Sunday, June 29, 2025
How AI Spots Estrus in Dairy Cows—Fast! #Sciencefather #researchawards
Thursday, June 26, 2025
How Avatar Detection Works in the Metaverse! 🚀 #Sciencefather #researchawards
The metaverse, a growing digital trend since 2022, offers immersive 3D environments where users, represented by avatars, can interact socially and economically. It has gained popularity due to global shifts like the COVID pandemic and climate change, which emphasized virtual collaboration. Major platforms like Second Life, Decentraland, Roblox, Fortnite, and Meta Horizon Worlds have shown how metaverse spaces are becoming more mainstream, with increasing user engagement and corporate investment.
A key part of the metaverse experience is the avatar—digital representations of users that interact within virtual worlds. These avatars can be recorded in metaverse recordings (MVRs), producing multimedia content like images or videos. MVRs have several applications, including VR training, experience sharing, and industrial simulations. However, to make use of these recordings effectively, especially in multimedia information retrieval (MMIR), there is a need to detect and classify avatars within the content.
This leads to the introduction of Avatar Detection, a specialized object detection task focused on identifying avatars in images and videos. While some platforms could provide metadata (Scene Raw Data) during live use, such data is often unavailable in recordings. Accurate avatar detection helps in organizing and retrieving content from large datasets, enabling semantic search, interaction analysis, and even identity recognition. As avatars reflect user actions and interactions, their detection becomes crucial for improving searchability and understanding content in the metaverse.
International Conference on Computer Vision
The International Research Awards on Computer Vision recognize groundbreaking contributions in the field of computer vision, honoring researchers, scientists and innovators whose work has significantly advanced the domain. This prestigious award highlights excellence in fundamental theories, novel algorithms and real-world applications, fostering progress in artificial intelligence, image processing and deep learning.
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How AI is Revolutionizing Ocean Life Analysis! 🌊🤖 #Sciencefather #researchawards #deeplearning
Over the past decade, the use of Remotely Operated Vehicles (ROVs) in marine
research has grown significantly due to advances in computational power and
robotics. These tools now allow marine biologists to gather high-quality
underwater video footage for analyzing marine life. However, challenges such as
low visibility, light scattering, and colour distortion hinder accurate object
detection and classification in these environments. As a result, researchers
have turned to computer vision methods—particularly deep learning models like
YOLO—for real-time detection of underwater objects such as fish and corals.
While YOLO-based models have shown strong performance in underwater fish
detection, current research remains limited in scope. Most existing datasets
and models, including FishNet, FishInTurbidWater, and FishDETECT, are
fish-centric and do not account for the broader ecological diversity,
particularly marine vegetation. There is a noticeable lack of well-defined
datasets and ontologies for identifying and classifying underwater plants,
which are essential for comprehensive marine ecosystem monitoring. Efforts like
CoralNet and CATNet's MSID dataset have broadened species categories, yet
marine vegetation remains underrepresented.
To bridge this gap, we introduce FjordVision, a
hierarchical deep learning framework designed for detecting and classifying
both marine vegetation and fauna in Esefjorden, Norway. FjordVision includes
the Esefjorden Marine Vegetation Segmentation Dataset (EMVSD),
featuring over 17,000 annotated images with more than 30,000 labelled marine
objects. Leveraging YOLOv8 for instance segmentation and enhanced with a
taxonomically structured classification model, FjordVision improves on
traditional flat classification by categorizing objects into binary, class,
genus, and species levels. This approach delivers more ecologically relevant
insights, making FjordVision a vital tool for biodiversity monitoring and
marine conservation.
Wednesday, June 25, 2025
How AI Tracks Fish in 3D Underwater! 🐟 #Sciencefather #researchawards #artificialintelligence
Indoor recirculating aquaculture systems (RAS) are advanced setups designed
to improve aquaculture productivity by integrating components such as water
circulation, filtration, oxygen supply, and microbial filters. These systems
support high-density fish farming while maintaining water quality. Given their
complexity, automated monitoring technologies like target detection and
tracking are essential for observing fish behavior. Behavior such as reduced
swimming or surface gathering can indicate stress, illness, or environmental
issues like low dissolved oxygen, highlighting the need for continuous
monitoring.
