Parallel Detection Algorithm Research Articles

Performance Analysis of a Multicore Approach Proposed for Efficient Community Detection and Recommendation System

Community detection is a well-known area of research that yields essential results in various fields such as social media, biological networks, pandemic spread, recommendation systems, etc. There are two important areas for improvement in existing community detection algorithms: quality of community formed should be improved and a parallel approach to community detection is needed to handle massive data. In this paper, we have proposed a three-step parallel algorithm, Par-Com, using the concept of k Clique and modularity optimization to address both of the above issues. The proposed algorithm increases the execution speed and also improves the quality of the community formed by optimizing modularity. Par-Com uses dynamic load balancing on the multicore architecture of Supercomputer ParamShivay. We have also evaluated Par-Com’s performance against nine sequential and three parallel community detection algorithms on varying size datasets, i.e. karate, macaque, email, immuno, soc-epinions, facebook, and com-Friendster. The experiment result shows that Par-Com outperforms other algorithms under consideration with up to 45% increase in modularity and up to 84% increase in execution speed. Par-Com is also capable of detecting overlapping communities, fuzzy membership of each node, most influential node in each community formed, and outlier nodes. Nodes within a community that have the highest influence are deemed as experts. The choices made by an expert in a particular community are served as recommendations to other users within that community.

Real-time laser spot detection and tracking system based on parallel multi-target detection and determination algorithm.

Laser spot detection and tracking play a critical role in laser techniques. However, traditional detection and tracking systems tend to be bulky and lack portability. Therefore, there is a growing emphasis on developing high-performance and miniaturized systems based on the field programmable gate array (FPGA). In this paper, a novel parallel multi-target detection and determination algorithm is proposed to address the issue of current FPGA-based systems' ineffective detection of laser spots in complex environments. Our simulation results demonstrate that the algorithm can effectively detect laser spots in complex environments. It can process a frame with an 800 × 480 resolution in only 7.88ms at a 50MHz image processing frequency, which means it can process more than 100 f/s and meet the real-time detection requirements. Such excellent detection performance is challenging to achieve with central processing units and advanced RISC machine microprocessors. Then, the algorithm is further deployed on an FPGA to build a prototype laser spot detection and tracking system. Practical tests show that the system can achieve a spot detection accuracy of around 90% under different luminous intensities, indicating excellent robustness of the designed algorithm. Besides, with the use of a piezoelectric actuator, speedy and precise tracking of the laser spot is implemented. The characteristics of speedy response, self-latching in power off, and no electromagnetic interference of the piezoelectric actuator give the system tremendous advantages in developing high-precision wireless communication control technology, which further broadens the application of the proposed system.

Modified parallel nested-layer particle swarm optimization algorithm for fast bifurcation point detection and its software implementation

This paper reports on the development of straightforward software for bifurcation point detection in discrete-time dynamical systems with a fast and versatile algorithm. Nested-layer particle swarm optimization (NLPSO) is an effective general-purpose bifurcation point detection strategy. In contrast to traditional gradient-based methods, the NLPSO method does not require derivatives of the objective functions. Note that NLPSO can incur high time costs, and parallel computation is practical. The software reported in this study employs modified NLPSO that is optimized for parallel computing. The proposed algorithm is fast and easy to use with limited knowledge of bifurcation point detection, algorithms, or parallel computing techniques.

A parallel feature selection algorithm for detection of cancer biomarkers

Biomarker plays an important role in early disease diagnosis including cancer. The World Health Organization defines a biomarker as any structure or process in the body that is measurable and affects the prognosis or outcome of the disease. Today, biomarkers can be identified using bioinformatics tools. The detection of biomarkers in the field of bioinformatics is considered more as a problem of feature selection. Many feature selection algorithms have been used for biomarker discovery however these algorithms do not have enough accuracy or have computational complexity. For this reason, the researchers discard the high accuracy algorithms because they are time consuming. We redesigned an efficient algorithm based on parallel algorithms. We used the Cancer Genome Atlas (TCGA) including breast cancer patients. The proposed algorithm has the same accuracy and increases the speed of algorithm.

Parallel anomaly detection algorithm for cybersecurity on the highspeed train control system.

With the rapid development of the high-speed train industry, the high-speed train control system has now been exposed to a complicated network environment full of dangers. This paper provides a speculative parallel data detection algorithm to rapidly detect the potential threats and ensure data transmission security in the railway network. At first, the structure of the high-speed train control data received by the railway control center was analyzed and divided tentatively into small chunks to eliminate the inside dependencies. Then the traditional threat detection algorithm based on deterministic finite automaton was reformed by the speculative parallel optimization so that the inline relationship's influences that affected the data detection order could be avoided. At last, the speculative parallel detection algorithm would inspect the divided data chunks on a distributed platform. With the help of both the speculative parallel technique and the distributed platform, the detection deficiency for train control data was improved significantly. The results showed that the proposed algorithm exhibited better performance and scalability when compared with the traditional, non-parallel detection method, and massive train control data could be inspected and processed promptly. Now it has been proved by practical use that the proposed algorithm was stable and reliable. Our local train control center was able to quickly detect the anomaly and make a fast response during the train control data transmission by adopting the proposed algorithm.

