OpenRaiser/Intern-Atlas huggingface dataset:
Topology optimization of pressure-loaded multi-material structures
Venue: arXiv cs.CE 2023
Year: 2023

Permitting multiple materials within a topology optimization setting increases the search space of the technique, which facilitates obtaining high-performing and efficient optimized designs. Structures with multiple materials involving fluidic pressure loads find various applications. However, dealing with the design-dependent nature of the pressure loads is challenging in topology optimization that gets even more pronounced with a multi-material framework. This paper provides a density-based topology optimization method to design fluidic pressure loadbearing multi-material structures. The design domain is parameterized using hexagonal elements as they ensure nonsingular connectivity. Pressure modeling is performed using the Darcy law with a conceptualized drainage term. The flow coefficient of each element is determined using a smooth Heaviside function considering its solid and void states. The consistent nodal loads are determined using the standard finite element methods. Multiple materials is modeled using the extended SIMP scheme. Compliance minimization with volume constraints is performed to achieve optimized loadbearing structures. Few examples are presented to demonstrate the efficacy and versatility of the proposed approach. The optimized results contain the prescribed amount of different materials.
Question-Answering System Extracts Information on Injection Drug Use from Clinical Notes
Venue: arXiv cs.AI 2023
Year: 2023

Background: Injection drug use (IDU) is a dangerous health behavior that increases mortality and morbidity. Identifying IDU early and initiating harm reduction interventions can benefit individuals at risk. However, extracting IDU behaviors from patients' electronic health records (EHR) is difficult because there is no International Classification of Disease (ICD) code and the only place IDU information can be indicated is unstructured free-text clinical notes. Although natural language processing can efficiently extract this information from unstructured data, there are no validated tools. Methods: To address this gap in clinical information, we design and demonstrate a question-answering (QA) framework to extract information on IDU from clinical notes. Our framework involves two main steps: (1) generating a gold-standard QA dataset and (2) developing and testing the QA model. We utilize 2323 clinical notes of 1145 patients sourced from the VA Corporate Data Warehouse to construct the gold-standard dataset for developing and evaluating the QA model. We also demonstrate the QA model's ability to extract IDU-related information on temporally out-of-distribution data. Results: Here we show that for a strict match between gold-standard and predicted answers, the QA model achieves 51.65% F1 score. For a relaxed match between the gold-standard and predicted answers, the QA model obtains 78.03% F1 score, along with 85.38% Precision and 79.02% Recall scores. Moreover, the QA model demonstrates consistent performance when subjected to temporally out-of-distribution data. Conclusions: Our study introduces a QA framework designed to extract IDU information from clinical notes, aiming to enhance the accurate and efficient detection of people who inject drugs, extract relevant information, and ultimately facilitate informed patient care.
Sentence Level Curriculum Learning for Improved Neural Conversational Models
Venue: arXiv cs.CL 2023
Year: 2023

Designing machine intelligence to converse with a human user necessarily requires an understanding of how humans participate in conversation, and thus conversation modeling is an important task in natural language processing. New breakthroughs in architecture and data gathering continue to push the performance of such conversational AI models. However, designs neglect the gradual buildup in sentence structure and complexity experienced by humans as we learn to communicate. During training, our model accepts one or more sentences as input and attempts to predict the next sentence in the conversation one word at a time, so our goal is to separate training into segments, with each segment's corpus comprised of longer sentence pairs than the previous one. This will mimic the desired "buildup" component of human learning. We begin with only "short" length sentence pairs, then only "medium" length pairs, and so on. A majority of our experiments were toward optimizing this technique, ensuring a proper representation of the technique's potential, since many of the details were new questions. Our segment-trained models were then able to achieve lower validation loss at the end of training than models trained with standard text preparation. This segmented training is straightforward to implement and our results provide a general direction for future research to implement and improve it.
Attacking Perceptual Similarity Metrics
Venue: arXiv cs.CV 2023
Year: 2023

Perceptual similarity metrics have progressively become more correlated with human judgments on perceptual similarity; however, despite recent advances, the addition of an imperceptible distortion can still compromise these metrics. In our study, we systematically examine the robustness of these metrics to imperceptible adversarial perturbations. Following the two-alternative forced-choice experimental design with two distorted images and one reference image, we perturb the distorted image closer to the reference via an adversarial attack until the metric flips its judgment. We first show that all metrics in our study are susceptible to perturbations generated via common adversarial attacks such as FGSM, PGD, and the One-pixel attack. Next, we attack the widely adopted LPIPS metric using spatial-transformation-based adversarial perturbations (stAdv) in a white-box setting to craft adversarial examples that can effectively transfer to other similarity metrics in a black-box setting. We also combine the spatial attack stAdv with PGD ($\ell_\infty$-bounded) attack to increase transferability and use these adversarial examples to benchmark the robustness of both traditional and recently developed metrics. Our benchmark provides a good starting point for discussion and further research on the robustness of metrics to imperceptible adversarial perturbations.
Python Tool for Visualizing Variability of Pareto Fronts over Multiple Runs
Venue: arXiv cs.AI 2023
Year: 2023

