Bridging the cultural divide: the emergence of Global Language Programs at Boston University

As the fourth largest private research institution in the United States Boston University (BU) serves more than 18,000 students, and approximately seven percent study a second language. Since 2007, when the President unveiled his Strategic Plan, the overall scope and diversity of foreign language instruction across campus and through BU’s Office of International Programs has increased dramatically. He is clearly fulfilling his mandate to strengthen the quality of the faculty, strive for excellence in undergraduate education, emphasize interdisciplinary studies, and deepen connections to the city of Boston and the world.1 The unveiling of his plan coincided with the arrival of a new Dean in the College of Arts and Sciences (CAS) who recognized that BU’s assets in languages could be developed into a signature strength of the College, and made a special commitment to nurturing the less commonly taught languages that cannot rely on prior student preparation.Accepted manuscrip

Remember Gerhard Richter in the Thunderstorm of Beethoven: The Influence of Cross-Sensory Coupling on Memory, Intercultural Communication, and the Verbalization of Paintings and Sounds

This interdisciplinary study focuses on the perception and verbalization of messages conveyed through instrumental music, soundscapes, and contemporary paintings. International young-adult university students learning German participated in a series of experiments conducted at Friedrich Schiller University in Jena, Germany. To incorporate globalization and cultural difference into this analysis, the author compared the reactions of Western and Asian participants to auditory and visual stimuli. This paper explores the concepts of mixed media, cross-sensory coupling, and esthetic synesthesia, and throws new light on the contribution of cross-sensory coupling to verbalization and to long-term memory processes, from encoding to retrieval. In addition, the author demonstrates how intercultural communication is based upon universal emotions aroused by contemporary paintings, instrumental music and soundscapes

A Comprehensive Study of ImageNet Pre-Training for Historical Document Image Analysis

Automatic analysis of scanned historical documents comprises a wide range of image analysis tasks, which are often challenging for machine learning due to a lack of human-annotated learning samples. With the advent of deep neural networks, a promising way to cope with the lack of training data is to pre-train models on images from a different domain and then fine-tune them on historical documents. In the current research, a typical example of such cross-domain transfer learning is the use of neural networks that have been pre-trained on the ImageNet database for object recognition. It remains a mostly open question whether or not this pre-training helps to analyse historical documents, which have fundamentally different image properties when compared with ImageNet. In this paper, we present a comprehensive empirical survey on the effect of ImageNet pre-training for diverse historical document analysis tasks, including character recognition, style classification, manuscript dating, semantic segmentation, and content-based retrieval. While we obtain mixed results for semantic segmentation at pixel-level, we observe a clear trend across different network architectures that ImageNet pre-training has a positive effect on classification as well as content-based retrieval

CuisineNet: Food Attributes Classification using Multi-scale Convolution Network

Diversity of food and its attributes represents the culinary habits of peoples from different countries. Thus, this paper addresses the problem of identifying food culture of people around the world and its flavor by classifying two main food attributes, cuisine and flavor. A deep learning model based on multi-scale convotuional networks is proposed for extracting more accurate features from input images. The aggregation of multi-scale convolution layers with different kernel size is also used for weighting the features results from different scales. In addition, a joint loss function based on Negative Log Likelihood (NLL) is used to fit the model probability to multi labeled classes for multi-modal classification task. Furthermore, this work provides a new dataset for food attributes, so-called Yummly48K, extracted from the popular food website, Yummly. Our model is assessed on the constructed Yummly48K dataset. The experimental results show that our proposed method yields 65% and 62% average F1 score on validation and test set which outperforming the state-of-the-art models.Comment: 8 pages, Submitted in CCIA 201