A parallel multi-objective evolutionary algorithm for community detection in large-scale complex networks

Community detection in large-scale complex networks has recently received significant attention as the volume of available data is becoming larger. The use of evolutionary algorithms (EAs) for community detection in large-scale networks has gained considerable popularity because these algorithms are fairly effective in networks with a relatively small number of nodes. In this paper, we propose a parallel multi-objective EA, called PMOEA, for community detection in large-scale networks, where the communities associated with key network nodes are detected in parallel. Specifically, we develop a multi-objective and a single-objective EA. The former is used to detect the communities of a key node instead of all communities in the network. The latter obtains the communities in the entire network using the previously detected communities of each key node. The performance of the proposed method was verified on both large-scale synthetic benchmark networks and real-world networks. The results demonstrated the superiority of PMOEA over six EA-based and two non-EA-based community-detection algorithms for large-scale networks.

Multiscale Parallel Algorithm for Early Detection of Tomato Gray Mold in a Complex Natural Environment.

Plant disease detection technology is an important part of the intelligent agricultural Internet of Things monitoring system. The real natural environment requires the plant disease detection system to have extremely high real time detection and accuracy. The lightweight network MobileNetv2-YOLOv3 model can meet the real-time detection, but the accuracy is not enough to meet the actual needs. This study proposed a multiscale parallel algorithm MP-YOLOv3 based on the MobileNetv2-YOLOv3 model. The proposed method put forward a multiscale feature fusion method, and an efficient channel attention mechanism was introduced into the detection layer of the network to achieve feature enhancement. The parallel detection algorithm was used to effectively improve the detection performance of multiscale tomato gray mold lesions while ensuring the real-time performance of the algorithm. The experimental results show that the proposed algorithm can accurately and real-time detect multiscale tomato gray mold lesions in a real natural environment. The F1 score and the average precision reached 95.6 and 93.4% on the self-built tomato gray mold detection dataset. The model size was only 16.9 MB, and the detection time of each image was 0.022 s.

Parallel discrepancy detection and incremental detection

This paper studies how to catch duplicates, mismatches and conflicts in the same process. We adopt a class of entity enhancing rules that embed machine learning predicates, unify entity resolution and conflict resolution, and are collectively defined across multiple relations. We detect discrepancies as violations of such rules. We establish the complexity of discrepancy detection and incremental detection problems with the rules; they are both NP-complete and W[1]-hard. To cope with the intractability and scale with large datasets, we develop parallel algorithms and parallel incremental algorithms for discrepancy detection. We show that both algorithms are parallelly scalable, i.e. , they guarantee to reduce runtime when more processors are used. Moreover, the parallel incremental algorithm is relatively bounded. The complexity bounds and algorithms carry over to denial constraints, a special case of the entity enhancing rules. Using real-life and synthetic datasets, we experimentally verify the effectiveness, scalability and efficiency of the algorithms.

POD: A Parallel Outlier Detection Algorithm Using Weighted kNN

Outlier detection algorithms based on k nearest neighbors ( kNN) can effectively find outliers from massive data, but most algorithms are difficult to adapt to high-dimensional data sets. In order to highlight the importance of attributes in k nearest neighbors, we propose a weighted kNN query method, which uses the Z-order curve to find the kNN. The method first applies information entropy to calculate each attribute weight, and then uses the Z-order curve to encode high-dimensional data into Z-value. The weighted kNN of each object are searched according to its Z-value. Meanwhile, a novel outlier detection algorithm is presented based on the minimum distance and average distance between each object and its weighted kNN. On this basis, we propose a parallel outlier detection algorithm called POD to improve the efficiency of the outlier detection. Finally, we implement and evaluate POD algorithm on a 10-nodes Hadoop cluster, on which synthetic and UCI standard data are tested. Experimental results show that POD achieves high performance in terms of effectiveness, scalability and extensibility.

