A complete list of live websites using the Contact Form 7 Dynamic Text Extension technology, compiled through global website indexing conducted by WebTechSurvey.

Recently, dynamic text presentation, such as scrolling text, has been widely used. Texts are often presented at constant timing and speed in conventional dynamic text presentation. However, dynamic text presentation enables visually presented texts to indicate timing information, such as prosody, and the texts might influence the impression of reading. In this paper, we examined this possibility by focusing on the temporal features of digital text in which texts are represented sequentially and with varying speed, duration, and timing. We call this “textual prosody.” We used three types of textual prosody: “Recorded,” “Shuffled,” and “Constant.” Recorded prosody is the reproduction of a reader’s reading with pauses and varying speed that simulates talking. Shuffled prosody randomly shuffles the time course of speed and pauses in the recorded type. Constant prosody has a constant presentation speed and provides no timing information. Experiment 1 examined the effect of textual prosody on people with normal hearing. Participants read dynamic text with textual prosody silently and rated their impressions of texts. The results showed that readers with normal hearing preferred recorded textual prosody and constant prosody at the optimum speed (6 letters/second). Recorded prosody was also preferred at a low presentation speed. Experiment 2 examined the characteristics of textual prosody using an articulatory suppression paradigm. The results showed that some textual prosody was stored in the articulatory loop despite it being presented visually. In Experiment 3, we examined the effect of textual prosody with readers with hearing loss. The results demonstrated that readers with hearing loss had positive impressions at relatively low presentation speeds when the recorded prosody was presented. The results of this study indicate that the temporal structure is processed regardless of whether the input is visual or auditory. Moreover, these results suggest that textual prosody can enrich reading not only in people with normal hearing but also in those with hearing loss, regardless of acoustic experiences.

Text-conditioned human motion generation has experienced significant advancements with diffusion models trained on extensive motion capture data and corresponding textual annotations. However, extending such success to 3D dynamic human-object interaction (HOI) generation faces notable challenges, primarily due to the lack of large-scale interaction data and comprehensive descriptions that align with these interactions.

A complete list of live websites using the Dynamic Content for Elementor technology, compiled through global website indexing conducted by WebTechSurvey.

Dynamic Content Delivery Market Outlook

The global market size for Dynamic Content Delivery was valued at approximately USD 5.8 billion in 2023 and is projected to reach USD 15.3 billion by 2032, expanding at a compound annual growth rate (CAGR) of 11.4% during the forecast period. This significant growth is driven by the increasing demand for personalized customer experiences, advances in technology, and the rising trend of digital transformation across various industries.

One of the primary growth factors contributing to the expansion of the Dynamic Content Delivery market is the surging demand for personalized customer experiences. Companies across sectors are increasingly leveraging dynamic content to cater to the unique preferences and behaviors of their customers. This approach not only enhances user engagement but also significantly improves conversion rates, thereby driving revenue growth. Moreover, the growing reliance on data analytics and machine learning technologies enables businesses to deliver more targeted and relevant content, further boosting market demand.

The rapid advancements in technology, particularly in artificial intelligence (AI) and machine learning (ML), are also playing a crucial role in the growth of the Dynamic Content Delivery market. These technologies empower organizations to analyze vast amounts of data in real-time, allowing them to make more informed decisions and optimize content delivery strategies. The integration of AI and ML in content delivery systems enhances the efficiency and effectiveness of content personalization, making it a valuable tool for businesses aiming to stay competitive in the digital landscape.

Another significant driver of market growth is the increasing trend of digital transformation across industries. As businesses continue to shift towards digital platforms, the need for dynamic and engaging content becomes more critical. Industries such as retail, media and entertainment, healthcare, and BFSI are heavily investing in dynamic content delivery solutions to enhance their digital presence and improve customer interactions. This widespread adoption of digital technologies is expected to sustain the market's growth momentum over the forecast period.

