在线仇恨言论已成为小时的需求。但是，由于几种地缘政治和文化原因，对此类活动的禁令是不可行的。为了减少问题的严重性，在本文中，我们介绍了一项新颖的任务，仇恨言语归一化，旨在削弱在线帖子表现出的仇恨强度。仇恨言语归一化的意图不是支持仇恨，而是为用户提供对非讨厌的垫脚石，同时为在线平台提供更多时间来监视用户行为的任何改进。为此，我们手动策划了平行语料库 - 仇恨文本及其标准化的同行（标准化文本较不憎恨，更良性）。我们介绍了NACL，这是一个简单而有效的仇恨言语归一化模型，该模型在三个阶段运行 - 首先，它测量了原始样本的仇恨强度；其次，它标识了其中的仇恨跨度；最后，它通过解释仇恨跨度来降低仇恨强度。我们进行了广泛的实验，以通过三向评估（内在，外部和人类研究）来衡量NaCl的功效。我们观察到，NaCl优于六个基准-NACL的强度预测得分为0.1365 RMSE，在SPAN识别中获得0.622 F1分数，而82.27 BLEU和80.05的差异和80.05的困惑为归一化​​文本生成。我们进一步显示了NACL在其他平台上的普遍性（Reddit，Facebook，GAB）。将NaCl的交互式原型放在一起进行用户研究。此外，该工具正在WIPRO AI的真实环境中部署，这是其在线平台上处理有害内容的任务的一部分。

Automatic medical image classification is a very important field where the use of AI has the potential to have a real social impact. However, there are still many challenges that act as obstacles to making practically effective solutions. One of those is the fact that most of the medical imaging datasets have a class imbalance problem. This leads to the fact that existing AI techniques, particularly neural network-based deep-learning methodologies, often perform poorly in such scenarios. Thus this makes this area an interesting and active research focus for researchers. In this study, we propose a novel loss function to train neural network models to mitigate this critical issue in this important field. Through rigorous experiments on three independently collected datasets of three different medical imaging domains, we empirically show that our proposed loss function consistently performs well with an improvement between 2%-10% macro f1 when compared to the baseline models. We hope that our work will precipitate new research toward a more generalized approach to medical image classification.

The aim of this study is to define importance of predictors for black box machine learning methods, where the prediction function can be highly non-additive and cannot be represented by statistical parameters. In this paper we defined a ``Generalized Variable Importance Metric (GVIM)'' using the true conditional expectation function for a continuous or a binary response variable. We further showed that the defined GVIM can be represented as a function of the Conditional Average Treatment Effect (CATE) squared for multinomial and continuous predictors. Then we propose how the metric can be estimated using using any machine learning models. Finally we showed the properties of the estimator using multiple simulations.

Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions. While these capabilities have led to widespread adoption, most LLMs are developed by resource-rich organizations and are frequently kept from the public. As a step towards democratizing this powerful technology, we present BLOOM, a 176B-parameter open-access language model designed and built thanks to a collaboration of hundreds of researchers. BLOOM is a decoder-only Transformer language model that was trained on the ROOTS corpus, a dataset comprising hundreds of sources in 46 natural and 13 programming languages (59 in total). We find that BLOOM achieves competitive performance on a wide variety of benchmarks, with stronger results after undergoing multitask prompted finetuning. To facilitate future research and applications using LLMs, we publicly release our models and code under the Responsible AI License.

Cartoons are an important part of our entertainment culture. Though drawing a cartoon is not for everyone, creating it using an arrangement of basic geometric primitives that approximates that character is a fairly frequent technique in art. The key motivation behind this technique is that human bodies - as well as cartoon figures - can be split down into various basic geometric primitives. Numerous tutorials are available that demonstrate how to draw figures using an appropriate arrangement of fundamental shapes, thus assisting us in creating cartoon characters. This technique is very beneficial for children in terms of teaching them how to draw cartoons. In this paper, we develop a tool - shape2toon - that aims to automate this approach by utilizing a generative adversarial network which combines geometric primitives (i.e. circles) and generate a cartoon figure (i.e. Mickey Mouse) depending on the given approximation. For this purpose, we created a dataset of geometrically represented cartoon characters. We apply an image-to-image translation technique on our dataset and report the results in this paper. The experimental results show that our system can generate cartoon characters from input layout of geometric shapes. In addition, we demonstrate a web-based tool as a practical implication of our work.

