Reviewerly/exHarmony huggingface dataset:
Development Tools

This chapter addresses practical tools that help in the development of solutions in Reinforcement Learning (RL). Among them, we highlight some of the main libraries and frameworks available, such as TensorFlow, Keras, and OpenAI Gym, which allow the implementation of RL algorithms more efficiently. Furthermore, some useful data sources for conducting experiments in RL are discussed to improve the performance of such algorithms.
Cryptocurrency Portfolios Using Heuristics

Given the support from academic studies for heuristic (naive) asset allocation strategies, this study compares the performance of seven heuristics, including four new heuristics, in forming a portfolio of six popular cryptocurrencies. As many cryptocurrency traders are retail investors, they are likely to use heuristics, rather than sophisticated optimization procedures. Our empirical analysis shows little difference in the out-of-sample performance of these seven strategies, indicating that it does not matter which heuristic is used by cryptocurrency investors. Therefore, retail investors might as well use the simplest heuristic (1/N) strategy, whose performance has been widely studied and found to be comparable with that of portfolio optimization models.
Practice with Code

In this chapter, a case study on the development of an autonomous cab using Artificial Intelligence (AI) in Python is presented. Details of the environment are discussed, and the action of the agent without the use of AI is exemplified. Afterward, the implementation of a Reinforcement Learning (RL) algorithm is presented in a simplified way, commenting on and explaining the code in detail. The differences and advantages of using RL in these types of problems are illustrated. The developed system is made available for testing and future implementation.
Concepts

This chapter discusses the fundamental concepts needed to understand the entire system involved in Reinforcement Learning. Terms such as agent, environment, actions, rewards, policies, and value function are presented. Examples and analogies are provided to illustrate each of these concepts, from the structuring of problems using the Markov Chain to Watkins and Dayan’s proposal and its application in the Bellman Equation. Finally, the different classes and particularities of algorithms that have achieved success in this field of research are presented.
Evaluation of the Development Value of Emotional Data Mining in Mass Media Using the RBTM Model

With the proliferation of the internet, many social platforms continue to emerge, giving rise to a surge in user-generated content. Consequently, abundant textual information permeates the virtual realm, wielding a certain impact on public opinion orientation. One can discern the prevailing sentiment prevailing in society by scrutinizing the latent emotional undercurrents embedded within vast news texts. This article presents an RBTM model adept at extracting and analyzing emotional information from mass media, thus lending invaluable insights into the evolution of the intelligent news industry. The empirical findings substantiate the RBTM model’s preeminence over its counterparts, evinced by its superior training duration and predictive prowess. Notably, the RBTM model efficaciously deciphers emotional inclinations within news content during practical applications, obviating the need for extensive manual inspection while curtailing analysis time by 49% across departmental endeavors. As an outcome, this paper deliberates upon the tantalizing prospects of intelligent news analysis methods contingent upon emotional information extraction, thereby paving the way for a formidable future in media comprehension.
Simulation-Aided SAR Target Classification via Dual-Branch Reconstruction and Subdomain Alignment

Convolutional neural networks (CNNs) are widely used in image classification, but such methods often require massive labeled data as learning resources. On the one hand, synthetic aperture radar (SAR) image interpretation is difficult, resulting in the lack of large-scale data sets with high-quality labels. On the other hand, CNNs are not explainable enough to provide reliable and trusted application services for SAR target recognition. To solve the above problems, physics-based electromagnetic simulated images are used to alleviate the shortage of real data with annotations, and explainability analysis methods are introduced to explain the basis of network decision-making. To address the domain gap between simulated and measured data, we propose a novel network integrating dual-branch image reconstruction and subdomain alignment (DBRSA). The network completes the reconstruction of simulated and measured images through the domain-shared encoder and domain-specific decoders, thereby helping the encoder to learn feature extraction methods independent of labels. In addition, the network aligns the feature vectors of similar targets obtained from different domains according to the real or pseudo labels of the samples, so as to further improve the classification accuracy. The experimental results and model decision analysis results demonstrate that the proposed network can improve the performance reliably by reducing the attention to the background noise and increasing the attention to the shadows and contours of the target, effectively reducing the dependence on the number of sample labels in practical application scenarios.
Deep Learning Model Portability for Domain-Agnostic Device Fingerprinting

