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ComPath: User Interest Mining in Heterogeneous Signed Social Networks for Internet of People

IEEE INTERNET OF THINGS JOURNAL 2021年第8期8卷 7024-7035页

作者： Dhelim, Sahraoui Ning, Huansheng Aung, Nyothiri Univ Sci & Technol Beijing Sch Comp & Commun Engn Beijing 100083 Peoples R China

The Internet of People (IoP) is a human-centric computing paradigm, where the people are not considered merely as end users, but become the center of the computing architecture. The computing model of IoP requires that the system understand the social characters of the users, such as the users' emotions, personality types, and interests. User interest detection is an important task in IoP. In this article, we propose a user interest detection framework for user interest detection in the context of a signed social network for IoP. First, we propose a new proximity function that measures the similarity between users based on their interests/disinterests with respect to the relative popularity of these interests/disinterests among other users. Second, we propose a greedy community detection algorithm that detects communities of users with common interests with possible overlapping communities using the adaptive clique relaxation technique. Finally, we introduce a novel link prediction algorithm named ComPath that leverages the community affiliation information to predict the unknown links in heterogeneous signed social networks. Experimental results show that ComPath outperforms other computational-based baselines as well as deep-learning-based baselines especially in the cold start phase with only a few training data.

关键词： Social networking (online) Prediction algorithms Internet of Things Predictive models Heuristic algorithms detection algorithms Clustering algorithms Community detection Internet of People (IoP) link prediction signed social networks similarity measurement user interests

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A UAV Video Dataset for Mosaicking and Change detection From Low-Altitude Flights

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IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS 2020年第6期50卷 2139-2149页

作者： Avola, Danilo Cinque, Luigi Foresti, Gian Luca Martinel, Niki Pannone, Daniele Piciarelli, Claudio Univ Udine Dept Math Comp Sci & Phys I-33100 Udine Italy Sapienza Univ Dept Comp Sci I-00185 Rome Italy

In recent years, the technology of small-scale unmanned aerial vehicles (UAVs) has steadily improved in terms of flight time, automatic control, and image acquisition. This has lead to the development of several applications for low-altitude tasks, such as vehicle tracking, person identification, and object recognition. These applications often require to stitch together several video frames to get a comprehensive view of large areas (mosaicking), or to detect differences between images or mosaics acquired at different times (change detection). However, the datasets used to test mosaicking and change detection algorithms are typically acquired at high-altitudes, thus ignoring the specific challenges of low-altitude scenarios. The purpose of this paper is to fill this gap by providing the UAV mosaicking and change detection dataset. It consists of 50 challenging aerial video sequences acquired at low-altitude in different environments with and without the presence of vehicles, persons, and objects, plus metadata and telemetry. In addition, this paper provides some performance metrics to evaluate both the quality of the obtained mosaics and the correctness of the detected changes. Finally, the results achieved by two baseline algorithms, one for mosaicking and one for detection, are presented. The aim is to provide a shared performance reference that can be used for comparison with future algorithms that will be tested on the dataset.

关键词： Video sequences Change detection algorithms Cameras detection algorithms Task analysis Telemetry Unmanned aerial vehicles Change detection dataset low-altitude flights mosaicking unmanned aerial vehicle (UAV)

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Low-Complexity Multistage Optimal detection for SVD-Based Hybrid Millimeter Wave MIMO Systems

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2020年第11期69卷 13216-13228页

作者： Wang, Xinyi Liu, Wenlong Ding, Shuxue Gao, Yuanlong Jin, Minglu Dalian Univ Technol Sch Informat & Commun Engn Dalian 116024 Peoples R China Guilin Univ Elect Technol Sch Artificial Intelligence Guilin 541004 Peoples R China

