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A Robust Algorithm Based on Link Label Propagation for Identifying Functional Modules From Protein-Protein Interaction Networks

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IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2022年第3期19卷 1435-1448页

作者： Jiang, Hao Zhan, Fei Wang, Congtao Qiu, Jianfeng Su, Yansen Zheng, Chunhou Zhang, Xingyi Zeng, Xiangxiang Anhui Univ Sch Comp Sci & Technol Minist Educ Key Lab Intelligent Comp & Signal Proc Hefei 230601 Peoples R China Hunan Univ Sch Informat Sci & Engn Changsha 410082 Peoples R China

Identifying functional modules in protein-protein interaction (PPI) networks elucidates cellular organization and mechanism. Various methods have been proposed to identify the functional modules in PPI networks, but most of these methods do not consider the noisy links in PPI networks. They achieve a competitive performance on the PPI networks without noisy links, but the performance of these methods considerably deteriorates in the noisy PPI networks. Furthermore, the noisy links are inevitable in the PPI networks. In this paper, we propose a novel link-driven label propagation algorithm (LLPA) to identify functional modules in PPI networks. The LLPA first find link clusters in PPI networks, and then the functional modules are identified from the link clusters. Two strategies aimed to ensure the robustness of LLPA are proposed. One strategy involves the proposed LLPA updating the link labels in accordance with the designed weight of the link, which can reduce the incidence of noisy links. The other strategy involves the filtration of some noisy labels from the link clusters to further reduce the influence of noisy links. The performance evaluation on three real PPI networks shows that LLPA outperforms other eight state-of-the-art detection algorithms in terms of accuracy and robustness.

关键词： Noise measurement Proteins Robustness Clustering algorithms Detection algorithms Signal processing algorithms partitioning algorithms Functional modules identification link label propagation nosiy link protein-protein interaction network

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An Efficient Reversible Digital Oil Painting Technique for Smartphone and Tablet Users

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IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS 2022年第6期69卷 6420-6428页

作者： Dutta, Tanima Mohanty, Sabyasachi Gupta, Hari Prabhat Banaras Hindu Univ Indian Inst Technol Varanasi 221005 Uttar Pradesh India

With the increase in mobility in technology, artists are looking for ways to create paintings using their tablets and smartphones while on the move. Digital oil painting is a revolutionary technique where an artist uses digital paintbrush to simulate the traditional bristle paintbrush of a painting to get an oil paint image. In this article, we propose a novel realistic technique for reversible digital oil painting to accelerate the users to (re)paint an oil paint image to convey the surrounding atmosphere, express their emotions and feelings, or showcase a time of day or type of weather through that painting. Realism is all about accuracy. To repaint, the original image is retrieved from the existing oil paint image by reversing the pixels to its original value. We derive a mathematical expression to determine the probability of a pixel, with a set of neighbor pixels, being oil painted to get an oil paint image of the desired quality, which reduces the time and space complexities of the technique. We also develop prototypes on Android, Windows, and Ubuntu operating systems that successfully implement the proposed technique.

关键词： Oils Painting Paints partitioning algorithms Image color analysis Smart devices Meteorology Digital oil paints reversible painting smartphone usage spatial filtering

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ESA-Stream: Efficient Self-Adaptive Online Data Stream Clustering

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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2022年第2期34卷 617-630页

作者： Li, Yanni Li, Hui Wang, Zhi Liu, Bing Cui, Jiangtao Fei, Hang Xidian Univ Sch Comp Sci & Technol Xian 710071 Peoples R China Xidian Univ State Key Lab Integrated Serv Networks Xian 710071 Peoples R China Peking Univ Wangxuan Inst Comp Technol Beijing 100871 Peoples R China Univ Illinois Dept Comp Sci Chicago IL 60607 USA

Many big data applications produce a massive amount of high-dimensional, real-time, and evolving streaming data. Clustering such data streams with both effectiveness and efficiency are critical for these applications. Although there are well-known data stream clustering algorithms that are based on the popular online-offline framework, these algorithms still face some major challenges. Several critical questions are still not answer satisfactorily: How to perform dimensionality reduction effectively and efficiently in the online dynamic environment? How to enable the clustering algorithm to achieve complete real-time online processing? How to make algorithm parameters learn in a self-supervised or self-adaptive manner to cope with high-speed evolving streams? In this paper, we focus on tackling these challenges by proposing a fully online data stream clustering algorithm (called ESA-Stream) that can learn parameters online dynamically in a self-adaptive manner, speedup dimensionality reduction, and cluster data streams effectively and efficiently in an online and dynamic environment. Experiments on a wide range of synthetic and real-world data streams show that ESA-Stream outperforms state-of-the-art baselines considerably in both effectiveness and efficiency.