The cover is the face of a book and is a point of attraction for the readers. Designing book covers is an essential task in the publishing industry. One of the main challenges in creating a book cover is representing the theme of the book's content in a single image. In this research, we explore ways to produce a book cover using artificial intelligence based on the fact that there exists a relationship between the summary of the book and its cover. Our key motivation is the application of text-to-image synthesis methods to generate images from given text or captions. We explore several existing text-to-image conversion techniques for this purpose and propose an approach to exploit these frameworks for producing book covers from provided summaries. We construct a dataset of English books that contains a large number of samples of summaries of existing books and their cover images. In this paper, we describe our approach to collecting, organizing, and pre-processing the dataset to use it for training models. We apply different text-to-image synthesis techniques to generate book covers from the summary and exhibit the results in this paper.

孟加拉国手语（BDSL）与其他标志语言一样 - 对于普通人来说很难学习，尤其是在表达信件时。在这张海报中，我们提出了Persign，该系统可以通过引入标志手势来重现人的形象。我们使此操作个性化，这意味着生成的图像可以保持人的初始图像轮廓 - 脸部，肤色，服装，背景 - 不变，同时适当地改变了手，手掌和手指位置。我们使用图像到图像翻译技术并构建相应的唯一数据集来完成任务。我们认为，翻译的图像可以减少签名者（使用手语的人）和非签名者之间的沟通差距，而无需事先了解BDSL。

基于各种非负矩阵分解（NMF）方法为成本函数添加了新术语，以使模型适应特定任务，例如聚类或保留减少空间中的某些结构属性（例如，局部不变性）。附加的术语主要由高参数加权，以控制整体公式的平衡，以指导优化过程实现目标。结果是一种参数化的NMF方法。但是，NMF方法采用了无监督的方法来估计分解矩阵。因此，不能保证使用新的特征执行预测（例如分类）的能力。这项工作的目的是设计一个进化框架，以学习参数化NMF的超参数，并以监督的方式估算分解矩阵，以更适合分类问题。此外，我们声称，将基于NMF的算法分别应用于不同的类对，而不是将其应用于整个数据集，从而提高了矩阵分解过程的有效性。这导致训练具有不同平衡参数值的多个参数化的NMF算法。采用了交叉验证组合学习框架，并使用遗传算法来识别最佳参数值集。我们对真实和合成数据集进行的实验证明了所提出的方法的有效性。

在现代资本市场中，由于各种社会，财务，政治和其他动态因素，股票的价格通常被认为是高度波动和不可预测的。借助计算和周到的投资，股票市场可以通过最少的资本投资来确保可观的利润，而错误的预测可以轻松地为投资者带来灾难性的财务损失。本文介绍了最近引入的机器学习模型 - 变压器模型的应用，以预测孟加拉国领先的证券交易所达卡证券交易所（DSE）的未来价格。变压器模型已被广泛用于自然语言处理和计算机视觉任务，但据我们所知，从未在DSE进行股票价格预测任务。最近，介绍了代表时间序列功能的Time2VEC编码，使得可以采用变压器模型进行股票价格预测。本文集中于基于变压器的模型的应用，以根据其历史和每周的数据来预测DSE中列出的八个特定股票的价格转移。我们的实验证明了大多数股票的有希望的结果和可接受的根平方误差。

有条件的生成对抗网络（CGANS）在课堂条件生成任务中显示出卓越的结果。为了同时控制多个条件，CGAN需要多标签训练数据集，其中可以将多个标签分配给每个数据实例。然而，巨大的注释成本限制了在现实世界中多标签数据集的可访问性。因此，我们探索称为单个正设置的实用设置，其中每个数据实例仅由一个没有明确的负标签的一个正标记。为了在单个正面设置中生成多标签数据，我们提出了一种基于马尔可夫链蒙特卡洛方法的新型抽样方法，称为单一标记（S2M）采样。作为一种广泛适用的“附加”方法，我们提出的S2M采样使现有的无条件和有条件的gans能够以最小的注释成本绘制高质量的多标签数据。在真实图像数据集上进行的广泛实验可以验证我们方法的有效性和正确性，即使与经过完全注释的数据集训练的模型相比。