Miracle-dz/UniFAR-FTUs huggingface dataset:
Learning Self-Supervised Traversability With Navigation Experiences of Mobile Robots: A Risk-Aware Self-Training Approach

Mobile robots operating in outdoor environments face the challenge of navigating various terrains with different degrees of difficulty. Therefore, traversability estimation is crucial for safe and efficient robot navigation. Current approaches utilize a robot's driving experience to learn traversability in a self-supervised fashion. However, providing sufficient and diverse experience to the robot is difficult in many practical applications. In this paper, we propose a self-supervised traversability learning method that adapts to challenging terrains with limited prior experience. One key aspect is to enable prioritized learning of scarce yet high-risk terrains by using a risk-sensitive approach. To this end, we train a neural network through a risk-aware instance weighting scheme. Another key aspect is to leverage traversability pseudo-labels on the basis of a self-training scheme. The proposed confidence-regularized self-training generates high-quality pseudo-labels, thereby achieving reliable data augmentation for unexperienced terrains. The effectiveness of the proposed method is verified in extensive real-world experiments, ranging from structured urban environments to complex rugged terrains.
Low-Rank Active Learning for Generating Speech-Drive Human Face Animation

Emotion&speech-based human facial animation technique can be considered as a useful application in many artificial intelligent systems. Given a speech signal, the recognizer output a sequence of the phoneme and emotion pairs. Thereby, we calculate the sequence of viseme and expression pairs accordingly, which are subsequently transformed to a consistent and synchronous video describing facial animation. This article introduces a novel facial animation technique that can intelligently generates real human face animation videos by leveraging an emotional speech. More specifically, we first extract acoustic features sufficiently discriminative to the emotion and phoneme pairs. And the corresponding sequence of phoneme and emotion pairs are computed. Next, we propose a low-rank active learning paradigm for discovering multiple key facial frames that can best represent the above phoneme and emotion pairs in the feature subspace. We associate each phoneme and emotion pair with a key facial frame, based on which the well-known morphing technique fits the associated key facial frames to a smooth animated facial video. We focus on generating multiple transitional facial frames between pairwise temporally adjacent key ones. Experiments demonstrated that the synthesized facial videos look real, smooth, and synchronous with different male/female speeches.
What Is Machine Learning

Machine learning is an evolving branch of computational algorithms that are designed to emulate human intelligence by learning from the surrounding environment. They are considered the working horse in the new era of the so-called big data. Techniques based on machine learning have been applied successfully in diverse fields ranging from pattern recognition, computer vision, spacecraft engineering, finance, entertainment, and computational biology to biomedical and medical applications. More than half of the patients with cancer receive ionizing radiation (radiotherapy) as part of their treatment, and it is the main treatment modality at advanced stages of local disease. Radiotherapy involves a large set of processes that not only span the period from consultation to treatment but also extend beyond that to ensure that the patients have received the prescribed radiation dose and are responding well. The degrees of the complexity of these processes can vary and may involve several stages of sophisticated human-machine interactions and decision making, which would naturally invite the use of machine learning algorithms into optimizing and automating these processes including but not limited to radiation physics quality assurance, contouring and treatment planning, image-guided radiotherapy, respiratory motion management, treatment response modeling, and outcomes prediction. The ability of machine learning algorithms to learn from current context and generalize into unseen tasks would allow improvements in both the safety and efficacy of radiotherapy practice leading to better outcomes.
Non-Invasive HRV Protocol and New Index to Assess Internal Training Load During Basketball Warm Up

The aim is to establish an specific, non-invasive, sub-maximum and applicable day-to-day protocol to assess the internal training load (IL) for basketball players using Heart Rate Variability (HRV) analysis before practice or competitions. Another aim is to propose a new IL index as a standardized parameter (%), to manage IL before practice sessions or competition. Twelve amateur male basketball players (age: 26.5 ± 8.8 years) completed HRV tests before a submaximal and intermittent drill (Activity Lay Up Wheel; ALUWIL 6min-test). Players performed a 5on5 scrimmage (MATCH) the following day during 10 min, completing rate of perceived exertion (RPE), total quality recovery (TQR) and wellness questionnaires, and continuous heart rate recordings. Players showed higher values of IL parameters based on heart rate (summatory heart rate zones, SHRZ; and training impulse, TRIMP), higher RPE values and lower recovery values during MATCH than ALUWIL (p<0.01). The parameter % of summatory of heart rate zones (SHRZ) showed stronger correlations than SHRZ and TRIMP during MATCH (p<0.001), and significant relations with RMSSD (square of the mean root of the adjacent R-R intervals) during the HRV test (p<0.05). During the ALUWIL test, %SHRZ shows higher and stronger correlations than SHRZ with RRmean (mean of the R-R intervals) (-.807) but similar than SHRZ with RMSSD. In this specific context, ALUWIL test could offer reliable and applied information as a standardized and integrated protocol to assess internal training load in basketball players using cardiac parameters before practice and games. The parameters RRMean, RMSSD and %SHRZ could be a specific indicator for IL management on basketball. However, more research is needed to validate these proposals in a complex context. Key words: HRV, internal training load, basketball, heart rate, submaximal test, SHRZ.
Comparison of the Artificial Neural Network's Approximations for the Levenberg-Marquardt Algorithm and the Gradient Descent Optimization on Datasets