Among monitoring techniques, 3D target tracking
stands out for its ability to accurately capture fish movements and behavior in
three-dimensional space. This enables more detailed behavioral metrics such as
swimming speed, spatial distribution, and depth. While 2D tracking is limited
by the lack of depth data and is commonly used for animals on flat surfaces, 3D
tracking is more suitable for fish that swim freely in all directions. Of the
available 3D tracking systems, underwater parallel stereo vision offers the
most promise for aquaculture due to its cost-effectiveness, single imaging
medium, and accurate depth perception without the complications of air-water
refraction.
To
address the limitations of current 3D tracking methods—such as high
computational costs and accuracy loss in noisy underwater environments—a
two-stage 3D multi-fish tracking (TMT) model has been proposed. In the first
stage, it uses YOLOv8x and DeepSORT to extract fish patches from stereo images.
In the second stage, it applies patch-based stereo matching, improved
Semi-global Matching (SGM), and point cloud filtering to calculate 3D
positions. By focusing only on fish-containing patches, the TMT model improves
tracking accuracy, reduces computational load, and streamlines the 3D fish behavior
monitoring process in RAS environments.
International Conference on Computer Vision
The International Research Awards on Computer Vision recognize groundbreaking contributions in the field of computer vision, honoring researchers, scientists and innovators whose work has significantly advanced the domain. This prestigious award highlights excellence in fundamental theories, novel algorithms and real-world applications, fostering progress in artificial intelligence, image processing and deep learning.
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Monday, June 23, 2025
Counting Rice Grains with AI: Fast & Accurate! #Sciencefather #researchawards #artificialintelligence
Rice (Oryza
sativa) is a key global staple, accounting for around 25% of total grain
production with about 800 million tons harvested yearly. As cultivated land
declines, it's essential to develop high-yielding rice varieties. One critical
factor in determining yield is the number of grains per panicle. Traditionally,
measuring this involves labor-intensive steps like manual threshing and
counting, which are time-consuming and inefficient. Moreover, due to grain
occlusion—where grains overlap each other—existing image-based methods struggle
to maintain both speed and accuracy in grain counting.
Advancements in deep learning have shown great promise for automating crop
analysis. Object detection algorithms like Faster R-CNN and YOLO have been
successfully applied to count seeds and grains in crops like wheat and rice.
For example, researchers achieved over 99% accuracy in counting threshed rice
grains by combining feature pyramid networks with convolutional neural
networks. However, these methods often depend on manual threshing, which is not
ideal for large-scale or real-time applications. Detecting grains in their
natural form—still attached and possibly overlapping—remains a major challenge.
Direct counting of rice grains in their natural form is difficult due to dense
distribution, overlapping grains, and differences in shape and color across
varieties. Current approaches that rely on deep learning sometimes require
threshing or image preprocessing to overcome occlusion. To improve accuracy and
reduce labor, researchers have begun integrating multiple deep learning
models—such as object detection, image classification, and segmentation
networks. For instance, combining classification models to first identify
panicle morphology before detection has shown promise in enhancing accuracy.
There is an urgent need for a method that quickly and accurately counts rice
grains in natural conditions with minimal manual effort.
International Conference on Computer Vision
The International Research Awards on Computer Vision recognize groundbreaking contributions in the field of computer vision, honoring researchers, scientists and innovators whose work has significantly advanced the domain. This prestigious award highlights excellence in fundamental theories, novel algorithms and real-world applications, fostering progress in artificial intelligence, image processing and deep learning.
