Search Area Research Articles

An Efficient Incremental Query Recommendation based Query Ranking Model

To retrieve the information from web, search area query estimation depending on the minor query logs is selected as an outstanding method. The log data will be developed very quickly. When new data models are developed the existing models should be updated. In this paper to keep the data up to date a new improved query ranking model is implemented. The new data is added to the existing model incrementally. The results shows the incremental query ranking model based techniques are better efficient than the existing query ranking model with better accuracy.

Long-term tracking with transformer and template update

Aiming at the tracking failure due to the disappearance of the target in the long-term target tracking process, this paper proposes a long-term target tracking network based on the visual transformer and template update. First of all, we construct a feature extraction network based on the transformer and adopt a knowledge distillation strategy to improve the effectiveness of the network for global feature extraction. Secondly, in the modeling transformer, the target features are fully fused with the search area features by using encoder, and the position information in the target query is learned by the decoder. Then, target predictions are performed on the information from the encoder–decoder to obtain tracking results. Meanwhile, we design a score head model to judge the validity of the dynamic template of the current frame before tracking in the next frame. We select the appropriate dynamic template for the tracking of the next frame according to the score result. In this paper, we performed extensive experiments on LaSOT, VOT2021-LT, TrackingNet, TLP, and UAV123 datasets, and the experimental results prove the effectiveness of our method. In particular, it exceeds STARK by 0.8 % (F score) on VOT2021-LT, 1.0 % (S score) on LaSOT, and TrackingNet exceed STARK by 1.1 % (NP score), which also demonstrates the superiority of the method in this paper.

Based on 5G network, use the unmanned aerial vehicles to live stream in VR

5G network has begun to step into people’s horizons. The character of high transmitting speed and low latency can enable some applications to do better in their fields. This article focuses on the new application of the UAV (unmanned aerial vehicles) based on the 5G network. Live streaming is a new entertaining way that is popular worldwide. In this article, the theoretical analysis of the 5G network and VR shows the development of mobile communication technology, the essential features of 5G network, which demonstrate the advantage of 5G, and the Internet requirement of VR. To reduce network bandwidth demand, a different view information transmission can distribute the transmission data of the side angle of view into the in front version. A Popularity Balance Method(PB) for UAVs in the same area layer) method based on the division of the entire search area into multiple area layers to change the pattern of message delivery to the area layer closer to the destination node. Advanced wireless systems (AWS) can make safe, effective operations. Suggestions about using Content Delivery Network (CDN) to solve the live stream problems are also given.

Algorithmic Signal Accuracy Improvement of Solid-State Wave Gyroscopes

Possible ways to algorithmically improve the accuracy of the output signals of solid-state wave gyroscopes of reduced manufacturing quality are discussed - if their resonators have residual mechanical defects in the form of anisotropy of the elastic and damping properties. For this purpose, narrowband amplitude-phase models of oscillations of resonators at the second angular harmonic, recorded in resonant variables, were used. Next, the transition to arbitrary fixed axes, including the instrument axes of the measuring device, is made. A transition to the movable axes of the working and quadrature standing waves was also made to reveal parametric dependencies in the drift function of the output signal of the gyroscope. On this basis, seven main stages of sequential system algorithmic improvement of output signal accuracy are outlined. Among them, the article mainly considers options for combining calibration operations for systematic drift functions of the output signal with new opportunities to improve the accuracy of the signal calculation algorithm. The highlighted options are: the plotting of two-dimensional calibration functions; application of theoretical relations to reduce the complexity of calibration of the drift function; separation of drift functions into calibrated and measured parts. To calibrate the coefficients of the theoretical parts of the drift functions, different models of their identification in the mode of free oscillations of the gyroscope resonator are considered. At the same time, models of oscillations in the movable axes of standing waves and fixed instrument axes were used. Additionally, for the case of a single-electrode parametric excitation system, the possibilities of controlling anisotropy of the elastic and damping properties of gyroscope resonators in the operating mode of exploitation are briefly indicated. The task of identifying the parameters of reference models of oscillations of resonators in order to form alternative algorithms for improving the accuracy of the measurement signal of gyroscopes was also set. The development of the described areas of search for ways to algorithmically improve the accuracy of the signal of solid-state wave gyroscopes with significant residual processing errors of their resonators in practice is expected in the process of their direct development in specific operating conditions.

