Tag Patterns Research Articles

Random laser ablated tags for anticounterfeiting purposes and towards physically unclonable functions

Anticounterfeiting tags affixed to products offer a practical solution to combat counterfeiting. To be effective, these tags must be economical, capable of ultrafast production, mass-producible, easy to authenticate, and automatable. We present a universal laser ablation technique that rapidly generates intrinsic, randomly distributed craters (in under a second) on laser-sensitive materials using a nanosecond pulsed infrared laser. The laser and scanning line parameters are balanced to produce randomly distributed craters. The tag patterns demonstrate high randomness, which is analyzed using pattern recognition algorithms and root mean square error deviation. The optical image information of the tag is digitized with a fixed bit uniformity of 0.5 without employing any debiasing algorithm. The efficacy of tags for anticounterfeiting is presented by securing the challenge associated with each tag. Statistical NIST tests are successfully performed on responses generated from both single and multiple tags, demonstrating the true randomness of the sequence of binary digits. The single(multiple) tag(s) achieved an actual encoding capacity of approximately 10391 (10518) and a low false rate (both positive and negative) on the order of 10−58 (10−50). Our findings introduce a laser-based method for anticounterfeiting tag generation, allowing for ultrafast and straightforward product processing with minimal fabrication and tag cost.

Identifying the Patterns of Author-Generated Tags to Library and Information Science Papers in The Academic Social Networks: Focusing on Academia.edu

This research aims to identify some patterns of author (as user) generated tags to the papers of library and information science field in Academia.edu. The research method is typically based on text analysis and word frequency distribution. The population contains over 6000 papers tagged in Academia.edu, and their abstracts were extracted from 159 English journals of the library and information science (LIS) field in the Scopus database. The growth of different types of tags in terms of the number of their words (one-word, two-word, three-word, and four-word and more), as well as the total number of tags over time, appeared as a logistic curve. It was also found that two-word tags had the most matching (54.92%) and four-word tags or more the least matching (1.76%) with different sections of papers (title, abstract, and authors’ keywords). The total tags matched 7.5% with the title, 76.61% with the abstract, and 15.89% with the authors’ keywords. Regarding the reuse of tags, it was revealed that on the one hand, 38.8% of the tags had been reused; on the other hand, 16% of the tags were reused in the first year, and more than 50% of the tags were reused in the first three years. Finally, it can be said that the users’ consensus on specific terms can identify the new patterns of users’ tagging at least partially compatible with professional indexing concepts, and by focusing on the most widely used tags and their sustainable distribution, the weighting of indexing terms and even classification schemes may be achieved.

Tagged-to-Cine MRI Sequence Synthesis via Light Spatial-Temporal Transformer.

Tagged magnetic resonance imaging (MRI) has been successfully used to track the motion of internal tissue points within moving organs. Typically, to analyze motion using tagged MRI, cine MRI data in the same coordinate system are acquired, incurring additional time and costs. Consequently, tagged-to-cine MR synthesis holds the potential to reduce the extra acquisition time and costs associated with cine MRI, without disrupting downstream motion analysis tasks. Previous approaches have processed each frame independently, thereby overlooking the fact that complementary information from occluded regions of the tag patterns could be present in neighboring frames exhibiting motion. Furthermore, the inconsistent visual appearance, e.g., tag fading, across frames can reduce synthesis performance. To address this, we propose an efficient framework for tagged-to-cine MR sequence synthesis, leveraging both spatial and temporal information with relatively limited data. Specifically, we follow a split-and-integral protocol to balance spatialtemporal modeling efficiency and consistency. The light spatial-temporal transformer (LiST2) is designed to exploit the local and global attention in motion sequence with relatively lightweight training parameters. The directional product relative position-time bias is adapted to make the model aware of the spatial-temporal correlation, while the shifted window is used for motion alignment. Then, a recurrent sliding fine-tuning (ReST) scheme is applied to further enhance the temporal consistency. Our framework is evaluated on paired tagged and cine MRI sequences, demonstrating superior performance over comparison methods.

