Patterns In Databases Research Articles

An Improved Multimodal Biometric Identification System Employing Score-Level Fuzzification of Finger Texture and Finger Vein Biometrics.

This research work focuses on a Near-Infra-Red (NIR) finger-images-based multimodal biometric system based on Finger Texture and Finger Vein biometrics. The individual results of the biometric characteristics are fused using a fuzzy system, and the final identification result is achieved. Experiments are performed for three different databases, i.e., the Near-Infra-Red Hand Images (NIRHI), Hong Kong Polytechnic University (HKPU) and University of Twente Finger Vein Pattern (UTFVP) databases. First, the Finger Texture biometric employs an efficient texture feature extracting algorithm, i.e., Linear Binary Pattern. Then, the classification is performed using Support Vector Machine, a proven machine learning classification algorithm. Second, the transfer learning of pre-trained convolutional neural networks (CNNs) is performed for the Finger Vein biometric, employing two approaches. The three selected CNNs are AlexNet, VGG16 and VGG19. In Approach 1, before feeding the images for the training of the CNN, the necessary preprocessing of NIR images is performed. In Approach 2, before the pre-processing step, image intensity optimization is also employed to regularize the image intensity. NIRHI outperforms HKPU and UTFVP for both of the modalities of focus, in a unimodal setup as well as in a multimodal one. The proposed multimodal biometric system demonstrates a better overall identification accuracy of 99.62% in comparison with 99.51% and 99.50% reported in the recent state-of-the-art systems.

A star identification algorithm for rolling shutter exposure based on Hough transform

The star identification algorithm usually identifies stars by angular distance matching. However, under high dynamic conditions, the rolling shutter effect distorts the angular distances between the measured and true star positions, leading to plethoric false matches and requiring complex and time-consuming verification for star identification. Low identification rate hinders the application of low-noise and cost-effective rolling shutter image sensors. In this work, we first study a rolling shutter distortion model of angular distances between stars, and then propose a novel three-stage star identification algorithm to identify distorted star images captured by the rolling shutter star sensor. The first stage uses a modified grid algorithm with adaptive error tolerance and an expanded pattern database to efficiently eliminate spurious matches. The second stage performs angular velocity estimation based on Hough transform to verify the matches that follow the same distortion pattern. The third stage applies a rolling shutter error correction method for further verification. Both the simulation and night sky image test demonstrate the effectiveness and efficiency of our algorithm under high dynamic conditions. The accuracy of angular velocity estimation method by Hough transform is evaluated and the root mean square error is below 0.5 (°)/s. Our algorithm achieves a 95.7% identification rate at an angular velocity of 10 (°)/s, which is much higher than traditional algorithms.

Bordering in the archives: An investigation into a digital archive of the Irish asylum and refugee determination

Creating borders and borderlands is a key role of the contemporary state. Drawing on an investigation of the Irish state bordering complex determining asylum and refugee claims in Ireland, this article illustrates the importance of the actions of individual state agents and the cultures of practice in state agencies in bordering and statecraft. Building on scholarship studying the state, I conceive of statecraft as cultures of practices, understood within a feminist geopolitical framework. Studying these cultures of practice reveals the ways state agencies and state agents produce knowledge and how they work within complex power-geometries of social and political geographies. I investigate the Irish asylum determination process using an archival ethnography methodology to encounter an online archive of refugee appeals decisions in Ireland. This article shows one result of this encounter: a published database of asylum decision patterns and practices of the refugee appeals tribunals and of individual Tribunal members issuing decisions to people seeking asylum in Ireland. I argue this approach: (1) addresses real needs for systematic evidence of practice of Tribunal members, as requested by asylum seekers and legal representatives; (2) investigates cultures of practice in decision-making bodies in the asylum complex of the Irish state; (3) and shows the importance of archives of asylum decision-making in the state practice of bordering and statecraft. Analysis shows an asylum determination culture heavily dependent on who is doing the deciding, the decision-maker assigned is a major element in outcomes of asylum claims. I also propose a framework for identifying statecraft and bordering as the collective project of groups and individuals embedded within the state; this framework provides productive and systematic evidence of bordering and the ways individuals and groups go about doing statecraft in the asylum determination process.

