pwc-archive/papers-with-abstracts huggingface dataset:
InfoVisDial: An Informative Visual Dialogue Dataset by Bridging Large Multimodal and Language Models
Authors: Bingbing Wen, Zhengyuan Yang, JianFeng Wang, Zhe Gan, Bill Howe, Lijuan Wang
Date: 2023-12-21 00:00:00
Tasks: Image Description

In this paper, we build a visual dialogue dataset, named InfoVisDial, which provides rich informative answers in each round even with external knowledge related to the visual content. Different from existing datasets where the answer is compact and short, InfoVisDial contains long free-form answers with rich information in each round of dialogue. For effective data collection, the key idea is to bridge the large-scale multimodal model (e.g., GIT) and the language models (e.g., GPT-3). GIT can describe the image content even with scene text, while GPT-3 can generate informative dialogue based on the image description and appropriate prompting techniques. With such automatic pipeline, we can readily generate informative visual dialogue data at scale. Then, we ask human annotators to rate the generated dialogues to filter the low-quality conversations.Human analyses show that InfoVisDial covers informative and diverse dialogue topics: $54.4\%$ of the dialogue rounds are related to image scene texts, and $36.7\%$ require external knowledge. Each round's answer is also long and open-ended: $87.3\%$ of answers are unique with an average length of $8.9$, compared with $27.37\%$ and $2.9$ in VisDial. Last, we propose a strong baseline by adapting the GIT model for the visual dialogue task and fine-tune the model on InfoVisDial. Hopefully, our work can motivate more effort on this direction.
Decoupling Representation and Knowledge for Few-Shot Intent Classification and Slot Filling
Authors: Jie Han, Yixiong Zou, Haozhao Wang, Jun Wang, Wei Liu, Yao Wu, Tao Zhang, Ruixuan Li
Date: 2023-12-21 00:00:00
Tasks: intent-classification, Intent Classification, Intent Classification and Slot Filling, Relation, slot-filling, Slot Filling

Few-shot intent classification and slot filling are important but challenging tasks due to the scarcity of finely labeled data. Therefore, current works first train a model on source domains with sufficiently labeled data, and then transfer the model to target domains where only rarely labeled data is available. However, experience transferring as a whole usually suffers from gaps that exist among source domains and target domains. For instance, transferring domain-specific-knowledge-related experience is difficult. To tackle this problem, we propose a new method that explicitly decouples the transferring of general-semantic-representation-related experience and the domain-specific-knowledge-related experience. Specifically, for domain-specific-knowledge-related experience, we design two modules to capture intent-slot relation and slot-slot relation respectively. Extensive experiments on Snips and FewJoint datasets show that our method achieves state-of-the-art performance. The method improves the joint accuracy metric from 27.72% to 42.20% in the 1-shot setting, and from 46.54% to 60.79% in the 5-shot setting.
Understanding and Estimating Domain Complexity Across Domains
Authors: Katarina Doctor, Mayank Kejriwal, Lawrence Holder, Eric Kildebeck, Emma Resmini, Christopher Pereyda, Robert J. Steininger, Daniel V. Olivença
Date: 2023-12-20 00:00:00
Tasks: AI Agent, Diversity, Navigate

Artificial Intelligence (AI) systems, trained in controlled environments, often struggle in real-world complexities. We propose a general framework for estimating domain complexity across diverse environments, like open-world learning and real-world applications. This framework distinguishes between intrinsic complexity (inherent to the domain) and extrinsic complexity (dependent on the AI agent). By analyzing dimensionality, sparsity, and diversity within these categories, we offer a comprehensive view of domain challenges. This approach enables quantitative predictions of AI difficulty during environment transitions, avoids bias in novel situations, and helps navigate the vast search spaces of open-world domains.
Fortify Your Defenses: Strategic Budget Allocation to Enhance Power Grid Cybersecurity
Authors: Rounak Meyur, Sumit Purohit, Braden K. Webb
Date: 2023-12-20 00:00:00
Tasks: Asset Management

