TagX data annotation services are a set of tools and processes used to accurately label and classify large amounts of data for use in machine learning and artificial intelligence applications. The services are designed to be highly accurate, efficient, and customizable, allowing for a wide range of data types and use cases.

The process typically begins with a team of trained annotators reviewing and categorizing the data, using a variety of annotation tools and techniques, such as text classification, image annotation, and video annotation. The annotators may also use natural language processing and other advanced techniques to extract relevant information and context from the data.

Once the data has been annotated, it is then validated and checked for accuracy by a team of quality assurance specialists. Any errors or inconsistencies are corrected, and the data is then prepared for use in machine learning and AI models.

TagX annotation services can be applied to a wide range of data types, including text, images, videos, and audio. The services can be customized to meet the specific needs of each client, including the type of data, the level of annotation required, and the desired level of accuracy.

TagX data annotation services provide a powerful and efficient way to prepare large amounts of data for use in machine learning and AI applications, allowing organizations to extract valuable insights and improve their decision-making processes.

Nexdata provides high-quality Speech Data services for speech cleaning, speech transcription, phoneme annotation etc, with word accuracy of 99.5% and phoneme segmentation of 0.01s.

Machine-assisted object detection and classification of fish species from Baited Remote Underwater Video Station (BRUVS) surveys using deep learning algorithms presents an opportunity for optimising analysis time and rapid reporting of marine ecosystem statuses. Training object detection algorithms for BRUVS analysis presents significant challenges: the model requires training datasets with bounding boxes already applied identifying the location of all fish individuals in a scene, and it requires training datasets identifying species with labels. In both cases, substantial volumes of data are required and this is currently a manual, labour-intensive process, resulting in a paucity of the labelled data currently required for training object detection models for species detection. Here, we present a “machine-assisted” approach for i) a generalised model to automate the application of bounding boxes to any underwater environment containing fish and ii) fish detection and classification to species identification level, up to 12 target species. A catch-all “fish” classification is applied to fish individuals that remain unidentified due to a lack of available training and validation data. Machine-assisted bounding box annotation was shown to detect and label fish on out-of-sample datasets with a recall between 0.70 and 0.89 and automated labelling of 12 targeted species with an F1 score of 0.79. On average, 12% of fish were given a bounding box with species labels and 88% of fish were located and given a fish label and identified for manual labelling. Taking a combined, machine-assisted approach presents a significant advancement towards the applied use of deep learning for fish species detection in fish analysis and workflows and has potential for future fish ecologist uptake if integrated into video analysis software. Manual labelling and classification effort is still required, and a community effort to address the limitation presented by a severe paucity of training data would improve automation accuracy and encourage increased uptake.

Manually created neuroanatomically labeled MRI brain scans with unique subjects, 5 subjects scanned twice.

This dataset consists of sentences extracted from BGS memoirs, DECC/OGA onshore hydrocarbons well reports and Mineral Reconnaissance Programme (MRP) reports. The sentences have been annotated to enable the dataset to be used as labelled training data for a Named Entity Recognition model and Entity Relation Extraction model, both of which are Natural Language Processing (NLP) techniques that assist with extracting structured data from unstructured text. The entities of interest are rock formations, geological ages, rock types, physical properties and locations, with inter-relations such as overlies, observedIn. The entity labels for rock formations and geological ages in the BGS memoirs were an extract from earlier published work https://github.com/BritishGeologicalSurvey/geo-ner-model https://zenodo.org/records/4181488 . The data can be used to fine tune a pre-trained large language model using transfer learning, to create a model that can be used in inference mode to automatically create the labels, thereby creating structured data useful for geological modelling and subsurface characterisation. The data is provided in JSONL(Relation) format which is the export format from doccano open source text annotation software (https://doccano.github.io/doccano/) used to create the labels. The source documents are already publicly available, but the MRP and DECC reports are only published in pdf image form. These latter documents had to undergo OCR and resulted in lower quality text and a lower quality training data. The majority of the labelled data is from the higher quality BGS memoirs text. The dataset is a proof of concept. Minimal peer review of the labelling has been conducted so this should not be treated as a gold standard labelled dataset, and it is of insufficient volume to build a performant model. The development of this training data and the text processing scripts were supported by a grant from UK Government Office for Technology Transfer (GOTT) Knowledge Asset Grant Fund Project 10083604

EDM-HSE Dataset

EDM-HSE is an open audio dataset containing a collection of code-generated drum recordings in the style of modern electronic house music. It includes 8,000 audio loops recorded in uncompressed stereo WAV format, created using custom audio samples and a MIDI drum dataset. The dataset also comes with paired JSON files containing MIDI note numbers (pitch) and tempo data, intended for supervised training of generative AI audio models.

