Detecting and recognizing characters in Greek papyri with YOLOv8, DeiT and SimCLR Robert Turnbull, Evelyn Mannix First place in character recognition challenge Second place in character detection challenge Best recall and precision results for detection and recognition results for IoU 0.5 Releasing prediction results in multiple formats for 4500+ Oxyrhynchus Papyri images Abstract The capacity to isolate and recognize individual characters from facsimile images of papyrus manuscripts yields rich opportunities for digital analysis. For this reason the'ICDAR 2023 Competition on Detection and Recognition of Greek Letters on Papyri' was held as part of the 17 We used an ensemble of YOLOv8 models to detect and classify individual characters and employed two different approaches for refining the character predictions, including a transformer based DeiT approach and a ResNet-50 model trained on a large corpus of unlabelled data using SimCLR, a self-supervised learning method. Our submission won the recognition challenge with a mAP of 42.2%, and was runner-up in the detection challenge with a mean average precision (mAP) of 51.4%. At the more relaxed intersection over union threshold of 0.5, we achieved the highest mean average precision and mean average recall results for both detection and classification. We ran our prediction pipeline on more than 4,500 images from the Oxyrhynchus Papyri to illustrate the utility of our approach, and we release the results publicly in multiple formats.

Text watermarking algorithms play a crucial role in the copyright protection of textual content, yet their capabilities and application scenarios have been limited historically. The recent developments in large language models (LLMs) have opened new opportunities for the advancement of text watermarking techniques. LLMs not only enhance the capabilities of text watermarking algorithms through their text understanding and generation abilities but also necessitate the use of text watermarking algorithms for their own copyright protection. This paper conducts a comprehensive survey of the current state of text watermarking technology, covering four main aspects: (1) an overview and comparison of different text watermarking techniques; (2) evaluation methods for text watermarking algorithms, including their success rates, impact on text quality, robustness, and unforgeability; (3) potential application scenarios for text watermarking technology; (4) current challenges and future directions for development. This survey aims to provide researchers with a thorough understanding of text watermarking technology, thereby promoting its further advancement.

Electrophysiological nature of neuronal networks allows to reveal various interactions between different cell units at a very short time-scales. One of the many challenges in analyzing these signals is to retrieve the morphology and functionality of a given network. In this work we developed a computational model, based on Reservoir Computing Network (RCN) architecture, which decodes the spatio-temporal data from electro-physiological measurements of neuronal cultures and reconstructs the network structure on a macroscopic domain, representing the connectivity between neuronal units. We demonstrate that the model can predict the connectivity map of the network with higher accuracy than the common methods such as Cross-Correlation and Transfer-Entropy. In addition, we experimentally demonstrate the ability of the model to predict a network response to a specific input, such as localized stimulus.

Detecting text generated by modern large language models is thought to be hard, as both LLMs and humans can exhibit a wide range of complex behaviors. However, we find that a score based on contrasting two closely related language models is highly accurate at separating human-generated and machine-generated text. Based on this mechanism, we propose a novel LLM detector that only requires simple calculations using a pair of pre-trained LLMs. The method, called Binoculars, achieves state-of-the-art accuracy without any training data. It is capable of spotting machine text from a range of modern LLMs without any model-specific modifications. We comprehensively evaluate Binoculars on a number of text sources and in varied situations. Over a wide range of document types, Binoculars detects over 90% of generated samples from ChatGPT (and other LLMs) at a false positive rate of 0.01%, despite not being trained on any ChatGPT data.

New York City (NYC) topped the global chart for the worst air pollution in June 2023, owing to the wildfire smoke drifting in from Canada. This unprecedented situation caused significant travel disruptions and shifts in traditional activity patterns of NYC residents. This study utilized large-scale social media data to study different crisis activity concerns (i.e., evacuation, staying indoors, shopping, and recreational activities among others) in the emergence of the 2023 Canadian wildfire smoke in NYC. In this regard, one week (June 02 through June 09, 2023) geotagged Twitter data from NYC were retrieved and used in the analysis. The tweets were processed using advanced text classification techniques and later integrated with national databases such as Social Security Administration data, Census, and American Community Survey. Finally, a model has been developed to make community inferences of different activity concerns in a major wildfire. The findings suggest, during wildfires, females are less likely to engage in discussions about evacuation, trips for medical, social, or recreational purposes, and commuting for work, likely influenced by workplaces maintaining operations despite poor air quality. There were also racial disparities in these discussions, with Asians being more likely than Hispanics to discuss evacuation and work commute, and African Americans being less likely to discuss social and recreational activities. Additionally, individuals from low-income neighborhoods and non-higher education students expressed fewer concerns about evacuation. This study provides valuable insights for policymakers, emergency planners, and public health officials, aiding them in formulating targeted communication strategies and equitable emergency response plans.

This study proposes a novel method to understand the factors affecting individuals' perception of transport accessibility, socioeconomic disparity, and public infrastructure. As opposed to the time consuming and expensive survey-based approach, this method can generate organic large-scale responses from social media and develop statistical models to understand individuals' perceptions of various transportation issues. This study retrieved and analyzed 36,098 tweets from New York City from March 19, 2020, to May 15, 2022. A state-of-the-art natural language processing algorithm is used for text mining and classification. A data fusion technique has been adopted to generate a series of socioeconomic traits that are used as explanatory variables in the model. The model results show that females and individuals of Asian origin tend to discuss transportation accessibility more than their counterparts, with those experiencing high neighborhood traffic also being more vocal. However, disadvantaged individuals, including the unemployed and those living in low-income neighborhoods or in areas with high natural hazard risks, tend to communicate less about such issues. As for socioeconomic disparity, individuals of Asian origin and those experiencing various types of air pollution are more likely to discuss these topics on Twitter, often with a negative sentiment. However, unemployed, or disadvantaged individuals, as well as those living in areas with high natural hazard risks or expected losses, are less inclined to tweet about this subject. Lack of internet accessibility could be a reason why many disadvantaged individuals do not tweet about transport accessibility and subsidized internet could be a possible solution.

