Real-Time Robotic Presentation Skill Scoring Using Multi-Model Analysis and Fuzzy Delphi-Analytic Hierarchy Process

Existing methods for scoring student presentations predominantly rely on computer-based implementations and do not incorporate a robotic multi-classification model. This limitation can result in potential misclassification issues as these approaches lack active feature learning capabilities due to fixed camera positions. Moreover, these scoring methods often solely focus on facial expressions and neglect other crucial factors, such as eye contact, hand gestures and body movements, thereby leading to potential biases or inaccuracies in scoring. To address these limitations, this study introduces Robotics-based Presentation Skill Scoring (RPSS), which employs a multi-model analysis. RPSS captures and analyses four key presentation parameters in real time, namely facial expressions, eye contact, hand gestures and body movements, and applies the fuzzy Delphi method for criteria selection and the analytic hierarchy process for weighting, thereby enabling decision makers or managers to assign varying weights to each criterion based on its relative importance. RPSS identifies five academic facial expressions and evaluates eye contact to achieve a comprehensive assessment and enhance its scoring accuracy. Specific sub-models are employed for each presentation parameter, namely EfficientNet for facial emotions, DeepEC for eye contact and an integrated Kalman and heuristic approach for hand and body movements. The scores are determined based on predefined rules. RPSS is implemented on a robot, and the results highlight its practical applicability. Each sub-model is rigorously evaluated offline and compared against benchmarks for selection. Real-world evaluations are also conducted by incorporating a novel active learning approach to improve performance by leveraging the robot's mobility. In a comparative evaluation with human tutors, RPSS achieves a remarkable average agreement of 99%, showcasing its effectiveness in assessing students' presentation skills.

Semantic visual simultaneous localization and mapping (SLAM) using deep learning for dynamic scenes

Simultaneous localization and mapping (SLAM) is a fundamental problem in robotics and computer vision. It involves the task of a robot or an autonomous system navigating an unknown environment, simultaneously creating a map of the surroundings, and accurately estimating its position within that map. While significant progress has been made in SLAM over the years, challenges still need to be addressed. One prominent issue is robustness and accuracy in dynamic environments, which can cause uncertainties and errors in the estimation process. Traditional methods using temporal information to differentiate static and dynamic objects have limitations in accuracy and applicability. Nowadays, many research trends have leaned towards utilizing deep learning-based methods which leverage the capabilities to handle dynamic objects, semantic segmentation, and motion estimation, aiming to improve accuracy and adaptability in complex scenes. This article proposed an approach to enhance monocular visual odometry's robustness and precision in dynamic environments. An enhanced algorithm using the semantic segmentation algorithm DeeplabV3+ is used to identify dynamic objects in the image and then apply the motion consistency check to remove feature points belonging to dynamic objects. The remaining static feature points are then used for feature matching and pose estimation based on ORB-SLAM2 using the Technical University of Munich (TUM) dataset. Experimental results show that our method outperforms traditional visual odometry methods in accuracy and robustness, especially in dynamic environments. By eliminating the influence of moving objects, our method improves the accuracy and robustness of visual odometry in dynamic environments. Compared to the traditional ORB-SLAM2, the results show that the system significantly reduces the absolute trajectory error and the relative pose error in dynamic scenes. Our approach has significantly improved the accuracy and robustness of the SLAM system's pose estimation.

3D-DIoU: 3D Distance Intersection over Union for Multi-Object Tracking in Point Cloud

