ChatGPT as a Math Questioner? Evaluating ChatGPT on Generating Pre-university Math Questions

Mathematical questioning is crucial for assessing students problem-solving skills. Since manually creating such questions requires substantial effort, automatic methods have been explored. Existing state-of-the-art models rely on fine-tuning strategies and struggle to generate questions that heavily involve multiple steps of logical and arithmetic reasoning. Meanwhile, large language models(LLMs) such as ChatGPT have excelled in many NLP tasks involving logical and arithmetic reasoning. Nonetheless, their applications in generating educational questions are underutilized, especially in the field of mathematics. To bridge this gap, we take the first step to conduct an in-depth analysis of ChatGPT in generating pre-university math questions. Our analysis is categorized into two main settings: context-aware and context-unaware. In the context-aware setting, we evaluate ChatGPT on existing math question-answering benchmarks covering elementary, secondary, and ternary classes. In the context-unaware setting, we evaluate ChatGPT in generating math questions for each lesson from pre-university math curriculums that we crawl. Our crawling results in TopicMath, a comprehensive and novel collection of pre-university math curriculums collected from 121 math topics and 428 lessons from elementary, secondary, and tertiary classes. Through this analysis, we aim to provide insight into the potential of ChatGPT as a math questioner.Comment: Accepted at the 39th ACM/SIGAPP Symposium On Applied Computing (SAC 2024), Main Conferenc

A Large-Scale Study of a Sleep Tracking and Improving Device with Closed-loop and Personalized Real-time Acoustic Stimulation

Various intervention therapies ranging from pharmaceutical to hi-tech tailored solutions have been available to treat difficulty in falling asleep commonly caused by insomnia in modern life. However, current techniques largely remain ill-suited, ineffective, and unreliable due to their lack of precise real-time sleep tracking, in-time feedback on the therapies, an ability to keep people asleep during the night, and a large-scale effectiveness evaluation. Here, we introduce a novel sleep aid system, called Earable, that can continuously sense multiple head-based physiological signals and simultaneously enable closed-loop auditory stimulation to entrain brain activities in time for effective sleep promotion. We develop the system in a lightweight, comfortable, and user-friendly headband with a comprehensive set of algorithms and dedicated own-designed audio stimuli. We conducted multiple protocols from 883 sleep studies on 377 subjects (241 women, 119 men) wearing either a gold-standard device (PSG), Earable, or both concurrently. We demonstrate that our system achieves (1) a strong correlation (0.89 +/- 0.03) between the physiological signals acquired by Earable and those from the gold-standard PSG, (2) an 87.8 +/- 5.3% agreement on sleep scoring using our automatic real-time sleep staging algorithm with the consensus scored by three sleep technicians, and (3) a successful non-pharmacological stimulation alternative to effectively shorten the duration of sleep falling by 24.1 +/- 0.1 minutes. These results show that the efficacy of Earable exceeds existing techniques in intentions to promote fast falling asleep, track sleep state accurately, and achieve high social acceptance for real-time closed-loop personalized neuromodulation-based home sleep care.Comment: 33 pages, 8 figure

Pre-treatment potential of electro-coagulation process using aluminum and titanium electrodes for instant coffee processing wastewater

This study aimed at investigating the potential of electrocoagulation (EC) process using Al-Al and Al-Ti electrodes for the pre-treatment of instant coffee processing wastewater. Effects of various operating conditions, including cell voltage, time of treatment, inter-electrode distance, pH of solution, solution conductivity and agitation speed on the removals of chemical oxygen demand (COD) and color were considered. The maximum removal of COD and color was achieved at 87% and 99%, respectively, corresponding to COD and color in the effluents of 359-384 mg/L and 58-101 Pt-Co. Biodegradability of treated wastewater was significantly improved since BOD5/COD increased from initial value of 0.42 to 0.65 after treatment. Nether mixing nor adding of electrolyte was recommended. Moreover, the COD removal kinetics during EC process appeared to follow the first-order kinetic model. The operating costs were also determined as a reference for cost assessment of the treatment

Optimization of protein extraction from "Cam" rice bran by response surface methodology