RF (Radio Frequency) device fingerprinting approaches using deep learning have recently emerged as potential methods of identifying devices solely based on their RF transmissions. However, these recently proposed approaches suffer from the domain portability problem, in that when the deep learning models used for fingerprinting are trained on data collected on one (source) domain but tested on data collected on a different (target) domain, the models will not perform well. For example of a change in the receiver used for data collection, in the day on which data was captured, or in the configuration settings of transmitters amounts to a change in the domain. This work proposes a technique that uses metric learning and model calibration to enable a model trained with data from one source domain to perform well on data collected on another target domain. This is accomplished with only a small amount of training data from the target domain and without changing the weights of the model, which makes the technique computationally quick. The effectiveness of the proposed technique is assessed using RF data captured using a testbed of real devices and under various different setup scenarios. Our results show that the proposed technique is viable and useful for networks with limited computational resources and applications with time-critical missions.
UAV Flight Path Planning Based on Multi-Strategy Improved White Sharks Optimization

Due to the development of its own technology, the Unmanned Aerial Vehicle (UAV) play an increasingly important role in today’s social production practice. The complex and changeable environment requires the development of innovative UAV path planning algorithms. In order to meet the requirements of the increasingly complex UAV flight environment, a new UAV flight path planning algorithm based on a version of the White Sharks Optimization (WSO) is proposed in this research. Firstly, the terrain matrix is used to establish the three-dimensional terrain environment and constraint function, and then WSO is improved for handling the path planning. In the process of path planning, multi-trajectory search, nonlinear convergence factor and the model of fish movement behavior are adopted to enrich the population diversity, excavate the search space, speed up the convergence and reduce the likelihood of falling into local optima. Based on the simulation results, it can be observed that the proposed algorithm outperforms in terms of optimization accuracy, convergence speed, and robustness, leading to improved outcomes in UAV flight path planning.
Broadband Digital Over-the-Air Computation for Wireless Federated Edge Learning

This paper presents the first orthogonal frequency-division multiplexing(OFDM)-based digital over-the-air computation (AirComp) system for wireless federated edge learning, where multiple edge devices transmit model data simultaneously using non-orthogonal OFDM subcarriers, and the edge server aggregates data directly from the superimposed signal. Existing analog AirComp systems often assume perfect phase alignment via channel precoding and utilize uncoded analog transmission for model aggregation. In contrast, our digital AirComp system leverages digital modulation and channel codes to overcome phase asynchrony, thereby achieving accurate model aggregation for phase-asynchronous multi-user OFDM systems. To realize a digital AirComp system, we develop a medium access control (MAC) protocol that allows simultaneous transmissions from different users using non-orthogonal OFDM subcarriers, and put forth joint channel decoding and aggregation decoders tailored for convolutional and LDPC codes. To verify the proposed system design, we build a digital AirComp prototype on the USRP software-defined radio platform, and demonstrate a real-time LDPC-coded AirComp system with up to four users. Trace-driven simulation results on test accuracy versus SNR show that: 1) analog AirComp is sensitive to phase asynchrony in practical multi-user OFDM systems, and the test accuracy performance fails to improve even at high SNRs; 2) our digital AirComp system outperforms two analog AirComp systems at all SNRs, and approaches the optimal performance when SNR <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\geq$</tex-math></inline-formula> 6 dB for two-user LDPC-coded AirComp, demonstrating the advantage of digital AirComp in phase-asynchronous multi-user OFDM systems.
Greek Literary Papyri Dating Benchmark

Dating papyri accurately is crucial not only to editing their texts but also for our understanding of palaeography and the history of writing, ancient scholarship, material culture, networks in antiquity, etc. Most ancient manuscripts offer little evidence regarding the time of their production, forcing papyrologists to date them on palaeographical grounds, a method often criticized for its subjectivity. In this work, with data obtained from the Collaborative Database of Dateable Greek Bookhands, an online collection of objectively dated Greek papyri, we created a dataset of literary papyri, which can be used for computational papyri dating. We also experimented on this dataset, by fine-tuning four convolutional neural networks pre-trained on generic images.
Efficient Annotation of Medieval Charters