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Sunday, June 22, 2025
How Deep Transfer Learning is Revolutionizing Online Civil Dispute Consultations! #Sciencefather #researchawards #deeplearning
The rapid increase in civil disputes and the
limited capacity of legal systems have challenged the effectiveness of
traditional dispute resolution methods. Online Dispute Resolution (ODR)
platforms—such as China’s Internet Court, British Columbia’s Civil Resolution
Tribunal, and the UK’s Online Court—have emerged as promising solutions. A core
component of these platforms is the Classification of Online Consultation (COC),
which helps route civil legal questions to the appropriate departments.
However, manual classification is inefficient and error-prone, especially as
civil disputes become more diverse and complex.
COC tasks rely heavily on text classification, but several issues hinder
accurate results. Many platforms lack sufficient and balanced training data,
while the short, colloquial, and vague nature of users’ input makes it
difficult for traditional machine learning models to perform well.
Additionally, the use of Chinese text introduces further complexity due to
limited labeled data and grammatical challenges. These factors collectively
result in poor classification accuracy and hinder the effectiveness of civil
dispute resolution services online.
To address these challenges, the study introduces a deep transfer
learning-based classification method called CMDTL (Cross-platform Mapping with
Deep Transfer Learning). By transferring knowledge from richer data sources and
applying advanced techniques like joint distribution adaptation and improved
marginal Fisher analysis, this method significantly improves accuracy despite
limited and unbalanced data. It also uses ontology modeling to clarify legal
concepts, ensuring a more accurate understanding of the user’s legal queries.
This approach ultimately aims to enhance the efficiency and precision of online
civil dispute consultations.
International Conference on Computer Vision
The International Research Awards on Computer Vision recognize groundbreaking contributions in the field of computer vision, honoring researchers, scientists and innovators whose work has significantly advanced the domain. This prestigious award highlights excellence in fundamental theories, novel algorithms and real-world applications, fostering progress in artificial intelligence, image processing and deep learning.
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Saturday, June 21, 2025
Deep Learning Magic: Modeling Threshold Curves #Sciencefather #researchawards #deeplearning
Neurons
are specialized cells responsible for transmitting electrical signals
throughout the body, enabling communication between the brain, muscles, and
other tissues. This signal transmission is possible due to their excitability —
the ability to generate short-lived electrical impulses in response to external
stimuli. Interestingly, the concept of excitability is not unique to neurons;
it applies broadly to systems like cardiac tissue, calcium signaling in cells,
and even predator–prey dynamics. These systems, known as excitable media,
are typically modeled using nonlinear reaction–diffusion equations, which
describe how activity spreads and interacts within a medium.
A key
feature of excitable media is the existence of a threshold — a stimulus must
surpass a certain critical value to trigger sustained wave propagation. This
study focuses on a one-component bistable reaction–diffusion system described
by the Zeldovich–Frank–Kamenetsky (ZFK) or Nagumo equation. By setting a
rectangular initial stimulus and applying no-flux boundary conditions, we
investigate whether the system’s response decays or leads to a propagating
wavefront. The outcome depends on both the spatial extent and amplitude of the
stimulus, and we aim to map the critical strength-extent curve that
separates these two regimes.
Solving nonlinear partial differential equations in excitable systems is
challenging, especially under complex conditions. Traditional methods like
spectral collocation or meshfree schemes have provided numerical solutions, but
recent advances in scientific machine learning, such as Physics-Informed Neural
Networks (PINNs), offer a new paradigm. PINNs embed physical laws into the
learning process, enabling accurate, data-efficient modeling of complex
systems. In this work, we apply PINNs and transfer learning techniques to
predict the strength-extent curve, improving computational efficiency and
allowing precise identification of critical thresholds in excitable media
dynamics.
International Conference on Computer Vision
The International Research Awards on Computer Vision recognize groundbreaking contributions in the field of computer vision, honoring researchers, scientists and innovators whose work has significantly advanced the domain. This prestigious award highlights excellence in fundamental theories, novel algorithms and real-world applications, fostering progress in artificial intelligence, image processing and deep learning.