The approximations obtained by gradient descent optimization on a set of datasets were compared with the results obtained with the Levenberg-Marquardt Optimization Method (LMOM) on the same datasets. The datasets, which comprised three orthogonal databases obtained from MATLAB's Neural Network toolbox accompanying databases, were normalized and serially loaded to the artificial neural network Graphical User Interface (GUI) designed by the researchers. The GUI built with Visual Studio Programming Language (VSPL) implements a gradient descent optimization scheme of the back-propagation algorithm. The characteristics of each database for determination of the termination criteria were approximated from the developed feature extractive iteration algorithm. Revalidation sessions of the LMOM on the sampled datasets showed significant spuriousness in outputted results when compared with the gradient descent optimization results which although slow in attaining convergence produced results that can be closely replicated. Analysis of the F-statistics and the Receiver Operating Characteristics (ROC) for the sampled datasets results of both methods also showed that the gradient descent method demonstrated significant accuracy and parsimony in approximating the nonlinear solutions in the datasets when compared with the results from LMOM processing. Additionally, in this research, an algorithm for deducing and producing the ROC for analyzed Artificial Neural Network (ANN) sessions was also developed and implemented using VSPL.
The Increase of HDL-C Levels using Ethanol Extract of Vernonia amygdalina Leaves in Wistar Rats Models of Metabolic Syndrome

Introduction: Metabolic syndrome (MetS) is a cluster of metabolic factors, including low high-density lipoprotein cholesterol (HDL-C) levels. Vernonia amygdalina (VA) leaves have been documented for their anti-cholesterol properties. The purpose of this study to evaluate ethanol extract of VA leaves in rising HDL-C levels in the MetS model.Methods: An experimental study using pre-and post-test design was conducted using 30 male Wistar rats divided into five groups randomly. KN group served as the control group. KP group was the MetS model without ethanol extract administration. Meanwhile, P1, P2, and P3 were MetS models and were administered ethanol extract of VA leaves administration (50mg/kg, 100mg/kg, and 150 mg/kg) for 28 days. The data were analysed using paired t-test and one-way ANOVA (α=0,05). Results: This study discovered that administering the extract in P1, P2, and P3 significantly improved HDL levels. Furthermore, increasing the dose resulted in significantly greater HDL levels. VA leaves contain phytochemicals that have impacts on cholesterol levels in the blood. Flavonoids and tannin inhibit ester cholesterol formation. Terpenoid and flavonoid hinder HMG-CoA. Saponin contributes to free cholesterol transporter, reducing oxidative stress and increasing LDL receptors. Lastly, vitamin C raises catabolism levels and clearance LDL.Conclusion: There are increases in HDL levels in the MetS model group receiving ethanol extract VA leaves.
Trust-Based Optimized Reporting for Detection and Prevention of Black Hole Attacks in Low-Power and Lossy Green IoT Networks

The Internet of Things (IoT) is empowering various sectors and aspects of daily life. Green IoT systems typically involve Low-Power and Lossy Networks (LLNs) with resource-constrained nodes. Lightweight routing protocols, such as the Routing Protocol for Low-Power and Lossy Networks (RPL), are increasingly being applied for efficient communication in LLNs. However, RPL is susceptible to various attacks, such as the black hole attack, which compromises network security. The existing black hole attack detection methods in Green IoT rely on static thresholds and unreliable metrics to compute trust scores. This results in increasing false positive rates, especially in resource-constrained IoT environments. To overcome these limitations, we propose a delta-threshold-based trust model called the Optimized Reporting Module (ORM) to mitigate black hole attacks in Green IoT systems. The proposed scheme comprises both direct trust and indirect trust and utilizes a forgetting curve. Direct trust is derived from performance metrics, including honesty, dishonesty, energy, and unselfishness. Indirect trust requires the use of similarity. The forgetting curve provides a mechanism to consider the most significant and recent feedback from direct and indirect trust. To assess the efficacy of the proposed scheme, we compare it with the well-known trust-based attack detection scheme. Simulation results demonstrate that the proposed scheme has a higher detection rate and low false positive alarms compared to the existing scheme, confirming the applicability of the proposed scheme in green IoT systems.
Cross-level transformation of creativity from entrepreneurs to organizations

