Personalized Purchase Prediction of Market Baskets with Wasserstein-Based Sequence Matching

Personalization in marketing aims at improving the shopping experience of customers by tailoring services to individuals. In order to achieve this, businesses must be able to make personalized predictions regarding the next purchase. That is, one must forecast the exact list of items that will comprise the next purchase, i.e., the so-called market basket. Despite its relevance to firm operations, this problem has received surprisingly little attention in prior research, largely due to its inherent complexity. In fact, state-of-the-art approaches are limited to intuitive decision rules for pattern extraction. However, the simplicity of the pre-coded rules impedes performance, since decision rules operate in an autoregressive fashion: the rules can only make inferences from past purchases of a single customer without taking into account the knowledge transfer that takes place between customers. In contrast, our research overcomes the limitations of pre-set rules by contributing a novel predictor of market baskets from sequential purchase histories: our predictions are based on similarity matching in order to identify similar purchase habits among the complete shopping histories of all customers. Our contributions are as follows: (1) We propose similarity matching based on subsequential dynamic time warping (SDTW) as a novel predictor of market baskets. Thereby, we can effectively identify cross-customer patterns. (2) We leverage the Wasserstein distance for measuring the similarity among embedded purchase histories. (3) We develop a fast approximation algorithm for computing a lower bound of the Wasserstein distance in our setting. An extensive series of computational experiments demonstrates the effectiveness of our approach. The accuracy of identifying the exact market baskets based on state-of-the-art decision rules from the literature is outperformed by a factor of 4.0.Comment: Accepted for oral presentation at 25th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2019

Predicting Agricultural Commodities Prices with Machine Learning: A Review of Current Research

Agricultural price prediction is crucial for farmers, policymakers, and other stakeholders in the agricultural sector. However, it is a challenging task due to the complex and dynamic nature of agricultural markets. Machine learning algorithms have the potential to revolutionize agricultural price prediction by improving accuracy, real-time prediction, customization, and integration. This paper reviews recent research on machine learning algorithms for agricultural price prediction. We discuss the importance of agriculture in developing countries and the problems associated with crop price falls. We then identify the challenges of predicting agricultural prices and highlight how machine learning algorithms can support better prediction. Next, we present a comprehensive analysis of recent research, discussing the strengths and weaknesses of various machine learning techniques. We conclude that machine learning has the potential to revolutionize agricultural price prediction, but further research is essential to address the limitations and challenges associated with this approach

Music-Driven Group Choreography

Music-driven choreography is a challenging problem with a wide variety of industrial applications. Recently, many methods have been proposed to synthesize dance motions from music for a single dancer. However, generating dance motion for a group remains an open problem. In this paper, we present $\rm AIOZ-GDANCE$, a new large-scale dataset for music-driven group dance generation. Unlike existing datasets that only support single dance, our new dataset contains group dance videos, hence supporting the study of group choreography. We propose a semi-autonomous labeling method with humans in the loop to obtain the 3D ground truth for our dataset. The proposed dataset consists of 16.7 hours of paired music and 3D motion from in-the-wild videos, covering 7 dance styles and 16 music genres. We show that naively applying single dance generation technique to creating group dance motion may lead to unsatisfactory results, such as inconsistent movements and collisions between dancers. Based on our new dataset, we propose a new method that takes an input music sequence and a set of 3D positions of dancers to efficiently produce multiple group-coherent choreographies. We propose new evaluation metrics for measuring group dance quality and perform intensive experiments to demonstrate the effectiveness of our method. Our project facilitates future research on group dance generation and is available at: https://aioz-ai.github.io/AIOZ-GDANCE/Comment: accepted in CVPR 202

Multi-dimensional data refining strategy for effective fine-tuning LLMs

Data is a cornerstone for fine-tuning large language models, yet acquiring suitable data remains challenging. Challenges encompassed data scarcity, linguistic diversity, and domain-specific content. This paper presents lessons learned while crawling and refining data tailored for fine-tuning Vietnamese language models. Crafting such a dataset, while accounting for linguistic intricacies and striking a balance between inclusivity and accuracy, demands meticulous planning. Our paper presents a multidimensional strategy including leveraging existing datasets in the English language and developing customized data-crawling scripts with the assistance of generative AI tools. A fine-tuned LLM model for the Vietnamese language, which was produced using resultant datasets, demonstrated good performance while generating Vietnamese news articles from prompts. The study offers practical solutions and guidance for future fine-tuning models in languages like Vietnamese

