OpenRaiser/Intern-Atlas huggingface dataset:
Textual Data Mining for Financial Fraud Detection: A Deep Learning Approach
Venue: arXiv cs.CL 2023
Year: 2023

In this report, I present a deep learning approach to conduct a natural language processing (hereafter NLP) binary classification task for analyzing financial-fraud texts. First, I searched for regulatory announcements and enforcement bulletins from HKEX news to define fraudulent companies and to extract their MD&A reports before I organized the sentences from the reports with labels and reporting time. My methodology involved different kinds of neural network models, including Multilayer Perceptrons with Embedding layers, vanilla Recurrent Neural Network (RNN), Long-Short Term Memory (LSTM), and Gated Recurrent Unit (GRU) for the text classification task. By utilizing this diverse set of models, I aim to perform a comprehensive comparison of their accuracy in detecting financial fraud. My results bring significant implications for financial fraud detection as this work contributes to the growing body of research at the intersection of deep learning, NLP, and finance, providing valuable insights for industry practitioners, regulators, and researchers in the pursuit of more robust and effective fraud detection methodologies.
Recurrent Multi-scale Transformer for High-Resolution Salient Object Detection
Venue: arXiv cs.CV 2023
Year: 2023

Salient Object Detection (SOD) aims to identify and segment the most conspicuous objects in an image or video. As an important pre-processing step, it has many potential applications in multimedia and vision tasks. With the advance of imaging devices, SOD with high-resolution images is of great demand, recently. However, traditional SOD methods are largely limited to low-resolution images, making them difficult to adapt to the development of High-Resolution SOD (HRSOD). Although some HRSOD methods emerge, there are no large enough datasets for training and evaluating. Besides, current HRSOD methods generally produce incomplete object regions and irregular object boundaries. To address above issues, in this work, we first propose a new HRS10K dataset, which contains 10,500 high-quality annotated images at 2K-8K resolution. As far as we know, it is the largest dataset for the HRSOD task, which will significantly help future works in training and evaluating models. Furthermore, to improve the HRSOD performance, we propose a novel Recurrent Multi-scale Transformer (RMFormer), which recurrently utilizes shared Transformers and multi-scale refinement architectures. Thus, high-resolution saliency maps can be generated with the guidance of lower-resolution predictions. Extensive experiments on both high-resolution and low-resolution benchmarks show the effectiveness and superiority of the proposed framework. The source code and dataset are released at: https://github.com/DrowsyMon/RMFormer.
Revisiting Prompt Engineering via Declarative Crowdsourcing
Venue: arXiv cs.DB 2023
Year: 2023

Large language models (LLMs) are incredibly powerful at comprehending and generating data in the form of text, but are brittle and error-prone. There has been an advent of toolkits and recipes centered around so-called prompt engineering-the process of asking an LLM to do something via a series of prompts. However, for LLM-powered data processing workflows, in particular, optimizing for quality, while keeping cost bounded, is a tedious, manual process. We put forth a vision for declarative prompt engineering. We view LLMs like crowd workers and leverage ideas from the declarative crowdsourcing literature-including leveraging multiple prompting strategies, ensuring internal consistency, and exploring hybrid-LLM-non-LLM approaches-to make prompt engineering a more principled process. Preliminary case studies on sorting, entity resolution, and imputation demonstrate the promise of our approach
Exploiting Generalization in Offline Reinforcement Learning via Unseen State Augmentations
Venue: arXiv cs.LG 2023
Year: 2023

Offline reinforcement learning (RL) methods strike a balance between exploration and exploitation by conservative value estimation -- penalizing values of unseen states and actions. Model-free methods penalize values at all unseen actions, while model-based methods are able to further exploit unseen states via model rollouts. However, such methods are handicapped in their ability to find unseen states far away from the available offline data due to two factors -- (a) very short rollout horizons in models due to cascading model errors, and (b) model rollouts originating solely from states observed in offline data. We relax the second assumption and present a novel unseen state augmentation strategy to allow exploitation of unseen states where the learned model and value estimates generalize. Our strategy finds unseen states by value-informed perturbations of seen states followed by filtering out states with epistemic uncertainty estimates too high (high error) or too low (too similar to seen data). We observe improved performance in several offline RL tasks and find that our augmentation strategy consistently leads to overall lower average dataset Q-value estimates i.e. more conservative Q-value estimates than a baseline.
Generative Benchmark Creation for Table Union Search
Venue: arXiv cs.DB 2023
Year: 2023