Catching Numeric Inconsistencies in Graphs

Numeric inconsistencies are common in real-life knowledge bases and social networks. To catch such errors, we extend graph functional dependencies with linear arithmetic expressions and built-in comparison predicates, referred to as numeric graph dependencies (NGDs). We study fundamental problems for NGDs. We show that their satisfiability, implication, and validation problems are Σ p 2 -complete, Π p 2 -complete, and coNP-complete, respectively. However, if we allow non-linear arithmetic expressions, even of degree at most 2, the satisfiability and implication problems become undecidable. In other words, NGDs strike a balance between expressivity and complexity. To make practical use of NGDs, we develop an incremental algorithm IncDect to detect errors in a graph G using NGDs in response to updates Δ G to G . We show that the incremental validation problem is coNP-complete. Nonetheless, algorithm IncDect is localizable, i.e., its cost is determined by small neighbors of nodes in Δ G instead of the entire G . Moreover, we parallelize IncDect such that it guarantees to reduce running time with the increase of processors. In addition, to strike a balance between the efficiency and accuracy, we also develop polynomial-time parallel algorithms for detection and incremental detection of top-ranked inconsistencies. Using real-life and synthetic graphs, we experimentally verify the scalability and efficiency of the algorithms.

Signal Detection Based on Parallel Group Detection Algorithm for High-Load Massive MIMO Systems

In this paper, a parallel group detection (PGD) algorithm is proposed in order to address the degradation in the bit error rate (BER) performance of linear detectors when they are used in high-load massive MIMO systems. The algorithm is constructed by converting the equivalent extended massive MIMO system into two subsystems, which can be simultaneously detected by the classical detection procedures. Then, using the PGD and the classical ZF as well as the QR-decomposition- (QRD-) based detectors, we proposed two new detectors, called ZF-based PGD (ZF-PGD) and QRD-based PGD (QRD-PGD). The PGD is further combined with the sorted longest basis (SLB) algorithm to make the signal recovery more accurate, thereby resulting in two new detectors, namely, the ZF-PGD-SLB and the QRD-PGD-SLB. Various complexity evaluations and simulations prove that the proposed detectors can significantly improve the BER performance compared to their classical linear and QRD counterparts with the practical complexity levels. Hence, our proposed detectors can be used as efficient means of estimating the transmitted signals in high-load massive MIMO systems.

Coverage optimization of visual sensor networks for observing 3-D objects: survey and comparison

Coverage is a basic and critical issue for design and deployment of visual sensor networks, however, the optimization problem is very challenging especially when considering coverage of three-dimensional (3-D) scenarios. This paper provides a brief survey of mainstream coverage optimization methods for visual sensor networks, including the greedy algorithm, genetic algorithm (GA), particle swarm optimization (PSO), binary integer programming (BIP) and differential evolution algorithm (DE). We provide an efficient open-source C++ implementation of these algorithms and compare their performance on a typical camera deployment problem for coverage of 3-D objects. In order to improve the computational efficiency, a parallel visual occlusion detection approach is proposed and implemented with graphic processing units (GPUs), which are then integrated into all of the aforementioned optimization approaches for a fair comparison. Evaluation results show that (1) the proposed parallel occlusion detection algorithm largely improves the computational efficiency; (2) among the five typical approaches, BIP has the best coverage performance yet with the highest time cost, and greedy algorithm is the fastest approach at the price of coverage performance; GA, PSO, and DE achieve a compromise between the performance and the time cost, while DE has better coverage performance and less time cost than PSO and GA. These results could serve as engineering guidelines and baselines for further improvement of coverage optimization algorithms.

A Low-Complexity Near-Maximum-Likelihood Parallel Detection Algorithm with Imperfect Channel State Information

The existing parallel detectors can approach the performance of the maximum likelihood detector (MLD). But, the required computational complexity is very huge for QAM with large constellation size. In this letter, a novel parallel detector, which focuses on the process of channel gain matrix instead of received signals, is proposed. The complex analysis shows that the new parallel detector’s computational complexity at least reduces an order than typical parallel detectors’ and its detection performance still keep near optimal even with imperfect channel state information.

A Novel Fast Linear Iteration Detection Algorithm in MU-Massive MIMO Systems

Massive MIMO is an extremely attractive technology for the potential of improving transmission capacity significantly. However, the minimum mean square error (MMSE) detection algorithm has also high complexity because it involves matrix inversion in obtaining the near-optimal detection for uplink multiuser massive MIMO (MU-Massive MIMO) systems. Iteration detection is effective in avoiding matrix inversion, and several iteration methods have been proposed by academia as well. In this paper, we propose a parallel fast linear iteration detection (FLID) algorithm for uplink MU-Massive MIMO systems. The proposed FLID algorithm can accelerate the convergence rate through reducing the spectral radius $$\rho ({\mathbf {B}})$$ of the re-derived iteration matrix B, and reach almost the same near-optimal detection as MMSE through a parallel iteration. The computational complexity of FLID reduces from $$O(K^{3})$$ to $$O(K^{2})$$ . Simulation results show that the proposed FLID algorithm can also achieve the same detection performance to the traditional MMSE algorithm with lower complexity.

