Automatic Search Research Articles

Linear Attacks On SNOW 3G And SNOW-V Using Automatic Search

Abstract In this paper, a linear attack model of SNOW 3G and SNOW-V based on automatic search technology is proposed. We first describe the linear approximation of Finite State Machine transformation, which allows a wider range of automatic search, then model it with the automatic search technology based on SAT/SMT program. Adopting this generic method, we seek out a binary linear approximation of SNOW 3G with correlation of $2^{-21.92}$ which has been verified by test. Treating this binary approximation as a mask of an 8-bit distribution in a fixed field, we provide a method to obtain the 8-bit distribution. The binary approximation is used in a fast correlation attack with expected time and memory complexity $2^{184.67}$, given $2^{173.96}$ key stream words. For the full version of SNOW-V, considering the linear relationship between Linear Feedback Shift Register parts at three successive moments, we search out a distinguisher with correlation of $2^{-175.51}$, which results in a distinguishing attack with an expected complexity of $2^{351.02}$.

A Mobile DNN Training Processor With Automatic Bit Precision Search and Fine-Grained Sparsity Exploitation

A Mobile DNN Training Processor With Automatic Bit Precision Search and Fine-Grained Sparsity Exploitation

Evaluation of Inspection Efficiency of Diatom Artificial Intelligence Search System Based on Scanning Electron Microscope.

To explore the application values of diatom artificial intelligence (AI) search system in the diagnosis of drowning. The liver and kidney tissues of 12 drowned corpses were taken and were performed with the diatom test, the view images were obtained by scanning electron microscopy (SEM). Diatom detection and forensic expert manual identification were carried out under the thresholds of 0.5, 0.7 and 0.9 of the diatom AI search system, respectively. Diatom recall rate, precision rate and image exclusion rate were used to detect and compare the efficiency of diatom AI search system. There was no statistical difference between the number of diatoms detected in the target marked by the diatom AI search system and the number of diatoms identified manually (P>0.05); the recall rates of the diatom AI search system were statistically different under different thresholds (P<0.05); the precision rates of the diatom AI system were statistically different under different thresholds(P<0.05), and the highest precision rate was 53.15%; the image exclusion rates of the diatom AI search system were statistically different under different thresholds (P<0.05), and the highest image exclusion rate was 99.72%. For the same sample, the time taken by the diatom AI search system to identify diatoms was only 1/7 of that of manual identification. Diatom AI search system has a good application prospect in drowning cases. Its automatic diatom search ability is equal to that of experienced forensic experts, and it can greatly reduce the workload of manual observation of images.

A preliminary analysis on optimization of core flow distribution

Optimization of core flow distribution is closely related with thermal safety, and a good match between core flow distribution and power distribution in reactor core is helpful for the promotion of thermal-hydraulic design, especially for the reactor with natural circulation whose driving force is limited. Flow distribution can be contrived by the reasonable resistance arrangement of core inlet in reactor design. In this study, an automatic solution search algorithm on optimizing inlet resistance distribution of reactor core is designed and established, and also an automatic core flow distribution optimization code coupled with COBRAIIIC code is developed in place of the method by skill and experience of user. A natural circulation reactor is selected to be tested, and the results show that the coolant temperature difference of assembly channel outlets at a specific time point of fuel cycle life can be eliminated by optimizing the core flow distribution. Further, for the flow distribution optimization during fuel cycle life which could be regarded as a global optimization problem with range limits of independent variable, simulated annealing algorithm is applied to solve it. A good optimization result that the coolant temperature difference of the assembly flow channel outlets during a fuel cycle life is reduced effectively is obtained, which provides a support for further enhancement of reactor system performance.

An Automatic Search and Energy-Saving Continuous Tracking Algorithm for Underwater Targets Based on Prediction and Neural Network

Underwater target search and tracking has become a technical hotspot in underwater sensor networks (UWSNs). Unfortunately, the complex and changeable marine environment creates many obstacles for localization and tracking. This paper proposes an automatic search and energy-saving continuous tracking algorithm for underwater targets based on prediction and neural network (ST-BPN). Firstly, the network contains active sensor nodes that can transmit detection signal. When analyzing the reflected signal spectrum, a modified convolutional neural network M-CNN is built to search the target. Then, based on the relationship between propagation delay and target location, a localization algorithm which can resist the influence of clock asynchrony LA-AIC is designed. Thirdly, a scheme based on consensus filtering TS-PSMCF is used to track the target. It is worth mentioning that a predictive switching mechanism, PSM, is added to the tracking process to adjust the working state of nodes. Simulation results show that the recognition accuracy of M-CNN is as high as 99.7%, the location accuracy of LA-AIC is 92.3% higher than that of traditional methods, and the tracking error of TS-PSMCF is kept between 0 m and 5 m.

