Dynamic Search Research Articles

Practical and Dynamic Attribute-Based Keyword Search Supporting Numeric Comparisons Over Encrypted Cloud Data

The attribute-based keyword search (ABKS), which simultaneously achieves searching and fine-grained access control over encrypted data, is frequently applied in cloud computing environments characterized by data storage and sharing. Recently, inspired by attribute-based encryption (ABE) and searchable encryption (SE) primitives, several ABKS schemes have been presented. However, almost all existing ABKS schemes actually only provide an attribute-based keyword equality match function and do not have a structural index to support practical search efficiency and dynamic data updates in real-world applications. To the best of our knowledge, this study is the first to realize an attribute-based keyword search construction supporting numerical comparison expressions with the practical search efficient and dynamic data update capacity (ABKS-NICEST), based on our proposed attribute-based keyword secure search scheme supporting numerical comparison expressions (ABKS-NICE) and an <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">exclusive OR-chain</i> -based inverted index structure. To the best of our knowledge, ABKS-NICEST is the first attributed-based keyword search scheme with practical search efficiency and dynamic data update capacity. In addition, numerical values are an important and common attribute, so providing comparison expressions among numerical values can greatly enhance the expressivity of access policy. Therefore, we use the prefix membership verification technique to design a method to support any numeric comparison expression in a flexible and uniform manner. Through theoretical and experimental evaluations, we determine that ABKS-NICEST is the most efficient ABKS scheme.

Dynamic Heterogeneous Graph Attention Neural Architecture Search

Dynamic heterogeneous graph neural networks (DHGNNs) have been shown to be effective in handling the ubiquitous dynamic heterogeneous graphs. However, the existing DHGNNs are hand-designed, requiring extensive human efforts and failing to adapt to diverse dynamic heterogeneous graph scenarios. In this paper, we propose to automate the design of DHGNN, which faces two major challenges: 1) how to design the search space to jointly consider the spatial-temporal dependencies and heterogeneous interactions in graphs; 2) how to design an efficient search algorithm in the potentially large and complex search space. To tackle these challenges, we propose a novel Dynamic Heterogeneous Graph Attention Search (DHGAS) method. Our proposed method can automatically discover the optimal DHGNN architecture and adapt to various dynamic heterogeneous graph scenarios without human guidance. In particular, we first propose a unified dynamic heterogeneous graph attention (DHGA) framework, which enables each node to jointly attend its heterogeneous and dynamic neighbors. Based on the framework, we design a localization space to determine where the attention should be applied and a parameterization space to determine how the attention should be parameterized. Lastly, we design a multi-stage differentiable search algorithm to efficiently explore the search space. Extensive experiments on real-world dynamic heterogeneous graph datasets demonstrate that our proposed method significantly outperforms state-of-the-art baselines for tasks including link prediction, node classification and node regression. To the best of our knowledge, DHGAS is the first dynamic heterogeneous graph neural architecture search method.

K-Means Binary Search Centroid with Dynamic Cluster for Java Island Health Clustering

This study is focused on determining the health status of each district/city in Java using the K-means Binary Search Centroid and Dynamic Kmeans algorithms. The research data uses data on the health profile of Java Island in 2020. Comparative algorithms were tested using the Davies Bound Index and Calinski-Harabasz Index methods on the traditional k-means algorithm and dynamic binary search centroid k-means. Based on the test, 5 clusters were found in the distribution area, including 11 regions with very high health quality cluster 1, 24 regions with high health quality, 28 regions with moderate health quality, and 28 clusters 4 with low health quality, 45 regions, and cluster 5 with poor health quality is 11 regions, with the best validation value of DBI 1.8175 and CHI 67.7868. Overall optimization of the dynamic k-means algorithm based on binary search centroid results in a better average cluster quality and a smaller number of iterations than the traditional k-means algorithm. The test results can be used as one of the best methods in evaluating the level of health in the Java Island area and a reference for decision-making in determining policies for related agencies.