AI-assisted Learning for Electronic Engineering Courses in High Education

This study evaluates the efficacy of ChatGPT as an AI teaching and learning support tool in an integrated circuit systems course at a higher education institution in an Asian country. Various question types were completed, and ChatGPT responses were assessed to gain valuable insights for further investigation. The objective is to assess ChatGPT's ability to provide insights, personalized support, and interactive learning experiences in engineering education. The study includes the evaluation and reflection of different stakeholders: students, lecturers, and engineers. The findings of this study shed light on the benefits and limitations of ChatGPT as an AI tool, paving the way for innovative learning approaches in technical disciplines. Furthermore, the study contributes to our understanding of how digital transformation is likely to unfold in the education sector

Gestational diabetes diagnosed in third trimester of pregnancy: an observation at a Hospital of Women and Children in Vietnam

Background. Gestational diabetes mellitus (GDM) remains a significant concern within the medical community due to its high risk, as well as its serious side effects on both the mothers and the fetuses. This study aims to assess the prevalence and the risk factors of gestational diabetes mellitus in pregnant women at Da Nang Hospital for Women and Children.Methods. A cross-sectional study was conducted on 706 pregnant women at 2428 weeks of gestation at Da Nang hospital to determine the prevalence of gestational diabetes. Multivariate regression analysis was used to clarify the independent risk factors associated with gestational diabetes. All participants were interviewed and tested for the oral glucose tolerance test (OGTT) to identify the number of gestational diabetes, which was diagnosed according to the American Diabetes Association (ADA) diagnostic criteria in 2014.Results. Gestational diabetes prevalence was 10.2%; categorized by the number of matched diagnostic criteria: 1 criterion: 7.1%; 2 criteria: 2.1%; 3 criteria: 1.0%. There are four independent risk factors for gestational diabetes determined through multivariate regression analysis: maternal age > 30 years (OR = 2.376), a history of gestational diabetes (OR = 12.211), pre-pregnancy BMI ≥ 23 kg/m2 (OR = 10.775), a history of fetal macrosomia > 3800 g (OR = 4.655). The risk of gestational diabetes in the group with risk factors was 6.21 times higher than that in the group with no risk factors.Conclusion. More attention should be paid to the risk factors for gestational diabetes, such as maternal age > 30 years, a history of gestational diabetes, pre-pregnancy BMI ≥ 23 kg/m2, a history of fetal macrosomia > 3800 g in all pregnant women

Performance of multi-hop cognitive MIMO relaying networks with joint constraint of intercept probability and limited interference

In this paper, we propose a multi-hop multiple input multiple output (MIMO) decode-and-forward relaying protocol in cognitive radio networks. In this protocol, a multi-antenna secondary source attempts to send its data to a multi-antenna secondary destination with assistance of multiple intermediate multi-antenna nodes, in presence of a multi-antenna secondary eavesdropper. A primary network includes a primary transmitter and a primary receiver which are equipped with multiple antennas, and use transmit antenna selection (TAS) and selection combining (SC) to communicate with each other. Operating on the underlay spectrum sharing method, the secondary source and relay nodes have to adjust their transmit power so that the outage performance of the primary network is not harmful and satisfy the quality of service (QoS). Moreover, these secondary nodes also reduce their transmit power so that the intercept probability (IP) at the eavesdropper at each hop is below a desired value. To improve the outage performance of the secondary network under the joint constraint of IP and limited interference, the TAS/SC method is employed to relay the source data hop-by-hop to the destination. We derived exact closed-form expressions of the end-to-end (e2e) outage probability (OP) and IP of the proposed protocol over Rayleigh fading channels. Monte Carlo simulations are then performed to verify the theoretical derivations

Lecane (Rotifera: Lecanidae) community in psammon habitat in Central Coast Vietnam: Diversity and relation to environmental condition

Characteristics of the Lecane (Rotifera) community in psammon in Central Coast Vietnam were investigated. A total of 50 taxa were identified in samples collected at hygropsammon zones of temporary pools, contributing 4 new species to rotifers' record of Vietnam. Psammonxenic species accounted for the largest percentage of Lecane community with 82%, followed by psammophiles (12%) and psammonbionts (6%). Influences of some environmental factors on the distribution of psammic lecanids were also observed. This group of organisms showed a slight tendency towards sand with grain sizes larger than 125 µm. Besides, other abiotic factors including pH, total phosphorus (TP) and total dissolved solids (TDS) were also found to significantly related to the distribution of some common Lecane species