Understanding customer malling behavior in an urban shopping mall using smartphones

Abstract This paper presents a novel customer malling behavior modeling framework for an urban shopping mall. As an automated computing framework using smartphones, it is designed to provide comprehensive understanding of customer behavior. We prototype the framework in a real-world urban shopping mall. Development consists of three steps; customer data collection, customer trace extraction, and behavior model analysis. We extract customer traces from a collection of 701-hour sensor data from 195 in-situ customers who installed our logging application at Android Market. The practical behavior model is created from the real traces. It has a multi-level structure to provide the holistic understanding of customer behavior from physical movement to service semantics. As far as we know, it is the first work to understand complex customer malling behavior in offline shopping malls

Iterative Compression of End-to-End ASR Model using AutoML

Increasing demand for on-device Automatic Speech Recognition (ASR) systems has resulted in renewed interests in developing automatic model compression techniques. Past research have shown that AutoML-based Low Rank Factorization (LRF) technique, when applied to an end-to-end Encoder-Attention-Decoder style ASR model, can achieve a speedup of up to 3.7x, outperforming laborious manual rank-selection approaches. However, we show that current AutoML-based search techniques only work up to a certain compression level, beyond which they fail to produce compressed models with acceptable word error rates (WER). In this work, we propose an iterative AutoML-based LRF approach that achieves over 5x compression without degrading the WER, thereby advancing the state-of-the-art in ASR compression

AdNext: A Visit-Pattern-Aware Mobile Advertising System for Urban Commercial Complexes

As smartphones have become prevalent, mobile advertising is getting significant attention as being not only a killer application in future mobile commerce, but also as an important business model of emerging mobile applications to monetize them. In this paper, we present AdNext, a visit-pattern-aware mobile advertising system for urban commercial complexes. AdNext can provide highly relevant ads to users by predicting places that the users will next visit. AdNext predicts the next visit place by learning the sequential visit patterns of commercial complex users in a collective manner. As one of the key enabling techniques for AdNext, we develop a probabilistic prediction model that predicts users ’ next visit place from their place visit history. To automatically collect the users ’ place visit history by smartphones, we utilize Wi-Fi-based indoor localization. We demonstrate the feasibility of AdNext by evaluating the accuracy of the prediction model. For the evaluation, we used a dataset collected from COEX Mall, the largest commercial complex in South Korea. Also, we implemented an initial prototype of AdNext with the latest smartphones, and deployed it in COEX Mall

Automated Hybrid Model for Detecting Perineural Invasion in the Histology of Colorectal Cancer

Perineural invasion (PNI) is a well-established independent prognostic factor for poor outcomes in colorectal cancer (CRC). However, PNI detection in CRC is a cumbersome and time-consuming process, with low inter-and intra-rater agreement. In this study, a deep-learning-based approach was proposed for detecting PNI using histopathological images. We collected 530 regions of histology from 77 whole-slide images (PNI, 100 regions; non-PNI, 430 regions) for training. The proposed hybrid model consists of two components: a segmentation network for tumor and nerve tissues, and a PNI classifier. Unlike a “black-box” model that is unable to account for errors, the proposed approach enables false predictions to be explained and addressed. We presented a high performance, automated PNI detector, with the area under the curve (AUC) for the receiver operating characteristic (ROC) curve of 0.92. Thus, the potential for the use of deep neural networks in PNI screening was proved, and a possible alternative to conventional methods for the pathologic diagnosis of CRC was provided

Strategic locations for logistics distribution centers along the Belt and Road : explorative analysis and research agenda

Since the inception of China's Belt and Road Initiative (BRI) in 2013, the associated infrastructure and transport and economic corridor developments have been widely addressed in the research field of transportation, logistics and supply chain management. Such developments open windows of opportunity for accommodating trade flows in new or upgraded intermediate hub nodes and gateway locations along the BRI corridors. This paper aims to propose strategic locations for global logistics distribution centers (LDCs) along the Belt and Road from the viewpoint of China, considering regional economic and trade blocks, maritime transport routes, China's overseas port developments, China Railway Express services, trade conflicts between China and US, and deteriorated mobility of resources and human power caused by COVID-19. We present a set of strategic locations for establishing LDCs by analyzing qualitative and quantitative facility location factors supported by the findings in existing literature. Eight locations for global LDCs are identified in the Sub-Saharan region, Sri Lanka, the Middle East, Northern Oceania, Southern Europe, Northern Europe, and key dry hub port locations in Minsk, Belarus and Northeast Asia along the Silk Road Economic Belt. Furthermore, we present a research agenda with applicable methods