Regionally, North America holds the largest market share in the Dynamic Content Delivery market, driven by the high adoption rate of advanced technologies and the presence of major market players. The Asia Pacific region is expected to witness the highest growth rate during the forecast period, fueled by the rapid digitalization efforts in countries like China, India, and Japan. The increasing internet penetration and mobile usage in these regions further contribute to the growing demand for dynamic content delivery solutions.

As the digital landscape continues to evolve, the role of a Cloud Content Delivery Network has become increasingly pivotal in ensuring seamless and efficient content delivery. A Cloud Content Delivery Network (CDN) leverages a network of servers distributed globally to cache and deliver content closer to the user's location. This not only reduces latency but also enhances the speed and reliability of content delivery, which is crucial for businesses aiming to provide a superior user experience. By utilizing a CDN, companies can effectively manage high traffic volumes and ensure consistent performance, even during peak usage times. This capability is particularly beneficial for organizations with a global audience, as it allows them to deliver content swiftly and efficiently across different regions.

Component Analysis

The Dynamic Content Delivery market can be segmented by components into Software and Services. The software segment encompasses various platforms and tools that enable the creation, management, and delivery of dynamic content. This segment is expected to witness robust growth due to the increasing demand for advanced content management systems and personalization tools. Businesses are increasingly investing in sophisticated software solutions that can analyze user data and deliver tailored content in real-time, thereby enhancing customer engagement and satisfaction.

On the other hand, the services segment includes professional services, such as consulting, implementation, and support, as well as managed services. The demand for these services is driven by the need for expert guidance and support in deploying and managing dynamic content delive

A complete list of live websites using the Dynamic Content Gallery Plugin technology, compiled through global website indexing conducted by WebTechSurvey.

According to Cognitive Market Research, the global Dynamic Content Delivery market size will be USD XX million in 2024. It will expand at a compound annual growth rate (CAGR) of 11.00% 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 XX million in 2024 and will grow at a compound annual growth rate (CAGR) of 9.2% from 2024 to 2031.
Europe accounted for a market share of over 30% of the global revenue with a market size of USD XX million.
Asia Pacific held a market share of around 23% of the global revenue with a market size of USD XX million in 2024 and will grow at a compound annual growth rate (CAGR) of 13.0% from 2024 to 2031.
Latin America had a market share of more than 5% of the global revenue with a market size of USD XX million in 2024 and will grow at a compound annual growth rate (CAGR) of 10.4% 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 XX million in 2024 and will grow at a compound annual growth rate (CAGR) of 10.7% from 2024 to 2031.
Personalized content delivery held the highest dynamic content delivery market revenue share in 2024.

Market Dynamics of Dynamic Content Delivery Market

Key Drivers for Dynamic Content Delivery Market

Expansion of eCommerce and Online Retail Requiring Dynamic Content Delivery

The rapid growth of eCommerce and online retail has significantly increased the need for dynamic content delivery systems. As more consumers turn to online shopping, businesses are under pressure to provide seamless, personalized experiences that can adapt in real-time to user behavior. Dynamic content delivery enables retailers to offer tailored product recommendations, targeted advertising, and localized content, all of which are crucial for enhancing customer engagement and driving sales. This technology allows eCommerce platforms to quickly respond to changing market conditions, ensuring that content remains relevant and appealing to diverse audiences. As online retail continues to expand, the demand for sophisticated dynamic content delivery solutions will only intensify, driving further innovation in this space.

Growing Demand for Personalized User Experiences Across Digital Platforms

The demand for personalized user experiences has become a central driver in the evolution of digital platforms. As consumers increasingly seek content that aligns with their individual preferences and behaviors, businesses are investing heavily in dynamic content delivery technologies that enable real-time personalization. This trend is particularly evident in sectors like media and entertainment, online gaming, and eCommerce, where personalized content can significantly enhance user engagement and satisfaction. By leveraging data analytics and AI, companies can tailor content to individual users, delivering more relevant and engaging experiences. This growing demand for personalization is reshaping digital strategies across industries, making dynamic content delivery a critical component of modern digital ecosystems.