Interactive Dynamic Presentation (IDP) and Semantic Faceted Search and Browsing (SFB) of the Wittgenstein Nachlass

Abstract In 2000 the Wittgenstein Archives at the University of Bergen (WAB) published the CD-ROM edition of Wittgenstein’s Nachlass: The Bergen Electronic Edition (BEE). Moreover, since then WAB has worked towards complementing the static CD-ROM edition with an interactive web platform that additionally allows more user-specific and more user-tailored utilizations of WAB’s Nachlass resources. The paper describes two specific web service tools of this platform: Interactive Dynamic Presentation (IDP) of the Wittgenstein Nachlass and Semantic Faceted Search and Browsing (SFB) of Wittgenstein domain metadata. The paper argues that it is only when these two tools are fully implemented and functional that WAB can adequately serve the scholarly needs of the Wittgenstein Nachlass user community. The paper discusses some selected features and functionalities of the two tools in detail. An earlier version of this paper was published as „Complementing Static Scholarly Editions with Dynamic Research Platforms: Interactive Dynamic Presentation (IDP) and Semantic Faceted Search and Browsing (SFB) for the Wittgenstein Nachlass“ in Selected Papers from the CLARIN Annual Conference 2020, ed. by C. Navarretta &amp; M. Eskevich (Utrecht: CLARIN ERIC). In writing this paper I have benefitted from comments by K. De Smedt, N. Gangopadhyay, Ø. Gjesdal, J. Hendrickson, C. Huitfeldt, H. Al Ruweh, the Clarin 2020 conference reviewers and editors as well as Jasmin Trächtler.

Drilling Path Planning of Rock-Drilling Jumbo Using a Vehicle-Mounted 3D Scanner

Achieving intelligent rock excavation is an important development direction in underground engineering construction. Currently, some rock-drilling jumbos are able to perform autonomous operations under ideal contour surfaces. However, irregular contour surfaces resulting from factors such as rock characteristics, drilling deviation, and blasting effects present a significant challenge for automated drilling under non-ideal surfaces, which constrains the intelligentization of rock excavation. To address this issue, this paper proposes a method for extracting contour surfaces and planning drilling paths based on a vehicle-mounted 3D scanner. This method effectively extracts contour surfaces and optimizes drilling paths, thereby improving work efficiency and safety. Specifically, the proposed method includes: (i) the real-time scanning of cross-sectional contours using a vehicle-mounted 3D scanner to construct an accurate three-dimensional point-cloud model and obtain contour over-digging information; the acquired data are compared with theoretical drilling maps in the vehicle’s coordinate system to re-plan the blasting-hole point set; (ii) the development of a volume-based dynamic search algorithm based on the irregularities of contour surfaces to detect potential collisions between holes; and (iii) the conversion of the drilling sequence planning based on the new blasting hole point set into a traveling salesman problem (TSP), and optimization using a Hybrid Greedy Genetic Algorithm (HGGA) to achieve path traversal of all drilling positions. The effectiveness of the proposed method was verified using rock excavation in a certain mine as an example. The results show that the overall recognition rate of the contour over-digging reached over 80%, the number of arm collisions was significantly reduced, and the distance traveled by the drilling rig was reduced by 35% using the improved genetic algorithm-based rock-drilling rig path planning.

Adaptive multi-surrogate and module-based optimization algorithm for high-dimensional and computationally expensive problems

Surrogate-assisted evolutionary algorithms (SAEAs) have been widely used for solving computationally expensive optimization problems. However, the performance of SAEAs deteriorates drastically for high-dimensional problems since the accuracy of surrogates may decrease to an insupportably low level in high dimensions which profoundly affect subsequent evolution optimization. To overcome this issue, this paper proposes an adaptive multi-surrogate and module-based optimization algorithm named AMSMO. Unlike existing algorithms that adopt sole surrogate models, AMSMO develops a real-time autoselection of kernels approach for building the most promising RBF. AMSMO makes use of five modules to promote the optimization quality, including: a. global search module; b. local search module; c. semi-local search module; d. convergence module; e. restart module. Specifically, we provide a novel distance- and objective-value-based criterion for global search. The semi-local search module applies a dynamic search criterion that makes the algorithm explore at the early search stage and exploit at the final stage. A cluster-based restart criterion is proposed to address the stagnation of optimization. Several benchmark functions with dimensions varying from 30 to 100 and computationally intensive practical simulation–optimization problems are adopted to validate the proposed algorithm. The empirical experiments demonstrate that the proposed algorithm outperforms other state-of-the-art algorithms in most cases.

