pwc-archive/papers-with-abstracts huggingface dataset:
AV-Lip-Sync+: Leveraging AV-HuBERT to Exploit Multimodal Inconsistency for Video Deepfake Detection
Authors: Sahibzada Adil Shahzad, Ammarah Hashmi, Yan-Tsung Peng, Yu Tsao, Hsin-Min Wang
Date: 2023-11-05 00:00:00
Tasks: DeepFake Detection, Face Swapping, Self-Supervised Learning, Video Forensics

Multimodal manipulations (also known as audio-visual deepfakes) make it difficult for unimodal deepfake detectors to detect forgeries in multimedia content. To avoid the spread of false propaganda and fake news, timely detection is crucial. The damage to either modality (i.e., visual or audio) can only be discovered through multi-modal models that can exploit both pieces of information simultaneously. Previous methods mainly adopt uni-modal video forensics and use supervised pre-training for forgery detection. This study proposes a new method based on a multi-modal self-supervised-learning (SSL) feature extractor to exploit inconsistency between audio and visual modalities for multi-modal video forgery detection. We use the transformer-based SSL pre-trained Audio-Visual HuBERT (AV-HuBERT) model as a visual and acoustic feature extractor and a multi-scale temporal convolutional neural network to capture the temporal correlation between the audio and visual modalities. Since AV-HuBERT only extracts visual features from the lip region, we also adopt another transformer-based video model to exploit facial features and capture spatial and temporal artifacts caused during the deepfake generation process. Experimental results show that our model outperforms all existing models and achieves new state-of-the-art performance on the FakeAVCeleb and DeepfakeTIMIT datasets.
Extraction of Atypical Aspects from Customer Reviews: Datasets and Experiments with Language Models
Authors: Smita Nannaware, Erfan Al-Hossami, Razvan Bunescu
Date: 2023-11-05 00:00:00

A restaurant dinner may become a memorable experience due to an unexpected aspect enjoyed by the customer, such as an origami-making station in the waiting area. If aspects that are atypical for a restaurant experience were known in advance, they could be leveraged to make recommendations that have the potential to engender serendipitous experiences, further increasing user satisfaction. Although relatively rare, whenever encountered, atypical aspects often end up being mentioned in reviews due to their memorable quality. Correspondingly, in this paper we introduce the task of detecting atypical aspects in customer reviews. To facilitate the development of extraction models, we manually annotate benchmark datasets of reviews in three domains - restaurants, hotels, and hair salons, which we use to evaluate a number of language models, ranging from fine-tuning the instruction-based text-to-text transformer Flan-T5 to zero-shot and few-shot prompting of GPT-3.5.
Compute at Scale: A Broad Investigation into the Data Center Industry
Authors: Konstantin Pilz, Lennart Heim
Date: 2023-11-05 00:00:00

This report characterizes the data center industry and its importance for AI development. Data centers are industrial facilities that efficiently provide compute at scale and thus constitute the engine rooms of today's digital economy. As large-scale AI training and inference become increasingly computationally expensive, they are dominantly executed from this designated infrastructure. Key features of data centers include large-scale compute clusters that require extensive cooling and consume large amounts of power, the need for fast connectivity both within the data center and to the internet, and an emphasis on security and reliability. The global industry is valued at approximately $250B and is expected to double over the next seven years. There are likely about 500 large (above 10 MW) data centers globally, with the US, Europe, and China constituting the most important markets. The report further covers important actors, business models, main inputs, and typical locations of data centers.
New Approach for an Affective Computing-Driven Quality of Experience (QoE) Prediction
Authors: Joshua Bègue, Mohamed Aymen Labiod, Abdelhamid Melloulk
Date: 2023-11-05 00:00:00
Tasks: EEG, Electroencephalogram (EEG), Emotion Recognition