The abundance of cyber-physical components in modern day power grid with their diverse hardware and software vulnerabilities has made it difficult to protect them from advanced persistent threats (APTs). An attack graph depicting the propagation of potential cyber-attack sequences from the initial access point to the end objective is vital to identify critical weaknesses of any cyber-physical system. A cyber security personnel can accordingly plan preventive mitigation measures for the identified weaknesses addressing the cyber-attack sequences. However, limitations on available cybersecurity budget restrict the choice of mitigation measures. We address this aspect through our framework, which solves the following problem: given potential cyber-attack sequences for a cyber-physical component in the power grid, find the optimal manner to allocate an available budget to implement necessary preventive mitigation measures. We formulate the problem as a mixed integer linear program (MILP) to identify the optimal budget partition and set of mitigation measures which minimize the vulnerability of cyber-physical components to potential attack sequences. We assume that the allocation of budget affects the efficacy of the mitigation measures. We show how altering the budget allocation for tasks such as asset management, cybersecurity infrastructure improvement, incident response planning and employee training affects the choice of the optimal set of preventive mitigation measures and modifies the associated cybersecurity risk. The proposed framework can be used by cyber policymakers and system owners to allocate optimal budgets for various tasks required to improve the overall security of a cyber-physical system.
The Adaptive Arms Race: Redefining Robustness in AI Security
Authors: Ilias Tsingenopoulos, Vera Rimmer, Davy Preuveneers, Fabio Pierazzi, Lorenzo Cavallaro, Wouter Joosen
Date: 2023-12-20 00:00:00

Despite considerable efforts on making them robust, real-world AI-based systems remain vulnerable to decision based attacks, as definitive proofs of their operational robustness have so far proven intractable. Canonical robustness evaluation relies on adaptive attacks, which leverage complete knowledge of the defense and are tailored to bypass it. This work broadens the notion of adaptivity, which we employ to enhance both attacks and defenses, showing how they can benefit from mutual learning through interaction. We introduce a framework for adaptively optimizing black-box attacks and defenses under the competitive game they form. To assess robustness reliably, it is essential to evaluate against realistic and worst-case attacks. We thus enhance attacks and their evasive arsenal together using RL, apply the same principle to defenses, and evaluate them first independently and then jointly under a multi-agent perspective. We find that active defenses, those that dynamically control system responses, are an essential complement to model hardening against decision-based attacks; that these defenses can be circumvented by adaptive attacks, something that elicits defenses being adaptive too. Our findings, supported by an extensive theoretical and empirical investigation, confirm that adaptive adversaries pose a serious threat to black-box AI-based systems, rekindling the proverbial arms race. Notably, our approach outperforms the state-of-the-art black-box attacks and defenses, while bringing them together to render effective insights into the robustness of real-world deployed ML-based systems.
Zero-1-to-3: Domain-level Zero-shot Cognitive Diagnosis via One Batch of Early-bird Students towards Three Diagnostic Objectives
Authors: Weibo Gao, Qi Liu, Hao Wang, Linan Yue, Haoyang Bi, Yin Gu, Fangzhou Yao, Zheng Zhang, Xin Li, Yuanjing He
Date: 2023-12-20 00:00:00
Tasks: cognitive diagnosis, Diagnostic, Domain Adaptation, Transfer Learning