Improving news headline text generation quality through frequent POS-Tag patterns analysis

Original synthetic content writing is one of the human abilities that algorithms aspire to emulate. The advent of sophisticated algorithms, especially based on neural networks has shown promising results in recent times. A watershed moment was witnessed when the attention mechanism was introduced which paved the way for transformers, a new exciting architecture in natural language processing. Recent sensations like GPT and BERT for synthetic text generation rely on NLP transformers. Although, GPT and BERT-based models are capable of generating creative text given they are properly trained on abundant data, however, the generated text suffers the quality aspect when limited data is available. This is especially an issue for low-resource languages where labeled data is still scarce. In such cases, the generated text, more often than not, lacks the proper sentence structure, thus unreadable. This study proposes a post-processing step in text generation that improves the quality of generated text through the GPT model. The proposed post-processing step is based on the analysis of POS tagging patterns in the original text and accepts only those generated sentences from GPT which satisfy POS patterns that are originally learned from the data. We exploit the GPT model to generate English headlines by utilizing Australian Broadcasting Corporation (ABC) news dataset. Furthermore, for assessing the applicability of the model in low-resource languages, we also train the model on the Urdu news dataset for Urdu news headlines generation. The experiments presented in this paper on these datasets from high- and low-resource languages show that the performance of generated headlines has a significant improvement by using the proposed headline POS pattern extraction. We evaluate the performance through subjective evaluation as well as using text generation quality metrics like BLEU and ROUGE.

GeneMiner: A Classification Approach for Detection of XSS Attacks on Web Services.

According to OWASP 2021, cross-site scripting (XSS) attacks are increasing through specially crafted XML documents. The attacker injects a malicious payload with a new pattern and combination of scripts, functions, and tags that deceits the existing security mechanisms in web services. This paper proposes an approach, GeneMiner, encompassing GeneMiner-E to extract new features and GeneMiner-C for classification of input payloads as malicious and nonmalicious. The proposed approach evolves itself to the changing patterns of attack payloads and identifies adversarial XSS attacks. The experiments have been conducted by collecting data from open source and generating various combinations of scripts, functions, and tags using an incremental genetic algorithm. The experimental results show that the proposed approach effectively detects newly crafted malicious XSS payloads with an accuracy of 98.5%, which is better than the existing classification techniques. The approach learns variations in the existing attack sample space and identifies the new attack payloads with reduced efforts.

Photodynamic Activation of the Cholecystokinin 1 Receptor with Tagged Genetically Encoded Protein Photosensitizers: Optimizing the Tagging Patterns.

Cholecystokinin 1 receptor (CCK1R) is activated photodynamically. For this to happen insitu, genetically encoded protein photosensitizers (GEPP) may be tagged to natively expressed CCK1R, but how to best tag GEPP has not been examined. Therefore, GEPP (miniSOG or KillerRed) was tagged to CCK1R and light-driven photodynamic CCK1R activation was monitored by Fura-2 fluorescent calcium imaging, to screen for optimized tagging patterns. Blue light-emitting diode irradiation of CHO-K1 cells expressing miniSOG fused to N- or C-terminus of CCK1R was found to both trigger persistent calcium oscillations-a hallmark of permanent photodynamic CCK1R activation. Photodynamic CCK1R activation was accomplished also with miniSOG fused to N-terminus of CCK1R via linker (GlySerGly)4 or 8 , but not linker (GSG)12 or an internal ribosomal entry site insert. KillerRed fused to N- or C-terminus of CCK1R after white light irradiation resulted in similar activation of in-frame CCK1R. Photodynamic CCK1R activation in miniSOG-CCK1R-CHO-K1 cells was blocked by singlet oxygen (1 O2 ) quencher uric acid or Trolox C, corroborating the role of 1 O2 as the reactive intermediate. It is concluded that photodynamic CCK1R activation can be achieved either with direct GEPP fusion to CCK1R or fusion via a short linker, fusion via long linkers might serve as the internal control.

