pwc-archive/papers-with-abstracts huggingface dataset:
CIFAKE: Image Classification and Explainable Identification of AI-Generated Synthetic Images
Authors: Jordan J. Bird, Ahmad Lotfi
Date: 2023-03-24 00:00:00
Tasks: Binary Classification, Classification, image-classification, Image Classification

Recent technological advances in synthetic data have enabled the generation of images with such high quality that human beings cannot tell the difference between real-life photographs and Artificial Intelligence (AI) generated images. Given the critical necessity of data reliability and authentication, this article proposes to enhance our ability to recognise AI-generated images through computer vision. Initially, a synthetic dataset is generated that mirrors the ten classes of the already available CIFAR-10 dataset with latent diffusion which provides a contrasting set of images for comparison to real photographs. The model is capable of generating complex visual attributes, such as photorealistic reflections in water. The two sets of data present as a binary classification problem with regard to whether the photograph is real or generated by AI. This study then proposes the use of a Convolutional Neural Network (CNN) to classify the images into two categories; Real or Fake. Following hyperparameter tuning and the training of 36 individual network topologies, the optimal approach could correctly classify the images with 92.98% accuracy. Finally, this study implements explainable AI via Gradient Class Activation Mapping to explore which features within the images are useful for classification. Interpretation reveals interesting concepts within the image, in particular, noting that the actual entity itself does not hold useful information for classification; instead, the model focuses on small visual imperfections in the background of the images. The complete dataset engineered for this study, referred to as the CIFAKE dataset, is made publicly available to the research community for future work.
Improving Prediction Performance and Model Interpretability through Attention Mechanisms from Basic and Applied Research Perspectives
Authors: Shunsuke Kitada
Date: 2023-03-24 00:00:00
Tasks: Prediction

With the dramatic advances in deep learning technology, machine learning research is focusing on improving the interpretability of model predictions as well as prediction performance in both basic and applied research. While deep learning models have much higher prediction performance than traditional machine learning models, the specific prediction process is still difficult to interpret and/or explain. This is known as the black-boxing of machine learning models and is recognized as a particularly important problem in a wide range of research fields, including manufacturing, commerce, robotics, and other industries where the use of such technology has become commonplace, as well as the medical field, where mistakes are not tolerated. This bulletin is based on the summary of the author's dissertation. The research summarized in the dissertation focuses on the attention mechanism, which has been the focus of much attention in recent years, and discusses its potential for both basic research in terms of improving prediction performance and interpretability, and applied research in terms of evaluating it for real-world applications using large data sets beyond the laboratory environment. The dissertation also concludes with a summary of the implications of these findings for subsequent research and future prospects in the field.
Interpretable Anomaly Detection via Discrete Optimization
Authors: Simon Lutz, Florian Wittbold, Simon Dierl, Benedikt Böing, Falk Howar, Barbara König, Emmanuel Müller, Daniel Neider
Date: 2023-03-24 00:00:00
Tasks: Anomaly Detection, Decision Making

Anomaly detection is essential in many application domains, such as cyber security, law enforcement, medicine, and fraud protection. However, the decision-making of current deep learning approaches is notoriously hard to understand, which often limits their practical applicability. To overcome this limitation, we propose a framework for learning inherently interpretable anomaly detectors from sequential data. More specifically, we consider the task of learning a deterministic finite automaton (DFA) from a given multi-set of unlabeled sequences. We show that this problem is computationally hard and develop two learning algorithms based on constraint optimization. Moreover, we introduce novel regularization schemes for our optimization problems that improve the overall interpretability of our DFAs. Using a prototype implementation, we demonstrate that our approach shows promising results in terms of accuracy and F1 score.
Distributed Silhouette Algorithm: Evaluating Clustering on Big Data
Authors: Marco Gaido
Date: 2023-03-24 00:00:00
Tasks: Clustering

