A fast Algorithm for Automatic Segmentation of Pancreas Histological Images f...Tathagata Bandyopadhyay
This work is mostly focused on automatic segmentation of Islets of Langerhans and its different cells from microscopic images ( histological image) of pancreas, to identify pre-diabetic condition.
Comparing Machine Learning Algorithms in Text MiningAndrea Gigli
In this project I compare different Machine Learning Algorithm on different Text Mining Tasks.
ML algorithms: Naive Bayes, Support Vector Machine, Decision Trees, Random Forest, Ordinal Regression as ML task
Tasks considered: Classifying Positive and Negative Reviews, Predicting Review Stars, Quantifying Sentiment Over Time, Detecting Fake Reviews
The Nonwoven Uniformity has been tried to derive here with a mathematical expression which can be helpful for people to understand very easily and communicate this with meaningful way.
Uniformity of Web Formation_Thermal Bonded Nonwovens Fabricabhijit12491
In this study we are trying to establish mathematical formula to expressing the uniformity level for thermal bonded nonwovens fabric. Hoping this will be interesting and helpful.
A fast Algorithm for Automatic Segmentation of Pancreas Histological Images f...Tathagata Bandyopadhyay
This work is mostly focused on automatic segmentation of Islets of Langerhans and its different cells from microscopic images ( histological image) of pancreas, to identify pre-diabetic condition.
Comparing Machine Learning Algorithms in Text MiningAndrea Gigli
In this project I compare different Machine Learning Algorithm on different Text Mining Tasks.
ML algorithms: Naive Bayes, Support Vector Machine, Decision Trees, Random Forest, Ordinal Regression as ML task
Tasks considered: Classifying Positive and Negative Reviews, Predicting Review Stars, Quantifying Sentiment Over Time, Detecting Fake Reviews
The Nonwoven Uniformity has been tried to derive here with a mathematical expression which can be helpful for people to understand very easily and communicate this with meaningful way.
Uniformity of Web Formation_Thermal Bonded Nonwovens Fabricabhijit12491
In this study we are trying to establish mathematical formula to expressing the uniformity level for thermal bonded nonwovens fabric. Hoping this will be interesting and helpful.
General conclusions
- Current methods used by the industry to evaluate protein quality are not capable of detecting existing differences among SBM
- The composition and the protein quality of SBM vary with the origin of the bean
- Different matrixes should be used for SBM of different origins, NIR technology might help
- Proteases might improve the uniformity and nutritive value of SBM batches
CHI'16 Journal "A Mouse With Two Optical Sensors That Eliminates Coordinate D...Byungjoo Lee
Presented by Byungjoo Lee at CHI'16 San Jose
ABSTRACT
The computer mouse is rarely used for drawing due to its body-fixed coordinate system, which creates a stroke that differs from the user’s original hand movement. In this study, we resolve this problem by implementing a new mouse called StereoMouse, which eliminates the rotational disturbance of the coordinate system in real-time. StereoMouse is a special mouse with two optical sensors, and its coordinate orientation at the beginning of a stroke is maintained throughout the movement by measuring and compensating for the angular deviation estimated from those sensors. The drawing performance of StereoMouse was measured by means of having users perform the task of repeatedly drawing a basic shape. The results of this experiment showed that StereoMouse eliminated the horizontal drift typically observed in a stroke drawn by a normal mouse. Consequently, StereoMouse allowed the users to draw shapes at a 10.6% faster mean speed with a 10.4% shorter travel time than a normal mouse would. Furthermore, StereoMouse showed 37.1% lower chance of making incorrect gesture input than the normal mouse.