关键词： Clustering algorithms Heuristic algorithms Real-time systems partitioning algorithms Dimensionality reduction Clustering methods Indexes Self-adaptive data stream online clustering

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Federated Matrix Factorization: Algorithm Design and Application to Data Clustering

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2022年 70卷 1625-1640页

作者： Wang, Shuai Chang, Tsung-Hui Chinese Univ Hong Kong Shenzhen Res Inst Big Data Shenzhen 518172 Peoples R China Chinese Univ Hong Kong Sch Sci & Engn Shenzhen 518172 Peoples R China Peng Cheng Lab Shenzhen 518066 Peoples R China

Recent demands on data privacy have called for federated learning (FL) as a new distributed learning paradigm in massive and heterogeneous networks. Although many FL algorithms have been proposed, few of them have considered the matrix factorization (MF) model, which is known to have a vast number of signal processing and machine learning applications. Since the MF problem involves two blocks of variables and the variables are usually subject to constraints related to specific solution structure, it requires new FL algorithm designs to achieve communication-efficient MF in heterogeneous data networks. In this paper, we address the challenge by proposing two new federated MF (FedMF) algorithms, namely, FedMAvg and FedMGS, based on the model averaging and gradient sharing principles, respectively. Both FedMAvg and FedMGS adopt multiple steps of local updates per communication round to speed up convergence, and allow only a randomly sampled subset of clients to communicate with the server for reducing the communication cost. Convergence properties for the two algorithms are thoroughly analyzed, which delineate the impacts of heterogeneous data distribution, local update number, and partial client communication on the algorithm performance, and guide the design of proposed algorithms. By focusing on a data clustering task, extensive experiment results are presented to examine the practical performance of proposed algorithms, as well as demonstrating their efficacy over the existing distributed clustering algorithms.

关键词： Clustering algorithms Signal processing algorithms Servers Distributed databases Convergence partitioning algorithms Data models Federated learning matrix factorization model averaging gradient sharing clustering

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A Novel Approximate Spectral Clustering Algorithm With Dense Cores and Density Peaks

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IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS 2022年第4期52卷 2348-2360页

作者： Cheng, Dongdong Huang, Jinlong Zhang, Sulan Zhang, Xiaohua Luo, Xin Yangtze Normal Univ Coll Big Data & Intelligent Engn Chongqing 408100 Peoples R China Hiroshima Inst Technol Dept Informat & Commun Hiroshima 7315193 Japan Chinese Acad Sci Chongqing Key Lab Big Data & Intelligent Comp Chongqing 400714 Peoples R China Chinese Acad Sci Chongqing Inst Green & Intelligent Technol Chongqing Engn Res Ctr Big Data Applicat Smart Ci Chongqing 400714 Peoples R China Univ Chinese Acad Sci Chongqing Sch Chongqing 400714 Peoples R China

Spectral clustering is becoming more and more popular because it has good performance in discovering clusters with varying characteristics. However, it suffers from high computational cost, unstable clustering results and noises. This work presents a novel approximate spectral clustering based on dense cores and density peaks, called DCDP-ASC. It first finds a reduced data set by introducing the concept of dense cores;then defines a new distance based on the common neighborhood of dense cores and calculates geodesic distances between dense cores according to the new defined distance;after that constructs a decision graph with a parameter-free local density and geodesic distance for obtaining initial centers;finally calculates the similarity between dense cores with their new defined geodesic distance, employs normalized spectral clustering method to divide dense cores, and expands the result on dense cores to the whole data set by assigning each point to its representative. The results on some challenging data sets and the comparison of our algorithm with some other excellent methods demonstrate that the proposed method DCDP-ASC is more advantageous in identifying complex structured clusters containing a lot of noises.