Data management has traditionally relied on synthetic data generators to generate structured benchmarks, like the TPC suite, where we can control important parameters like data size and its distribution precisely. These benchmarks were central to the success and adoption of database management systems. But more and more, data management problems are of a semantic nature. An important example is finding tables that can be unioned. While any two tables with the same cardinality can be unioned, table union search is the problem of finding tables whose union is semantically coherent. Semantic problems cannot be benchmarked using synthetic data. Our current methods for creating benchmarks involve the manual curation and labeling of real data. These methods are not robust or scalable and perhaps more importantly, it is not clear how robust the created benchmarks are. We propose to use generative AI models to create structured data benchmarks for table union search. We present a novel method for using generative models to create tables with specified properties. Using this method, we create a new benchmark containing pairs of tables that are both unionable and non-unionable but related. We thoroughly evaluate recent existing table union search methods over existing benchmarks and our new benchmark. We also present and evaluate a new table search methods based on recent large language models over all benchmarks. We show that the new benchmark is more challenging for all methods than hand-curated benchmarks, specifically, the top-performing method achieves a Mean Average Precision of around 60%, over 30% less than its performance on existing manually created benchmarks. We examine why this is the case and show that the new benchmark permits more detailed analysis of methods, including a study of both false positives and false negatives that were not possible with existing benchmarks.
Scalable and Equitable Math Problem Solving Strategy Prediction in Big Educational Data
Venue: arXiv cs.LG 2023
Year: 2023

Understanding a student's problem-solving strategy can have a significant impact on effective math learning using Intelligent Tutoring Systems (ITSs) and Adaptive Instructional Systems (AISs). For instance, the ITS/AIS can better personalize itself to correct specific misconceptions that are indicated by incorrect strategies, specific problems can be designed to improve strategies and frustration can be minimized by adapting to a student's natural way of thinking rather than trying to fit a standard strategy for all. While it may be possible for human experts to identify strategies manually in classroom settings with sufficient student interaction, it is not possible to scale this up to big data. Therefore, we leverage advances in Machine Learning and AI methods to perform scalable strategy prediction that is also fair to students at all skill levels. Specifically, we develop an embedding called MVec where we learn a representation based on the mastery of students. We then cluster these embeddings with a non-parametric clustering method where we progressively learn clusters such that we group together instances that have approximately symmetrical strategies. The strategy prediction model is trained on instances sampled from these clusters. This ensures that we train the model over diverse strategies and also that strategies from a particular group do not bias the DNN model, thus allowing it to optimize its parameters over all groups. Using real world large-scale student interaction datasets from MATHia, we implement our approach using transformers and Node2Vec for learning the mastery embeddings and LSTMs for predicting strategies. We show that our approach can scale up to achieve high accuracy by training on a small sample of a large dataset and also has predictive equality, i.e., it can predict strategies equally well for learners at diverse skill levels.
A new approach for evaluating internal cluster validation indices
Venue: arXiv cs.LG 2023
Year: 2023

A vast number of different methods are available for unsupervised classification. Since no algorithm and parameter setting performs best in all types of data, there is a need for cluster validation to select the actually best-performing algorithm. Several indices were proposed for this purpose without using any additional (external) information. These internal validation indices can be evaluated by applying them to classifications of datasets with a known cluster structure. Evaluation approaches differ in how they use the information on the ground-truth classification. This paper reviews these approaches, considering their advantages and disadvantages, and then suggests a new approach.
Developability Approximation for Neural Implicits through Rank Minimization
Venue: arXiv cs.CV 2023
Year: 2023

Developability refers to the process of creating a surface without any tearing or shearing from a two-dimensional plane. It finds practical applications in the fabrication industry. An essential characteristic of a developable 3D surface is its zero Gaussian curvature, which means that either one or both of the principal curvatures are zero. This paper introduces a method for reconstructing an approximate developable surface from a neural implicit surface. The central idea of our method involves incorporating a regularization term that operates on the second-order derivatives of the neural implicits, effectively promoting zero Gaussian curvature. Implicit surfaces offer the advantage of smoother deformation with infinite resolution, overcoming the high polygonal constraints of state-of-the-art methods using discrete representations. We draw inspiration from the properties of surface curvature and employ rank minimization techniques derived from compressed sensing. Experimental results on both developable and non-developable surfaces, including those affected by noise, validate the generalizability of our method.
State Estimation of Continuum Robots: A Nonlinear Constrained Moving Horizon Approach
Venue: arXiv cs.RO 2023
Year: 2023

