Search Speed Research Articles

Penerapan Algoritma String Matching dan Regular Expression pada Aplikasi Kamus Besar Bahasa Indonesia (KBBI)

The Kamus Besar Bahasa Indonesia (KBBI) application is a software or application that allows users to access and search for the meaning of a single word in Indonesian along with its spelling. Indonesian has a complex morphological structure, where words can change form through affixation (addition of prefixes or suffixes), reduplication, or internal changes that create many dialectal variations and spelling variations that can affect word forms and writing patterns. This causes a variety of words that are similar but different in writing. On the other hand, that the Indonesian Dictionary application needs speed and accuracy of word searches then the program makes adjustments to those in the dictionary. Therefore, researchers analyze based on the data that has been obtained by the KBBI application using string matching (brute force) and regular expression algorithms. Researchers hope that the algorithms that have been analyzed can help solve problems in the KBBI application in conducting word searches to find the meaning of spelling funds.

An Adaptive Lion Swarm Optimization Algorithm Incorporating Tent Chaotic Search and Information Entropy

This paper proposes an improved adaptive lion swarm optimization (LSO) algorithm integrating the chaotic search strategy and information entropy to address the problem that the standard LSO algorithm has slow convergence and easily falls into the local optimum in later iterations. At first, an adaptive factor is introduced to improve tent chaotic mapping and used for population position initialization to enhance population diversity and realize uniform traversal while ensuring random distribution, ultimately improving the global search ability. Second, to address the problem that the cub selection strategy is blind, resulting in insufficient traversal in the early stage, a dynamic step-size perturbation factor is established using the second-order norm and information entropy. Adaptive parameters are used to dynamically adjust the selection probability of different cub behaviors based on the number of iterations to suppress the premature convergence of the algorithm. Finally, tent chaotic search is employed to adaptively adjust the search range and improve the individuals with poor fitness through multiple neighborhood points of the local optimal solution, further improving the algorithm’s search speed and accuracy. Experimental results on 18 benchmark functions revealed that the proposed algorithm yields superior performance in terms of convergence speed, optimization accuracy, and ability to jump out of the local optimal solution compared with the standard LSO, gray wolf optimizer, and particle swarm optimization algorithms. Furthermore, the improved LSO algorithm was used to optimize the initial weights and thresholds of the BP neural network, and the effectiveness of the proposed algorithm was further verified by studying the house price prediction problem using two real-world datasets.

The influence of the stimulus category on the characteristics of eye movements in visual search

Introduction. The speed and efficiency of visual search are important characteristics of the activities of a significant range of specialists working with visual information. Specialists have carefully studied the features of visual search from the point of view of cognitive psychology and in connection with the modeling of oculomotor activity. It has been shown that its effectiveness and the parameters of search eye movements are influenced by both stimulation factors and some cognitive factors (features of the subject's preset, the influence of previous search stages, etc.). We assume that the search features are also related to how well a person represents the desired stimulus: whether it belongs to a familiar semantic category, how accurate and unambiguous its visual representation is.
 The study aims to explore the relationship between the parameters of eye movements in visual search and the features of the mental representation of the target object.
 Materials and methods. We have created sets of stimuli belonging to different semantic categories, characterized by different accuracy of mental representations, but having similar sensory qualities. Each object (both the target stimulus and the distractor) in the search field was a black circle on a white background with four black segments inside. The segments formed one of three configurations: a) a face; b) a Roman numeral; c) a random configuration. The study participants were asked to find in a matrix of 84 objects a specific target stimulus belonging to one of the three specified categories. The scientists recorded the participant's eye movements during the search.
 Results. The results of the study generally confirmed the hypothesis about the relationship of search characteristics with the category of the target object. The smallest number of errors, the minimum duration of fixation and the maximum amplitude of saccades characterized the search for Roman numerals — objects with a well-formed and well-defined visual representation. The most difficult, as expected, was the search for objects representing random configurations of segments.
 Limitations. Since the study was largely exploratory in nature, the results obtained require clarification on a wider sample. It is possible that the characteristics of the search were influenced by such a factor not controlled in the study as the configuration of the lines, namely, the presence or absence of their intersections, which should be taken into account in further work.
 Conclusions. The better the visual representation of the target object is formed, the more successful its search is. In this case, eye movements are characterized by a shorter duration of fixation and a higher frequency of saccades of greater amplitude.
 Ethics. The study was conducted in compliance with the ethical principles set out in the Helsinki Declaration of the World Medical Association, all participants gave voluntary informed consent.