Multipulse operation in Mamyshev oscillator: influence of external seed source

We study the influence of an external seed source on the startup of a fiber Mamyshev oscillator (MO). Depending on the seed source parameters, while keeping MO parameters fixed, we observe diverse mode-locked operations from the MO, generating stable single, double, triple and even up to a group of seven pulses. The probability of the occurrence of such mode-locked operations is studied. There can be different operating regimes in a particular multipulse operation. We find that there is an optimum spectral width of the seed source for which the probability of generating mode-locked stable train of single pulses from a fiber MO is maximum. Further, we find that the power of the seed source has less influence on the mode-locking state of the fiber MO; however, the probability of single or double pulse operation is relatively higher than that of multipulse operation at higher power of the seed source. These experimental observations will enrich the database of multipulse patterns and help in understanding pulse dynamics in the fiber MO. Suitably chosen seed source parameters can generate the desired pulse pattern. Such tailored outputs could have prospective applications in laser micromachining and high bit rate data transfer in optical networks.

Carboxypeptidase inhibitors from Solanaceae as a new subclass of pathogenesis related peptide aiming biotechnological targets for plant defense.

Background: Plant protease inhibitors play a crucial role in inhibiting proteases produced by phytopathogens and exhibiting inhibitory effects on nematodes, fungi, and insects, making them promising candidates for crop protection. Specifically, carboxypeptidase inhibitors, a subset of proteinase inhibitors, have been extensively studied in potato and tomato of Solanaceae plant family. However, further research is needed to fully understand the functions and biotechnological potential of those inhibitors in plants. This work aimed to in silico characterize carboxypeptidase inhibitors from Solanaceae as potential antimicrobial and defense agents focused on biotechnological targets. Methods: The methodology employed involved search in UniProt, PDB, KNOTTIN, NCBI, and MEROPS databases for solanaceous carboxypeptidase inhibitors, phylogenetic relationships and conservation patterns analyzes using MEGA-X software and Clustal Omega/MView tools, physicochemical properties and antimicrobial potential prediction using ProtParam, ToxinPred, iAMPred, and APD3 tools, and structural features prediction using PSIPRED. Results and discussion: A systematic literature search was conducted to identify relevant studies on Solanaceae carboxypeptidase inhibitors and their activities against pathogens. The selected studies were reviewed and the main findings compiled. The characterization of Solanaceae carboxypeptidase inhibitors proposed for the first time the global sequence consensus motif CXXXCXXXXDCXXXXXCXXC, shedding light on carboxypeptidase inhibitors distribution, sequence variability, and conservation patterns. Phylogenetic analysis showed evolutionary relationships within the Solanaceae family, particularly in Capsicum, Nicotiana, and Solanum genera. Physicochemical characteristics of those peptides indicated their similarity to antimicrobial peptides. Predicted secondary structures exhibited variations, suggesting a broad spectrum of action, and studies had been demonstrated their activities against various pathogens. Conclusion: Carboxypeptidase inhibitors are being proposed here as a new subclass of PR-6 pathogenesis-related proteins, which will aid in a focused understanding of their functional roles in plant defense mechanisms. These findings confirm the Solanaceae carboxypeptidase inhibitors potential as defense agents and highlight opportunities for their biotechnological applications in pathogen control.

CAPEC ontology

CAPEC is an effort coordinated by MITRE Corporation. Its aim is attack pattern database structured in taxonomies. CAPEC is available as XML document from its project site. CAPEC structure and content are under permanent change and development. It is still not mature database but may be never will.CAPEC, CWE, and CVE are databases devoted to attacks, weaknesses, and vulnerabilities. They refer each other forming a knowledge ecosystem in cybersecurity area.Traditional approach for knowledge presentation as information does not work well with conceptualizations under dynamics of this ecosystem and particularly of CAPEC. In this paper an alternative approach to CAPEC knowledge presentation is proposed, as ontology. First, CAPEC structure and content are discussed and then ontology structure is introduced. CAPEC as ontology opens doors to ``open world'' concept that is more adequate to ecosystem dynamics.