Multi-object tracking (MOT) is a prominent and important study in point cloud processing and computer vision. The main objective of MOT is to predict full tracklets of several objects in point cloud. Occlusion and similar objects are two common problems that reduce the algorithm's performance throughout the tracking phase. The tracking performance of current MOT techniques, which adopt the 'tracking-by-detection' paradigm, is degrading, as evidenced by increasing numbers of identification (ID) switch and tracking drifts because it is difficult to perfectly predict the location of objects in complex scenes that are unable to track. Since the occluded object may have been visible in former frames, we manipulated the speed and location position of the object in the previous frames in order to guess where the occluded object might have been. In this paper, we employed a unique intersection over union (IoU) method in three-dimension (3D) planes, namely a distance IoU non-maximum suppression (DIoU-NMS) to accurately detect objects, and consequently we use 3D-DIoU for an object association process in order to increase tracking robustness and speed. By using a hybrid 3D DIoU-NMS and 3D-DIoU method, the tracking speed improved significantly. Experimental findings on the Waymo Open Dataset and nuScenes dataset, demonstrate that our multistage data association and tracking technique has clear benefits over previously developed algorithms in terms of tracking accuracy. In comparison with other 3D MOT tracking methods, our proposed approach demonstrates significant enhancement in tracking performances.

Facies analysis and distribution of Late Palaeogene deep‐water massive sandstones in submarine‐fan lobes, NW Borneo

Deep‐water massive sandstones (DWMS) are characterized by large volumes of sand accumulations which are considered as potential reservoir intervals in deep‐marine environments. Lithological variations and bed thickness statistics are used to interpret the distribution of massive sandstones in a deep‐marine fan‐lobe system. These massive sandstones are interpreted based on lithological heterogeneities and detailed facies analysis in seventeen exposed sections of the Late Palaeogene deposits in Sabah, NW Borneo. Sedimentary logs containing details of lithology textures and structures were used to recognize nine sedimentary facies of DWMS. These lithofacies were then grouped into three sedimentary facies associations: (1) massive facies association with basal part of turbiditic Bouma sequence, (2) massive facies association having soft‐sediment deformation structures, and (3) massive facies association with erosional features. The facies analysis portrays inner to middle submarine fan deposition and was later applied to reconstruct the distribution of a channel‐lobe complex. Individual sandstone bed thicknesses vary from 1 m to more than 8 m and the number of massive sandstones in submarine lobes range from less than 10% to more than 50%. The thicknesses of massive sandstones in channels are more than 8 m, whereas distal lobes have thicknesses from 1–2 m only. These sandstones are concentrated in channels, proximal and medial lobe settings that can also be verified from calculating the average of all maximum thickness of massive sand intervals that is, 8.91 m. The lithological heterogeneities and the processes associated with the deposition of these massive sandstones are vital for potential hydrocarbon reservoirs in the deep‐marine environments around the globe.

Enhancing data transmission in duct air quality monitoring using mesh network strategy for LoRa

Duct air quality monitoring (DAQM) is a typical process for building controls, with multiple infections outbreaks reported over time linked with duct system defilement. Various research works have been published with analyses on the air quality inside ducting systems using microcontrollers and low-cost smart sensors instead of conventional meters. However, researchers face problems sending data within limited range and cross-sections inside the duct to the gateway using available wireless technologies, as the transmission is entirely a non-line-of-sight. Therefore, this study developed a new instrument for DAQM to integrate microcontrollers and sensors with a mobile robot using LoRa as the wireless communication medium. The main contribution of this paper is the evaluation of mesh LoRa strategies using our instrument to overcome network disruption problems at the cross-sections and extend the coverage area within the duct environment. A mobile LoRa-based data collection technique is implemented for various data sensors such as DHT22, MQ7, MQ2, MQ135, and DSM50A to identify carbon monoxide, carbon dioxide, smoke, and PM2.5 levels. This study analyzed the efficiency of data transmission and signal strength to cover the air duct environment using several network topologies. The experimental design covered four different scenarios with different configurations in a multi-story building. The network performance evaluations focused on the packet delivery ratio (PDR) and the received signal strength indicator (RSSI). Experimental results in all scenarios showed an improvement in Packet Delivery Ratio (PDR) and significant improvement in the coverage area in the mesh network setup. The results conclude that the transmission efficiency and coverage area are significantly enhanced using the proposed LoRa mesh network and potentially expanded in larger duct environments.