With the intensification of competition in the business environment, organizational creativity is increasingly becoming crucial for organizations to build competitive advantages and promote organizational development. For innovative enterprises, their entrepreneurs largely determine the development orientation of the enterprise. They are one of the most critical factors determining the level of corporate innovation, but there need to be more effective creativity transformation path to pursue innovation development. The findings in this study show that entrepreneurial individual creativity has a significant positive effect on organizational creativity, platform leadership mediates the path of creativity transformation across hierarchical levels, and organizational culture has positive moderating effect between platform leadership and organizational creativity. The study results explain the transformation mechanism of creativity from the entrepreneur's perspective, expand the potential transformation path of organizational creativity, and are instructive for enhancing organizational creativity.
PULMONARY SOUND DESIGN USING MAX 9814 SENSOR WITH NEXTION VIEW

This study aims to develop a tool that can assist nurses in examining patients by displaying sounds and signals on a TFT LCD screen. This tool uses the MAX 9814 sound sensor, which converts sound into an electrical signal. This sensor will generate a voltage when the diaphragm inside it moves back and forth. To design this tool, researchers used a series of high pass filters and low pass filters with a cut-off frequency of 333 Hz - 714 kHz. During testing of this tool using the MAX 9814 sensor mounted on a stethoscope, the signal appears stable on the TFT LCD screen and sound can be played properly. However, if the placement of the stethoscope is not correct during the examination, this can also affect the signal and sound produced. This tool should not produce a clear and loud sound when checking, because if this happens, the inspection cannot be carried out optimally. This research also produces a signal shape that is almost similar to the signal on the phantom. It is important to note that the MAX 9814 can work optimally if it is not exposed to environmental noise. Therefore, if this equipment is used in a noisy environment, the signal and sound produced may be disturbed by environmental noise. This research was conducted with the aim of making it easier for doctors and nurses to carry out portable examinations, as well as to monitor signals and sounds easily.
Feasibility Study of the Proposed Northern Belize Biological Corridors Project (NBBCP) Volume II: Monitoring Report

This report is an ambitious step toward establishment of a system-wide assemblage of monitoring techniques on a scale unprecedented in Belize. It describes both nontechnical and highly scientific monitoring techniques. The latter will require significant start-up funding with some bolstering through the proximal years. This program has the potential to more closely approach the problem of accelerating deforestation via decisions that lack connectivity with the whole than any other, largely because it involves local communities at several levels. • Monitoring the physical parameters should be monitored on the landscape level (forest cover) by means of bi-annual review of most recent aerial and LANDSAT data. • The easiest monitoring method, in the absence of time and qualified observers, is a simple logbook in which wildlife observations are recorded.
Exploring the Challenges in Covering Dental Services through Complementary Insurance in Iran: A Qualitative Study

Background Financial protection is crucial for attaining universal health coverage. The inclusion of costly dental services in insurance plans poses a significant challenge for all parties involved in the insurance sector. This study aimed to investigate the challenges of covering dental services by complementary insurance in Iran during 2020–2021. Materials and Methods This qualitative research was conducted in Iran during 2020–2021. A triangulation of methods and data sources were employed to achieve a comprehensive perspective. In-depth semistructured interviews were conducted on an individual basis, and all national documents, rules, regulations, and instructions pertaining to complementary dental insurance were thoroughly reviewed. Purposeful sampling was used to select participants from all stakeholder groups engaged in dental insurance coverage, including (1) health system policymakers, (2) insurers, (3) policyholders, (4) care providers (dentists), and (5) insured people. Six open-ended questions were formulated to explore various facets of dental insurance, including (1) development, (2) management, (3) population coverage, (4) premium calculation, (5) services coverage, and (6) payment and reimbursement mechanisms. With the consent of the participants, all interviews were recorded and transcribed verbatim. The gathered data were evaluated using a framework analysis approach in the MAXQDA20 software. Finally, the primary themes, each encompassing multiple subthemes, were identified and presented. Results A total of 26 interviews were conducted with five groups of interviewees, and nine national documents were evaluated. Six themes were extracted, which included 18 codes from the interviews and seven codes from the documents. The extracted themes were as follows: (1) Insurance commitments and service coverage, (2) reimbursement system, (3) information system, (4) economic issues, (5) population coverage, and (6) regulation and supervision. The high cost of dental services was the most frequent challenge, followed by the insurance commitments and service coverage. Conclusions The delivery of dental services through complementary insurance in Iran primarily faces economic and service coverage challenges. The resolution hinges on the collaboration between basic and complementary insurance sectors, the development of a unified information system for insured individuals, and the implementation of a risk-adjusted premium plan.
Exploration of the Management Mode and Quality Evaluation of Entrepreneurship Education in Colleges and Universities Based on Data Mining