Artificial intelligence for target symptoms of Thai herbal medicine by web scraping

Machine learning (ML) is implementing artificial intelligence (AI) research within medicine that has made dramatic progress in recent years. In addition to standard treatments, the role of complementary and alternative medicine should be mentioned. Traditional Thai medicine has received growing acceptance as a complementary approach to modern medicine by using local herbs. A vast amount of Thai herbal knowledge and information is freely available on the Internet. The reader must evaluate each website and decide to use trustworthy and appropriate information. This study aimed to acquire Thai herbal knowledge recorded in the Thai language system on the Internet by scraping websites using programming techniques. The knowledge was extracted with programming, and the types of Thai herbs were classified corresponding to target symptoms by the machine learning algorithm. The ML method organized the process when sufficient achievement was reached in order to give reliable and high accuracy results from the training data set. The validation of extracted knowledge was achieved by using the part-of-speech tag patterns analysis. This study showed that the programming and machine learning system was appropriate for obtaining and classifying Thai herbal medicines knowledge.

Native-resolution myocardial principal Eulerian strain mapping using convolutional neural networks and Tagged Magnetic Resonance Imaging

BackgroundAssessment of regional myocardial function at native pixel-level resolution can play a crucial role in recognizing the early signs of the decline in regional myocardial function. Extensive data processing in existing techniques limits the effective resolution and accuracy of the generated strain maps. The purpose of this study is to compute myocardial principal strain maps εp1 and εp2 from tagged MRI (tMRI) at the native image resolution using deep-learning local patch convolutional neural network (CNN) models (DeepStrain). MethodsFor network training, validation, and testing, realistic tMRI datasets were generated and consisted of 53,606 cine images simulating the heart, the liver, blood pool, and backgrounds, including ranges of shapes, positions, motion patterns, noise, and strain. In addition, 102 in-vivo image datasets from three healthy subjects, and three Pulmonary Arterial Hypertension patients, were acquired and used to assess the network's in-vivo performance.Four convolutional neural networks were trained for mapping input tagging patterns to corresponding ground-truth principal strains using different cost functions. Strain maps using harmonic phase analysis (HARP) were obtained with various spectral filtering settings for comparison. CNN and HARP strain maps were compared at the pixel level versus the ground-truth and versus the least-loss in-vivo maps using Pearson correlation coefficients (R) and the median error and Inter-Quartile Range (IQR) histograms. ResultsCNN-based local patch DeepStrain maps at a phantom resolution of 1.1mm × 1.1 mm and in-vivo resolution of 2.1mm × 1.6 mm were artifact-free with multiple fold improvement with εp1 ground-truth median error of 0.009(0.007) vs. 0.32(0.385) using HARP and εp2 ground-truth error of 0.016(0.021) vs. 0.181(0.08) using HARP. CNN-based strain maps showed substantially higher agreement with the ground-truth maps with correlation coefficients R > 0.91 for εp1 and εp2 compared to R < 0.21 and R < 0.82 for HARP-generated maps, respectively. ConclusionCNN-generated Eulerian strain mapping permits artifact-free visualization of myocardial function at the native image resolution.

Malapposition of graft-host interface after penetrating keratoplasty (PK) and deep anterior lamellar keratoplasty (DALK): an optical coherence tomography study

