communications' impact. But why are some communications more impactful? While some argue that content drives success, we suggest that style, or the way ideas are presented, plays an important role. To test style's importance, we examine it in a context where content should be paramount: academic research. While scientists often see writing as a disinterested way to communicate unobstructed truth, a multi‐method investigation indicates that writing style shapes impact. To separate content from style, we focus on a unique class of words linked to style (i.e., function words such as “and,” “the,” and “on”) that are devoid of content. Natural language processing of almost 30,000 articles from a range of disciplines finds that function words explain 4–11% of overall variance explained and 11–27% of language content's impact on citations. Additional analyses examine particular style features that may shape success, and why, highlighting the role of writing simplicity, personal voice, and temporal perspective. Experiments further indicate the causal impact of style. The results suggest ways to boost communication's impact and highlight the value of natural language processing for understanding the success of ideas.
High-Performance Embedded System for Offline Signature Verification Problem Using Machine Learning

This paper proposes a high-performance embedded system for offline Urdu handwritten signature verification. Though many signature datasets are publicly available in languages such as English, Latin, Chinese, Persian, Arabic, Hindi, and Bengali, no Urdu handwritten datasets were available in the literature. So, in this work, an Urdu handwritten signature dataset is created. The proposed embedded system is then used to distinguish genuine and forged signatures based on various features, such as length, pattern, and edges. The system consists of five steps: data acquisition, pre-processing, feature extraction, signature registration, and signature verification. A majority voting (MV) algorithm is used for improved performance and accuracy of the proposed embedded system. In feature extraction, an improved sinusoidal signal multiplied by a Gaussian function at a specific frequency and orientation is used as a 2D Gabor filter. The proposed framework is tested and compared with existing handwritten signature verification methods. Our test results show accuracies of 66.8% for ensemble, 86.34% for k-nearest neighbor (KNN), 93.31% for support vector machine (SVM), and 95.05% for convolutional neural network (CNN). After applying the majority voting algorithm, the overall accuracy can be improved to 95.13%, with a false acceptance rate (FAR) of 0.2% and a false rejection rate (FRR) of 41.29% on private dataset. To test the generalization ability of the proposed model, we also test it on a public dataset of English handwritten signatures and achieve an overall accuracy of 97.46%.
Federated Learning-Based Lightweight Two-Factor Authentication Framework with Privacy Preservation for Mobile Sink in the Social IoMT

The social Internet of Medical Things (S-IoMT) highly demands dependable and non-invasive device identification and authentication and makes data services more prevalent in a reliable learning system. In real time, healthcare systems consistently acquire, analyze, and transform a few operational intelligence into actionable forms through digitization to capture the sensitive information of the patient. Since the S-IoMT tries to distribute health-related services using IoT devices and wireless technologies, protecting the privacy of data and security of the device is so crucial in any eHealth system. To fulfill the design objectives of eHealth, smart sensing technologies use built-in features of social networking services. Despite being more convenient in its potential use, a significant concern is a security preventing potential threats and infringement. Thus, this paper presents a lightweight two-factor authentication framework (L2FAK) with privacy-preserving functionality, which uses a mobile sink for smart eHealth. Formal and informal analyses prove that the proposed L2FAK can resist cyberattacks such as session stealing, message modification, and denial of service, guaranteeing device protection and data integrity. The learning analysis verifies the features of the physical layer using federated learning layered authentication (FLLA) to learn the data characteristics by exploring the learning framework of neural networks. In the evaluation, the core scenario is implemented on the TensorFlow Federated framework to examine FLLA and other relevant mechanisms on two correlated datasets, namely, MNIST and FashionMNIST. The analytical results show that the proposed FLLA can analyze the protection of privacy features effectively in order to guarantee an accuracy ≈89.83% to 93.41% better than other mechanisms. Lastly, a real-time testbed demonstrates the significance of the proposed L2FAK in achieving better quality metrics, such as transmission efficiency and overhead ratio than other state-of-the-art approaches.
Shrinkage heteroscedastic discriminant algorithms for classifying multi-class high-dimensional data: Insights from a national health survey

In many practical data applications, there are often a large number of pre-processed heteroscedastic features. Discriminant analysis is a standard statistical learning method that is useful for classifying such multivariate features. It is well known in literature that the Linear Discriminant Analysis (LDA) is quite sub-optimal for the analysis of high-dimensional heteroscedastic data because of the inherent singularity and instability of the within-class variance. However, shrinkage discriminant analysis (SDA) and its variants often perform better due to its robustness against inherent multicollinearity and heteroscedasticity. In this article, we propose some newly modified discriminant classification algorithms based on the SDA and compare their sensitivities with those of other competing algorithms. The empirical application show that the proposed algorithms perform moderately well for datasets with high dimensions and unequal co-variance structures when applied to simulated and nutrition data with inherent heteroscedasticity and outliers. The sensitivity and precision of the algorithms for the target classes ranges from 70%–100%. The balanced accuracy of all the algorithms ranges from 50 to 75% for the three-class problem considered. Heteroscedastic discriminant algorithm performs moderately well with high sensitivity for classifying health data with high and low dimensions.
Model and Data Integrated Transfer Learning for Unstructured Map Text Detection

The emergence of the third information wave makes extensive maps available to be generated by volunteered ways, never specially designed and generated by professional institutes alone. These large-scale images-based volunteered maps created by the public provide plentiful geographical information regarding a place while posing a challenge for recognizing the unstructured text in these maps for previous approaches to standard map text detection. Map text or map annotations denote the critical element of map content. To achieve the detection of unstructured map text, this paper proposed an integrated data-based and model-based transfer learning model, which mainly respectively included data augmentation techniques and adaptive fine-tuning, to reinforce the state-of-the-art CNNs by transferring the OCR knowledge for detecting the unstructured text units in volunteered maps. The experiment proved that our proposed framework can effectively reinforce the state-of-the-art CNN in detecting unstructured map text. We hope our research results can contribute to unstructured map text detection and recognition.