A Modification of the Monte Carlo Filtering Approach for Correcting Negative SEA Loss Factors

Monte Carlo Filtering (MCF) is one of the methods of Experimental Statistical Energy Analysis (E-SEA), which allows the correction of negative LFs (Loss Factors). In this article, a modification of the MCF method, called DESA (Diagonal Expansion of the Search Area), is proposed. The technique applies a non-uniform extension of the search area when generating a population of normalized energy matrices. The degree of expansion of the search area is controlled by the Diagonal Penalty Factor (DPF). The authors demonstrated the method’s effectiveness on a system that could not be identified in several frequency bands by the classical MCF method. After applying DESA, it was possible to fill in the problematic bands that were missing CLF (coupling loss factor) and DLF (damping loss factor) values. The paper also proposes a way to minimize the errors introduced by using overly high DPF values.

Artificial Intelligence in the Russian Media and Journalism: The Issue of Ethics

Artificial intelligence is gradually being integrated into the work of Russian media and journalists, spreading to various areas of media practices, in particular, information search and fact-checking; speech, sound, and image recognition; prioritization of topics, creation of texts, and their targeted distribution, etc. Such Russian media companies as Interfax, Sports.ru, RIA Novosti are regarded as leaders in this field. With the development of AI, the professional media community inevitably faces new challenges in the ethical regulation of media practices, their correlation with the daily moral issues for editors and journalists. Based on the rich history of foreign and domestic discourse concerning the philosophical and ethical problems of artificial intelligence, as well as analyzing the codes of ethical regulation of AI developments in different countries, the authors highlight the main provisions that need to be taken into account in the process of developing ethical standards for the use of artificial intelligence in media and journalism. The main issues that require mandatory codification include: preservation of the right of the audience to receive information about the interaction with artificial intelligence and transparency in this area on the part of editorial offices; a ban on the transfer of powers to artificial intelligence in matters of moral choice when preparing media content; joint responsibility of the editorial board and software developers for the consequences of the work of artificial intelligence systems, determining the range of potential topics for which the use of artificial intelligence is ethically unacceptable, etc. These recommendations are an invitation to a discussion in media and academic communities about the need for ethical codification of the use of artificial intelligence technologies in media and journalism.

The Importance of Communication in Political Campaign

Political communication is not enough just by combining two definitions, communication and politics. It has its own concept, although it is simply a combination of the two concepts. Communication and politics in the discourse of human science are two areas of search that can each be said to be relatively independent. But both have similarities because they have the same material object, namely humans. The similarity of these material objects makes the two disciplines cannot avoid the meeting of the field of study. The main goal of political communication is to convey political information, shape the political image of the candidate, form public opinion and repel ridicule and accusations from the opposition. An election candidate who masters political communication effectively will get the support of the next voters to win the election. Mistakes in political communication will tarnish the image of an election candidate and erode his political career. An election crisis occurs when the election outcome is unclear, particularly when there is no reliable and agreed-upon mechanism for determining the winner. This paper uses agenda-setting theory (agenda setting) as the basis of the study. Study data were collected through content analysis methods. This paper examines political communication stages before the election, which includes research, goal setting, developing campaign messages, and voter relations plans.

