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2021 International Conference on Electronic Communication, computer science and Technology, ECCST 2021

作者： Hu, Bingyi Gao, Jixun Hu, Yanxin Wang, Huaichen Liu, Jialei School of Computer Science and Technology Henan Polytechnic University Jiaozuo454000 China School of Computer Henan University of Engineering Zhengzhou451191 China School of Software Engineering Anyang Normal University Anyang455000 China

The energy consumption of traditional mobile network is mainly reflected by task calculation and data transmission. The edge server in mobile edge computing is placed at the edge of the network closer to the mobile terminal device. When the computing resources of the mobile terminal device are insufficient, the task can be offloaded to the edge server for processing. This paper discusses the energy consumption optimization in the Mobile Edge Computing (MEC) system, which is composed of the edge server and mobile devices at the edge of the network. Firstly, the system model of MEC is elaborated. Secondly, the energy consumption optimization for mobile devices and base stations is studied. Which oriented to the mobile device energy consumption optimization is divided into multiple users - single MEC server scenarios of energy consumption optimization and multiple users - the energy optimization of MEC server scenario, and enumerate the oriented to the base station and a new method for the optimization of energy consumption for mobile equipment energy consumption optimization method, in addition to the insufficiencies of these methods and the prospect of the future are also explained. © Published under licence by IOP Publishing Ltd.

关键词： Mobile edge computing

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Depth Completion using Galerkin Attention

Depth Completion using Galerkin Attention

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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Yinuo Xu Xuesong Zhang School of Computer Science (National Pilot Software Engineering School) Beijing University of Posts and Telecommunications Beijing China

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

Current depth completion methods usually employ a pair of calibrated RGB and depth sensors to reconstruct a dense depth map. Although RGB (dense) and depth (sparse) measurements are collected from the same underlying scene, they reflect different physical characteristics and thus it remains rather intricate how the devised RGB guidance scheme can effectively leads to a faithful depth recovery. Different from existing 3D geometry representations, such as point cloud, voxels or meshes, we propose to define 3D scenes as vector-valued functions, f : Ω ∋ (u, v) ↦ (r, g, b, d) ∈ ℝ 4 , mapping from the image plane Ω to RGBD vectors. This scene function representation brings two benefits: 1) allowing for the adaptation of the Galerkin method to explore the nodal basis of the scene function space, and 2) transforming the irregularly scattered (X, Y, Z) points in the Euclidean space into the depth function defined over the regular grid in the image plane. We further leverage these two benefits within a deep neural network, characterized by an efficient Galerkin attention-based RGBD function embedding to effectively explore the interaction of color and depth information, and by the utilization of equivariant convolution operation on the RGBD feature map as efficient basic blocks. Experiments show that the proposed method achieves significant performance improvement over state-of-the-arts. Code at https://***/ZXS-Labs/DCGA.

关键词： Point cloud compression Geometry Three-dimensional displays Image color analysis Sensor phenomena and characterization Vectors Encoding Method of moments Intelligent robots Image reconstruction

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General or Specific? Retrofitting AI and IoT-Enabled Hydroponic Grow Tents for Use in Different Climates Across Rural Africa

General or Specific? Retrofitting AI and IoT-Enabled Hydropo...

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International Conference on Emerging Trends in Networks and computer Communications (ETNCC)

作者： Sarina Till Taryn Wilson Rikurora Karamata Franco Beukes Metaramuje Kaundje Heike Winschiers-Theophilus School of Computer Science IIE Durban South Africa Software Engineering NUST Windhoek Namibia

ISBN: (数字)9798350353266

ISBN: (纸本)9798350353273

Climate change threatens African subsistence farming with erratic rainfall, droughts, and seasonal shifts causing devastating crop failures. Hydroponic farming, often recommended to mitigate these threats, requires constant monitoring and knowledge, which can be overwhelming for first-time growers. Research has explored using the Internet of Things (IoT) and Artificial Intelligence (AI) to automate and monitor hydroponic systems. Still, few studies have tested these systems in the field with farmers. Our previous work involved an eight-month deployment of an AI and IoT-enabled hydroponic system with rural subsistence farmers in South Africa. We conducted focus groups and observed the hydroponic systems using a qualitative approach. This paper discusses lessons learned from deploying the system in Namibia. We found that generalized AI models are unlikely to succeed due to differences in water quality, climate, participant readiness, and internet connectivity. Therefore, AI and IoT systems for rural Africa must be tailor-made to be functional and effective for crop growth.

