OpenRaiser/Intern-Atlas huggingface dataset:
Target-Guided Composed Image Retrieval
Venue: arXiv cs.MM 2023
Year: 2023

Composed image retrieval (CIR) is a new and flexible image retrieval paradigm, which can retrieve the target image for a multimodal query, including a reference image and its corresponding modification text. Although existing efforts have achieved compelling success, they overlook the conflict relationship modeling between the reference image and the modification text for improving the multimodal query composition and the adaptive matching degree modeling for promoting the ranking of the candidate images that could present different levels of matching degrees with the given query. To address these two limitations, in this work, we propose a Target-Guided Composed Image Retrieval network (TG-CIR). In particular, TG-CIR first extracts the unified global and local attribute features for the reference/target image and the modification text with the contrastive language-image pre-training model (CLIP) as the backbone, where an orthogonal regularization is introduced to promote the independence among the attribute features. Then TG-CIR designs a target-query relationship-guided multimodal query composition module, comprising a target-free student composition branch and a target-based teacher composition branch, where the target-query relationship is injected into the teacher branch for guiding the conflict relationship modeling of the student branch. Last, apart from the conventional batch-based classification loss, TG-CIR additionally introduces a batch-based target similarity-guided matching degree regularization to promote the metric learning process. Extensive experiments on three benchmark datasets demonstrate the superiority of our proposed method.
ReOnto: A Neuro-Symbolic Approach for Biomedical Relation Extraction
Venue: arXiv cs.CL 2023
Year: 2023

Relation Extraction (RE) is the task of extracting semantic relationships between entities in a sentence and aligning them to relations defined in a vocabulary, which is generally in the form of a Knowledge Graph (KG) or an ontology. Various approaches have been proposed so far to address this task. However, applying these techniques to biomedical text often yields unsatisfactory results because it is hard to infer relations directly from sentences due to the nature of the biomedical relations. To address these issues, we present a novel technique called ReOnto, that makes use of neuro symbolic knowledge for the RE task. ReOnto employs a graph neural network to acquire the sentence representation and leverages publicly accessible ontologies as prior knowledge to identify the sentential relation between two entities. The approach involves extracting the relation path between the two entities from the ontology. We evaluate the effect of using symbolic knowledge from ontologies with graph neural networks. Experimental results on two public biomedical datasets, BioRel and ADE, show that our method outperforms all the baselines (approximately by 3\%).
ImmersiveNeRF: Hybrid Radiance Fields for Unbounded Immersive Light Field Reconstruction
Venue: arXiv cs.CV 2023
Year: 2023

This paper proposes a hybrid radiance field representation for unbounded immersive light field reconstruction which supports high-quality rendering and aggressive view extrapolation. The key idea is to first formally separate the foreground and the background and then adaptively balance learning of them during the training process. To fulfill this goal, we represent the foreground and background as two separate radiance fields with two different spatial mapping strategies. We further propose an adaptive sampling strategy and a segmentation regularizer for more clear segmentation and robust convergence. Finally, we contribute a novel immersive light field dataset, named THUImmersive, with the potential to achieve much larger space 6DoF immersive rendering effects compared with existing datasets, by capturing multiple neighboring viewpoints for the same scene, to stimulate the research and AR/VR applications in the immersive light field domain. Extensive experiments demonstrate the strong performance of our method for unbounded immersive light field reconstruction.
MLGuard: Defend Your Machine Learning Model!
Venue: arXiv cs.SE 2023
Year: 2023

Machine Learning (ML) is used in critical highly regulated and high-stakes fields such as finance, medicine, and transportation. The correctness of these ML applications is important for human safety and economic benefit. Progress has been made on improving ML testing and monitoring of ML. However, these approaches do not provide i) pre/post conditions to handle uncertainty, ii) defining corrective actions based on probabilistic outcomes, or iii) continual verification during system operation. In this paper, we propose MLGuard, a new approach to specify contracts for ML applications. Our approach consists of a) an ML contract specification defining pre/post conditions, invariants, and altering behaviours, b) generated validation models to determine the probability of contract violation, and c) an ML wrapper generator to enforce the contract and respond to violations. Our work is intended to provide the overarching framework required for building ML applications and monitoring their safety.
SemProtector: A Unified Framework for Semantic Protection in Deep Learning-based Semantic Communication Systems
Venue: arXiv cs.CR 2023
Year: 2023