Restraint Factor for the Dynamic Content Delivery Market

Data privacy concerns and stringent regulations impacting personalized content delivery

Data privacy concerns and stringent regulations are major challenges impacting the personalized content delivery market. As consumers become more aware of how their personal data is used, they demand greater transparency and control, leading to stricter data protection laws like GDPR in Europe and CCPA in California. These regulations impose significant compliance burdens on companies, restricting their ability to collect and use personal data for content personalization. Violations can result in hefty fines and damage to brand reputation. Consequently, businesses must navigate a complex regulatory landscape while still trying to deliver personalized experiences. Balancing the need for personalization with privacy concerns is becoming increasingly difficult, potentially slowing the adoption and effectiveness of dynamic content delivery solutions.

Impact of Covid-19 on the Dynamic Content Delivery Market

The COVID-19 pandemic had a profound impact on the Dynamic Content Delivery Market, accelerating its growth as more consumers shifted to digital platforms for entertainment, shopping, education, and work. With lockdowns and social di...

No description is available. Visit https://dataone.org/datasets/doi%3A10.18739%2FA2J678X27 for complete metadata about this dataset.

ULB ChocoFountainBxl sequence by LISA ULB

The test sequence "ULB ChocoFountainBxl" is provided by Daniele Bonatto, Sarah Fachada, Mehrdad Teratani and Gauthier Lafruit, members of the LISA department, EPB (Ecole Polytechnique de Bruxelles), ULB (Université Libre de Bruxelles), Belgium.

View Synthesis Sample

License

License Creative Commons 4.0 - CC BY 4.0

Terms of Use

Any kind of publication or report using this sequence should refer to the following references.

[1] Daniele Bonatto, Sarah Fachada, Mehrdad Teratani, Gauthier Lafruit, "ULB ChocoFountainBxl", Zenodo, 10.5281/zenodo.5960227, 2022.

@misc{bonatto_chocofountainbxl_2022,
title = {{ULB} {ChocoFountainBxl}},
author = {Bonatto, Daniele and Fachada, Sarah and Teratani, Mehrdad and Lafruit, Gauthier},
publisher = {Zenodo}
month = feb,
year = {2022},
doi = {10.5281/zenodo.5960227}
}

[2] A. Schenkel, D. Bonatto, S. Fachada, H.-L. Guillaume, et G. Lafruit, "Natural Scenes Datasets for Exploration in 6DOF Navigation", in 2018 International Conference on 3D Immersion (IC3D), Brussels, Belgium, déc. 2018, p. 1-8. doi: 10.1109/IC3D.2018.8657865.

@inproceedings{schenkel_natural_b_2018,
address = {Brussels, Belgium},
title = {Natural {Scenes} {Datasets} for {Exploration} in {6DOF} {Navigation}},
isbn = {978-1-5386-7590-8},
url = {https://doi.org/10.1109/IC3D.2018.8657865},
doi = {10.1109/IC3D.2018.8657865},
language = {en},
urldate = {2019-04-11},
booktitle = {2018 {International} {Conference} on {3D} {Immersion} ({IC3D})},
publisher = {IEEE},
author = {Schenkel, Arnaud and Bonatto, Daniele and Fachada, Sarah and Guillaume, Henry-Louis and Lafruit, Gauthier},
month = dec,
year = {2018},
pages = {1--8}
}

Production

Laboratory of Image Synthesis and Analysis, LISA department, Ecole Polytechnique de Bruxelles, Universite Libre de Bruxelles, Belgium.

Content

This dataset contains a dynamic test scene created using the acquisition system described in [2] (3x5 array with a baseline of 10 cm (vertical) and 15cm horizontal).
We provide color corrected [4] 97 frames RGB textures (YUV420p10le format) captured using 15 4k micro studio Blackmagic cameras (3840x2160 pixels @ 30 fps cropped to 3712x2064).
We also provide corresponding depth maps (YUV420p16le format) estimated using MPEG's Immersive Video Depth Estimation (IVDE) [5] and refined using PDR [6].

The scene display two actors interacting with difficult objects to render in view synthesis. In particular the scene contains transparent, specular and smooth areas objects.
The videos were taken in a controlled light environment.

