Leveraging Transfer Learning for Robust Multimodal Positioning Systems using Smartphone Multi-sensor Data

Indoor positioning has been widely researched in recent years due to its high demand for developing localization services and its complexity in GPS-denied environments. However, the diversity of indoor spaces and temporal variation of local conditions impose the need for building specific and periodic calibrations at high cost for deployment and maintenance of these localization systems. A robust positioning solution that overcomes these challenges is yet to be available. Previous systems achieve good performance when specializing their solution to the unique characteristics of the deployment site. The drive is now to automatically model these localization solutions on the sensor data from each site with the least amount of effort. We propose to accelerate the model adaptation to new deployment sites by using transfer learning of a multimodal deep neural network architecture. We demonstrate that the required training data is drastically reduced compared to training the model from scratch, while also boosting its accuracy, due to the additional knowledge from pretraining on other sites. The resulting model is also fault-tolerant, showing good performance in missing modalities experiment. Our research opens the way toward scalable and cost efficient localization systems

Comparative Analysis of Microbial Community between Mechanized and Traditional Stack Fermentation of Fermented Grains for Fuhexiangxing Baijiu

High-throughput sequencing technology was used to analyze the microbial community of fermented grains for Fuhexiangxing baijiu during mechanized and traditional stack fermentation. The results showed that microbial exchange, succession and enrichment occurred during the fermentation process. In both fermented grains, the microbial communities were composed of four major bacterial genera including Bacillus, Weissella, Acetobacter and Ralstonia, and six major fungal genera including Aspergillus, Lichtheimia, Candida, Pichia, Wickerhamomyces and Saccharomyces. The diversity of bacteria was significantly higher in mechanized than traditional stack fermentation, while the diversity of fungi was significantly lower in traditional than mechanized stack fermentation. Moreover, bacterial diversity was lower on the surface than in the interior of fermented grains, whereas fungal diversity was higher on the surface than in the interior of fermented grains. These results provide a basis for continuous optimization of the mechanized brewing process of Fuhexiangxing baijiu and for improving the quality of base baijiu

FabricTouch: A Multimodal Fabric Assessment Touch Gesture Dataset to Slow Down Fast Fashion

Touch exploration of fabric is used to evaluate its properties, and it could further be leveraged to understand a consumer’s sensory experience and preference so as to support them in real time to make careful clothing purchase decisions. In this paper, we open up opportunities to explore the use of technology to provide such support with our FabricTouch dataset, i.e., a multimodal dataset of fabric assessment touch gestures. The dataset consists of bilateral forearm movement and muscle activity data captured while 15 people explored 114 different garments in total to evaluate them according to 5 properties (warmth, thickness, smoothness, softness, and flexibility). The dataset further includes subjective ratings of the garments with respect to each property and ratings of pleasure experienced in exploring the garment through touch. We further report baseline work on automatic detection. Our results suggest that it is possible to recognise the type of fabric property that a consumer is exploring based on their touch behaviour. We obtained mean F1 score of 0.61 for unseen garments, for 5 types of fabric property. The results also highlight the possibility of additionally recognizing the consumer’s subjective rating of the fabric when the property being rated is known, mean F1 score of 0.97 for unseen subjects, for 3 rating levels

ON THE ROBUSTNESS OF THE BLOCK BOOTSTRAP PANEL UNIT ROOT TEST

Abstract. Palm, Smeekes and Urbain (2011, PSU) proposed a bootstrap panel unit root test which can deal with a rather general cross-sectional dependency structure, including (but not exclusive to) the popular common factor framework. However, the robustness of the test is not fully investigated in their simulation study. In this article, we did Monte Carlo simulations to study the robustness of the tests proposed by PSU from simple to complex cross-sectional dependence structures. We compare the PSU test with two other representative tests in second generation panel unit root tests, the test proposed b