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Friday, June 20, 2025
Laser Ultrasonic Detection: Next-Gen LAM Defect Finder! #Sciencefather #researchawards
Laser Additive Manufacturing (LAM) is an advanced technique that uses
high-energy lasers to build complex metal parts layer by layer with high
precision, efficiency, and minimal material waste. It has wide applications in
industries such as aerospace, medical, and automotive. However, the LAM process
faces challenges due to non-equilibrium thermodynamics, which often cause
metallurgical defects like cracks and pores. If not detected during printing,
these flaws can grow and affect the final part's quality and structural
integrity, limiting the broader adoption of LAM.
To ensure quality and reliability, several online nondestructive testing (NDT)
methods are used, including X-ray computed tomography, infrared thermography,
optical photography, structured light imaging, and ultrasonic detection. Among
them, laser ultrasonic testing stands out due to its non-contact,
high-temperature resistance, and ability to generate multiple wave modes in one
pulse, which helps identify both surface and internal defects. Recent studies
have shown the potential of laser ultrasonics in real-time monitoring of
mechanical properties and defect detection during LAM processes.
Despite advancements, challenges such as surface roughness and environmental
noise reduce the clarity of ultrasonic signals. Post-processing methods like
SAFT and TFM improve resolution but are time-consuming and require heavy data
storage. To address these issues, this study introduces a novel ultrasonic
imaging method—Variable Time Window Intensity Mapping (VTWIM) with adaptive 2σ
thresholds. This approach adapts to changing noise levels and enables rapid,
accurate detection of submillimeter defects in real time, demonstrating
significant promise for improving LAM quality control.
International Conference on Computer Vision
The International Research Awards on Computer Vision recognize groundbreaking contributions in the field of computer vision, honoring researchers, scientists and innovators whose work has significantly advanced the domain. This prestigious award highlights excellence in fundamental theories, novel algorithms and real-world applications, fostering progress in artificial intelligence, image processing and deep learning.
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Wednesday, June 18, 2025
AR & VR: The Future of Learning #Sciencefather #researchawards #augmentedreality #virtualreality
Augmented Reality (AR) and Virtual Reality (VR) are transforming the way
education is delivered by offering immersive and interactive learning
experiences. These technologies go beyond traditional lectures and textbooks by
allowing students to engage deeply with complex topics through virtual
simulations, 3D models, and digital overlays. They also empower educators to
customize content to suit individual learning styles, enhancing both
comprehension and retention. Virtual field trips, science experiments, and
historical recreations are now possible without physical boundaries, enriching
the overall learning experience.
While AR and VR are often mentioned together, they serve different functions.
AR overlays digital content onto the real-world environment, enhancing what we
see and interact with, whereas VR creates a fully digital environment that
immerses users through the use of headsets or glasses. These technologies have
found practical applications in sectors like education, healthcare,
manufacturing, and retail. In education, they are especially valuable for
simulating real-life scenarios, allowing students to gain practical skills and
professional experience in a safe and controlled digital space.
Recent studies show a significant rise in research on AR and VR in education
over the past twelve years. This trend highlights their growing importance and
effectiveness in online, mobile, and hybrid learning environments. Research has
explored both the benefits—such as increased student engagement and interactive
content—and the challenges, including cost and accessibility. By analyzing past
developments and identifying gaps in the literature, ongoing research aims to
guide future innovations and expand the use of AR and VR in modern educational
systems.