: China has also continuously applied it to practice in its development process, and has carried out a lot of research and practice. Experiments and promotion have been carried out in major colleges and universities, which has laid a solid foundation for college students' entrepreneurial education thoughts and management methods. This paper used data mining technology to evaluate the teachers in entrepreneurship teaching, so as to improve the entrepreneurial quality of college students and promote their transformation from entrepreneurship to employment. The results in this paper showed that teachers in the age group A2 (31-35) had the highest proportion of excellent teaching quality evaluation results among all age groups, accounting for 47%; teachers with the title of J3 (associate professor) had the highest proportion of excellent teaching quality evaluation results among all teacher titles, accounting for 82%; teachers with E2 (master's degree) education had the highest proportion of excellent teaching quality evaluation results among all teachers' qualifications, accounting for 71.4%. According to the data, teachers of moderate age with associate professor or above and master's degree or above are more likely to teach excellent students, thereby promoting the further improvement of the entrepreneurial management education models in colleges and universities.
Paradisiacal Lived Experiences of Tourists in Islas de Gigantes

This study explores the viewpoints and experiences of tourists at Islas de Gigantes, a captivating tourist destination located in the Philippines. This research aims to unravel the complex factors that influence tourists' perception of Gigantes as an authentic paradise, using a combination of interviews and observations. This study explores the underlying motives of tourists, their interactions with the local environment and community, and their overall level of contentment during their travels. Moreover, it carefully examines the complex relationship between tourism and the remarkable variety of biodiversity of plants and animals found on these islands. It places special importance on the long-term viability of activities like scallop harvesting and their compatibility with efforts to protect the environment and promote the welfare of local communities. This analysis examines the economic consequences of tourism for local communities, emphasizing the crucial role that tourism plays in promoting local economic growth while also acknowledging the possible disruptions that may occur. The study reveals intricate patterns and underlying patterns, enhancing our understanding of idyllic travel experiences. This research considerably contributes to the global discussion on paradisiacal locations by exposing these findings to a comparison analysis with previous literature. Additionally, it provides practical suggestions to individuals involved in the tourism industry, highlighting the significance of responsible and sustainable actions that protect the natural surroundings and conserve the local cultural heritage. Although acknowledging the inherent constraints of any research undertaking, this study provides guidance for future inquiries. This statement proposes potential areas for future research, including longitudinal studies and evaluations of climate resilience, with the aim of improving the sustainability and resilience of tourism in picturesque locations in an increasingly volatile world. This research offers valuable insights into the enticing appeal of Islas de Gigantes, as well as enhances our comprehension of how pleasant destinations can coexist harmoniously with the environment and local communities, serving as an exemplary model for responsible and sustainable tourism practices worldwide.
Significance of the Influence of Leadership Model, Morale, and Satisfaction on Performance Consistency

Maintaining performance while carrying out tasks is not easy, many things make emotions and work behavior inconsistent. Therefore, external factors are needed as a reminder, for example the role of leadership. The presence of the leader is one of the factors of self-control in carrying out tasks, because its presence is considered to be a supervisor for the implementation of tasks. In addition, self-factors such as a sense of morale and job satisfaction are internal factors that affect performance consistency. This research was conducted, to be able to see the role of internal and external factors of employees on the consistency of performance in carrying out their duties. The method used is quantitative with statistical regression. The research subjects were teachers at a boarding-based Islamic school in the city of Bogor. A total of 41 respondents were used for questioning through the census technique. The results of the study explain that a good leader can be a motivation for employees in carrying out their duties. Because employees feel, cared for and recognized their existence. In addition, the factors of enthusiasm and job satisfaction are factors that are thought to be able to increase self-enthusiasm in carrying out tasks.