由于其各种潜在应用及其众多挑战，视频生成是机器学习中相对较新的，但流行的主题。视频生成中的当前方法为用户提供了很少或根本没有控制通过对生成视频中的对象被移动并位于每个帧的精确规范，即，用户无法明确控制每个对象如何视频应该移动。在本文中，我们提出了一种新颖的方法，该方法允许用户通过在这些对象上绘制边界框，然后在所需路径中移动这些框来移动所有数量的单个初始帧的对象。我们的模型利用两个AutoEncoders完全分解视频中的运动和内容信息，并实现与众所周知的基线和现有方法的结果相当。

It does not matter whether it is a job interview with Tech Giants, Wall Street firms, or a small startup; all candidates want to demonstrate their best selves or even present themselves better than they really are. Meanwhile, recruiters want to know the candidates' authentic selves and detect soft skills that prove an expert candidate would be a great fit in any company. Recruiters worldwide usually struggle to find employees with the highest level of these skills. Digital footprints can assist recruiters in this process by providing candidates' unique set of online activities, while social media delivers one of the largest digital footprints to track people. In this study, for the first time, we show that a wide range of behavioral competencies consisting of 16 in-demand soft skills can be automatically predicted from Instagram profiles based on the following lists and other quantitative features using machine learning algorithms. We also provide predictions on Big Five personality traits. Models were built based on a sample of 400 Iranian volunteer users who answered an online questionnaire and provided their Instagram usernames which allowed us to crawl the public profiles. We applied several machine learning algorithms to the uniformed data. Deep learning models mostly outperformed by demonstrating 70% and 69% average Accuracy in two-level and three-level classifications respectively. Creating a large pool of people with the highest level of soft skills, and making more accurate evaluations of job candidates is possible with the application of AI on social media user-generated data.

Multi-objective feature selection is one of the most significant issues in the field of pattern recognition. It is challenging because it maximizes the classification performance and, at the same time, minimizes the number of selected features, and the mentioned two objectives are usually conflicting. To achieve a better Pareto optimal solution, metaheuristic optimization methods are widely used in many studies. However, the main drawback is the exploration of a large search space. Another problem with multi-objective feature selection approaches is the interaction between features. Selecting correlated features has negative effect on classification performance. To tackle these problems, we present a novel multi-objective feature selection method that has several advantages. Firstly, it considers the interaction between features using an advanced probability scheme. Secondly, it is based on the Pareto Archived Evolution Strategy (PAES) method that has several advantages such as simplicity and its speed in exploring the solution space. However, we improve the structure of PAES in such a way that generates the offsprings, intelligently. Thus, the proposed method utilizes the introduced probability scheme to produce more promising offsprings. Finally, it is equipped with a novel strategy that guides it to find the optimum number of features through the process of evolution. The experimental results show a significant improvement in finding the optimal Pareto front compared to state-of-the-art methods on different real-world datasets.

In medical image analysis, low-resolution images negatively affect the performance of medical image interpretation and may cause misdiagnosis. Single image super-resolution (SISR) methods can improve the resolution and quality of medical images. Currently, Generative Adversarial Networks (GAN) based super-resolution models have shown very good performance. Real-Enhanced Super-Resolution Generative Adversarial Network (Real-ESRGAN) is one of the practical GAN-based models which is widely used in the field of general image super-resolution. One of the challenges in medical image super-resolution is that, unlike natural images, medical images do not have high spatial resolution. To solve this problem, we can use transfer learning technique and fine-tune the model that has been trained on external datasets (often natural datasets). In our proposed approach, the pre-trained generator and discriminator networks of the Real-ESRGAN model are fine-tuned using medical image datasets. In this paper, we worked on chest X-ray and retinal images and used the STARE dataset of retinal images and Tuberculosis Chest X-rays (Shenzhen) dataset for fine-tuning. The proposed model produces more accurate and natural textures, and its outputs have better detail and resolution compared to the original Real-ESRGAN outputs.

随着虚拟助手变得越来越多样化和专业，对应用或特定品牌唤醒的需求也是如此。但是，通常用于训练尾流检测器的特定于唤醒特定的数据集是昂贵的。在本文中，我们探索了两种技术来利用声音建模数据，以提高大唱歌的语音识别，以改善专用的尾流探测器：转移学习和知识蒸馏。我们还探讨了这些技术如何与时间同步训练目标相互作用以提高检测潜伏期。实验显示在开源“嘿STHIPS”数据集中，并且内部远场数据集更具挑战性。使用大型声学模型中的电话同步目标和知识蒸馏，我们能够提高两个数据集的数据集尺寸的精度，同时降低延迟。

设计机器学习算法准确但公平，而不是基于任何敏感属性进行区分，对于社会接受对关键应用的AI至关重要。在本文中，我们提出了一种新颖的公平表示方法，称为R \'enyi公平信息瓶颈方法（RFIB），该方法包含了代表性的效用，公平性和紧凑性的约束，并将其应用于图像分类。我们方法的一个关键属性是，与大多数先前的工作相比，我们认为人口统计学奇偶ant和均衡的赔率是公平的约束，从而使对这两个标准的满意度更加细致。利用各种方法，我们表明我们的目标产生了涉及经典信息瓶颈（IB）措施的损失函数，并根据r \'enyi nyi nyi差异$ \ alpha $在共同信息上的r \'enyi差异ib术语IB术语测量紧凑度上建立上限在输入及其编码嵌入之间。在三个不同的图像数据集（Eyepacs，celeba和Fairface）上进行实验，我们研究了$ \ alpha $参数的影响以及其他两个可调IB参数对实现效用/公平性权衡目标的影响，并表明$ \ \ \ \ Alpha $参数提供了一个额外的自由度，可用于控制表示的紧凑性。我们使用各种效用，公平性和复合效用/公平指标评估方法的性能，表明RFIB的表现优于当前最新方法。