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6th International Conference on Electronic information technology and computer Engineering, EITCE 2022

作者： Zhu, Tingting Wu, Huarui Chen, Cheng Zhu, Huaji School of Computer Science and Communication Engineering JiangSu University ZhenJiang China Big Data and Intelligence Department Research Center of Information Technology Beijing Academy of Agriculture and Forestry Sciences Key Laboratory of Digital Village Technology Ministry of Agriculture and Rural Affairs BeiJing China Industrialization Department Research Center of Information Technology Beijing Academy of Agriculture and Forestry Sciences Key Laboratory of Digital Village Technology Ministry of Agriculture and Rural Affairs BeiJing China

ISBN: (纸本)9781450397148

Under the background of the in-depth implementation of the Rural Revitalization Strategy, the conditions of public service facilities for the elderly have improved significantly in rural areas. However, the problem is that the asymmetry between the intelligent management and construction of the corresponding elderly care services is more prominent. It leads to the lagging management of elderly care services, poor service information, and other problems. In order to solve the above series of problems, we propose an IoT-based rural community elderly care service management system, which can effectively assist community elderly care. The designs of structure, function, and key technologies are discussed in this article. To provide more timely and accurate services for the elderly, the system can realize real-time data collection of behavior, vital signs, and the surrounding environment of the elderly, monitor and warn of abnormal situations. © 2022 Association for Computing Machinery.

关键词： Rural areas

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A Lightweight Convolutional Neural Network with Representation Self-challenge for Fingerprint Liveness Detection

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computers, Materials & Continua 2022年第10期73卷 719-733页

作者： Jie Chen Chengsheng Yuan Chen Cui Zhihua Xia Xingming Sun Thangarajah Akilan School of Computer Science Nanjing University of Information Science and TechnologyNanjing210044China Key Laboratory of Public Security Information Application Based on Big-Data Architecture Ministry of Public SecurityZhejiang Police CollegeHangzhou310053China Jiangsu Yuchi Blockchain Research Institute Nanjing210044China Department of Software Engineering Lakehead UniversityThunder BayON P7B 5E1Canada

Fingerprint identification systems have been widely deployed in many occasions of our daily ***,together with many advantages,they are still vulnerable to the presentation attack(PA)by some counterfeit *** address challenges from PA,fingerprint liveness detection(FLD)technology has been proposed and gradually attracted people’s *** vast majority of the FLD methods directly employ convolutional neural network(CNN),and rarely pay attention to the problem of overparameterization and over-fitting of models,resulting in large calculation force of model deployment and poor model *** at filling this gap,this paper designs a lightweight multi-scale convolutional neural network method,and further proposes a novel hybrid spatial pyramid pooling block to extract abundant features,so that the number of model parameters is greatly reduced,and support multi-scale true/fake fingerprint ***,the representation self-challenge(RSC)method is used to train the model,and the attention mechanism is also adopted for optimization during execution,which alleviates the problem of model over-fitting and enhances generalization of detection ***,experimental results on two publicly benchmarks:LivDet2011 and LivDet2013 sets,show that our method achieves outstanding detection results for blind materials and *** size of the model parameters is only 548 KB,and the average detection error of cross-sensors and cross-materials are 15.22 and 1 respectively,reaching the highest level currently available.

关键词： FLD lightweight multi-scale RSC blind materials

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Stochastic configuration networks with CPU-GPU implementation for large-scale data analytics

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information Sciences 2024年 667卷

作者： Li, Junqi Wang, Dianhui Department of Computer Science and Information Technology La Trobe University MelbourneVIC3086 Australia Artificial Intelligence Research Institute China University of Mining and Technology Xuzhou221116 China State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang110819 China

Stochastic configuration networks (SCNs) are a class of randomized learner models that have garnered increasing attention in data analytics. In the original implementation of SCN, the pseudo-inverse of the hidden layer output matrix is used to evaluate output weights. However, it is quite challenging to compute the pseudo-inverse of the hidden layer output matrices for large-scale datasets with high complexity, which occurs in many real-world applications. This paper aims to accelerate the learning process of SCNs by employing the well-known alternating direction method of multipliers (ADMM) solver and CPU-GPU heterogeneous computing technique. Empirical results on two benchmark datasets demonstrate the efficiency and effectiveness of the proposed method for large-scale data analytics. © 2024 The Author(s)

关键词： Data Analytics

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An incentive mechanism for crowdsourcing markets with social welfare maximization in cloud-edge computing

An incentive mechanism for crowdsourcing markets with social...