Binary dynamic stochastic search algorithm with support vector regression for feature selection in low-velocity impact localization problem

Locating low-velocity impacts (LVIs) on composite plates precisely is necessary. Support vector regression (SVR) is an effective method in addressing the LVI localization problem, whereas a large number of impact features may lead to a slow computational rate and over-fitting of the SVR model. In this paper, a binary dynamic stochastic search algorithm (BDSS) is proposed by introducing a threshold factor and the encoding concept of genetic algorithm into the original dynamic stochastic search algorithm (DSS). Then, a feature selection method is proposed by combining BDSS with SVR, which is called BDSS-SVR. BDSS-SVR as a wrapper method can simultaneously implement dimensionality reduction of impact features and optimize the SVR model’s parameters. Furthermore, a novel LVI localization method based on BDSS-SVR and multi-domain features is designed for accurately detecting the LVIs on a carbon fiber reinforced plastic (CFRP) plate. To analyze the performance of BDSS-SVR, a LVI localization system based on fiber Bragg grating (FBG) sensors is applied to the conduction of two experiments. Two additional control parameters of BDSS-SVR, including the threshold factor and weight coefficient, are tuned in the first experiment. Moreover, the statistical results in the second experiment illustrate that BDSS-SVR outperforms optimized SVR using DSS, feature selection methods based on five state-of-the-art algorithms and SVR, and five machine learning methods. For fifteen random LVIs on the CFRP plate, BDSS-SVR effectively reduces the number of impact features and provides satisfactory localization accuracy. The maximum, minimum, and average errors are 4.659 mm, 0.024 mm, and 3.065 mm, respectively.

K-Means Binary Search Centroid With Dynamic Cluster for Java Island Health Clustering

This study is focused on determining the health status of each district/city in Java using the K-means Binary Search Centroid and Dynamic Kmeans algorithms. The research data uses data on the health profile of Java Island in 2020. Comparative algorithms were tested using the Davies Bound Index and Calinski-Harabasz Index methods on the traditional k-means algorithm and dynamic binary search centroid k-means. Based on the test, 5 clusters were found in the distribution area, including 11 regions with very high health quality cluster 1, 24 regions with high health quality, 28 regions with moderate health quality, and 28 clusters 4 with low health quality, 45 regions, and cluster 5 with deficient health quality is 11 regions, with the best validation value of DBI 1.8175 and CHI 67.7868. Overall optimization of the dynamic k-means algorithm based on binary search centroid results in a better average cluster quality and a smaller number of iterations than the traditional k-means algorithm. The test results can be used as one of the best methods in evaluating the level of health in the Java Island area and a reference for decision-making in determining policies for related agencies

DE-DARTS: Neural architecture search with dynamic exploration

Neural architecture search (NAS) methods automatically find optimal neural networks without human assistance. Numerous algorithms for NAS have been studied to find architectures with gradient-based search. Differentiable architecture search (DARTS), one of the key papers of gradient-based search, dramatically reduced search cost, and showed outstanding performance through continuous relaxation and meta-learning based approximation. However, one of the issues with DARTS is that the gradient-based search process is biased due to the nested bi-level optimization structure, and the greedy behavior of the gradient descent. As a result, there is a problem that search spaces are limited to a limited set of architectures. To overcome the bias of the gradient-based search in network architecture search (NAS), we used a dynamic search method. This technique allows gradient-based search to have exploration. In this paper, we present a novel approach, namely, Dynamic-Exploration DARTS (DE-DARTS). For effective exploration, we use dynamic attention networks (DANs) in DE-DARTS, which change model architectures based on input data. As our DANs are activated early in the search, more diverse architectures are considered, depending on input data at the beginning of search. Our algorithm is evaluated in multiple image classification datasets including CIFAR-10, and ImageNet, and shows improved performance.

