这里提出的研究提供了对Ghazal的数字洞察力 - 乌尔都语诗歌中最受赞赏的类型。这项研究探讨了4,754级诗人生产的4,754个诗人，探讨了Urdu Ghazal的主要特征，使其比其他形式更受欢迎和钦佩。提供了详细的解释，以及用于表达爱情，自然，鸟类和鲜花等的单词类型。也认为是诗人在诗歌中对其所属的人讨论的方式。使用多维缩放进行数值分析诗歌风格，以揭示引起批评者注意力的不同诗意作品的词汇分集和相似性/差异，例如iqbal和ghalib，mir taqi mir和mir dard。这里产生的分析对于计算风格学，神经认知诗学和情感分析特别有利。

Migraine is a high-prevalence and disabling neurological disorder. However, information migraine management in real-world settings could be limited to traditional health information sources. In this paper, we (i) verify that there is substantial migraine-related chatter available on social media (Twitter and Reddit), self-reported by migraine sufferers; (ii) develop a platform-independent text classification system for automatically detecting self-reported migraine-related posts, and (iii) conduct analyses of the self-reported posts to assess the utility of social media for studying this problem. We manually annotated 5750 Twitter posts and 302 Reddit posts. Our system achieved an F1 score of 0.90 on Twitter and 0.93 on Reddit. Analysis of information posted by our 'migraine cohort' revealed the presence of a plethora of relevant information about migraine therapies and patient sentiments associated with them. Our study forms the foundation for conducting an in-depth analysis of migraine-related information using social media data.

Dengue fever is a virulent disease spreading over 100 tropical and subtropical countries in Africa, the Americas, and Asia. This arboviral disease affects around 400 million people globally, severely distressing the healthcare systems. The unavailability of a specific drug and ready-to-use vaccine makes the situation worse. Hence, policymakers must rely on early warning systems to control intervention-related decisions. Forecasts routinely provide critical information for dangerous epidemic events. However, the available forecasting models (e.g., weather-driven mechanistic, statistical time series, and machine learning models) lack a clear understanding of different components to improve prediction accuracy and often provide unstable and unreliable forecasts. This study proposes an ensemble wavelet neural network with exogenous factor(s) (XEWNet) model that can produce reliable estimates for dengue outbreak prediction for three geographical regions, namely San Juan, Iquitos, and Ahmedabad. The proposed XEWNet model is flexible and can easily incorporate exogenous climate variable(s) confirmed by statistical causality tests in its scalable framework. The proposed model is an integrated approach that uses wavelet transformation into an ensemble neural network framework that helps in generating more reliable long-term forecasts. The proposed XEWNet allows complex non-linear relationships between the dengue incidence cases and rainfall; however, mathematically interpretable, fast in execution, and easily comprehensible. The proposal's competitiveness is measured using computational experiments based on various statistical metrics and several statistical comparison tests. In comparison with statistical, machine learning, and deep learning methods, our proposed XEWNet performs better in 75% of the cases for short-term and long-term forecasting of dengue incidence.

DeepAngle is a machine learning-based method to determine the contact angles of different phases in the tomography images of porous materials. Measurement of angles in 3--D needs to be done within the surface perpendicular to the angle planes, and it could become inaccurate when dealing with the discretized space of the image voxels. A computationally intensive solution is to correlate and vectorize all surfaces using an adaptable grid, and then measure the angles within the desired planes. On the contrary, the present study provides a rapid and low-cost technique powered by deep learning to estimate the interfacial angles directly from images. DeepAngle is tested on both synthetic and realistic images against the direct measurement technique and found to improve the r-squared by 5 to 16% while lowering the computational cost 20 times. This rapid method is especially applicable for processing large tomography data and time-resolved images, which is computationally intensive. The developed code and the dataset are available at an open repository on GitHub (https://www.github.com/ArashRabbani/DeepAngle).

This paper introduces and presents a new language named MAIL (Malware Analysis Intermediate Language). MAIL is basically used for building malware analysis and detection tools. MAIL provides an abstract representation of an assembly program and hence the ability of a tool to automate malware analysis and detection. By translating binaries compiled for different platforms to MAIL, a tool can achieve platform independence. Each MAIL statement is annotated with patterns that can be used by a tool to optimize malware analysis and detection.

Visible light positioning has the potential to yield sub-centimeter accuracy in indoor environments, yet conventional received signal strength (RSS)-based localization algorithms cannot achieve this because their performance degrades from optical multipath reflection. However, this part of the optical received signal is deterministic due to the often static and predictable nature of the optical wireless channel. In this paper, the performance of optical channel impulse response (OCIR)-based localization is studied using an artificial neural network (ANN) to map embedded features of the OCIR to the user equipment's location. Numerical results show that OCIR-based localization outperforms conventional RSS techniques by two orders of magnitude using only two photodetectors as anchor points. The ANN technique can take advantage of multipath features in a wide range of scenarios, from using only the DC value to relying on high-resolution time sampling that can result in sub-centimeter accuracy.

