OpenRaiser/Intern-Atlas huggingface dataset:
Stochastic Vision Transformers with Wasserstein Distance-Aware Attention
Venue: arXiv cs.CV 2023
Year: 2023

Self-supervised learning is one of the most promising approaches to acquiring knowledge from limited labeled data. Despite the substantial advancements made in recent years, self-supervised models have posed a challenge to practitioners, as they do not readily provide insight into the model's confidence and uncertainty. Tackling this issue is no simple feat, primarily due to the complexity involved in implementing techniques that can make use of the latent representations learned during pre-training without relying on explicit labels. Motivated by this, we introduce a new stochastic vision transformer that integrates uncertainty and distance awareness into self-supervised learning (SSL) pipelines. Instead of the conventional deterministic vector embedding, our novel stochastic vision transformer encodes image patches into elliptical Gaussian distributional embeddings. Notably, the attention matrices of these stochastic representational embeddings are computed using Wasserstein distance-based attention, effectively capitalizing on the distributional nature of these embeddings. Additionally, we propose a regularization term based on Wasserstein distance for both pre-training and fine-tuning processes, thereby incorporating distance awareness into latent representations. We perform extensive experiments across different tasks such as in-distribution generalization, out-of-distribution detection, dataset corruption, semi-supervised settings, and transfer learning to other datasets and tasks. Our proposed method achieves superior accuracy and calibration, surpassing the self-supervised baseline in a wide range of experiments on a variety of datasets.
ArcMMLU: A Library and Information Science Benchmark for Large Language Models
Venue: arXiv cs.CL 2023
Year: 2023

In light of the rapidly evolving capabilities of large language models (LLMs), it becomes imperative to develop rigorous domain-specific evaluation benchmarks to accurately assess their capabilities. In response to this need, this paper introduces ArcMMLU, a specialized benchmark tailored for the Library & Information Science (LIS) domain in Chinese. This benchmark aims to measure the knowledge and reasoning capability of LLMs within four key sub-domains: Archival Science, Data Science, Library Science, and Information Science. Following the format of MMLU/CMMLU, we collected over 6,000 high-quality questions for the compilation of ArcMMLU. This extensive compilation can reflect the diverse nature of the LIS domain and offer a robust foundation for LLM evaluation. Our comprehensive evaluation reveals that while most mainstream LLMs achieve an average accuracy rate above 50% on ArcMMLU, there remains a notable performance gap, suggesting substantial headroom for refinement in LLM capabilities within the LIS domain. Further analysis explores the effectiveness of few-shot examples on model performance and highlights challenging questions where models consistently underperform, providing valuable insights for targeted improvements. ArcMMLU fills a critical gap in LLM evaluations within the Chinese LIS domain and paves the way for future development of LLMs tailored to this specialized area.
DQSSA: A Quantum-Inspired Solution for Maximizing Influence in Online Social Networks (Student Abstract)
Venue: arXiv cs.SI 2023
Year: 2023

Influence Maximization is the task of selecting optimal nodes maximising the influence spread in social networks. This study proposes a Discretized Quantum-based Salp Swarm Algorithm (DQSSA) for optimizing influence diffusion in social networks. By discretizing meta-heuristic algorithms and infusing them with quantum-inspired enhancements, we address issues like premature convergence and low efficacy. The proposed method, guided by quantum principles, offers a promising solution for Influence Maximisation. Experiments on four real-world datasets reveal DQSSA's superior performance as compared to established cutting-edge algorithms.
Seg2Reg: Differentiable 2D Segmentation to 1D Regression Rendering for 360 Room Layout Reconstruction
Venue: arXiv cs.CV 2023
Year: 2023

State-of-the-art single-view 360-degree room layout reconstruction methods formulate the problem as a high-level 1D (per-column) regression task. On the other hand, traditional low-level 2D layout segmentation is simpler to learn and can represent occluded regions, but it requires complex post-processing for the targeting layout polygon and sacrifices accuracy. We present Seg2Reg to render 1D layout depth regression from the 2D segmentation map in a differentiable and occlusion-aware way, marrying the merits of both sides. Specifically, our model predicts floor-plan density for the input equirectangular 360-degree image. Formulating the 2D layout representation as a density field enables us to employ `flattened' volume rendering to form 1D layout depth regression. In addition, we propose a novel 3D warping augmentation on layout to improve generalization. Finally, we re-implement recent room layout reconstruction methods into our codebase for benchmarking and explore modern backbones and training techniques to serve as the strong baseline. Our model significantly outperforms previous arts. The code will be made available upon publication.
Meta-Prior: Meta learning for Adaptive Inverse Problem Solvers
Venue: arXiv cs.CV 2023
Year: 2023