CT liver segmentation using artificial bee colony optimizationAboul Ella Hassanien
This presentation in the workshop of Intelligent Systems and Application (ISA2017), held at faculty of computers and information, Banha university on Saturday 13 May 2017
COMPUTING THE GROWTH RATE OF STEM CELLS USING DIGITAL IMAGE PROCESSING Pratyusha Mahavadi
The aim is to compute the growth rate of stem cells by using segmentation, feature extraction and pattern recognition which are the fundamental methods of digital image processing. DRLSE algorithm is applied for segmenting images. The DRLSE algorithm is an amalgamation of Canny Edge Detector algorithm and DRLSE method, which uses the four well potential function. Features are extracted from segmented images using GLCM method and finally Support Vector Machine (SVM) is used for pattern recognition and classification of stem cells.
NEURAL NETWORKS AND BOOTSTRAP SIMULATION IN PREDICTION OF OUTCOME OF NON-SMALL CELL LUNG CANCER PATIENTS AFTER COMPLETE LOBECTOMIES AND PNEUMONECTOMIES
STUDIES ON INTEGRATED BIO-HYDROGEN PRODUCTION PROCESS-EXPERIMENTAL AND MODELINGArghya_D
In the project “Studies on integrated biohydrogen production process-Experimental and Modeling”,a co-culture (mixture of two microorganisms in a single reactor) study of a dark fermentative and photofermentative microorganism was done to assess its hydrogen production performance. For modeling purpose, Artificial Neural Network and Genetic Algorithm has been used as a stochastic technique. The optimized data from batch study was successfully used to run a photobioreactor in continuous mode. A mechanistic model was developed for a continuous co-culture setup using data from literature and solved using MATLAB.
General conclusions
- Current methods used by the industry to evaluate protein quality are not capable of detecting existing differences among SBM
- The composition and the protein quality of SBM vary with the origin of the bean
- Different matrixes should be used for SBM of different origins, NIR technology might help
- Proteases might improve the uniformity and nutritive value of SBM batches
CHI'16 Journal "A Mouse With Two Optical Sensors That Eliminates Coordinate D...Byungjoo Lee
Presented by Byungjoo Lee at CHI'16 San Jose
ABSTRACT
The computer mouse is rarely used for drawing due to its body-fixed coordinate system, which creates a stroke that differs from the user’s original hand movement. In this study, we resolve this problem by implementing a new mouse called StereoMouse, which eliminates the rotational disturbance of the coordinate system in real-time. StereoMouse is a special mouse with two optical sensors, and its coordinate orientation at the beginning of a stroke is maintained throughout the movement by measuring and compensating for the angular deviation estimated from those sensors. The drawing performance of StereoMouse was measured by means of having users perform the task of repeatedly drawing a basic shape. The results of this experiment showed that StereoMouse eliminated the horizontal drift typically observed in a stroke drawn by a normal mouse. Consequently, StereoMouse allowed the users to draw shapes at a 10.6% faster mean speed with a 10.4% shorter travel time than a normal mouse would. Furthermore, StereoMouse showed 37.1% lower chance of making incorrect gesture input than the normal mouse.
CT liver segmentation using artificial bee colony optimizationAboul Ella Hassanien
This presentation in the workshop of Intelligent Systems and Application (ISA2017), held at faculty of computers and information, Banha university on Saturday 13 May 2017
COMPUTING THE GROWTH RATE OF STEM CELLS USING DIGITAL IMAGE PROCESSING Pratyusha Mahavadi
The aim is to compute the growth rate of stem cells by using segmentation, feature extraction and pattern recognition which are the fundamental methods of digital image processing. DRLSE algorithm is applied for segmenting images. The DRLSE algorithm is an amalgamation of Canny Edge Detector algorithm and DRLSE method, which uses the four well potential function. Features are extracted from segmented images using GLCM method and finally Support Vector Machine (SVM) is used for pattern recognition and classification of stem cells.
