Robot-assisted endovascular navigation provides significant advantages, including reduced radiation exposure for surgeons and improved patient safety. However, a major challenge is to control curvilinear instruments like guidewires precisely for smooth and accurate navigation while adapting to anatomical variations and external forces. Traditional segmentation-based approaches struggle with real-time prediction of the guidewire’s evolving shape, limiting their effectiveness in navigation tasks. In this paper, we propose SplineFormer, an explainable transformer network that predicts the continuous, structured representation of the guidewire as a B-spline. This formulation enables a compact, smooth, and explainable state representation that facilitates downstream navigation. By leveraging SplineFormer’s predictions within an imitation learning framework, our system successfully performs autonomous endovascular navigation. Experimental results show that SplineFormer achieves a 50% success rate when fully autonomously cannulating the Brachiocephalic Artery in a real robotic setup, demonstrating its potential for improved autonomous navigation in endovascular interventions.

Accurate quantification of intraoperative blood loss is crucial for enhancing patient safety and the success rate of surgeries. Traditional estimation techniques, mainly reliant on visual assessments, are prone to significant inaccuracies due to their subjective nature. This study introduces MDCare, an innovative deep learning-integrated system designed to substantially improve the precision of blood loss quantification using surgical sponges. By integrating advanced hardware components, including a mass sensor and webcam, with sophisticated algorithms like ResNet-18 and YOLOv4, MDCare achieves classification accuracy up to 96.2% and sponge detection accuracies above 91% for both synthetic and real blood scenarios. The system processes images at 7.4 frames per second, aligning with the exigent pace of surgical environments, thereby supporting surgeons with real-time, accurate blood loss data essential for timely and informed decision-making. The contributions of the paper are: (1) Demonstrating the application of advanced machine learning models in a critical clinical setting, achieving significantly higher accuracy in blood loss estimation compared to traditional methods; (2) Validating the system's efficacy in real-time surgical environments, thereby enhancing the decision-making process and potentially reducing postoperative complications; (3) Setting a new standard in surgical care by integrating a complex system into real-world clinical workflows, showcasing its adaptability and potential for widespread adoption. Future work will focus on expanding the dataset and refining the algorithms to ensure the MDCare system's robustness and adaptability across surgical settings. The findings underscore the potential of MDCare to automate and refine critical aspects of surgery, marking a significant advancement in surgical care.

To avoid the exposure of original source code in a Web application, the variable names in JS code deployed in the wild are often replaced by short, meaningless names, thus making the code extremely difficult to manually understand and analysis. This paper presents JSNeat, an information retrieval (IR)-based approach to recover the variable names in minified JS code. JSNeat follows a data-driven approach to recover names by searching for them in a large corpus of open-source JS code. We use three types of contexts to match a variable in given minified code against the corpus including the context of the properties and roles of the variable, the context of that variable and relations with other variables under recovery, and the context of the task of the function to which the variable contributes. We performed several empirical experiments to evaluate JSNeat on the dataset of more than 322K JS files with 1M functions, and 3.5M variables with 176K unique variable names. We found that JSNeat achieves a high accuracy of 69.1%, which is the relative improvements of 66.1% and 43% over two state-of-the-art approaches JSNice and JSNaughty, respectively. The time to recover for a file or a variable with JSNeat is twice as fast as with JSNice and 4x as fast as with JNaughty, respectively.