Deep neural networks have become a foundational tool for addressing imaging inverse problems. They are typically trained for a specific task, with a supervised loss to learn a mapping from the observations to the image to recover. However, real-world imaging challenges often lack ground truth data, rendering traditional supervised approaches ineffective. Moreover, for each new imaging task, a new model needs to be trained from scratch, wasting time and resources. To overcome these limitations, we introduce a novel approach based on meta-learning. Our method trains a meta-model on a diverse set of imaging tasks that allows the model to be efficiently fine-tuned for specific tasks with few fine-tuning steps. We show that the proposed method extends to the unsupervised setting, where no ground truth data is available. In its bilevel formulation, the outer level uses a supervised loss, that evaluates how well the fine-tuned model performs, while the inner loss can be either supervised or unsupervised, relying only on the measurement operator. This allows the meta-model to leverage a few ground truth samples for each task while being able to generalize to new imaging tasks. We show that in simple settings, this approach recovers the Bayes optimal estimator, illustrating the soundness of our approach. We also demonstrate our method's effectiveness on various tasks, including image processing and magnetic resonance imaging.
Semi-supervised Semantic Segmentation via Boosting Uncertainty on Unlabeled Data
Venue: arXiv cs.CV 2023
Year: 2023

We bring a new perspective to semi-supervised semantic segmentation by providing an analysis on the labeled and unlabeled distributions in training datasets. We first figure out that the distribution gap between labeled and unlabeled datasets cannot be ignored, even though the two datasets are sampled from the same distribution. To address this issue, we theoretically analyze and experimentally prove that appropriately boosting uncertainty on unlabeled data can help minimize the distribution gap, which benefits the generalization of the model. We propose two strategies and design an uncertainty booster algorithm, specially for semi-supervised semantic segmentation. Extensive experiments are carried out based on these theories, and the results confirm the efficacy of the algorithm and strategies. Our plug-and-play uncertainty booster is tiny, efficient, and robust to hyperparameters but can significantly promote performance. Our approach achieves state-of-the-art performance in our experiments compared to the current semi-supervised semantic segmentation methods on the popular benchmarks: Cityscapes and PASCAL VOC 2012 with different train settings.
Evaluating the Impact of Flaky Simulators on Testing Autonomous Driving Systems
Venue: arXiv cs.SE 2023
Year: 2023

Simulators are widely used to test Autonomous Driving Systems (ADS), but their potential flakiness can lead to inconsistent test results. We investigate test flakiness in simulation-based testing of ADS by addressing two key questions: (1) How do flaky ADS simulations impact automated testing that relies on randomized algorithms? and (2) Can machine learning (ML) effectively identify flaky ADS tests while decreasing the required number of test reruns? Our empirical results, obtained from two widely-used open-source ADS simulators and five diverse ADS test setups, show that test flakiness in ADS is a common occurrence and can significantly impact the test results obtained by randomized algorithms. Further, our ML classifiers effectively identify flaky ADS tests using only a single test run, achieving F1-scores of $85$%, $82$% and $96$% for three different ADS test setups. Our classifiers significantly outperform our non-ML baseline, which requires executing tests at least twice, by $31$%, $21$%, and $13$% in F1-score performance, respectively. We conclude with a discussion on the scope, implications and limitations of our study. We provide our complete replication package in a Github repository.
Mavericks at BLP-2023 Task 1: Ensemble-based Approach Using Language Models for Violence Inciting Text Detection
Venue: arXiv cs.CL 2023
Year: 2023

This paper presents our work for the Violence Inciting Text Detection shared task in the First Workshop on Bangla Language Processing. Social media has accelerated the propagation of hate and violence-inciting speech in society. It is essential to develop efficient mechanisms to detect and curb the propagation of such texts. The problem of detecting violence-inciting texts is further exacerbated in low-resource settings due to sparse research and less data. The data provided in the shared task consists of texts in the Bangla language, where each example is classified into one of the three categories defined based on the types of violence-inciting texts. We try and evaluate several BERT-based models, and then use an ensemble of the models as our final submission. Our submission is ranked 10th in the final leaderboard of the shared task with a macro F1 score of 0.737.
Efficient Baseline for Quantitative Precipitation Forecasting in Weather4cast 2023
Venue: arXiv cs.LG 2023
Year: 2023

Accurate precipitation forecasting is indispensable for informed decision-making across various industries. However, the computational demands of current models raise environmental concerns. We address the critical need for accurate precipitation forecasting while considering the environmental impact of computational resources and propose a minimalist U-Net architecture to be used as a baseline for future weather forecasting initiatives.
Pre-registration for Predictive Modeling
Venue: arXiv cs.LG 2023
Year: 2023