The singular value decomposition (SVD) based hybrid processing for millimeter wave (mmWave) multiple-input multiple-output (MIMO) systems attempts to eliminate mutual interference between data streams by processing the original channel matrix to an approximate diagonal matrix. However, the non-zero off-diagonal entries will result in performance loss. To improve the performance with low-complexity, we propose a multistage optimal detection algorithm for SVD-based hybrid mmWave MIMO systems. At the first stage, a decision criterion is proposed by effectively exploiting the characteristic that the magnitudes of off-diagonal entries of the baseband channel matrix are relatively small. Through this decision criterion, some elements of global optimal solution can be determined. The ratio of the determined elements to all elements is analyzed to reveal the advantage of proposed decision criterion. At the second stage, we further propose a decision feedback algorithm to detect more elements of global optimal solution. If all elements are determined, the detection is complete, otherwise it enters the third stage. In the case of third stage, we apply the maximum likelihood (ML) detection algorithm to solve the residual small-scale optimization problem containing only few undetermined elements. Numerical experiments show that the number of elements required to be determined by ML detection is relatively very small or zero, in most situations, that is why the algorithm has a significantly lower complexity.

关键词： MIMO communication Detectors Antenna arrays detection algorithms Transmission line matrix methods Computer architecture Matrix decomposition Millimeter wave detection hybrid processing multiple-input multiple-output (MIMO)

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Particle Swarm Optimization Algorithm for Detecting Distributed Predicates

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IEEE ACCESS 2021年 9卷 105286-105296页

作者： Al Maghayreh, Eslam Dhahiri, Habib Albogamy, Fahad Al Rahhal, Mohamad Mahmoud Mahmood, Awais Othman, Esam Elkilani, Wail S. King Saud Univ Fac Appl Comp Sci Riyadh 11451 Saudi Arabia Yarmouk Univ Dept Comp Sci Irbid 21163 Jordan Taif Univ Turabah Univ Coll Comp Sci Program At Taif 26571 Saudi Arabia

Metaheuristic algorithms are widely used to solve NP-complete problems in several domains. Distributed predicates detection is a fundamental distributed systems problem that has many useful applications. The problem of distributed predicates detection, in general, is known to be an NP-complete problem. In this paper, we developed a detection algorithm inspired by the particle swarm optimization algorithm, one of the well-known metaheuristic algorithms applied to solve problems in several domains. The proposed detection algorithm deal with distributed predicates under the possibly modality. We compared the performance of the proposed distributed predicates detection algorithm with several other detection algorithms. The experimental results reveal the effectiveness of the suggested distributed predicates detection algorithm.

关键词： detection algorithms Particle swarm optimization Licenses Statistics Sociology Search problems Runtime Artificial intelligence computational intelligence debugging distributed predicates detection distributed systems monitoring particle swarm optimization runtime verification testing

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Towards designing secure in-vehicle network architectures using community detection algorithms

Towards designing secure in-vehicle network architectures us...

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IEEE Vehicular Networking Conference (VNC)

作者： Pierre Kleberger Nasser Nowdehi Tomas Olovsson Department of Computer Science and Engineering Chalmers University of Technology Gothenburg Sweden Volvo Car Corporation Gothenburg Sweden

Efforts in securing the in-vehicle network have resulted in a number of proposed security mechanisms in recent years. However, so far little attention has been given to the actual architecture of the in-vehicle network. An approach within in-vehicle network design is to divide the network into domains, where each domain consists of a set of Electronic Control Units (ECUs) that handles some united functionality, e.g., body control, powertrain, and telematics. Still, this approach is based on “best engineering practice” and there is room for improvements. In this paper, we study real traffic from a modern car and we try to divide the in-vehicle network into domains using automated partitioning algorithms. To find the optimum division, we select four community detection algorithms, known from social network analysis, and we evaluate their ability to find these domains. We conclude that community detection algorithms can be used to identify in-vehicle domains based on the message types (signals) used in the in-vehicle network and we demonstrate this by applying the algorithms to real data. The approach is not limited to only message types, but domains can also be identified based on other criteria, such as frequency of messages, payload sizes, or Automotive Safety Integrity Levels (ASILs). We also conclude that the identification of good domains can facilitate the implementation of security measures. Therefore, we believe that the approach has great potential to help engineers in deriving secure in-vehicle network architectures during the design of a vehicle.