In human interactions, emotion recognition is crucial. For this reason, the topic of computer-vision approaches for automatic emotion recognition is currently being extensively researched. Processing multi-channel electroencephalogram (EEG) information is one of the most researched methods for automatic emotion recognition. This paper presents a new model for an affective computing-driven Quality of Experience (QoE) prediction. In order to validate the proposed model, a publicly available dataset is used. The dataset contains EEG, ECG, and respiratory data and is focused on a multimedia QoE assessment context. The EEG data are retained on which the differential entropy and the power spectral density are calculated with an observation window of three seconds. These two features were extracted to train several deep-learning models to investigate the possibility of predicting QoE with five different factors. The performance of these models is compared, and the best model is optimized to improve the results. The best results were obtained with an LSTM-based model, presenting an F1-score from 68% to 78%. An analysis of the model and its features shows that the Delta frequency band is the least necessary, that two electrodes have a higher importance, and that two other electrodes have a very low impact on the model's performances.
PotholeGuard: A Pothole Detection Approach by Point Cloud Semantic Segmentation
Authors: Sahil Nawale, Dhruv Khut, Daksh Dave, Gauransh Sawhney, Pushkar Aggrawal, Dr. Kailas Devadakar
Date: 2023-11-05 00:00:00
Tasks: Image Segmentation, Segmentation, Semantic Segmentation

Pothole detection is crucial for road safety and maintenance, traditionally relying on 2D image segmentation. However, existing 3D Semantic Pothole Segmentation research often overlooks point cloud sparsity, leading to suboptimal local feature capture and segmentation accuracy. Our research presents an innovative point cloud-based pothole segmentation architecture. Our model efficiently identifies hidden features and uses a feedback mechanism to enhance local characteristics, improving feature presentation. We introduce a local relationship learning module to understand local shape relationships, enhancing structural insights. Additionally, we propose a lightweight adaptive structure for refining local point features using the K nearest neighbor algorithm, addressing point cloud density differences and domain selection. Shared MLP Pooling is integrated to learn deep aggregation features, facilitating semantic data exploration and segmentation guidance. Extensive experiments on three public datasets confirm PotholeGuard's superior performance over state-of-the-art methods. Our approach offers a promising solution for robust and accurate 3D pothole segmentation, with applications in road maintenance and safety.
Assessing the Promise and Pitfalls of ChatGPT for Automated Code Generation
Authors: Muhammad Fawad Akbar Khan, Max Ramsdell, Erik Falor, Hamid Karimi
Date: 2023-11-05 00:00:00
Tasks: Code Generation, Language Modeling, Language Modelling, Large Language Model

This paper presents a comprehensive evaluation of the code generation capabilities of ChatGPT, a prominent large language model, compared to human programmers. A novel dataset of 131 code-generation prompts across 5 categories was curated to enable robust analysis. Code solutions were generated by both ChatGPT and humans for all prompts, resulting in 262 code samples. A meticulous manual assessment methodology prioritized evaluating correctness, comprehensibility, and security using 14 established code quality metrics. The key findings reveal ChatGPT's strengths in crafting concise, efficient code with advanced constructs, showcasing strengths in data analysis tasks (93.1% accuracy) but limitations in visual-graphical challenges. Comparative analysis with human code highlights ChatGPT's inclination towards modular design and superior error handling. Additionally, machine learning models effectively distinguished ChatGPT from human code with up to 88% accuracy, suggesting detectable coding style disparities. By providing profound insights into ChatGPT's code generation capabilities and limitations through quantitative metrics and qualitative analysis, this study makes valuable contributions toward advancing AI-based programming assistants. The curated dataset and methodology offer a robust foundation for future research in this nascent domain. All data and codes are available on https://github.com/DSAatUSU/ChatGPT-promises-and-pitfalls.
A Critical Perceptual Pre-trained Model for Complex Trajectory Recovery
Authors: Dedong Li, Ziyue Li, Zhishuai Li, Lei Bai, Qingyuan Gong, Lijun Sun, Wolfgang Ketter, Rui Zhao
Date: 2023-11-05 00:00:00
Tasks: Trajectory Recovery