Enhanced chimp optimization algorithm for high level synthesis of digital filters

The HLS of digital filters is a complex optimization task in electronic design automation that increases the level of abstraction for designing and scheming digital circuits. The complexity of this issue attracting the interest of the researcher and solution of this issue is a big challenge for the researcher. The scientists are trying to present the various most powerful methods for this issue, but keep in mind these methods could be trapped in the complex space of this problem due to own weaknesses. Due to shortcomings of these methods, we are trying to design a new framework with the mixture of the phases of the powerful approaches for high level synthesis of digital filters in this work. This modification has been done by merging the chimp optimizer with sine cosine functions. The sine cosine phases helped in enhancing the exploitation phase of the chimp optimizer and also ignored the local optima in the search area during the searching of new shortest paths. The algorithms have been applied on 23-standard test suites and 14-digital filters for verifying the performance of the algorithms. Experimental results of single and multi-objective functions have been compared in terms of best score, best maxima, average, standard deviation, execution time, occupied area and speed respectively. Furthermore, by analyzing the effectiveness of the proposed algorithm with the recent algorithms for the HLS digital filters design, this can be concluded that the proposed method dominates the other two methods in HLS digital filters design. Another prominent feature of the proposed system in addition to the stated enhancement, is its rapid runtime, lowest delay, occupied area and lowest power in achieving an appropriate response. This could greatly reduce the cost of systems with broad dimensions while increasing the design speed.

A fast deconvolution method for multiple sound source localization based on Hilbert curve

To improve the resolution and efficiency of the multiple sound source localization, a fast deconvolution method based on the Hilbert curve, called CLEAN-VH, is developed in this paper. Firstly, the initial cross-spectral matrix of the measured array signal is decomposed by eigenvalue decomposition, and a new cross-spectral matrix function is constructed using the eigenvalues and eigenvectors. Then, the function is used to update the cross-spectral matrix in the process of deconvolution iteration (CLEAN), and the Hilbert curve is introduced to improve the computational efficiency of the output power in the monitoring area. Finally, the distribution of multiple sound sources can be obtained by superimposing the clean beams and residual power spectrum. The superiority of CLEAN-VH is proved by analyzing the key parameters of the method. The search area optimization based on Hilbert curve greatly alleviates the grid mismatch problem and improves the performance of the proposed method. Through the simulation and experimental comparisons with the other three algorithms, such as CLEAN, FB and CLEAN-SC, the proposed CLEAN-VH achieves high-resolution localization of multiple sound sources, better dynamic range and higher computation efficiency. The proposed method provides another choice for the localization of multiple sound sources.

Hybrid Approach for Automated Test Data Generation

Software testing has long been thought to be a good technique to improve the software quality and reliability. Path testing is the most reliable software testing technique and the key method for improving software quality among all testing approaches. On the other hand, test data quality has a big impact on the software testing activity’s ability to detect errors or defects. To solving testing problem, one must locate the entire search space for the relevant input data to encompass the different paths in the testable program. To satisfy path coverage, it is vital test to look at the accumulated test data across the thorough search area. A new approach based on ant colony optimization and negative selection algorithm (HACO-NSA) is presented in this research which overcome the flaws associated with search-based test data by generated automated test data. The optimum path testing objective is to generate appropriate test data to maximise coverage and to enhance the test data’s efficacy, as a result, the test data’s adequacy is validated using a path-based fitness function. In the NSA generation stage, the suggested method alters the new detectors creation using ACO. The proposed approach is evaluated for metrics such as average coverage, average generation, average time, and success rate and comparison has been done with random testing, ant colony optimization and negative selection algorithm Different benchmark programs have been used for object-oriented system. The findings show that the hybrid methodology escalates the coverage percentage and curtail test data size, reduces the redundancy in data and enhances the efficiency. The proposed approach is follows IEEE 829-2008 test documentation in entire testing process.