Application of the equivalent source method to preserve and reproduce the spatial characteristics of the sound source

Many works have been done to make a database of sound sources' radiation patterns, such as musical instruments. However, the conventional approaches were mainly based on the farfield measurement at some selected points on the spherical surface. A huge amount of data was presented in every spherical coordinate's angular direction. To overcome the problem, a method based on the equivalent source method(ESM) is proposed to record and exchange information on the spatial radiation characteristics from a sound source. By utilizing the principle of acoustical holography, complicated radiation patterns and strengths of an actual source can be approximately described by expanding the spherical harmonics in various orders. Such a method enables preserving the radiation pattern information of sound sources keeping only details on the position coordinate of equivalent sources and the coefficient of each order of spherical harmonics, which can be regarded as a standard format. If the proposed approach is used, a more precise and large amount of information can be stored with a smaller size of data than the conventional dataset.

On suspicious tracks: Machine-learning based approaches to detect cartels in railway-infrastructure procurement

In railway infrastructure, construction and maintenance is typically procured using competitive procedures such as auctions. However, these procedures only fulfill their purpose – using (taxpayers’) money efficiently – if bidders do not collude. Employing a unique dataset of the Swiss Federal Railways, we present two methods in order to detect potential collusion: First, we apply machine learning to screen tender databases for suspicious patterns. Second, we establish a novel category-managers’ tool, which allows for sequential and decentralized screening. To the best of our knowledge, our study represents the first attempt to adapt and implement machine-learning-based price screens within the context of a railway-infrastructure market.

The Effects of Online Social Identity Signals on Retailer Demand

Recent shifts in societal discourse have led digital platforms to support equity, inclusivity, and diversity by introducing identity-signaling features, for example, indicators of owner race or gender. In this work, we explore whether, when, and how using those features may impact retailer demand. We tackle this question via a multimethod study. We begin by conducting a controlled experiment on Prolific.co , presenting subjects with actual Google Places business profiles for a set of Black-owned restaurants in Chicago. We randomly vary the presence of Black- and women-owned labels in these profiles and assess subjects’ expectations of popularity and quality along various restaurant dimensions. Our results demonstrate that the Black-owned label, in particular, drives significant increases in all outcomes with the effects arising primarily from Black and democratically liberal subjects. Next, we conduct an archival analysis of the effect label adoption has on the physical foot traffic that retailers receive based on SafeGraph’s mobile patterns database. Our difference-in-differences estimations yield consistent results; we find evidence of a positive average effect on foot traffic volumes. Further, we show that a rise in visitors from liberal-leaning geographies drives these effects. We discuss implications for digital platform operators and retailers. This paper was accepted by Duncan Simester, marketing. Supplemental Material: The online appendix and data are available at https://doi.org/10.1287/mnsc.2023.01168 .

Two-Step Identification Method and Experimental Verification of Weld Damage at Joints in Spatial Grid Structures

Welded joints in grid structures are susceptible to damage and destruction when exposed to random excitation. The complexity of the grid structure poses challenges for realizing the damage recognition of welded joints. In this study, a two-step method is proposed specifically for damage identification of welded joints in grid structures, combining wavelet analysis and fuzzy pattern recognition to accurately identify the location and extent of damage in welded joints. Firstly, the structure is divided based on the analysis of the influence range of joint damage. Key joints are selected within the sub-regions where sensors are installed, and the acceleration response of these key joints is measured. Wavelet analysis is then applied to identify the sub-regions where weld damage occurs. Secondly, an equivalent finite element model is established for joints with varying degrees of damage. The damage index, calculated as the ratio of the absolute value of the difference in the first-order element strain mode of the members, increases with the degree of damage during joint weld damage. By monitoring the changes in the damage index of sensitive members, which exhibit significant changes with varying weld damage degrees, a damage pattern database is constructed for each sub-region. The membership degree between joint damage and the patterns in the pattern database is then calculated to determine the location and degree of weld damage. To validate the effectiveness of the proposed method, an experiment was conducted using a grid structure model with replaceable members. Highly sensitive FBG sensors were designed to measure the acceleration response of the joints, resulting in accurate identification of damaged sub-regions solely through the measurement of key joint acceleration responses. Furthermore, within the damaged sub-regions, the fuzzy pattern recognition method precisely determined the location and degree of weld damage in the joints. The experimental results demonstrate that the proposed method effectively reduces the complexity of the structure by dividing the grid structure into sub-regions, and enables the two-step identification method to achieve successful damage identification for the joints in the grid structure with high efficiency and accuracy.