Securing Network Information System Design: An Efficient Tool for DSP Undocumented Instruction Mining

As recently studied, the undocumented instructions in embedded processors that may cause catastrophic results for devices have become one of the main threats to system security. To tackle this issue, in this paper, we propose an undocumented instruction mining tool for digital signal processors named DSPUIM that can find out the undocumented instructions from the frequently used Digital Signal Processors (DSP) in network information systems. First, we analyzed the characteristics of the DSP instruction format to compress the instruction search space and improve the instruction search speed. Second, according to the public instruction set of DSPs, we built an instruction disassembly framework that helped us to identify all the undefined instructions. Finally, by testing the executability of undefined instructions automatically, we obtained the undocumented instructions for target DSPs. To demonstrate the effectiveness of our tool, we applied it on ten DSP processors of Texas Instruments (TI) and mined 335 undocumented instructions from them within 5 min. Some undocumented instructions have malicious functions, such as changing registers and denial of service, posing a security threat to the network devices using DSPs.

A feature and conjunction visual search immersive virtual reality serious game for measuring spatial and distractor inhibition attention using response time and action kinematics

ABSTRACT Background Treisman (1980) proposed that visual-spatial attention to targets presented with distractors involves parallel and serial cognition. When the target is different from distractors by a single feature, the number of distractors does not influence search speed (parallel). However, when the target is different from the distractor by a conjunction of features, increased numbers of distractors increase task difficulty (serial). Here, we developed a serious game in immersive virtual reality (IVR) for evaluating spatial and distractor inhibition attention. Methods We tested 60 healthy participants. They performed the serious game in which they had to find a target mole wearing a red miner’s helmet. In the single feature parallel conditions, the distractor moles wore blue (miner’s or horned) helmets, and in the conjunction feature serial conditions, the distractor moles wore blue miner’s helmets or red horned helmets. There were 11–17–23 distractors. Responses were made with the dominant hand by hitting the target with a virtual hammer. We measured mean response time (RT), mean velocity (MV) and coefficient of variance of speed (CV). Results Participants were significantly slower (RT and MV) and showed greater CV when responding to targets in conjunction compared to single feature search tasks. Further, participants were slower (RT and MV) and showed greater CV when the number of distractors increased. A significant interaction between search tasks and distractors showed that RT and CV only increased with distractor number for the conjunction search tasks. MV decreased with distractor number for both single and conjunction tasks, with a stronger decrease for conjunction relative to single feature search. Conclusion The results replicated previous findings, providing support for the use of immersive virtual reality technology for the simultaneous evaluation of spatial and distractor inhibition attention using complex 3D objects.

Inadequate load output diagnosis of ultra-supercritical thermal power units based on MIWOA multi-label random forest

Aiming at the problem of considerable economic losses caused by the large fluctuation range of response load and Automatic Generation Control (AGC) commanded load of ultra-supercritical thermal power units, an inadequate load output diagnosis model of multi-label random forest with multi-improved whale optimization algorithm (MIWOA-MLRF) is proposed. Thermal power units are a kind of high-dimensional, nonlinear, and complex industrial complex, which brings difficulty for conventional mechanistic models in comprehensively analyzing the inadequate output causes of the units. With the help of extensive data analysis and artificial intelligence algorithms, a multi-label random forest (MLRF) for inadequate output cause analysis is constructed. To improve the accuracy of WOA for MLRF classification, a good point set strategy is used to optimize the initial population distribution of WOA, and an improved convergence factor is used to control the speed of WOA search. Three Gaussian simulation datasets with a different number of features and labels and three sets of thermal power operation data with varying periods are utilized. The test results show that the missing alarm rates (MAR) of MIWOA-MLRF are 0.301, 0.621, and 0.802 under three sets of Gaussian data tests, which are the smallest values among all analytical models. Under three sets of thermal power unit operation data, the average values of MIWOA-MLRF's MAR are 2.2%, 0.2%, and 1.5%, respectively, which have lower missing alarm rates compared with other algorithms. Furthermore, in the false alarm rate (FAR) comparison, the FAR of MIWOA-MLRF decreases significantly compared to MLRF. In the optimization time comparison, the optimization time required for MIWOA-MLRF is about 1000 s, which saves 33% on average compared to the optimization time of other algorithms. The result indicates that MIWOA-MLRF can guarantee the same better real-time performance with reduced FAR and MAR of inadequate output cause classification.

