66 research outputs found
Leveraging Symmetry in Multi-Agent Code Generation: A Cross-Verification Collaboration Protocol for Competitive Programming
No full textCompetitive programming has emerged as a critical benchmark for evaluating large language models (LLMs) in solving algorithmic problems under competitive conditions. Existing methods, such as the Sequential One-Agent Pipeline (SOP) approach, suffer from significant limitations, including the inability to effectively manage semantic drift across multiple stages, a lack of coordinated adversarial testing, and suboptimal final solutions. These issues lead to high rates of wrong answer (WA) and time-limit exceeded (TLE) errors, especially on complex problems. In this paper, we propose the Cross-Verification Collaboration Protocol (CVCP), a multi-agent framework that integrates symmetry detection, symmetry-guided adversarial testing, Round-Trip Review Protocol (RTRP), and Asynchronous Voting Resolution (AVR) to address these shortcomings. We evaluate our method on the CodeELO dataset, showing significant improvements in performance, with Elo Ratings increasing by up to 7.1% and Pass Rates for hard problems improving by as much as 1.8 times compared to the SOP baseline
Physiological measurement based automatic driver cognitive distraction detection
Full text linkVehicle safety and road safety are two important issues. They are related to each other and road accidents are mostly caused by driver distraction. Issues related to driver distraction like eating, drinking, talking to a passenger, using IVIS (In-Vehicle Information System) and thinking something unrelated to driving are some of the main reasons for road accidents. Driver distraction can be categorized into 3 different types: visual distraction, manual distraction and cognitive distraction. Visual distraction is when driver's eyes are off the road and manual distraction is when the driver takes one or both hands off the steering wheel and places the hand/s on something that is not related to the driving safety. Cognitive distraction whereas happens when a driver's mind is not on the road. It has been found that cognitive distraction is the most dangerous among the three because the thinking process can induce a driver to view and/or handle something unrelated to the safety information while driving a vehicle. This study proposes a physiological measurement to detect driver cognitive distraction. Features like lips, eyebrows, mouth movement, eye movement, gaze rotation, head rotation and blinking frequency are used for the purpose. Three different sets of experiments were conducted. The first experiment was conducted in a lab with faceLAB cameras and served as a pilot study to determine the correlation between mouth movement and eye movement during cognitive distraction. The second experiment was conducted in a real traffic environment using faceAPI cameras to detect movement on lips and eyebrows. The third experiment was also conducted in a real traffic environment. However, both faceLAB and faceAPI toolkits were combined to capture more features. A reliable and stable classification algorithm called Dynamic Bayesian Network (DBN) was used as the main algorithm for analysis. A few more others algorithms like Support Vector Machine (SVM), Logistic Regression (LR), AdaBoost and Static Bayesian Network (SBN) were also used for comparison. Results showed that DBN is the best algorithm for driver cognitive distraction detection. Finally a comparison was also made to evaluate results from this study and those by other researchers. Experimental results showed that lips and eyebrows used in this study are strongly correlated and have a significant role in improving cognitive distraction detection
Improved Deep Learning Model for Workpieces of Rectangular Pipeline Surface Defect Detection
No full textThis study introduces a novel approach to address challenges in workpiece surface defect identification. It presents an enhanced Single Shot MultiBox Detector model, incorporating attention mechanisms and multi-feature fusion. The research methodology involves carefully curating a dataset from authentic on-site factory production, enabling the training of a model with robust real-world generalization. Leveraging the Single Shot MultiBox Detector model lead to improvements integrating channel and spatial attention mechanisms in the feature extraction network. Diverse feature extraction methods enhance the network’s focus on crucial information, improving its defect detection efficacy. The proposed model achieves a significant Mean Average Precision (mAP) improvement, reaching 99.98% precision, a substantial 3% advancement over existing methodologies. Notably, the proposed model exhibits a tendency for the values of the P-R curves in object detection for each category to approach 1, which allows a better balance between the requirements of real-time detection and precision. Within the threshold range of 0.2 to 1, the model maintains a stable level of precision, consistently remaining between 0.99 and 1. In addition, the average running speed is 2 fps lower compared to other models, and the reduction in detection speed after the model improvement is kept within 1%. The experimental results indicate that the model excels in pixel-level defect identification, which is crucial for precise defect localization. Empirical experiments validate the algorithm’s superior performance. This research represents a pivotal advancement in workpiece surface defect identification, combining technological innovation with practical efficacy
Eye and mouth movements extraction for driver cognitive distraction detection
No full textCognitive distraction is happened when a driver's mind is off the road. It happened when a driver is looking on the road but his mind is doing a thinking process. It has been found that, cognitive distraction is the most dangerous type of driver distractions. This has been presented in the comparison table and stem plot between Control Experiment result and Task Experiment result. Information from eye movement and mouth movement are obtained using the faceLab cameras and their correlation is discussed here. Two sets of experiment (Control and Task) with 6 participants were completed for this paper. Results were presented in scatter diagram to show the correlation between eye and mouth movements. Stem plot is to show the different result obtained between control and task experiment