Path planning based on reinforcement learning

With the wide application of mobile robots in industry, path planning has always been a difficult problem for mobile robots. Reinforcement learning algorithms such as Q-learning play a huge role in path planning. Traditional Q-learning algorithm mainly uses - greedy search policy. But for a fixed search factor -greedy. For example, the problems of slow convergence speed, time-consuming and many continuous action transformations (such as the number of turns during robot movement) are not conducive to the stability requirements of mobile robots in industrial transportation. Especially for the transportation of dangerous chemicals, continuous transformation of turns will increase the risk of objects toppling. This paper proposes a new method based on - greedy 's improved dynamic search strategy is used to improve the stability of mobile robots in motion planning. The experiment shows that the dynamic search strategy converges faster, consumes less time, has less continuous transformation times of action, and has higher motion stability in the test environment.

Optimizing the parameters of hybrid active power filters through a comprehensive and dynamic multi-swarm gravitational search algorithm

This paper introduces a dynamic comprehensive multi-swarm gravitational search algorithm with non-uniform mutation (cdGSA-2m) for optimizing hybrid active power filter (HAPF) parameters. The cdGSA-2m algorithm divides the population into two heterogeneous sub-populations with different learning mechanisms. The first sub-population focuses on exploitation with comprehensive learning, while the second sub-population emphasizes exploration with a dynamic multi-swarm strategy (DMS) with non-uniform mutation. Additionally, a non-linear adaptive parameter balances the search capabilities of the DMS sub-population. These mechanisms effectively control the exploration, exploitation, and diversity of the population. To analyse the effectiveness of implemented mechanisms in the cdGSA-2m, diversity and exploration–exploitation are discussed at length with a few experiments. An independent structural study of cdGSA-2m is also carried out to understand the effects of comprehensive learning, dynamic multi-swarm strategy, and non-uniform mutation mechanisms in the algorithm. The proposed cdGSA-2m is tested on IEEE-CEC 2017 benchmark problems, and the results of these problems are compared with twenty-two state-of-the-art algorithms in terms of mean error values and standard deviation of the fitness values. Based on the results, the algorithms are ranked against each other, along with statistical validation of the results. The proposed cdGSA-2 m also solves two case studies of the HAPF model and compares the results with existing state-of-the-art algorithms for the HAPF model. Experimental results demonstrate that the cdGSA-2m algorithm has better accuracy, convergence rate, search capability, and stability. The overall ranking of the algorithms suggests that cdGSA-2 m has outstanding potential to deal with difficult optimization problems.

The “desire to conform” and dynamic search by a committee

The “desire to conform” and dynamic search by a committee

Swarm Intelligence in Cooperative Environments: -Step Dynamic Tree Search Algorithm Overview

Reinforcement learning tree-based planning methods have been gaining popularity in the last few years due to their success in single-agent domains, where a perfect simulator model is available: for example, Go and chess strategic board games. This paper pretends to extend tree search algorithms to the multiagent setting in a decentralized structure, dealing with scalability issues and exponential growth of computational resources. The dynamic tree search combines forward planning and direct temporal-difference updates, outperforming markedly conventional tabular algorithms such as learning and state-action-reward-state-action (SARSA). Future state transitions and rewards are predicted with a model built and learned from real interactions between agents and the environment. This paper analyzes the developed algorithm in the hunter–pursuit cooperative game against stochastic and intelligent evaders. The dynamic tree search aims to adapt single-agent tree search learning methods to the multiagent boundaries and is demonstrated to be a remarkable advance as compared to conventional temporal-difference techniques.