Overview

The EDM-HSE Dataset was developed using an algorithmic framework to generate probable drum notations commonly played by EDM music producers. For supervised training with labeled data, a variational mixing technique was applied to the rendered audio files. This method systematically includes or excludes drum notes, assisting the model in recognizing patterns and relationships between drum instruments, thereby enhancing its generalization capabilities.

The primary purpose of this dataset is to provide accessible content for machine learning applications in music and audio. Potential use cases include generative music, feature extraction, tempo detection, audio classification, rhythm analysis, drum synthesis, music information retrieval (MIR), sound design and signal processing.

Specifications

• 8,000 audio loops (approximately 17 hours)
• 16-bit WAV format
• Tempo range: 120–130 BPM
• Paired label data (WAV + JSON)
• Variational drum patterns
• Subgenre styles (Big room, electro, minimal, classic)

A JSON file is provided for referencing and converting MIDI note numbers to text labels. You can update the text labels to suit your preferences.

License

This dataset was compiled by WaivOps, a crowdsourced music project managed by the sound label company Patchbanks. All recordings have been compiled by verified sources for copyright clearance.

The EDM-HSE dataset is licensed under Creative Commons Attribution 4.0 International (CC BY 4.0).

Additional Info

Please note that this dataset has not been fully reviewed and may contain minor notational errors or audio defects.

For audio examples or more information about this dataset, please refer to the GitHub repository.

PPE Dataset for Workplace Safety

Overview

The "PPE Dataset" is a robust and diverse collection of images designed for the development and enhancement of machine learning models in the realm of workplace safety. This dataset focuses on the detection and classification of various types of personal protective equipment (PPE) typically used in industrial and construction environments. The goal is to facilitate automated monitoring systems that ensure adherence to safety protocols, thereby contributing to the prevention of workplace accidents and injuries.

Class Types

The dataset comprises annotated images spanning four primary PPE categories:

Boots: Safety footwear, including steel-toe and insulated boots. Helmet: Various types of safety helmets and hard hats. Person: Individuals, both with and without PPE, to enhance person detection alongside PPE recognition. Vest: High-visibility vests, reflective safety gear for visibility in low-light conditions. Ear-protection: adding images Mask: Respiratory masks adding images Glass: Safety glasses adding images Glove: Safety Gloves adding images Safety cones: to be added Each class is annotated to provide precise bounding boxes, ensuring high-quality data for model training.

Current Status and Timeline

Phase 1 - Collection: Gathering images from diverse sources, focusing on different environments, lighting conditions, and angles. Phase 2 - Annotation: Ongoing process of labeling the images with accurate bounding boxes. Phase 3 - Model Training: Scheduled to commence post-annotation, targeting advanced object detection models like YOLOv8 & YOLO-NAS.

Contribution and Labeling Guidelines We welcome contributions from the community! If you wish to contribute images or assist with annotations:

Image Contributions: Please ensure images are high-resolution and showcase clear instances of PPE usage. Annotation Guidelines: Follow the standard annotation format as per Roboflow's Annotation Guide. Your contributions will play a vital role in enhancing workplace safety through AI-driven solutions.

According to Cognitive Market Research, the global Label Printer Applicator market size will be USD 3124.2 million in 2024. It will expand at a compound annual growth rate (CAGR) of 5.00% from 2024 to 2031.

North America held the major market share for more than 40% of the global revenue with a market size of USD 1249.68 million in 2024 and will grow at a compound annual growth rate (CAGR) of 3.2% from 2024 to 2031.
Europe accounted for a market share of over 30% of the global revenue with a market size of USD 937.26 million.
Asia Pacific held a market share of around 23% of the global revenue with a market size of USD 718.57 million in 2024 and will grow at a compound annual growth rate (CAGR) of 7.0% from 2024 to 2031.
Latin America had a market share of more than 5% of the global revenue with a market size of USD 156.21 million in 2024 and will grow at a compound annual growth rate (CAGR) of 4.4% from 2024 to 2031.
Middle East and Africa had a market share of around 2% of the global revenue and was estimated at a market size of USD 62.48 million in 2024 and will grow at a compound annual growth rate (CAGR) of 4.7% from 2024 to 2031.
The pharmaceutical industry is increasing at the fastest rate of the Label Printer Applicator industry

