OpenRaiser/Intern-Atlas huggingface dataset:
$M^{2}$Fusion: Bayesian-based Multimodal Multi-level Fusion on Colorectal Cancer Microsatellite Instability Prediction
Venue: arXiv cs.CV 2024
Year: 2024

Colorectal cancer (CRC) micro-satellite instability (MSI) prediction on histopathology images is a challenging weakly supervised learning task that involves multi-instance learning on gigapixel images. To date, radiology images have proven to have CRC MSI information and efficient patient imaging techniques. Different data modalities integration offers the opportunity to increase the accuracy and robustness of MSI prediction. Despite the progress in representation learning from the whole slide images (WSI) and exploring the potential of making use of radiology data, CRC MSI prediction remains a challenge to fuse the information from multiple data modalities (e.g., pathology WSI and radiology CT image). In this paper, we propose $M^{2}$Fusion: a Bayesian-based multimodal multi-level fusion pipeline for CRC MSI. The proposed fusion model $M^{2}$Fusion is capable of discovering more novel patterns within and across modalities that are beneficial for predicting MSI than using a single modality alone, as well as other fusion methods. The contribution of the paper is three-fold: (1) $M^{2}$Fusion is the first pipeline of multi-level fusion on pathology WSI and 3D radiology CT image for MSI prediction; (2) CT images are the first time integrated into multimodal fusion for CRC MSI prediction; (3) feature-level fusion strategy is evaluated on both Transformer-based and CNN-based method. Our approach is validated on cross-validation of 352 cases and outperforms either feature-level (0.8177 vs. 0.7908) or decision-level fusion strategy (0.8177 vs. 0.7289) on AUC score.
Authorship Obfuscation in Multilingual Machine-Generated Text Detection
Venue: arXiv cs.CL 2024
Year: 2024

High-quality text generation capability of recent Large Language Models (LLMs) causes concerns about their misuse (e.g., in massive generation/spread of disinformation). Machine-generated text (MGT) detection is important to cope with such threats. However, it is susceptible to authorship obfuscation (AO) methods, such as paraphrasing, which can cause MGTs to evade detection. So far, this was evaluated only in monolingual settings. Thus, the susceptibility of recently proposed multilingual detectors is still unknown. We fill this gap by comprehensively benchmarking the performance of 10 well-known AO methods, attacking 37 MGT detection methods against MGTs in 11 languages (i.e., 10 $\times$ 37 $\times$ 11 = 4,070 combinations). We also evaluate the effect of data augmentation on adversarial robustness using obfuscated texts. The results indicate that all tested AO methods can cause evasion of automated detection in all tested languages, where homoglyph attacks are especially successful. However, some of the AO methods severely damaged the text, making it no longer readable or easily recognizable by humans (e.g., changed language, weird characters).
Consolidating Trees of Robotic Plans Generated Using Large Language Models to Improve Reliability
Venue: arXiv cs.RO 2024
Year: 2024

The inherent probabilistic nature of Large Language Models (LLMs) introduces an element of unpredictability, raising concerns about potential discrepancies in their output. This paper introduces an innovative approach aims to generate correct and optimal robotic task plans for diverse real-world demands and scenarios. LLMs have been used to generate task plans, but they are unreliable and may contain wrong, questionable, or high-cost steps. The proposed approach uses LLM to generate a number of task plans as trees and amalgamates them into a graph by removing questionable paths. Then an optimal task tree can be retrieved to circumvent questionable and high-cost nodes, thereby improving planning accuracy and execution efficiency. The approach is further improved by incorporating a large knowledge network. Leveraging GPT-4 further, the high-level task plan is converted into a low-level Planning Domain Definition Language (PDDL) plan executable by a robot. Evaluation results highlight the superior accuracy and efficiency of our approach compared to previous methodologies in the field of task planning.
Deep Fuzzy Framework for Emotion Recognition using EEG Signals and Emotion Representation in Type-2 Fuzzy VAD Space
Venue: arXiv cs.HC 2024
Year: 2024