Research of natural language processing based on dynamic search corpus in cultural translation and emotional analysis

Research of natural language processing based on dynamic search corpus in cultural translation and emotional analysis

UAV-Aided Computation Offloading in Mobile-Edge Computing Networks: A Stackelberg Game Approach

Unmanned aerial vehicles (UAVs) are considered as a promising method to provide additional computation capability and wide coverage for mobile users (MUs), especially when MUs are not within the communication range of the infrastructure. In this article, a UAV-aided mobile-edge computing (MEC) network, including one UAV-MEC server, one BS-MEC server, and several MUs, is investigated for computation offloading, in which the edge service provider (ESP) manages two kinds of servers. It is considered that MUs have a large number of computation tasks to conduct, while the ESP has idle computational resources. MUs can choose to offload their tasks to the ESP to reduce their pressure and cost, and the ESP can make a profit by selling computational resources. The interaction among the ESP and MUs is modeled as a Stackelberg game, and both the ESP and MUs want to maximize their utility. The proposed game is analyzed by using the backward induction method, and it is proved that a unique Nash equilibrium can be achieved in the game. Then, a gradient-based dynamic iterative search algorithm (GDISA) is proposed to get the approximate optimal solution. Finally, the effectiveness of GDISA is verified by extensive simulations, and the results show that GDISA performs better than other benchmark methods under different scenarios.

Query Join Order Optimization Method Based on Dynamic Double Deep Q-Network

A join order directly affects database query performance and computational overhead. Deep reinforcement learning (DRL) can explore efficient query plans while not exhausting the search space. However, the deep Q network (DQN) suffers from the overestimation of action values in query optimization, which can lead to limited query performance. In addition, ε-greedy exploration is not efficient enough and does not enable deep exploration. Accordingly, in this paper, we propose a dynamic double DQN (DDQN) order selection method(DDOS) for join order optimization. First, the method models the join query as a Markov decision process (MDP), then solves the DRL model by integrating the network model DQN and DDQN weighting into the DRL model’s estimation error problem in query joining, and finally improves the quality of developing query plans. And actions are selected using a dynamic progressive search strategy to improve the randomness and depth of exploration and accumulate a high information gain of exploration. The performance of the proposed method is compared with those of dynamic programming, heuristic algorithms, and DRL optimization methods based on the query set Join Order Benchmark (JOB). The experimental results show that the proposed method can effectively improve the query performance with a favorable generalization ability and robustness, and outperforms other baselines in multi-join query applications.

The same phase creates a unique visual rhythm unifying moving elements in time.

Attention can be selectively tuned to particular features at different spatial locations or objects. The deployment of attention can be guided by properties, such as color, orientation, and so forth, as guiding features. What might be such guiding features for visual stimuli under dynamic rhythmic conditions? We asked specifically what might be the parameters that attract attention when perceiving a visual rhythm. We used a visual search paradigm, in which a dynamic search display consisted of vertically "bouncing balls" with regular rhythms. The search target was defined by a unique visual rhythm (i.e., with either a shorter or longer period) among rhythmic distractors sharing an identical period. We modulated amplitudes and phases of the distractor balls systematically. The results showed a crucial factor of the phase, not the amplitude. If the phase is violated, the target suddenly "pops out" as an "oddball," showing an efficient parallel search. The findings indicate in general the essential role of the phase in conjunction with amplitude and period for visual rhythm perception. Furthermore, a higher saliency of moving objects with a higher frequency component has also been disclosed.

A novel sequential switching quadratic particle swarm optimization scheme with applications to fast tuning of PID controllers

In this work, a sequential switching quadratic particle swarm optimization (SSQPSO) scheme is investigated, where the velocity update mechanism is improved to enhance the convergence performance. Considering the sequential characteristics (related to evolution factors) of the evolution process, a Markov chain with special probability transition matrix is employed to characterize the switching of evolution state. With the help of the mean distance, the concept of population density is first put forward in the dynamic search region enclosed by all particles. Then, taking into account the change of the population density in different generations, two quadratic acceleration terms are introduced into the velocity update model based on the Hadamard product, where four evolution-state-dependent acceleration coefficients are also adopted. The positivity or negativity of the quadratic acceleration terms is retained by resorting to the matrix sign functions. Several widely utilized benchmark functions (including two unimodal and multimodal functions) are employed to evaluate the search capability of the studied SSQPSO scheme. The experimental consequences illustrate that the performance of the developed SSQPSO scheme is superior to that of some popular particle swarm optimization (PSO) schemes. To further demonstrate the effectiveness in practical engineering, the addressed SSQPSO scheme is successfully applied to achieve the fast parameter tuning of the proportional-integral-derivative controller in a spring-mass-damper system.