关键词： Communities Vehicles Security Partitioning algorithms detection algorithms Image edge detection Algorithm design and analysis

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Uplink Non-Orthogonal Multiple Access With Golden Codeword Constellation

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IEEE ACCESS 2021年 9卷 150966-150974页

作者： Xu, Hongjun Pillay, Narushan Univ KwaZulu Natal Sch Engn ZA-4041 Durban South Africa

Non-orthogonal multiple access (NOMA) is a technique to improve spectral efficiency. In uplink NOMA (UL-NOMA) systems, mobile multiusers are globally synchronized to share the same time and frequency resources, and transmit their own independent symbols to the base station (BS). This paper proposes an UL-NOMA system with Golden codeword constellation (GCC). In the proposed UL-NOMA system, two users, the center user and the edge user, transmit their own independent Golden codewords to the BS. Compared to the conventional UL-NOMA systems, the proposed UL-NOMA system not only preserves the spectral efficiency, but also improves error performance. The fast essentially maximum likelihood (FE-ML) detection with dynamic signal detection subset (DSDS) is proposed to decode the Golden codewords. A lower bound on error performance for both the center user and edge user is further derived. Simulation results show that the derived lower bound well predicts the error performance of UL-NOMA with GCC. Simulation results also show that the proposed UL-NOMA system outperforms the conventional UL-NOMA system by at least 2 dB signal-to-noise ratio (SNR) for both the center user and edge user at a bit error rate of 2 x 10(-5). Finally, both complexity analysis and simulation results show that the proposed FE-ML with DSDS result in a 68% complexity reduction compared to the FE-ML with SDS at an SNR of 23 dB for the center user transmitting 64QAM and the edge user transmitting 16QAM.

关键词： Codes NOMA Complexity theory Signal to noise ratio Signal detection Fading channels detection algorithms Dynamic signal detection subset fast essentially ML detection golden code golden codeword non-orthogonal multiple access QR decomposition based detection signal detection subset

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Multiple Object Tracking for Occluded Particles

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IEEE ACCESS 2021年 9卷 1524-1532页

作者： Qian, Yifei Ji, Ru Duan, Yuping Yang, Runhuai Anhui Med Univ Dept Biomed Engn Hefei 230022 Peoples R China

The precise detection and tracking of multiple particles under a microscope are of significance in the research of the individual and cluster behavior of dynamic bacteria and subcellular structures. However, the existing detection algorithms cannot separate occluded particles from each other, and most of the tracking algorithms aimed to address the occlusion involve several uncertainties. In this paper, a two-step detection algorithm based on the threshold segmentation and morphological open operation has been developed for identify non-fluorescent labeled particles under microscope, which could separate micro-contact targets. Moreover, we have proposed a novel correlation algorithm that can exploit the strengths of the global shortest path algorithm and Hungarian algorithm, which updating online in real time and considering the occlusion among particles. The proposed approach could achieve the temporal optimal match and spatial optimal solution by utilizing the multi-frame information. Moreover, the proposed method could realize the tracking of occluded particles tracking, and outperform the single global shortest path algorithm and Hungarian algorithm. The proposed method was successfully applied to six real image sequences with the maximum number of particles per frame ranging from 23 to 55, as well as a synthetic and fluorescent labeled sequence. The results of the contrast experiments demonstrated that the proposed algorithm is practical and can realize real-time tracking.

关键词： Target tracking Filtering algorithms detection algorithms Image edge detection Fluorescence Uncertainty Morphological operations Microscopic image multiple particles tracking target occlusion global data association

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Correlations Between Panoramic Imagery and Gamma-Ray Background in an Urban Area

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IEEE TRANSACTIONS ON NUCLEAR SCIENCE 2021年第12期68卷 2818-2834页

作者： Bandstra, M. S. Quiter, B. J. Bilton, K. J. Curtis, J. C. Goldenberg, S. Joshi, T. H. Y. Salathe, M. Lawrence Berkeley Natl Lab Appl Nucl Phys Program Berkeley CA 94720 USA Univ Calif Berkeley Dept Nucl Engn Berkeley CA 94720 USA Coll William & Mary Comp Sci Dept Williamsburg VA 23185 USA