关键词： Clustering algorithms Manifolds Matrix decomposition Sparse matrices partitioning algorithms Approximation algorithms Level measurement Approximate spectral clustering common neighborhood-based distance dense cores density peaks geodesic distance

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Set partitioning in Hierarchical Trees for Point Cloud Attribute Compression

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IEEE SIGNAL PROCESSING LETTERS 2021年 28卷 1903-1907页

作者： Souto, Andre L. Figueiredo, Victor F. Chou, Philip A. de Queiroz, Ricardo L. Univ Brasilia Elect Engn Dept BR-70910900 Brasilia DF Brazil Google Seattle WA 98013 USA Univ Brasilia Comp Sci Dept BR-70910900 Brasilia DF Brazil

We propose an embedded attribute encoding method for point clouds based on set partitioning in hierarchical trees (SPIHT). The encoder is used with the region-adaptive hierarchical transform, which has been a popular transform for point cloud coding and is included in the standard geometry-based point cloud coder (G-PCC). The result is an encoder that is efficient, scalable, and embedded. That is, higher compression is achieved by trimming the full bit-stream. G-PCC's RAHT coefficient prediction prevents the straightforward incorporation of SPIHT into G-PCC. However, our results over other RAHT-based coders are promising, improving over the original, non-predictive RAHT encoder, while providing the key functionality of being embedded.

关键词： Encoding Three-dimensional displays partitioning algorithms Image coding Transforms Sorting Standards Bit-plane encoding point cloud RAHT SPIHT

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An Efficient Framework for Clustered Federated Learning

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IEEE TRANSACTIONS ON INFORMATION THEORY 2022年第12期68卷 8076-8091页

作者： Ghosh, Avishek Chung, Jichan Yin, Dong Ramchandran, Kannan Univ Calif San Diego Halicioglu Data Sci Inst La Jolla CA 92093 USA Univ Calif Berkeley EECS Dept Berkeley CA 94720 USA DeepMind Mountain View CA 94043 USA

We address the problem of federated learning (FL) where users are distributed and partitioned into clusters. This setup captures settings where different groups of users have their own objectives (learning tasks) but by aggregating their data with others in the same cluster (same learning task), they can leverage the strength in numbers in order to perform more efficient federated learning. For this new framework of clustered federated learning, we propose the Iterative Federated Clustering Algorithm (IFCA), which alternately estimates the cluster identities of the users and optimizes model parameters for the user clusters via gradient descent. We analyze the convergence rate of this algorithm first in a linear model with squared loss and then for generic strongly convex and smooth loss functions. We show that in both settings, with good initialization, IFCA is guaranteed to converge, and discuss the optimality of the statistical error rate. In particular, for the linear model with two clusters, we can guarantee that our algorithm converges as long as the initialization is slightly better than random. When the clustering structure is ambiguous, we propose to train the models by combining IFCA with the weight sharing technique in multi-task learning. In the experiments, we show that our algorithm can succeed even if we relax the requirements on initialization with random initialization and multiple restarts. We also present experimental results showing that our algorithm is efficient in non-convex problems such as neural networks. We demonstrate the benefits of IFCA over the baselines on several clustered FL benchmarks.

关键词： Clustering algorithms Collaborative work Convergence Data models partitioning algorithms Distributed databases Task analysis Federated learning clustering alternating minimization

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Split Computing based Quantized PoseNet Model for Distributed AI Architecture in IoT-Edge 15

Split Computing based Quantized PoseNet Model for Distribute...

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15th International Conference on Communication Systems and Networks (COMSNETS)

作者： Karjee, Jyotirmoy Naik, Praveen S. Anand, Kartik Byadgi, Chandrashekhar S. Dabbiru, Ramesh Babu Venkat Srinidhi, N. Samsung R&D Inst India Bangalore Karnataka India

ISBN: (纸本)9781665477062

In recent times, Internet of Things (IoT) devices is gaining popularity in advanced wireless technology (i.e., 5G). However, in 5G applications (say in edge platform), the IoT devices have limited computation & processing capabilities which makes it challenging to execute Deep Neural Network (DNN) models on them. To address this, we introduce Split Computing technology, to partition DNN inference layers based on the computational capabilities (such as bandwidth, battery level and processing power, etc.) of IoT and edge (computationally powerful) devices, respectively. To validate split computing, we propose a framework called Distributed Artificial Intelligence (DAI) architecture. We use the architecture for a fitness application (use-case) where we detect the pose of a person for our proposed Quantized Split PoseNet DNN (QSP-DNN) algorithm which partitions the DNN layers among IoT device and edge based on Wi-Fi bandwidth. We perform experiments to validate the QSP-DNN algorithm using DAI architecture. The QSP-DNN with DAI compares split execution (computed among IoT device & edge) for partial offload and full-offload executed on edge device. The result shows that using QSP-DNN in DAI architecture provides split execution performing 20.76 % improvement compared to the full offload case.