BackgroudPrevious studies of internal graft-host malappositions have not dealt with the precise ways in which each malapposition affected post-penetrating keratoplasty (post-PK) visual outcomes. In this study, we reviewed our post-PK and post-deep anterior lamellar keratoplasty (post-DALK) keratoconic patients and used anterior segment optical coherence tomography (AS-OCT) to evaluate the associations between graft-host interface (GHI) characteristics and visual outcomes.MethodsNovel GHI metrics included: mean graft-host touch (GHT), total prevalence of malapposition proportion (Pm), frequency of apposition (F), size of malapposition (Sm), junctional graft thickness (Tg), junctional host thickness (Th) and the absolute value of difference between Tg and Th (|Tg-Th|). We connected the external and internal junction points of GHI (GHT) and drew a straight line through the central point, perpendicular to both sides of the cornea. Tg and Th were the thicknesses at cross-points 1 mm away from the meeting point on the external side of the graft and host, respectively. Linear regression analysis was used to describe associations between GHI metrics and postsurgical visual outcomes [logarithm of minimum angle of resolution best-corrected visual acuity (logMAR BCVA), spherical equivalent diopter (SE), diopter of spherical power (DS), diopter of cylindrical power (DC) and keratometric astigmatism (Astig value)].ResultsWe enrolled 22 post-PK and 23 post-DALK keratoconic patients. Compared with the regular-apposition results, GHT was decreased in step and gape patterns, and increased in hill and tag patterns. SE increased averagely by 6.851, 5.428 and 5.164 diopter per 1% increase in: F (step) [β = 6.851; 95% Confidence interval (CI) = 2.975–10.727; P = 0.001]; F (graft step) [β = 5.428; 95% CI = 1.685–9.171; P = 0.005]; and Pm [β = 5.164; 95%CI = 0.913–9.146; P = 0.018], respectively. SE increased averagely by 0.31 diopter per 10-μm increment in |Tg-Th| [β = 0.031; 95% CI = 0.009–0.054; P = 0.007]. LogMAR BCVA increased (on average) by 0.01 per 10-μm increment in both GHT [β = 0.001; 95% CI = 0–0.002; P = 0.030]. and Tg [β = 0.001; 95% CI = 0.001–0.002; P = 0.001]. Astig value increased on average by 0.17 diopter per 10-μm increment in Sm [β = 0.017; 95% CI = 0–0.033; P = 0.047].ConclusionThis investigation of GHI characteristics suggests explanations for varied ametropia in keratoconic eyes and has potential significance as a reference for promoting pre-surgical planning and technology for corneal transplantation.

Leveraging Web Semantic Knowledge in Word Representation Learning

Much recent work focuses on leveraging semantic lexicons like WordNet to enhance word representation learning (WRL) and achieves promising performance on many NLP tasks. However, most existing methods might have limitations because they require high-quality, manually created, semantic lexicons or linguistic structures. In this paper, we propose to leverage semantic knowledge automatically mined from web structured data to enhance WRL. We first construct a semantic similarity graph, which is referred as semantic knowledge, based on a large collection of semantic lists extracted from the web using several pre-defined HTML tag patterns. Then we introduce an efficient joint word representation learning model to capture semantics from both semantic knowledge and text corpora. Compared with recent work on improving WRL with semantic resources, our approach is more general, and can be easily scaled with no additional effort. Extensive experimental results show that our approach outperforms the state-of-the-art methods on word similarity, word sense disambiguation, text classification and textual similarity tasks.

A gradient-based optical-flow cardiac motion estimation method for cine and tagged MR images.

A new method is proposed to quantify the myocardial motion from both 2D C(ine)-MRI and T(agged)-MRI sequences. The tag pattern offers natural landmarks within the image that makes it possible to accurately quantify the motion within the myocardial wall. Therefore, several methods have been proposed for T-MRI. However, the lack of salient features within the cardiac wall in C-MRI hampers local motion estimation. Our method aims to ensure the local intensity and shape features invariance during motion through the iterative minimization of a cost function via a random walk scheme. The proposed approach is evaluated on realistic simulated C-MRI and T-MRI sequences. The results show more than 53% improvements on displacement estimation, and more than 24% on strain estimation for both C-MRI and T-MRI sequences, as compared to state-of-the-art cardiac motion estimators. Preliminary experiments on clinical data have shown a good ability of the proposed method to detect abnormal motion patterns related to pathology. If those results are confirmed on large databases, this would open up the possibility for more accurate diagnosis of cardiac function from standard C-MRI examinations and also the retrospective study of prior studies.