Recently, the representation of emotions in the Valence, Arousal and Dominance (VAD) space has drawn enough attention. However, the complex nature of emotions and the subjective biases in self-reported values of VAD make the emotion model too specific to a particular experiment. This study aims to develop a generic model representing emotions using a fuzzy VAD space and improve emotion recognition by utilizing this representation. We partitioned the crisp VAD space into a fuzzy VAD space using low, medium and high type-2 fuzzy dimensions to represent emotions. A framework that integrates fuzzy VAD space with EEG data has been developed to recognize emotions. The EEG features were extracted using spatial and temporal feature vectors from time-frequency spectrograms, while the subject-reported values of VAD were also considered. The study was conducted on the DENS dataset, which includes a wide range of twenty-four emotions, along with EEG data and subjective ratings. The study was validated using various deep fuzzy framework models based on type-2 fuzzy representation, cuboid probabilistic lattice representation and unsupervised fuzzy emotion clusters. These models resulted in emotion recognition accuracy of 96.09\%, 95.75\% and 95.31\%, respectively, for the classes of 24 emotions. The study also included an ablation study, one with crisp VAD space and the other without VAD space. The result with crisp VAD space performed better, while the deep fuzzy framework outperformed both models. The model was extended to predict cross-subject cases of emotions, and the results with 78.37\% accuracy are promising, proving the generality of our model. The generic nature of the developed model, along with its successful cross-subject predictions, gives direction for real-world applications in the areas such as affective computing, human-computer interaction, and mental health monitoring.
Generating Maximal Configurations and Their Variants Using Code Metrics
Venue: arXiv cs.SE 2024
Year: 2024

Testing configurable systems continues to be challenging and costly. Generation of configurations for testing tends to use either techniques based on semantic sampling (e.g., logical formulas over configuration variables, often called presence conditions) or structural code metrics (e.g., code coverage). In this paper we describe our hybrid approaches that combine these two kinds of techniques to good effect. We present new configuration-generation algorithms that leverage constraint solving (SAT and MaxSAT) and configuration fuzzing, and implement our approach in a configuration-generation framework, CONFIZZ. CONFIZZ both enables the generation of maximal configurations (maximal sets of presence conditions that can be satisfied together) and performs code-metric guided configuration fuzzing. Results from evaluation on BusyBox, a highly configurable benchmark, show that our MaxSAT-based configuration generation achieves better coverage for several code metrics. Results also show that, when high coverage of multiple configurations is needed, CONFIZZ's presence-condition fuzzing outperforms alternatives.
Delivery Line Tracking Robot
Venue: arXiv cs.RO 2024
Year: 2024

The project we embarked on is making an electronic robot that can deliver a package along a set route through infrared sensors. It uses the infrared sensors to determine if the path it is following is correct or if it is off course. This is determined by sending off a photon to reflect off the path and determines if it is on a light surface by the amount of light emitted back or if it is a dark surface by the amount of light that is not present. In addition to following a line, the user can stop and start the robot at any interval through the infrared remote control. The project is a combination of the practical parts of machinery with the software part of coding in Arduino which is a coding subsect of C++. This can lead to endless possibilities that could help a wide variety of people from all ranges of life, especially with those that live with disabilities
The Pulse of Fileless Cryptojacking Attacks: Malicious PowerShell Scripts
Venue: arXiv cs.CR 2024
Year: 2024

Fileless malware predominantly relies on PowerShell scripts, leveraging the native capabilities of Windows systems to execute stealthy attacks that leave no traces on the victim's system. The effectiveness of the fileless method lies in its ability to remain operational on victim endpoints through memory execution, even if the attacks are detected, and the original malicious scripts are removed. Threat actors have increasingly utilized this technique, particularly since 2017, to conduct cryptojacking attacks. With the emergence of new Remote Code Execution (RCE) vulnerabilities in ubiquitous libraries, widespread cryptocurrency mining attacks have become prevalent, often employing fileless techniques. This paper provides a comprehensive analysis of PowerShell scripts of fileless cryptojacking, dissecting the common malicious patterns based on the MITRE ATT&CK framework.
Parallel $k$-Core Decomposition with Batched Updates and Asynchronous Reads
Venue: arXiv cs.DC 2024
Year: 2024