NEURAL NETWORKS AND BOOTSTRAP SIMULATION IN PREDICTION OF OUTCOME OF NON-SMALL CELL LUNG CANCER PATIENTS AFTER COMPLETE LOBECTOMIES AND PNEUMONECTOMIES
STUDIES ON INTEGRATED BIO-HYDROGEN PRODUCTION PROCESS-EXPERIMENTAL AND MODELINGArghya_D
In the project “Studies on integrated biohydrogen production process-Experimental and Modeling”,a co-culture (mixture of two microorganisms in a single reactor) study of a dark fermentative and photofermentative microorganism was done to assess its hydrogen production performance. For modeling purpose, Artificial Neural Network and Genetic Algorithm has been used as a stochastic technique. The optimized data from batch study was successfully used to run a photobioreactor in continuous mode. A mechanistic model was developed for a continuous co-culture setup using data from literature and solved using MATLAB.
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Generative AI Deep Dive: Advancing from Proof of Concept to ProductionAggregage
Join Maher Hanafi, VP of Engineering at Betterworks, in this new session where he'll share a practical framework to transform Gen AI prototypes into impactful products! He'll delve into the complexities of data collection and management, model selection and optimization, and ensuring security, scalability, and responsible use.
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
A tale of scale & speed: How the US Navy is enabling software delivery from l...sonjaschweigert1
Rapid and secure feature delivery is a goal across every application team and every branch of the DoD. The Navy’s DevSecOps platform, Party Barge, has achieved:
- Reduction in onboarding time from 5 weeks to 1 day
- Improved developer experience and productivity through actionable findings and reduction of false positives
- Maintenance of superior security standards and inherent policy enforcement with Authorization to Operate (ATO)
Development teams can ship efficiently and ensure applications are cyber ready for Navy Authorizing Officials (AOs). In this webinar, Sigma Defense and Anchore will give attendees a look behind the scenes and demo secure pipeline automation and security artifacts that speed up application ATO and time to production.
We will cover:
- How to remove silos in DevSecOps
- How to build efficient development pipeline roles and component templates
- How to deliver security artifacts that matter for ATO’s (SBOMs, vulnerability reports, and policy evidence)
- How to streamline operations with automated policy checks on container images
PHP Frameworks: I want to break free (IPC Berlin 2024)Ralf Eggert
In this presentation, we examine the challenges and limitations of relying too heavily on PHP frameworks in web development. We discuss the history of PHP and its frameworks to understand how this dependence has evolved. The focus will be on providing concrete tips and strategies to reduce reliance on these frameworks, based on real-world examples and practical considerations. The goal is to equip developers with the skills and knowledge to create more flexible and future-proof web applications. We'll explore the importance of maintaining autonomy in a rapidly changing tech landscape and how to make informed decisions in PHP development.
This talk is aimed at encouraging a more independent approach to using PHP frameworks, moving towards a more flexible and future-proof approach to PHP development.
FIDO Alliance Osaka Seminar: The WebAuthn API and Discoverable Credentials.pdf
Analysis of Hierarchical Multi-Content Text Classification Model on B-SHARP Dataset for Early Detection of Alzheimer’s Disease
1. Analysis of Hierarchical Multi-Content Text
Classi
fi
cation Model on B-SHARP Dataset
for Early Detection of Alzheimer’s Disease
Asia-Paci
fi
c Chapter of the Association for Computational Linguistic
s
Presented by Jinho D. Cho
i
October 28, 2020
♠
Renxuan A. Li, ♦
Ihab Hajjar, ♦
Felicia Goldstein, ♠
Jinho D. Choi
♠
Department of Computer Science, ♦
Department of Neurolog
y
Emory University, Atlanta GA, USA
jinho.choi@emory.edu
3. Mild Cognition Impairment (MCI)
3
Mild Cognitiv
e
Impairment
Mil
d
Dementia
Moderat
e
Dementia
Sever
e
Dementia
Impairment does not Interfere
with activities or daily living
First work to detec
t
MCI using NLP
Impairment starts Interfering
with activities or daily living
4. B-SHARP Dataset
4
Brain, Stress, Hypertension, and Aging Research Program
Collect 1-2 minute recordings for 3 tasks from MCI patients and Control subjects.