ВДОСКОНАЛЕННЯ ПОШУКУ СКУПЧЕНЬ РИБИ ЗАСОБАМИ КІНЕМАТИЧНОГО ПРОЕКТУВАННЯ

The article deals with sea and river commercial fisheries. It describes a possible way to increase the efficiency of the movements of fishing vessels when they are looking for accumulations (school) of fish. The improvement of the search movements of ships is based on the use of means and methods of kinematic design, which is part of modern descriptive geometry. In kinematic design, all design components are in independent spatial displacements at different speeds. In this case, the created mathematical dependencies make it possible to determine the coordinates of the spatial displacements of the design object, which here is the wanted accumulation (school) of fish. The basis of the proposed method for searching fish concentrations is the use of an additional search floating facility, such as a boat or motor boat. The fishing vessel and the auxiliary boat are equipped with search sonar equipment. A fishing vessel in the water area of the search area can drift or move at a slow speed along a given trajectory, and the search boat can accompany it and move around it along a circle of a certain radius, due to the range of the search equipment. The aim of the study was to create a methodology for increasing the efficiency of the movement of fishing vessels in search of fish concentrations. Among the main objectives of the study was the development of mathematical dependencies for determining the coordinates of the identified fish aggregations and creating a methodology for an approximate estimate of the volume of fish in its identified aggregation. The advantages of the proposed methodology for improving the search movements of fishing vessels are: - reduction of fuel consumption for the movement of the vessel in the water area of the investigated fish search area; - levelling by the kinematic design of the negative impact of false sonar signals reflected from the bottom of the reservoir. Keywords: marine fishing, fish search, sonar, sketch geometry, kinematic design, search trajectory, mathematical dependence.

Most significant hotspot detection using improved particle swarm optimizers

Significant circular hotspot detection (SCHD) aims at identifying those circular regions in a spatial space where the occurrence of a particular activity is uncommonly higher than the surrounding areas. Plentiful societal applications make SCHD a problem of utmost interest. In many domains, detection of the most significant circular hotspot (MSCHD) is of further usefulness. Well-timed detection of the most significant hotspot helps crucially for short term response planning (STRP) in situations like a disease outbreak, police vigilance, etc. State of the art methods like SaTScan, identify circular hotspots by listing all possible circles in the search area, followed by a statistical significance test, making it a very high computational cost problem. Considering their high costs, these methods are inefficient in applications related to STRP. To reduce the computational time of SaTScan, two randomized versions of the SaTScan algorithm are presented in this paper. Further, the MSCHD problem is modeled as an optimization problem and three improved variants of Particle Swarm Optimizer (PSO) namely I-PSO, HCL-PSO and Ensemble PSO are applied to detect the most significant hotspots. The comparative and sensitivity analysis are performed using synthetic datasets. The comparative analysis of SaTScan and the presented PSO based schemes is made in terms of the quality of identified hotspots and the computational time. Results reveal that the PSO based schemes (I-PSO, HCL-PSO, and Ensemble PSO) are promising and far efficient than randomized and traditional SaTScan algorithms. Further, for the datasets containing only a single hotspot, the performance of all PSO based schemes is at par with each other. However, for datasets with more than one hotspot in the study area, HCL-PSO has lower average rank than I-PSO and Ensemble PSO schemes and hence seems more promising for MSCHD. The superiority of HCL-PSO based hotspot detection is also validated using Friedman Test. Finally, the presented schemes are applied to the case study of Chicago city for identification of different types of crime hotspots.

Improved Particle Swarm Localization Algorithm Based on Evolutionary Mechanism

Aiming at the problem of low localization accuracy and poor robustness when conventional localization algorithms are used for the localization of WSN nodes, an improved particle swarm localization algorithm based on an evolutionary mechanism is proposed. To reduce the ranging error in a complex environment, the anchor box algorithm is improved by using ranging information, and the search area is reduced by the improved algorithm to determine the possible region where unknown nodes exist. Then the objective function is established to transform the localization problem into a bounded optimization problem. The conventional particle swarm optimization algorithm is improved and genetic operators such as crossover and mutation are introduced to improve the search accuracy and global optimization ability of the algorithm. The simulation results show that, compared with the conventional positioning algorithm, the proposed algorithm has smaller positioning errors and stronger robustness.

항공기탑재 레이다의 자세와 위치 변화를 고려한 적응적인 동시운용모드 빔 스케줄링 기법

The purpose of an interleaved mode is to simultaneously provide two or more modes to the user. For this, the time resources allocated to each mode are reduced, and thus, the performance of each mode is degraded. Therefore, a scheduling method is required that can minimize the performance degradation of each mode by appropriately allocating resources. In the interleaved mode that operates the ground imaging and air-to-air search/track modes, the image update interval is longer than that of the single mode operation, and air-to-air tracking performance can be degraded; therefore, scheduling must be planned considering them. In this paper, to always obtain images above a certain proportion of the current search area while minimizing the degradation of air-to-air tracking performance, a scheduling algorithm that adaptively adjusts the image update interval by utilizing the posture and position change of the radar-mounted aircraft is proposed.