Cognitive diagnosis seeks to estimate the cognitive states of students by exploring their logged practice quiz data. It plays a pivotal role in personalized learning guidance within intelligent education systems. In this paper, we focus on an important, practical, yet often underexplored task: domain-level zero-shot cognitive diagnosis (DZCD), which arises due to the absence of student practice logs in newly launched domains. Recent cross-domain diagnostic models have been demonstrated to be a promising strategy for DZCD. These methods primarily focus on how to transfer student states across domains. However, they might inadvertently incorporate non-transferable information into student representations, thereby limiting the efficacy of knowledge transfer. To tackle this, we propose Zero-1-to-3, a domain-level zero-shot cognitive diagnosis framework via one batch of early-bird students towards three diagnostic objectives. Our approach initiates with pre-training a diagnosis model with dual regularizers, which decouples student states into domain-shared and domain-specific parts. The shared cognitive signals can be transferred to the target domain, enriching the cognitive priors for the new domain, which ensures the cognitive state propagation objective. Subsequently, we devise a strategy to generate simulated practice logs for cold-start students through analyzing the behavioral patterns from early-bird students, fulfilling the domain-adaption goal. Consequently, we refine the cognitive states of cold-start students as diagnostic outcomes via virtual data, aligning with the diagnosis-oriented goal. Finally, extensive experiments on six real-world datasets highlight the efficacy of our model for DZCD and its practical application in question recommendation. The code is publicly available at https://github.com/bigdata-ustc/Zero-1-to-3.
DSPy Assertions: Computational Constraints for Self-Refining Language Model Pipelines
Authors: Arnav Singhvi, Manish Shetty, Shangyin Tan, Christopher Potts, Koushik Sen, Matei Zaharia, Omar Khattab
Date: 2023-12-20 00:00:00
Tasks: Language Modeling, Language Modelling, Prompt Engineering, Question Answering, Text Generation

Chaining language model (LM) calls as composable modules is fueling a new way of programming, but ensuring LMs adhere to important constraints requires heuristic "prompt engineering". We introduce LM Assertions, a programming construct for expressing computational constraints that LMs should satisfy. We integrate our constructs into the recent DSPy programming model for LMs, and present new strategies that allow DSPy to compile programs with LM Assertions into more reliable and accurate systems. We also propose strategies to use assertions at inference time for automatic self-refinement with LMs. We report on four diverse case studies for text generation and find that LM Assertions improve not only compliance with imposed rules but also downstream task performance, passing constraints up to 164% more often and generating up to 37% more higher-quality responses. Our reference implementation of LM Assertions is integrated into DSPy at https://github.com/stanfordnlp/dspy
Transparency and Privacy: The Role of Explainable AI and Federated Learning in Financial Fraud Detection
Authors: Tomisin Awosika, Raj Mani Shukla, Bernardi Pranggono
Date: 2023-12-20 00:00:00
Tasks: Federated Learning, Fraud Detection, Privacy Preserving

Fraudulent transactions and how to detect them remain a significant problem for financial institutions around the world. The need for advanced fraud detection systems to safeguard assets and maintain customer trust is paramount for financial institutions, but some factors make the development of effective and efficient fraud detection systems a challenge. One of such factors is the fact that fraudulent transactions are rare and that many transaction datasets are imbalanced; that is, there are fewer significant samples of fraudulent transactions than legitimate ones. This data imbalance can affect the performance or reliability of the fraud detection model. Moreover, due to the data privacy laws that all financial institutions are subject to follow, sharing customer data to facilitate a higher-performing centralized model is impossible. Furthermore, the fraud detection technique should be transparent so that it does not affect the user experience. Hence, this research introduces a novel approach using Federated Learning (FL) and Explainable AI (XAI) to address these challenges. FL enables financial institutions to collaboratively train a model to detect fraudulent transactions without directly sharing customer data, thereby preserving data privacy and confidentiality. Meanwhile, the integration of XAI ensures that the predictions made by the model can be understood and interpreted by human experts, adding a layer of transparency and trust to the system. Experimental results, based on realistic transaction datasets, reveal that the FL-based fraud detection system consistently demonstrates high performance metrics. This study grounds FL's potential as an effective and privacy-preserving tool in the fight against fraud.
In-Context Reinforcement Learning for Variable Action Spaces
Authors: Viacheslav Sinii, Alexander Nikulin, Vladislav Kurenkov, Ilya Zisman, Sergey Kolesnikov
Date: 2023-12-20 00:00:00
Tasks: In-Context Reinforcement Learning, Multi-Armed Bandits, reinforcement-learning, Reinforcement Learning