Market Dynamics of Label Printer Applicator Market

Key Drivers for Label Printer Applicator Market

Growth of e-commerce and online retail

Primary drivers of market growth for Label Printer Applicator markets are increases in e-commerce and online retail. Businesses have increasingly undertaken their operations online. Therefore, the number of shipments directly to customers has increased. This shipping and handling requirement calls for effective labeling solutions in place to track accurately and deliver effectively. Automatic labeling processing increases speed and accuracy in fulfillment operations mainly due to the role played by label printer applicators. Additionally, e-commerce's requirement for personalized packaging and branding acts as a stimulating factor for the demand of high-quality labels. With the growth in online retail, reliance on advanced labeling technologies will increase, drive innovation and fuel investment in the Label Printer Applicator market. For instance, Zebra Technologies Corporation introduced the ZD621 and ZD611 printer series, which are built for high-speed printing and efficiency to meet the growing demand from online shops. According to their reports, Zebra's solutions have helped firms cut labeling time by up to 50%, resulting in dramatically improved fulfillment processes

Increasing demand for compliance and regulatory labeling

Increasing demand for compliance and regulatory labeling drives market growth in the Label Printer Applicator market significantly. Various industries, such as food and pharmaceuticals, are bound by strict regulations requiring accurate and clear labeling to ensure consumer safety and product authenticity. As governments around the world strictly enforce these regulations, businesses have a need to invest in reliable labeling solutions that can meet the compliance requirements. This legal obligation compliance requirement increases the demand for the label printer applicators. At the same time, firms are encouraged to upgrade their labeling technologies in pursuit of higher accuracy and efficiency. The focus on compliance is going to create a strong market setting in which innovativity in the growth of labeling solutions is going to thrive.

Restraint Factor for the Label Printer Applicator Market

High initial investment costs for advanced systems

High capital-intensive setup costs for advanced label printer applicator systems do not allow for major market penetration. Such systems come as a massive investment for most firms, especially the small- and medium-sized ones. They may find it difficult to raise the proper finances for such installations. This, as an effect, slows down the pace of adoption where less capital-intensive but less effective labeling systems are opted instead. The need for continual upkeep and probable improvement can stretch more the budget, and this becomes a discourager for investing in quality systems. This results in limited growth of the overall market because companies are unwilling to shift to more modern labeling technologies that ensure productivity as well as efficiency over the long term.

Impact of Covid-19 on the Label Printer...

Background: The composition of tissue types present within a wound is a useful indicator of its healing progression and could be helpful in guiding its treatment. Additionally, this measure is clinically used in wound healing tools (e.g. BWAT) to assess risk and recommend treatment. However, the identification of wound tissue and the estimation of their relative composition is highly subjective and variable. This results in incorrect assessments being reported, leading to downstream impacts including inappropriate dressing selection, failure to identify wounds at risk of not healing, or failure to make appropriate referrals to specialists. Objective: To measure inter-and intra-rater variability in manual tissue segmentation and quantification among a cohort of wound care clinicians. To determine if an objective assessment of tissue types (i.e., size, amount) can be achieved using a deep convolutional neural network that predicts wound tissue types. The proposed objective measurement by machine learning model’s performance is reported in terms of mean intersection over union (mIOU) between model prediction and the ground truth labels. Finally, to compare the performance of the model wound tissue identification by a cohort of wound care clinicians. Methods: A dataset of 58 anonymized wound images of various types of chronic wounds from Swift Medical’s Wound Database was used to conduct the inter-rater and intra-rater agreement study. The dataset was split into 3 subsets, with 50% overlap between subsets to measure intra-rater agreement. Four different tissue types (epithelial, granulation, slough and eschar) within the wound bed were independently labelled by the 5 wound clinicians using a browser-based image annotation tool. Each subset was labelled at one-week intervals. Inter-rater and intra rater agreement was computed. Next, two separate deep convolutional neural networks architectures were developed for wound segmentation and tissue segmentation and are used in sequence in the proposed workflow. These models were trained using 465,187 wound image-label pairs and 17,000 image-label pairs respectively. This is by far the largest and most diverse reported dataset of labelled wound images used for training deep learning models for wound and wound tissue segmentation. This allows our models to be robust, unbiased towards skin tones and generalize well to unseen data. The deep learning model architectures were designed to be fast and nimble to allow them to run in near real-time on mobile devices. Results: We observed considerable variability when a cohort of wound clinicians was tasked to label the different tissue types within the wound using a browser-based image annotation tool. We report poor to moderate inter-rater agreement in identifying tissue types in chronic wound images. A very poor Krippendorff alpha value of 0.014 for inter-rater variability when identifying epithelization has been observed, while granulation is most consistently identified by the clinicians. The intra-rater ICC(3,1) (Intra-Class Correlation) however indicates raters are relatively consistent when labelling the same image multiple times over a period of time. Our deep learning models achieved a mean intersection over union (mIOU) of 0.8644 and 0.7192 for wound and tissue segmentation respectively. A cohort of wound clinicians, by consensus, rated 91% of the tissue segmentation results to be between fair and good in terms of tissue identification and segmentation quality. Conclusions: Our inter-rater agreement study validates that clinicians may exhibit considerable variability when identifying and visually estimating tissue proportion within the wound bed. The proposed deep learning model provides objective tissue identification and measurements to assist clinicians in documenting the wound more accurately. Our solution works on off-the-shelf mobile devices and was trained with the largest and most diverse chronic wound dataset ever reported and leading to a robust model when deployed. The proposed solution brings us a step closer to more accurate wound documentation and may lead to improved healing outcomes when deployed at scale.