In the big data era, the key feature that each algorithm needs to have is the possibility of efficiently running in parallel in a distributed environment. The popular Silhouette metric to evaluate the quality of a clustering, unfortunately, does not have this property and has a quadratic computational complexity with respect to the size of the input dataset. For this reason, its execution has been hindered in big data scenarios, where clustering had to be evaluated otherwise. To fill this gap, in this paper we introduce the first algorithm that computes the Silhouette metric with linear complexity and can easily execute in parallel in a distributed environment. Its implementation is freely available in the Apache Spark ML library.
Online Learning for the Random Feature Model in the Student-Teacher Framework
Authors: Roman Worschech, Bernd Rosenow
Date: 2023-03-24 00:00:00

Deep neural networks are widely used prediction algorithms whose performance often improves as the number of weights increases, leading to over-parametrization. We consider a two-layered neural network whose first layer is frozen while the last layer is trainable, known as the random feature model. We study over-parametrization in the context of a student-teacher framework by deriving a set of differential equations for the learning dynamics. For any finite ratio of hidden layer size and input dimension, the student cannot generalize perfectly, and we compute the non-zero asymptotic generalization error. Only when the student's hidden layer size is exponentially larger than the input dimension, an approach to perfect generalization is possible.
Improved Adversarial Training Through Adaptive Instance-wise Loss Smoothing
Authors: Lin Li, Michael Spratling
Date: 2023-03-24 00:00:00
Tasks: Adversarial Attack, Adversarial Robustness, Data Augmentation

Deep neural networks can be easily fooled into making incorrect predictions through corruption of the input by adversarial perturbations: human-imperceptible artificial noise. So far adversarial training has been the most successful defense against such adversarial attacks. This work focuses on improving adversarial training to boost adversarial robustness. We first analyze, from an instance-wise perspective, how adversarial vulnerability evolves during adversarial training. We find that during training an overall reduction of adversarial loss is achieved by sacrificing a considerable proportion of training samples to be more vulnerable to adversarial attack, which results in an uneven distribution of adversarial vulnerability among data. Such "uneven vulnerability", is prevalent across several popular robust training methods and, more importantly, relates to overfitting in adversarial training. Motivated by this observation, we propose a new adversarial training method: Instance-adaptive Smoothness Enhanced Adversarial Training (ISEAT). It jointly smooths both input and weight loss landscapes in an adaptive, instance-specific, way to enhance robustness more for those samples with higher adversarial vulnerability. Extensive experiments demonstrate the superiority of our method over existing defense methods. Noticeably, our method, when combined with the latest data augmentation and semi-supervised learning techniques, achieves state-of-the-art robustness against $\ell_{\infty}$-norm constrained attacks on CIFAR10 of 59.32% for Wide ResNet34-10 without extra data, and 61.55% for Wide ResNet28-10 with extra data. Code is available at https://github.com/TreeLLi/Instance-adaptive-Smoothness-Enhanced-AT.
SEAL: Semantic Frame Execution And Localization for Perceiving Afforded Robot Actions
Authors: Cameron Kisailus, Daksh Narang, Matthew Shannon, Odest Chadwicke Jenkins
Date: 2023-03-24 00:00:00
Tasks: Robot Manipulation

Recent advances in robotic mobile manipulation have spurred the expansion of the operating environment for robots from constrained workspaces to large-scale, human environments. In order to effectively complete tasks in these spaces, robots must be able to perceive, reason, and execute over a diversity of affordances, well beyond simple pick-and-place. We posit the notion of semantic frames provides a compelling representation for robot actions that is amenable to action-focused perception, task-level reasoning, action-level execution, and integration with language. Semantic frames, a product of the linguistics community, define the necessary elements, pre- and post- conditions, and a set of sequential robot actions necessary to successfully execute an action evoked by a verb phrase. In this work, we extend the semantic frame representation for robot manipulation actions and introduce the problem of Semantic Frame Execution And Localization for Perceiving Afforded Robot Actions (SEAL) as a graphical model. For the SEAL problem, we describe our nonparametric Semantic Frame Mapping (SeFM) algorithm for maintaining belief over a finite set of semantic frames as the locations of actions afforded to the robot. We show that language models such as GPT-3 are insufficient to address generalized task execution covered by the SEAL formulation and SeFM provides robots with efficient search strategies and long term memory needed when operating in building-scale environments.
Learning Reward Machines in Cooperative Multi-Agent Tasks
Authors: Leo Ardon, Daniel Furelos-Blanco, Alessandra Russo
Date: 2023-03-24 00:00:00
Tasks: Multi-agent Reinforcement Learning