РЕАЛИЗАЦИЯ СВЕРТОЧНЫХ НЕЙРОННЫХ СЕТЕЙ НА ВСТРАИВАЕМЫХ УСТРОЙСТВАХ С ОГРАНИЧЕННЫМ ВЫЧИСЛИТЕЛЬНЫМ РЕСУРСОМ

Large amounts of video data captured by sensor sensors in various spectral ranges, the significantsize of convolutional neural network architectures create problems with the implementation ofneural network algorithms on peripheral devices due to significant limitations of computing resourceson embedded computing devices. The article discusses the use of algorithms for automatic search andpattern recognition based on machine learning methods, implemented on embedded devices with acomputing resource Graphics Processing Unit. Detection convolutional neural networks «You OnlyLook Once V3» and «You Only Look Once V3-Tiny» are used as a search and pattern recognition algorithm,which are implemented on embedded computing devices of the NVIDIA Jetson line, located indifferent price ranges and with different computing resources ... Also, in the work, the estimates ofalgorithms on embedded devices are experimentally calculated for such indicators as power consumption,forward passage time of a convolutional neural network, and detection accuracy.On the basis of solutions implemented, both at the hardware level and in software, presented byNVIDIA, it becomes possible to use deep neural network algorithms based on the convolutionoperation in real time. Computational optimization methods offered by NVIDIA are considered.Experimental studies of the influence of computations with reduced accuracy on the speed andaccuracy of object detection in images of the investigated architectures of convolutional neuralnetworks, which were previously trained on a sample of images consisting of the PASCAL VOC2007 and PASCAL VOC 2012 datasets, have been carried out.

Automatic tube lens design from stock optics for microscope remote-refocusing systems.

The remote-refocusing approach of Botcherby et al. [Opt. Lett.32, 2007 (2007)10.1364/OL.32.002007] has been applied widely to 2D and 3D fluorescence microscopes to enable rapid refocusing of the optical system without mechanically perturbing the sample. In order for this approach to operate correctly, it requires that the overall magnification of the first two microscope systems matches the ratio of the refractive indices in sample and intermedia image spaces. However, commercially available tube lenses are not always suitable to produce the desired overall magnification. Therefore, a practical approach to produce tube lenses with low expense and diffraction-limited performance is required. Tube lenses can be formed using a pair of stock achromatic doublets, however, selecting appropriate pairs of achromatic doublets from stock optics is a time-consuming process, as many combinations can be considered. In this paper, we present two software packages (Catalogue Generator and Doublet Selector) developed in MATLAB that use the application programming interface (ZOS-API) to the Zemax OpticStudio optical design software to realise an automatic search of stock achromatic doublets to produce microscope tube lenses with a specified focal length, entrance pupil diameter and maximum design field angle. An algorithm to optimise principal plane positions in versions of OpticStudio before 20.2 was also introduced to enable the use of older software versions. To evaluate the performance of Catalogue Generator and Doublet Selector, we used them to generate ten tube lens designs. All of the software-produced tube lenses have a better optical performance than those using manually selected pairs of stock doublets lenses.

Automatic Approach to Explore the Multireaction Mechanism for Medium-Sized Bimolecular Reactions via Collision Dynamics Simulations and Transition State Searches

We develop a broadly applicable computational method for the automatic exploration of the bimolecular multireaction mechanism. The current methodology mainly involves the high-energy Born-Oppenheimer molecular dynamics (BOMD) simulation and the successive reaction pathway construction. Several computational tricks are introduced, which include the selection of the reactive regions based on the electronic structure calculations and the employment of the virtual collision dynamics simulations with the monitoring of the atomic distance before the BOMD simulation. These prescreening steps largely reduce the number of trajectories in the BOMD simulations and significantly save the computational cost. The hidden Markov model combined with the modified atomic connectivity matrix is used for the detection of reaction events in each BOMD trajectory. Starting from several geometries close to the reaction events, the further intermediate optimization and transition state searches are conducted. The proposed method allows us to build the complicated multireaction mechanism of medium-sized bimolecular systems automatically. Here, we examine the feasibility and efficiency of the current method by its performance in searching the mechanisms of two prototype reactions in environmental science, which are the penicillin G anion + H2O and penicillin G anion + OH radical reactions. The result indicates that the proposed theoretical method is a powerful protocol for the automatic search of the bimolecular reaction mechanisms for medium-sized compounds.