Diplomatics, the analysis of medieval charters, is a major field of research in which paleography is applied. Annotating data, if performed by laymen, needs validation and correction by experts. In this paper, we propose an effective and efficient annotation approach for charter segmentation, essentially reducing it to object detection. This approach allows for a much more efficient use of the paleographer’s time and produces results that can compete and even outperform pixel-level segmentation in some use cases. Further experiments shed light on how to design a class ontology in order to make the best use of annotators’ time and effort. Exploiting the presence of calibration cards in the image, we further annotate the data with the physical length in pixels and train regression neural networks to predict it from image patches.
Collaborative Annotation and Computational Analysis of Hieratic

We introduce a new open-source web application for the collaborative annotation and study of hieratic texts, in the form of facsimiles and/or photographs. Functionality includes the automatic classification of occurrences of hieratic signs in Unicode, aided by image processing and optical character recognition techniques. Relying on various forms of dimensionality reduction, the tool also allows visualization of differences in sign shapes between texts, periods, genres, and geographical areas. This is motivated by recent work that demonstrated the value of dimensionality reduction for such purposes as identifying scribal hands. The interactive user interface can also be used for teaching students to read hieratic.
Detection of Buried Complex Text. Case of Onomatopoeia in Comics Books

Recent advances in scene text detection, boosted by deep neural networks, have revolutionized our ability to identify text in complex visual environments, including historical documents, newspapers, administrative records, and even text in the wild. However, traditional word-level bounding boxes struggle to capture irregular text shapes. Onomatopoeias in comic books, which epitomize those complex textual elements, are often mixed and buried with graphical components, that make fail conventional detection methods. In this paper, we propose an innovative approach for text segmentation based on the Unet architecture and wisely integrating pre and post-processing. The method is specifically designed to accurately detect intricate text shapes and configurations. This cutting-edge technic offers exceptional accuracy, paving the way for innovative text detection applications in various environments. The evaluations were carried out on the KABOOM-ONOMATOPOEIA dataset and show the relevance of our method in comparison with methods of the literature, which makes it a promising tool in the field of scene text detection.
MSCA-UNet: Multi-Scale Convolutional Attention UNet for Automatic Cell Counting Using Density Regression

The quantification of cell numbers in microscopy images plays a vital role in biomedical research and medical diagnosis. Presently, deep regression networks are widely employed to generate cell density maps, and the number of cells is obtained by integrating the density maps. However, automating cell counting remains challenging due to the variability in cell morphology, the diversity of cell types, and the interference of image backgrounds. This paper aims to address the central question: ’Can we design a robust and efficient deep learning model that can effectively count cells in microscopy images, regardless of these challenges?’ To tackle this issue, we propose a novel multi-scale convolutional attention UNet (MSCA-UNet) based on density regression. Compared with other advanced density regression methods, our method introduces two key innovations. Firstly, we employ an MSCA block with multi-scale interaction ability as an encoder component, which, when combined with spatial attention, enhances the extraction of cell details and spatial information. Secondly, the design of the asymmetric UNet allows the encoder to extract more global information and better understand the image. In the meantime, using smaller convolutional kernels and strides in the decoder helps to restore image details and edge information, resulting in improved network performance. Our method outperformed other advanced methods on three publicly available benchmark cell datasets, including the synthetic bacterial (VGG) dataset, the modified bone marrow (MBM) dataset, and the human subcutaneous adipose tissue (ADI) dataset.
Using GANs for Domain Adaptive High Resolution Synthetic Document Generation

In this paper we are addressing one specific problem of Context-Adaptive Document Analysis: the generation of specific learning data. While many Document Analysis solutions exist for well described cases, it is often difficult to adapt them to other contexts. We present an ongoing research methodology for generating synthetic documents that are not only language and layout-specific but also compatible with user-defined application contexts. In the contemporary digital landscape, corporations face a considerable challenge: the scarcity of high-resolution, accurately labeled data, specifically in the context of French language documents, which often exhibit stylistic nuances distinct from English counterparts. This paper addresses the need for high-resolution synthetic documents, establishing it as the primary impetus behind our research. While the DocSynth model [2] (on which our work is based) represented a preliminary step towards the generation of realistic document images, it was impeded by its incapacity to produce high-resolution outputs. Our research aims to overcome this restriction, enhancing the DocSynth model’s capacity to generate high-resolution document images. Additionally, we strive to equip it with the ability to recognize and generate text, effectively addressing a crucial demand within the industry.
Toward Improved Reliability of Deep Learning Based Systems Through Online Relabeling of Potential Adversarial Attacks