MSNP: A Massively Parallel Algorithm for Large-Scale SNP Detection

Single Nucleotide Polymorphism (SNP) detection is a fundamental procedure of whole genome analysis. SOAPsnp, a classic tool for detection, would take more than one week to analyze one typical human genome, which limits the efficiency of downstream analyses. In this paper, we present mSNP, an optimized version of SOAPsnp, which leverages Intel Xeon Phi coprocessors for large-scale SNP detection. Firstly, we redesigned the essential data structures of SOAPsnp, which significantly reduces memory footprint and improves computing efficiency. Then we developed a coordinated parallel framework for a higher hardware utilization of both CPU and Xeon Phi. Also, we tailored the data structures and operations to utilize the wide VPU of Xeon Phi to improve data throughput. Last but not the least, we proposeed a read-based window division strategy to improve throughput and obtain better load balance. mSNP is the first SNP detection tool empowered by Xeon Phi. We achieved a 38x single thread speedup on CPU, without any loss in precision. Moreover, mSNP successfully scaled to 4,096 nodes on Tianhe-2. Our experiments demonstrate that mSNP is efficient and scalable for large-scale human genome SNP detection.

A parallel immune genetic algorithm for community detection in complex networks

We propose a complex network community discovery method based on parallel immune genetic algorithm for the problem of low efficiency, slow convergence rate and population degradation in community mining method based on genetic algorithm. The algorithm is making use of the principle of parallel immune system to ensure the diversity of population, and enhancing the searching ability by using a single path crossover operator in the initial population and crossover operation, in the initial population and crossover operation to enhance the ability to find the best use of the single path crossover operator, while using the improved character encoding and adaptive mutation operator to further reduce the search space, and improve the population degradation phenomenon. Experiments show that the improved parallel immune genetic algorithm is used to find the problem of complex network community with high accuracy, effectiveness and efficiency.

A parallel immune genetic algorithm for community detection in complex networks

A parallel immune genetic algorithm for community detection in complex networks

Parallel incremental dynamic community detection algorithm based on Spark

Parallel incremental dynamic community detection algorithm based on Spark

Parallel quantum-inspired evolutionary algorithms for community detection in social networks

The world around us may be viewed as a network of entities interconnected via their social, economic, and political interactions. These entities and their interactions form a social network. A social network is often modeled as a graph whose nodes represent entities, and edges represent interactions between these entities. These networks are characterized by the collective latent behavior that does not follow trivially from the behaviors of the individual entities in the network. One such behavior is the existence of hierarchy in the network structure, the sub-networks being popularly known as communities. Discovery of the community structure in a social network is a key problem in social network analysis as it refines our understanding of the social fabric. Not surprisingly, the problem of detecting communities in social networks has received substantial attention from the researchers.In this paper, we propose parallel implementations of recently proposed community detection algorithms that employ variants of the well-known quantum-inspired evolutionary algorithm (QIEA). Like any other evolutionary algorithm, a quantum-inspired evolutionary algorithm is also characterized by the representation of the individual, the evaluation function, and the population dynamics. However, individual bits called qubits, are in a superposition of states. As chromosomes evolve individually, the quantum-inspired evolutionary algorithms (QIEAs) are intrinsically suitable for parallelization.In recent years, programmable graphics processing units — GPUs, have evolved into massively parallel environments with tremendous computational power. NVIDIA® compute unified device architecture (CUDA®) technology, one of the leading general-purpose parallel computing architectures with hundreds of cores, can concurrently run thousands of computing threads. The paper proposes novel parallel implementations of quantum-inspired evolutionary algorithms in the field of community detection on CUDA-enabled GPUs.The proposed implementations employ a single-population fine-grained approach that is suited for massively parallel computations. In the proposed approach, each element of a chromosome is assigned to a separate thread. It is observed that the proposed algorithms perform significantly better than the benchmark algorithms. Further, the proposed parallel implementations achieve significant speedup over the serial versions. Due to the highly parallel nature of the proposed algorithms, an increase in the number of multiprocessors and GPU devices may lead to a further speedup.

A scalable method for link prediction in large real world networks

Link prediction has become an important task, especially with the rise of large-scale, complex and dynamic networks. The emerging research area of network dynamics and evolution is directly related to predicting new interactions between objects, a possibility in the near future. Recent studies show that the precision of link prediction can be improved to a great extent by including community information in the prediction methods. As traditional community-based link prediction algorithms can run only on stand-alone computers, they are not well suited for most of the large networks. Graph parallelization can be one solution to such problems. Bulk Synchronous Parallel (BSP) programming model is a recently emerged framework for parallelizing graph algorithms. In this paper, we propose a hybrid similarity measure for link prediction in real world networks. We also propose a scalable method for community structure-based link prediction on large networks. This method uses a parallel label propagation algorithm for community detection and a parallel community information-based Adamic–Adar measure for link prediction. We have developed these algorithms using Bulk Synchronous Parallel programming model and tested them with large networks of various domains.