Parameter identification method of J–A hysteresis model based on improved sparrow search algorithm

Aiming at the weak capacity of the parameter identification methods of Jiles–Atherton (J–A) hysteresis model, such as slow convergence speed and low convergence accuracy, an improved sparrow search algorithm (IM-SSA) is proposed based on mutation crossover operator and T-distribution perturbation strategy in this paper to improve the convergence speed and accuracy of parameter identification method. Firstly, the initial population of the sparrow search algorithm is enriched by Tent chaotic sequence, which expand the search area. Then, the adaptive crossover and mutation operator is introduced into the finders population to enrich the diversity of the finders population and balance the global and local search ability of the algorithm. Secondly, the T-distribution perturbation or differential mutation is used to perturb the population after each iteration according to the individual characteristics, which can avoid the population singularity in the later stage of the algorithm and enhance the ability of jump out of the local optimal value of the algorithm. Finally, the proposed parameter identification method is used to identify the parameters of the J–A hysteresis model, which are used to simulate the magnetic properties of Non-oriented silicon steel sheets with different magnetic induction intensity. The calculation result is compared with the experiment ones. The results proved that the proposed method has faster convergence speed and higher convergence accuracy.

Effect of a temperature gradient on the behaviour of an endangered Mexican topminnow and an invasive freshwater fish

Climate change and biological invasions are two of the major threats to biodiversity. They could act synergistically to the detriment of natives as non-native species may be more plastic and resilient when facing changing environments. The twoline skiffia (Skiffia bilineata) is an endangered Mexican topminnow that cohabits with invasive guppies (Poecilia reticulata) in some areas in central Mexico. Guppies have been found to take advantage from associating with the twoline skiffia and are considered partially responsible for the decline of its populations. Refuge use and exploratory behaviours are trade-offs between being safe from the unknown and the opportunity to explore novel areas in search for better resources or to disperse. The aim of this study is to investigate how a change in temperature affects the refuge use and exploratory behaviours for both species. We found that temperature affects the refuge use of twoline skiffias, and the swimming activity of both species. Skiffias explored the rock more than guppies regardless of the temperature scenario. Also, smaller fish spent more time performing exploratory behaviours than bigger ones. Our study is the first to test the effect of temperature on the refuge use and exploratory behaviour of a goodeid species, and our results contribute to the idea that some natives could be more affected by climate change than some invaders.

Object semantic-guided graph attention feature fusion network for Siamese visual tracking

The similarity matching between the template and the search area plays a key role in Siamese-based trackers. Most Siamese-based trackers adopt correlation operation to perform feature fusion on the template branch and search branch for similarity matching. However, the correlation operation directly uses the template feature to slide the window on the search area feature without distinguishing the discriminant part of the target and the background noise, which blurs the spatial information of the response feature. To address this issue, this work proposes a novel object semantic-guided graph attention feature fusion network that both removes background information and focuses on the discriminative part of the object. The proposed network effectively removes background noise by utilizing an adaptive template instead of the fixed-size template used by the correlation operation. The network also models the contextual semantic relations of the target and uses the resulting semantic relations to guide the feature fusion process in a part-based manner, thereby accurately highlighting the discriminative parts of the target. Therefore, the problem of blurring response feature caused by correlation operation is effectively resolved. Furthermore, we propose an object-aware prediction network to learn object-aware features for classification and regression task, which effectively improves the discriminative ability of the prediction network. Experiments on many challenging benchmarks like OTB-100, LaSOT, TColor-128, GOT-10k and VOT2019, show that our methods achieves excellent performance.