Estimating of Fraction of Weakly Deformable Erythrocytes in a Blood Sample Based on the Method of Laser Ektacytometry and a Database of Simulated Diffraction Patterns

Estimating of Fraction of Weakly Deformable Erythrocytes in a Blood Sample Based on the Method of Laser Ektacytometry and a Database of Simulated Diffraction Patterns

Computing Domain Abstractions for Optimal Classical Planning with Counterexample-Guided Abstraction Refinement

Abstraction heuristics are the state of the art in optimal classical planning as heuristic search. A popular method for computing abstractions is the counterexample-guided abstraction refinement (CEGAR) principle, which has successfully been used for projections, which are the abstractions underlying pattern databases, and Cartesian abstractions. While projections are simple and fast to compute, Cartesian abstractions subsume projections and hence allow more fine-grained abstractions, however at the expense of efficiency. Domain abstractions are a third class of abstractions between projections and Cartesian abstractions in terms of generality. Yet, to the best of our knowledge, they are only briefly considered in the planning literature but have not been used for computing heuristics yet. We aim to close this gap and compute domain abstractions by using the CEGAR principle. Our empirical results show that domain abstractions compare favorably against projections and Cartesian abstractions.

Efficient Evaluation of Large Abstractions for Decoupled Search: Merge-and-Shrink and Symbolic Pattern Databases

Abstraction heuristics are a state-of-the-art technique to solve classical planning problems optimally. A common approach is to precompute many small abstractions and combine them admissibly using cost partitioning. Recent work has shown that this approach does not work out well when using such heuristics for decoupled state space search, where search nodes represent potentially large sets of states. This is due to the fact that admissibly combining the estimates of several heuristics without sacrificing accuracy is NP-hard for decoupled states. In this work we propose to use a single large abstraction instead. We focus on merge-and-shrink and symbolic pattern database heuristics, which are designed to produce such abstractions. For these heuristics, we prove that the evaluation of decoupled states is NP-hard in general, but we also identify conditions under which it is polynomial. We introduce algorithms for both the general and the polynomial case. Our experimental evaluation shows that single large abstraction heuristics lead to strong performance when the heuristic evaluation is polynomial.

Improving patch-based simulation using Generative Adversial Networks

Multiple-Point Simulation (MPS) is a geostatistical simulation technique commonly used to model complex geological patterns and subsurface heterogeneity. There have been a great variety of implementation methods developed within MPS, of which Patch-Based Simulation is a more recently developed class. While we have witnessed great progress in Patch-Based algorithms lately, they are still faced with two challenges: conditioning to point data and the occurrence of verbatim copy. Both of them are partly due to finite size of Training image, from which a limited size of pattern database is constructed. To address these questions, we propose a novel approach that we call Generative-Patched-Simulation (GPSim), which is based on Generative Adversarial Networks (GAN). With this method, we are able to generate sufficient (in theory an infinite) number of new patches based on the current pattern database. As demonstrated by the results on a simple 2D binary image, this approach shows its potential to address the two issues and thus improve Patch-based Simulation methods.

Editorial: Ontology-based Knowledge Presentation and Computational Linguistics for Semantic Big Social Data Analytics in Asian Social Networks

Data-driven ontology-based knowledge (OK) presentation and computational linguistics for evolving semantic Asian social networks (ASNs) can make one of the most important platforms that provide robust and real-time data mapping in massive access across the heterogeneous big data sources in the web that is named OK-ASN. It benefits from computational intelligence, web-of-things (WoT) architecture, semantic features, statistical learning and pattern recognition, database management, computer vision, cyber-security, and language processing. OK-ASN is a critical strategy for WoT big data mining and enterprises from social media to medical and industrial sectors.