Coalition Structure Generation in Edge Computing Environment With Multitasking Concurrency

Edge computing (EC) is a distributed computing paradigm that brings computation and data storage closer to the data sources. With the rapid development of EC, offloading scientific computing and data processing tasks from end devices to edge nodes (ENs) can satisfy these tasks’ requirements. However, a single EN with limited computing capacity is usually insufficient to handle these tasks. Hence, using a coalition structure (CS) of many ENs to handle multiple concurrent tasks in an EC environment becomes a feasible scheme. Still, many existing methods cannot be used directly in this scenario because of enormous CS solution space, low search speed, long-running time, poor optimal solution quality, etc. In response, we propose an arbitrary discrete political optimizer (ADPO) algorithm with a discrete recent-past position updating strategy to explore the potential CS space. Unlike other heuristic algorithms, ADPO further improves the better solutions by interacting with each other in the parliamentary affairs phase. After that, we formulate an integer programming (IP) optimization model to alleviate the low search speed or long time consuming. Finally, extensive experiments demonstrate that ADPO is superior to the existing heuristic algorithm. In addition, the IP model excels in exiting algorithms at the running time (milliseconds level) and resource occupied ratio.

A nuclear magnetic resonance proxy model for predicting movable fluid of rocks based on adaptive ensemble learning

The movable fluid percentage and movable fluid porosity of rocks are important parameters for evaluating the development potential of petroleum reservoirs, which are usually determined by expensive and time-consuming low-field nuclear magnetic resonance (NMR) experiments combined with centrifugation. In this study, an NMR proxy model based on adaptive ensemble learning was proposed to predict the rock movable fluid indexes efficiently and economically. We established adaptive ensemble learning via an opposite political optimizer (AEL-OPO), which adaptively combines 33 base learners through political optimization to increase the prediction accuracy of the NMR proxy model. To improve the generalization ability of the AEL-OPO, opposition-based learning was introduced to improve the global search speed and stability of the political optimizer. Accessible petrophysical parameters, such as rock density, porosity, permeability, average throat radius, and maximum throat radius, were used as a training set, a validation set, and a test set. The prediction results show that our new strategy outperforms the other 33 base learners, with R2 (coefficient of determination) values of 84.64% in movable fluid percentage and 74.09% in movable fluid porosity.

Association Rule Mining through Combining Hybrid Water Wave Optimization Algorithm with Levy Flight

Association rule mining (ARM) is one of the most important tasks in data mining. In recent years, swarm intelligence algorithms have been effectively applied to ARM, and the main challenge has been to achieve a balance between search efficiency and the quality of the mined rules. As a novel swarm intelligence algorithm, the water wave optimization (WWO) algorithm has been widely used for combinatorial optimization problems, with the disadvantage that it tends to fall into local optimum solutions and converges slowly. In this paper, a novel hybrid ARM method based on WWO with Levy flight (LWWO) is proposed. The proposed method improves the solution of WWO by expanding the search space through Levy flight while effectively increasing the search speed. In addition, this paper employs the hybrid strategy to enhance the diversity of the population in order to obtain the global optimal solution. Moreover, the proposed ARM method does not generate frequent items, unlike traditional algorithms (e.g., Apriori), thus reducing the computational overhead and saving memory space, which increases its applicability in real-world business cases. Experiment results show that the performance of the proposed hybrid algorithms is significantly better than that of the WWO and LWWO in terms of quality and number of mined rules.

USV path planning algorithm based on plant growth

Unmanned surface vehicles (USVs) have been widely used in patrol, exploration and military applications, replacing manned vessels in dangerous areas of the sea where humans cannot reach or where there is a high risk. When USVs are used for a variety of tasks in the complex and changing marine environment, autonomous navigation and obstacle avoidance are essential technologies for USVs to be equipped. In response to the current problems of slow search speed, low collision avoidance efficiency and long paths in path planning, this paper proposes a PGR (Plant Grow Route) algorithm. It is based on the principle of phototropism of plant growth, which guides USVs to avoid obstacles and reach target points through the magnitude of light intensity. Experiments show that this algorithm can successfully avoid static obstacles and allow USVs to follow the route with the strongest light intensity to reach the target point safely, and it requires short search time and high efficiency. The improved G-DWA algorithm is used to implement the PGR algorithm for local path planning, and local path planning is used as a key development direction for this experiment in the future.

Research on electrical load distribution using an improved bacterial foraging algorithm

This paper proposes an improved bacterial foraging algorithm for electrical load distribution to impro-ve power plants’ efficiency and reduce energy consumption costs. In the chemotaxis stage, the adaptive step size is introduced to accelerate the random search speed compared with the traditional algorithm. In the replication stage, a hybrid crisscross operator is proposed to replace the traditional binary replication method in the algorithm to ensure the diversity of the population and improve the efficiency of the algorithm. The adaptive dynamic probability is used instead of the initial fixed probability to improve the global search performance of the algorithm. The mathematical model of electrical load distribution in a natural power plant is established, and the improved bacterial foraging algorithm is used to solve the model. Through comparative analysis of two power plant unit experiments, it is proved that the results of the improved algorithm can reduce 3.671% and 1.06% respectively compared with the particle swarm optimization algorithm, and 7.26% and 1.37% respectively compared with the traditional bacterial foraging algorithm, which can significantly reduce the coal consumption of the power plant.