The trajectory on the road traffic is commonly collected at a low sampling rate, and trajectory recovery aims to recover a complete and continuous trajectory from the sparse and discrete inputs. Recently, sequential language models have been innovatively adopted for trajectory recovery in a pre-trained manner: it learns road segment representation vectors, which will be used in the downstream tasks. However, existing methods are incapable of handling complex trajectories: when the trajectory crosses remote road segments or makes several turns, which we call critical nodes, the quality of learned representations deteriorates, and the recovered trajectories skip the critical nodes. This work is dedicated to offering a more robust trajectory recovery for complex trajectories. Firstly, we define the trajectory complexity based on the detour score and entropy score and construct the complexity-aware semantic graphs correspondingly. Then, we propose a Multi-view Graph and Complexity Aware Transformer (MGCAT) model to encode these semantics in trajectory pre-training from two aspects: 1) adaptively aggregate the multi-view graph features considering trajectory pattern, and 2) higher attention to critical nodes in a complex trajectory. Such that, our MGCAT is perceptual when handling the critical scenario of complex trajectories. Extensive experiments are conducted on large-scale datasets. The results prove that our method learns better representations for trajectory recovery, with 5.22% higher F1-score overall and 8.16% higher F1-score for complex trajectories particularly. The code is available at https://github.com/bonaldli/ComplexTraj.
The New Frontier of Cybersecurity: Emerging Threats and Innovations
Authors: Daksh Dave, Gauransh Sawhney, Pushkar Aggarwal, Nitish Silswal, Dhruv Khut
Date: 2023-11-05 00:00:00

In today's digitally interconnected world, cybersecurity threats have reached unprecedented levels, presenting a pressing concern for individuals, organizations, and governments. This study employs a qualitative research approach to comprehensively examine the diverse threats of cybersecurity and their impacts across various sectors. Four primary categories of threats are identified and analyzed, encompassing malware attacks, social engineering attacks, network vulnerabilities, and data breaches. The research delves into the consequences of these threats on individuals, organizations, and society at large. The findings reveal a range of key emerging threats in cybersecurity, including advanced persistent threats, ransomware attacks, Internet of Things (IoT) vulnerabilities, and social engineering exploits. Consequently, it is evident that emerging cybersecurity threats pose substantial risks to both organizations and individuals. The sophistication and diversity of these emerging threats necessitate a multi-layered approach to cybersecurity. This approach should include robust security measures, comprehensive employee training, and regular security audits. The implications of these emerging threats are extensive, with potential consequences such as financial loss, reputational damage, and compromised personal information. This study emphasizes the importance of implementing effective measures to mitigate these threats. It highlights the significance of using strong passwords, encryption methods, and regularly updating software to bolster cyber defenses.
AIOps-Driven Enhancement of Log Anomaly Detection in Unsupervised Scenarios
Authors: Daksh Dave, Gauransh Sawhney, Dhruv Khut, Sahil Nawale, Pushkar Aggrawal, Prasenjit Bhavathankar
Date: 2023-11-05 00:00:00
Tasks: Anomaly Detection

Artificial intelligence operations (AIOps) play a pivotal role in identifying, mitigating, and analyzing anomalous system behaviors and alerts. However, the research landscape in this field remains limited, leaving significant gaps unexplored. This study introduces a novel hybrid framework through an innovative algorithm that incorporates an unsupervised strategy. This strategy integrates Principal Component Analysis (PCA) and Artificial Neural Networks (ANNs) and uses a custom loss function to substantially enhance the effectiveness of log anomaly detection. The proposed approach encompasses the utilization of both simulated and real-world datasets, including logs from SockShop and Hadoop Distributed File System (HDFS). The experimental results are highly promising, demonstrating significant reductions in pseudo-positives. Moreover, this strategy offers notable advantages, such as the ability to process logs in their raw, unprocessed form, and the potential for further enhancements. The successful implementation of this approach showcases a remarkable reduction in anomalous logs, thus unequivocally establishing the efficacy of the proposed methodology. Ultimately, this study makes a substantial contribution to the advancement of log anomaly detection within AIOps platforms, addressing the critical need for effective and efficient log analysis in modern and complex systems.
Get the Ball Rolling: Alerting Autonomous Robots When to Help to Close the Healthcare Loop
Authors: Jiaxin Shen, Yanyao Liu, ZiMing Wang, Ziyuan Jiao, Yufeng Chen, Wenjuan Han
Date: 2023-11-05 00:00:00

