本文介绍了一个名为Bangrawriting的孟加拉手写数据集，其中包含260个不同个性和年龄的个人的单页手写。每个页面都包含边界框的边界框以及写作的Unicode表示。该数据集总共包含21,234个单词和32,787个字符。此外，该数据集包括5,470个孟加拉词汇的独特单词。除了通常的单词外，数据集还包括261个可理解的覆盖物和450个手写罢工和错误。所有的边界盒和单词标签都是手动生成的。该数据集可用于复杂的光学字符/单词识别，作者识别，手写单词分割和单词生成。此外，该数据集适用于提取基于年龄的和基于性别的笔迹变化。

当今智能城市中产生的大型视频数据从其有目的的用法角度引起了人们的关注，其中监视摄像机等是最突出的资源，是为大量数据做出贡献的最突出的资源，使其自动化分析成为计算方面的艰巨任务。和精确。暴力检测（VD）在行动和活动识别域中广泛崩溃，用于分析大型视频数据，以了解由于人类而引起的异常动作。传统上，VD文献基于手动设计的功能，尽管开发了基于深度学习的独立模型的进步用于实时VD分析。本文重点介绍了深度序列学习方法以及检测到的暴力的本地化策略。该概述还介入了基于机器学习的初始图像处理和基于机器学习的文献及其可能具有的优势，例如针对当前复杂模型的效率。此外，讨论了数据集，以提供当前模型的分析，并用对先前方法的深入分析得出的VD域中的未来方向解释了他们的利弊。

缺陷预测是最受欢迎的研究主题之一，因为它有可能最大程度地减少软件质量保证工作。现有方法已经从复杂性和开发者指标等各个角度检查了缺陷预测。但是，这些都没有考虑用于缺陷预测的编程样式。本文旨在分析风格指标对项目内部和交叉对象缺陷预测的影响。为了预测，使用了4种广泛使用的机器学习算法，即幼稚的贝叶斯，支持向量机，决策树和逻辑回归。该实验是在5个流行的开源项目的14个版本上进行的。检查F1，精度和召回以评估结果。结果表明，风格指标是缺陷的良好预测指标。

由于它们的蔓延越来越多，对神经网络预测的信心变得越来越重要。然而，基本的神经网络不会透露确定性估计或遭受超过或置信度。许多研究人员一直在努力了解和量化神经网络预测中的不确定性。结果，已经提出了已经确定了不同类型和不确定性的来源，并且已经提出了一种测量和量化神经网络中不确定性的各种方法。这项工作概述了神经网络中的不确定性估计，评论最近领域的进步，突出了当前的挑战，并确定了潜在的研究机会。它旨在向任何兴趣在神经网络中的不确定性估计感兴趣的概述和介绍，而无需预先展现在该领域的先验知识。给出了对最关键的不确定性来源的全面介绍，并分离到可还原的模型不确定性，并提出了未降低的数据不确定性。基于确定性神经网络，贝叶斯神经网络，神经网络集合的这些不确定性和测试时间数据增强方法的建模以及这些领域的不同分支以及讨论了最新的发展。对于实际应用，我们讨论了不同的不确定性措施，校准神经网络的方法，并概述现有基线和实施。来自不同领域的广泛挑战的不同示例概念了关于实际应用中不确定性的需求和挑战。此外，讨论了当前特派团和安全关键现实世界应用程序的实际限制，并讨论了对更广泛使用此类方法的下一个步骤的展望。

Diabetic Retinopathy (DR) is considered one of the primary concerns due to its effect on vision loss among most people with diabetes globally. The severity of DR is mostly comprehended manually by ophthalmologists from fundus photography-based retina images. This paper deals with an automated understanding of the severity stages of DR. In the literature, researchers have focused on this automation using traditional machine learning-based algorithms and convolutional architectures. However, the past works hardly focused on essential parts of the retinal image to improve the model performance. In this paper, we adopt transformer-based learning models to capture the crucial features of retinal images to understand DR severity better. We work with ensembling image transformers, where we adopt four models, namely ViT (Vision Transformer), BEiT (Bidirectional Encoder representation for image Transformer), CaiT (Class-Attention in Image Transformers), and DeiT (Data efficient image Transformers), to infer the degree of DR severity from fundus photographs. For experiments, we used the publicly available APTOS-2019 blindness detection dataset, where the performances of the transformer-based models were quite encouraging.