Hyperparameter optimization is crucial to achieving high performance in deep learning. On top of the performance, other criteria such as inference time or memory requirement often need to be optimized due to some practical reasons. This motivates research on multi-objective optimization (MOO). However, Pareto fronts of MOO methods are often shown without considering the variability caused by random seeds and this makes the performance stability evaluation difficult. Although there is a concept named empirical attainment surface to enable the visualization with uncertainty over multiple runs, there is no major Python package for empirical attainment surface. We, therefore, develop a Python package for this purpose and describe the usage. The package is available at https://github.com/nabenabe0928/empirical-attainment-func.
Watermarking Text Generated by Black-Box Language Models
Venue: arXiv cs.CL 2023
Year: 2023

LLMs now exhibit human-like skills in various fields, leading to worries about misuse. Thus, detecting generated text is crucial. However, passive detection methods are stuck in domain specificity and limited adversarial robustness. To achieve reliable detection, a watermark-based method was proposed for white-box LLMs, allowing them to embed watermarks during text generation. The method involves randomly dividing the model vocabulary to obtain a special list and adjusting the probability distribution to promote the selection of words in the list. A detection algorithm aware of the list can identify the watermarked text. However, this method is not applicable in many real-world scenarios where only black-box language models are available. For instance, third-parties that develop API-based vertical applications cannot watermark text themselves because API providers only supply generated text and withhold probability distributions to shield their commercial interests. To allow third-parties to autonomously inject watermarks into generated text, we develop a watermarking framework for black-box language model usage scenarios. Specifically, we first define a binary encoding function to compute a random binary encoding corresponding to a word. The encodings computed for non-watermarked text conform to a Bernoulli distribution, wherein the probability of a word representing bit-1 being approximately 0.5. To inject a watermark, we alter the distribution by selectively replacing words representing bit-0 with context-based synonyms that represent bit-1. A statistical test is then used to identify the watermark. Experiments demonstrate the effectiveness of our method on both Chinese and English datasets. Furthermore, results under re-translation, polishing, word deletion, and synonym substitution attacks reveal that it is arduous to remove the watermark without compromising the original semantics.
DopUS-Net: Quality-Aware Robotic Ultrasound Imaging based on Doppler Signal
Venue: arXiv cs.CV 2023
Year: 2023

Medical ultrasound (US) is widely used to evaluate and stage vascular diseases, in particular for the preliminary screening program, due to the advantage of being radiation-free. However, automatic segmentation of small tubular structures (e.g., the ulnar artery) from cross-sectional US images is still challenging. To address this challenge, this paper proposes the DopUS-Net and a vessel re-identification module that leverage the Doppler effect to enhance the final segmentation result. Firstly, the DopUS-Net combines the Doppler images with B-mode images to increase the segmentation accuracy and robustness of small blood vessels. It incorporates two encoders to exploit the maximum potential of the Doppler signal and recurrent neural network modules to preserve sequential information. Input to the first encoder is a two-channel duplex image representing the combination of the grey-scale Doppler and B-mode images to ensure anatomical spatial correctness. The second encoder operates on the pure Doppler images to provide a region proposal. Secondly, benefiting from the Doppler signal, this work first introduces an online artery re-identification module to qualitatively evaluate the real-time segmentation results and automatically optimize the probe pose for enhanced Doppler images. This quality-aware module enables the closed-loop control of robotic screening to further improve the confidence and robustness of image segmentation. The experimental results demonstrate that the proposed approach with the re-identification process can significantly improve the accuracy and robustness of the segmentation results (dice score: from 0:54 to 0:86; intersection over union: from 0:47 to 0:78).
Is a Mean Difference of 0.46 Relevant? Towards Determining the Smallest Effect Size of Interest for Visual Aesthetics of Websites
Venue: arXiv cs.HC 2023
Year: 2023