In this study, we propose a lung nodule detection scheme which fully incorporates the clinic workflow of radiologists. Particularly, we exploit Bi-Directional Maximum intensity projection (MIP) images of various thicknesses (i.e., 3, 5 and 10mm) along with a 3D patch of CT scan, consisting of 10 adjacent slices to feed into self-distillation-based Multi-Encoders Network (MEDS-Net). The proposed architecture first condenses 3D patch input to three channels by using a dense block which consists of dense units which effectively examine the nodule presence from 2D axial slices. This condensed information, along with the forward and backward MIP images, is fed to three different encoders to learn the most meaningful representation, which is forwarded into the decoded block at various levels. At the decoder block, we employ a self-distillation mechanism by connecting the distillation block, which contains five lung nodule detectors. It helps to expedite the convergence and improves the learning ability of the proposed architecture. Finally, the proposed scheme reduces the false positives by complementing the main detector with auxiliary detectors. The proposed scheme has been rigorously evaluated on 888 scans of LUNA16 dataset and obtained a CPM score of 93.6\%. The results demonstrate that incorporating of bi-direction MIP images enables MEDS-Net to effectively distinguish nodules from surroundings which help to achieve the sensitivity of 91.5% and 92.8% with false positives rate of 0.25 and 0.5 per scan, respectively.

人类活动识别（HAR）是使用有效的机器学习（ML）方法将传感器数据解释为人类运动的问题。 HAR系统依靠来自不受信任的用户的数据，使他们容易受到数据中毒攻击的影响。在中毒攻击中，攻击者操纵传感器读数以污染训练集，从而误导了har以产生错误的结果。本文介绍了针对HAR系统的标签翻转数据中毒攻击的设计，在数据收集阶段，传感器读数的标签发生了恶意更改。由于传感环境中的噪音和不确定性，这种攻击对识别系统构成了严重威胁。此外，当将活动识别模型部署在安全至关重要的应用中时，标记翻转攻击的脆弱性是危险的。本文阐明了如何通过基于智能手机的传感器数据收集应用程序在实践中进行攻击。据我们所知，这是一项较早的研究工作，它通过标签翻转中毒探索了攻击HAR模型。我们实施了提出的攻击并根据以下机器学习算法进行活动识别模型进行测试：多层感知器，决策树，随机森林和XGBoost。最后，我们评估了针对拟议攻击的基于K-Nearest邻居（KNN）的防御机制的有效性。

Granger因果关系（GC）检验是一种著名的统计假设检验，用于研究一个时期的过去是否影响了另一个时间的未来。它有助于回答一个问题序列是否有助于预测。 Granger因果关系检测的标准传统方法通常假设线性动力学，但是这种简化在许多现实世界应用中不存在，例如，神经科学或基因组学本质上是非线性的。在这种情况下，施加线性模型，例如向量自回旋（VAR）模型可能会导致对真正的Granger因果相互作用的不一致估计。机器学习（ML）可以学习数据集中的隐藏模式（DL）在学习复杂系统的非线性动力学方面表现出巨大的希望。 Tank等人的最新工作建议通过使用神经网络结合对可学习的权重的稀疏性惩罚来克服VAR模型中线性简化的问题。在这项工作中，我们基于Tank等人引入的想法。我们提出了几类新的模型，这些模型可以处理潜在的非线性。首先，我们介绍了学识渊博的内核var（lekvar）模型 - var模型的扩展，这些模型也学习了通过神经网络参数的内核。其次，我们表明可以通过脱钩的惩罚直接将滞后和单个时间序列的重要性分解。这种去耦提供了更好的缩放，并使我们可以将滞后选择嵌入RNN中。最后，我们提出了一种支持迷你批次的新培训算法，并且它与常用的自适应优化器（例如Adam）兼容。癫痫患者的电脑电图（EEG）数据研究了在19个EEG通道之前，期间和之后的GC演变。

在这项研究中，我们为心脏磁共振图像中心室分割掩模的时间外推定了一种像素跟踪方法。像素跟踪过程从心脏周期的末端框架开始，使用可用的手动分割图像来预测终端节感应分割掩码。Superpixels方法用于将原始图像分为较小的单元格，在每个时间范围内，新标签都分配给图像单元，从而导致跟踪心脏壁元件通过不同框架的运动。将收缩期末端的履带掩膜与已经可用的手动分割面罩进行了比较，并且发现骰子得分在0.81至0.84之间。考虑到所提出的方法不一定需要培训数据集这一事实，在培训数据受到限制的情况下，这可能是一种有吸引力的深度学习分割方法的替代方法。