Image-based neural architecture automatic search method for hyperspectral image classification

Convolutional neural networks (CNNs) have shown excellent performance for hyperspectral image (HSI) classification due to their characteristics of both local connectivity and sharing weights. Nevertheless, with the in-depth study of network architecture, merely manual empirical design can no longer meet the current scenario needs. In addition, the existing CNN-based frameworks are heavily affected by the redundant three-dimensional cubes of the input and result in inefficient description issues of HSIs. We propose an image-based neural architecture automatic search framework (I-NAS) as an alternative to CNN. First, to alleviate the redundant spectral–spatial distribution, I-NAS feeds a full image into the framework via a label masking fashion. Second, an end-to-end cell-based structure search space is considered to enrich the feature representation. Then, it determined the optimal cells by employing a gradient descent search algorithm. Finally, the well-trained CNN architecture is automatically constructed by stacking the optimal cells. The experimental results from two real HSI datasets indicate that our proposal can provide a competitive performance in classification.

A forensic-driven data model for automatic vehicles events analysis

Digital vision technologies emerged exponentially in all living areas to watch, play, control, or track events. Security checkpoints have benefited also from those technologies by integrating dedicated cameras in studied locations. The aim is to manage the vehicles accessing the inspection security point and fetching for any suspected ones. However, the gathered data volume continuously increases each day, making their analysis very hard and time-consuming. This paper uses semantic-based techniques to model the data flow between the cameras, checkpoints, and administrators. It uses ontologies to deal with the increased data size and its automatic analysis. It considers forensics requirements throughout the creation of the ontology modules to ensure the records’ admissibility for any possible investigation purposes. Ontology-based data modeling will help in the automatic events search and correlation to track suspicious vehicles efficiently.

Solar Controller with Automatic Search Technology for the Maximum Power Point with Autonomy Display

In our work, we have been interested in conducting technological research on the use of photovoltaic energy for lighting. In fact, we have produced a stand-alone photovoltaic system with automatic search for the maximum power point, consisting of a photovoltaic module, a solar regulator, a storage battery and a digital control by microcontroller. The solar field is connected to the input of the regulator and the battery to its output. When the battery voltage is lower than the regulation voltage, the controller operates the photovoltaic generator at maximum power Pmpp and transfers this power to the output. In addition to the protection function, this regulator ensures tracking of the maximum power point (MPPT) and allows the photovoltaic generator to deliver its power whatever the variation of climatic conditions (sunshine and temperature). The main role of the solar regulator is the continuous monitoring of the state of charge of the battery to ensure its protection against overcharging and excessive discharging, the coupling and decoupling of the user as well as its maintenance. The principle of operation is based on controlling a DC-DC converter by a rectangular “PWM” signal generated by a PIC16F874 microcontroller which also controls the entire system. The results of simulation after realization were presented to illustrate the operation of the regulator by curves.

Cognitive and Discursive Peculiarities of the Place-Names Ekaterinburg and Chelyabinsk in the 19th Century British Media Discourse

The study explores the cognitive and discursive aspects of the place names Ekaterinburg and Chelyabinsk in the 19th century British media discourse (1800–1899). The focus on massmedia discourse is due to its double function: not only to reflect the world around, but also to form the public opinion. The research takes an interdisciplinary approach, incorporating a wide range of linguistic and historical methods such as definitional, source study, cognitive-matrix, discourse, comparative and chronological analyses. Automatic and manual searches were used to form the corpus of the study. The sources are British explanatory and encyclopedic dictionaries, geographic reference books, and the digitized archive of historical media texts — the British Newspaper Archive. The authors identify the variants of the place names spelling in modern English and in the 19th century British media discourse (1800–1899), reveal the components of place names’ cognitive matrix: political and economic peculiarities, territory, flora and culture (Chelyabinsk), political and economic peculiarities, territory, culture, flora, fauna, history, religion (Ekaterinburg), specify those extra-linguistic factors that determined the use of the Ural place names in British media discourse. The authors argue that economic, political factors, natural resources are the key for the media’s interest in Ekaterinburg. Compared to Ekaterinburg, the British interest in Chelyabinsk became more consistent only in the late 19th century and was mainly due to economic factors and the building of the Trans-Sib. The coverage of events related to Chelyabinsk and Ekaterinburg correlates with the general attitude to the Russian Empire in the 19th century British media discourse. The authors conclude that place names present a particularly valuable source of information regarding their use in a different linguistic and cultural environment.