Deep neural networks have shown vulnerability to well-designed inputs called adversarial examples. Researchers in industry and academia have proposed many adversarial example defense techniques. However, they offer partial but not full robustness. Thus, complementing them with another layer of protection is a must, especially for mission-critical applications. This article proposes a novel online selection and relabeling algorithm (OSRA) that opportunistically utilizes a limited number of crowdsourced workers to maximize the machine learning (ML) system's robustness. The OSRA strives to use crowdsourced workers effectively by selecting the most suspicious inputs and moving them to the crowdsourced workers to be validated and corrected. As a result, the impact of adversarial examples gets reduced, and accordingly, the ML system becomes more robust. We also proposed a heuristic threshold selection method that contributes to enhancing the prediction system's reliability. We empirically validated our proposed algorithm and found that it can efficiently and optimally utilize the allocated budget for crowdsourcing. It is also effectively integrated with a state-of-the-art black box defense technique, resulting in a more robust system. Simulation results show that the OSRA can outperform a random selection algorithm by 60% and achieve comparable performance to an optimal offline selection benchmark. They also show that OSRA's performance has a positive correlation with system robustness.
KangaiSet: A Dataset for Visual Emotion Recognition on Manga

This paper presents KangaiSet, a dataset for facial emotion recognition on Manga. This dataset is based on the japanese manga books available in the Manga109 dataset and focuses on visual aspects. While faces are considered as the main emotional information carrier, artists employ various graphic cues through the panels to highlight different aspects of a character’s emotional states. Consequently, for each face image, pictures corresponding to the character’s body and the whole panel are also included so they can be studied simultaneously.
Beyond Human Forgeries: An Investigation into Detecting Diffusion-Generated Handwriting

Methods for detecting forged handwriting are usually based on the assumption that the forged handwriting is produced by humans. Authentic-looking handwriting, however, can also be produced synthetically. Diffusion-based generative models have recently gained popularity as they produce striking natural images and are also able to realistically mimic a person’s handwriting. It is, therefore, reasonable to assume that these models will be used to forge handwriting in the near future, adding a new layer to handwriting forgery detection. We show for the first time that the identification of synthetic handwritten data is possible by a small Convolutional Neural Network (ResNet18) reaching accuracies of 90%. We further investigate the existence of distinct discriminative features in synthetic handwriting data produced by latent diffusion models that could be exploited to build stronger detection methods. Our experiments indicate that the strongest discriminative features do not come from generation artifacts, letter shapes, or the generative model’s architecture, but instead originate from real-world artifacts in genuine handwriting that are not reproduced by generative methods.
Evaluation of Model Quantization Method on Vitis-AI for Mitigating Adversarial Examples

Adversarial examples (AEs) are typical model evasion attacks and security threats in deep neural networks (DNNs). One of the countermeasures is adversarial training (AT), and it trains DNNs by using a training dataset containing AEs to achieve robustness against AEs. On the other hand, the robustness obtained by AT greatly decreases when its parameters are quantized from a 32-bit float into an 8-bit integer to execute DNNs on edge devices with restricted hardware resources. Preliminary experiments in this study show that robustness is reduced by the fine-tuning process, in which a quantized model is trained with clean samples to reduce quantization errors. We propose quantization-aware adversarial training (QAAT) to address this problem, optimizing DNNs by conducting AT in quantization flow. In this study, we constructed a QAAT model using Vitis-AI provided by Xilinx. We implemented the QAAT model on the evaluation board ZCU104, equipped with Zynq UltraScale+, and demonstrate the robustness against AEs.
A Survey and Approach to Chart Classification

Charts represent an essential source of visual information in documents and facilitate a deep understanding and interpretation of information typically conveyed numerically. In the scientific literature, there are many charts, each with its stylistic differences. Recently the document understanding community has begun to address the problem of automatic chart understanding, which begins with chart classification. In this paper, we present a survey of the current state-of-the-art techniques for chart classification and discuss the available datasets and their supported chart types. We broadly classify these contributions as traditional approaches based on ML, CNN, and Transformers. Furthermore, we carry out an extensive comparative performance analysis of CNN-based and transformer-based approaches on the recently published CHARTINFO UB-UNITECH PMC dataset for the CHART-Infographics competition at ICPR 2022. The data set includes 15 different chart categories, including 22,923 training images and 13,260 test images. We have implemented a vision-based transformer model that produces state-of-the-art results in chart classification.