加权CSP（WCSP）的重新定义（WCSP）的重新定位概念（也称为WCSPS的等价 - 保存的变换）是众所周知的并且在许多算法中找到其使用以近似或绑定最佳WCSP值。相比之下，已经提出了超级reparamureIzations的概念（这是保留或增加每个任务的WCSP目标的权重的变化），但从未详细研究过。为了填补这一差距，我们展示了一些超级reparamizations的理论属性，并将它们与重新定位化的差异进行比较。此外，我们提出了一种用于使用超级Reparamizations计算（最大化版本）WCSP的最佳值的上限的框架。我们表明原则上可以采用任意（在某些技术条件下）约束传播规则来改善绑定。特别是对于电弧一致性，该方法减少到已知的虚拟AC（VAC）算法。新的，我们实施了Singleton ARC一致性（SAC）的方法，并将其与WCSPS在公共基准上的其他强大局部常量进行比较。结果表明，从SAC获得的界限对于许多实例组优越。

Independent Component Analysis (ICA) is an algorithm originally developed for finding separate sources in a mixed signal, such as a recording of multiple people in the same room speaking at the same time. It has also been used to find linguistic features in distributional representations. In this paper, we used ICA to analyze words embeddings. We have found that ICA can be used to find semantic features of the words and these features can easily be combined to search for words that satisfy the combination. We show that only some of the independent components represent such features, but those that do are stable with regard to random initialization of the algorithm.

This paper presents a conversational AI platform called Flowstorm. Flowstorm is an open-source SaaS project suitable for creating, running, and analyzing conversational applications. Thanks to the fast and fully automated build process, the dialogues created within the platform can be executed in seconds. Furthermore, we propose a novel dialogue architecture that uses a combination of tree structures with generative models. The tree structures are also used for training NLU models suitable for specific dialogue scenarios. However, the generative models are globally used across applications and extend the functionality of the dialogue trees. Moreover, the platform functionality benefits from out-of-the-box components, such as the one responsible for extracting data from utterances or working with crawled data. Additionally, it can be extended using a custom code directly in the platform. One of the essential features of the platform is the possibility to reuse the created assets across applications. There is a library of prepared assets where each developer can contribute. All of the features are available through a user-friendly visual editor.

Modeling perception sensors is key for simulation based testing of automated driving functions. Beyond weather conditions themselves, sensors are also subjected to object dependent environmental influences like tire spray caused by vehicles moving on wet pavement. In this work, a novel modeling approach for spray in lidar data is introduced. The model conforms to the Open Simulation Interface (OSI) standard and is based on the formation of detection clusters within a spray plume. The detections are rendered with a simple custom ray casting algorithm without the need of a fluid dynamics simulation or physics engine. The model is subsequently used to generate training data for object detection algorithms. It is shown that the model helps to improve detection in real-world spray scenarios significantly. Furthermore, a systematic real-world data set is recorded and published for analysis, model calibration and validation of spray effects in active perception sensors. Experiments are conducted on a test track by driving over artificially watered pavement with varying vehicle speeds, vehicle types and levels of pavement wetness. All models and data of this work are available open source.

Within an operational framework, covers used by a steganographer are likely to come from different sensors and different processing pipelines than the ones used by researchers for training their steganalysis models. Thus, a performance gap is unavoidable when it comes to out-of-distributions covers, an extremely frequent scenario called Cover Source Mismatch (CSM). Here, we explore a grid of processing pipelines to study the origins of CSM, to better understand it, and to better tackle it. A set-covering greedy algorithm is used to select representative pipelines minimizing the maximum regret between the representative and the pipelines within the set. Our main contribution is a methodology for generating relevant bases able to tackle operational CSM. Experimental validation highlights that, for a given number of training samples, our set covering selection is a better strategy than selecting random pipelines or using all the available pipelines. Our analysis also shows that parameters as denoising, sharpening, and downsampling are very important to foster diversity. Finally, different benchmarks for classical and wild databases show the good generalization property of the extracted databases. Additional resources are available at github.com/RonyAbecidan/HolisticSteganalysisWithSetCovering.