Industrial Thermal Transfer Labels Market: Dynamics and Outlook The global industrial thermal transfer labels market is projected to reach a valuation of XX million by 2033, expanding at a CAGR of 4.86% during the forecast period. The growth can be attributed to the rising demand for advanced labeling solutions that enhance productivity and efficiency in industrial settings. The increased adoption of automation and the need for durable and reliable labels in various end-user industries are driving the demand for these labels. Key factors propelling the market include the growing use of thermal transfer labels in food and beverages, healthcare, logistics, and transportation industries. The labels provide essential product information, facilitate inventory management, and enhance product safety. Moreover, technological advancements such as RFID integration and tamper-proof labels are further fueling market expansion. However, factors such as environmental concerns and regulations pertaining to label sustainability may pose certain challenges to the market's growth. Recent developments include: In January 2020, Epson America Inc. announced the new TM-L90II LFC thermal label printer at the NRF 2020 Retail's Big Show. The new label printer is a flexible label printer that supports 40-, 58-, and 80mm full media for flexible printing options. Replacing the TM-L90 Plus LFC models, this flexible and adaptable thermal label printer supports liner-free printing and receipt printing and features a back-feed functionality and label-taken sensor., In August 2020, Sato America introduced a new thermal printer to the North American market. It is a new wide format label printer, SG112-ex series, with Automatic label loading, fast and consistent throughput, print speeds up to 6 inches per second, and multi-language display.. Key drivers for this market are: Rise in the Demand for Thermal Transfer Labels in the Healthcare Industry Aided by Growth in Volume Sales, Material Advancements, Specifically in the Case of Polyester-based Labels Expected to Drive the Demand in the Electronics and Food and Beverages Sector; Growing Online Sales. Potential restraints include: Increased Cost of Raw Materials. Notable trends are: Healthcare is Expected to Register Significant Growth Rate.

Applying state-of-the-art machine learning techniques to medical images requires a thorough selection and normalization of input data. One of such steps in digital mammography screening for breast cancer is the labeling and removal of special diagnostic views, in which diagnostic tools or magnification are applied to assist in assessment of suspicious initial findings. As a common task in medical informatics is prediction of disease and its stage, these special diagnostic views, which are only enriched among the cohort of diseased cases, will bias machine learning disease predictions. In order to automate this process, here, we develop a machine learning pipeline that utilizes both DICOM headers and images to predict such views in an automatic manner, allowing for their removal and the generation of unbiased datasets. We achieve AUC of 99.72% in predicting special mammogram views when combining both types of models. Finally, we apply these models to clean up a dataset of about 772,000 images with expected sensitivity of 99.0%. The pipeline presented in this paper can be applied to other datasets to obtain high-quality image sets suitable to train algorithms for disease detection.