Artificial intelligence maturity model: a systematic literature review

Organizations in various industries have widely developed the artificial intelligence (AI) maturity model as a systematic approach. This study aims to review state-of-the-art studies related to AI maturity models systematically. It allows a deeper understanding of the methodological issues relevant to maturity models, especially in terms of the objectives, methods employed to develop and validate the models, and the scope and characteristics of maturity model development. Our analysis reveals that most works concentrate on developing maturity models with or without their empirical validation. It shows that the most significant proportion of models were designed for specific domains and purposes. Maturity model development typically uses a bottom-up design approach, and most of the models have a descriptive characteristic. Besides that, maturity grid and continuous representation with five levels are currently trending in maturity model development. Six out of 13 studies (46%) on AI maturity pertain to assess the technology aspect, even in specific domains. It confirms that organizations still require an improvement in their AI capability and in strengthening AI maturity. This review provides an essential contribution to the evolution of organizations using AI to explain the concepts, approaches, and elements of maturity models.

MULTILABEL OVER-SAMPLING AND UNDER-SAMPLING WITH CLASS ALIGNMENT FOR IMBALANCED MULTILABEL TEXT CLASSIFICATION

Simultaneous multiple labelling of documents, also known as multilabel text classification, will not perform optimally if the class is highly imbalanced. Class imbalanced entails skewness in the fundamental data for distribution that leads to more difficulty in classification. Random over-sampling and under-sampling are common approaches to solve the class imbalanced problem. However, these approaches have several drawbacks; the under-sampling is likely to dispose of useful data, whereas the over-sampling can heighten the probability of overfitting. Therefore, a new method that can avoid discarding useful data and overfitting problems is needed. This study proposes a method to tackle the class imbalanced problem by combining multilabel over-sampling and under-sampling with class alignment (ML-OUSCA). In the proposed ML-OUSCA, instead of using all the training instances, it draws a new training set by over-sampling small size classes and under-sampling big size classes. To evaluate our proposed ML-OUSCA, evaluation metrics of average precision, average recall and average F-measure on three benchmark datasets, namely, Reuters-21578, Bibtex, and Enron datasets, were performed. Experimental results showed that the proposed ML-OUSCA outperformed the chosen baseline random resampling approaches; K-means SMOTE and KNN-US. Thus, based on the results, we can conclude that designing a resampling method based on the class imbalanced together with class alignment will improve multilabel classification even better than just the random resampling method.

Malay Version of the mHealth App Usability Questionnaire (M-MAUQ): Translation, Adaptation, and Validation Study

BACKGROUND

Mobile health (mHealth) apps play an important role in delivering education, providing advice on treatment, and monitoring patients' health. Good usability of mHealth apps is essential to achieve the objectives of mHealth apps efficiently. To date, there are questionnaires available to assess the general system usability but not explicitly tailored to precisely assess the usability of mHealth apps. Hence, the mHealth App Usability Questionnaire (MAUQ) was developed with 4 versions according to the type of app (interactive or standalone) and according to the target user (patient or provider). Standalone MAUQ for patients comprises 3 subscales, which are ease of use, interface and satisfaction, and usefulness.

OBJECTIVE

This study aimed to translate and validate the English version of MAUQ (standalone for patients) into a Malay version of MAUQ (M-MAUQ) for mHealth app research and usage in future in Malaysia.

METHODS

Forward and backward translation and harmonization of M-MAUQ were conducted by Malay native speakers who also spoke English as their second language. The process began with a forward translation by 2 independent translators followed by harmonization to produce an initial translated version of M-MAUQ. Next, the forward translation was continued by another 2 translators who had never seen the original MAUQ. Lastly, harmonization was conducted among the committee members to resolve any ambiguity and inconsistency in the words and sentences of the items derived with the prefinal adapted questionnaire. Subsequently, content and face validations were performed with 10 experts and 10 target users, respectively. Modified kappa statistic was used to determine the interrater agreement among the raters. The reliability of the M-MAUQ was assessed by 51 healthy young adult mobile phone users. Participants needed to install the MyFitnessPal app and use it for 2 days for familiarization before completing the designated task and answer the M-MAUQ. The MyFitnessPal app was selected because it is one among the most popular installed mHealth apps globally available for iPhone and Android users and represents a standalone mHealth app.