关键词： Performance evaluation Computational modeling Image edge detection Computer architecture Bandwidth Inference algorithms partitioning algorithms

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Area partitioning by Intelligent UAVs for effective path planning using Evolutionary algorithms

Area Partitioning by Intelligent UAVs for effective path pla...

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International Conference on Computer Communication and Informatics (ICCCI)

作者： Akshya. J P.L.K. Priyadarsini School of Computing SASTRA Deemed to be University Thanjavur India

ISBN: (纸本)9781728158754;9781728192994

Unmanned Aerial Vehicles (UAVs) have emerged as one of the most researched topics in recent times. They are being deployed in ample applications without any human intervention. The use of autonomous UAVs has given rise to many new challenges. Path planning has always been one of the most critical problems when it comes to the deployment of UAVs in real-time applications. The current work focuses on the area partitioning algorithm considering a few significant parameters such as static obstacles and altitude of UAVs. For any given geographic area, the proposed area partitioning algorithm finds a division of the entire area of interest by effectively partitioning into rectangles. The algorithm merges two or more equal-sized rectangles based on the obstacle position, resulting in a rectilinear partitioning of the area. Two mid-points are computed for each partition and are further used for the computation of an optimal path. An optimal path for the UAV is found by constructing a graph from the mid-points, posing the problem as Travelling Salesperson Problem(TSP), and finding a solution using Firefly Algorithm(FA) and Particle Swarm Optimization(PSO). Effective comparison is done to find the optimal path using both techniques. The obtained results show that FA outperforms PSO.

关键词： Heuristic algorithms Path planning Unmanned aerial vehicles Real-time systems partitioning algorithms Particle swarm optimization Informatics

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Cluster-Based Multi-Carrier Hybrid Beamforming for Massive Device Terahertz Communications

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IEEE TRANSACTIONS ON COMMUNICATIONS 2022年第5期70卷 3407-3420页

作者： Yuan, Hang Yang, Nan Ding, Xuhui Han, Chong Yang, Kai An, Jianping Beijing Inst Technol Sch Informat & Elect Beijing 100081 Peoples R China Australian Natl Univ Sch Engn Canberra ACT 2600 Australia Beijing Inst Technol Sch Cyberspace Sci & Technol Beijing 100081 Peoples R China Shanghai Jiao Tong Univ Univ Michigan Shanghai Jiao Tong Univ UM SJTU Joi Shanghai 200240 Peoples R China

We propose a cluster-based multi-carrier beam division multiple access (MC-BDMA) scheme to enable massive device terahertz (THz) communications with the dynamic-subarray hybrid beamforming (HBF) architecture. This scheme is motivated by a limitation of conventional HBF architectures, i.e., the number of users cannot exceed the number of radio frequency (RF) chains. By exploiting the unique properties of THz channels, such as high distance-and-frequency dependence, high sparsity, and small angular spread, we propose multiple users in the same cluster to be supported by a single RF chain with distance-aware multi-carrier modulation. Moreover, we propose different clusters to be divided by BDMA that is accomplished through HBF design. In our proposed scheme, we first design a novel antenna selection and subarray partition algorithm for the dynamic-subarray HBF architecture to compensate for performance loss caused by diverse channels among users. We then design an algorithm for multi-carrier HBF which maximizes the achievable throughput and eliminates the inter-beam interference. Numerical results show that our proposed cluster-based MC-BDMA scheme with the dynamic-subarray HBF architecture provides an almost 100% spectral efficiency gain and a 60% energy efficiency gain over the conventional non-cluster HBF scheme in massive device communications.

关键词： Radio frequency Computer architecture Array signal processing partitioning algorithms Heuristic algorithms Wireless communication Switches Terahertz communications massive device communications multi-carrier hybrid beamforming dynamic beam division multiple access

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