Hybrid Multilevel Thresholding Image Segmentation Approach for Brain MRI.

A brain tumor is an abnormal growth of tissues inside the skull that can interfere with the normal functioning of the neurological system and the body, and it is responsible for the deaths of many individuals every year. Magnetic Resonance Imaging (MRI) techniques are widely used for detection of brain cancers. Segmentation of brain MRI is a foundational process with numerous clinical applications in neurology, including quantitative analysis, operational planning, and functional imaging. The segmentation process classifies the pixel values of the image into different groups based on the intensity levels of the pixels and a selected threshold value. The quality of the medical image segmentation extensively depends on the method which selects the threshold values of the image for the segmentation process. The traditional multilevel thresholding methods are computationally expensive since these methods thoroughly search for the best threshold values to maximize the accuracy of the segmentation process. Metaheuristic optimization algorithms are widely used for solving such problems. However, these algorithms suffer from the problem of local optima stagnation and slow convergence speed. In this work, the original Bald Eagle Search (BES) algorithm problems are resolved in the proposed Dynamic Opposite Bald Eagle Search (DOBES) algorithm by employing Dynamic Opposition Learning (DOL) at the initial, as well as exploitation, phases. Using the DOBES algorithm, a hybrid multilevel thresholding image segmentation approach has been developed for MRI image segmentation. The hybrid approach is divided into two phases. In the first phase, the proposed DOBES optimization algorithm is used for the multilevel thresholding. After the selection of the thresholds for the image segmentation, the morphological operations have been utilized in the second phase to remove the unwanted area present in the segmented image. The performance efficiency of the proposed DOBES based multilevel thresholding algorithm with respect to BES has been verified using the five benchmark images. The proposed DOBES based multilevel thresholding algorithm attains higher Peak Signal-to-Noise ratio (PSNR) and Structured Similarity Index Measure (SSIM) value in comparison to the BES algorithm for the benchmark images. Additionally, the proposed hybrid multilevel thresholding segmentation approach has been compared with the existing segmentation algorithms to validate its significance. The results show that the proposed algorithm performs better for tumor segmentation in MRI images as the SSIM value attained using the proposed hybrid segmentation approach is nearer to 1 when compared with ground truth images.

Inspection path planning of free-form surfaces based on improved cuckoo search algorithm

To address the problems of long run times, long path length and low efficiencies of traditional intelligent algorithms to optimise free-form surface inspection path algorithms, this paper proposes a method based on an improved cuckoo search algorithm. Since the basic cuckoo search algorithm suffers from problems such as low search efficiency and the tendency to fall into local optimum solutions, the basic cuckoo search algorithm is improved by using a parameter adaptive adjustment strategy and dynamic neighbourhood search strategy, so that the improved cuckoo search algorithm can obtain the optimised inspection path stably and quickly. The local composition of the free-form surface inspection path and the corresponding mathematical model are first analysed, and then traditional intelligent algorithms and the improved cuckoo search algorithm are applied to optimise the mathematical model. The results of inspection experiments conducted with an engine impeller showed that the improved cuckoo search algorithm reduced the length of the optimised inspection path by at least 8.6%, reduced the algorithm run time by at least 35%, and improved the inspection efficiency by at least 1.2% compared to those of the genetic algorithm, simulated annealing algorithm, and ant colony Optimisation algorithm. The improved cuckoo search algorithm allows for effective free-form surface inspection path Optimisation and an improved inspection efficiency.