Amid rising concerns of reproducibility and generalizability in predictive modeling, we explore the possibility and potential benefits of introducing pre-registration to the field. Despite notable advancements in predictive modeling, spanning core machine learning tasks to various scientific applications, challenges such as overlooked contextual factors, data-dependent decision-making, and unintentional re-use of test data have raised questions about the integrity of results. To address these issues, we propose adapting pre-registration practices from explanatory modeling to predictive modeling. We discuss current best practices in predictive modeling and their limitations, introduce a lightweight pre-registration template, and present a qualitative study with machine learning researchers to gain insight into the effectiveness of pre-registration in preventing biased estimates and promoting more reliable research outcomes. We conclude by exploring the scope of problems that pre-registration can address in predictive modeling and acknowledging its limitations within this context.
Transport Equation based Physics Informed Neural Network to predict the Yield Strength of Architected Materials
Venue: arXiv cs.LG 2023
Year: 2023

In this research, the application of the Physics-Informed Neural Network (PINN) model is explored to solve transport equation-based Partial Differential Equations (PDEs). The primary objective is to analyze the impact of different activation functions incorporated within the PINN model on its predictive performance, specifically assessing the Mean Squared Error (MSE) and Mean Absolute Error (MAE). The dataset used in the study consists of a varied set of input parameters related to strut diameter, unit cell size, and the corresponding yield stress values. Through this investigation the aim is to understand the effectiveness of the PINN model and the significance of choosing appropriate activation functions for solving complex PDEs in real-world applications. The outcomes suggest that the choice of activation function may have minimal influence on the model's predictive accuracy for this particular problem. The PINN model showcases exceptional generalization capabilities, indicating its capacity to avoid overfitting with the provided dataset. The research underscores the importance of striking a balance between performance and computational efficiency while selecting an activation function for specific real-world applications. These valuable findings contribute to advancing the understanding and potential adoption of PINN as an effective tool for solving challenging PDEs in diverse scientific and engineering domains.
Preserving The Safety And Confidentiality Of Data Mining Information In Health Care: A literature review
Venue: arXiv cs.CR 2023
Year: 2023

Daily, massive volume of data are produced due to the internet of things' rapid development, which has now permeated the healthcare industry. Recent advances in data mining have spawned a new field of a study dubbed privacy-preserving data mining (PPDM). PPDM technique or approach enables the extraction of actionable insight from enormous volume of data while safeguarding the privacy of individual information and benefiting the entire society Medical research has taken a new course as a result of data mining with healthcare data to detect diseases earlier and improve patient care. Data integration necessitates the sharing of sensitive patient information. However, substantial privacy issues are raised in connection with the storage and transmission of potentially sensitive information. Disclosing sensitive information infringes on patients' privacy. This paper aims to conduct a review of related work on privacy-preserving mechanisms, data protection regulations, and mitigating tactics. The review concluded that no single strategy outperforms all others. Hence, future research should focus on adequate techniques for privacy solutions in the age of massive medical data and the standardization of evaluation standards.
DeFi Security: Turning The Weakest Link Into The Strongest Attraction
Venue: arXiv cs.CR 2023
Year: 2023

The primary innovation we pioneer -- focused on blockchain information security -- is called the Safe-House. The Safe-House is badly needed since there are many ongoing hacks and security concerns in the DeFi space right now. The Safe-House is a piece of engineering sophistication that utilizes existing blockchain principles to bring about greater security when customer assets are moved around. The Safe-House logic is easily implemented as smart contracts on any decentralized system. The amount of funds at risk from both internal and external parties -- and hence the maximum one time loss -- is guaranteed to stay within the specified limits based on cryptographic fundamentals. To improve the safety of the Safe-House even further, we adapt the one time password (OPT) concept to operate using blockchain technology. Well suited to blockchain cryptographic nuances, our secondary advancement can be termed the one time next time password (OTNTP) mechanism. The OTNTP is designed to complement the Safe-House making it even more safe. We provide a detailed threat assessment model -- discussing the risks faced by DeFi protocols and the specific risks that apply to blockchain fund management -- and give technical arguments regarding how these threats can be overcome in a robust manner. We discuss how the Safe-House can participate with other external yield generation protocols in a secure way. We provide reasons for why the Safe-House increases safety without sacrificing the efficiency of operation. We start with a high level intuitive description of the landscape, the corresponding problems and our solutions. We then supplement this overview with detailed discussions including the corresponding mathematical formulations and pointers for technological implementation. This approach ensures that the article is accessible to a broad audience.
Presentation Attack detection using Wavelet Transform and Deep Residual Neural Net
Venue: arXiv cs.CR 2023
Year: 2023