Federated reinforcement learning approach for detecting uncertain deceptive target using autonomous dual UAV system

This paper develops a cooperative federated reinforcement learning (RL) strategy that enables two unmanned aerial vehicles (UAVs) to cooperate in learning and predicting the movements of an intelligent deceptive target in a given search area. The proposed strategy allows the UAVs to autonomously cooperate, through information exchange of the gained experience to maximize the target detection performance and accelerate the learning speed while maintaining privacy. Specifically, we consider a monitoring model that includes a search area, a charging station, two cooperative UAVs, an intelligent deceptive uncertain moving target, and a fake (false) target. Each UAV is equipped with a limited-capacity rechargeable battery and a communication unit for exchanging the gained experience. The problem of maximizing the detection probability of the uncertain deceptive target using cooperative UAVs is mathematically modeled as a search-benefit maximization problem, which is then reformulated as a Markov decision process (MDP) due to the uncertainty nature of the problem. Because there is no prior information on the targets’ movement, a cooperative RL, is utilized to tackle the problem. The proposed cooperative RL-based algorithm is a distributed collaborative mechanism that enables the two UAVs, i.e., agents, to individually interact with the operating environment and maximize their cumulative rewards by converging to a shared policy while achieving privacy. Simulation results indicate that a cooperative RL-based dual UAV system can noticeably improve the target detection probability, reduce the detection performance, and accelerate the learning speed.

CDX-301 prevents radiation-induced dysregulation of miRNA expression and biogenesis

Risks of radiation exposure necessitate the development ofradioprophylactic drugs. We have reported the efficacy of CDX-301, a recombinantly developed human protein form of Fms-related tyrosine kinase 3 ligand (Flt3L), as a radioprophylactic and radiomitigatory agent. Here, we performed global microRNA profiling to further understand the mechanism of action of CDX-301. We find that CDX-301 administration 24h prior to total body irradiation prevents radiation-induced dysregulation of microRNA biogenesis and expression in murine serum and spleen samples in a time- and tissue-dependent manner. Further analysis shows that activation of the HOTAIR regulatory pathway has a prominent function in radiation-induced injury responses, which is inhibited by pre-treatment with CDX-301. Moreover, CDX-301 attenuates radiation-induced dysregulation of several cellular functions such as inflammatory and immune responses. In corroboration, we also find that pre-treatment with CDX-301 restores the expression of bone marrow aplasia markers and inflammatory cytokines and growth factors, as well as the expression of genes associated with MAP kinase and TGF-β pathways that are altered by radiation. Our findings provide new insights into CDX-301-mediated molecular and cellular mechanisms and point to a possible novel radioprotective drug for the prevention of irradiation-induced injury and hematopoietic acute radiation syndrome.

Implementation of Features Selection Based on Dragonfly Optimization Algorithm

Nowadays increasing dimensionality of data produces several issues in machine learning. Therefore, it is needed to decrease the number of features by choosing just the most important ones and eliminating duplicate features, also reducing the number of features that are important to the model. For this purpose, many methodologies known as Feature Selection are applied. In this study, a feature selection approach is proposed based on Swarm Intelligence methods, which search for the best points in the search area to achieve optimization. In this paper, a wrapper feature selection technique based on the Dragonfly algorithm is proposed. The dragonfly optimization technique is used to find the optimal subset of features that could accurately classify breast cancer as benign or malignant. Many times, the fitness function is defined as classification accuracy. In this study, hard vote classes are employed as a model developed to evaluate feature subsets that have been chosen. It is used as an evaluation function (fitness function) to evaluate each dragonfly in the population. The proposed ensemble hard voting classifier utilizes a combination of five machine-learning algorithms to produce a binary classification for feature selection: Support Vector Machine (SVM), K-Nearest Neighbors (K-NN), Naive Bayes (NB), Decision Tree (DT), and Random Forest (RF). According to the results of the experiments, the voting ensemble classifier has the greatest accuracy value among the single classifiers. The proposed method showed that when training the subset features, the accuracy generated by the voting classifier is high at 98.24%, whereas the training of all features achieved an accuracy of 96.49%. The proposed approach makes use of the UCI repository's Wisconsin Diagnostic Breast Cancer (WDBC) Dataset. Which consists of 569 instances and 30 features.