To facilitate the advancement of research in healthcare robots without human intervention or commands, we introduce the Autonomous Helping Challenge, along with a crowd-sourcing large-scale dataset. The goal is to create healthcare robots that possess the ability to determine when assistance is necessary, generate useful sub-tasks to aid in planning, carry out these plans through a physical robot, and receive feedback from the environment in order to generate new tasks and continue the process. Besides the general challenge in open-ended scenarios, Autonomous Helping focuses on three specific challenges: autonomous task generation, the gap between the current scene and static commonsense, and the gap between language instruction and the real world. Additionally, we propose Helpy, a potential approach to close the healthcare loop in the learning-free setting.
Automated Camera Calibration via Homography Estimation with GNNs
Authors: Giacomo D'Amicantonio, Egor Bondarev, Peter H. N. de With
Date: 2023-11-05 00:00:00
Tasks: Camera Calibration, Homography Estimation, Retrieval

Over the past few decades, a significant rise of camera-based applications for traffic monitoring has occurred. Governments and local administrations are increasingly relying on the data collected from these cameras to enhance road safety and optimize traffic conditions. However, for effective data utilization, it is imperative to ensure accurate and automated calibration of the involved cameras. This paper proposes a novel approach to address this challenge by leveraging the topological structure of intersections. We propose a framework involving the generation of a set of synthetic intersection viewpoint images from a bird's-eye-view image, framed as a graph of virtual cameras to model these images. Using the capabilities of Graph Neural Networks, we effectively learn the relationships within this graph, thereby facilitating the estimation of a homography matrix. This estimation leverages the neighbourhood representation for any real-world camera and is enhanced by exploiting multiple images instead of a single match. In turn, the homography matrix allows the retrieval of extrinsic calibration parameters. As a result, the proposed framework demonstrates superior performance on both synthetic datasets and real-world cameras, setting a new state-of-the-art benchmark.
FloodBrain: Flood Disaster Reporting by Web-based Retrieval Augmented Generation with an LLM
Authors: Grace Colverd, Paul Darm, Leonard Silverberg, Noah Kasmanoff
Date: 2023-11-05 00:00:00
Tasks: Humanitarian, Question Answering, Retrieval, Retrieval-augmented Generation, Text Summarization

Fast disaster impact reporting is crucial in planning humanitarian assistance. Large Language Models (LLMs) are well known for their ability to write coherent text and fulfill a variety of tasks relevant to impact reporting, such as question answering or text summarization. However, LLMs are constrained by the knowledge within their training data and are prone to generating inaccurate, or "hallucinated", information. To address this, we introduce a sophisticated pipeline embodied in our tool FloodBrain (floodbrain.com), specialized in generating flood disaster impact reports by extracting and curating information from the web. Our pipeline assimilates information from web search results to produce detailed and accurate reports on flood events. We test different LLMs as backbones in our tool and compare their generated reports to human-written reports on different metrics. Similar to other studies, we find a notable correlation between the scores assigned by GPT-4 and the scores given by human evaluators when comparing our generated reports to human-authored ones. Additionally, we conduct an ablation study to test our single pipeline components and their relevancy for the final reports. With our tool, we aim to advance the use of LLMs for disaster impact reporting and reduce the time for coordination of humanitarian efforts in the wake of flood disasters.
scBeacon: single-cell biomarker extraction via identifying paired cell clusters across biological conditions with contrastive siamese networks
Authors: Chenyu Liu, Yong Jin Kweon, Jun Ding
Date: 2023-11-05 00:00:00
Tasks: Diagnostic

