Study on the identification and classification of fish is challenging and valuable because of its role in advancing the marine and agricultural fields. This research has benefits interms of monitoring fish populations...
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programming can help K-12 students to develop their 21st-century core skills. Despite the benefits, programming is not common to be delivered in Indonesian K-12 education. There is a need to understand potential chall...
programming can help K-12 students to develop their 21st-century core skills. Despite the benefits, programming is not common to be delivered in Indonesian K-12 education. There is a need to understand potential challenges in introducing programming to K-12 students. We developed a questionnaire survey covering four identified dimensions of challenges: administrative, facilities, teachers, and students. We also asked about common programming assessments and their preferred software features for teaching programming. Forty K-12 teachers were invited to complete the survey. The responses were analyzed with thematic analysis using a bigram-based Latent Dirichlet Allocation topic modeling and descriptive statistics. Our study shows that the challenges include limited learning modules, an insufficient number of computers, limited programming skills, and limited computational thinking skills. Scratch was the most common programming language used and many programming assessments were about debugging a program or writing a small program. Visualization and animation can be helpful in teaching programming.
The development of blockchain technology across the globe has seen to be vast, including those related to visual arts. In Islam, anything new: especially those that never existed before, needs to be, obligatory discus...
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This research aims to help the user monitor their heart’s condition and notify other people in case of an emergency due to an abnormal heartbeat (normal heartbeat around 60-100 beats per minute). From the heart rate ...
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The capacity to deceptively move in hilly terrains is fundamental to agents in simulation systems for tactical and strategic military training. Such an ability to deceive the adversary can ensure a relevant advantage ...
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ISBN:
(数字)9798331508296
ISBN:
(纸本)9798331508302
The capacity to deceptively move in hilly terrains is fundamental to agents in simulation systems for tactical and strategic military training. Such an ability to deceive the adversary can ensure a relevant advantage by hiding the real goals of a mission. Using pairs of real and deceptive mission goals, this paper investigates the planning of realistic deceptive routes for simulated agents. With real-world terrain elevation maps, it shows how to explore pathfinding algorithms with relevant path-smoothing characteristics (Theta* and WJPS*, contrasting with the standard $A^{*}$ algorithm) in terrains with pronounced relief features. The study analyzes the effects of terrain elevation costs and the representation of relief contour lines on the determination of more realistic deceptive paths. This work also investigates how users can adjust a Last Topographic Deceptive Point ($L D P_{T}$) calculation to enhance the pathfinding algorithm’s ability to produce more deceptively dense and topographically aware routes. Experimental results for different deceptive topographic path planning strategies are evaluated according to statistical models showing that Theta*, despite being slower than the base $A^{*}$ method in most cases, generated smoothed paths while maintaining a similar deception density for the proposed strategies. On the other hand, WJPS outperformed both in execution time for certain strategies while maintaining the smoothed path characteristic and resulting in a path with lower deceptive capacity.
As the availability of data is increasing everyday, the need to reflect on how to make these data meaningful and impactful becomes vital. Current data paradigms have provided data life cycles that often focus on data ...
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This work studied message communications on patient portals and examined both the longitudinal trends and the correlations with characteristics of message senders. We analyzed over 5.6 million secure messages sent on ...
This work studied message communications on patient portals and examined both the longitudinal trends and the correlations with characteristics of message senders. We analyzed over 5.6 million secure messages sent on the Mayo Clinic patient portal between February 18, 2010, and December 31, 2017. We studied the longitudinal changes in the number of portal messages, patient senders’ demographics and medical conditions (PheCodes), and provider senders’ care settings (e.g., primary or specialty) and practice roles (e.g., physician, nurse practitioner, and registered nurses). When compared to non-message-senders, patient message senders had a significantly higher proportion of the demographics: age 41-60, female, married, white, and English-speaking. From 2010-2017, an individual patient sent an average of 9.8 messages per person while a provider sent 418.4. The average number of PheCodes for all patients regardless of portal usage increased from 7.5 +/-6.9 in 2010 to 10.7 +/- 10.1 in 2017. The Pearson correlation coefficient between average PheCodes per patient and average messages per patient was 0.273 (p < 0.0001). Physicians were the largest proportion of message composers in both primary and specialty care (36.20% of primary, 37.54% of specialty). Starting 2013 onwards, specialty providers comprised the majority of portal providers while primary care providers remained stable around 20-22%. Our results show that patient portals are playing an increasingly significant role in supporting patient-provider communications. The longitudinal growth also sheds light on the possible challenge of communication overload for providers and the healthcare system.
This paper introduces HAAQI-Net, a non-intrusive music audio quality assessment model for hearing aid users. Unlike traditional methods such as Hearing Aid Audio Quality Index (HAAQI), which requires intrusive referen...
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This paper introduces HAAQI-Net, a non-intrusive music audio quality assessment model for hearing aid users. Unlike traditional methods such as Hearing Aid Audio Quality Index (HAAQI), which requires intrusive reference signal comparisons, HAAQI-Net offers a more accessible and computationally efficient alternative. Leveraging a bidirectional long short-term memory architecture with attention mechanisms and features extracted from a pre-trained BEATs model, it can predict HAAQI scores directly from music audio clips and hearing loss patterns. The experimental results demonstrate that, compared to the traditional HAAQI as the reference, HAAQI-Net achieves a linear correlation coefficient (LCC) of 0.9368, a Spearman's rank correlation coefficient (SRCC) of 0.9486, and a mean squared error (MSE) of 0.0064, while significantly reducing the inference time from 62.52 seconds to 2.54 seconds. Furthermore, a knowledge distillation strategy was applied, reducing the parameters by 75.85% and inference time by 96.46%, while maintaining strong performance (LCC: 0.9071, SRCC: 0.9307, MSE: 0.0091). To expand its capabilities, HAAQI-Net was adapted to predict subjective human scores, mean opinion score (MOS), by fine-tuning. This adaptation significantly improved the prediction accuracy. Furthermore, the robustness of HAAQI-Net was evaluated under varying sound pressure level (SPL) conditions, revealing optimal performance at a reference SPL of 65 dB, with the accuracy gradually decreasing as SPL deviated from this point. The advancements in subjective score prediction, SPL robustness, and computational efficiency position HAAQI-Net as a reliable solution for music audio quality assessment, significantly contributing to the development of efficient and accurate models in audio signal processing and hearing aid technology.
Most of the recent work in psychedelic neuroscience has been done using non-invasive neuroimaging, with data recorded from the brains of adult volunteers under the influence of a variety of drugs. While this data prov...
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Deep learning algorithms are dependent on the availability of large-scale annotated clinical text datasets. The lack of such publicly available datasets is the biggest bottleneck for the development of clinical Natura...
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