RESULTS

The content validity index for the relevancy and clarity of M-MAUQ were determined to be 0.983 and 0.944, respectively, which indicated good relevancy and clarity. The face validity index for understandability was 0.961, which indicated that users understood the M-MAUQ. The kappa statistic for every item in M-MAUQ indicated excellent agreement between the raters (κ ranging from 0.76 to 1.09). The Cronbach α for 18 items was .946, which also indicated good reliability in assessing the usability of the mHealth app.

CONCLUSIONS

The M-MAUQ fulfilled the validation criteria as it revealed good reliability and validity similar to the original version. M-MAUQ can be used to assess the usability of mHealth apps in Malay in the future.

High Resistivity Reservoirs (Causes And Effects): Sahara Field, Murzuq Basin, Libya

High and low resistivity values is an alarming phenomenon that is usually associated with a very complicated reservoir history and worth looking into. Ordovician sandstone reservoirs are the primary oil producers in the Murzuq basin oil fields that is characterized with an average porosity of 14%, permeability range 410-10,760 md and clean quartz aranite composition. More than fifty wells were drilled in Sahara oil field, but only four of them were announced to have high resistivity values more than 100k ohm-m and ten others to be considered as low resistivity wells (below 50 ohm-m). Therefore, average deep resistivity was mapped in both water and oil legs using all available data set, and the top reservoir was employed as a trend map. They showed distinctive trends for low resistivity readings in oil-leg and confirmed the extreme deep resistivity nature for the wells (W7, W8, W9, and W10). Height above oil water contact and capillary pressure was also calculated for all the wells and revealed a high pressure (400 psi) at the location of the high resistivity wells. As a result, of higher capillary pressure in thicker reservoir area oil might have been able to displace water through geological time by benefitting of more considerable height above oil-water contact, higher connate pressure, and buoyancy forces support, which resulted in occupying all the larger pores and pushed the water into minor scattered pores leading to gradual alteration of reservoir wettability from water to oil-wet. Hence, the brine fluids will no longer be connected to each other inside the pore system. Therefore, they will lose their contribution to resistivity readings, and the resistivity tool will encounter a more resistant medium, which in turn will lead to underestimation of water saturation.

Malay Version of the mHealth App Usability Questionnaire (M-MAUQ): Translation, Adaptation, and Validation Study (Preprint).. [DOI: 10.2196/preprints.24457] [Citation(s) in RCA: 0] [Impact Index Per Article: 0]

BACKGROUND

Mobile health (mHealth) apps play an important role in delivering education, providing advice on treatment, and monitoring patients’ health. Good usability of mHealth apps is essential to achieve the objectives of mHealth apps efficiently. To date, there are questionnaires available to assess the general system usability but not explicitly tailored to precisely assess the usability of mHealth apps. Hence, the mHealth App Usability Questionnaire (MAUQ) was developed with 4 versions according to the type of app (interactive or standalone) and according to the target user (patient or provider). Standalone MAUQ for patients comprises 3 subscales, which are ease of use, interface and satisfaction, and usefulness.

OBJECTIVE

This study aimed to translate and validate the English version of MAUQ (standalone for patients) into a Malay version of MAUQ (M-MAUQ) for mHealth app research and usage in future in Malaysia.

METHODS

Forward and backward translation and harmonization of M-MAUQ were conducted by Malay native speakers who also spoke English as their second language. The process began with a forward translation by 2 independent translators followed by harmonization to produce an initial translated version of M-MAUQ. Next, the forward translation was continued by another 2 translators who had never seen the original MAUQ. Lastly, harmonization was conducted among the committee members to resolve any ambiguity and inconsistency in the words and sentences of the items derived with the prefinal adapted questionnaire. Subsequently, content and face validations were performed with 10 experts and 10 target users, respectively. Modified kappa statistic was used to determine the interrater agreement among the raters. The reliability of the M-MAUQ was assessed by 51 healthy young adult mobile phone users. Participants needed to install the MyFitnessPal app and use it for 2 days for familiarization before completing the designated task and answer the M-MAUQ. The MyFitnessPal app was selected because it is one among the most popular installed mHealth apps globally available for iPhone and Android users and represents a standalone mHealth app.