IDENTIFICATION OF THE ELECTRICAL IMPEDANCE OF AN IMMOBILIZED TRANSDUCER AND THE MECHANICAL IMPEDANCE OF AN ELECTRODYNAMIC LOUDSPEAKER USING AUTOMATIC FIT OF THE FORCE FACTOR BL

This paper describes a method for determining mechanical and electrical impedances of an electrodynamic transducer based on the automatic brute-force search of the force factor Bl. Also, the way of identifying electrical apparent resistance and apparent inductance of the voice coil is shown as an example of deeper loudspeaker analysis that proposed method allows to achieve. The measured signals are voltage on speaker terminals, voice coil current and membrane displacement. Measuring displacement in addition to voltage and current allows to completely separate transducer mechanical subsystem from electrical and apply various known modeling approaches to them separately.

A systematic literature review of sustainability indicators in sustainability reporting

In the past decade studies related to sustainability indicators (SIs), as a measure of sustainable development (SD), have steeply risen across several domains. Therefore, a systematic literature review (SLR) is required to assess the current state of studies on SIs. The current SLR identifies, evaluates, and synthesises the available literature on SIs published between 1994-2020 with an aim to explore the current development in the field and assess future research directions. The published literature from Web of Science and Scopus database was identified through automatic and manual search techniques and a total of 248 usable studies were finalised after inclusion-exclusion and quality assessment. Thematic analysis of the studies revealed that majority of the studies were from the theme - urban development area, followed by water resources and environment development, and the themes like social sustainability, social equity, dairy, green accounting and biotechnology are less explored.

AutoNAS: Automatic Neural Architecture Search for Hyperspectral Unmixing

Owing to the powerful and automatic representation capabilities, deep learning (DL) techniques have made significant breakthroughs and progress in hyperspectral unmixing (HU). Among the DL approaches, autoencoders (AEs) have become a widely-used and promising network architecture. However, these AE-based methods heavily rely on manual design and may not be a good fit for specific datasets. To unmix hyperspectral images more intelligently, we propose an automatic neural architecture search model for HU, AutoNAS for short, to determine the optimal network architecture by considering channel configurations and convolution kernels simultaneously. In AutoNAS, the self-supervised training mechanism based on hyperspectral images is first designed for generating the training samples of the supernet. Then, the affine parameter sharing strategy is adopted by applying different affine transformations on the supernet weights in the training phase, which enables finding the optimal channel configuration. Furthermore, on the basis of the obtained channel configuration, the evolutionary algorithm with additional computational constraints is introduced into networks to achieve flexible convolution kernel search by evaluating unmixing results of different architectures in the supernet. Extensive experiments conducted on four hyperspectral datasets demonstrate the effectiveness and superiority of the proposed AutoNAS in comparison with several state-of-the-art unmixing algorithms.

APPLICATION OF PIEZOSURGERY IN EXTRACTION OF IMPACTED THIRD MOLARS

Removal of impacted permanent third molars is considered to be one of the most common and routine oral surgical procedures. This intervention is usually performed in the classical way with the use of rotatory &nbsp;instruments and burs. As an alternative to this classical approach, piezosurgery can be used, which is an osteotomy technique based on ultrasonic vibrations. The crucial advantage of piezosurgery is that it is inert to soft tissues. The aim of this study was to compare piezosurgery with the rotatory osteotomy technique, with particular reference to the time required to perform the intervention and the intensity of postoperative sequelae: pain, swelling, and trismus. This paper summarizes published experiences and knowledge of piezosurgery, with special regard to the extraction of mandibular third molars. For the purposes of this research, an automatic detailed search was performed on the electronic database PubMed for the period 2012-2022. Keywords used in the search were: piezosurgery, impacted third molars. The initial filtration resulted in 47 scientific papers, 17 of which met the selection criteria. Of particular interest were papers such as Meta-analyzes and systematic reviews. A review of the literature indicates that although patients undergoing piezosurgery required longer operating times, they had less postoperative pain, swelling, and trismus. Keywords: impacted molar, piezosurgery. &nbsp

Обзор тенденций развития технологий интернета вещей в интеллектуальных транспортных системах