This paper presents a novel approach to Multi-Agent Reinforcement Learning (MARL) that combines cooperative task decomposition with the learning of reward machines (RMs) encoding the structure of the sub-tasks. The proposed method helps deal with the non-Markovian nature of the rewards in partially observable environments and improves the interpretability of the learnt policies required to complete the cooperative task. The RMs associated with each sub-task are learnt in a decentralised manner and then used to guide the behaviour of each agent. By doing so, the complexity of a cooperative multi-agent problem is reduced, allowing for more effective learning. The results suggest that our approach is a promising direction for future research in MARL, especially in complex environments with large state spaces and multiple agents.
Communicating Complex Decisions in Robot-Assisted Therapy
Authors: Carl Bettosi, Kefan Chen, Ryan Shah, Lynne Baillie
Date: 2023-03-24 00:00:00
Tasks: Decision Making

Socially Assistive Robots (SARs) have shown promising potential in therapeutic scenarios as decision-making instructors or motivational companions. In human-human therapy, experts often communicate the thought process behind the decisions they make to promote transparency and build trust. As research aims to incorporate more complex decision-making models into these robots to drive better interaction, the ability for the SAR to explain its decisions becomes an increasing challenge. We present the latest examples of complex SAR decision-makers. We argue that, based on the importance of transparent communication in human-human therapy, SARs should incorporate such components into their design. To stimulate discussion around this topic, we present a set of design considerations for researchers.
SPEC: Summary Preference Decomposition for Low-Resource Abstractive Summarization
Authors: Yi-Syuan Chen, Yun-Zhu Song, Hong-Han Shuai
Date: 2023-03-24 00:00:00
Tasks: Abstractive Text Summarization, Few-Shot Learning, Meta-Learning, Transfer Learning

Neural abstractive summarization has been widely studied and achieved great success with large-scale corpora. However, the considerable cost of annotating data motivates the need for learning strategies under low-resource settings. In this paper, we investigate the problems of learning summarizers with only few examples and propose corresponding methods for improvements. First, typical transfer learning methods are prone to be affected by data properties and learning objectives in the pretext tasks. Therefore, based on pretrained language models, we further present a meta learning framework to transfer few-shot learning processes from source corpora to the target corpus. Second, previous methods learn from training examples without decomposing the content and preference. The generated summaries could therefore be constrained by the preference bias in the training set, especially under low-resource settings. As such, we propose decomposing the contents and preferences during learning through the parameter modulation, which enables control over preferences during inference. Third, given a target application, specifying required preferences could be non-trivial because the preferences may be difficult to derive through observations. Therefore, we propose a novel decoding method to automatically estimate suitable preferences and generate corresponding summary candidates from the few training examples. Extensive experiments demonstrate that our methods achieve state-of-the-art performance on six diverse corpora with 30.11%/33.95%/27.51% and 26.74%/31.14%/24.48% average improvements on ROUGE-1/2/L under 10- and 100-example settings.
'Team-in-the-loop': Ostrom's IAD framework 'rules in use' to map and measure contextual impacts of AI
Authors: Deborah Morgan, Youmna Hashem, John Francis, Saba Esnaashari, Vincent J. Straub, Jonathan Bright
Date: 2023-03-24 00:00:00
Tasks: Decision Making, Vocal Bursts Intensity Prediction