Task 1
:
Daily Activity
Task 2
:
Room Environment
Task 3
:
Picture Description
5. B-SHARP Dataset
5
1st-visit 2nd-visit 3rd-visit Recordings MoCA BNT
Control 185 100 50 385 26.2 (±2.6) 14.2 (±1.2)
MCI 141 68 28 265 21.5 (±3.5) 13.4 (±1.5)
Total 326 168 78 650 24.2 (±3.8) 13.9 (±1.4)
Subject make multiple visits to take more recordings.
The term between the previous and each visit is 1 year.
Tokens Sentences Nouns Verbs Conjuncts Complex Discourse
Q1
Control 186.6 (±60.4) 10.4 (±4.5) 28.1 (±9.6) 30.4 (±11.5) 8.5 (±4.5) 2.3 (±1.7) 8.1 (±5.4)
MCI 175.6 (±54.5) 9.8 (±4.1) 23.7 (±8.3) 29.3 (±10.4) 8.5 (±4.2) 2.0 (±1.6) 9.2 (±6.0)
Q2
Control 191.5 (±11.8) 11.7 (±4.7) 41.1 (±13.3) 24.3 (±11.2) 6.6 (±4.5) 3.6 (±2.7) 7.1 (±4.8)
MCI 178.6 (±11.7) 11.6 (±4.7) 36.7 (±12.1) 23.2 (±10.6) 6.4 (±4.4) 2.9 (±2.3) 8.4 (±5.3)
Q3
Control 193.4 (±63.4) 12.6 (±5.4) 39.5 (±13.5) 28.4 (±10.1) 8.0 (±4.8) 3.3 (±2.1) 6.1 (±5.5)
MCI 187.8 (±63.4) 12.7 (±5.1) 36.2 (±13.2) 27.7 (±10.9) 7.2 (±4.2) 2.6 (±2.0) 7.3 (±5.5)
All
Control 578.1 (±149.8) 34.5 (±10.7) 110.5 (±27.9) 84.2 (±25.4) 23.5 (±10.1) 9.3 (±4.5) 21.4 (±13.0)
MCI 548.7 (±140.6) 34.0 (±10.5) 98.1 (±26.1) 81.2 (±24.1) 22.5 (±9.7) 7.7 (±4.2) 25.3 (±15.0)
p 0.0110 0.5541 < 0.0001 0.1277 0.2046 < 0.0001 0.0006
Table 1: Average counts and their standard deviations of linguistic features per transcript in the B-SHARP dataset.
7. Experiments
7
5-folds Cross Validation
Transformer Encoder
s
BERT (Devlin et al., 2019
)
RoBERTa (Liu et al., 2020
)
ALBERT (Lan et al., 2019)
CV0 CV1 CV2 CV3 CV4 ALL
Control 77 77 77 77 77 385
MCI 53 53 53 53 53 265
Control 37 37 37 37 37 185
MCI 27 28 28 29 29 141
Recordings
Subjects
Subjects in each set are mutually exclusive to the other sets.
8. Evaluation
8
BERT RoBERTa ALBERT
Q1 Q2 Q3 Q1 Q2 Q3 Q1 Q2 Q3
ACC 67.6 (±0.4) 69.0 (±1.2) 67.7 (±0.7) 69.0 (±1.5) 69.9 (±0.2) 65.2 (±0.3) 67.6 (±1.5) 69.5 (±0.3) 66.6 (±1.3)
SEN 48.9 (±1.8) 57.1 (±2.5) 41.5 (±3.6) 44.3 (±4.5) 55.3 (±1.2) 37.1 (±3.7) 45.9 (±1.9) 52.2 (±0.6) 37.4 (±3.3)
SPE 80.4 (±1.2) 77.3 (±2.8) 85.2 (±3.0) 85.8 (±2.1) 79.7 (±0.7) 84.5 (±3.0) 82.6 (±3.7) 81.4 (±0.3) 86.8 (±3.3)
Table 3: Model performance on the individual tasks. ACC: accuracy, SEN: sensitivity, SPE: specificity.