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作者： Xu, Xiaolong Cai, Qing Zhang, Guoming Zhang, Jie Tian, Wei Zhang, Xiaorui Liu, Alex X. School of Computer and Software Nanjing University of Information Science and Technology Jiangsu China Jiangsu Engineering Centre of Network Monitoring Nanjing University of Information Science and Technology Jiangsu China State Key Laboratory for Novel Software Technology Nanjing University Jiangsu China Department of Computer Science and Engineering Michigan State University MI United States

Crowdsourcing is emerging as a powerful paradigm that utilizes the distributed devices to sense, collect, and upload data to satisfy the requirements of the users. Currently, with the popularity of edge computing, edge device users can act as recruiters or participants to publish or perform crowdsourcing tasks and share feedback. However, due to the individual selfishness, it is still a challenge to maximize the social welfare distribution of all the participants and the recruiters for the crowdsourcing market. In view of this challenge, an incentive mechanism for the crowdsourcing market with social welfare maximization in the cloud-edge computing is designed in this paper. Technically, a double action model under the cloud-edge computing framework is proposed first, which aims to maximize the social welfare maximization and meanwhile meet the demands of incentive compatibility, individual rationality, market clearing, and budget constraint. Secondly, a corresponding incentive mechanism is designed based on the double auction model to achieve the market fairness. Experimental evaluation and comparison analysis are conducted to validate the efficiency and effectiveness of the mechanism. © 2018 John Wiley & Sons, Ltd.

关键词： Crowdsourcing

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Image Hiding Using Invertible Neural Network and Similarity of Bits Pairs

SSRN

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SSRN 2023年

作者： Ping, Ping Li, Qianwen Guo, Bobiao Xu, Feng Sibanda, Festus Mao, Yingchi The College of Computer and Information Hohai University Jiangsu Nanjing210098 China The Key Laboratory of Water Big Data Technology of Ministry of Water Resources Hohai University Jiangsu Nanjing210098 China

The objective of image hiding is to conceal a secret image in a cover image inconspicuously. The challenges for image hiding focus on the embedding capacity, image quality and security. To contend with these challenges, this paper presents a novel image hiding method using Invertible Rescaling Net (IRN) and Similarity of Bits Pairs (SBP). Because of the downscaling ability of IRN, our method is able to hide a secret image into a cover image with the same dimensions. Moreover, the superior rescaling capabilities of IRN enable us to produce a recovered image of high-quality. We improve the SBP embedding technique and combine it with differential coding to boost the quality of the stego image. In the experiments, the quality of the recovered secret image and stego image is assessed using various quality evaluation metrics, and for the security, the anti-steganalysis performance is also evaluated in this paper. Extensive experimental results reveal that the quality of both recovered secret image and stego-image in our method outperforms several advanced methods and provides a high degree of security. © 2023, The Authors. All rights reserved.