RESULTS

The content validity index for the relevancy and clarity of M-MAUQ were determined to be 0.983 and 0.944, respectively, which indicated good relevancy and clarity. The face validity index for understandability was 0.961, which indicated that users understood the M-MAUQ. The kappa statistic for every item in M-MAUQ indicated excellent agreement between the raters (κ ranging from 0.76 to 1.09). The Cronbach α for 18 items was .946, which also indicated good reliability in assessing the usability of the mHealth app.

CONCLUSIONS

The M-MAUQ fulfilled the validation criteria as it revealed good reliability and validity similar to the original version. M-MAUQ can be used to assess the usability of mHealth apps in Malay in the future.

Pedestrian Detection using Triple Laser Range Finders

<p>Pedestrian detection is one of the important features in autonomous ground vehicle (AGV). It ensures the capability for safety navigation in urban environment. Therefore, the detection accuracy became a crucial part which leads to implementation using Laser Range Finder (LRF) for better data representation. In this study, an improved laser configuration and fusion technique is introduced by implementation of triple LRFs in two layers with Pedestrian Data Analysis (PDA) to recognize multiple pedestrians. The PDA integrates various features from feature extraction process for all clusters and fusion of multiple layers for better recognition. The experiments were conducted in various occlusion scenarios such as intersection, closed-pedestrian and combine scenarios. The analysis of the laser fusion and PDA for all scenarios showed an improvement of detection where the pedestrians were represented by various detection categories which solve occlusion issues when low number<br />of laser data were obtained.</p>

Development of Sports Nutrition Educational Tools (NutriSportExTM): A Web-Application for Malaysian National Athletes

The use of electronics or technology, to improve the subjects’ knowledge, attitude and practice (KAP) in nutrition or their compliance in recording their diet, for general primary care is not uncommon. However, there are more to be explored to improve sports nutrition KAP. The objective of this study was to develop a web app tool for athletes to increases their KAP in sports nutrition and to test on their acceptance towards the web app tool. This study was divided into 3 phases. Phase I involves the development of the web app. In Phase II, a focus group of 20 people was recruited to test the web app tool. Phase III was to evaluate the effectiveness of the web app in increasing athletes’ sports nutrition KAP and their acceptance of the web app. 30 athletes were recruited to use this web app for a week, a PRE- and POST-intervention KAP questionnaire were given. There was a strong correlation between athletes’ attitude towards this tool and intention to use in the future (r = 0.675). The more this web app tool was perceived as useful, the more likely they will visit the web app (r=0.589). Although the web app was well-accepted among the athletes, their KAP score was significantly reduced after the intervention (p= 0.011). The reduction of scores was due to the lack of consistent usage on the web app. In conclusion, it requires more facilitating support from the coach, longer training time, reminders and entertainment features for consistent usage, to improve the athletes’ KAP in sports nutrition via the web app.

Observations on the trypanosomosis problem outside the tsetse belts of Sudan

Entomological and trypanosomosis surveys were conducted in the Blue Nile area between Admazien and Khartoum. The surveys showed the area to be tsetse free. The only species of trypanosome found to infect cattle in this study was Trypanosoma vivax, which infected some of the local cattle that had no history of entering tsetse belts. The prevalence of disease varied with the season. High disease prevalence coincided with the periods when tabanid and stomoxy flies were abundant. The study showed that the months when biting flies were most numerous coincided with trypanosomosis outbreaks, but even minimal numbers of these flies may cause the cycle of mechanical transmission to continue in stable enzootic conditions.