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226 results on '"Jia, Gangyong"'

52. Coordinate Task and Memory Management for Improving Power Efficiency

Author

Jia, Gangyong, Li, Xi, Wan, Jian, Wang, Chao, Dai, Dong, Jiang, Congfeng, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Kołodziej, Joanna, editor, Di Martino, Beniamino, editor, Talia, Domenico, editor, and Xiong, Kaiqi, editor

Published
2013
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61. i2APP: A Two-Step Machine Learning Framework For Antiparasitic Peptides Identification

Author

Jiang, Minchao, Zhang, Renfeng, Xia, Yixiao, Jia, Gangyong, Yin, Yuyu, Wang, Pu, Wu, Jian, and Ge, Ruiquan

Subjects
Genetics, Molecular Medicine, Genetics (clinical)
Abstract

Parasites can cause enormous damage to their hosts. Studies have shown that antiparasitic peptides can inhibit the growth and development of parasites and even kill them. Because traditional biological methods to determine the activity of antiparasitic peptides are time-consuming and costly, a method for large-scale prediction of antiparasitic peptides is urgently needed. We propose a computational approach called i2APP that can efficiently identify APPs using a two-step machine learning (ML) framework. First, in order to solve the imbalance of positive and negative samples in the training set, a random under sampling method is used to generate a balanced training data set. Then, the physical and chemical features and terminus-based features are extracted, and the first classification is performed by Light Gradient Boosting Machine (LGBM) and Support Vector Machine (SVM) to obtain 264-dimensional higher level features. These features are selected by Maximal Information Coefficient (MIC) and the features with the big MIC values are retained. Finally, the SVM algorithm is used for the second classification in the optimized feature space. Thus the prediction model i2APP is fully constructed. On independent datasets, the accuracy and AUC of i2APP are 0.913 and 0.935, respectively, which are better than the state-of-arts methods. The key idea of the proposed method is that multi-level features are extracted from peptide sequences and the higher-level features can distinguish well the APPs and non-APPs.

Published
2022
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91. STC: an intelligent trash can system based on both NB-IoT and edge computing for smart cities.

Author

Jia, Gangyong, Zhu, Yujie, Han, Guangjie, Chan, Sammy, and Shu, Lei

Subjects
REFUSE containers, EDGE computing, SMART cities, CONSUMPTION (Economics), ORGANIC wastes, INTERNET of things
Abstract

The intelligent treatment of urban garbage is an important component of creating a smart city and also solves several problems associated with urban garbage. Many traditional garbage cans are widely distributed, leading to great consumption of human and material, untimely government. Therefore, in this paper, we propose an intelligent system based on edge computing and the narrow-band Internet of things (NB-IoT) for monitoring smart trash cans (STCs). The experimental results demonstrate that the proposed STC system improves the load capacity of garbage cans, requires few human and material resources, and allows for intelligent management of garbage in smart cities. [ABSTRACT FROM AUTHOR]

Published
2020
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97. Coordinate Channel-Aware Page Mapping Policy and Memory Scheduling for Reducing Memory Interference Among Multimedia Applications

Author

Universitat Politècnica de València. Departamento de Comunicaciones - Departament de Comunicacions, National Science Foundation, China, Natural Science Foundation of Zhejiang Province, Jia, Gangyong, Han, Guangjie, Li, Aohan, Lloret, Jaime, Universitat Politècnica de València. Departamento de Comunicaciones - Departament de Comunicacions, National Science Foundation, China, Natural Science Foundation of Zhejiang Province, Jia, Gangyong, Han, Guangjie, Li, Aohan, and Lloret, Jaime

Abstract

"© 2017 IEEE. Personal use of this material is permitted. Permissíon from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertisíng or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.", [EN] In a modern multicore system, memory is shared among more and more concurrently running multimedia applications. Therefore, memory contention and interference are more andmore serious, inducing system performance degradation significantly, the performance degradation of each thread differently, unfairness in resource sharing, and priority inversion, even starvation. In this paper, we propose an approach of coordinating channel-aware page mapping policy and memory scheduling (CCPS) to reduce intermultimedia application interference in a memory system. The idea is to map the data of different threads to different channels, together with memory scheduling. The key principles of the policies of page mapping and memory scheduling are: 1) the memory address space, the thread priority, and the load balance; and 2) prioritizing a low-memory request thread, a row-buffer hit access, and an older request. We evaluate the CCPS on a variety of mixed single-thread and multithread benchmarks and system configurations, and we compare them with four previously proposed state-of-the-art interference-reducing policies. Experimental results demonstrate that the CCPS improves the performance while reducing the energy consumption significantly; moreover, the CCPS incurs a much lower hardware overhead than the current existing policies.

Published
2017

98. Virtual Page Behavior Based Page Management Policy for Hybrid Main Memory in Cloud Computing

Author

Xuan Zhang, Guangjie Han, Wan Jian, Jia Gangyong, Huang Jie, and Li-You Huizi

Subjects
Hardware_MEMORYSTRUCTURES, 021103 operations research, Page fault, Computer science, 0211 other engineering and technologies, Registered memory, 02 engineering and technology, computer.software_genre, Memory map, Memory management, Demand paging, Virtual memory, 0202 electrical engineering, electronic engineering, information engineering, Operating system, Interleaved memory, 020201 artificial intelligence & image processing, Page, computer
Abstract

A new generation memory, Non-Volatile Memory (NVM), such as Phase-Change Memory (PCM), has been adopted together with DRAM in the main memory to form the hybrid main memory for low energy consumption and high capacity. The biggest challenge of hybrid memory is how to decrease the average memory access cost for the higher cost of NVM's read/write operation. Currently, most researches are based on migration. However, the page migration itself is a high cost operation. And the migration based policy produces many migration operations, which induces high cost in memory access. Therefore, in order to decrease the cost, we present a virtual page behavior based page management policy (VBPM) in this paper. According to the virtual pages' behavior, we allocate virtual pages into DRAM or PCM physical pages correspondingly. The whole process is migration independent. The experimental results show our VBPM decreases the average memory access time by 24%, moreover, VBPM improves real-time performance in critical path.

Published
2016
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