Dysgraphia, a handwriting learning disability, has a serious negative impact on children's academic results, daily life and overall wellbeing. Early detection of dysgraphia allows for an early start of a targeted intervention. Several studies have investigated dysgraphia detection by machine learning algorithms using a digital tablet. However, these studies deployed classical machine learning algorithms with manual feature extraction and selection as well as binary classification: either dysgraphia or no dysgraphia. In this work, we investigated fine grading of handwriting capabilities by predicting SEMS score (between 0 and 12) with deep learning. Our approach provide accuracy more than 99% and root mean square error lower than one, with automatic instead of manual feature extraction and selection. Furthermore, we used smart pen called SensoGrip, a pen equipped with sensors to capture handwriting dynamics, instead of a tablet, enabling writing evaluation in more realistic scenarios.

视觉和语言（V+L）模型的最新进展对医疗保健领域产生了有希望的影响。但是，这样的模型难以解释如何以及为什么做出特定决定。此外，模型透明度和域专业知识的参与是机器学习模型进入该领域的关键成功因素。在这项工作中，我们研究了局部替代解释性技术来克服黑盒深度学习模型的问题。我们探讨了使用本地替代物与基础V+L结合使用本地替代物与域专业知识相似的可行性，以生成多模式的视觉和语言解释。我们证明，这种解释可以作为指导该领域数据科学家和机器学习工程师的指导模型培训的有益反馈。

评估组织内组织和分支机构的效率对于经理来说是一个具有挑战性的问题。评估标准允许组织对其内部单位进行排名，确定其在竞争对手方面的立场，并实施改进和发展目的的策略。在评估银行分支机构的方法中，非参数方法吸引了近年来研究人员的注意。最广泛使用的非参数方法之一是数据包络分析（DEA），可带来有希望的结果。但是，静态DEA方法并未考虑模型中的时间。因此，本文使用动态DEA（DDEA）方法在三年内评估伊朗银行的分支机构（2017-2019）。然后将结果与静态DEA进行比较。对分支进行排名后，使用K-均值方法聚类。最后，引入了一种全面的敏感性分析方法，以帮助管理人员决定更改变量以将分支从一个群集转移到更有效的变量。

我们介绍了AARGH，这是一个面向任务的对话框系统，该系统结合了单个模型中的检索和生成方法，旨在改善对话框管理和输出的词汇多样性。该模型采用了一种新的响应选择方法，该方法基于动作感知训练目标和简化的单编码检索架构，该方法使我们能够构建端到端检索增强生成模型，在该模型中，检索和生成共享大多数参数。在Multiwoz数据集上，我们表明我们的方法与最先进的基线相比，在维持或改善状态跟踪和上下文响应生成性能的同时，产生了更多的输出。

机器学习（ML）为生物处理工程的发展做出了重大贡献，但其应用仍然有限，阻碍了生物过程自动化的巨大潜力。用于模型构建自动化的ML可以看作是引入另一种抽象水平的一种方式，将专家的人类集中在生物过程开发的最认知任务中。首先，概率编程用于预测模型的自动构建。其次，机器学习会通过计划实验来测试假设并进行调查以收集信息性数据来自动评估替代决策，以收集基于模型预测不确定性的模型选择的信息数据。这篇评论提供了有关生物处理开发中基于ML的自动化的全面概述。一方面，生物技术和生物工程社区应意识到现有ML解决方案在生物技术和生物制药中的应用的限制。另一方面，必须确定缺失的链接，以使ML和人工智能（AI）解决方案轻松实施在有价值的生物社区解决方案中。我们总结了几个重要的生物处理系统的ML实施，并提出了两个至关重要的挑战，这些挑战仍然是生物技术自动化的瓶颈，并减少了生物技术开发的不确定性。没有一个合适的程序；但是，这项综述应有助于确定结合生物技术和ML领域的潜在自动化。