Continuum robots, made from flexible materials with continuous backbones, have several advantages over traditional rigid robots. Some of them are the ability to navigate through narrow or confined spaces, adapt to irregular or changing environments, and perform tasks in proximity to humans. However, one of the challenges in using continuum robots is the difficulty in accurately estimating their state, such as their tip position and curvature. This is due to the complexity of their kinematics and the inherent uncertainty in their measurement and control. This paper proposes a moving horizon estimation (MHE) approach for estimating the robot's state, including its tip position and shape parameters. Our approach involves minimizing the error between measurement samples from an IMU attached to the robot's tip and the estimated state along the estimation horizon using an inline optimization problem. We demonstrate the effectiveness of our approach through simulation and experimental results. Our approach can potentially improve the accuracy and robustness of state estimation and control for continuum robots. It can be applied to various applications such as surgery, manufacturing, and inspection.
Zero-shot Skeleton-based Action Recognition via Mutual Information Estimation and Maximization
Venue: arXiv cs.CV 2023
Year: 2023

Zero-shot skeleton-based action recognition aims to recognize actions of unseen categories after training on data of seen categories. The key is to build the connection between visual and semantic space from seen to unseen classes. Previous studies have primarily focused on encoding sequences into a singular feature vector, with subsequent mapping the features to an identical anchor point within the embedded space. Their performance is hindered by 1) the ignorance of the global visual/semantic distribution alignment, which results in a limitation to capture the true interdependence between the two spaces. 2) the negligence of temporal information since the frame-wise features with rich action clues are directly pooled into a single feature vector. We propose a new zero-shot skeleton-based action recognition method via mutual information (MI) estimation and maximization. Specifically, 1) we maximize the MI between visual and semantic space for distribution alignment; 2) we leverage the temporal information for estimating the MI by encouraging MI to increase as more frames are observed. Extensive experiments on three large-scale skeleton action datasets confirm the effectiveness of our method. Code: https://github.com/YujieOuO/SMIE.
Exploring Security Practices in Infrastructure as Code: An Empirical Study
Venue: arXiv cs.CR 2023
Year: 2023

Cloud computing has become popular thanks to the widespread use of Infrastructure as Code (IaC) tools, allowing the community to conveniently manage and configure cloud infrastructure using scripts. However, the scripting process itself does not automatically prevent practitioners from introducing misconfigurations, vulnerabilities, or privacy risks. As a result, ensuring security relies on practitioners understanding and the adoption of explicit policies, guidelines, or best practices. In order to understand how practitioners deal with this problem, in this work, we perform an empirical study analyzing the adoption of IaC scripted security best practices. First, we select and categorize widely recognized Terraform security practices promulgated in the industry for popular cloud providers such as AWS, Azure, and Google Cloud. Next, we assess the adoption of these practices by each cloud provider, analyzing a sample of 812 open-source projects hosted on GitHub. For that, we scan each project configuration files, looking for policy implementation through static analysis (checkov). Additionally, we investigate GitHub measures that might be correlated with adopting these best practices. The category Access policy emerges as the most widely adopted in all providers, while Encryption in rest are the most neglected policies. Regarding GitHub measures correlated with best practice adoption, we observe a positive, strong correlation between a repository number of stars and adopting practices in its cloud infrastructure. Based on our findings, we provide guidelines for cloud practitioners to limit infrastructure vulnerability and discuss further aspects associated with policies that have yet to be extensively embraced within the industry.
Fixed Inter-Neuron Covariability Induces Adversarial Robustness
Venue: arXiv cs.LG 2023
Year: 2023

The vulnerability to adversarial perturbations is a major flaw of Deep Neural Networks (DNNs) that raises question about their reliability when in real-world scenarios. On the other hand, human perception, which DNNs are supposed to emulate, is highly robust to such perturbations, indicating that there may be certain features of the human perception that make it robust but are not represented in the current class of DNNs. One such feature is that the activity of biological neurons is correlated and the structure of this correlation tends to be rather rigid over long spans of times, even if it hampers performance and learning. We hypothesize that integrating such constraints on the activations of a DNN would improve its adversarial robustness, and, to test this hypothesis, we have developed the Self-Consistent Activation (SCA) layer, which comprises of neurons whose activations are consistent with each other, as they conform to a fixed, but learned, covariability pattern. When evaluated on image and sound recognition tasks, the models with a SCA layer achieved high accuracy, and exhibited significantly greater robustness than multi-layer perceptron models to state-of-the-art Auto-PGD adversarial attacks \textit{without being trained on adversarially perturbed data
A Benchmarking Study of Matching Algorithms for Knowledge Graph Entity Alignment
Venue: arXiv cs.DB 2023
Year: 2023

