pwc-archive/papers-with-abstracts huggingface dataset:
SimplyMime: A Control at Our Fingertips
Authors: Sibi Chakkaravarthy Sethuraman, Gaurav Reddy Tadkapally, Athresh Kiran, Saraju P. Mohanty, Anitha Subramanian
Date: 2023-04-22 00:00:00
Tasks: Gesture Recognition, Hand Gesture Recognition, Hand-Gesture Recognition

The utilization of consumer electronics, such as televisions, set-top boxes, home theaters, and air conditioners, has become increasingly prevalent in modern society as technology continues to evolve. As new devices enter our homes each year, the accumulation of multiple infrared remote controls to operate them not only results in a waste of energy and resources, but also creates a cumbersome and cluttered environment for the user. This paper presents a novel system, named SimplyMime, which aims to eliminate the need for multiple remote controls for consumer electronics and provide the user with intuitive control without the need for additional devices. SimplyMime leverages a dynamic hand gesture recognition architecture, incorporating Artificial Intelligence and Human-Computer Interaction, to create a sophisticated system that enables users to interact with a vast majority of consumer electronics with ease. Additionally, SimplyMime has a security aspect where it can verify and authenticate the user utilising the palmprint, which ensures that only authorized users can control the devices. The performance of the proposed method for detecting and recognizing gestures in a stream of motion was thoroughly tested and validated using multiple benchmark datasets, resulting in commendable accuracy levels. One of the distinct advantages of the proposed method is its minimal computational power requirements, making it highly adaptable and reliable in a wide range of circumstances. The paper proposes incorporating this technology into all consumer electronic devices that currently require a secondary remote for operation, thus promoting a more efficient and sustainable living environment.
Stimulating student engagement with an AI board game tournament
Authors: Ken Hasselmann, Quentin Lurkin
Date: 2023-04-22 00:00:00
Tasks: AI Agent, Board Games

Strong foundations in basic AI techniques are key to understanding more advanced concepts. We believe that introducing AI techniques, such as search methods, early in higher education helps create a deeper understanding of the concepts seen later in more advanced AI and algorithms courses. We present a project-based and competition-based bachelor course that gives second-year students an introduction to search methods applied to board games. In groups of two, students have to use network programming and AI methods to build an AI agent to compete in a board game tournament-othello was this year's game. Students are evaluated based on the quality of their projects and on their performance during the final tournament. We believe that the introduction of gamification, in the form of competition-based learning, allows for a better learning experience for the students.
Detecting Political Opinions in Tweets through Bipartite Graph Analysis: A Skip Aggregation Graph Convolution Approach
Authors: Xingyu Peng, Zhenkun Zhou, Chong Zhang, Ke Xu
Date: 2023-04-22 00:00:00
Tasks: Decision Making, Graph Neural Network, Node Classification

Public opinion is a crucial factor in shaping political decision-making. Nowadays, social media has become an essential platform for individuals to engage in political discussions and express their political views, presenting researchers with an invaluable resource for analyzing public opinion. In this paper, we focus on the 2020 US presidential election and create a large-scale dataset from Twitter. To detect political opinions in tweets, we build a user-tweet bipartite graph based on users' posting and retweeting behaviors and convert the task into a Graph Neural Network (GNN)-based node classification problem. Then, we introduce a novel skip aggregation mechanism that makes tweet nodes aggregate information from second-order neighbors, which are also tweet nodes due to the graph's bipartite nature, effectively leveraging user behavioral information. The experimental results show that our proposed model significantly outperforms several competitive baselines. Further analyses demonstrate the significance of user behavioral information and the effectiveness of skip aggregation.
Detecting Adversarial Faces Using Only Real Face Self-Perturbations
Authors: Qian Wang, Yongqin Xian, Hefei Ling, Jinyuan Zhang, Xiaorui Lin, Ping Li, Jiazhong Chen, Ning Yu
Date: 2023-04-22 00:00:00
Tasks: Face Detection