Timing of physical activity across adulthood on later-life cognition: 30 years follow-up in the 1946 British birth cohort

BackgroundTo assess how timing, frequency and maintenance of being physically active, spanning over 30 years in adulthood, is associated with later-life cognitive function.MethodsParticipants (n=1417, 53% female) were from the prospective...

Application of A* Algorithm Based on Extended Neighborhood Priority Search in Multi-Scenario Maps

The robustness of the traditional A* algorithm of path planning is poor due to its excessive number of traversal nodes, slow search speed, and large turning angle. Aiming to solve the above problems, a multi-scenario adaptive A* algorithm based on extended neighborhood priority search is proposed. Firstly, this algorithm designs the heuristic function that can adapt to various scene changes by quantifying the scene map information, and the search weight is adjusted adaptively to enhance the robustness and adaptability of the algorithm. Secondly, the search strategy based on extended neighborhood priority is adopted to improve the orientation of the algorithm, and the redundant node removal strategy is used to smooth the path to reduce the number of traversed nodes and the turning angle. Finally, simulation tests are conducted in several representative map environments. The test results show that the proposed algorithm is superior to the traditional A* algorithm due to its stronger robustness and significantly improved performance metrics, with an 84.95% reduction in the number of traversal nodes, an 83.84% reduction in the number of path nodes, a 62.28% reduction in turning points on the path, a 77.38% reduction in the total turning angle, and a 58.47% reduction in the search time.

A Hybrid STA Based on Nelder–Mead Simplex Search and Quadratic Interpolation

State transition algorithm (STA) is a metaheuristic method for global optimization. However, due to the insufficient utilization of historical information, it still suffers from slow convergence speed and low solution accuracy on specific problems in the later stages. This paper proposes a hybrid STA based on Nelder–Mead (NM) simplex search and quadratic interpolation (QI). In the exploration stage, NM simplex search utilizes the historical information of STA to generate promising solutions. In the exploitation stage, QI utilizes the historical information to enhance the local search capacity. The proposed method uses an eagle strategy to maximize the efficiency and stability. The proposed method successfully combines the merits of the three distinct approaches: the powerful exploration capacity of STA, the fast convergence speed of NM simplex search and the strong exploitation capacity of QI. The hybrid STA is evaluated using 15 benchmark functions with dimensions of 20, 30, 50 and 100. Moreover, the results are statistically analyzed using the Wilcoxon signed-rank sum test. In addition, the applicability of the hybrid STA to solve real-world problems is assessed using the wireless sensor network localization problem. Compared with six state-of-the-art metaheuristic methods, the experimental results demonstrate the superiority and effectiveness of the proposed method.

Addition of Out-of-Population Search in JADE

JADE is an optimization algorithm that uses probability distributions to adaptively select parameters. However, it does not take into account the search for regions outside the solution population, so it can be improved by adding an efficient out-of-population search such as the Nelder-Mead method. In this study, a simple method with a small number of parameters to add out-of-population search was considered while keeping the search speed as high as possible, and its effectiveness was confirmed through numerical experiments.

Efficacy improvement in searching MEDLINE database using a novel PubMed visual analytic system: EEEvis.

PubMed is the most extensively used database and search engine in the biomedical and healthcare fields. However, users could experience several difficulties in acquiring their target papers facing massive numbers of search results, especially in their unfamiliar fields. Therefore, we developed a novel user interface for PubMed and conducted three steps of study: step A, a preliminary user survey with 76 medical experts regarding the current usability for the biomedical literature search task at PubMed; step B is implementing EEEvis, a novel interactive visual analytic system for the search task; step C, a randomized user study comparing PubMed and EEEvis. First, we conducted a Google survey of 76 medical experts regarding the unmet needs of PubMed and the user requirements for a novel search interface. According to the data of preliminary Google survey, we implemented a novel interactive visual analytic system for biomedical literature search. This EEEvis provides enhanced literature data analysis functions including (1) an overview of the bibliographic features including publication date, citation count, and impact factors, (2) an overview of the co-authorship network, and (3) interactive sorting, filtering, and highlighting. In the randomized user study of 24 medical experts, the search speed of EEEvis was not inferior to PubMed in the time to reach the first article (median difference 3 sec, 95% CI -2.1 to 8.5, P = 0.535) nor in the search completion time (median difference 8 sec, 95% CI -4.7 to 19.1, P = 0.771). However, 22 participants (91.7%) responded that they are willing to use EEEvis as their first choice for a biomedical literature search task, and 21 participants (87.5%) answered the bibliographic sorting and filtering functionalities of EEEvis as a major advantage. EEEvis could be a supplementary interface for PubMed that can enhance the user experience in the search for biomedical literature.