Recently proliferated semantic communications (SC) aim at effectively transmitting the semantics conveyed by the source and accurately interpreting the meaning at the destination. While such a paradigm holds the promise of making wireless communications more intelligent, it also suffers from severe semantic security issues, such as eavesdropping, privacy leaking, and spoofing, due to the open nature of wireless channels and the fragility of neural modules. Previous works focus more on the robustness of SC via offline adversarial training of the whole system, while online semantic protection, a more practical setting in the real world, is still largely under-explored. To this end, we present SemProtector, a unified framework that aims to secure an online SC system with three hot-pluggable semantic protection modules. Specifically, these protection modules are able to encrypt semantics to be transmitted by an encryption method, mitigate privacy risks from wireless channels by a perturbation mechanism, and calibrate distorted semantics at the destination by a semantic signature generation method. Our framework enables an existing online SC system to dynamically assemble the above three pluggable modules to meet customized semantic protection requirements, facilitating the practical deployment in real-world SC systems. Experiments on two public datasets show the effectiveness of our proposed SemProtector, offering some insights of how we reach the goal of secrecy, privacy and integrity of an SC system. Finally, we discuss some future directions for the semantic protection.
Zero-shot information extraction from radiological reports using ChatGPT
Venue: arXiv cs.CL 2023
Year: 2023

Electronic health records contain an enormous amount of valuable information, but many are recorded in free text. Information extraction is the strategy to transform the sequence of characters into structured data, which can be employed for secondary analysis. However, the traditional information extraction components, such as named entity recognition and relation extraction, require annotated data to optimize the model parameters, which has become one of the major bottlenecks in building information extraction systems. With the large language models achieving good performances on various downstream NLP tasks without parameter tuning, it becomes possible to use large language models for zero-shot information extraction. In this study, we aim to explore whether the most popular large language model, ChatGPT, can extract useful information from the radiological reports. We first design the prompt template for the interested information in the CT reports. Then, we generate the prompts by combining the prompt template with the CT reports as the inputs of ChatGPT to obtain the responses. A post-processing module is developed to transform the responses into structured extraction results. We conducted the experiments with 847 CT reports collected from Peking University Cancer Hospital. The experimental results indicate that ChatGPT can achieve competitive performances for some extraction tasks compared with the baseline information extraction system, but some limitations need to be further improved.
Why Change My Design: Explaining Poorly Constructed Visualization Designs with Explorable Explanations
Venue: arXiv cs.HC 2023
Year: 2023

Although visualization tools are widely available and accessible, not everyone knows the best practices and guidelines for creating accurate and honest visual representations of data. Numerous books and articles have been written to expose the misleading potential of poorly constructed charts and teach people how to avoid being deceived by them or making their own mistakes. These readings use various rhetorical devices to explain the concepts to their readers. In our analysis of a collection of books, online materials, and a design workshop, we identified six common explanation methods. To assess the effectiveness of these methods, we conducted two crowdsourced studies (each with N = 125) to evaluate their ability to teach and persuade people to make design changes. In addition to these existing methods, we brought in the idea of Explorable Explanations, which allows readers to experiment with different chart settings and observe how the changes are reflected in the visualization. While we did not find significant differences across explanation methods, the results of our experiments indicate that, following the exposure to the explanations, the participants showed improved proficiency in identifying deceptive charts and were more receptive to proposed alterations of the visualization design. We discovered that participants were willing to accept more than 60% of the proposed adjustments in the persuasiveness assessment. Nevertheless, we found no significant differences among different explanation methods in convincing participants to accept the modifications.
Communication Lower Bounds for Cryptographic Broadcast Protocols
Venue: arXiv cs.CR 2023
Year: 2023

Broadcast protocols enable a set of $n$ parties to agree on the input of a designated sender, even facing attacks by malicious parties. In the honest-majority setting, randomization and cryptography were harnessed to achieve low-communication broadcast with sub-quadratic total communication and balanced sub-linear cost per party. However, comparatively little is known in the dishonest-majority setting. Here, the most communication-efficient constructions are based on Dolev and Strong (SICOMP '83), and sub-quadratic broadcast has not been achieved. On the other hand, the only nontrivial $ω(n)$ communication lower bounds are restricted to deterministic protocols, or against strong adaptive adversaries that can perform "after the fact" removal of messages. We provide new communication lower bounds in this space, which hold against arbitrary cryptography and setup assumptions, as well as a simple protocol showing near tightness of our first bound. 1) We demonstrate a tradeoff between resiliency and communication for protocols secure against $n-o(n)$ static corruptions. For example, $Ω(n\cdot {\sf polylog}(n))$ messages are needed when the number of honest parties is $n/{\sf polylog}(n)$; $Ω(n\sqrt{n})$ messages are needed for $O(\sqrt{n})$ honest parties; and $Ω(n^2)$ messages are needed for $O(1)$ honest parties. Complementarily, we demonstrate broadcast with $O(n\cdot{\sf polylog}(n))$ total communication facing any constant fraction of static corruptions. 2) Our second bound considers $n/2 + k$ corruptions and a weakly adaptive adversary that cannot remove messages "after the fact." We show that any broadcast protocol within this setting can be attacked to force an arbitrary party to send messages to $k$ other parties. This rules out, for example, broadcast facing 51% corruptions in which all non-sender parties have sublinear communication locality.
Layer-wise training for self-supervised learning on graphs
Venue: arXiv cs.LG 2023
Year: 2023