How to identify those equivalent entities between knowledge graphs (KGs), which is called Entity Alignment (EA), is a long-standing challenge. So far, many methods have been proposed, with recent focus on leveraging Deep Learning to solve this problem. However, we observe that most of the efforts has been paid to having better representation of entities, rather than improving entity matching from the learned representations. In fact, how to efficiently infer the entity pairs from this similarity matrix, which is essentially a matching problem, has been largely ignored by the community. Motivated by this observation, we conduct an in-depth analysis on existing algorithms that are particularly designed for solving this matching problem, and propose a novel matching method, named Bidirectional Matching (BMat). Our extensive experimental results on public datasets indicate that there is currently no single silver bullet solution for EA. In other words, different classes of entity similarity estimation may require different matching algorithms to reach the best EA results for each class. We finally conclude that using PARIS, the state-of-the-art EA approach, with BMat gives the best combination in terms of EA performance and the algorithm's time and space complexity.
AI Chatbots as Multi-Role Pedagogical Agents: Transforming Engagement in CS Education
Venue: arXiv cs.AI 2023
Year: 2023

This study investigates the use of Artificial Intelligence (AI)-powered, multi-role chatbots as a means to enhance learning experiences and foster engagement in computer science education. Leveraging a design-based research approach, we develop, implement, and evaluate a novel learning environment enriched with four distinct chatbot roles: Instructor Bot, Peer Bot, Career Advising Bot, and Emotional Supporter Bot. These roles, designed around the tenets of Self-Determination Theory, cater to the three innate psychological needs of learners - competence, autonomy, and relatedness. Additionally, the system embraces an inquiry-based learning paradigm, encouraging students to ask questions, seek solutions, and explore their curiosities. We test this system in a higher education context over a period of one month with 200 participating students, comparing outcomes with conditions involving a human tutor and a single chatbot. Our research utilizes a mixed-methods approach, encompassing quantitative measures such as chat log sequence analysis, and qualitative methods including surveys and focus group interviews. By integrating cutting-edge Natural Language Processing techniques such as topic modelling and sentiment analysis, we offer an in-depth understanding of the system's impact on learner engagement, motivation, and inquiry-based learning. This study, through its rigorous design and innovative approach, provides significant insights into the potential of AI-empowered, multi-role chatbots in reshaping the landscape of computer science education and fostering an engaging, supportive, and motivating learning environment.
Multi-level Map Construction for Dynamic Scenes
Venue: arXiv cs.RO 2023
Year: 2023

In dynamic scenes, both localization and mapping in visual SLAM face significant challenges. In recent years, numerous outstanding research works have proposed effective solutions for the localization problem. However, there has been a scarcity of excellent works focusing on constructing long-term consistent maps in dynamic scenes, which severely hampers map applications. To address this issue, we have designed a multi-level map construction system tailored for dynamic scenes. In this system, we employ multi-object tracking algorithms, DBSCAN clustering algorithm, and depth information to rectify the results of object detection, accurately extract static point clouds, and construct dense point cloud maps and octree maps. We propose a plane map construction algorithm specialized for dynamic scenes, involving the extraction, filtering, data association, and fusion optimization of planes in dynamic environments, thus creating a plane map. Additionally, we introduce an object map construction algorithm targeted at dynamic scenes, which includes object parameterization, data association, and update optimization. Extensive experiments on public datasets and real-world scenarios validate the accuracy of the multi-level maps constructed in this study and the robustness of the proposed algorithms. Furthermore, we demonstrate the practical application prospects of our algorithms by utilizing the constructed object maps for dynamic object tracking.
Top K Relevant Passage Retrieval for Biomedical Question Answering
Venue: arXiv cs.CL 2023
Year: 2023

Question answering is a task that answers factoid questions using a large collection of documents. It aims to provide precise answers in response to the user's questions in natural language. Question answering relies on efficient passage retrieval to select candidate contexts, where traditional sparse vector space models, such as TF-IDF or BM25, are the de facto method. On the web, there is no single article that could provide all the possible answers available on the internet to the question of the problem asked by the user. The existing Dense Passage Retrieval model has been trained on Wikipedia dump from Dec. 20, 2018, as the source documents for answering questions. Question answering (QA) has made big strides with several open-domain and machine comprehension systems built using large-scale annotated datasets. However, in the clinical domain, this problem remains relatively unexplored. According to multiple surveys, Biomedical Questions cannot be answered correctly from Wikipedia Articles. In this work, we work on the existing DPR framework for the biomedical domain and retrieve answers from the Pubmed articles which is a reliable source to answer medical questions. When evaluated on a BioASQ QA dataset, our fine-tuned dense retriever results in a 0.81 F1 score.