智能仪表测量值虽然对于准确的需求预测至关重要，但仍面临一些缺点，包括消费者的隐私，数据泄露问题，仅举几例。最近的文献探索了联合学习（FL）作为一种有前途的隐私机器学习替代方案，该替代方案可以协作学习模型，而无需将私人原始数据暴露于短期负载预测中。尽管有着美德，但标准FL仍然容易受到棘手的网络威胁，称为拜占庭式攻击，这是由错误和/或恶意客户进行的。因此，为了提高联邦联邦短期负载预测对拜占庭威胁的鲁棒性，我们开发了一个最先进的基于私人安全的FL框架，以确保单个智能电表的数据的隐私，同时保护FL的安全性模型和架构。我们提出的框架利用了通过符号随机梯度下降（SignsGD）算法的梯度量化的想法，在本地模型培训后，客户仅将梯度的“符号”传输到控制中心。当我们通过涉及一组拜占庭攻击模型的基准神经网络的实验突出显示时，我们提出的方法会非常有效地减轻此类威胁，从而优于常规的FED-SGD模型。

近年来，由于其在金融，网络安全和医学等广泛的领域中的应用，近年来，归因网络中的异常检测受到了极大的关注。传统方法不能在属性网络的设置上采用以解决异常检测问题。这种方法的主要局限性是它们固有地忽略了数据特征之间的关系信息。随着基于深度学习和图神经网络技术的快速爆炸，由于深度技术在提取复杂关系方面的潜力，因此在归因网络上发现稀有对象已大大发展。在本文中，我们提出了有关异常检测的新架构。设计这种体系结构的主要目标是利用多任务学习，以增强检测性能。基于多任务的基于学习的异常检测仍处于起步阶段，现有文献中只有少数研究迎合了同样的研究。我们合并了社区检测和多视图表示学习技术，以从属性网络中提取明显和互补的信息，并随后融合捕获的信息以获得更好的检测结果。该体系结构中采用的两个主要组成部分（即社区特定的学习和多视图表示学习）之间的相互合作展示了一种有希望的解决方案，以达到更有效的结果。

联合学习（FL）是一种有效的分布式机器学习范式，以隐私的方式采用私人数据集。 FL的主要挑战是，END设备通常具有各种计算和通信功能，其培训数据并非独立且分布相同（非IID）。由于在移动网络中此类设备的通信带宽和不稳定的可用性，因此只能在每个回合中选择最终设备（也称为参与者或客户端的参与者或客户端）。因此，使用有效的参与者选择方案来最大程度地提高FL的性能，包括最终模型的准确性和训练时间，这一点至关重要。在本文中，我们对FL的参与者选择技术进行了评论。首先，我们介绍FL并突出参与者选择期间的主要挑战。然后，我们根据其解决方案来审查现有研究并将其分类。最后，根据我们对该主题领域最新的分析的分析，我们为FL的参与者选择提供了一些未来的指示。

随着智能设备的扩散和通信中的旋转，配电系统逐渐从被动，手动操作和不灵活的，到大规模互连的网络物理智能电网，以解决未来的能源挑战。然而，由于部署的大规模复杂性和资源限制，若干尖端技术的集成引入了几种安全和隐私漏洞。最近的研究趋势表明，虚假数据注入（FDI）攻击正成为整个智能电网范式内最恶毒的网络威胁之一。因此，本文介绍了对积极分配系统内的直接投资袭击事件的最近进展的全面调查，并提出了分类法，以对智能电网目标进行外商直接投资威胁。相关研究与攻击方法和对电力分配网络的影响形成鲜明对比和总结。最后，我们确定了一些研究差距并推荐了一些未来的研究方向，以指导和激励前瞻性研究人员。

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.

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.

The number of international benchmarking competitions is steadily increasing in various fields of machine learning (ML) research and practice. So far, however, little is known about the common practice as well as bottlenecks faced by the community in tackling the research questions posed. To shed light on the status quo of algorithm development in the specific field of biomedical imaging analysis, we designed an international survey that was issued to all participants of challenges conducted in conjunction with the IEEE ISBI 2021 and MICCAI 2021 conferences (80 competitions in total). The survey covered participants' expertise and working environments, their chosen strategies, as well as algorithm characteristics. A median of 72% challenge participants took part in the survey. According to our results, knowledge exchange was the primary incentive (70%) for participation, while the reception of prize money played only a minor role (16%). While a median of 80 working hours was spent on method development, a large portion of participants stated that they did not have enough time for method development (32%). 25% perceived the infrastructure to be a bottleneck. Overall, 94% of all solutions were deep learning-based. Of these, 84% were based on standard architectures. 43% of the respondents reported that the data samples (e.g., images) were too large to be processed at once. This was most commonly addressed by patch-based training (69%), downsampling (37%), and solving 3D analysis tasks as a series of 2D tasks. K-fold cross-validation on the training set was performed by only 37% of the participants and only 50% of the participants performed ensembling based on multiple identical models (61%) or heterogeneous models (39%). 48% of the respondents applied postprocessing steps.

Time-critical control applications typically pose stringent connectivity requirements for communication networks. The imperfections associated with the wireless medium such as packet losses, synchronization errors, and varying delays have a detrimental effect on performance of real-time control, often with safety implications. This paper introduces multi-service edge-intelligence as a new paradigm for realizing time-critical control over wireless. It presents the concept of multi-service edge-intelligence which revolves around tight integration of wireless access, edge-computing and machine learning techniques, in order to provide stability guarantees under wireless imperfections. The paper articulates some of the key system design aspects of multi-service edge-intelligence. It also presents a temporal-adaptive prediction technique to cope with dynamically changing wireless environments. It provides performance results in a robotic teleoperation scenario. Finally, it discusses some open research and design challenges for multi-service edge-intelligence.

One of the weaknesses of classical (fuzzy) rough sets is their sensitivity to noise, which is particularly undesirable for machine learning applications. One approach to solve this issue is by making use of fuzzy quantifiers, as done by the vaguely quantified fuzzy rough set (VQFRS) model. While this idea is intuitive, the VQFRS model suffers from both theoretical flaws as well as from suboptimal performance in applications. In this paper, we improve on VQFRS by introducing fuzzy quantifier-based fuzzy rough sets (FQFRS), an intuitive generalization of fuzzy rough sets that makes use of general unary and binary quantification models. We show how several existing models fit in this generalization as well as how it inspires novel ones. Several binary quantification models are proposed to be used with FQFRS. We conduct a theoretical study of their properties, and investigate their potential by applying them to classification problems. In particular, we highlight Yager's Weighted Implication-based (YWI) binary quantification model, which induces a fuzzy rough set model that is both a significant improvement on VQFRS, as well as a worthy competitor to the popular ordered weighted averaging based fuzzy rough set (OWAFRS) model.

In classic reinforcement learning algorithms, agents make decisions at discrete and fixed time intervals. The physical duration between one decision and the next becomes a critical hyperparameter. When this duration is too short, the agent needs to make many decisions to achieve its goal, aggravating the problem's difficulty. But when this duration is too long, the agent becomes incapable of controlling the system. Physical systems, however, do not need a constant control frequency. For learning agents, it is desirable to operate with low frequency when possible and high frequency when necessary. We propose a framework called Continuous-Time Continuous-Options (CTCO), where the agent chooses options as sub-policies of variable durations. Such options are time-continuous and can interact with the system at any desired frequency providing a smooth change of actions. The empirical analysis shows that our algorithm is competitive w.r.t. other time-abstraction techniques, such as classic option learning and action repetition, and practically overcomes the difficult choice of the decision frequency.