Maintaining a dynamic $k$-core decomposition is an important problem that identifies dense subgraphs in dynamically changing graphs. Recent work by Liu et al. [SPAA 2022] presents a parallel batch-dynamic algorithm for maintaining an approximate $k$-core decomposition. In their solution, both reads and updates need to be batched, and therefore each type of operation can incur high latency waiting for the other type to finish. To tackle most real-world workloads, which are dominated by reads, this paper presents a novel hybrid concurrent-parallel dynamic $k$-core data structure where asynchronous reads can proceed concurrently with batches of updates, leading to significantly lower read latencies. Our approach is based on tracking causal dependencies between updates, so that causally related groups of updates appear atomic to concurrent readers. Our data structure guarantees linearizability and liveness for both reads and updates, and maintains the same approximation guarantees as prior work. Our experimental evaluation on a 30-core machine shows that our approach reduces read latency by orders of magnitude compared to the batch-dynamic algorithm, up to a $\left(4.05 \cdot 10^{5}\right)$-factor. Compared to an unsynchronized (non-linearizable) baseline, our read latency overhead is only up to a $3.21$-factor greater, while improving accuracy of coreness estimates by up to a factor of $52.7$.
Belief Miner: A Methodology for Discovering Causal Beliefs and Causal Illusions from General Populations
Venue: arXiv cs.HC 2024
Year: 2024

Causal belief is a cognitive practice that humans apply everyday to reason about cause and effect relations between factors, phenomena, or events. Like optical illusions, humans are prone to drawing causal relations between events that are only coincidental (i.e., causal illusions). Researchers in domains such as cognitive psychology and healthcare often use logistically expensive experiments to understand causal beliefs and illusions. In this paper, we propose Belief Miner, a crowdsourcing method for evaluating people's causal beliefs and illusions. Our method uses the (dis)similarities between the causal relations collected from the crowds and experts to surface the causal beliefs and illusions. Through an iterative design process, we developed a web-based interface for collecting causal relations from a target population. We then conducted a crowdsourced experiment with 101 workers on Amazon Mechanical Turk and Prolific using this interface and analyzed the collected data with Belief Miner. We discovered a variety of causal beliefs and potential illusions, and we report the design implications for future research.
JustiLM: Few-shot Justification Generation for Explainable Fact-Checking of Real-world Claims
Venue: arXiv cs.CL 2024
Year: 2024

Justification is an explanation that supports the veracity assigned to a claim in fact-checking. However, the task of justification generation is previously oversimplified as summarization of fact-check article authored by fact-checkers. Therefore, we propose a realistic approach to generate justification based on retrieved evidence. We present a new benchmark dataset called ExClaim for \underline{Ex}plainable fact-checking of real-world \underline{Claim}s, and introduce JustiLM, a novel few-shot \underline{Justi}fication generation based on retrieval-augmented \underline{L}anguage \underline{M}odel by using fact-check articles as auxiliary resource during training only. Experiments show that JustiLM achieves promising performance in justification generation compared to strong baselines, and can also enhance veracity classification with a straightforward extension.
BanglaNet: Bangla Handwritten Character Recognition using Ensembling of Convolutional Neural Network
Venue: arXiv cs.CV 2024
Year: 2024

Handwritten character recognition is a crucial task because of its abundant applications. The recognition task of Bangla handwritten characters is especially challenging because of the cursive nature of Bangla characters and the presence of compound characters with more than one way of writing. In this paper, a classification model based on the ensembling of several Convolutional Neural Networks (CNN), namely, BanglaNet is proposed to classify Bangla basic characters, compound characters, numerals, and modifiers. Three different models based on the idea of state-of-the-art CNN models like Inception, ResNet, and DenseNet have been trained with both augmented and non-augmented inputs. Finally, all these models are averaged or ensembled to get the finishing model. Rigorous experimentation on three benchmark Bangla handwritten characters datasets, namely, CMATERdb, BanglaLekha-Isolated, and Ekush has exhibited significant recognition accuracies compared to some recent CNN-based research. The top-1 recognition accuracies obtained are 98.40%, 97.65%, and 97.32%, and the top-3 accuracies are 99.79%, 99.74%, and 99.56% for CMATERdb, BanglaLekha-Isolated, and Ekush datasets respectively.
Toward Clinically Trustworthy Deep Learning: Applying Conformal Prediction to Intracranial Hemorrhage Detection
Venue: arXiv cs.CV 2024
Year: 2024