Currently, interest in the development of Internet of Things technologies is increasingly penetrating into the field of intelligent transport systems. The presented review is devoted to the current research situation, analysis and classification of publications on the topic in terms of scientific, temporal, geographical, etc. aspects in order to determine promising development trends. The Scopus Publication Database serves as the source for the selection. Analysis and selection were carried out both manually and with the help of special software for automatic keyword search. The sample can be considered representative: about 2,000 publications from more than 15,537 sources. The statistical characteristics of the study of the application of the Internet of Things technology in intelligent transport systems, obtained as a result of the analysis, were visualized in graphical form by diagrams of various types. Statistics were presented in accordance with various criteria: problem statement, scope, publication activity on different continents, countries, affiliation with authors, research and industrial organizations, publishers, citation index, distribution of temporary research activities in the period 2017-2022, etc. As a result of the analysis, the most relevant set of works was formed, on the basis of which classifications of promising areas of research and methods for the development of intelligent transport systems were developed.

Musical Instrument Recognition with a Convolutional Neural Network and Staged Training

Musical instrument recognition is an important task within the broad field of Music Information Retrieval. It helps to build recommendation systems, compute similarity between musical compositions and enable automatic search in music collections with regard to the instrument. The task has two variants differed by the difficulty level. The simpler one is classification based on the sound of a single instrument, while the more difficult challenge is to recognize the predominant instrument in polyphonic recordings. In this paper, we used a convolutional neural network to solve both of these problems. As the analysis of monotimbral recordings is relatively easy, we used the knowledge acquired during solving it to train a neural network to tackle the more complex predominant instrument recognition problem. Within this staged training scenario, we also examined the impact of introducing some intermediate stages during the training sessions. The results showed that such a training approach has a potential to improve the accuracy of classification.

Object-Based Attention Mechanism for Color Calibration of UAV Remote Sensing Images in Precision Agriculture

Color calibration is a critical step for Unmanned Aerial Vehicles (UAV) remote sensing, especially in precision agriculture, which relies mainly on correlating color changes to specific quality attributes, e.g., plant health, disease, and pest stresses. In UAV remote sensing, the exemplar-based color transfer is popularly used for color calibration, where the automatic search for the semantic correspondences is the key to ensuring the color transfer accuracy. However, the existing attention mechanisms encounter difficulties in building the precise semantic correspondences between the reference image and the target one, in which the normalized cross correlation is often computed for feature reassembling. As a result, the color transfer accuracy is inevitably decreased by the disturbance from the semantically unrelated pixels, leading to semantic mismatch due to the absence of semantic correspondences. In this paper, we proposed an unsupervised object-based attention mechanism (OBAM) to suppress the disturbance of the semantically unrelated pixels, along with a further introduced weight-adjusted AdaIN (WAA) method to tackle the challenges caused by the absence of semantic correspondences. By embedding the proposed modules into a photorealistic style transfer method with progressive stylization, the color transfer accuracy can be improved while better preserving the structural details. We evaluated our approach on the UAV data of different crop types including rice, beans, and cotton. Extensive experiments demonstrate that our proposed method outperforms several state-of-the-art methods. As our approach requires no annotated labels, it can be easily embedded into the off-the-shelf color transfer approaches. Relevant codes and configurations will be available at http://github.com/huanghsheng/object-based-attention-mechanism.

Category of speech system in modeling affective processes (based on the material of network communication)

The goal of this paper is to convey the meaning of the category of speech system for computer cognitive modeling. By implementing it, the authors prove the importance of supplementing relational-situational analysis, based linguistically on the concept of functional syntax, with the technology of creating templates which are the formalization of those sections of stylisticspeech system that mark the mental processes under study. A review of works on speech system since the 1920s is given. It is shown that in recent decades, when studying speech system, the primary interest of linguists lies in the processes of verbal communication and in its main unit — a text. It is noted that the quantitative aspects of the language functioning in various areas and communicative situations are being examined not only in quantitative linguistics but also in the works on artificial intelligence. We prove the hypothesis that the given approach to the automatic analysis of texts is realized in all cases of social communicative practice, when to express the certain affective states or cognitive actions, we can find relatively stable ways of choosing and using linguistic means. From psychological point of view, this is the area of compressed internal actions, which, if necessary, can be expanded and expressed in the form of a verbal reaction or a verbal report. It is a broad research field covering the typologies underlying the known psychological inventories or recorded in descriptions of human behavior in various spheres of activity. The paper demonstrates the resources to analyze stylistic-speech system for the automatic search of professional crisis signs in social networks. The extensive material presented by the fragments of 86 network discussions describes the patterns of choice and use of multi-level language units to express the typical emotional states of currently experienced professional crisis. The linguistic markers of the mentioned states are considered in detail.