When searching for radiological sources in an urban area, a vehicle-borne detector system will often measure complex, varying backgrounds primarily from natural gamma-ray sources. Much work has been focused on developing spectral algorithms that retain sensitivity and minimize the false-positive rate even in the presence of such spectral and temporal variability. However, information about the environment surrounding the detector system might also provide useful clues about the expected background, which if incorporated into an algorithm, could improve performance. Recent work has focused on extensive measuring and modeling of urban areas with the goal of understanding how these complex backgrounds arise. This work presents an analysis of panoramic video images and gamma-ray background data collected in Oakland, California, by the radiological multisensor analysis platform (RadMAP) vehicle. Features were extracted from the panoramic images by semantically labeling the images and then convolving the labeled regions with the detector response. A linear model was used to relate the image-derived features to gamma-ray spectral features obtained using nonnegative matrix factorization (NMF) under different regularizations. We find some gamma-ray background features correlate strongly with image-derived features that measure the response-adjusted solid angle subtended by sky and buildings, and we discuss the implications for the development of future, contextually aware detection algorithms.

关键词： Gamma-rays Detectors Feature extraction Cameras Urban areas Sensors detection algorithms Environmental radiation effects gamma-ray background gamma-ray detectors gamma-ray spectroscopy non-negative matrix factorization (NMF) radiation environment characterization security applications

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Change detection Algorithm for Multi-Temporal Remote Sensing Images Based on Adaptive Parameter Estimation

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IEEE ACCESS 2020年 8卷 106083-106096页

作者： Chen, Yu Ming, Zutao Menenti, Massimo China Univ Geosci Sch Geog & Informat Engn Wuhan 430078 Peoples R China Delft Univ Technol Fac Civil Engn & Geosci Dept Geosci & Remote Sensing NL-2628 CN Delft Netherlands

This paper proposes a multi-temporal image change detection algorithm based on adaptive parameter estimation, which is used to solve the problems of severe interference of coherent speckle noise and the retention of detailed information about changing regions in synthetic aperture radar remote sensing images. The change area in the initial differential image has local consistency and global prominence. By detecting the significant area to locate similar change areas, the coherent speckle noise outside the area can be eliminated. The use of hierarchical FCM clustering to automatically generate training samples can improve the reliability of training samples. In addition, in order to increase the distinction between the changed area and the non-changed area, a sparse automatic encoder is used to extract the changed features and generate a change detection map. Experiments using 4 sets of SAR images show that the algorithm can effectively reduce the effect of speckle noise on detection accuracy, the extraction of changing areas is more complete and meticulous, and the false detection rate is greatly reduced. Since the images in different time phases will be disturbed by weather, clouds, sea water, etc., the target segmentation algorithm can be used to extract the target of interest and highlight the changing area. Principal component analysis and k-means clustering method are used to reduce the influence of isolated pixels, and change information is extracted to obtain different images. The experiment uses four sets of image data of islands and reefs. The experiment proves that the algorithm can well eliminate external interference, improve the accuracy of change detection, and have a good detection effect on the area of islands and reefs. The adaptive parameter estimation plays a good role in the detection of changing areas, and the visual effect is better, which can improve the accuracy of the detection results.

关键词： Remote sensing Image segmentation Feature extraction Change detection algorithms Sparse matrices Synthetic aperture radar detection algorithms Adaptive parameters multi-temporal remote sensing images change detection model superficial superposition

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A Comprehensive Review of Polyphonic Sound Event detection

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IEEE ACCESS 2020年 8卷 103339-103373页

作者： Chan, T. K. Chin, Cheng Siong Newcastle Univ Fac Sci Agr & Engn Singapore 599493 Singapore Xylem Water Solut Singapore Private Ltd Singapore 609935 Singapore

One of the most amazing functions of the human auditory system is the ability to detect all kinds of sound events in the environment. With the technologies and hardware advances, polyphonic Sound Event detection (SED) can be developed to mimic the ability of the human auditory system. However, the development of a SED system is no trivial task, and several different factors often hinder accuracy. Although there are several overview papers available, most of them only provide a theoretical overview of algorithms used with little discussion. Thus, to the best of the authors' knowledge, there is no comprehensive review that covers this particular domain. Therefore, this paper aims to provide an in-depth discussion of different methodologies proposed by various authors that include the features used, detection algorithms, and their corresponding accuracy and limitations. Additional information on possible trends is also discussed that can be useful for future development works.

关键词： Deep learning detection algorithms Gaussian mixture model non-negative matrix factorization polyphonic sound event detection

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