Biometric authentication is becoming more prevalent for secured authentication systems. However, the biometric substances can be deceived by the imposters in several ways. Among other imposter attacks, print attacks, mask attacks, and replay attacks fall under the presentation attack category. The bio-metric images, especially the iris and face, are vulnerable to different presentation attacks. This research applies deep learning approaches to mitigate presentation attacks in a biometric access control system. Our contribution in this paper is two-fold: First, we applied the wavelet transform to extract the features from the biometric images. Second, we modified the deep residual neural net and applied it to the spoof datasets in an attempt to detect the presentation attacks. This research applied the proposed approach to biometric spoof datasets, namely ATVS, CASIA two class, and CASIA cropped image sets. The datasets used in this research contain images that are captured in both a controlled and uncontrolled environment along with different resolutions and sizes. We obtained the best accuracy of 93% on the ATVS Iris datasets. For CASIA two class and CASIA cropped datasets, we achieved test accuracies of 91% and 82%, respectively.
A Case for Competent AI Systems $-$ A Concept Note
Venue: arXiv cs.CY 2023
Year: 2023

The efficiency of an AI system is contingent upon its ability to align with the specified requirements of a given task. How-ever, the inherent complexity of tasks often introduces the potential for harmful implications or adverse actions. This note explores the critical concept of capability within AI systems, representing what the system is expected to deliver. The articulation of capability involves specifying well-defined out-comes. Yet, the achievement of this capability may be hindered by deficiencies in implementation and testing, reflecting a gap in the system's competency (what it can do vs. what it does successfully). A central challenge arises in elucidating the competency of an AI system to execute tasks effectively. The exploration of system competency in AI remains in its early stages, occasionally manifesting as confidence intervals denoting the probability of success. Trust in an AI system hinges on the explicit modeling and detailed specification of its competency, connected intricately to the system's capability. This note explores this gap by proposing a framework for articulating the competency of AI systems. Motivated by practical scenarios such as the Glass Door problem, where an individual inadvertently encounters a glass obstacle due to a failure in their competency, this research underscores the imperative of delving into competency dynamics. Bridging the gap between capability and competency at a detailed level, this note contributes to advancing the discourse on bolstering the reliability of AI systems in real-world applications.
CRAFT: Contextual Re-Activation of Filters for face recognition Training
Venue: arXiv cs.CV 2023
Year: 2023

The first layer of a deep CNN backbone applies filters to an image to extract the basic features available to later layers. During training, some filters may go inactive, mean ing all weights in the filter approach zero. An inactive fil ter in the final model represents a missed opportunity to extract a useful feature. This phenomenon is especially prevalent in specialized CNNs such as for face recogni tion (as opposed to, e.g., ImageNet). For example, in one the most widely face recognition model (ArcFace), about half of the convolution filters in the first layer are inactive. We propose a novel approach designed and tested specif ically for face recognition networks, known as "CRAFT: Contextual Re-Activation of Filters for Face Recognition Training". CRAFT identifies inactive filters during training and reinitializes them based on the context of strong filters at that stage in training. We show that CRAFT reduces fraction of inactive filters from 44% to 32% on average and discovers filter patterns not found by standard training. Compared to standard training without reactivation, CRAFT demonstrates enhanced model accuracy on standard face-recognition benchmark datasets including AgeDB-30, CPLFW, LFW, CALFW, and CFP-FP, as well as on more challenging datasets like IJBB and IJBC.
A Metadata Generation System with Semantic Understanding for Video Retrieval in Film Production
Venue: arXiv cs.MM 2023
Year: 2023

In film production, metadata plays an important role in original raw video indexing and classification within the industrial post-production software. Inspired by deep visual-semantic methods, we propose an automated image information extraction process to extend the diversity of metadata entities for massive large-scale raw video searching and retrieval. In this paper, we introduce the proposed system architecture and modules, integrating semantic annotation models and user-demand-oriented information fusion. We conducted experiments to validate the effectiveness of our system on Film Raw Video Semantic Annotation Dataset (Film-RVSAD) and Slate Board Template Dataset (SBTD), two benchmark datasets built for cinematography-related semantic annotation and slate detection. Experimental results show that the proposed system provides an effective strategy to improve the efficiency of metadata generation and transformation, which is necessary and convenient for collaborative work in the filmmaking process.
Which way is `right'?: Uncovering limitations of Vision-and-Language Navigation model
Venue: arXiv cs.CV 2023
Year: 2023

The challenging task of Vision-and-Language Navigation (VLN) requires embodied agents to follow natural language instructions to reach a goal location or object (e.g. `walk down the hallway and turn left at the piano'). For agents to complete this task successfully, they must be able to ground objects referenced into the instruction (e.g.`piano') into the visual scene as well as ground directional phrases (e.g.`turn left') into actions. In this work we ask the following question -- to what degree are spatial and directional language cues informing the navigation model's decisions? We propose a series of simple masking experiments to inspect the model's reliance on different parts of the instruction. Surprisingly we uncover that certain top performing models rely only on the noun tokens of the instructions. We propose two training methods to alleviate this concerning limitation.