Han Su

University of Electronic Science and Technology of China

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Kai Zheng

University of Electronic Science and Technology of China

Xiaofang Zhou

Hong Kong University of Science and Technology

Shuncheng Liu

Huawei Technologies (China)

Bolong Zheng

Huazhong University of Science and Technology

Kai Zheng

University of Electronic Science and Technology of China

Yan Zhao

Aalborg University

Kai Zeng

Huawei Technologies (China)

Yuyang Xia

University of Electronic Science and Technology of China

Haozhou Wang

China Southern Power Grid (China)

Guohui Li

Huazhong University of Science and Technology

Cooperative Institutions

University of Electronic Science and Technology of China

Chinese Academy of Sciences

Czech Academy of Sciences, Institute of Biophysics

Czech Academy of Sciences

Huawei Technologies (China)

Sun Yat-sen University

Czech Academy of Sciences, Institute of Experimental Botany

University of Queensland

Tsinghua University

National University of Defense Technology

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Automatic recipe cuisine classification by ingredients

Han Su Ting-Wei Lin Cheng–Te Li Man-Kwan Shan Janet Chang

With the growth of recipe sharing services, online cooking recipes associated with ingredients and cooking procedures are available. Many recipe sharing sites have devoted to the development of recipe recommendation mechanism. While most food related research has been on recipe recommendation, little effort has been done on analyzing the correlation between recipe cuisines and ingredients. In this paper, we aim to investigate the underlying cuisine-ingredient connections by exploiting the classification techniques, including associative classification and support vector machine. Our study conducted on food.com data provides insights about which cuisines are the most similar and what are the essential ingredients for a cuisine, with an application to automatic cuisine labeling for recipes.

Recipe

Ingredient

10.1145/2638728.2641335

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Citations (35)

SMART: A Decision-Making Framework with Multi-modality Fusion for Autonomous Driving Based on Reinforcement Learning

Lecture notes in computer science (2023)

Yuyang Xia Shuncheng Liu Rui Hu Quanlin Yu Xiushi Feng

Autonomous system (mathematics)

10.1007/978-3-031-30678-5_33

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Citations (4)

A Review on Application of Waste Tire in Concrete

Jurnal Teknik Lingkungan : Electronic Journal of Civil and Environmental Engineering (2016)

Mohammad Arab Pour Yazdi Jinsheng Yang Long Yi-hui Han Su

10.5281/zenodo.1338638

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Citations (13)

Making sense of trajectory data: A partition-and-summarization approach

Han Su Kai Zheng Kai Zeng Jiamin Huang Shazia Sadiq

Due to the prevalence of GPS-enabled devices and wireless communication technology, spatial trajectories that describe the movement history of moving objects are being generated and accumulated at an unprecedented pace. However, a raw trajectory in the form of sequence of timestamped locations does not make much sense for humans without semantic representation. In this work we aim to facilitate human's understanding of a raw trajectory by automatically generating a short text to describe it. By formulating this task as the problem of adaptive trajectory segmentation and feature selection, we propose a partition-and-summarization framework. In the partition phase, we first define a set of features for each trajectory segment and then derive an optimal partition with the aim to make the segments within each partition as homogeneous as possible in terms of their features. In the summarization phase, for each partition we select the most interesting features by comparing against the common behaviours of historical trajectories on the same route and generate short text description for these features. For empirical study, we apply our solution to a real trajectory dataset and have found that the generated text can effectively reflect the important parts in a trajectory.

10.1109/icde.2015.7113348

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Citations (42)

Predicting Passenger’s Public Transportation Travel Route Using Smart Card Data

Lecture notes in computer science (2018)

Yang Chen Wei Chen Bolong Zheng Tieke He Kai Zheng

Leverage (statistics)

Baseline (sea)

10.1007/978-3-319-96893-3_15

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Citations (0)

Efficient Aggregate Farthest Neighbour Query Processing on Road Networks

Lecture notes in computer science (2014)

Haozhou Wang Kai Zheng Han Su Jiping Wang Shazia Sadiq

Fraction (chemistry)

10.1007/978-3-319-08608-8_2

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Citations (7)

EGL: Efficient Graph Learning with Safety Constrains for Heterogeneous Trajectory Prediction

Lecture notes in computer science (2023)

Weijie Lian Yuze Wang Ximu Zeng Shuncheng Liu Yuyang Xia

10.1007/978-3-031-35415-1_5

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Citations (0)

EA-MLM: Error-aware Masked Language Modeling for Grammatical Error Correction

Junyi He Xia Li Han Su Chen Xin-yin Hao Yang

In recent years, BERT has been used in the task of grammatical error correction (GEC) and achieved good performance. However, few previous studies have investigated the incorporation of real grammatical errors into BERT for the GEC task. We argue that the distribution of GEC data (containing several types of errors) is different from the distribution of BERT pre-training data (usually error-free). To fill this gap, in this paper, we extend masked language modeling and propose a novel error-aware masked language modeling strategy (EA-MLM) to fine-tune BERT so that the representation distribution of the pre-trained BERT is better adapted to the GEC task. We conduct extensive experiments on public datasets and the experimental results demonstrate the effectiveness of our proposed method.

Representation

Training set

10.1109/ialp54817.2021.9675154

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Citations (1)

A Novel Secure Routing System in Overlay Environment

Lecture notes in computer science (2005)

Han Su Yun Wang

Policy-based routing

Overlay network

10.1007/11590354_17

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Citations (0)

PATROL

Zhi Xu Shuncheng Liu Zi‐Niu Wu Xu Chen Kai Zeng

The largest portion of urban congestion is caused by 'phantom' traffic jams, causing significant delay travel time, fuel waste, and air pollution. It frequently occurs in high-density traffics without any obvious signs of accidents or roadworks. The root cause of 'phantom' traffic jams in one-lane traffics is the sudden change in velocity of some vehicles (i.e. harsh driving behavior (HDB)), which may generate a chain reaction with accumulated impact throughout the vehicles along the lane. This paper makes the first attempt to address this notorious problem in a one-lane traffic environment through velocity control of autonomous vehicles. Specifically, we propose a velocity control framework, called PATROL (sPAtial-temporal ReinfOrcement Learning). First, we design a spatial-temporal graph inside the reinforcement learning model to process and extract the information (e.g. velocity and distance difference) of multiple vehicles ahead across several historical time steps in the interactive environment. Then, we propose an attention mechanism to characterize the vehicle interactions and an LSTM structure to understand the vehicles' driving patterns through time. At last, we modify the reward function used in previous velocity control works to enable the autonomous driving agent to predict the HDB of preceding vehicles and smoothly adjust its velocity, which could alleviate the chain reaction caused by HDB. We conduct extensive experiments to demonstrate the effectiveness and superiority of PATROL in alleviating the 'phantom' traffic jam in simulation environments. Further, on the real-world velocity control dataset, our method significantly outperforms the existing methods in terms of driving safety, comfortability, and efficiency.

10.1145/3459637.3482283

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Citations (11)