关键词： Computational modeling Hydroponics Crops Africa Water quality Internet of Things Artificial intelligence Monitoring Farming Context modeling

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A Novel Multi-Atlas Fusion Model Based On Contrastive Learning For Functional Connectivity Graph Diagnosis

A Novel Multi-Atlas Fusion Model Based On Contrastive Learni...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Jiayu Zhang Dexuan Xu Yiwei Lou Yu Huang School of Software & Microelectronics Peking University China School of Computer Science Peking University China National Engineering Research Center for Software Engineering Peking University China

Functional connectivity (FC) graph analysis is an important method for diagnosing brain disorders using functional magnetic resonance imaging (fMRI). Existing FC graph diagnosis approaches preprocess the brain by dividing it into specific regions using atlases. However, relying on a single atlas exclusively for data preprocessing fails to fully harness the potential of the medical prior knowledge embedded within these atlases. To address this issue, this paper proposes a functional connectivity graph representation learning method that integrates multiple atlases and multiple views. This self-supervised approach to representation learning aligns features across different atlases through contrastive learning. Building upon this, we introduce an improved finite field- of-view multi-head attention mechanism for feature fusion. This mechanism is used to initialize a spectral graph convolutional network's nodes (GCN) with fusion features obtained through cross-atlas alignment. Meanwhile, edge initialization takes into account the inherent correlations among multiple independent atlas fusion features. The proposed method is validated on multi-site datasets and demonstrates superiority across various metrics. The experimental section provides visualizations of the learned feature alignment and consistency, evaluating the effectiveness of contrastive learning.

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Delivery Time Prediction Using Large-Scale Graph Structure Learning Based on Quantile Regression 39

Delivery Time Prediction Using Large-Scale Graph Structure L...

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39th IEEE International Conference on Data engineering, ICDE 2023

作者： Zhang, Lei Zhou, Xin Zeng, Zhiwei Cao, Yiming Xu, Yonghui Wang, Mingliang Wu, Xingyu Liu, Yong Cui, Lizhen Shen, Zhiqi Shandong University School of Software Jinan China Jinan China Alibaba-NTU Singapore Joint Research Institute Singapore Nanyang Technological University School of Computer Science and Engineering Singapore Alibaba Group China

ISBN: (纸本)9798350322279

Predicting Estimated Time of Arrival (ETA) for packages is a critical problem in e-commerce. The prediction is often made based on spatial (sending and receiving addresses), temporal (payment time), and context (merchants) attributes. Existing methods usually formalize this task as an Origin-Destination (OD) ETA prediction problem and exploit the attribute relations with graph learning. However, most existing methods make use of fixed and manually defined graph structures, which are often not optimal for downstream ETA task and hence lead to unsatisfactory prediction results. In addition, current ETA models tend to focus on prediction accuracy without considering fulfillment rate. This may lead to a low fulfillment rate in practice, i.e., actual delivery time is much longer than estimations provided by models, which consequently exacerbates the frustrating experiences for users. To address these issues, we propose a novel Graph Structure Learning-based Quantile Regression (GSL-QR) model for e-commerce ETA prediction in this paper. Specifically, we utilize graph structure learning to dynamically update the spatial and temporal relation graphs of orders and learn optimal graph structures and graph embeddings guided by downstream ETA prediction task. To guarantee both prediction accuracy and order fulfillment rate, we design a multi-objective quantile regression in GSL-QR that can find the Pareto solution of the problem. In order to extend GSL to large-scale real-world graphs, we devise a Fast Sampling-based Graph Structure Learning (FS-GSL) method, which can significantly reduce the computational complexity of graph structure learning. Finally, we conduct comprehensive experiments on three industrial datasets collected from Alibaba e-commerce platform. The results demonstrate that the proposed model can significantly outperform baselines on both ETA prediction accuracy and order fulfillment rate. © 2023 IEEE.