Imagine conducting a study to determine whether users rate the visual aesthetics of your website more positively than your competitors. To assess users' perceptions of both websites, you use a validated survey scale for visual aesthetics, and you observe a statistically significant difference in users' ratings of the visual aesthetics of the two websites of 0.5 on a 7-point Likert-type scale. However, determining whether such a difference is practically (and theoretically) meaningful is challenging. In this paper, I follow the procedure outlined in Anvari & Lakens (2021) to determine the smallest subjectively experienced difference in VisAWI-s ratings using an anchor-based method. A sample of N = 249 participants rated and compared screenshots of eight websites in an online survey. I determined an estimate of a population-specific mean difference of 0.4, or in POMP units 6.58%, which translates to a mean difference of 0.46 with the 7-point Likert-type scale of the VisAWI-s. These values suggest that differences in VisAWI-s scores exceeding these estimates, such as the 0.5 mentioned above, are likely noticeable and meaningful to users. However, the estimate of this smallest subjectively experienced difference is affected by the overall visual aesthetics rating of the stimuli used. Researchers can use this effect size to inform study design and sample size planning. Still, whenever possible, they should aim to determine a domain- and research-design-specific smallest effect size of interest.
Causal Analysis for Robust Interpretability of Neural Networks
Venue: arXiv cs.LG 2023
Year: 2023

Interpreting the inner function of neural networks is crucial for the trustworthy development and deployment of these black-box models. Prior interpretability methods focus on correlation-based measures to attribute model decisions to individual examples. However, these measures are susceptible to noise and spurious correlations encoded in the model during the training phase (e.g., biased inputs, model overfitting, or misspecification). Moreover, this process has proven to result in noisy and unstable attributions that prevent any transparent understanding of the model's behavior. In this paper, we develop a robust interventional-based method grounded by causal analysis to capture cause-effect mechanisms in pre-trained neural networks and their relation to the prediction. Our novel approach relies on path interventions to infer the causal mechanisms within hidden layers and isolate relevant and necessary information (to model prediction), avoiding noisy ones. The result is task-specific causal explanatory graphs that can audit model behavior and express the actual causes underlying its performance. We apply our method to vision models trained on classification tasks. On image classification tasks, we provide extensive quantitative experiments to show that our approach can capture more stable and faithful explanations than standard attribution-based methods. Furthermore, the underlying causal graphs reveal the neural interactions in the model, making it a valuable tool in other applications (e.g., model repair).
Survey of Malware Analysis through Control Flow Graph using Machine Learning
Venue: arXiv cs.CR 2023
Year: 2023

Malware is a significant threat to the security of computer systems and networks which requires sophisticated techniques to analyze the behavior and functionality for detection. Traditional signature-based malware detection methods have become ineffective in detecting new and unknown malware due to their rapid evolution. One of the most promising techniques that can overcome the limitations of signature-based detection is to use control flow graphs (CFGs). CFGs leverage the structural information of a program to represent the possible paths of execution as a graph, where nodes represent instructions and edges represent control flow dependencies. Machine learning (ML) algorithms are being used to extract these features from CFGs and classify them as malicious or benign. In this survey, we aim to review some state-of-the-art methods for malware detection through CFGs using ML, focusing on the different ways of extracting, representing, and classifying. Specifically, we present a comprehensive overview of different types of CFG features that have been used as well as different ML algorithms that have been applied to CFG-based malware detection. We provide an in-depth analysis of the challenges and limitations of these approaches, as well as suggest potential solutions to address some open problems and promising future directions for research in this field.
Convex Geometric Trajectory Tracking using Lie Algebraic MPC for Autonomous Marine Vehicles
Venue: arXiv cs.RO 2023
Year: 2023

Controlling marine vehicles in challenging environments is a complex task due to the presence of nonlinear hydrodynamics and uncertain external disturbances. Despite nonlinear model predictive control (MPC) showing potential in addressing these issues, its practical implementation is often constrained by computational limitations. In this paper, we propose an efficient controller for trajectory tracking of marine vehicles by employing a convex error-state MPC on the Lie group. By leveraging the inherent geometric properties of the Lie group, we can construct globally valid error dynamics and formulate a quadratic programming-based optimization problem. Our proposed MPC demonstrates effectiveness in trajectory tracking through extensive-numerical simulations, including scenarios involving ocean currents. Notably, our method substantially reduces computation time compared to nonlinear MPC, making it well-suited for real-time control applications with long prediction horizons or involving small marine vehicles.
Response to "The digital pound: a new form of money for households and businesses"
Venue: arXiv cs.CY 2023
Year: 2023

This document constitutes a response to a Consultation Paper published by the Bank of England and HM Treasury, "The digital pound: a new form of money for households and businesses?", the latest document in a series that includes "Central Bank Digital Currency: opportunities, challenges and design" in 2020 and "New forms of digital money" in 2021. The Consultation Paper concerns the adoption of central bank digital currency (CBDC) for retail use in the United Kingdom by the Bank of England. We shall address the consultation questions directly in the third section of this document.
Skin Deep: Investigating Subjectivity in Skin Tone Annotations for Computer Vision Benchmark Datasets
Venue: arXiv cs.CV 2023
Year: 2023