Untargeted approaches and thus biological interpretation of metabolomics results are still hampered by the reliable assignment of the global metabolome as well as classification and (putative) identification of metabolites. In this work we present an liquid chromatography-mass spectrometry (LC-MS)–based stable isotope assisted approach that combines global metabolome and tracer based isotope labeling for improved characterization of (unknown) metabolites and their classification into tracer derived submetabolomes. To this end, wheat plants were cultivated in a customized growth chamber, which was kept at 400 ± 50 ppm 13CO2 to produce highly enriched uniformly 13C-labeled sample material. Additionally, native plants were grown in the greenhouse and treated with either 13C9-labeled phenylalanine (Phe) or 13C11-labeled tryptophan (Trp) to study their metabolism and biochemical pathways. After sample preparation, liquid chromatography-high resolution mass spectrometry (LC-HRMS) analysis and automated data evaluation, the results of the global metabolome- and tracer-labeling approaches were combined. A total of 1,729 plant metabolites were detected out of which 122 respective 58 metabolites account for the Phe- and Trp-derived submetabolomes. Besides m/z and retention time, also the total number of carbon atoms as well as those of the incorporated tracer moieties were obtained for the detected metabolite ions. With this information at hand characterization of unknown compounds was improved as the additional knowledge from the tracer approaches considerably reduced the number of plausible sum formulas and structures of the detected metabolites. Finally, the number of putative structure formulas was further reduced by isotope-assisted annotation tandem mass spectrometry (MS/MS) derived product ion spectra of the detected metabolites. A major innovation of this paper is the classification of the metabolites into submetabolomes which turned out to be valuable information for effective filtering of database hits based on characteristic structural subparts. This allows the generation of a final list of true plant metabolites, which can be characterized at different levels of specificity.

Understanding the metabolic pathways of mycobacteria is essential to identify novel antibiotics and to compose synergistic antibiotic regimens against tuberculosis, one of the world’s most deadly infectious diseases with >1.7 Mio yearly deaths. We present a novel proteomics approach for the dynamic measurement of the nascent fractions of specific proteins. We use nontargeted stable isotope incorporation to label the nascent proteins after adding glycerol-1,3-13C2. The analysis is performed using matrix-assisted laser desorption–ionization time-of-flight mass spectrometry (MALDI-TOF-MS) with a self-programmed script, allowing quantitative data. We compared the de novo synthesis of proteins under regular growth conditions and the effect of four antimicrobials, including rifampicin as a first-line drug, linezolid and bedaquiline as second-line drugs, and benzithiazinone-043 as promising drug candidates against tuberculosis. Changes in the synthesis of individual proteins, either due to antimicrobial action or due to regulations in the organism, can be followed in high temporal resolution within the 1/2 doubling cycle of mycobacteria. The analysis of de novo protein synthesis offers a fast screening and testing tool, allowing assessment of the onset and extent of antimycobacterial activity or regulatory phenotypes in different organisms. Due to the untargeted approach, it can be used in model strains and clinical isolates alike and does not require genetic modifications. The dynamic readout and labeling reveal the onset of action of drugs or drug candidates and allow for the prediction of synergistic effects of several substances.

This dataset is a collection of 5,049 images of cotton leaf surfaces acquired with a hand-held microscope to develop deep learning models for leaf hairiness and assist Cotton breeders in their variety selection efforts. These images were collected from two populations (A: 3,276 images; B: 1,773 images) over the 2021-2022 season in a field located at Australian Cotton Research Institute, -30.21, 149.60, Narrabri, NSW, Australia. Populations and genotypes have been anonymized to protect germplasm Intellectual Property.

This dataset is being released together with our HairNet2 paper (Farazi et al 2024). See below for links to related Datasets and Publications.

Lineage: Plant genotypes and growth conditions: Two cotton populations called A and B, were selected for their heterogeneous leaf hairiness, with population A being generally less hairy than population B. Both populations were planted in the summer growing season of 2021-22 at ACRI. Seeds of each genotype were planted in a field on the 23rd of October 2021 at a planting density of 10-12 plants/m2 in rows spaced at 1m. Each genotype was grown in a single 13m plot.

Leaf selection and imaging Leaf samples from these plant populations were collected on the 2nd and 6th of March 2022 (at 19 weeks, first open boll stage). Leaf 3 was harvested from 10 plants per genotype, placed in a paper bag and imaged the same day using the same protocol and equipment as in Rolland, Farazi et al 2022, with the following distinctions: - for population A, two images were collected per leaf: one along the central midvein and one on the leaf blade. - for population B, one image was collected per leaf: along the central midvein. The abaxial side of leaves were imaged at a magnification of about 31x with a portable AM73915 Dino-lite Edge 3.0 (AnMo Electronics Corporation, Taiwan) microscope equipped with a RK-04F folding manual stage (AnMo Electronics Corporation, Taiwan) and connected to a digital tablet running DinoCapture 2.0 (AnMo Electronics Corporation, Taiwan). The exact angle of the mid-vein in each image was not fixed. However, either end of the mid-vein was always cut by the left and right borders of the field of view, and never by the top and bottom ones.