关键词： Image quality

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BeFA: A General Behavior-driven Feature Adapter for Multimedia Recommendation

arXiv

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arXiv 2024年

作者： Fan, Qile Yu, Penghang Tan, Zhiyi Bao, Bing-Kun Lu, Guanming School of Communications and Information Engineering Nanjing University of Posts and Telecommunications Nanjing China Jiangsu Key Laboratory of Intelligent Information Processing and Communication Technology Nanjing China School of Computer Science Nanjing University of Posts and Telecommunications Nanjing China

Multimedia recommender systems focus on utilizing behavioral information and content information to model user preferences. Typically, it employs pre-trained feature encoders to extract content features, then fuses them with behavioral features. However, pre-trained feature encoders often extract features from the entire content simultaneously, including excessive preference-irrelevant details. We speculate that it may result in the extracted features not containing sufficient features to accurately reflect user preferences. To verify our hypothesis, we introduce an attribution analysis method for visually and intuitively analyzing the content features. The results indicate that certain items’ content features exhibit the issues of information drift and information omission, reducing the expressive ability of features. Building upon this finding, we propose an effective and efficient general Behavior-driven Feature Adapter (BeFA) to tackle these issues. This adapter reconstructs the content feature with the guidance of behavioral information, enabling content features accurately reflecting user preferences. Extensive experiments demonstrate the effectiveness of the adapter across all multimedia recommendation methods. Our code is made publicly available on https://***/fqldom/BeFA. Copyright © 2024, The Authors. All rights reserved.

关键词： Recommender systems

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Three-dimensional dynamic probabilistic shaping high-security transmission scheme based on dual physical layer encryption for seven-core fibers

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Chinese Optics Letters 2025年第03期 19-25页

作者：毛雅亚刘博黄景瑞任建新陈帅东吴翔宇吴泳锋宋秀敏戚志鹏崔杰 Institute of Optics and Electronics Nanjing University of Information Science & Technology Jiangsu Key Laboratory for Optoelectronic Detection of Atmosphere and Ocean Nanjing University of Information Science & Technology Jiangsu International Joint Laboratory on Meteorological Photonics and Optoelectronic Detection Nanjing University of Information Science & Technology School of Computer Science and Technology Tongji University

In this paper,a carrier-less amplitude and phase modulation passive optical network （CAP-PON） scheme is proposed based on dynamic probabilistic shaping （DPS） and Rubik’s cube encryption in optical access *** key is generated from a novel five-dimensional entangled chaos model for dynamic probabilistic shaping and Rubik’s cube *** verify the performance of the encryption scheme,an experimental demonstration of 70 Gb/s （7×10 Gb/s） encrypted DPS-3D-CAP signal transmission over 2 km weakly coupled 7-core fiber is *** key space of the new five-dimensional entangled chaos model reaches 10173,and the interference level reaches 100%.Experimental results show that the receiver sensitivity increases by 1.47 d B compared to the conventional uniform 3D-CAP due to the introduction of dynamic probabilistic shaping.

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Machine Translation Testing via Syntactic Tree Pruning

arXiv

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arXiv 2024年

作者： Zhang, Quanjun Zhai, Juan Fang, Chunrong Liu, Jiawei Sun, Weisong Hu, Haichuan Wang, Qingyu State Key Laboratory for Novel Software Technology Nanjing University Jiangsu Nanjing210093 China Manning College of Information & Computer Sciences University of Massachusetts Amherst AmherstMA01003 United States

Machine translation systems have been widely adopted in our daily life, making life easier and more convenient. Unfortunately, erroneous translations may result in severe consequences, such as financial losses. This requires to improve the accuracy and the reliability of machine translation systems. However, it is challenging to test machine translation systems because of the complexity and intractability of the underlying neural models. To tackle these challenges, we propose a novel metamorphic testing approach by syntactic tree pruning (STP) to validate machine translation systems. Our key insight is that a pruned sentence should have similar crucial semantics compared with the original sentence. Specifically, STP (1) proposes a core semantics-preserving pruning strategy by basic sentence structures and dependency relations on the level of syntactic tree representation;(2) generates source sentence pairs based on the metamorphic relation;(3) reports suspicious issues whose translations break the consistency property by a bag-of-words model. We further evaluate STP on two state-of-the-art machine translation systems (i.e., Google Translate and Bing Microsoft Translator) with 1,200 source sentences as inputs. The results show that STP accurately finds 5,073 unique erroneous translations in Google Translate and 5,100 unique erroneous translations in Bing Microsoft Translator (400% more than state-of-the-art techniques), with 64.5% and 65.4% precision, respectively. The reported erroneous translations vary in types and more than 90% of them are not found by state-of-the-art techniques. There are 9,393 erroneous translations unique to STP, which is 711.9% more than state-of-the-art techniques. Moreover, STP is quite effective in detecting translation errors for the original sentences with a recall reaching 74.0%, improving state-of-the-art techniques by 55.1% on average. © 2024, CC BY.