The views are disposed as follow:

In addition to the images and their depth maps, an accurate camera calibration file is provided following the format of [8].

The dataset contains:

- a `camera.json` file in OMAF coordinates system (Camera position: X: forwards, Y:left, Z: up, Rotation: yaw, pitch, roll) [9],
- a `view_synthesis_config.zip` folder containing configuration files for RVS [7,8] to synthesize every view with its closest 4 neighbors in a "plus" configuration,
- a `view_synthesis_results.zip` folder containing videos (scaled to 710x516) corresponding to the configuration files in `view_synthesis_config` and a multiview videos displaying all the results merged together. Views synthesized with RVS [7,8],
- a `vXY_depth_3712x2064_yuv420p16le.zip` Depth maps for every XY view in yuv420p16le format,
- a `vXY_texture_3712x2064_yuv420p10le.zip` RGB textures for every XY view in yuv420p10le format.

References and links

[4] A. Dziembowski, D. Mieloch, S. Różek and M. Domański, "Color Correction for Immersive Video Applications," in IEEE Access, vol. 9, pp. 75626-75640, 2021, doi: 10.1109/ACCESS.2021.3081870.
[5] D. Mieloch, O. Stankiewicz and M. Domański, "Depth Map Estimation for Free-Viewpoint Television and Virtual Navigation", IEEE Access, vol. 8, pp. 5760-5776, 2020, doi: 10.1109/ACCESS.2019.2963487.
[6] D. Mieloch, A. Dziembowski and M. Domański, "Depth Map Refinement for Immersive Video," in IEEE Access, vol. 9, pp. 10778-10788, 2021, doi: 10.1109/ACCESS.2021.3050554.
[7] D. Bonatto, S. Fachada, S. Rogge, A. Munteanu and G. Lafruit, "Real-Time Depth Video-Based Rendering for 6-DoF HMD Navigation and Light Field Displays," in IEEE Access, vol. 9, pp. 146868-146887, 2021, doi: 10.1109/ACCESS.2021.3123529.
[8] S. Fachada, B. Kroon, D. Bonatto, B. Sonneveldt, et G. Lafruit, "Reference View Synthesizer (RVS) 2.0 manual, [N17759]", july. 2018.
[9] S. Fachada, D. Bonatto, M. Teratani, and G. Lafruit, "Intechopen - View Synthesis tool for VR Immersive Video", 2022.

Acknowledgments

[G1] EU project HoviTron, Grant Agreement n$^o$951989 on Interactive Technologies, Horizon 2020.
[G2] Innoviris, the Brussels Institute for Research and Innovation, Belgium, under contract No.: 2015-DS-39a/b & 2015-R-39c/d, 3DLicorneA.
[G3] Sarah Fachada is a Research Fellow of the Fonds de la Recherche Scientifique - FNRS, Belgium.

North America Dynamic Content Delivery market size will be USD XX million in 2024 and will grow at a compound annual growth rate (CAGR) of 9.2% from 2024 to 2031. North America has emerged as a prominent participant, and its sales revenue is estimated to reach USD XX Million by 2031. Advanced technological infrastructure and high digital adoption drive growth in North America.

perturbed toxic text dataset (from Jigsaw dataset), managed by the types of perturbations. There are 9 types of perturbations:

insert repeat maskword homoglyph swap remove abbrs./slangs distract words distract sentences

Introduction

There are several works based on Natural Language Processing on newspaper reports. Mining opinions from headlines [ 1 ] using Standford NLP and SVM by Rameshbhaiet. Al.compared several algorithms on a small and large dataset. Rubinet. al., in their paper [ 2 ], created a mechanism to differentiate fake news from real ones by building a set of characteristics of news according to their types. The purpose was to contribute to the low resource data available for training machine learning algorithms. Doumitet. al.in [ 3 ] have implemented LDA, a topic modeling approach to study bias present in online news media.