End-to-end training of graph neural networks (GNN) on large graphs presents several memory and computational challenges, and limits the application to shallow architectures as depth exponentially increases the memory and space complexities. In this manuscript, we propose Layer-wise Regularized Graph Infomax, an algorithm to train GNNs layer by layer in a self-supervised manner. We decouple the feature propagation and feature transformation carried out by GNNs to learn node representations in order to derive a loss function based on the prediction of future inputs. We evaluate the algorithm in inductive large graphs and show similar performance to other end to end methods and a substantially increased efficiency, which enables the training of more sophisticated models in one single device. We also show that our algorithm avoids the oversmoothing of the representations, another common challenge of deep GNNs.
Hawkeye: Change-targeted Testing for Android Apps based on Deep Reinforcement Learning
Venue: arXiv cs.SE 2023
Year: 2023

Android Apps are frequently updated to keep up with changing user, hardware, and business demands. Ensuring the correctness of App updates through extensive testing is crucial to avoid potential bugs reaching the end user. Existing Android testing tools generate GUI events focussing on improving the test coverage of the entire App rather than prioritising updates and its impacted elements. Recent research has proposed change-focused testing but relies on random exploration to exercise the updates and impacted GUI elements that is ineffective and slow for large complex Apps with a huge input exploration space. We propose directed testing of App updates with Hawkeye that is able to prioritise executing GUI actions associated with code changes based on deep reinforcement learning from historical exploration data. Our empirical evaluation compares Hawkeye with state-of-the-art model-based and reinforcement learning-based testing tools FastBot2 and ARES using 10 popular open-source and 1 commercial App. We find that Hawkeye is able to generate GUI event sequences targeting changed functions more reliably than FastBot2 and ARES for the open source Apps and the large commercial App. Hawkeye achieves comparable performance on smaller open source Apps with a more tractable exploration space. The industrial deployment of Hawkeye in the development pipeline also shows that Hawkeye is ideal to perform smoke testing for merge requests of a complicated commercial App.
Prior Knowledge Guided Network for Video Anomaly Detection
Venue: arXiv cs.CV 2023
Year: 2023

Video Anomaly Detection (VAD) involves detecting anomalous events in videos, presenting a significant and intricate task within intelligent video surveillance. Existing studies often concentrate solely on features acquired from limited normal data, disregarding the latent prior knowledge present in extensive natural image datasets. To address this constraint, we propose a Prior Knowledge Guided Network(PKG-Net) for the VAD task. First, an auto-encoder network is incorporated into a teacher-student architecture to learn two designated proxy tasks: future frame prediction and teacher network imitation, which can provide better generalization ability on unknown samples. Second, knowledge distillation on proper feature blocks is also proposed to increase the multi-scale detection ability of the model. In addition, prediction error and teacher-student feature inconsistency are combined to evaluate anomaly scores of inference samples more comprehensively. Experimental results on three public benchmarks validate the effectiveness and accuracy of our method, which surpasses recent state-of-the-arts.
On the Robustness of Post-hoc GNN Explainers to Label Noise
Venue: arXiv cs.LG 2023
Year: 2023

Proposed as a solution to the inherent black-box limitations of graph neural networks (GNNs), post-hoc GNN explainers aim to provide precise and insightful explanations of the behaviours exhibited by trained GNNs. Despite their recent notable advancements in academic and industrial contexts, the robustness of post-hoc GNN explainers remains unexplored when confronted with label noise. To bridge this gap, we conduct a systematic empirical investigation to evaluate the efficacy of diverse post-hoc GNN explainers under varying degrees of label noise. Our results reveal several key insights: Firstly, post-hoc GNN explainers are susceptible to label perturbations. Secondly, even minor levels of label noise, inconsequential to GNN performance, harm the quality of generated explanations substantially. Lastly, we engage in a discourse regarding the progressive recovery of explanation effectiveness with escalating noise levels.
Survival Prediction from Imbalance colorectal cancer dataset using hybrid sampling methods and tree-based classifiers
Venue: arXiv cs.LG 2023
Year: 2023