关键词： Software testing

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A Compact Broadband Metasurface Antenna Based on Hybrid Via-Wall Structures

A Compact Broadband Metasurface Antenna Based on Hybrid Via-...

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International Applied Computational Electromagnetics Society Symposium (ACES)

作者： Dongxu Chen Haotian Liu Wanchen Yang Jiaqi Chen Ning Cao Wenquan Che Quan Xue Computer and Information College Hohai University Nanjing Jiangsu China Guangdong Key Laboratory of Millimeter-Waves and Terahertz The School of Electronic and Information Engineering South China University of Technology Guangzhou China

In this work, a compact broadband metasurface (MTS) antenna using hybrid via-wall structures is firstly proposed. First, different via-wall MTS structures are derived from a conventional square MTS structure by folding the square patches downward and loading stacked patches horizontally in various configurations. These modifications effectively lower the resonance frequency and enable miniaturization to varying degrees. Furtherly, for a multi-resonance wideband feature, a hybrid MTS structure consisting of different via-wall structures is presented and then utilized to construct a MTS-based antenna by employing a slot-coupled feeding structure. With these hybrid via-wall structures that resonant at different frequencies, the MTS-based antenna can exhibit a wideband impedance matching. Finally, Theoretical results demonstrate that the proposed antenna achieves remarkable performance. It features a wide impedance bandwidth of 53.4% (5.41~9.35GHz) and delivers a high gain of approximately 5.9dBi within a compact overall size only $0.44\lambda_{0}\times 0.44\lambda_{0}\times 0.07 \lambda_{0}$ .

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Geo-Enhanced High-Order Feature Interaction Network for Cloud API QoS Prediction 19th

Geo-Enhanced High-Order Feature Interaction Network for Clou...

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19th CCF Conference on computer Supported Cooperative Work and Social Computing, ChineseCSCW 2024

作者： Ma, Xinhong Zeng, Yehong Qi, Mingyang Si, Yali Chen, Zhen School of Information Science and Engineering Yanshan University Qinhuangdao066004 China Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province Yanshan University Qinhuangdao066004 China Changshu Institute of Technology Jiangsu Changshu215500 China

ISBN: (纸本)9789819623754

In the era of open cloud, cloud API is an important component and key enabling technology to achieve efficient data transmission, artificial intelligence algorithm empowerment, and software development cost reduction and efficiency. With the proliferation of cloud API with similar functionality, quality of service (QoS) becomes essential for differentiating cloud API performance, and QoS-driven prediction for cloud API has emerged as a critical topic for high quality cloud API selection. However, existing methods often inadequately utilize contextual information in resource-constrained scenario, limiting the improvement of cloud API prediction performance. We in this paper propose a geo-enhanced high-order feature interaction network (GHFIN) for cloud API QoS prediction. Firstly, a correlation analysis was conducted on real-world QoS dataset to explore the relationship between QoS data and geospatial context. Then, we design a geo-enhanced strategy that effectively utilizes the hierarchical relationships of geospatial entities (country, province, city, and district) through an offline real-time coordinate conversion module. On this basis, a geo-enhanced cloud API QoS prediction network GHFIN was designed, which aims to learn the high-order feature interactions through layer-wise feature stacking. Experimental results on two real-world QoS datasets illustrate the effectiveness of GHFIN compared to the classic and state-of-the-art methods, and our extensive analysis shows how the geospatial context and high-order feature interaction positively impact the performance of cloud API QoS prediction. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Quality of service

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