Recently, it has been shown that transformers pre-trained on diverse datasets with multi-episode contexts can generalize to new reinforcement learning tasks in-context. A key limitation of previously proposed models is their reliance on a predefined action space size and structure. The introduction of a new action space often requires data re-collection and model re-training, which can be costly for some applications. In our work, we show that it is possible to mitigate this issue by proposing the Headless-AD model that, despite being trained only once, is capable of generalizing to discrete action spaces of variable size, semantic content and order. By experimenting with Bernoulli and contextual bandits, as well as a gridworld environment, we show that Headless-AD exhibits significant capability to generalize to action spaces it has never encountered, even outperforming specialized models trained for a specific set of actions on several environment configurations. Implementation is available at: https://github.com/corl-team/headless-ad.
MonoCoder: Domain-Specific Code Language Model for HPC Codes and Tasks
Authors: Tal Kadosh, Niranjan Hasabnis, Vy A. Vo, Nadav Schneider, Neva Krien, Mihai Capota, Abdul Wasay, Nesreen Ahmed, Ted Willke, Guy Tamir, Yuval Pinter, Timothy Mattson, Gal Oren
Date: 2023-12-20 00:00:00
Tasks: Code Generation, Language Modeling, Language Modelling

With easier access to powerful compute resources, there is a growing trend in AI for software development to develop large language models (LLMs) to address a variety of programming tasks. Even LLMs applied to tasks from the high-performance computing (HPC) domain are huge in size and demand expensive compute resources for training. This is partly because LLMs for HPC tasks are obtained by finetuning existing LLMs that support several natural and/or programming languages. We found this design choice confusing - why do we need LLMs trained on natural languages and programming languages unrelated to HPC for HPC-specific tasks? In this line of work, we aim to question choices made by existing LLMs by developing smaller language models (LMs) for specific domains - we call them domain-specific LMs. Specifically, we start with HPC as a domain and build an HPC-specific LM, named MonoCoder, which is orders of magnitude smaller than existing LMs but delivers better performance on non-HPC and HPC codes. Specifically, we pre-trained MonoCoder on an HPC-specific dataset (named HPCorpus) of C and C++ programs mined from GitHub. We evaluated the performance of MonoCoder against state-of-the-art multi-lingual LLMs. Results demonstrate that MonoCoder, although much smaller than existing LMs, outperforms other LLMs on normalized-perplexity tests (in relation to model size) while also delivering competing CodeBLEU scores for high-performance and parallel code generations. In other words, results suggest that MonoCoder understands HPC code better than state-of-the-art LLMs.
Unlocking Pre-trained Image Backbones for Semantic Image Synthesis
Authors: Tariq Berrada, Jakob Verbeek, Camille Couprie, Karteek Alahari
Date: 2023-12-20 00:00:00
Proceeding: CVPR 2024 1
Tasks: Conditional Image Generation, image-classification, Image Classification, Image Generation, Image-to-Image Translation

Semantic image synthesis, i.e., generating images from user-provided semantic label maps, is an important conditional image generation task as it allows to control both the content as well as the spatial layout of generated images. Although diffusion models have pushed the state of the art in generative image modeling, the iterative nature of their inference process makes them computationally demanding. Other approaches such as GANs are more efficient as they only need a single feed-forward pass for generation, but the image quality tends to suffer on large and diverse datasets. In this work, we propose a new class of GAN discriminators for semantic image synthesis that generates highly realistic images by exploiting feature backbone networks pre-trained for tasks such as image classification. We also introduce a new generator architecture with better context modeling and using cross-attention to inject noise into latent variables, leading to more diverse generated images. Our model, which we dub DP-SIMS, achieves state-of-the-art results in terms of image quality and consistency with the input label maps on ADE-20K, COCO-Stuff, and Cityscapes, surpassing recent diffusion models while requiring two orders of magnitude less compute for inference.
Generate E-commerce Product Background by Integrating Category Commonality and Personalized Style
Authors: Haohan Wang, Wei Feng, Yaoyu Li, Zheng Zhang, Jingjing Lv, Junjie Shen, Zhangang Lin, Jingping Shao
Date: 2023-12-20 00:00:00
Tasks: 2k