However, there are not many NLP research invested in studying COVID-19. Most applications include classification of chest X-rays and CT-scans to detect presence of pneumonia in lungs [ 4 ], a consequence of the virus. Other research areas include studying the genome sequence of the virus[ 5 ][ 6 ][ 7 ] and replicating its structure to fight and find a vaccine. This research is crucial in battling the pandemic. The few NLP based research publications are sentiment classification of online tweets by Samuel et el [ 8 ] to understand fear persisting in people due to the virus. Similar work has been done using the LSTM network to classify sentiments from online discussion forums by Jelodaret. al.[ 9 ]. NKK dataset is the first study on a comparatively larger dataset of a newspaper report on COVID-19, which contributed to the virus’s awareness to the best of our knowledge.

2 Data-set Introduction

2.1 Data Collection

We accumulated 1000 online newspaper report from United States of America (USA) on COVID-19. The newspaper includes The Washington Post (USA) and StarTribune (USA). We have named it as “Covid-News-USA-NNK”. We also accumulated 50 online newspaper report from Bangladesh on the issue and named it “Covid-News-BD-NNK”. The newspaper includes The Daily Star (BD) and Prothom Alo (BD). All these newspapers are from the top provider and top read in the respective countries. The collection was done manually by 10 human data-collectors of age group 23- with university degrees. This approach was suitable compared to automation to ensure the news were highly relevant to the subject. The newspaper online sites had dynamic content with advertisements in no particular order. Therefore there were high chances of online scrappers to collect inaccurate news reports. One of the challenges while collecting the data is the requirement of subscription. Each newspaper required $1 per subscriptions. Some criteria in collecting the news reports provided as guideline to the human data-collectors were as follows:

The headline must have one or more words directly or indirectly related to COVID-19.

The content of each news must have 5 or more keywords directly or indirectly related to COVID-19.

The genre of the news can be anything as long as it is relevant to the topic. Political, social, economical genres are to be more prioritized.

Avoid taking duplicate reports.

Maintain a time frame for the above mentioned newspapers.

To collect these data we used a google form for USA and BD. We have two human editor to go through each entry to check any spam or troll entry.

2.2 Data Pre-processing and Statistics

Some pre-processing steps performed on the newspaper report dataset are as follows:

Remove hyperlinks.

Remove non-English alphanumeric characters.

Remove stop words.

Lemmatize text.

While more pre-processing could have been applied, we tried to keep the data as much unchanged as possible since changing sentence structures could result us in valuable information loss. While this was done with help of a script, we also assigned same human collectors to cross check for any presence of the above mentioned criteria.

The primary data statistics of the two dataset are shown in Table 1 and 2.

Table 1: Covid-News-USA-NNK data statistics

No of words per headline

7 to 20

No of words per body content

150 to 2100

Table 2: Covid-News-BD-NNK data statistics No of words per headline

10 to 20

No of words per body content

100 to 1500

2.3 Dataset Repository

We used GitHub as our primary data repository in account name NKK^1. Here, we created two repositories USA-NKK^2 and BD-NNK^3. The dataset is available in both CSV and JSON format. We are regularly updating the CSV files and regenerating JSON using a py script. We provided a python script file for essential operation. We welcome all outside collaboration to enrich the dataset.

3 Literature Review

Natural Language Processing (NLP) deals with text (also known as categorical) data in computer science, utilizing numerous diverse methods like one-hot encoding, word embedding, etc., that transform text to machine language, which can be fed to multiple machine learning and deep learning algorithms.

Some well-known applications of NLP includes fraud detection on online media sites[ 10 ], using authorship attribution in fallback authentication systems[ 11 ], intelligent conversational agents or chatbots[ 12 ] and machine translations used by Google Translate[ 13 ]. While these are all downstream tasks, several exciting developments have been made in the algorithm solely for Natural Language Processing tasks. The two most trending ones are BERT[ 14 ], which uses bidirectional encoder-decoder architecture to create the transformer model, that can do near-perfect classification tasks and next-word predictions for next generations, and GPT-3 models released by OpenAI[ 15 ] that can generate texts almost human-like. However, these are all pre-trained models since they carry huge computation cost. Information Extraction is a generalized concept of retrieving information from a dataset. Information extraction from an image could be retrieving vital feature spaces or targeted portions of an image; information extraction from speech could be retrieving information about names, places, etc[ 16 ]. Information extraction in texts could be identifying named entities and locations or essential data. Topic modeling is a sub-task of NLP and also a process of information extraction. It clusters words and phrases of the same context together into groups. Topic modeling is an unsupervised learning method that gives us a brief idea about a set of text. One commonly used topic modeling is Latent Dirichlet Allocation or LDA[17].