As deep learning (DL) continues to demonstrate its ability in radiological tasks, it is critical that we optimize clinical DL solutions to include safety. One of the principal concerns in the clinical adoption of DL tools is trust. This study aims to apply conformal prediction as a step toward trustworthiness for DL in radiology. This is a retrospective study of 491 non-contrast head CTs from the CQ500 dataset, in which three senior radiologists annotated slices containing intracranial hemorrhage (ICH). The dataset was split into definite and challenging subsets, where challenging images were defined to those in which there was disagreement among readers. A DL model was trained on 146 patients (10,815 slices) from the definite data (training dataset) to perform ICH localization and classification for five classes of ICH. To develop an uncertainty-aware DL model, 1,546 cases of the definite data (calibration dataset) was used for Mondrian conformal prediction (MCP). The uncertainty-aware DL model was tested on 8,401 definite and challenging cases to assess its ability to identify challenging cases. After the MCP procedure, the model achieved an F1 score of 0.920 for ICH classification on the test dataset. Additionally, it correctly identified 6,837 of the 6,856 total challenging cases as challenging (99.7% accuracy). It did not incorrectly label any definite cases as challenging. The uncertainty-aware ICH detector performs on par with state-of-the-art models. MCP's performance in detecting challenging cases demonstrates that it is useful in automated ICH detection and promising for trustworthiness in radiological DL.
KTVIC: A Vietnamese Image Captioning Dataset on the Life Domain
Venue: arXiv cs.CV 2024
Year: 2024

Image captioning is a crucial task with applications in a wide range of domains, including healthcare and education. Despite extensive research on English image captioning datasets, the availability of such datasets for Vietnamese remains limited, with only two existing datasets. In this study, we introduce KTVIC, a comprehensive Vietnamese Image Captioning dataset focused on the life domain, covering a wide range of daily activities. This dataset comprises 4,327 images and 21,635 Vietnamese captions, serving as a valuable resource for advancing image captioning in the Vietnamese language. We conduct experiments using various deep neural networks as the baselines on our dataset, evaluating them using the standard image captioning metrics, including BLEU, METEOR, CIDEr, and ROUGE. Our findings underscore the effectiveness of the proposed dataset and its potential contributions to the field of image captioning in the Vietnamese context.
Deep Shape-Texture Statistics for Completely Blind Image Quality Evaluation
Venue: arXiv cs.CV 2024
Year: 2024

Opinion-Unaware Blind Image Quality Assessment (OU-BIQA) models aim to predict image quality without training on reference images and subjective quality scores. Thereinto, image statistical comparison is a classic paradigm, while the performance is limited by the representation ability of visual descriptors. Deep features as visual descriptors have advanced IQA in recent research, but they are discovered to be highly texture-biased and lack of shape-bias. On this basis, we find out that image shape and texture cues respond differently towards distortions, and the absence of either one results in an incomplete image representation. Therefore, to formulate a well-round statistical description for images, we utilize the shapebiased and texture-biased deep features produced by Deep Neural Networks (DNNs) simultaneously. More specifically, we design a Shape-Texture Adaptive Fusion (STAF) module to merge shape and texture information, based on which we formulate qualityrelevant image statistics. The perceptual quality is quantified by the variant Mahalanobis Distance between the inner and outer Shape-Texture Statistics (DSTS), wherein the inner and outer statistics respectively describe the quality fingerprints of the distorted image and natural images. The proposed DSTS delicately utilizes shape-texture statistical relations between different data scales in the deep domain, and achieves state-of-the-art (SOTA) quality prediction performance on images with artificial and authentic distortions.
No-Clean-Reference Image Super-Resolution: Application to Electron Microscopy
Venue: arXiv cs.CV 2024
Year: 2024