This article explores how the 'rules in use' from Ostrom's Institutional Analysis and Development Framework (IAD) can be developed as a context analysis approach for AI. AI risk assessment frameworks increasingly highlight the need to understand existing contexts. However, these approaches do not frequently connect with established institutional analysis scholarship. We outline a novel direction illustrated through a high-level example to understand how clinical oversight is potentially impacted by AI. Much current thinking regarding oversight for AI revolves around the idea of decision makers being in-the-loop and, thus, having capacity to intervene to prevent harm. However, our analysis finds that oversight is complex, frequently made by teams of professionals and relies upon explanation to elicit information. Professional bodies and liability also function as institutions of polycentric oversight. These are all impacted by the challenge of oversight of AI systems. The approach outlined has potential utility as a policy tool of context analysis aligned with the 'Govern and Map' functions of the National Institute of Standards and Technology (NIST) AI Risk Management Framework; however, further empirical research is needed. Our analysis illustrates the benefit of existing institutional analysis approaches in foregrounding team structures within oversight and, thus, in conceptions of 'human in the loop'.
PowerPruning: Selecting Weights and Activations for Power-Efficient Neural Network Acceleration
Authors: Richard Petri, Grace Li Zhang, Yiran Chen, Ulf Schlichtmann, Bing Li
Date: 2023-03-24 00:00:00
Tasks: Efficient Neural Network

Deep neural networks (DNNs) have been successfully applied in various fields. A major challenge of deploying DNNs, especially on edge devices, is power consumption, due to the large number of multiply-and-accumulate (MAC) operations. To address this challenge, we propose PowerPruning, a novel method to reduce power consumption in digital neural network accelerators by selecting weights that lead to less power consumption in MAC operations. In addition, the timing characteristics of the selected weights together with all activation transitions are evaluated. The weights and activations that lead to small delays are further selected. Consequently, the maximum delay of the sensitized circuit paths in the MAC units is reduced even without modifying MAC units, which thus allows a flexible scaling of supply voltage to reduce power consumption further. Together with retraining, the proposed method can reduce power consumption of DNNs on hardware by up to 78.3% with only a slight accuracy loss.
Physical Backdoor Trigger Activation of Autonomous Vehicle using Reachability Analysis
Authors: Wenqing Li, Yue Wang, Muhammad Shafique, Saif Eddin Jabari
Date: 2023-03-24 00:00:00
Tasks: Autonomous Vehicles

Recent studies reveal that Autonomous Vehicles (AVs) can be manipulated by hidden backdoors, causing them to perform harmful actions when activated by physical triggers. However, it is still unclear how these triggers can be activated while adhering to traffic principles. Understanding this vulnerability in a dynamic traffic environment is crucial. This work addresses this gap by presenting physical trigger activation as a reachability problem of controlled dynamic system. Our technique identifies security-critical areas in traffic systems where trigger conditions for accidents can be reached, and provides intended trajectories for how those conditions can be reached. Testing on typical traffic scenarios showed the system can be successfully driven to trigger conditions with near 100% activation rate. Our method benefits from identifying AV vulnerability and enabling effective safety strategies.
Paraphrase Detection: Human vs. Machine Content
Authors: Jonas Becker, Jan Philip Wahle, Terry Ruas, Bela Gipp
Date: 2023-03-24 00:00:00
Tasks: Diversity