Physiological measurement used in real time experiment to detect driver cognitive distraction
No full textThis paper discusses about lips and eyebrows are used to detect driver cognitive distraction by using faceAPI toolkit. A few number of classification algorithms like Support Vector Machine (SVM), Logistic Regression (LR) and Static Bayesian Network (SBN) and Dynamic Bayesian Network (DBN) have been used for accuracy rate comparison
Non intrusive physiological measurement for driver cognitive distraction detection: Eye and mouth movements
No full textDriver distractions can be categorized into 3 major parts:-visual, cognitive and manual. Visual and manual distraction on a driver can be physically detected. However, assessing cognitive distraction is difficult since it is more of an “internal” distraction rather than any easily measured “external” distraction. There are several methods available that can be used to detect cognitive driver distraction. Physiological measurements, performance measures (primary and secondary tasks) and rating scales are some of the well-known measures to detect cognitive distraction. This study focused on physiological measurements, specifically on a driver's eye and mouth movements. Six different participants were involved in our experiment. The duration of the experiment was 8 minutes and 49 seconds for each participant. Eye and mouth movements were obtained using the FaceLab Seeing Machine cameras and their magnitude of the r-values were found more than 60% thus proving that they are strongly correlated to each other
Non-intrusive physiological measurement for driver cognitive distraction: Eye and mouth movement
No full textDriver distractions can be categorized into 3 major parts: visual, cognitive and manual. Visual and manual distraction on a driver can be physically detected. However, assessing cognitive distraction is difficult since it is more of an “internal” distraction rather than any easily measured “external” distraction. There are several methods available that can be used to detect cognitive driver distraction. Physiological measurements, performance measurements (primary and secondary tasks) and rating scales are some of the well-known measurements to detect cognitive distraction. This study focused on physiological measurements, specifically on a driver’s eye and mouth movements. Six different participants were involved in our experiment. The duration of the experiment was 8 minutes and 49 seconds for each participant. Eye and mouth movements were obtained using the FaceLAB Seeing Machine cameras and their magnitudes of the r-values were found more than 60% thus proving that they are strongly correlated to each other
Bayesian Network
No full textAbstract Driver distractions can be categorized into 3 major parts: visual, cognitive and manual. Visual and manual distraction on a driver can be physically detected. However, assessing cognitive distraction is difficult since it is more of an “internal ” distraction rather than any easily measured “external ” distraction. There are several methods available that can be used to detect driver cognitive. Physiological measurements, performance measurements (primary and secondary tasks) and rating scales are some of the well-known measurements usually used to detect cognitive distraction. This study focused on physiological measurements, specifically on a driver’s eye and mouth movements. Six different participants were involved in our experiment. The duration of the experiment was 8 minutes and 49 seconds for each participant. Eye and mouth movements were obtained using the FaceLAB Seeing Machine cameras and their magnitudes of the r-values were found more than 60% thus proving that they are strongly correlated to each other. 1
Physiological measurement used in real time experiment to detect driver cognitive distraction
No full textPicture superiority effect in authentication systems for the blind and visually impaired on a smartphone platform
No full textPictures are more likely to be remembered than words or text. For smartphone authentication, graphical password interfaces employing both visual objects and auditory cues are more memorable than textual password interfaces among sighted people because the graphical interface evokes visual imagery in the brain. However, interfaces employing visual imagery have not been studied for the blind and visually impaired. The objective of this research is to demonstrate that graphical password interfaces, designed to evoke visual imagery among blind and visually impaired users, improve the ease of use of smartphone authentication systems. We developed and tested two graphical password systems, BlindLoginV2, which employs object picture superiority effect and AudioBlindLogin, which employs auditory cues to enrich the picture superiority effect. We collected quantitative metrics measuring login speed, configuration time and failure rates immediately after training, 1 h later, 1 day later and 1 week later and qualitative evidence through face-to-face interviews. This study shows that blind and visually impaired users benefit from the picture superiority effect and passwords are more memorable, quicker to key in with greater accuracy as compared to 4-character textual password interfaces. Using the authentication system as an example, we demonstrate that visual imagery can be evoked in blind and visually impaired users through careful design of smartphone interfaces and when paired with additional sensory cues such as audio, can significantly improve the ease-of-use and thereby enhance access among visually impaired users to the rich array of security features available in smartphones
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