Adversarial attacks aim to disturb the functionality of a target system by adding specific noise to the input samples, bringing potential threats to security and robustness when applied to facial recognition systems. Although existing defense techniques achieve high accuracy in detecting some specific adversarial faces (adv-faces), new attack methods especially GAN-based attacks with completely different noise patterns circumvent them and reach a higher attack success rate. Even worse, existing techniques require attack data before implementing the defense, making it impractical to defend newly emerging attacks that are unseen to defenders. In this paper, we investigate the intrinsic generality of adv-faces and propose to generate pseudo adv-faces by perturbing real faces with three heuristically designed noise patterns. We are the first to train an adv-face detector using only real faces and their self-perturbations, agnostic to victim facial recognition systems, and agnostic to unseen attacks. By regarding adv-faces as out-of-distribution data, we then naturally introduce a novel cascaded system for adv-face detection, which consists of training data self-perturbations, decision boundary regularization, and a max-pooling-based binary classifier focusing on abnormal local color aberrations. Experiments conducted on LFW and CelebA-HQ datasets with eight gradient-based and two GAN-based attacks validate that our method generalizes to a variety of unseen adversarial attacks.
Learning Symbolic Representations Through Joint GEnerative and DIscriminative Training
Authors: Emanuele Sansone, Robin Manhaeve
Date: 2023-04-22 00:00:00
Tasks: Clustering, Self-Supervised Learning

We introduce GEDI, a Bayesian framework that combines existing self-supervised learning objectives with likelihood-based generative models. This framework leverages the benefits of both GEnerative and DIscriminative approaches, resulting in improved symbolic representations over standalone solutions. Additionally, GEDI can be easily integrated and trained jointly with existing neuro-symbolic frameworks without the need for additional supervision or costly pre-training steps. We demonstrate through experiments on real-world data, including SVHN, CIFAR10, and CIFAR100, that GEDI outperforms existing self-supervised learning strategies in terms of clustering performance by a significant margin. The symbolic component further allows it to leverage knowledge in the form of logical constraints to improve performance in the small data regime.
Input Augmentation with SAM: Boosting Medical Image Segmentation with Segmentation Foundation Model
Authors: Yizhe Zhang, Tao Zhou, Shuo Wang, Peixian Liang, Danny Z. Chen
Date: 2023-04-22 00:00:00
Tasks: Image Segmentation, Medical Image Segmentation, Segmentation, Semantic Segmentation

The Segment Anything Model (SAM) is a recently developed large model for general-purpose segmentation for computer vision tasks. SAM was trained using 11 million images with over 1 billion masks and can produce segmentation results for a wide range of objects in natural scene images. SAM can be viewed as a general perception model for segmentation (partitioning images into semantically meaningful regions). Thus, how to utilize such a large foundation model for medical image segmentation is an emerging research target. This paper shows that although SAM does not immediately give high-quality segmentation for medical image data, its generated masks, features, and stability scores are useful for building and training better medical image segmentation models. In particular, we demonstrate how to use SAM to augment image input for commonly-used medical image segmentation models (e.g., U-Net). Experiments on three segmentation tasks show the effectiveness of our proposed SAMAug method. The code is available at \url{https://github.com/yizhezhang2000/SAMAug}.
Dialectical language model evaluation: An initial appraisal of the commonsense spatial reasoning abilities of LLMs
Authors: Anthony G Cohn, Jose Hernandez-Orallo
Date: 2023-04-22 00:00:00
Tasks: Language Model Evaluation, Language Modeling, Language Modelling, Spatial Reasoning

Language models have become very popular recently and many claims have been made about their abilities, including for commonsense reasoning. Given the increasingly better results of current language models on previous static benchmarks for commonsense reasoning, we explore an alternative dialectical evaluation. The goal of this kind of evaluation is not to obtain an aggregate performance value but to find failures and map the boundaries of the system. Dialoguing with the system gives the opportunity to check for consistency and get more reassurance of these boundaries beyond anecdotal evidence. In this paper we conduct some qualitative investigations of this kind of evaluation for the particular case of spatial reasoning (which is a fundamental aspect of commonsense reasoning). We conclude with some suggestions for future work both to improve the capabilities of language models and to systematise this kind of dialectical evaluation.
A Semi-Supervised Framework for Misinformation Detection
Authors: Yueyang Liu, Zois Boukouvalas, Nathalie Japkowicz
Date: 2023-04-22 00:00:00
Tasks: Misinformation