Background and Objective: Colorectal cancer is a high mortality cancer. Clinical data analysis plays a crucial role in predicting the survival of colorectal cancer patients, enabling clinicians to make informed treatment decisions. However, utilizing clinical data can be challenging, especially when dealing with imbalanced outcomes. This paper focuses on developing algorithms to predict 1-, 3-, and 5-year survival of colorectal cancer patients using clinical datasets, with particular emphasis on the highly imbalanced 1-year survival prediction task. To address this issue, we propose a method that creates a pipeline of some of standard balancing techniques to increase the true positive rate. Evaluation is conducted on a colorectal cancer dataset from the SEER database. Methods: The pre-processing step consists of removing records with missing values and merging categories. The minority class of 1-year and 3-year survival tasks consists of 10% and 20% of the data, respectively. Edited Nearest Neighbor, Repeated edited nearest neighbor (RENN), Synthetic Minority Over-sampling Techniques (SMOTE), and pipelines of SMOTE and RENN approaches were used and compared for balancing the data with tree-based classifiers. Decision Trees, Random Forest, Extra Tree, eXtreme Gradient Boosting, and Light Gradient Boosting (LGBM) are used in this article. Method. Results: The performance evaluation utilizes a 5-fold cross-validation approach. In the case of highly imbalanced datasets (1-year), our proposed method with LGBM outperforms other sampling methods with the sensitivity of 72.30%. For the task of imbalance (3-year survival), the combination of RENN and LGBM achieves a sensitivity of 80.81%, indicating that our proposed method works best for highly imbalanced datasets. Conclusions: Our proposed method significantly improves mortality prediction for the minority class of colorectal cancer patients.
Marginalized Importance Sampling for Off-Environment Policy Evaluation
Venue: arXiv cs.LG 2023
Year: 2023

Reinforcement Learning (RL) methods are typically sample-inefficient, making it challenging to train and deploy RL-policies in real world robots. Even a robust policy trained in simulation requires a real-world deployment to assess their performance. This paper proposes a new approach to evaluate the real-world performance of agent policies prior to deploying them in the real world. Our approach incorporates a simulator along with real-world offline data to evaluate the performance of any policy using the framework of Marginalized Importance Sampling (MIS). Existing MIS methods face two challenges: (1) large density ratios that deviate from a reasonable range and (2) indirect supervision, where the ratio needs to be inferred indirectly, thus exacerbating estimation error. Our approach addresses these challenges by introducing the target policy's occupancy in the simulator as an intermediate variable and learning the density ratio as the product of two terms that can be learned separately. The first term is learned with direct supervision and the second term has a small magnitude, thus making it computationally efficient. We analyze the sample complexity as well as error propagation of our two step-procedure. Furthermore, we empirically evaluate our approach on Sim2Sim environments such as Cartpole, Reacher, and Half-Cheetah. Our results show that our method generalizes well across a variety of Sim2Sim gap, target policies and offline data collection policies. We also demonstrate the performance of our algorithm on a Sim2Real task of validating the performance of a 7 DoF robotic arm using offline data along with the Gazebo simulator.
One Wide Feedforward is All You Need
Venue: arXiv cs.CL 2023
Year: 2023

The Transformer architecture has two main non-embedding components: Attention and the Feed Forward Network (FFN). Attention captures interdependencies between words regardless of their position, while the FFN non-linearly transforms each input token independently. In this work we explore the role of the FFN, and find that despite taking up a significant fraction of the model's parameters, it is highly redundant. Concretely, we are able to substantially reduce the number of parameters with only a modest drop in accuracy by removing the FFN on the decoder layers and sharing a single FFN across the encoder. Finally we scale this architecture back to its original size by increasing the hidden dimension of the shared FFN, achieving substantial gains in both accuracy and latency with respect to the original Transformer Big.
Efficient Defense Against Model Stealing Attacks on Convolutional Neural Networks
Venue: arXiv cs.LG 2023
Year: 2023

Model stealing attacks have become a serious concern for deep learning models, where an attacker can steal a trained model by querying its black-box API. This can lead to intellectual property theft and other security and privacy risks. The current state-of-the-art defenses against model stealing attacks suggest adding perturbations to the prediction probabilities. However, they suffer from heavy computations and make impracticable assumptions about the adversary. They often require the training of auxiliary models. This can be time-consuming and resource-intensive which hinders the deployment of these defenses in real-world applications. In this paper, we propose a simple yet effective and efficient defense alternative. We introduce a heuristic approach to perturb the output probabilities. The proposed defense can be easily integrated into models without additional training. We show that our defense is effective in defending against three state-of-the-art stealing attacks. We evaluate our approach on large and quantized (i.e., compressed) Convolutional Neural Networks (CNNs) trained on several vision datasets. Our technique outperforms the state-of-the-art defenses with a $\times37$ faster inference latency without requiring any additional model and with a low impact on the model's performance. We validate that our defense is also effective for quantized CNNs targeting edge devices.