HumanSense Framework for Common Sense Labeling
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Solution Overview
Problem
Computing systems face difficulties in automatically labeling images and videos, which is a simple task for humans but tedious and costly when done manually, necessitating a hybrid approach to efficiently solve common sense problems.
Innovation Solution
A human computation framework, HumanSense, that integrates human input with computational processes to address common sense questions, ensuring high-quality solutions by distinguishing human from bot responses, preventing malicious attacks, and maintaining solution quality through CAPTCHA verification and answer distillation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual labeling is used to solve common sense problems, then accuracy is improved, but cost and time consumption increase
Solution Approach 1:
The system merges automated computational methods with human verification capabilities. Computers perform initial labeling using computer vision algorithms, while humans verify and correct labels when confidence thresholds are not met. This combination achieves high accuracy while maintaining efficient throughput by avoiding purely manual labeling for all cases.
Solution Approach 2:
The system implements self-service through automated confidence assessment and self-correction mechanisms. The computational system automatically evaluates its own confidence in generated labels and selectively routes uncertain cases to human reviewers. This reduces the burden on human operators while maintaining high accuracy standards.
2Productivity
If purely automated computing is used for labeling, then productivity is improved, but accuracy deteriorates due to inability to handle common sense problems
Solution Approach 1:
Human reviewers serve as intermediaries between automated computational systems and final label outputs. The system uses humans to verify and correct computational errors, particularly in common sense reasoning tasks where automated systems struggle. This intermediary layer ensures high accuracy while preserving the efficiency benefits of automated processing.
Solution Approach 2:
The system implements feedback loops where human corrections are used to retrain and improve computational models. Error patterns identified by human reviewers feed back into the computational system, enabling continuous improvement of automated labeling accuracy over time while maintaining high throughput.
3Measurement precision
If human verification is added to automated systems, then accuracy is improved, but system complexity increases
Solution Approach 1:
The verification system is segmented into modular components: confidence assessment modules, routing logic, human verification interfaces, and result aggregation. Each component performs a specific function independently, making the overall complex system manageable and maintainable. This modular architecture reduces complexity compared to monolithic designs.
Solution Approach 2:
The system dynamically adjusts its complexity based on problem difficulty and confidence levels. Simple problems with high computational confidence bypass human verification entirely, while only uncertain or complex problems route to human reviewers. This dynamic behavior reduces average system complexity while maintaining high accuracy when needed.
4Measurement precision
If human computation is used, then common sense problems are solved accurately, but vulnerability to malicious attacks increases
Solution Approach 1:
The system applies preliminary anti-action through CAPTCHA verification and bot detection mechanisms before human responses are collected. Malicious automated systems are identified and blocked in advance, preventing them from contaminating the answer pool. This preliminary defense reduces the need for extensive post-processing to filter malicious inputs.
Solution Approach 2:
The system converts the potential harm of malicious inputs into benefit by using adversarial examples to improve security. Attempts at malicious attacks are analyzed to identify new attack patterns, which then inform improved detection algorithms. This transforms security threats into opportunities for strengthening the system's defenses.
Data Source
AI summary
Technologies for a human computation framework suitable for answering common sense questions that are difficult for computers to answer but easy for humans to answer. The technologies support solving general common sense problems without a priori knowledge of the problems; support for determining whether an answer is from a bot or human so as to screen out spurious answers from bots; support for distilling answers collected from human users to ensure high quality solutions to the questions asked; and support for preventing malicious elements in or out of the system from attacking other system elements or contaminating the solutions produced by the system, and preventing users from being compensated without contributing answers.