Keyword extraction is a process of information extraction and sub-task of NLP to extract essential words and phrases from a text. TextRank [ 18 ] is an efficient keyword extraction technique that uses graphs to calculate the weight of each word and pick the words with more weight to it.

Word clouds are a great visualization technique to understand the overall ’talk of the topic’. The clustered words give us a quick understanding of the content.

4 Our experiments and Result analysis

We used the wordcloud library^4 to create the word clouds. Figure 1 and 3 presents the word cloud of Covid-News-USA- NNK dataset by month from February to May. From the figures 1,2,3, we can point few information:

In February, both the news paper have talked about China and source of the outbreak.

StarTribune emphasized on Minnesota as the most concerned state. In April, it seemed to have been concerned more.

Both the newspaper talked about the virus impacting the economy, i.e, bank, elections, administrations, markets.

Washington Post discussed global issues more than StarTribune.

StarTribune in February mentioned the first precautionary measurement: wearing masks, and the uncontrollable spread of the virus throughout the nation.

While both the newspaper mentioned the outbreak in China in February, the weight of the spread in the United States are more highlighted through out March till May, displaying the critical impact caused by the virus.

We used a script to extract all numbers related to certain keywords like ’Deaths’, ’Infected’, ’Died’ , ’Infections’, ’Quarantined’, Lock-down’, ’Diagnosed’ etc from the news reports and created a number of cases for both the newspaper. Figure 4 shows the statistics of this series. From this extraction technique, we can observe that April was the peak month for the covid cases as it gradually rose from February. Both the newspaper clearly shows us that the rise in covid cases from February to March was slower than the rise from March to April. This is an important indicator of possible recklessness in preparations to battle the virus. However, the steep fall from April to May also shows the positive response against the attack. We used Vader Sentiment Analysis to extract sentiment of the headlines and the body. On average, the sentiments were from -0.5 to -0.9. Vader Sentiment scale ranges from -1(highly negative to 1(highly positive). There were some cases

where the sentiment scores of the headline and body contradicted each other,i.e., the sentiment of the headline was negative but the sentiment of the body was slightly positive. Overall, sentiment analysis can assist us sort the most concerning (most negative) news from the positive ones, from which we can learn more about the indicators related to COVID-19 and the serious impact caused by it. Moreover, sentiment analysis can also provide us information about how a state or country is reacting to the pandemic. We used PageRank algorithm to extract keywords from headlines as well as the body content. PageRank efficiently highlights important relevant keywords in the text. Some frequently occurring important keywords extracted from both the datasets are: ’China’, Government’, ’Masks’, ’Economy’, ’Crisis’, ’Theft’ , ’Stock market’ , ’Jobs’ , ’Election’, ’Missteps’, ’Health’, ’Response’. Keywords extraction acts as a filter allowing quick searches for indicators in case of locating situations of the economy,

Latin America's Dynamic Content Delivery market will be USD XX million in 2024 and is estimated to grow at a compound annual growth rate (CAGR) of 10.4% from 2024 to 2031. The market is foreseen to reach USD XX million by 2031 due to the expanding eCommerce and mobile device use.