The inability to acquire clean high-resolution (HR) electron microscopy (EM) images over a large brain tissue volume hampers many neuroscience studies. To address this challenge, we propose a deep-learning-based image super-resolution (SR) approach to computationally reconstruct clean HR 3D-EM with a large field of view (FoV) from noisy low-resolution (LR) acquisition. Our contributions are I) Investigating training with no-clean references for $\ell_2$ and $\ell_1$ loss functions; II) Introducing a novel network architecture, named EMSR, for enhancing the resolution of LR EM images while reducing inherent noise; and, III) Comparing different training strategies including using acquired LR and HR image pairs, i.e., real pairs with no-clean references contaminated with real corruptions, the pairs of synthetic LR and acquired HR, as well as acquired LR and denoised HR pairs. Experiments with nine brain datasets showed that training with real pairs can produce high-quality super-resolved results, demonstrating the feasibility of training with non-clean references for both loss functions. Additionally, comparable results were observed, both visually and numerically, when employing denoised and noisy references for training. Moreover, utilizing the network trained with synthetically generated LR images from HR counterparts proved effective in yielding satisfactory SR results, even in certain cases, outperforming training with real pairs. The proposed SR network was compared quantitatively and qualitatively with several established SR techniques, showcasing either the superiority or competitiveness of the proposed method in mitigating noise while recovering fine details.
OBSeg: Accurate and Fast Instance Segmentation Framework Using Segmentation Foundation Models with Oriented Bounding Box Prompts
Venue: arXiv cs.CV 2024
Year: 2024

Instance segmentation in remote sensing images is a long-standing challenge. Since horizontal bounding boxes introduce many interference objects, oriented bounding boxes (OBBs) are usually used for instance identification. However, based on ``segmentation within bounding box'' paradigm, current instance segmentation methods using OBBs are overly dependent on bounding box detection performance. To tackle this problem, this paper proposes OBSeg, an accurate and fast instance segmentation framework using OBBs. OBSeg is based on box prompt-based segmentation foundation models (BSMs), e.g., Segment Anything Model. Specifically, OBSeg first detects OBBs to distinguish instances and provide coarse localization information. Then, it predicts OBB prompt-related masks for fine segmentation. Since OBBs only serve as prompts, OBSeg alleviates the over-dependence on bounding box detection performance of current instance segmentation methods using OBBs. Thanks to OBB prompts, OBSeg outperforms other current BSM-based methods using HBBs. In addition, to enable BSMs to handle OBB prompts, we propose a novel OBB prompt encoder. To make OBSeg more lightweight and further improve the performance of lightweight distilled BSMs, a Gaussian smoothing-based knowledge distillation method is introduced. Experiments demonstrate that OBSeg outperforms current instance segmentation methods on multiple datasets in terms of instance segmentation accuracy and has competitive inference speed. The code is available at https://github.com/zhen6618/OBBInstanceSegmentation.
MARIO: MAth Reasoning with code Interpreter Output -- A Reproducible Pipeline
Venue: arXiv cs.CL 2024
Year: 2024

Large language models (LLMs) have seen considerable advancements in natural language understanding tasks, yet there remains a gap to bridge before attaining true artificial general intelligence, especially concerning shortcomings in mathematical reasoning capabilities. We postulate that the inherent nature of LLM training, which focuses on predicting probabilities of next token, presents challenges in effectively modeling mathematical reasoning that demands exact calculations, both from data-driven and theoretical standpoints. In this paper, we address this challenge by enriching the data landscape and introducing a novel math dataset, enhanced with a capability to utilize a Python code interpreter. This dataset is derived from GSM8K and MATH and has been further refined through a combination of GPT-4 annotations, human review, and self-training processes, where the errors in the original GSM8K training set have been fixed. Additionally, we propose a tentative, easily replicable protocol for the fine-tuning of math-specific LLMs, which has led to a significant improvement in the performance of a 7B-parameter LLM on the GSM8K and MATH datasets. We are committed to advancing the field of mathematical reasoning in LLMs and, to that end, we have made source code for data generation / training / inference, and the model checkpoints publicly available at \url{https://github.com/MARIO-Math-Reasoning/MARIO}. We hope this will facilitate further research and development within the community.