This paper presents our solutions for the MediaEval 2022 task on DisasterMM. The task is composed of two subtasks, namely (i) Relevance Classification of Twitter Posts (RCTP), and (ii) Location Extraction from Twitter Texts (LETT). The RCTP subtask aims at differentiating flood-related and non-relevant social posts while LETT is a Named Entity Recognition (NER) task and aims at the extraction of location information from the text. For RCTP, we proposed four different solutions based on BERT, RoBERTa, Distil BERT, and ALBERT obtaining an F1-score of 0.7934, 0.7970, 0.7613, and 0.7924, respectively. For LETT, we used three models namely BERT, RoBERTa, and Distil BERTA obtaining an F1-score of 0.6256, 0.6744, and 0.6723, respectively.

Enterprise resource planning (ERP) software brings resources, data together to keep software-flow within business processes in a company. However, cloud computing's cheap, easy and quick management promise pushes business-owners for a transition from monolithic to a data-center/cloud based ERP. Since cloud-ERP development involves a cyclic process, namely planning, implementing, testing and upgrading, its adoption is realized as a deep recurrent neural network problem. Eventually, a classification algorithm based on long short term memory (LSTM) and TOPSIS is proposed to identify and rank, respectively, adoption features. Our theoretical model is validated over a reference model by articulating key players, services, architecture, functionalities. Qualitative survey is conducted among users by considering technology, innovation and resistance issues, to formulate hypotheses on key adoption factors.

Objective: Despite numerous studies proposed for audio restoration in the literature, most of them focus on an isolated restoration problem such as denoising or dereverberation, ignoring other artifacts. Moreover, assuming a noisy or reverberant environment with limited number of fixed signal-to-distortion ratio (SDR) levels is a common practice. However, real-world audio is often corrupted by a blend of artifacts such as reverberation, sensor noise, and background audio mixture with varying types, severities, and duration. In this study, we propose a novel approach for blind restoration of real-world audio signals by Operational Generative Adversarial Networks (Op-GANs) with temporal and spectral objective metrics to enhance the quality of restored audio signal regardless of the type and severity of each artifact corrupting it. Methods: 1D Operational-GANs are used with generative neuron model optimized for blind restoration of any corrupted audio signal. Results: The proposed approach has been evaluated extensively over the benchmark TIMIT-RAR (speech) and GTZAN-RAR (non-speech) datasets corrupted with a random blend of artifacts each with a random severity to mimic real-world audio signals. Average SDR improvements of over 7.2 dB and 4.9 dB are achieved, respectively, which are substantial when compared with the baseline methods. Significance: This is a pioneer study in blind audio restoration with the unique capability of direct (time-domain) restoration of real-world audio whilst achieving an unprecedented level of performance for a wide SDR range and artifact types. Conclusion: 1D Op-GANs can achieve robust and computationally effective real-world audio restoration with significantly improved performance. The source codes and the generated real-world audio datasets are shared publicly with the research community in a dedicated GitHub repository1.

Uncertainty quantification is crucial to inverse problems, as it could provide decision-makers with valuable information about the inversion results. For example, seismic inversion is a notoriously ill-posed inverse problem due to the band-limited and noisy nature of seismic data. It is therefore of paramount importance to quantify the uncertainties associated to the inversion process to ease the subsequent interpretation and decision making processes. Within this framework of reference, sampling from a target posterior provides a fundamental approach to quantifying the uncertainty in seismic inversion. However, selecting appropriate prior information in a probabilistic inversion is crucial, yet non-trivial, as it influences the ability of a sampling-based inference in providing geological realism in the posterior samples. To overcome such limitations, we present a regularized variational inference framework that performs posterior inference by implicitly regularizing the Kullback-Leibler divergence loss with a CNN-based denoiser by means of the Plug-and-Play methods. We call this new algorithm Plug-and-Play Stein Variational Gradient Descent (PnP-SVGD) and demonstrate its ability in producing high-resolution, trustworthy samples representative of the subsurface structures, which we argue could be used for post-inference tasks such as reservoir modelling and history matching. To validate the proposed method, numerical tests are performed on both synthetic and field post-stack seismic data.

In recent years distributional reinforcement learning has produced many state of the art results. Increasingly sample efficient Distributional algorithms for the discrete action domain have been developed over time that vary primarily in the way they parameterize their approximations of value distributions, and how they quantify the differences between those distributions. In this work we transfer three of the most well-known and successful of those algorithms (QR-DQN, IQN and FQF) to the continuous action domain by extending two powerful actor-critic algorithms (TD3 and SAC) with distributional critics. We investigate whether the relative performance of the methods for the discrete action space translates to the continuous case. To that end we compare them empirically on the pybullet implementations of a set of continuous control tasks. Our results indicate qualitative invariance regarding the number and placement of distributional atoms in the deterministic, continuous action setting.