The Text Analytics market is experiencing robust growth, projected to reach $10.49 billion in 2025 and exhibiting a Compound Annual Growth Rate (CAGR) of 39.90% from 2019 to 2033. This expansion is driven by several key factors. The increasing volume of unstructured textual data generated across various sectors, coupled with the need for efficient data analysis to extract actionable insights, is a major catalyst. Businesses across BFSI, healthcare, retail, and energy are leveraging text analytics for risk management, fraud detection, customer service improvements, and enhanced business intelligence. The shift towards cloud-based deployments further fuels market growth, offering scalability and cost-effectiveness. Advancements in Natural Language Processing (NLP) and machine learning algorithms are enabling more accurate and sophisticated text analysis, leading to better decision-making and improved operational efficiency. The competitive landscape is characterized by a mix of established players like IBM, SAS, and Microsoft, alongside innovative startups offering specialized solutions. Looking ahead, the market's growth trajectory is expected to remain strong. Continued technological advancements, expanding adoption across new industries, and the increasing demand for real-time insights will contribute significantly. However, challenges such as data privacy concerns, the need for skilled professionals to interpret analytical results, and the complexities of managing large and diverse datasets present potential restraints. To overcome these, companies are focusing on developing user-friendly platforms and investing in robust data security measures. The segmentation by deployment (on-premise vs. cloud), application (risk management, fraud detection, etc.), and end-user industry offers a granular understanding of market dynamics, facilitating targeted strategic initiatives for both vendors and investors. The Asia-Pacific region is projected to witness particularly rapid growth driven by digital transformation and increasing data generation across developing economies. This comprehensive report provides an in-depth analysis of the global Text Analytics market, offering invaluable insights for businesses seeking to leverage the power of text data. Covering the period from 2019 to 2033, with a focus on 2025, this report delves into market size, growth drivers, challenges, and future trends, equipping you with the knowledge to make strategic decisions. We analyze key segments including deployment models (on-premise, cloud), applications (risk management, fraud detection, business intelligence, social media analysis, customer care), and end-user industries (BFSI, healthcare, retail, etc.). The report also profiles leading players in the market, providing a competitive landscape overview. This research is crucial for investors, market entrants, and established players looking to navigate the dynamic Text Analytics landscape. Keywords: Text analytics market size, text analytics market growth, text analytics market trends, text analytics market share, text analytics applications, text analytics software, sentiment analysis, natural language processing (NLP), machine learning (ML), business intelligence, social media analytics, customer service analytics, risk management, fraud detection, text mining, big data analytics. Recent developments include: January 2023- Microsoft announced a new multibillion-dollar investment in ChatGPT maker Open AI. ChatGPT, automatically generates text based on written prompts in a more creative and advanced than the chatbots. Through this investment, the company will accelerate breakthroughs in AI, and both companies will commercialize advanced technologies., November 2022 - Tntra and Invenio have partnered to develop a platform that offers comprehensive data analysis on a firm. Throughout the process, Tntra offered complete engineering support and cooperation to Invenio. Tantra offers feeds, knowledge graphs, intelligent text extraction, and analytics, which enables Invenio to give information on seven parts of the business, such as false news identification, subject categorization, dynamic data extraction, article summaries, sentiment analysis, and keyword extraction.. Key drivers for this market are: Growing Demand for Social Media Analytics, Rising Practice of Predictive Analytics. Potential restraints include: Lack of Skilled Personnel and Awareness. Notable trends are: Retail and E-commerce to Hold a Significant Share in Text Analytics Market.

Text to Video AI Market size was valued at USD 0.16 Billion in 2024 and is projected to reach USD 1.40 Billion by 2031, growing at a CAGR of 36% during the forecasted period 2024 to 2031.

The Text to Video AI Market is driven by the increasing demand for dynamic content creation, especially in digital marketing, education, and social media. As companies seek efficient ways to produce engaging, high-quality video content without extensive resources, text-to-video AI offers an automated, cost-effective solution. Advancements in AI, particularly in natural language processing (NLP) and computer vision, are enhancing the quality and realism of video generation, making it easier for users to convert text into visually appealing videos. The growing preference for personalized and interactive content, coupled with rising internet penetration and video consumption across platforms, further fuels demand. Additionally, businesses are increasingly leveraging AI-driven video content to reach wider audiences, enhance storytelling, and improve customer engagement.