The spread of misinformation in social media outlets has become a prevalent societal problem and is the cause of many kinds of social unrest. Curtailing its prevalence is of great importance and machine learning has shown significant promise. However, there are two main challenges when applying machine learning to this problem. First, while much too prevalent in one respect, misinformation, actually, represents only a minor proportion of all the postings seen on social media. Second, labeling the massive amount of data necessary to train a useful classifier becomes impractical. Considering these challenges, we propose a simple semi-supervised learning framework in order to deal with extreme class imbalances that has the advantage, over other approaches, of using actual rather than simulated data to inflate the minority class. We tested our framework on two sets of Covid-related Twitter data and obtained significant improvement in F1-measure on extremely imbalanced scenarios, as compared to simple classical and deep-learning data generation methods such as SMOTE, ADASYN, or GAN-based data generation.
Unmatched uncertainty mitigation through neural network supported model predictive control
Authors: Mateus V. Gasparino, Prabhat K. Mishra, Girish Chowdhary
Date: 2023-04-22 00:00:00
Tasks: Model Predictive Control

This paper presents a deep learning based model predictive control (MPC) algorithm for systems with unmatched and bounded state-action dependent uncertainties of unknown structure. We utilize a deep neural network (DNN) as an oracle in the underlying optimization problem of learning based MPC (LBMPC) to estimate unmatched uncertainties. Generally, non-parametric oracles such as DNN are considered difficult to employ with LBMPC due to the technical difficulties associated with estimation of their coefficients in real time. We employ a dual-timescale adaptation mechanism, where the weights of the last layer of the neural network are updated in real time while the inner layers are trained on a slower timescale using the training data collected online and selectively stored in a buffer. Our results are validated through a numerical experiment on the compression system model of jet engine. These results indicate that the proposed approach is implementable in real time and carries the theoretical guarantees of LBMPC.
Lookahead Diffusion Probabilistic Models for Refining Mean Estimation
Authors: Guoqiang Zhang, Niwa Kenta, W. Bastiaan Kleijn
Date: 2023-04-22 00:00:00
Proceeding: CVPR 2023 1

We propose lookahead diffusion probabilistic models (LA-DPMs) to exploit the correlation in the outputs of the deep neural networks (DNNs) over subsequent timesteps in diffusion probabilistic models (DPMs) to refine the mean estimation of the conditional Gaussian distributions in the backward process. A typical DPM first obtains an estimate of the original data sample $\boldsymbol{x}$ by feeding the most recent state $\boldsymbol{z}_i$ and index $i$ into the DNN model and then computes the mean vector of the conditional Gaussian distribution for $\boldsymbol{z}_{i-1}$. We propose to calculate a more accurate estimate for $\boldsymbol{x}$ by performing extrapolation on the two estimates of $\boldsymbol{x}$ that are obtained by feeding $(\boldsymbol{z}_{i+1},i+1)$ and $(\boldsymbol{z}_{i},i)$ into the DNN model. The extrapolation can be easily integrated into the backward process of existing DPMs by introducing an additional connection over two consecutive timesteps, and fine-tuning is not required. Extensive experiments showed that plugging in the additional connection into DDPM, DDIM, DEIS, S-PNDM, and high-order DPM-Solvers leads to a significant performance gain in terms of FID score.
MAWSEO: Adversarial Wiki Search Poisoning for Illicit Online Promotion
Authors: Zilong Lin, Zhengyi Li, Xiaojing Liao, XiaoFeng Wang, Xiaozhong Liu
Date: 2023-04-22 00:00:00
Tasks: Articles

