The explosive growth of the social media community has increased many kinds of misinformation and is attracting tremendous attention from the research community. One of the most prevalent ways of misleading news is cheapfakes. Cheapfakes utilize non-AI techniques such as unaltered images with false context news to create false news, which makes it easy and “cheap” to create and leads to an abundant amount in the social media community. Moreover, the development of deep learning also opens and invents many domains relevant to news such as fake news detection, rumour detection, fact-checking, and verification of claimed images. Nevertheless, despite the impact on and harmfulness of cheapfakes for the social community and the real world, there is little research on detecting cheapfakes in the computer science domain. It is challenging to detect misused/false/out-of-context pairs of images and captions, even with human effort, because of the complex correlation between the attached image and the veracity of the caption content. Existing research focuses mostly on training and evaluating on given dataset, which makes the proposal limited in terms of categories, semantics and situations based on the characteristics of the dataset. In this paper, to address these issues, we aimed to leverage textual semantics understanding from the large corpus and integrated with different combinations of text-image matching and image captioning methods via ANN/Transformer boosting schema to classify a triple of (image, caption1, caption2) into OOC (out-of-context) and NOOC (no out-of-context) labels. We customized these combinations according to various exceptional cases that we observed during data analysis. We evaluate our approach using the dataset and evaluation metrics provided by the COSMOS baseline. Compared to other methods, including the baseline, our method achieves the highest Accuracy, Recall, and F1 scores.

The air quality index (AQI) forecast in big cities is an exciting study area in smart cities and healthcare on the Internet of Things. In recent years, a large number of empirical, academic, and review papers using machine learning (ML) for air quality analysis have been published. However, most of those studies focused on traditional centralized processing on a single machine, and there had been few surveys of federated learning (FL) in this field. This overview aims to fill this gap and provide newcomers with a broader perspective to inform future research on this topic, especially for the multi-model approach. In this survey, we went over the works that previous scholars have conducted in AQI forecast both in traditional ML approaches and FL mechanisms. Our objective is to comprehend previous research on AQI prediction including methods, models, data sources, achievements, challenges, and solutions applied in the past. We also convey a new path of using multi-model FL, which has piqued the computer science community’s interest recently.

To maintain and improve an amateur athlete’s fitness throughout training and to achieve peak performance in sports events, good nutrition and physical activity (general and training specifically) must be considered as important factors. In our context, the terminology “amateur athletes” represents those who want to practice sports to protect their health from sickness and diseases and improve their ability to join amateur athlete events (e.g., marathons). Unlike professional athletes with personal trainer support, amateur athletes mostly rely on their experience and feeling. Hence, amateur athletes need another way to be supported in monitoring and recommending more efficient execution of their activities. One of the solutions to (self-)coaching amateur athletes is collecting lifelog data (i.e., daily data captured from different sources around a person) to understand how daily nutrition and physical activities can impact their exercise outcomes. Unfortunately, not all factors of the lifelog data can contribute to understanding the mutual impact of nutrition, physical activities, and exercise frequency on improving endurance, stamina, and weight loss. Hence, there is no guarantee that analyzing all data collected from people can produce good insights towards having a good model to predict what the outcome will be. Besides, analyzing a rich and complicated dataset can consume vast resources (e.g., computational complexity, hardware, bandwidth), and this therefore does not suit deployment on IoT or personal devices. To meet this challenge, we propose a new method to (i) discover the optimal lifelog data that significantly reflect the relation between nutrition and physical activities and training performance and (ii) construct an adaptive model that can predict the performance for both large-scale and individual groups. Our suggested method produces positive results with low MAE and MSE metrics when tested on large-scale and individual datasets and also discovers exciting patterns and correlations among data factors.

Bài báo này trình bày phương pháp ứng dụng hệ luật mờ Standard Addictive Model (SAM) vào việc dự báo biểu điểm thi tại các cơ sở giáo dục. Chúng tôi đã xây dựng SAM qua các bước học máy như sau: Học cấu trúc hệ luật, học điều chỉnh thông số và học tối ưu hệ luật. Thực nghiệm trên độ khó của đề thi và học lực của người học được lấy từ số liệu thực tế tại Trường Cao đẳng Kinh tế-Tài chính Vĩnh Long. Quá trình thực nghiệm cho kết quả dự báo sát với thực tế. Qua đó góp phần nâng cao tính khoa học trong hoạt động đánh giá người học, một trong những nhiệm vụ quan trọng trong lĩnh vực khảo thí và đảm bảo chất lượng giáo dục.

Từ khóa-Hỗ trợ chẩn trị Đông y, hệ hỗ trợ ra quyết định, hệ suy diễn, hệ chuyên gia, khai phá dữ liệu. I. GIỚI THIỆU Ở Việt Nam, nền Y dược học cổ truyền (YDHCT) đã có từ rất lâu đời, gắn liền với sự phát triển truyền thống văn hóa dân tộc, có thể nói YDHCT là hệ thống y dược có vai trò và tiềm năng lớn trong sự nghiệp chăm sóc và bảo vệ sức khoẻ của nhân dân [5]. Hiện nay, hầu hết các Tỉnh đều có một bệnh viện Y dược cổ truyền (YDCT), các bệnh viện đa khoa tuyến tỉnh đều có khoa Y học cổ truyền (YHCT), các bệnh viện đa khoa tuyến huyện đều có khoa hoặc tổ YHCT, các trạm y tế cấp xã có triển khai hoạt động YHCT. Tuy nhiên, nguồn nhân lực về YDHCT rất mỏng và ít được đào tạo lại, các cơ sở vật chất và trang thiết bị cho việc điều trị, chăm sóc sức khỏe cộng đồng bằng YDHCT còn thiếu thốn, trong khi nhu cầu khám và điều trị bệnh bằng YHCT của người dân là rất cao.