The state-of-the-art methods for e-commerce product background generation suffer from the inefficiency of designing product-wise prompts when scaling up the production, as well as the ineffectiveness of describing fine-grained styles when customizing personalized backgrounds for some specific brands. To address these obstacles, we integrate the category commonality and personalized style into diffusion models. Concretely, we propose a Category-Wise Generator to enable large-scale background generation with only one model for the first time. A unique identifier in the prompt is assigned to each category, whose attention is located on the background by a mask-guided cross attention layer to learn the category-wise style. Furthermore, for products with specific and fine-grained requirements in layout, elements, etc, a Personality-Wise Generator is devised to learn such personalized style directly from a reference image to resolve textual ambiguities, and is trained in a self-supervised manner for more efficient training data usage. To advance research in this field, the first large-scale e-commerce product background generation dataset BG60k is constructed, which covers more than 60k product images from over 2k categories. Experiments demonstrate that our method could generate high-quality backgrounds for different categories, and maintain the personalized background style of reference images. BG60k will be available at \url{https://github.com/Whileherham/BG60k}.
Adaptive Training Meets Progressive Scaling: Elevating Efficiency in Diffusion Models
Authors: Wenhao Li, Xiu Su, Yu Han, Shan You, Tao Huang, Chang Xu
Date: 2023-12-20 00:00:00
Tasks: Decision Making, Denoising

Diffusion models have demonstrated remarkable efficacy in various generative tasks with the predictive prowess of denoising model. Currently, diffusion models employ a uniform denoising model across all timesteps. However, the inherent variations in data distributions at different timesteps lead to conflicts during training, constraining the potential of diffusion models. To address this challenge, we propose a novel two-stage divide-and-conquer training strategy termed TDC Training. It groups timesteps based on task similarity and difficulty, assigning highly customized denoising models to each group, thereby enhancing the performance of diffusion models. While two-stage training avoids the need to train each model separately, the total training cost is even lower than training a single unified denoising model. Additionally, we introduce Proxy-based Pruning to further customize the denoising models. This method transforms the pruning problem of diffusion models into a multi-round decision-making problem, enabling precise pruning of diffusion models. Our experiments validate the effectiveness of TDC Training, demonstrating improvements in FID of 1.5 on ImageNet64 compared to original IDDPM, while saving about 20\% of computational resources.
Towards Fair Graph Federated Learning via Incentive Mechanisms
Authors: Chenglu Pan, Jiarong Xu, Yue Yu, Ziqi Yang, Qingbiao Wu, Chunping Wang, Lei Chen, Yang Yang
Date: 2023-12-20 00:00:00
Tasks: Fairness, Federated Learning, Model Optimization

Graph federated learning (FL) has emerged as a pivotal paradigm enabling multiple agents to collaboratively train a graph model while preserving local data privacy. Yet, current efforts overlook a key issue: agents are self-interested and would hesitant to share data without fair and satisfactory incentives. This paper is the first endeavor to address this issue by studying the incentive mechanism for graph federated learning. We identify a unique phenomenon in graph federated learning: the presence of agents posing potential harm to the federation and agents contributing with delays. This stands in contrast to previous FL incentive mechanisms that assume all agents contribute positively and in a timely manner. In view of this, this paper presents a novel incentive mechanism tailored for fair graph federated learning, integrating incentives derived from both model gradient and payoff. To achieve this, we first introduce an agent valuation function aimed at quantifying agent contributions through the introduction of two criteria: gradient alignment and graph diversity. Moreover, due to the high heterogeneity in graph federated learning, striking a balance between accuracy and fairness becomes particularly crucial. We introduce motif prototypes to enhance accuracy, communicated between the server and agents, enhancing global model aggregation and aiding agents in local model optimization. Extensive experiments show that our model achieves the best trade-off between accuracy and the fairness of model gradient, as well as superior payoff fairness.