Enron is a well-known dataset in network science and text mining. It has been widely studied in academia. In network science, several different static networks appear in the literature. However, up to now, no dynamic network has been published, even though the email conversations have timestamps.We processed the original dataset to extract a dynamic network, In original dataset, It contains 158 nodes representing Enron employees between 1997 and 2002. All the addresses in the From and To fields of each email are considered, resulting in a network of 28802 nodes representing a distinct email addresses. A time span of one month is chosen for the time slices, generating 46 time slices. Two nodes are connected if the corresponding persons emailed each other during the given time slice. We did not make any distinction between sender and receiver, and thus produced an undirected dynamic network.

This dataset contains the data collected from Cuckoo and our own kernel driver after running 1000 malicious and 1000 clean samples.

The Kernel Driver folder contains subfolders that hold the API-calls from clean and malicious data. The folders holding data from running clean samples are ProcessIdClean, ProcessIdCleanHippo, ProcessIdCleanPippo, ProcessIdCleanZero. The folders holding data from running malicious samples are ProcessIdVirusShare500 and ProcessIdVirusShare1000. Within these folders are folders labeled as numbers with each number representing the running of a different sample. Within each run is a text file for each system call monitored (the text file's name is the system call's name that it contains the calls for). A new line is added to the file every time that system call is called.

In the Cuckoo folder, the subfolders containing clean data are CuckooClean, CuckooCleanHippo, and CuckooCleanPippo. CuckooVirusShare contains all of the results from running malware. These folders contain the standard data that Cuckoo offers.

This is the dataset for the paper "Disjoint-DABS: A Benchmark for Dynamic Aspect-Based Summarization in Disorganized Texts". It includes two sub-datasets converted from CNN/DailyMail (D-CnnDM.zip) and WikiHow (D-WikiHow.zip). We include the data with training, validation, and test split. The file for training the summarization model is at (WikiHowSep.zip and CnnDM.zip) We also include the small-scale data for D-WikiHow used for prompting experiments (D-WikiHow-sample). The generated summaries for all baselines for further research, especially for human evaluation is included (result.zip).

Europe Dynamic Content Delivery market USD XX million in 2024 and will grow at a compound annual growth rate (CAGR) of 9.5% from 2024 to 2031. Strong regulatory frameworks and a focus on digital innovation fuel market expansion in Europe is expected to aid the sales to USD XX million by 2031

With the rapid development of deep learning techniques, the generation and counterfeiting of multimedia material are becoming increasingly straightforward to perform. At the same time, sharing fake content on the web has become so simple that malicious users can create unpleasant situations with minimal effort. Also, forged media are getting more and more complex, with manipulated videos (e.g., deepfakes where both the visual and audio contents can be counterfeited) that are taking the scene over still images. The multimedia forensic community has addressed the possible threats that this situation could imply by developing detectors that verify the authenticity of multimedia objects. However, the vast majority of these tools only analyze one modality at a time. This was not a problem as long as still images were considered the most widely edited media, but now, since manipulated videos are becoming customary, performing monomodal analyses could be reductive. Nonetheless, there is a lack in the literature regarding multimodal detectors (systems that consider both audio and video components). This is due to the difficulty of developing them but also to the scarsity of datasets containing forged multimodal data to train and test the designed algorithms.

In this paper we focus on the generation of an audio-visual deepfake dataset. First, we present a general pipeline for synthesizing speech deepfake content from a given real or fake video, facilitating the creation of counterfeit multimodal material. The proposed method uses Text-to-Speech (TTS) and Dynamic Time Warping (DTW) techniques to achieve realistic speech tracks. Then, we use the pipeline to generate and release TIMIT-TTS, a synthetic speech dataset containing the most cutting-edge methods in the TTS field. This can be used as a standalone audio dataset, or combined with DeepfakeTIMIT and VidTIMIT video datasets to perform multimodal research. Finally, we present numerous experiments to benchmark the proposed dataset in both monomodal (i.e., audio) and multimodal (i.e., audio and video) conditions. This highlights the need for multimodal forensic detectors and more multimodal deepfake data.

For the initial version of TIMIT-TTS v1.0

Arxiv: https://arxiv.org/abs/2209.08000

TIMIT-TTS Database v1.0: https://zenodo.org/record/6560159