Visual scoring of images by human expert A human expert scored all CotLeaf-X images using arbitrary ordinal scales (0 − 5 for population A and 2 − 5.5 for population B), where higher numbers corresponded to images with more trichomes.

Private Label Food And Beverages Market size was valued at USD 431.99 Billion in 2023 and is projected to reach USD 709.26 Billion by 2030, growing at a CAGR of 7.35% during the forecast period 2024-2030.

Global Private Label Food And Beverages Market Drivers

The market drivers for the Private Label Food And Beverages Market can be influenced by various factors. These may include:

Cost Savings & Value for Money: Because private label products are often more affordable than national brands, consumers are drawn to them. Private labels are an appealing alternative since they are less expensive, particularly in recessionary times.
Retailers’ Attention Is Focused on Private Labels: Retailers have been making investments to grow and advertise their private label businesses. Private label items now place more of an emphasis on quality and innovation, which helps to win over customers’ loyalty and trust.
Customer Perception and Trust: Confidence has grown as a result of favorable customer experiences with private label goods. More customers are inclined to select private label products over established brands as long as shops make the necessary investments to uphold quality and satisfy customer expectations.
Innovation and customisation: To provide distinctive and superior private label items, retailers are investing more and more in product innovation and customisation. Private brands are able to stand out and attract a wider range of customers thanks to this innovation.
Retailer Control Over Supply Chain: When it comes to private label products, retailers have more influence over the supply chain. They can maintain consistent product quality, effectively manage inventories, and react swiftly to shifting consumer preferences thanks to this control.
Growth of E-Commerce: Retailers now have a platform to display and sell their private label products directly to customers thanks to the expansion of e-commerce. Customers can conveniently obtain a variety of private label solutions through online channels.
Consumer Preferences for Regional and Eco-Friendly Products: Retailers frequently source locally for their private label lines, stressing freshness and environmental responsibility. This has helped private label items, which have profited from the increased focus on sustainability and support for small businesses.
Better Packaging and Quality: Retailers have made investments to raise the caliber of their private label merchandise on par with or better than national brands. Furthermore, private label items’ appealing and educational packaging has added to their attractiveness on store shelves.
Global Retailer Expansion: Private label product growth has been aided by the big retailers’ foreign market expansion. Retailers use the power of their brands to reach a wider audience with private label products.

Dataset Introduction TFH_Annotated_Dataset is an annotated patent dataset pertaining to thin film head technology in hard-disk. To the best of our knowledge, this is the second labeled patent dataset public available in technology management domain that annotates both entities and the semantic relations between entities, the first one is 1.

The well-crafted information schema used for patent annotation contains 17 types of entities and 15 types of semantic relations as shown below.

Table 1 The specification of entity types

Table 2 The specification of relation types

There are 1010 patent abstracts with 3,986 sentences in this corpus . We use a web-based annotation tool named Brat2 for data labeling, and the annotated data is saved in '.ann' format. The benefit of 'ann' is that you can display and manipulate the annotated data once the TFH_Annotated_Dataset.zip is unzipped under corresponding repository of Brat.

TFH_Annotated_Dataset contains 22,833 entity mentions and 17,412 semantic relation mentions. With TFH_Annotated_Dataset, we run two tasks of information extraction including named entity recognition with BiLSTM-CRF3 and semantic relation extractionand with BiGRU-2ATTENTION[4]. For improving semantic representation of patent language, the word embeddings are trained with the abstract of 46,302 patents regarding magnetic head in hard disk drive, which turn out to improve the performance of named entity recognition by 0.3% and semantic relation extraction by about 2% in weighted average F1, compared to GloVe and the patent word embedding provided by Risch et al[5].

For named entity recognition, the weighted-average precision, recall, F1-value of BiLSTM-CRF on entity-level for the test set are 78.5%, 78.0%, and 78.2%, respectively. Although such performance is acceptable, it is still lower than its performance on general-purpose dataset by more than 10% in F1-value. The main reason is the limited amount of labeled dataset.

The precision, recall, and F1-value for each type of entity is shown in Fig. 4. As to relation extraction, the weighted-average precision, recall, F1-value of BiGRU-2ATTENTION for the test set are 89.7%, 87.9%, and 88.6% with no_edge relations, and 32.3%, 41.5%, 36.3% without no_edge relations.

