The monograph summarizes and analyzes the current state of development of computer and mathematical simulation and modeling, the automation of management processes, the use of information technologies in education, the design of information systems and software complexes, the development of computer telecommunication networks and technologies most areas that are united by the term Industry 4.0

This short report reviews the current state of the research and methodology on theoretical and practical aspects of Artificial Neural Networks (ANN). It was prepared to gather state-of-the-art knowledge needed to construct complex, hypercomplex and fuzzy neural networks. The report reflects the individual interests of the authors and, by now means, cannot be treated as a comprehensive review of the ANN discipline. Considering the fast development of this field, it is currently impossible to do a detailed review of a considerable number of pages. The report is an outcome of the Project 'The Strategic Research Partnership for the mathematical aspects of complex, hypercomplex and fuzzy neural networks' meeting at the University of Warmia and Mazury in Olsztyn, Poland, organized in September 2022.

Using robots in educational contexts has already shown to be beneficial for a student's learning and social behaviour. For levitating them to the next level of providing more effective and human-like tutoring, the ability to adapt to the user and to express proactivity is fundamental. By acting proactively, intelligent robotic tutors anticipate possible situations where problems for the student may arise and act in advance for preventing negative outcomes. Still, the decisions of when and how to behave proactively are open questions. Therefore, this paper deals with the investigation of how the student's cognitive-affective states can be used by a robotic tutor for triggering proactive tutoring dialogue. In doing so, it is aimed to improve the learning experience. For this reason, a concept learning task scenario was observed where a robotic assistant proactively helped when negative user states were detected. In a learning task, the user's states of frustration and confusion were deemed to have negative effects on the outcome of the task and were used to trigger proactive behaviour. In an empirical user study with 40 undergraduate and doctoral students, we studied whether the initiation of proactive behaviour after the detection of signs of confusion and frustration improves the student's concentration and trust in the agent. Additionally, we investigated which level of proactive dialogue is useful for promoting the student's concentration and trust. The results show that high proactive behaviour harms trust, especially when triggered during negative cognitive-affective states but contributes to keeping the student focused on the task when triggered in these states. Based on our study results, we further discuss future steps for improving the proactive assistance of robotic tutoring systems.

Sunquakes are seismic emissions visible on the solar surface, associated with some solar flares. Although discovered in 1998, they have only recently become a more commonly detected phenomenon. Despite the availability of several manual detection guidelines, to our knowledge, the astrophysical data produced for sunquakes is new to the field of Machine Learning. Detecting sunquakes is a daunting task for human operators and this work aims to ease and, if possible, to improve their detection. Thus, we introduce a dataset constructed from acoustic egression-power maps of solar active regions obtained for Solar Cycles 23 and 24 using the holography method. We then present a pedagogical approach to the application of machine learning representation methods for sunquake detection using AutoEncoders, Contrastive Learning, Object Detection and recurrent techniques, which we enhance by introducing several custom domain-specific data augmentation transformations. We address the main challenges of the automated sunquake detection task, namely the very high noise patterns in and outside the active region shadow and the extreme class imbalance given by the limited number of frames that present sunquake signatures. With our trained models, we find temporal and spatial locations of peculiar acoustic emission and qualitatively associate them to eruptive and high energy emission. While noting that these models are still in a prototype stage and there is much room for improvement in metrics and bias levels, we hypothesize that their agreement on example use cases has the potential to enable detection of weak solar acoustic manifestations.

Transfer learning on edge is challenging due to on-device limited resources. Existing work addresses this issue by training a subset of parameters or adding model patches. Developed with inference in mind, Inverted Residual Blocks (IRBs) split a convolutional layer into depthwise and pointwise convolutions, leading to more stacking layers, e.g., convolution, normalization, and activation layers. Though they are efficient for inference, IRBs require that additional activation maps are stored in memory for training weights for convolution layers and scales for normalization layers. As a result, their high memory cost prohibits training IRBs on resource-limited edge devices, and making them unsuitable in the context of transfer learning. To address this issue, we present MobileTL, a memory and computationally efficient on-device transfer learning method for models built with IRBs. MobileTL trains the shifts for internal normalization layers to avoid storing activation maps for the backward pass. Also, MobileTL approximates the backward computation of the activation layer (e.g., Hard-Swish and ReLU6) as a signed function which enables storing a binary mask instead of activation maps for the backward pass. MobileTL fine-tunes a few top blocks (close to output) rather than propagating the gradient through the whole network to reduce the computation cost. Our method reduces memory usage by 46% and 53% for MobileNetV2 and V3 IRBs, respectively. For MobileNetV3, we observe a 36% reduction in floating-point operations (FLOPs) when fine-tuning 5 blocks, while only incurring a 0.6% accuracy reduction on CIFAR10. Extensive experiments on multiple datasets demonstrate that our method is Pareto-optimal (best accuracy under given hardware constraints) compared to prior work in transfer learning for edge devices.