Remote Sensing Imagine Change Detection Using Semi-Supervised Neural Networks

By comparing and contrasting photographs of the same location taken at various periods using remote sensing technology, we may determine whether or not there have been any changes to the land cover there. Identifying shifts in a timely manner will facilitate the identification of natural disasters and climate change, the evaluation of damage, and the rehabilitation of affected areas. Both supervised and unsupervised methods have been utilised historically for change detection. In practise, specialists can only gather a small number of tagged patterns to use in solving the change detection issue. In this case, supervised techniques are ineffective, and if unsupervised methods are employed instead, some scarce but useful labels are wasted. In this dissertation, we offer a novel methodology to enhance change detection in the face of data scarcity by combining a small number of labelled patterns with a large number of unlabeled ones. In this study, many neural network architectures are employed to develop active and semi-supervised learning change detection methods. For better change detection when only a small number of labelled patterns are available, we redesign two unsupervised learning based neural networks, a modified self-organizing feature map and a Hopfield type neural network, in a semi-supervised framework.

Design and Development of Test-tube Label Automatic Printing and Pasting System Based on Database

Through analysis of the use requirements of tube labels in the hospital and the existing conditions of the hospital, the overall design of the system was determined. The technical issues, such as the integration of label printing and test-tube labeling, the selection of different types of test-tubes, the handling of the test-tubes with different size, and the acquisition of the information in the database by tag pattern generating software were determined. The purpose of the study is to develop the first full-automatic portable test-tube label printing and pasting system, integrated with printing and pasting, which can be linked with the database platform software. The actual operation of the system indicates that the printing and pasting system is in good condition and could be counted on.

Exploiting haptic facets: Users' sensemaking schemas as a path to design and personalization of experience

Our poor understanding of the connection between haptic effect engineering using controllable parameters like frequency, amplitude and rhythm and how sensations are comprehended by end-users hinders effective design. Haptic facets (categories of attributes that characterize collection items in different ways) are a way to describe, navigate and analyze the cognitive frameworks by which users make sense of qualitative and affective characteristics of haptic sensations. Embedded in tools, they will provide designers and end-users interested in customization with a road-mapped perceptual and cognitive design space. We previously compiled five haptic facets based on how people describe vibrations: physical, sensory, emotional, metaphoric, and usage examples.Here, we report a study in which we deployed these facets to identify underlying dimensions and cross-linkages in participants' perception of a 120-item vibration library. We show that the facets are crosslinked in people's minds, and discuss three scenarios where the facet-based organizational schemes, their linkages and consequent redundancies can support design, evaluation and personalization of expressive vibrotactile effects. Finally, we report between-subject variation (individual differences) and within-subject consistency (reliability) in participants' rating and tagging patterns to inform future progress on haptic evaluation. This facet-based approach is also applicable to other kinds of haptic sensations and even other modalities, and can inform multimodal experience design through a descriptive design language shared between different modalities. HighlightsEmpirically derived semantic dimensions of four VT facets.Between-facet linkages at dimensional and tag levels, and their implications for VT design.Analysis of individual variations in rating and annotating vibrations.A two-step methodology for annotating large VT sets, and data on its validity and reliability.A publicly available dataset of 120 vibrations and their annotations and dimensions.

Gyro in the Air

3D orientation tracking is an essential ingredient for many Internet-of-Things applications. Yet existing orientation tracking systems commonly require motion sensors that are only available on battery-powered devices. In this paper, we propose Tagyro, which attaches an array of passive RFID tags as orientation sensors on everyday objects. Tagyro uses a closed-form model to transform the run-time phase offsets between tags into orientation angle. To enable orientation tracking in 3D space, we found the key challenge lies in the imperfect radiation pattern of practical tags, caused by the antenna polarity, non-isotropic emission and electromagnetic coupling, which substantially distort phase measurement. We address these challenges by designing a set of phase sampling and recovery algorithms, which together enable reliable orientation sensing with 3 degrees of freedom. We have implemented a real-time version of Tagyro on a commodity RFID system. Our experiments show that Tagyro can track the 3D orientation of passive objects with a small error of 4°, at a processing rate of 37.7 samples per second.

Using Social Tags and User Rating Patterns for Collaborative Filtering

The overwhelming supply of online information on the Web makes finding better ways to separate important information from the noisy data ever more important. Recommender systems may help users deal with the information overloading issue, yet their performance appears to have stalled in currently available approaches. In this study, the authors propose and examine a novel user profiling approach that uses collaborative tagging information to enhance recommendation performance. They evaluate the proposed hybrid approach, illustrated in the context of movie recommendation. The authors also empirically evaluate various existing recommendation approaches (in comparison with the newly proposed approach) using sensitivity analyses to investigate the potential use of varied user rating or tagging patterns to improve recommendations accuracy. The results don't just indicate the effective and competitive performance of the suggested approach, but they also suggest important implications and directions for further research, including the potential associated with applying multiple recommendation approaches within a single system based on the different rating or tagging patterns of the user.