Despite the breakthroughs in biomarker discovery facilitated by differential gene analysis, challenges remain, particularly at the single-cell level. Traditional methodologies heavily rely on user-supplied cell annotations, focusing on individually expressed data, often neglecting the critical interactions between biological conditions, such as healthy versus diseased states. In response, here we introduce scBeacon, an innovative framework built upon a deep contrastive siamese network. scBeacon pioneers an unsupervised approach, adeptly identifying matched cell populations across varied conditions, enabling a refined differential gene analysis. By utilizing a VQ-VAE framework, a contrastive siamese network, and a greedy iterative strategy, scBeacon effectively pinpoints differential genes that hold potential as key biomarkers. Comprehensive evaluations on a diverse array of datasets validate scBeacon's superiority over existing single-cell differential gene analysis tools. Its precision and adaptability underscore its significant role in enhancing diagnostic accuracy in biomarker discovery. With the emphasis on the importance of biomarkers in diagnosis, scBeacon is positioned to be a pivotal asset in the evolution of personalized medicine and targeted treatments.
KITS: Inductive Spatio-Temporal Kriging with Increment Training Strategy
Authors: Qianxiong Xu, Cheng Long, Ziyue Li, Sijie Ruan, Rui Zhao, Zhishuai Li
Date: 2023-11-05 00:00:00

Sensors are commonly deployed to perceive the environment. However, due to the high cost, sensors are usually sparsely deployed. Kriging is the tailored task to infer the unobserved nodes (without sensors) using the observed source nodes (with sensors). The essence of kriging task is transferability. Recently, several inductive spatio-temporal kriging methods have been proposed based on graph neural networks, being trained based on a graph built on top of observed nodes via pretext tasks such as masking nodes out and reconstructing them. However, the graph in training is inevitably much sparser than the graph in inference that includes all the observed and unobserved nodes. The learned pattern cannot be well generalized for inference, denoted as graph gap. To address this issue, we first present a novel Increment training strategy: instead of masking nodes (and reconstructing them), we add virtual nodes into the training graph so as to mitigate the graph gap issue naturally. Nevertheless, the empty-shell virtual nodes without labels could have bad-learned features and lack supervision signals. To solve these issues, we pair each virtual node with its most similar observed node and fuse their features together; to enhance the supervision signal, we construct reliable pseudo labels for virtual nodes. As a result, the learned pattern of virtual nodes could be safely transferred to real unobserved nodes for reliable kriging. We name our new Kriging model with Increment Training Strategy as KITS. Extensive experiments demonstrate that KITS consistently outperforms existing kriging methods by large margins, e.g., the improvement over MAE score could be as high as 18.33%.
Time Series Synthesis Using the Matrix Profile for Anonymization
Authors: Audrey Der, Chin-Chia Michael Yeh, Yan Zheng, Junpeng Wang, Huiyuan Chen, Zhongfang Zhuang, Liang Wang, Wei zhang, Eamonn Keogh
Date: 2023-11-05 00:00:00
Tasks: Time Series

Publishing and sharing data is crucial for the data mining community, allowing collaboration and driving open innovation. However, many researchers cannot release their data due to privacy regulations or fear of leaking confidential business information. To alleviate such issues, we propose the Time Series Synthesis Using the Matrix Profile (TSSUMP) method, where synthesized time series can be released in lieu of the original data. The TSSUMP method synthesizes time series by preserving similarity join information (i.e., Matrix Profile) while reducing the correlation between the synthesized and the original time series. As a result, neither the values for the individual time steps nor the local patterns (or shapes) from the original data can be recovered, yet the resulting data can be used for downstream tasks that data analysts are interested in. We concentrate on similarity joins because they are one of the most widely applied time series data mining routines across different data mining tasks. We test our method on a case study of ECG and gender masking prediction. In this case study, the gender information is not only removed from the synthesized time series, but the synthesized time series also preserves enough information from the original time series. As a result, unmodified data mining tools can obtain near-identical performance on the synthesized time series as on the original time series.