To investigate the well-observed racial disparities in computer vision systems that analyze images of humans, researchers have turned to skin tone as more objective annotation than race metadata for fairness performance evaluations. However, the current state of skin tone annotation procedures is highly varied. For instance, researchers use a range of untested scales and skin tone categories, have unclear annotation procedures, and provide inadequate analyses of uncertainty. In addition, little attention is paid to the positionality of the humans involved in the annotation process--both designers and annotators alike--and the historical and sociological context of skin tone in the United States. Our work is the first to investigate the skin tone annotation process as a sociotechnical project. We surveyed recent skin tone annotation procedures and conducted annotation experiments to examine how subjective understandings of skin tone are embedded in skin tone annotation procedures. Our systematic literature review revealed the uninterrogated association between skin tone and race and the limited effort to analyze annotator uncertainty in current procedures for skin tone annotation in computer vision evaluation. Our experiments demonstrated that design decisions in the annotation procedure such as the order in which the skin tone scale is presented or additional context in the image (i.e., presence of a face) significantly affected the resulting inter-annotator agreement and individual uncertainty of skin tone annotations. We call for greater reflexivity in the design, analysis, and documentation of procedures for evaluation using skin tone.
The Good, the Bad, and the Missing: Neural Code Generation for Machine Learning Tasks
Venue: arXiv cs.SE 2023
Year: 2023

Machine learning (ML) has been increasingly used in a variety of domains, while solving ML programming tasks poses unique challenges because of the fundamentally different nature and construction from general programming tasks, especially for developers who do not have ML backgrounds. Automatic code generation that produces a code snippet from a natural language description can be a promising technique to accelerate ML programming tasks. In recent years, although many deep learning-based neural code generation models have been proposed with high accuracy, the fact that most of them are mainly evaluated on general programming tasks calls into question their effectiveness and usefulness in ML programming tasks. In this paper, we set out to investigate the effectiveness of existing neural code generation models on ML programming tasks. For our analysis, we select six state-of-the-art neural code generation models, and evaluate their performance on four widely used ML libraries, with newly-created 83K pairs of natural-language described ML programming tasks. Our empirical study reveals some good, bad, and missing aspects of neural code generation models on ML tasks, with a few major ones listed below. (Good) Neural code generation models perform significantly better on ML tasks than on non-ML tasks. (Bad) Most of the generated code is semantically incorrect. (Bad) Code generation models cannot significantly improve developers' completion time. (Good) The generated code can help developers write more correct code by providing developers with clues for using correct APIs. (Missing) The observation from our user study reveals the missing aspects of code generation for ML tasks, e.g., decomposing code generation for divide-and-conquer into two tasks: API sequence identification and API usage generation.
A Review of Data-driven Approaches for Malicious Website Detection
Venue: arXiv cs.CR 2023
Year: 2023

The detection of malicious websites has become a critical issue in cybersecurity. Therefore, this paper offers a comprehensive review of data-driven methods for detecting malicious websites. Traditional approaches and their limitations are discussed, followed by an overview of data-driven approaches. The paper establishes the data-feature-model-extension pipeline and the latest research developments of data-driven approaches, including data preprocessing, feature extraction, model construction and technology extension. Specifically, this paper compares methods using deep learning models proposed in recent years. Furthermore, the paper follows the data-feature-model-extension pipeline to discuss the challenges together with some future directions of data-driven methods in malicious website detection.
Modelling Human Visual Motion Processing with Trainable Motion Energy Sensing and a Self-attention Network
Venue: arXiv cs.AI 2023
Year: 2023

Visual motion processing is essential for humans to perceive and interact with dynamic environments. Despite extensive research in cognitive neuroscience, image-computable models that can extract informative motion flow from natural scenes in a manner consistent with human visual processing have yet to be established. Meanwhile, recent advancements in computer vision (CV), propelled by deep learning, have led to significant progress in optical flow estimation, a task closely related to motion perception. Here we propose an image-computable model of human motion perception by bridging the gap between biological and CV models. Specifically, we introduce a novel two-stages approach that combines trainable motion energy sensing with a recurrent self-attention network for adaptive motion integration and segregation. This model architecture aims to capture the computations in V1-MT, the core structure for motion perception in the biological visual system, while providing the ability to derive informative motion flow for a wide range of stimuli, including complex natural scenes. In silico neurophysiology reveals that our model's unit responses are similar to mammalian neural recordings regarding motion pooling and speed tuning. The proposed model can also replicate human responses to a range of stimuli examined in past psychophysical studies. The experimental results on the Sintel benchmark demonstrate that our model predicts human responses better than the ground truth, whereas the state-of-the-art CV models show the opposite. Our study provides a computational architecture consistent with human visual motion processing, although the physiological correspondence may not be exact.