GGNN: Graph-Based GPU Nearest Neighbor Search

Approximate nearest neighbor (ANN) search in high dimensions is an integral part of several computer vision systems and gains importance in deep learning with explicit memory representations. Since PQT, FAISS, and SONG started to leverage the massive parallelism offered by GPUs, GPU-based implementations are a crucial resource for today's state-of-the-art ANN methods. While most of these methods allow for faster queries, less emphasis is devoted to accelerating the construction of the underlying index structures. In this paper, we propose a novel GPU-friendly search structure based on nearest neighbor graphs and information propagation on graphs. Our method is designed to take advantage of GPU architectures to accelerate the hierarchical construction of the index structure and for performing the query. Empirical evaluation shows that GGNN significantly surpasses the state-of-the-art CPU- and GPU-based systems in terms of build-time, accuracy and search speed.

Multi-Objective Intercity Carpooling Route Optimization Considering Carbon Emission

In recent years, intercity carpooling has been vigorously developed in China. Considering the differences between intercity carpooling and intracity carpooling, this paper first defines the intercity carpooling path optimization problem with time window. Based on the balance of interests among passengers, platform, and government, a multi-objective function is constructed to minimize passenger cost, maximize platform revenue, and minimize carbon emission cost, with vehicle capacity, boarding and alighting points, vehicle service, and other constraints. Secondly, in order to further improve the coordination ability and search speed of the operator, this paper uses the particle swarm optimization algorithm to help the operator remember the previous search position and iterative information, and designs the PSO (Particle Swarm Optimization) improved NSGA-II (Non-dominated Sorting Genetic Algorithm) algorithm to solve the multi-objective model. Finally, the feasibility of the model is verified by numerical analysis of Xi’an–Xianyang intercity carpool. The results show that the path of vehicle 1 is 5-8-O-D-16-13, the path of vehicle 2 is 7-3-6-O-D-15-11-14, and the path of vehicle 3 is 2-1-4-O-D-12-10-9. Compared with NSGA-II algorithm, the PSO-NSGA-II algorithm designed in this paper has significant advantages in global search ability and convergence speed.

Data Search Process Optimization using Brute Force and Genetic Algorithm Hybrid Method

High accuracy and speed in data search, which are aims at finding the best solution to a problem, are essential. This study examines the brute force method, genetic algorithm, and two proposed algorithms which are the development of the brute force algorithm and genetic algorithm, namely Multiple Crossover Genetic, and Genetics with increments values. Brute force is a method with a direct approach to solving a problem based on the formulation of the problem and the definition of the concepts involved. A genetic algorithm is a search algorithm that uses genetic evolution that occurs in living things as its basis. This research selected the case of determining the pin series by looking for a match between the target and the search result. To test the suitability of the method, 100-time tests were conducted for each algorithm. The results of this study indicated that brute force has the highest average generation rate of 737146.3469 and an average time of 1960.4296, and the latter algorithm gets the best score with an average generation rate of 36.78 and an average time of 0.0642.

Visual expertise is more than meets the eye: an examination of holistic visual processing in radiologists and architects.

One of the dominant behavioral markers of visual-expert search strategy, holistic visual processing (HVP), suggests that experts process information from a larger region of space in conjunction with a more focused gaze pattern to improve search speed and accuracy. To date, extant literature suggests that visual search expertise is domain specific, including HVP and its associated behaviors. The current study is the first to use eye tracking to directly measure the HVP strategies of two expert groups, radiologists and architects, in comparison to one another and a novice control. In doing so, we replicated and extended this prior research: visual expertise is domain specific. However, our eye-tracking data indicate that contrary to this prior work, HVP strategies are transferable across domains. Yet, despite the transfer of HVP strategies, there is neither reduced search time nor greater accuracy in visual search outside of an expert's domain. Therefore, our data suggest that HVP behaviors are a particular form of visual search mechanism that, outside of an expert's native search-ecology, are not necessarily conducive to more general visual search success. It is in addition to explicit knowledge of an expert's domain, how to search, and where to search, that HVP strategies are their most effective for visual search success.