Academic citing Chen, L., Xu, S*., Zhu, L. et al. A deep learning based method for extracting semantic information from patent documents. Scientometrics 125, 289–312 (2020). https://doi.org/10.1007/s11192-020-03634-y

Paper link https://link.springer.com/article/10.1007/s11192-020-03634-y

REFERENCE 1 Pérez-Pérez, M., Pérez-Rodríguez, G., Vazquez, M., Fdez-Riverola, F., Oyarzabal, J., Oyarzabal, J., Valencia,A., Lourenço, A., & Krallinger, M. (2017). Evaluation of chemical and gene/protein entity recognition systems at BioCreative V.5: The CEMP and GPRO patents tracks. In Proceedings of the Bio-Creative V.5 challenge evaluation workshop, pp. 11–18.

2 Stenetorp, P., Pyysalo, S., Topić, G., Ohta, T., Ananiadou, S., & Tsujii, J. I. (2012). BRAT: a web-based tool for NLP-assisted text annotation. In Proceedings of the Demonstrations at the 13th Conference of the European Chapter of the Association for Computational Linguistics (pp. 102-107)

3 Huang, Z., Xu, W., &Yu, K. (2015). Bidirectional LSTM-CRF models for sequence tagging. arXiv preprint arXiv:1508.01991

[4] Han,X., Gao,T., Yao,Y., Ye,D., Liu,Z., Sun, M.(2019). OpenNRE: An Open and Extensible Toolkit for Neural Relation Extraction. arXiv preprint arXiv: 1301.3781

[5] Risch, J., & Krestel, R. (2019). Domain-specific word embeddings for patent classification. Data Technologies and Applications, 53(1), 108–122.

The hyperparameters of λsub, λrole, and λprob are 0.5, 0.25, and 0.25 for subjectivity; 0.25, 0.75, and 0 for clinical role; and 0.33, 0.33, and 0.33 for probable label.

The ’Me 163’ was a Second World War fighter airplane and a result of the German air force secret developments. One of these airplanes is currently owned and displayed in the historic aircraft exhibition of the ’Deutsches Museum’ in Munich, Germany. To gain insights with respect to its history, design and state of preservation, a complete CT scan was obtained using an industrial XXL-computer tomography scanner.

Using the CT data from the Me 163, all its details can visually be examined at various levels, ranging from the complete hull down to single sprockets and rivets. However, while a trained human observer can identify and interpret the volumetric data with all its parts and connections, a virtual dissection of the airplane and all its different parts would be quite desirable. Nevertheless, this means, that an instance segmentation of all components and objects of interest into disjoint entities from the CT data is necessary.

As of currently, no adequate computer-assisted tools for automated or semi-automated segmentation
of such XXL-airplane data are available, in a first step, an interactive data annotation and object labeling process has been established. So far, seven sub-volumes from the Me 163 airplane have been annotated and labeled, whose results can potentially be used for various new applications in the field of digital heritage, non-destructive testing, or machine-learning. These annotated and labeled data sets are available here.

According to Cognitive Market Research, the global Electronic Shelf Label System market size is USD 995.2 million in 2024 and will expand at a compound annual growth rate (CAGR) of 19.00% from 2024 to 2031.

North America held the major market of more than 40% of the global revenue with a market size of USD 398.08 million in 2024 and will grow at a compound annual growth rate (CAGR) of 17.2% from 2024 to 2031.
Europe accounted for a share of over 30% of the global market size of USD 298.56 million.
Asia Pacific held the market of around 23% of the global revenue with a market size of USD 228.90 million in 2024 and will grow at a compound annual growth rate (CAGR) of 21.0% from 2024 to 2031.
Latin America market of more than 5% of the global revenue with a market size of USD 49.76 million in 2024 and will grow at a compound annual growth rate (CAGR) of 18.4% from 2024 to 2031.
Middle East and Africa held the major market of around 2% of the global revenue with a market size of USD 19.90 million in 2024 and will grow at a compound annual growth rate (CAGR) of 18.7% from 2024 to 2031.
The 500 to 3,000 held the highest Electronic Shelf Label System market revenue share in 2024.

Market Dynamics of Electronic Shelf Label System Market

Key Drivers for Electronic Shelf Label System Market

Rising Trend of Retail Automation to Increase the Demand Globally

The retail industry is witnessing a big shift toward automation to beautify operational efficiency, lessen prices, and improve client reviews. An essential issue in this trend is the adoption of electronic shelf label (ESL) systems, which automate the method of updating rates and product information. By changing guide procedures with virtual answers, ESL structures allow shops to quickly modify expenses, reducing the likelihood of mistakes and ensuring correct information on keep cabinets. This automation not only reduces labor expenses related to manual label adjustments but also lets in for extra dynamic pricing strategies, facilitating real-time changes in response to marketplace developments. Ultimately, ESL systems assist the wider retail automation fashion industry by streamlining operations and enhancing the overall shopping experience.

