Intersemiotic Shifts in the Subtitle Translation of Journey to the West via a Multimodal Framework

Journey to the West is one of the four famous Chinese classical works and it was reproduced into TV drama both at home and abroad, which has aroused great concern. However, current studies mainly compare the English translated versions with Chinese one, focusing on the translation strategies for the verbal texts or on the cultural level. Therefore, this study explores different kinds of intersemiotic shifts in the subtitle translation of Journey to the West and reasons that have caused these intersemiotic shifts via a multimodal analytical framework. Ten episodes chosen according to the plots will be analyzed. Findings show that there are mainly six kinds of intersemiotic shifts in the Journey to the West. Image and para-/non-verbal modes can supplement the meaning expressed in the verbal texts

The relationship between video games, problem-solving skills, and academic performance from IT students’ perspective

Abstract. The advancement in information technology has contributed to growth in different industries. One such industry that has gained prominence in recent years is the gaming industry. The game industry today has become a major platform of entertainment, amassing a large community of players across the globe. As most people consider video game play only as a form of entertainment, the existential educational benefits are missed. While it is true that violent games could negatively affect the psychological health of its players, playing games right can contribute positively to the improvement of skills and academics. The objective of this study is to investigate the relationship between video game play, problem-solving, and academic performance on Information Technology (IT) students and to understand the kinds of video games that affect their problem-solving skills in their studies. A quantitative research method was used, collecting quantitative data from students of the Faculty of Information Technology and Electrical Engineering (ITEE) of the University of Oulu through an online survey. The results of the study indicate that strategy and puzzle games significantly improve the problem-solving skills of students through analytical, logical, and creative thinking. The results of this study can be used in the future to investigate other phenomena such as how video games differently affect students with IT background from other students and how video games could be used as an effective educational tool for IT students

Providing Informative Nutrition Facts for Food Ordering

Online food ordering services provide little to no information regarding nutritional facts. This makes it difficult for customers to make decisions based on their nutrition and health needs. This disclosure describes an online food ordering service that provides users the nutritional facts of a food item prior to order placement. The information is provided via a suitable user interface of a website or app that enables the user to drill down further to obtain more granular information. If the user permits, prior orders of the users are used to provide periodic feedback regarding eating behavior and nutritional intake

Interphase (Xiangji) Economic Principle and Targeted Poverty Alleviation: Strategic Breakthrough of Non-economic Factors

The theoretical model of the “interphase (xiangji) economic principle” proposes that in economic activities, if the complex problems caused by cross-ethnic and cross-culture contact are ignored, the restrictive effects of various “non-economic factors” must follow. Therefore, in the practice of the “targeted poverty alleviation program”, it is necessary to fully understand and grasp the restrictive roles of “non-economic factors”, assess the situation, maintain rational behaviors of cultural traditions, and effectively avoid and overcome its unfavorable factors on economic activities. Therefore, the poverty alleviation action can achieve twice the result with half the effort

Soil Image Segmentation Based on Mask R-CNN

The complex background in the soil image collected in the field natural environment will affect the subsequent soil image recognition based on machine vision. Segmenting the soil center area from the soil image can eliminate the influence of the complex background, which is an important preprocessing work for subsequent soil image recognition. For the first time, the deep learning method was applied to soil image segmentation, and the Mask R-CNN model was selected to complete the positioning and segmentation of soil images. Construct a soil image dataset based on the collected soil images, use the EISeg annotation tool to mark the soil area as soil, and save the annotation information; train the Mask R-CNN soil image instance segmentation model. The trained model can obtain accurate segmentation results for soil images, and can show good performance on soil images collected in different environments; the trained instance segmentation model has a loss value of 0.1999 in the training set, and the mAP of the validation set segmentation (IoU=0.5) is 0.8804, and it takes only 0.06s to complete image segmentation based on GPU acceleration, which can meet the real-time segmentation and detection of soil images in the field under natural conditions. You can get our code in the Conclusions. The homepage is https://github.com/YidaMyth.Comment: 4 pages, 5 figures, Published in 2023 3rd International Conference on Consumer Electronics and Computer Engineerin

Renmin University of China at TRECVID 2022: Improving Video Search by Feature Fusion and Negation Understanding

We summarize our TRECVID 2022 Ad-hoc Video Search (AVS) experiments. Our solution is built with two new techniques, namely Lightweight Attentional Feature Fusion (LAFF) for combining diverse visual / textual features and Bidirectional Negation Learning (BNL) for addressing queries that contain negation cues. In particular, LAFF performs feature fusion at both early and late stages and at both text and video ends to exploit diverse (off-the-shelf) features. Compared to multi-head self attention, LAFF is much more compact yet more effective. Its attentional weights can also be used for selecting fewer features, with the retrieval performance mostly preserved. BNL trains a negation-aware video retrieval model by minimizing a bidirectionally constrained loss per triplet, where a triplet consists of a given training video, its original description and a partially negated description. For video feature extraction, we use pre-trained CLIP, BLIP, BEiT, ResNeXt-101 and irCSN. As for text features, we adopt bag-of-words, word2vec, CLIP and BLIP. Our training data consists of MSR-VTT, TGIF and VATEX that were used in our previous participation. In addition, we automatically caption the V3C1 collection for pre-training. The 2022 edition of the TRECVID benchmark has again been a fruitful participation for the RUCMM team. Our best run, with an infAP of 0.262, is ranked at the second place teamwise

Infection and Infertility

Infection is a multifactorial process, which can be induced by a virus, bacterium, or parasite. It may cause many diseases, including obesity, cancer, and infertility. In this chapter, we focus our attention on the association of infection and fertility alteration. Numerous studies have suggested that genetic polymorphisms influencing infection are associated with infertility. So we also review the genetic influence on infection and risk of infertility