As a prominent instance of vandalism edits, Wiki search poisoning for illicit promotion is a cybercrime in which the adversary aims at editing Wiki articles to promote illicit businesses through Wiki search results of relevant queries. In this paper, we report a study that, for the first time, shows that such stealthy blackhat SEO on Wiki can be automated. Our technique, called MAWSEO, employs adversarial revisions to achieve real-world cybercriminal objectives, including rank boosting, vandalism detection evasion, topic relevancy, semantic consistency, user awareness (but not alarming) of promotional content, etc. Our evaluation and user study demonstrate that MAWSEO is capable of effectively and efficiently generating adversarial vandalism edits, which can bypass state-of-the-art built-in Wiki vandalism detectors, and also get promotional content through to Wiki users without triggering their alarms. In addition, we investigated potential defense, including coherence based detection and adversarial training of vandalism detection, against our attack in the Wiki ecosystem.
Nonverbal Cues in Human-Robot Interaction: A Communication Studies Perspective
Authors: Jacqueline Urakami, Katie Seaborn
Date: 2023-04-22 00:00:00

Communication between people is characterized by a broad range of nonverbal cues. Transferring these cues into the design of robots and other artificial agents that interact with people may foster more natural, inviting, and accessible experiences. In this position paper, we offer a series of definitive nonverbal codes for human-robot interaction (HRI) that address the five human sensory systems (visual, auditory, haptic, olfactory, gustatory) drawn from the field of communication studies. We discuss how these codes can be translated into design patterns for HRI using a curated sample of the communication studies and HRI literatures. As nonverbal codes are an essential mode in human communication, we argue that integrating robotic nonverbal codes in HRI will afford robots a feeling of "aliveness" or "social agency" that would otherwise be missing. We end with suggestions for research directions to stimulate work on nonverbal communication within the field of HRI and improve communication between human and robots.
On the Identification of the Energy related Issues from the App Reviews
Authors: Noshin Nawal
Date: 2023-04-22 00:00:00

The energy inefficiency of the apps can be a major issue for the app users which is discussed on App Stores extensively. Previous research has shown the importance of investigating the energy related app reviews to identify the major causes or categories of energy related user feedback. However, there is no study that efficiently extracts the energy related app reviews automatically. In this paper, we empirically study different techniques for automatic extraction of the energy related user feedback. We compare the accuracy, F1-score and run time of numerous machine-learning models with relevant feature combinations and relatively modern Neural Network-based models. In total, 60 machine learning models are compared to 30 models that we build using six neural network architectures and three word embedding models. We develop a visualization tool for this study through which a developer can traverse through this large-scale result set. The results show that neural networks outperform the other machine learning techniques and can achieve the highest F1-score of 0.935. To replicate the research results, we have open sourced the interactive visualization tool. After identifying the best results and extracting the energy related reviews, we further compare various techniques to help the developers automatically investigate the emerging issues that might be responsible for energy inefficiency of the apps. We experiment the previously used string matching with results obtained from applying two of the state-of-the-art topic modeling algorithms, OBTM and AOLDA. Finally, we run a qualitative study performed in collaboration with developers and students from different institutions to determine their preferences for identifying necessary topics from previously categorized reviews, which shows OBTM produces the most helpful results.
Identifying Appropriate Intellectual Property Protection Mechanisms for Machine Learning Models: A Systematization of Watermarking, Fingerprinting, Model Access, and Attacks
Authors: Isabell Lederer, Rudolf Mayer, Andreas Rauber
Date: 2023-04-22 00:00:00

The commercial use of Machine Learning (ML) is spreading; at the same time, ML models are becoming more complex and more expensive to train, which makes Intellectual Property Protection (IPP) of trained models a pressing issue. Unlike other domains that can build on a solid understanding of the threats, attacks and defenses available to protect their IP, the ML-related research in this regard is still very fragmented. This is also due to a missing unified view as well as a common taxonomy of these aspects. In this paper, we systematize our findings on IPP in ML, while focusing on threats and attacks identified and defenses proposed at the time of writing. We develop a comprehensive threat model for IP in ML, categorizing attacks and defenses within a unified and consolidated taxonomy, thus bridging research from both the ML and security communities.
Trust and Reliance in Consensus-Based Explanations from an Anti-Misinformation Agent
Authors: Takane Ueno, Yeongdae Kim, Hiroki Oura, Katie Seaborn
Date: 2023-04-22 00:00:00
Tasks: Explainable Artificial Intelligence (XAI), Misinformation