In the era of the fourth industrial revolution, it is essential to automate fault detection and diagnosis of machineries so that a warning system can be developed that will help to take an appropriate action before any catastrophic damage. Some machines health monitoring systems are used globally but they are expensive and need trained personnel to operate and analyse. Predictive maintenance and occupational health and safety culture are not available due to inadequate infrastructure, lack of skilled manpower, financial crisis, and others in developing countries. Starting from developing a cost-effective DAS for collecting fault data in this study, the effect of limited data and resources has been investigated while automating the process. To solve this problem, A feature engineering and data reduction method has been developed combining the concepts from wavelets, differential calculus, and signal processing. Finally, for automating the whole process, all the necessary theoretical and practical considerations to develop a predictive model have been proposed. The DAS successfully collected the required data from the machine that is 89% accurate compared to the professional manual monitoring system. SVM and NN were proposed for the prediction purpose because of their high predicting accuracy greater than 95% during training and 100% during testing the new samples. In this study, the combination of the simple algorithm with a rule-based system instead of a data-intensive system turned out to be hybridization by validating with collected data. The outcome of this research can be instantly applied to small and medium-sized industries for finding other issues and developing accordingly. As one of the foundational studies in automatic FDD, the findings and procedure of this study can lead others to extend, generalize, or add other dimensions to FDD automation.

Our paper proposes building a model through federated learning to automatically diagnose COVID from patient chest X-Rays The recent pandemic has delivered a desire for understanding efficiently and accurately. This model would aid as a useful supplement COVID-19 diagnoses among patients in hospitals. Currently, hospitals to doctors who are trying to quickly determine whether have difficulty diagnosing this novel respiratory illness from a patient needs to continue with the RT-PCR test from their chest a patient's chest X-Ray to another, simply because it is difficult X-Ray results. Initially, one might question why the model needs to compare COVID chest X-Ray between patients due to HIPAA to be trained in a federated setting - wouldn't it be enough to train compliance. In this paper, we aim to build a model to assist in the separate models per hospital? However, our concern is that the data diagnosis of COVID-19 while being HIPAA compliant through federated at a single hospital may be severely biased and would not be an learning, a distributed machine learning technique used to accurate representation of all patient chest X-Rays across hospitals train an algorithm across multiple decentralized devices with local [33]. Thus, we utilize federated learning techniques to ensure that data samples without sharing them [8]. Our model extends on existing the model will be able to generalize well to samples from the other work in the chest X-Ray diagnostic model space; we analyze hospitals. Additionally, since it is inherently possible for an attacker the best performing models in the CheXpert [15] competition and to learn personally identifiable information from a model, as can be build our own models that work effectively for our hospital data.

Roadside perception systems are increasingly crucial in enhancing traffic safety and facilitating cooperative driving for autonomous vehicles. Despite rapid technological advancements, a major challenge persists for this newly arising field: the absence of standardized evaluation methods and benchmarks for these systems. This limitation hampers the ability to effectively assess and compare the performance of different systems, thus constraining progress in this vital field. This paper introduces a comprehensive evaluation methodology specifically designed to assess the performance of roadside perception systems. Our methodology encompasses measurement techniques, metric selection, and experimental trial design, all grounded in real-world field testing to ensure the practical applicability of our approach. We applied our methodology in Mcity\footnote{\url{https://mcity.umich.edu/}}, a controlled testing environment, to evaluate various off-the-shelf perception systems. This approach allowed for an in-depth comparative analysis of their performance in realistic scenarios, offering key insights into their respective strengths and limitations. The findings of this study are poised to inform the development of industry-standard benchmarks and evaluation methods, thereby enhancing the effectiveness of roadside perception system development and deployment for autonomous vehicles. We anticipate that this paper will stimulate essential discourse on standardizing evaluation methods for roadside perception systems, thus pushing the frontiers of this technology. Furthermore, our results offer both academia and industry a comprehensive understanding of the capabilities of contemporary infrastructure-based perception systems.

Despite the revolutionary impact of AI and the development of locally trained algorithms, achieving widespread generalized learning from multi-modal data in medical AI remains a significant challenge. This gap hinders the practical deployment of scalable medical AI solutions. Addressing this challenge, our research contributes a self-supervised robust machine learning framework, OCT-SelfNet, for detecting eye diseases using optical coherence tomography (OCT) images. In this work, various data sets from various institutions are combined enabling a more comprehensive range of representation. Our method addresses the issue using a two-phase training approach that combines self-supervised pretraining and supervised fine-tuning with a mask autoencoder based on the SwinV2 backbone by providing a solution for real-world clinical deployment. Extensive experiments on three datasets with different encoder backbones, low data settings, unseen data settings, and the effect of augmentation show that our method outperforms the baseline model, Resnet-50 by consistently attaining AUC-ROC performance surpassing 77% across all tests, whereas the baseline model exceeds 54%. Moreover, in terms of the AUC-PR metric, our proposed method exceeded 42%, showcasing a substantial increase of at least 10% in performance compared to the baseline, which exceeded only 33%. This contributes to our understanding of our approach's potential and emphasizes its usefulness in clinical settings.