"Cam" rice bran was considered a waste product from rice, which is rich in natural compounds and protein owing to its outstanding nutritional value. This study aimed to establish an optimization model for extracting protein from rice bran, with two responses: extraction yield (%) and protein content (%). The variable parameters included were pH (8.5-9.5), stirring time (3.5-4.5 h), and enzyme incubation temperature (85-95°C). The coefficient of determination for both models were above 0.95, indicating a high correlation between the actual and estimated values. The maximum extraction yield and protein content were achieved when the conditions were set at pH of 9.02, stirring time of 4.02 h, and extraction temperature of 90.6°C. Under these optimum conditions, the predicted protein extracted from rice bran was 43.03% (moisture <13.0%), with an extraction yield of 15.9%. The findings of this study suggested that this protocol can enhance the utilization of rice bran and might be employed on a large scale in the food industry to exploit the nutritional source

TextANIMAR: Text-based 3D Animal Fine-Grained Retrieval

3D object retrieval is an important yet challenging task, which has drawn more and more attention in recent years. While existing approaches have made strides in addressing this issue, they are often limited to restricted settings such as image and sketch queries, which are often unfriendly interactions for common users. In order to overcome these limitations, this paper presents a novel SHREC challenge track focusing on text-based fine-grained retrieval of 3D animal models. Unlike previous SHREC challenge tracks, the proposed task is considerably more challenging, requiring participants to develop innovative approaches to tackle the problem of text-based retrieval. Despite the increased difficulty, we believe that this task has the potential to drive useful applications in practice and facilitate more intuitive interactions with 3D objects. Five groups participated in our competition, submitting a total of 114 runs. While the results obtained in our competition are satisfactory, we note that the challenges presented by this task are far from being fully solved. As such, we provide insights into potential areas for future research and improvements. We believe that we can help push the boundaries of 3D object retrieval and facilitate more user-friendly interactions via vision-language technologies.Comment: arXiv admin note: text overlap with arXiv:2304.0573

Current advances in seagrass research: A review from Viet Nam

Seagrass meadows provide valuable ecosystem services but are fragile and threatened ecosystems all over the world. This review highlights the current advances in seagrass research from Viet Nam. One goal is to support decision makers in developing science-based conservation strategies. In recent years, several techniques were applied to estimate the size of seagrass meadows. Independent from the method used, there is an alarming decline in the seagrass area in almost all parts of Viet Nam. Since 1990, a decline of 46.5% or 13,549 ha was found. Only in a few protected and difficult-to-reach areas was an increase observed. Conditions at those sites could be investigated in more detail to make suggestions for conservation and recovery of seagrass meadows. Due to their lifestyle and morphology, seagrasses take up compounds from their environment easily. Phytoremediation processes of Thalassia hemprichii and Enhalus acoroides are described exemplarily. High accumulation of heavy metals dependent on their concentration in the environment in different organs can be observed. On the one hand, seagrasses play a role in phytoremediation processes in polluted areas; on the other hand, they might suffer at high concentrations, and pollution will contribute to their overall decline. Compared with the neighboring countries, the total Corg stock from seagrass beds in Viet Nam was much lower than in the Philippines and Indonesia but higher than that of Malaysia and Myanmar. Due to an exceptionally long latitudinal coastline of 3,260 km covering cool to warm water environments, the seagrass species composition in Viet Nam shows a high diversity and a high plasticity within species boundaries. This leads to challenges in taxonomic issues, especially with the Halophila genus, which can be better deduced from genetic diversity/population structures of members of Hydrocharitaceae. Finally, the current seagrass conservation and management efforts in Viet Nam are presented and discussed. Only decisions based on the interdisciplinary cooperation of scientists from all disciplines mentioned will finally lead to conserve this valuable ecosystem for mankind and biodiversity

Flexible interactive retrieval SysTem 3.0 for visual lifelog exploration at LSC 2022

Building a retrieval system with lifelogging data is more complicated than with ordinary data due to the redundancies, blurriness, massive amount of data, various sources of information accompanying lifelogging data, and especially the ad-hoc nature of queries. The Lifelog Search Challenge (LSC) is a benchmarking challenge that encourages researchers and developers to push the boundaries in lifelog retrieval. For LSC'22, we develop FIRST 3.0, a novel and flexible system that leverages expressive cross-domain embeddings to enhance the searching process. Our system aims to adaptively capture the semantics of an image at different levels of detail. We also propose to augment our system with an external search engine to help our system with initial visual examples for unfamiliar concepts. Finally, we organize image data in hierarchical clusters based on their visual similarity and location to assist users in data exploration. Experiments show that our system is both fast and effective in handling various retrieval scenarios

A comprehensive study on the efficacy of a wearable sleep aid device featuring closed-loop real-time acoustic stimulation