关键词： Graphic methods

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The Practice of Applet-Cloud Development in Library Mobile Services 24

The Practice of Applet-Cloud Development in Library Mobile S...

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2024 Guangdong-Hong Kong-Macao Greater Bay Area International Conference on Digital Economy and Artificial Intelligence, DEAI 2024

作者： Xing, Yongwang Peng, Feifei Lin, Pengyang Yang, Hanxiao Wu, Zhuoran Gao, Kehan School of Computer Science National Pilot Software Engineering School Beijing University of Posts and Telecommunications Beijing100876 China School of Library Beijing University of Posts and Telecommunications Beijing100876 China School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing100876 China School of Economics and Management Beijing University of Posts and Telecommunications Beijing100876 China

ISBN: (纸本)9798400717147

This paper introduces an innovative reader service model, which is realized through the reasonable use of WeChat applets in library mobile services. The article first describes the design and development background of the library equipment full life cycle management system based on small program-cloud development. The system mainly focuses on equipment maintenance, inventory and inspection, and takes the whole life cycle of equipment as the main line, realizing the collection and real-time monitoring of equipment basic data and operation data. In this way, the library gets rid of the traditional paper-based office and realizes the scientific and institutionalized equipment management. At the same time, the article also introduces the overall design scheme and technical route of the system, and shows the operation effect of the system. Practice shows that this system based on small program-cloud development not only improves the digitalization and intelligence of library equipment management, but also comprehensively improves the level of library mobile services. The system has strong practicality and wide application prospects, and helps to improve the level of library mobile services. © 2024 ACM.

关键词： Life cycle

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A Machine Learning Approach for Predicting Air Quality Index in Smart Cities 5

A Machine Learning Approach for Predicting Air Quality Index...

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5th International Conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024

作者： Swamynathan, S. Sneha, N. Ramesh, S.P. Niranjana, R. Ponkumar, D. David Neels Saravanakumar, R. Karpagam Academy of Higher Education Department of Computer Applications Tamil Nadu Coimbatore India School of Computer Science & Engineering Department of Computer Science and Engineering Galgotias University Uttar Pradesh Greater Noida India Sri Krishna College of Engineering and Technology Department of Electronics and Communication Engineering Tamil Nadu Coimbatore India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Electronics and Communication Engineering Tamil Nadu Chennai India Iconix Software Solution Tamil Nadu Tirunelveli India

ISBN: (纸本)9798331518097

One essential natural resource, the air quality index has been deteriorated by economic activities. Many studies have been conducted about the forecasting of periods of unsafe atmospheric conditions;however, the majority of these studies are limited in their capacity to include seasonal and other factors due to insufficient longitudinal data. India noise pollution has compiled a 6-year dataset that has been used to build several prediction models. There are encouraging outcomes when AQI levels are predicted using machine learning methodologies such as support vector machines (SVMs) artificial neural networks (ANNs), and Naive Bayes (NB). However, in several tests involving datasets from three different locations, NB, ANN, and SVM performed well to maximize prediction performance. When it comes to mean absolute error (MAE), R-squared (R2), and Root-mean-square deviation (RMSE), the combined performance of stacks is unsurpassed. In the related work section, summarize the limitations of existing models, such as inadequate data preprocessing, low spatialtemporal resolution, limited feature selection, or overfitting in complex models. Highlight gaps in scalability, real-time adaptability, and generalization across regions, emphasizing the need for robust, efficient models tailored to air quality prediction. © 2024 IEEE.

关键词： Support vector machines

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Innovative and affordable wearable solution for suppression of hand tremors

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International Journal of Intelligent Systems Technologies and Applications 2021年第3期20卷 233-244页

作者： Vishnu, Dana Vasudevan, Shriram K. Dhanush, G.A. Siddharth, S. Raj, Kiran S. Department of Computer Science and Engineering Amrita School of Engineering Amrita Vishwa Vidyapeetham Coimbatore India Software Services MNC Bengaluru India