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How AR Makes Geometry Fun for Kids! 🚀📐 #Sciencefather #researchawards #augmentedreality
Monday, June 16, 2025
How Optical Microwaves Measure Gas-Liquid Flow #Sciencefather #researchawards #computervision
To address these limitations, the Optical
Carrier Microwave Interferometry (OCMI) technique has emerged as a promising
alternative. OCMI combines the advantages of optics and microwaves, offering
features like distributed sensing, high positioning accuracy, and insensitivity
to optical polarization. It has been successfully used for measuring physical
parameters like temperature, strain, pressure, and vibration. However,
conventional demodulation methods used with OCMI—such as dip frequency tracking
and phase demodulation—are typically limited to detecting one parameter at a
time and may struggle with low sensitivity or resolution, especially when
applied to complex systems like gas-liquid two-phase flow. These limitations
highlight the need for an improved approach that can handle the simultaneous
measurement of multiple flow parameters under dynamic industrial conditions.
In
response, this study proposes a new sensing method that integrates OCMI
technology with machine learning, specifically artificial neural networks
(ANN). By learning from data patterns, ANN models can simultaneously predict
gas and liquid flow rates, overcoming the limitations of traditional OCMI
demodulation. This approach not only enhances measurement accuracy and
adaptability to different flow patterns but also provides a foundation for
real-time monitoring in complex flow environments. The effectiveness of
different input schemes to the neural network is also investigated,
demonstrating the potential of combining advanced sensing techniques with
intelligent data processing to revolutionize multiphase flow measurement in
industrial applications.
International Conference on Computer Vision
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Sunday, June 15, 2025
Machine Learning vs. AI #Sciencefather #researchawards
Machine learning differ in AI
The terms "artificial intelligence" and "machine
learning" are often used interchangeably, but one is more specific than
the other.
Artificial intelligence (AI) is
the broader of the two terms. It originated in the 1950s and can be used to
describe any application or machine that mimics human intelligence. This
includes both simple programs, such as a virtual checkers player, and
sophisticated machines, such as self-driving cars. Some in the field
distinguish between AI tools that exist today and general artificial
intelligence—thinking, autonomous agents—that do not yet exist.
Machine learning describes a
subset of artificial intelligence. This term arose in the 1970s. Machine
learning is distinguished by a machine or program that is fed and trained on
existing data and then is able to find patterns, make predictions, or perform
tasks when it encounters data it has never seen before.
Machine learning can be thought of as the process of converting data and
experience into new knowledge, usually in the form of a mathematical model.
Once it is created, this model can then be used to perform other tasks. This
allows for the design of applications that would be too complex or time
consuming to develop without computer assistance. For example, a machine
learning system may be trained on millions of examples of labeled tumors in MRI
images. On the basis of these examples, the system recognizes patterns of
characteristics that constitute a tumor. This serves as a model that can then
determine if tumors are present in new MRI images. These systems are often able
to outperform experts.
Machine learning is a powerful tool that increasingly is incorporated
into more computer applications. Its ubiquity makes it harder to spot AI
applications that are not trained on data but that rely on
human-written and readable rules and facts. Applications that use artificial
intelligence but do not learn from or produce new results based on exposure to
data are sometimes referred to as "good old-fashioned AI" or
"GOFAI." And some are still in operation. For example, a
simple chatbot may address questions solely by supplying pre-written answers
that contain relevant keywords.
Finally, deep learning is a subset of machine learning.
Deep learning uses machine learning algorithms but structures the algorithms in
layers to create "artificial neural networks." These networks are
modeled after the human brain and have been effective in many situations. Deep
learning applications are most likely to provide an experience that feels like
interacting with a real human.
The International Research Awards on Computer Vision recognize groundbreaking contributions in the field of computer vision, honoring researchers, scientists and innovators whose work has significantly advanced the domain. This prestigious award highlights excellence in fundamental theories, novel algorithms and real-world applications, fostering progress in artificial intelligence, image processing and deep learning.
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Friday, June 13, 2025
How Big Data Powers Digital Transformation! 🚀 #Sciencefather #researchawards #datascience
Digital transformation has become a
vital driver of global economic growth, especially in the post-pandemic era.
With global digitalization investments projected to surpass $6.8 trillion
between 2022 and 2023, countries like China are playing a more prominent role.