CNN BERTe RoBERTae ALBERTe Be + Re Ae + Re Be + Ae + Re
ACC 69.5 (±0.2) 69.9 (±1.1) 71.6 (±1.5) 69.7 (±2.9) 72.2 (±0.7) 71.5 (±1.9) 74.1 (±0.3)
SEN 49.2 (±0.8) 57.6 (±3.4) 48.5 (±6.1) 46.2 (±8.3) 56.5 (±2.5) 51.7 (±1.3) 60.9 (±5.2)
SPE 83.5 (±0.9) 77.4 (±4.8) 87.5 (±1.8) 85.4 (±0.5) 83.1 (±0.9) 86.7 (±3.4) 84.0 (±2.4)
Table 4: Performance of ensemble models. Berte/RoBERTae/ALBERTe use transcript embeddings from all 3 tasks
trained by the BERT/RoBERTa/ALBERT models in Table 3, respectively. Be+Re uses transcript embeddings from
both Berte and RoBERTae (so the total of 6 embeddings), Ae+Re uses transcript embeddings from both ALBERTe
and RoBERTae (6 embeddings), and Be+Ae+Re uses transcript embeddings from all three models (9 embeddings).
BERT RoBERTa ALBERT
Q1 Q2 Q3 Q1 Q2 Q3 Q1 Q2 Q3
ACC 67.6 (±0.4) 69.0 (±1.2) 67.7 (±0.7) 69.0 (±1.5) 69.9 (±0.2) 65.2 (±0.3) 67.6 (±1.5) 69.5 (±0.3) 66.6 (±1.3)
SEN 48.9 (±1.8) 57.1 (±2.5) 41.5 (±3.6) 44.3 (±4.5) 55.3 (±1.2) 37.1 (±3.7) 45.9 (±1.9) 52.2 (±0.6) 37.4 (±3.3)
SPE 80.4 (±1.2) 77.3 (±2.8) 85.2 (±3.0) 85.8 (±2.1) 79.7 (±0.7) 84.5 (±3.0) 82.6 (±3.7) 81.4 (±0.3) 86.8 (±3.3)
Table 3: Model performance on the individual tasks. ACC: accuracy, SEN: sensitivity, SPE: specificity.
CNN BERTe RoBERTae ALBERTe Be + Re Ae + Re Be + Ae + Re
ACC 69.5 (±0.2) 69.9 (±1.1) 71.6 (±1.5) 69.7 (±2.9) 72.2 (±0.7) 71.5 (±1.9) 74.1 (±0.3)
SEN 49.2 (±0.8) 57.6 (±3.4) 48.5 (±6.1) 46.2 (±8.3) 56.5 (±2.5) 51.7 (±1.3) 60.9 (±5.2)
SPE 83.5 (±0.9) 77.4 (±4.8) 87.5 (±1.8) 85.4 (±0.5) 83.1 (±0.9) 86.7 (±3.4) 84.0 (±2.4)
Table 4: Performance of ensemble models. Berte/RoBERTae/ALBERTe use transcript embeddings from all 3 tasks
trained by the BERT/RoBERTa/ALBERT models in Table 3, respectively. Be+Re uses transcript embeddings from
both Berte and RoBERTae (so the total of 6 embeddings), Ae+Re uses transcript embeddings from both ALBERTe
and RoBERTae (6 embeddings), and Be+Ae+Re uses transcript embeddings from all three models (9 embeddings).
Performance on the Individual Tasks
Performance of the Ensemble Models
9. Conclusion
9
Introduced the new dataset, B-SHARP
,
for the detection of Mild Cognitive Impairment (MCI)
Presented Hierarchical Multi-Content Classi
fi
cation Mode
l
to jointly learn multiple documents from different tasks
Achieved the state-of-the-art results with an ensemble mode
l
using three types of transformer encoders
Please visit our lab webpag
e
http://nlp.cs.emory.edu