Difficulty falling asleep is one of the typical insomnia symptoms. However, intervention therapies available nowadays, ranging from pharmaceutical to hi-tech tailored solutions, remain ineffective due to their lack of precise real-time sleep tracking, in-time feedback on the therapies, and an ability to keep people asleep during the night. This paper aims to enhance the efficacy of such an intervention by proposing a novel sleep aid system that can sense multiple physiological signals continuously and simultaneously control auditory stimulation to evoke appropriate brain responses for fast sleep promotion. The system, a lightweight, comfortable, and user-friendly headband, employs a comprehensive set of algorithms and dedicated own-designed audio stimuli. Compared to the gold-standard device in 883 sleep studies on 377 subjects, the proposed system achieves (1) a strong correlation (0.89 ± 0.03) between the physiological signals acquired by ours and those from the gold-standard PSG, (2) an 87.8% agreement on automatic sleep scoring with the consensus scored by sleep technicians, and (3) a successful non-pharmacological real-time stimulation to shorten the duration of sleep falling by 24.1 min. Conclusively, our solution exceeds existing ones in promoting fast falling asleep, tracking sleep state accurately, and achieving high social acceptance through a reliable large-scale evaluation

Genetic Interaction Between Two VNTRs in the SLC6A4 Gene Regulates Nicotine Dependence in Vietnamese Men

Nicotine dependence is an addiction to tobacco products and a global public health concern. Association between the SLC6A4 polymorphisms and nicotine dependence is controversial. Two variable number tandem repeat (VNTR) domains, termed HTTLPR and STin2, in the SLC6A4 gene are well characterized transcriptional regulatory elements. Their polymorphism in the copy number of the repeat correlates with the particular personality and psychiatric traits. We analyzed nicotine dependence in 1,804 participants from Central Vietnam. The Fagerström Test (FTND) was used to evaluate the nicotine dependence and PCR was used to determine the SLC6A4 HTTLPR and STin2 VNTRs. The HTTLPR VNTR was associated with difficulties to refrain from smoking in a prohibiting environment. The STIn2 10/10 genotype was associated with (1) years of smoking, (2) difficulties to quit the first cigarette, and (3) higher number of cigarettes smoked per day (CPD). Stratification analysis was used to find the genetic interaction between these two VNTRs in nicotine dependence as they may synergistically regulate the SLC6A4 expression. Smokers with the S/S HTTLPR genotypes showed a much stronger association between STin2 10/10 variant and CPD. This finding is consistent with the molecular evidence for the functional interaction between HTTLPR and STin2 in cell line models, where STin2 has described as a stronger expressional regulator. Similarly, we found that STin2 is a much stronger modifier of smoking with 10/10 genotype related to higher nicotine dependence. The present study supports genetic interaction between HTTLPR and STin2 VNTRs in the regulation of nicotine dependence with the dominance of the STin2 effects. This finding could be explained by their differential effect on the SLC6A4 expression

Genetic Interaction Between Two VNTRs in the SLC6A4 Gene Regulates Nicotine Dependence in Vietnamese Men

Nicotine dependence is an addiction to tobacco products and a global public health concern. Association between the SLC6A4 polymorphisms and nicotine dependence is controversial. Two variable number tandem repeat (VNTR) domains, termed HTTLPR and STin2, in the SLC6A4 gene are well characterized transcriptional regulatory elements. Their polymorphism in the copy number of the repeat correlates with the particular personality and psychiatric traits. We analyzed nicotine dependence in 1,804 participants from Central Vietnam. The Fagerström Test (FTND) was used to evaluate the nicotine dependence and PCR was used to determine the SLC6A4 HTTLPR and STin2 VNTRs. The HTTLPR VNTR was associated with difficulties to refrain from smoking in a prohibiting environment. The STIn2 10/10 genotype was associated with (1) years of smoking, (2) difficulties to quit the first cigarette, and (3) higher number of cigarettes smoked per day (CPD). Stratification analysis was used to find the genetic interaction between these two VNTRs in nicotine dependence as they may synergistically regulate the SLC6A4 expression. Smokers with the S/S HTTLPR genotypes showed a much stronger association between STin2 10/10 variant and CPD. This finding is consistent with the molecular evidence for the functional interaction between HTTLPR and STin2 in cell line models, where STin2 has described as a stronger expressional regulator. Similarly, we found that STin2 is a much stronger modifier of smoking with 10/10 genotype related to higher nicotine dependence. The present study supports genetic interaction between HTTLPR and STin2 VNTRs in the regulation of nicotine dependence with the dominance of the STin2 effects. This finding could be explained by their differential effect on the SLC6A4 expression