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3. HERA: Partial Label Learning by Combining Heterogeneous Loss with Sparse and Low-Rank Regularization

Author

Lyu, Gengyu, Feng, Songhe, Jin, Yi, Dai, Guojun, Lang, Congyan, and Li, Yidong

Subjects
Computer Science - Machine Learning, Statistics - Machine Learning
Abstract

Partial Label Learning (PLL) aims to learn from the data where each training instance is associated with a set of candidate labels, among which only one is correct. Most existing methods deal with such problem by either treating each candidate label equally or identifying the ground-truth label iteratively. In this paper, we propose a novel PLL approach called HERA, which simultaneously incorporates the HeterogEneous Loss and the SpaRse and Low-rAnk procedure to estimate the labeling confidence for each instance while training the model. Specifically, the heterogeneous loss integrates the strengths of both the pairwise ranking loss and the pointwise reconstruction loss to provide informative label ranking and reconstruction information for label identification, while the embedded sparse and low-rank scheme constrains the sparsity of ground-truth label matrix and the low rank of noise label matrix to explore the global label relevance among the whole training data for improving the learning model. Extensive experiments on both artificial and real-world data sets demonstrate that our method can achieve superior or comparable performance against the state-of-the-art methods.

Published
2019

7. Quorum-based Localized Scheme for Duty Cycling in Asynchronous Sensor Networks

Author

Zhang, Jianhui, Tang, Shao-jie, Shen, Xingfa, Dai, Guojun, and Nayak, Amiya

Subjects
Computer Science - Networking and Internet Architecture
Abstract

Many TDMA- and CSMA-based protocols try to obtain fair channel access and to increase channel utilization. It is still challenging and crucial in Wireless Sensor Networks (WSNs), especially when the time synchronization cannot be well guaranteed and consumes much extra energy. This paper presents a localized and ondemand scheme ADC to adaptively adjust duty cycle based on quorum systems. ADC takes advantages of TDMA and CSMA and guarantees that (1) each node can fairly access channel based on its demand, (2) channel utilization can be increased by reducing competition for channel access among neighboring nodes, (3) every node has at least one rendezvous active time slot with each of its neighboring nodes even under asynchronization. The latency bound of data aggregation is analyzed under ADC to show that ADC can bound the latency under both synchronization and asynchronization. We conduct extensive experiments in TinyOS on a real test-bed with TelosB nodes to evaluate the performance of ADC. Comparing with B-MAC, ADC substantially reduces the contention for channel access and energy consumption, and improves network throughput., Comment: 11 pages, 13 figures, IEEE 8th International Conference on Mobile Ad hoc and Sensor Systems (MASS, 2011)

Published
2015

21. Multi-Source Data Repairing: A Comprehensive Survey.

Author

Ye, Chen, Duan, Haoyang, Zhang, Hengtong, Zhang, Hua, Wang, Hongzhi, and Dai, Guojun

Subjects
BIG data, DATA quality, DATA compression
Abstract

In the era of Big Data, integrating information from multiple sources has proven valuable in various fields. To ensure a high-quality supply of multi-source data, repairing different types of errors in the multi-source data becomes critical. This paper categorizes errors in multi-source data into entity information overlapping, attribute value conflicts, and attribute value inconsistencies. We first summarize existing repairing methods for these errors and then examine and review the study of the detection and repair of compound-type errors in multi-source data. Finally, we indicate further research directions in multi-source data repair. [ABSTRACT FROM AUTHOR]

Published
2023
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22. EEG-Based Index for Timely Detecting User's Drowsiness Occurrence in Automotive Applications

Author

Di Flumeri Gianluca, Ronca Vincenzo, Giorgi Andrea, Vozzi Alessia, Aricò Pietro, Sciaraffa Nicolina, Zeng Hong, Dai Guojun, Kong Wanzeng, Babiloni Fabio, Borghini Gianluca

Subjects
cognitive neuroscience, drowsiness, human factor, EEG, neurometrics, road safety, driving performance, neuroergonomics
Abstract