Enhanced Operational Efficiency to Propel Market Growth

Enhanced operational performance is a big gain of Electronic Shelf Label (ESL) structures, imparting outlets' actual-time product record updates. With ESL systems, price modifications, promotions, and centered advertising can be applied rapidly and as they should be, lowering delays and doing away with manual errors. This dynamic technique allows retailers to respond quickly to marketplace conditions, aggressive pricing, and seasonal promotions, which are mainly due to advanced operational efficiency. By making sure customers have the right to access accurate, up-to-date product data, ESL structures also enhance the buying revel, promoting purchaser belief and pleasure. The automation of pricing and product records contributes to a more agile retail environment, where retailers can recognize purchaser engagement and other fee-brought activities even while preserving operational accuracy.

Restraint Factor for the Electronic Shelf Label System Market

High Installation and Infrastructure Costs to Limit the Sales

High installation and infrastructure prices present a giant venture for the adoption of Electronic Shelf Label (ESL) systems, mainly for smaller outlets or people with limited budgets. The initial funding encompasses the cost of the labels, which may be widespread depending on store size and product range, along with the infrastructure required for wireless verbal exchange and power. Additional fees encompass software programs for coping with the system and integrating with current retail systems. This premature cost can be a first-rate barrier to entry, deterring shops from adopting the ESL era in spite of its long-term operational advantages. To overcome this hurdle, retailers may also want to weigh the initial prices in opposition to capacity efficiency profits and discover scalable or phased implementation processes.

Impact of Covid-19 on the Electronic Shelf Label System Market

The COVID-19 pandemic had a brilliant impact on the Electronic Shelf Label (ESL) System marketplace. As outlets faced unheard-of disruptions, from keep closures to supply chain challenges, the point of interest in operational performance and valu...

RibFrac dataset is a benchmark for developping algorithms on rib fracture detection, segmentation and classification. We hope this large-scale dataset could facilitate both clinical research for automatic rib fracture detection and diagnoses, and engineering research for 3D detection, segmentation and classification.

Due to size limit of zenodo.org, we split the whole RibFrac Training Set into 2 parts; This is the Training Set Part 1 of RibFrac dataset, including 300 CTs and the corresponding annotations. Files include:

ribfrac-train-images-1.zip: 300 chest-abdomen CTs in NII format (nii.gz).

ribfrac-train-labels-1.zip: 300 annotations in NII format (nii.gz).

ribfrac-train-info-1.csv: labels in the annotation NIIs.

public_id: anonymous patient ID to match images and annotations.

label_id: discrete label value in the NII annotations.

label_code: 0, 1, 2, 3, 4, -1

0: it is background

1: it is a displaced rib fracture

2: it is a non-displaced rib fracture

3: it is a buckle rib fracture

4: it is a segmental rib fracture

-1: it is a rib fracture, but we could not define its type due to ambiguity, diagnosis difficulty, etc. Ignore it in the classification task.

If you find this work useful in your research, please acknowledge the RibFrac project teams in the paper and cite this project as:

Liang Jin, Jiancheng Yang, Kaiming Kuang, Bingbing Ni, Yiyi Gao, Yingli Sun, Pan Gao, Weiling Ma, Mingyu Tan, Hui Kang, Jiajun Chen, Ming Li. Deep-Learning-Assisted Detection and Segmentation of Rib Fractures from CT Scans: Development and Validation of FracNet. EBioMedicine (2020). (DOI)

or using bibtex

@article{ribfrac2020, title={Deep-Learning-Assisted Detection and Segmentation of Rib Fractures from CT Scans: Development and Validation of FracNet}, author={Jin, Liang and Yang, Jiancheng and Kuang, Kaiming and Ni, Bingbing and Gao, Yiyi and Sun, Yingli and Gao, Pan and Ma, Weiling and Tan, Mingyu and Kang, Hui and Chen, Jiajun and Li, Ming}, journal={EBioMedicine}, year={2020}, publisher={Elsevier} }

The RibFrac dataset is a research effort of thousands of hours by experienced radiologists, computer scientists and engineers. We kindly ask you to respect our effort by appropriate citation and keeping data license.

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.