Leveraging its vast manufacturing base and leading in 5G infrastructure—with
about 70% of global 5G base stations—China has rapidly advanced its digital
capabilities. Government initiatives have been crucial in this transformation,
with national and local policies prioritizing the integration of digital
technologies across industries. These measures aim to promote high-quality
development and sustainable growth by encouraging enterprises to adopt digital
tools, upgrade business models, and build digital ecosystems.
Despite
progress, measuring the extent of enterprise digital transformation remains a
challenge. Most current methods rely on keyword analysis of company reports or
limited-scale indicators. Addressing this gap, the study constructs a
comprehensive measurement scale that captures various stages of transformation.
Furthermore, it explores how digitalization policies influence enterprise
transformation using a hierarchical regression model. By integrating
institutional theory, resource-based theory, and strategic transformation
theory, the study identifies key policy themes and reveals how factors like big
data capabilities and network infrastructure mediate the impact of policies.
This research not only enriches academic understanding but also offers actionable
insights for policymakers and businesses aiming to foster effective digital
transformation.
International Conference on Computer Vision
The International Research Awards on Computer Vision recognize
groundbreaking contributions in the field of computer vision, honoring
researchers, scientists and innovators whose work has significantly advanced
the domain. This prestigious award highlights excellence in fundamental
theories, novel algorithms and real-world applications, fostering progress in
artificial intelligence, image processing and deep learning.
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Data Science #Sciencefather #researchawards
Data Science
Data science combines math and statistics, specialized programming, advanced analytics, artificial intelligence (AI) and machine learning with specific subject matter expertise to uncover actionable insights hidden in an organization’s data. These insights can b
e used to guide decision making and strategic planning.
The accelerating volume of data sources, and subsequently data, has made data science is one of the fastest growing field across every industry. As a result, it is no surprise that the role of the data scientist was dubbed the “sexiest job of the 21st century” by Harvard Business Review. Organizations are increasingly reliant on them to interpret data and provide actionable recommendations to improve business outcomes.
The data science lifecycle involves various roles, tools, and processes, which enables analysts to glean actionable insights. Typically, a data science project undergoes the following stages:
- Data ingestion: The lifecycle begins with the data collection, both raw structured and unstructured data from all relevant sources using a variety of methods. These methods can include manual entry, web scraping, and real-time streaming data from systems and devices. Data sources can include structured data, such as customer data, along with unstructured data like log files, video, audio, pictures, the Internet of Things (IoT), social media, and more.
- Data storage and data processing: Since data can have different formats and structures, companies need to consider different storage systems based on the type of data that needs to be captured. Data management teams help to set standards around data storage and structure, which facilitate workflows around analytics, machine learning and deep learning models. This stage includes cleaning data, deduplicating, transforming and combining the data using ETL (extract, transform, load) jobs or other data integration technologies. This data preparation is essential for promoting data quality before loading into a data warehouse, data lake, or other repository.
- Data analysis: Here, data scientists conduct an exploratory data analysis to examine biases, patterns, ranges, and distributions of values within the data. This data analytics exploration drives hypothesis generation for a/b testing. It also allows analysts to determine the data’s relevance for use within modeling efforts for predictive analytics, machine learning, and/or deep learning. Depending on a model’s accuracy, organizations can become reliant on these insights for business decision making, allowing them to drive more scalability.
- Communicate: Finally, insights are presented as reports and other data visualizations that make the insights and their impact on business easier for business analysts and other decision-makers to understand. A data science programming language such as R or Python includes components for generating visualizations; alternately, data scientists can use dedicated visualization tools.
- International Conference on Computer Vision
- The International Research Awards on Computer Vision recognize groundbreaking contributions in the field of computer vision, honoring researchers, scientists and innovators whose work has significantly advanced the domain. This prestigious award highlights excellence in fundamental theories, novel algorithms and real-world applications, fostering progress in artificial intelligence, image processing and deep learning.
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