Hand tremor influences a huge number of individuals around the world. Tremor is characterised as unintended, cadenced muscle development. Albeit most tremors neither reason serious medical issues nor threaten life, most patients experience many troubles in performing everyday exercises. It is humiliating to numerous individuals, particularly more seasoned people. Tremor negatively affects personal satisfaction and decreases the ability to perform everyday life undertakings such as eating, drinking, writing, and changing clothing. Some patients with mild tremor do not visit doctors if tremor does not hinder their daily life activities. Their situations often worsen over time. There is currently very few to almost no external solution existing to cope with this medical condition. We have developed a glove that is able to dynamically conceive the motion of a user’s hand and ascertain the difference between intentional movement and unintentional movement. Further, we have quantified, verified and attempted to provide a haptic response to the users’ fingers with the help of coin motors to help counteract the unwanted vibration. Copyright © 2021 Inderscience Enterprises Ltd.

关键词： Internet of things

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Nexus: Efficient and Conflict-Equivalent DAG-Based Permissioned Blockchain Sharding

Nexus: Efficient and Conflict-Equivalent DAG-Based Permissio...

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International Workshop on Quality of Service

作者： Yuchao Zhang Xiaotian Wang Xiaofeng He School of Computer Science (National Pilot Software Engineering School) Beijing University of Posts and Telecommunications Beijing China

ISBN: (数字)9798350350128

ISBN: (纸本)9798350350135

Sharding is one of the most promising solutions to tackle the well-known scalability issue in blockchain by dividing the network into multiple parallel committees. However, inefficient and insecure handling of cross-shard transactions can lead to a decline in the system service quality. Existing permissioned blockchains are mainly based on the classic two-phase commit (2PC) and two-phase locking (2PL) from traditional distributed databases to implement cross-shard transactions with deterministic safety, but this also leads to issues such as decreased system throughput and frequent transaction aborts. In this paper, we introduce Nexus, an efficient and conflict-equivalent sharded permissioned blockchains to overcome the aforementioned challenges. The core idea of Nexus is to construct a directed acyclic graph (DAG) among all shards to establish a partial order of blocks, while leaving transaction dissemination and final ordering to be completed in parallel by each shard. Nexus alleviate the overhead of global ordering through a efficient consensus process, and ensure that all transactions with overlapping read and write sets can be committed in the same relative order across different shards. A prototype of Nexus is implemented and evaluated, and experimental results show that Nexus achieves approximately 70% and 50% throughput improvement over AHL and SharPer under default settings.

关键词： Sharding Directed acyclic graph Protocols Scalability Prototypes Quality of service Throughput

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Evolution of Web API Cooperation Network via Exploring Community Structure and Popularity

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第12期5卷 6659-6671页

作者： Kang, Guosheng Wang, Yang Liu, Jianxun Cao, Buqing Xiao, Yong Xu, Yu Hunan University of Science and Technology Hunan Provincial Key Laboratory for Services Computing and Novel Software Technology School of Computer Science and Engineering Xiangtan411201 China University of Cambridge Cambridge Baker Systems Genomics Initiative British Heart Foundation Cardiovascular Epidemiology Unit Department of Public Health and Primary Care CambridgeCB1 8RN United Kingdom

With the growing popularity of the Internet, Web applications have become increasingly essential in our daily lives. Web application programming interfaces (Web APIs) play a crucial role in facilitating interaction between applications. However, most Web service platforms are suffering from the imbalance of Web services now, many services of good quality but low popularity are difficult to be invoked even once and do not create direct connections with the users. Some graph-based Web service recommendation methods also often present a long-tailed distribution of recommended Web services due to limited Mashup-API invocation relationships. To relieve this problem and promote service recommendation, in this article, we propose a community structure and popularity-based approach by constructing an evolving cooperation network for Web APIs. We leverage the Louvain algorithm in community detection to assign community structure to each Web API and consider both the popularity and community structure in constructing the network. By optimizing the Barabαsi-Albert (BA) evolving network model, we demonstrate that our approach outperforms the BA, Bianconi-Barabαsi (BB), and popularity-similarity optimization (PSO) models in Web service clustering. Based on our proposed evolutionary network model for the evolutionary extension of API cooperation network and used for downstream Web service recommendation tasks, the experimental results also show that our recommended approach outperforms some other baseline models for Web service recommendation. © 2020 IEEE.

关键词： Web services

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