Human errors are widely considered among the major causes of road accidents. Furthermore, it is estimated that more than 90% of vehicle crashes causing fatal and permanent injuries are directly related to mental tiredness, fatigue, and drowsiness of the drivers. In particular, driving drowsiness is recognized as a crucial aspect in the context of road safety, since drowsy drivers can suddenly lose control of the car. Moreover, the driving drowsiness episodes mostly appear suddenly without any prior behavioral evidence. The present study aimed at characterizing the onset of drowsiness in car drivers by means of a multimodal neurophysiological approach to develop a synthetic electroencephalographic (EEG)-based index, able to detect drowsy events. The study involved 19 participants in a simulated scenario structured in a sequence of driving tasks under different situations and traffic conditions. The experimental conditions were designed to induce prominent mental drowsiness in the final part. The EEG-based index, so-called “MDrow index”, was developed and validated to detect the driving drowsiness of the participants. The MDrow index was derived from the Global Field Power calculated in the Alpha EEG frequency band over the parietal brain sites. The results demonstrated the reliability of the proposed MDrow index in detecting the driving drowsiness experienced by the participants, resulting also more sensitive and timely sensible with respect to more conventional autonomic parameters, such as the EyeBlinks Rate and the Heart Rate Variability, and to subjective measurements (self-reports).

Published
2022

38. Design and development of a prototype of hybrid superconducting receiver front-end for UMTS wireless network: First results and application perspectives

Author

Fabrizio, Ricci, Boffa, Vincenzo, Dai, Guojun, Grassano, Giuseppe, Mele, Renata, Tebano, Riccardo, Arena, Domenico, Bertin, Giorgio, Magnani, Nicola Pio, Zarba, Giovanna, Andreone, Antonello, Cassinese, Antonio, and Vaglio, Ruggero

Subjects
Mobile communication systems -- Research, Wireless communication systems -- Research, Functions, Elliptic -- Analysis, Superconductive devices -- Design and construction, Wireless technology, Business, Electronics, Electronics and electrical industries
Abstract

The main results of the activity aimed to develop a prototype of High Temperature Superconducting Receiver Front-End (HTS RFE) for specific application in 3rd generation mobile network, in particular in Universal Mobile Telecom System (UMTS) are described. The device is designed to reach very low noise figure on the receiving channel to employ the best performance in terms of sensitivity of the up-link channel.

Published
2005

49. A Mobile Sensing System for UrbanPM2.5Monitoring with Adaptive Resolution

Author

Shen Fangyao, Yifan Wu, Yidan Hu, Dai Guojun, Jin Fan, and Guo Hongjie

Subjects
Artificial neural network, Computer science, Grid method multiplication, Taxis, Experimental data, 020206 networking & telecommunications, 02 engineering and technology, 010501 environmental sciences, computer.software_genre, Grid, 01 natural sciences, symbols.namesake, Control and Systems Engineering, Adaptive resolution, 0202 electrical engineering, electronic engineering, information engineering, symbols, Data mining, Electrical and Electronic Engineering, Instrumentation, computer, Air quality index, Gaussian process, 0105 earth and related environmental sciences
Abstract

This paper develops a mobile sensing system, the first system used in adaptive resolution urban air quality monitoring. In this system, we employ several taxis as sensor carries to collect originalPM2.5data and collect a variety of datasets, including meteorological data, traffic status data, and geographical data in the city. This paper also presents a novel method AG-PCEM (Adaptive Grid-Probabilistic Concentration Estimation Method) to infer thePM2.5concentration for undetected grids using dynamic adaptive grids. We gradually collect the measurements throughout a year using a prototype system in Xiasha District of Hangzhou City, China. Experimental data has verified that the proposed system can achieve good performance in terms of computational cost and accuracy. The computational cost of AG-PCEM is reduced by about 40.2% compared with a static grid method PCEM under the condition of reaching the close accuracy, and the accuracy of AG-PCEM is far superior as widely used artificial neural network (ANN) and Gaussian process (GP), enhanced by 38.8% and 14.6%, respectively. The system can be expanded to wide-range air quality monitor by adjusting the initial grid resolution, and our findings can tell citizens actual air quality and help official management find pollution sources.

Published
2016

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