Statistical machine translation (SMT) is a fast-growing sub-field of computational linguistics. Until now, the most popular automatic metric to measure the quality of SMT is BiLingual Evaluation Understudy (BLEU) score. Lately, SMT along with the BLEU metric has been applied to a Software Engineering task named code migration. (In) Validating the use of BLEU score could advance the research and development of SMT-based code migration tools. Unfortunately, there is no study to approve or disapprove the use of BLEU score for source code. In this paper, we conducted an empirical study on BLEU score to (in) validate its suitability for the code migration task due to its inability to reflect the semantics of source code. In our work, we use human judgment as the ground truth to measure the semantic correctness of the migrated code. Our empirical study demonstrates that BLEU does not reflect translation quality due to its weak correlation with the semantic correctness of translated code. We provided counter-examples to show that BLEU is ineffective in comparing the translation quality between SMT-based models. Due to BLEU's ineffectiveness for code migration task, we propose an alternative metric RUBY, which considers lexical, syntactical, and semantic representations of source code. We verified that RUBY achieves a higher correlation coefficient with the semantic correctness of migrated code, 0.775 in comparison with 0.583 of BLEU score. We also confirmed the effectiveness of RUBY in reflecting the changes in translation quality of SMT-based translation models. With its advantages, RUBY can be used to evaluate SMT-based code migration models.

Unexpected interactions among features induce most bugs in a configurable software system. Exhaustively analyzing all the exponential number of possible configurations is prohibitively costly. Thus, various sampling techniques have been proposed to systematically narrow down the exponential number of legal configurations to be analyzed. Since analyzing all selected configurations can require a huge amount of effort, fault-based configuration prioritization, that helps detect faults earlier, can yield practical benefits in quality assurance. In this paper, we propose CoPro, a novel formulation of feature-interaction bugs via common program entities enabled/disabled by the features. Leveraging from that, we develop an efficient feature-interaction-aware configuration prioritization technique for a configurable system by ranking the configurations according to their total number of potential bugs. We conducted several experiments to evaluate CoPro on the ability to detect configuration-related bugs in a public benchmark. We found that CoPro outperforms the state-of-the-art configuration prioritization techniques when we add them on advanced sampling algorithms. In 78% of the cases, CoPro ranks the buggy configurations at the top 3 positions in the resulting list. Interestingly, CoPro is able to detect 17 not-yet-discovered feature-interaction bugs.

The tracking of ants in video is important for the analysis of their complex group behavior. However, the manual analysis of these videos is tedious and time consuming. Automated tracking methods tend to drift due to frequent occlusions during their interactions and similarity in appearance. Semi-automated tracking methods enable corrections of tracking errors by incorporating user interaction. Although it is much lower than manual analysis, the required user time of the existing method is still typically 23 times the actual video length. In this paper, we propose a new semi-automated method that achieves similar accuracy while reducing the user interaction time by (1) mitigating user wait time by incorporating a data association tracking method to separate the tracking from user correction, and (2) minimizing the number of candidates visualized for user during correction. This proposed method is able to reduce the user interaction time by 67% while maintaining the accuracy within 3% of the previous semi-automated method [11].

The automated tracking of social insects, such as ants, could dramatically increase the fidelity and amount of analyzed data for studying complex group behaviors. Recently, data association based multiple object tracking methods have shown promise in improving handling of occlusions. However, the tracking of ants in a colony is still challenging as (1) their motion is often sporadic and irregular and (2) they are mostly present the entire duration of video. In this paper, we propose to improve the data association based tracking of multiple ants. First, we model the ant's motion using a set of irregular motion features including random walk model. Second, we use the convergence of particle filter based tracking to match tracklets with a long temporal gap. Testing results of two-fold cross validation on a 10,000 frame video shows that our proposed method was able to reduce the number of fragments by 61% and ID switches by 57%.

Behavior analysis of social insects requires robust tracking over many frames. Automated tracking methods are not reliable for tracking over long video. And they are prone to a quick accumulation of error from one mis-tracking. However, searching and correcting of mis-tracking is time-consuming. In this paper, we present an efficient method for achieving robust tracking of multiple ants over a long video. First, our method minimizes the user wait time by speeding up tracking with a GPU. Second, it minimizes the amount of data the user needs to validate by automatically searching for potential errors and presenting them for user validation and correction. User studies with three participants on a 10,000 frame video demonstrates that (1) the speed of tracking is 16x faster with GPU optimization, (2) tracking accuracy was 96%, which is a 25% improvement over no user interaction, (3) users examined less than 0.6% of frames for validation and correction.