The growing prominence of large language models, such as GPT-4 and ChatGPT, has led to increased concerns over academic integrity due to the potential for machine-generated content and paraphrasing. Although studies have explored the detection of human- and machine-paraphrased content, the comparison between these types of content remains underexplored. In this paper, we conduct a comprehensive analysis of various datasets commonly employed for paraphrase detection tasks and evaluate an array of detection methods. Our findings highlight the strengths and limitations of different detection methods in terms of performance on individual datasets, revealing a lack of suitable machine-generated datasets that can be aligned with human expectations. Our main finding is that human-authored paraphrases exceed machine-generated ones in terms of difficulty, diversity, and similarity implying that automatically generated texts are not yet on par with human-level performance. Transformers emerged as the most effective method across datasets with TF-IDF excelling on semantically diverse corpora. Additionally, we identify four datasets as the most diverse and challenging for paraphrase detection.
Machine Psychology
Authors: Thilo Hagendorff, Ishita Dasgupta, Marcel Binz, Stephanie C. Y. Chan, Andrew Lampinen, Jane X. Wang, Zeynep Akata, Eric Schulz
Date: 2023-03-24 00:00:00
Tasks: Information Retrieval, Retrieval

Large language models (LLMs) show increasingly advanced emergent capabilities and are being incorporated across various societal domains. Understanding their behavior and reasoning abilities therefore holds significant importance. We argue that a fruitful direction for research is engaging LLMs in behavioral experiments inspired by psychology that have traditionally been aimed at understanding human cognition and behavior. In this article, we highlight and summarize theoretical perspectives, experimental paradigms, and computational analysis techniques that this approach brings to the table. It paves the way for a "machine psychology" for generative artificial intelligence (AI) that goes beyond performance benchmarks and focuses instead on computational insights that move us toward a better understanding and discovery of emergent abilities and behavioral patterns in LLMs. We review existing work taking this approach, synthesize best practices, and highlight promising future directions. We also highlight the important caveats of applying methodologies designed for understanding humans to machines. We posit that leveraging tools from experimental psychology to study AI will become increasingly valuable as models evolve to be more powerful, opaque, multi-modal, and integrated into complex real-world settings.
Interpretable Motion Planner for Urban Driving via Hierarchical Imitation Learning
Authors: Bikun Wang, Zhipeng Wang, Chenhao Zhu, Zhiqiang Zhang, Zhichen Wang, Penghong Lin, Jingchu Liu, Qian Zhang
Date: 2023-03-24 00:00:00
Tasks: Autonomous Driving, Decision Making, Imitation Learning, Motion Planning

Learning-based approaches have achieved remarkable performance in the domain of autonomous driving. Leveraging the impressive ability of neural networks and large amounts of human driving data, complex patterns and rules of driving behavior can be encoded as a model to benefit the autonomous driving system. Besides, an increasing number of data-driven works have been studied in the decision-making and motion planning module. However, the reliability and the stability of the neural network is still full of uncertainty. In this paper, we introduce a hierarchical planning architecture including a high-level grid-based behavior planner and a low-level trajectory planner, which is highly interpretable and controllable. As the high-level planner is responsible for finding a consistent route, the low-level planner generates a feasible trajectory. We evaluate our method both in closed-loop simulation and real world driving, and demonstrate the neural network planner has outstanding performance in complex urban autonomous driving scenarios.
AssetField: Assets Mining and Reconfiguration in Ground Feature Plane Representation
Authors: Yuanbo Xiangli, Linning Xu, Xingang Pan, Nanxuan Zhao, Bo Dai, Dahua Lin
Date: 2023-03-24 00:00:00
Proceeding: ICCV 2023 1
Tasks: Novel View Synthesis, Object

Both indoor and outdoor environments are inherently structured and repetitive. Traditional modeling pipelines keep an asset library storing unique object templates, which is both versatile and memory efficient in practice. Inspired by this observation, we propose AssetField, a novel neural scene representation that learns a set of object-aware ground feature planes to represent the scene, where an asset library storing template feature patches can be constructed in an unsupervised manner. Unlike existing methods which require object masks to query spatial points for object editing, our ground feature plane representation offers a natural visualization of the scene in the bird-eye view, allowing a variety of operations (e.g. translation, duplication, deformation) on objects to configure a new scene. With the template feature patches, group editing is enabled for scenes with many recurring items to avoid repetitive work on object individuals. We show that AssetField not only achieves competitive performance for novel-view synthesis but also generates realistic renderings for new scene configurations.