Using linguistic features to automatically extract web page title

Existing methods for extracting titles from HTML web page mostly rely on visual and structural features. However, this approach fails in the case of service-based web pages because advertisements are often given more visual emphasize than the main headlines. To improve the current state-of-the-art, we propose a novel method that combines statistical features, linguistic knowledge, and text segmentation. Using annotated English corpus, we learn the morphosyntactic characteristics of known titles and define a part-of-speech tag patterns that help to extract candidate phrases from the web page. To evaluate the proposed method, we compared two datasets Titler and Mopsi and evaluated the extracted features using four classifiers: Naïve Bayes, k-NN, SVM, and clustering. Experimental results show that the proposed method outperform the solution used by Google from 0.58 to 0.85 on Titler corpus and from 0.43 to 0.55 on Mopsi dataset, and offers a readily available solution for the title extraction problem.

ZeuScansion: A tool for scansion of English poetry

We present a finite state technology based system capable of performing metrical scansion of verse written in English. Scansion is the traditional task of analyzing the lines of a poem, marking the stressed and non-stressed elements, and dividing the line into metrical feet. The system's workflow is composed of several subtasks designed around finite state machines that analyze verse by performing tokenization, part of speech tagging, stress placement, and unknown word stress pattern guessing. The scanner also classifies its input according to the predominant type of metrical foot found. We also present a brief evaluation of the system using a gold standard corpus of human-scanned verse, on which a per-syllable accuracy of 86.78% is reached. The program uses open-source components and is released under the GNU GPL license.

Assessment of cardiac mass from tagged magnetic resonance images.

Tagged and cine magnetic resonance imaging (tMRI and cMRI) techniques are used for evaluating regional and global heart function, respectively. Measuring global function parameters directly from tMRI is challenging due to the obstruction of the anatomical structure by the tagging pattern. The purpose of this study was to develop a method for processing the tMRI images to improve the myocardium-blood contrast in order to estimate global function parameters from the processed images. The developed method consists of two stages: (1) removing the tagging pattern based on analyzing and modeling the signal distribution in the image's k-space, and (2) enhancing the blood-myocardium contrast based on analyzing the signal intensity variability in the two tissues. The developed method is implemented on images from twelve human subjects. Ventricular mass measured with the developed method showed good agreement with that measured from gold-standard cMRI images. Further, preliminary results on measuring ventricular volume using the developed method are presented. The promising results in this study show the potential of the developed method for evaluating both regional and global heart function from a single set of tMRI images, with associated reduction in scan time and patient discomfort.

Examination of Endogenous Rotund Expression and Function in Developing Drosophila Olfactory System Using CRISPR-Cas9-Mediated Protein Tagging.

The zinc-finger protein Rotund (Rn) plays a critical role in controlling the development of the fly olfactory system. However, little is known about its molecular function in vivo. Here, we added protein tags to the rn locus using CRISPR-Cas9 technology in Drosophila to investigate its subcellular localization and the genes that it regulates . We previously used a reporter construct to show that rn is expressed in a subset of olfactory receptor neuron (ORN) precursors and it is required for the diversification of ORN fates. Here, we show that tagged endogenous Rn protein is functional based on the analysis of ORN phenotypes. Using this method, we also mapped the expression pattern of the endogenous isoform-specific tags in vivo with increased precision. Comparison of the Rn expression pattern from this study with previously published results using GAL4 reporters showed that Rn is mainly present in early steps in antennal disc patterning, but not in pupal stages when ORNs are born. Finally, using chromatin immunoprecipitation, we showed a direct binding of Rotund to a previously identified regulatory element upstream of the bric-a-brac gene locus in the developing antennal disc.