The illusion of consensus occurs when people believe there is consensus across multiple sources, but the sources are the same and thus there is no "true" consensus. We explore this phenomenon in the context of an AI-based intelligent agent designed to augment metacognition on social media. Misinformation, especially on platforms like Twitter, is a global problem for which there is currently no good solution. As an explainable AI (XAI) system, the agent provides explanations for its decisions on the misinformed nature of social media content. In this late-breaking study, we explored the roles of trust (attitude) and reliance (behaviour) as key elements of XAI user experience (UX) and whether these influenced the illusion of consensus. Findings show no effect of trust, but an effect of reliance on consensus-based explanations. This work may guide the design of anti-misinformation systems that use XAI, especially the user-centred design of explanations.
PyTorch FSDP: Experiences on Scaling Fully Sharded Data Parallel
Authors: Yanli Zhao, Andrew Gu, Rohan Varma, Liang Luo, Chien-chin Huang, Min Xu, Less Wright, Hamid Shojanazeri, Myle Ott, Sam Shleifer, Alban Desmaison, Can Balioglu, Pritam Damania, Bernard Nguyen, Geeta Chauhan, Yuchen Hao, Ajit Mathews, Shen Li
Date: 2023-04-21 00:00:00

It is widely acknowledged that large models have the potential to deliver superior performance across a broad range of domains. Despite the remarkable progress made in the field of machine learning systems research, which has enabled the development and exploration of large models, such abilities remain confined to a small group of advanced users and industry leaders, resulting in an implicit technical barrier for the wider community to access and leverage these technologies. In this paper, we introduce PyTorch Fully Sharded Data Parallel (FSDP) as an industry-grade solution for large model training. FSDP has been closely co-designed with several key PyTorch core components including Tensor implementation, dispatcher system, and CUDA memory caching allocator, to provide non-intrusive user experiences and high training efficiency. Additionally, FSDP natively incorporates a range of techniques and settings to optimize resource utilization across a variety of hardware configurations. The experimental results demonstrate that FSDP is capable of achieving comparable performance to Distributed Data Parallel while providing support for significantly larger models with near-linear scalability in terms of TFLOPS.
Benchmarking Low-Shot Robustness to Natural Distribution Shifts
Authors: Aaditya Singh, Kartik Sarangmath, Prithvijit Chattopadhyay, Judy Hoffman
Date: 2023-04-21 00:00:00
Proceeding: ICCV 2023 1
Tasks: Benchmarking

Robustness to natural distribution shifts has seen remarkable progress thanks to recent pre-training strategies combined with better fine-tuning methods. However, such fine-tuning assumes access to large amounts of labelled data, and the extent to which the observations hold when the amount of training data is not as high remains unknown. We address this gap by performing the first in-depth study of robustness to various natural distribution shifts in different low-shot regimes: spanning datasets, architectures, pre-trained initializations, and state-of-the-art robustness interventions. Most importantly, we find that there is no single model of choice that is often more robust than others, and existing interventions can fail to improve robustness on some datasets even if they do so in the full-shot regime. We hope that our work will motivate the community to focus on this problem of practical importance.
Who's the Best Detective? LLMs vs. MLs in Detecting Incoherent Fourth Grade Math Answers
Authors: Felipe Urrutia, Roberto Araya
Date: 2023-04-21 00:00:00
Tasks: Math, Multiple-choice

Written answers to open-ended questions can have a higher long-term effect on learning than multiple-choice questions. However, it is critical that teachers immediately review the answers, and ask to redo those that are incoherent. This can be a difficult task and can be time-consuming for teachers. A possible solution is to automate the detection of incoherent answers. One option is to automate the review with Large Language Models (LLM). In this paper, we analyze the responses of fourth graders in mathematics using three LLMs: GPT-3, BLOOM, and YOU. We used them with zero, one, two, three and four shots. We compared their performance with the results of various classifiers trained with Machine Learning (ML). We found that LLMs perform worse than MLs in detecting incoherent answers. The difficulty seems to reside in recursive questions that contain both questions and answers, and in responses from students with typical fourth-grader misspellings. Upon closer examination, we have found that the ChatGPT model faces the same challenges.