Integrated approaches in a morphological analyzer of the Arabic language

This article presents a systemof a morphological analyzer of the Arabic language, by integrating several approaches and the viterbi algorithm. First approach is based on database for all thesurface patterns in the Arabic language, second approach is Buckwalter Arabic morphological analyzer and the last approach is based on finite state automaton. With the integration of correspondence tables between affixes in these approaches. The combination between these approaches in our analyzer is very important. Our analyzer is tested on a morphological corpus of 200,000 words, which generalize the words of the Arabic language. The effectiveness of the proposed approaches is demonstrated experimentally and the results obtained are comparable to the state of the art. Moreover, it shows the interest and the advantages of integrating these approaches are to improve our morphological analyzer.

Implementasi Data Mining FP-Growth Untuk Analisis Pola Pembelian Pada Transaksi Penjualan

In the business world, efforts are needed as much as possible in gaining profits. The accuracy of marketing strategies can be seen from the consumer spending pattern database obtained from sales transactions on fashion products that are usually purchased simultaneously by customers. Information about the Pattern of Purchasing Customer Shopping that is Inaccurate at the Ayu Collection Online Shop Shop has caused promotional policy to be one of the causes of the store to suffer losses. One way to get an accurate customer shopping pattern is to use data mining. One of the methods contained in data mining is the association analysis method, in the association analysis there are several algorithms, one of which is the FP-Growth algorithm. In this study several association rules were found by applying the Frequent Pattern (FP-Growth) algorithm from the transaction database Fashion sales at Ayu Collection Online Shop. This association rules will later be used as decision making material to develop successful marketing and sales strategies. The findings of this study are in the form of product recommendations, namely the proposal of two or more items based on the findings of the FP-Growth algorithm using a 50% confidence value and a minimum support of 40%, this study uses assistance from the rapidminer tools version 9.9.

Structural damage identification based on pattern matching using the flexibility change rate

AbstractIn this paper, a rapid identification method for postearthquake structural damage is proposed based on pattern matching using the flexibility change rate. In the first stage, a small number of dynamic signals are collected for extracting the modal information, which is used as a reference for model updating. Using the updated finite element model of the structure to be monitored, the characteristic vector of the flexibility change rate is calculated by modal analysis from the first two orders of modal information of various damage scenarios to establish a pattern database. For the real structure, the measured responses are used to identify the modal parameters to calculate the flexibility change rate characteristic vector, which is treated as the observed pattern. Then, the similarity between the observed pattern and the pattern in the pattern database is calculated using the Euclidean distance (ED). The damage location and severity of the measured structure are treated to be the same as the pattern corresponding to the minimum value of ED, so that the damage is identified. Numerical simulations and experiments of two types of concentrated mass structures, four floors, and six floors confirm that the proposed method can identify the damage well. In addition, the matching results based on different sensor combinations demonstrate that the method can quickly identify the damage location and severity of a structure even using a limited number of sensors. The proposed method can provide support for the government in rapid postearthquake emergency decision‐making.

The efficiency of Using protective clothing from the risks of pollutants on the health of the workers in the chemical plants

The success of protective clothing depends on the extent to standing against the pollutants during the functional performance. The consumer faces many types of fabrics or clothing with varying degrees of protection, especially protection from chemical pollutants, their degrees of toxicity, and the permeability properties of chemicals through various types of clothing during periods of exposing pollutants. There is Difficulty in the process of choosing between these clothes in order to reach a rational consumer decision. The research concerned studying the classification of protective clothing, the types of fabrics used, the connections used in sewing clothes and their effectiveness of protection against chemical hazards, according to the level of protection and permeability. The researcher designed a database of patterns of protective clothing for protection from chemical pollutants.

Identification of the Backsheet Type of Silicon Photovoltaic Modules from Encapsulant Fluorescence Images

Feasibility of the identification of backsheet (BS) composition of field-aged Si photovoltaic (PV) modules based on the analysis of characteristic patterns of UV-excited fluorescence (UVF) of ethylene vinyl acetate copolymer encapsulant is shown. The approach includes BS identification by vibrational spectroscopies and the formation of a database of BS-related characteristic UVF patterns. A multivariate principal component analysis of the UVF images of unknown samples combined with the UVF pattern database allows the BS type to be identified only from UVF images. The geometric place of particular samples in the principal component plots is indicative of the general degradation status of PV modules. The proposed method can be applied for high-throughput noncontact characterization of solar PV plants.