从随机实验获得的数据培训模型是做出良好决策的理想选择。但是，随机实验通常是耗时的，昂贵的，冒险的，不可行的或不道德的，决策者别无选择，只能依靠培训模型时在历史策略下收集的观察数据。这不仅为实践中的决策政策发挥了最佳作用，还为不同的数据收集协议对数据培训的各种政策的绩效的影响，或者在问题上的稳健性方面的稳健性，对问题的绩效提出了疑问诸如观察结果中的动作或奖励 - 特定延迟之类的特征。我们的目的是为了在LinkedIn优化销售渠道分配的问题回答此类问题，其中销售帐户（线索）需要分配给三个渠道之一，目的是在一段时间内最大程度地提高成功转换的数量。关键问题特征构成了观察分配结果的随机延迟，其分布既是通道和结果依赖性的。我们构建了一个离散的时间模拟，可以处理我们的问题功能并将其用于评估：a）基于历史规则的策略； b）有监督的机器学习政策（XGBOOST）； c）多臂强盗（MAB）策略，在涉及的不同情况下：i）用于培训的数据收集（观察性与随机分组）； ii）铅转换方案； iii）延迟分布。我们的仿真结果表明，Linucb是一种简单的mAB策略，始终优于其他策略，相对于基于规则的策略，实现了18-47％的提升

持续学习一系列任务是深度神经网络中的一个活跃领域。调查的主要挑战是灾难性遗忘或干扰以前任务的知识的现象。最近的工作调查了远期知识转移到新任务。向后转移以改善以前的任务中获得的知识的关注要少得多。通常，人们对知识转移如何有助于不断学习的任务有限。我们提出了一种在持续监督学习中进行知识转移的理论，该理论都考虑了前进和向后转移。我们旨在了解它们对越来越多知识的学习者的影响。我们得出这些转移机制中的每一种。这些界限对特定实现（例如深神经网络）是不可知的。我们证明，对于观察相关任务的持续学习者而言，前进和向后转移都可以随着观察到更多的任务而提高性能。

随着机器学习和系统社区努力通过自定义深度神经网络（DNN）加速器，多样的精度或量化水平以及模型压缩技术来实现更高的能源效率，因此需要设计空间探索框架，以结合量化意识的处理。在具有准确和快速的功率，性能和区域模型的同时，进入加速器设计空间。在这项工作中，我们提出了Quidam，这是一种高度参数化的量化量化DNN加速器和模型共探索框架。我们的框架可以促进对DNN加速器设计空间探索的未来研究，以提供各种设计选择，例如位精度，处理元素类型，处理元素的刮擦大小，全局缓冲区大小，总处理元素的数量和DNN配置。我们的结果表明，不同的精确度和处理元素类型会导致每个区域和能量性能方面的显着差异。具体而言，我们的框架标识了广泛的设计点，其中每个面积和能量的性能分别差异超过5倍和35倍。通过拟议的框架，我们表明，与最佳基于INT16的实施相比，轻巧的处理元素可在准确性结果上实现，每个区域的性能和能源改善高达5.7倍。最后，由于预先特征的功率，性能和区域模型的效率，Quidam可以将设计勘探过程加快3-4个数量级，因为它消除了每种设计的昂贵合成和表征的需求。

嵌入式机器学习（ML）系统现在已成为部署ML服务任务的主要平台，预计对于培训ML模型而言非常重要。随之而来的是，在严格的内存约束下，总体高效部署，尤其是低功率和高吞吐量实现的挑战。在这种情况下，与常规SRAM相比，由于其非挥发性，较高的细胞密度和可伸缩性特征，STT-MRAM和SOT-MRAM等非易失性记忆（NVM）技术具有显着优势。虽然先前的工作已经调查了NVM对通用应用的几种架构含义，但在这项工作中，我们提出了DeepNVM ++，这是一个综合框架，用于表征，模型和分析基于NVM的GPU架构中的基于NVM的CACHES，通过结合技术特异性的技术应用程序（DL）应用程序（DL）应用程序。电路级模型和各种DL工作负载的实际内存行为。 DEEPNVM ++依赖于使用常规SRAM和新兴STT-MRAM和SOT-MRAM Technologies实施的最后级别缓存的ISO容量和ISO区域性能和能量模型。在ISO容量的情况下，与常规的SRAM相比，STT-MRAM和SOT-MRAM可提供高达3.8倍和4.7倍的能量延迟产品（EDP）的降低以及2.4倍和2.8倍面积。在ISO-AREA假设下，STT-MRAM和SOT-MRAM可提供高达2.2倍和2.4倍的EDP降低，并且与SRAM相比，分别可容纳2.3倍和3.3倍的缓存能力。我们还执行可伸缩性分析，并表明与大型缓存能力相比，STT-MRAM和SOT-MRAM与SRAM相比实现了EDP的降低。 DEEPNVM ++在STT-/SOT-MRAM技术上进行了证明，可用于DL应用中GPU中最后一级缓存的任何NVM技术的表征，建模和分析。

冠状质量弹出（CME）是最地理化的空间天气现象，与大型地磁风暴有关，有可能引起电信，卫星网络中断，电网损失和故障的干扰。因此，考虑到这些风暴对人类活动的潜在影响，对CME的地理效果的准确预测至关重要。这项工作着重于在接近太阳CME的白光冠状动脉数据集中训练的不同机器学习方法，以估计这种新爆发的弹出是否有可能诱导地磁活动。我们使用逻辑回归，k-nearest邻居，支持向量机，向前的人工神经网络以及整体模型开发了二进制分类模型。目前，我们限制了我们的预测专门使用太阳能发作参数，以确保延长警告时间。我们讨论了这项任务的主要挑战，即我们数据集中的地理填充和无效事件的数量以及它们的众多相似之处以及可用变量数量有限的极端失衡。我们表明，即使在这种情况下，这些模型也可以达到足够的命中率。