非侵入性负载监控（NILM）试图通过从单个骨料测量中估算单个设备功率使用来节省能源。深度神经网络在尝试解决尼尔姆问题方面变得越来越流行。但是，大多数使用的模型用于负载识别，而不是在线源分离。在源分离模型中，大多数使用单任务学习方法，其中神经网络专门为每个设备培训。该策略在计算上是昂贵的，并且忽略了多个电器可以同时活跃的事实和它们之间的依赖性。其余模型不是因果关系，这对于实时应用很重要。受语音分离模型Convtas-Net的启发，我们提出了Conv-Nilm-Net，这是端到端尼尔姆的完全卷积框架。 Conv-NILM-NET是多元设备源分离的因果模型。我们的模型在两个真实数据集和英国销售的两个真实数据集上进行了测试，并且显然超过了最新技术的状态，同时保持尺寸明显小于竞争模型。

Understanding the facial expressions of our interlocutor is important to enrich the communication and to give it a depth that goes beyond the explicitly expressed. In fact, studying one's facial expression gives insight into their hidden emotion state. However, even as humans, and despite our empathy and familiarity with the human emotional experience, we are only able to guess what the other might be feeling. In the fields of artificial intelligence and computer vision, Facial Emotion Recognition (FER) is a topic that is still in full growth mostly with the advancement of deep learning approaches and the improvement of data collection. The main purpose of this paper is to compare the performance of three state-of-the-art networks, each having their own approach to improve on FER tasks, on three FER datasets. The first and second sections respectively describe the three datasets and the three studied network architectures designed for an FER task. The experimental protocol, the results and their interpretation are outlined in the remaining sections.

知识图（kgs）以（头，谓词，尾部） - 轨道的形式存储信息。为了增强具有新知识的公斤，研究人员提出了kg完成（kgc）任务的模型，例如链接预测；即，回答（H; P;？）或（？; P; t）查询。这种模型通常在固定测试集上使用平均指标进行评估。尽管对于跟踪进度有用，但平均的单分数指标无法透露模型到底学到的或未能学习的内容。为了解决这个问题，我们提出了KGXBoard：一个交互式框架，用于对有意义的数据子集进行精细颗粒评估，每个框架都测试了KGC模型的个人和可解释功能。在我们的实验中，我们强调了使用KGXBoard发现的发现，这是无法通过标准平均单分数指标来检测到的。

随着现实应用程序中AI系统的兴起，需要可靠和值得信赖的AI。一个基本方面是可解释的AI系统。但是，关于应如何评估可解释的AI系统的商定标准。受图灵测试的启发，我们引入了一个以人为本的评估框架，领先的领域专家接受或拒绝AI系统和另一个领域专家的解决方案。通过比较提供的解决方案的接受率，我们可以评估AI系统与域专家相比的性能，以及AI系统的解释（如果提供）是否可以理解。该设置与图灵测试相当 - 可以作为各种以人为中心的AI系统评估的框架。我们通过提出两个实例来证明这一点：（1）评估系统的分类准确性，可以选择合并标签不确定性； （2）评估以人为中心确定提供的解释的有用性。

基于训练学习的脱毛方法需要大量的模糊和尖锐的图像对。不幸的是，现有的合成数据集还不够现实，对其进行训练的Deblurring模型无法有效处理真正的模糊图像。尽管最近提出了真实的数据集，但它们提供了有限的场景和相机设置，并且为不同的设置捕获真实数据集仍然具有挑战性。为了解决这一问题，本文分析了各种因素，这些因素引入了真实和合成模糊图像之间的差异。为此，我们提出了RSBlur，这是一个具有真实图像的新型数据集和相应的尖锐图像序列，以详细分析真实和合成模糊之间的差异。使用数据集，我们揭示了不同因素在模糊生成过程中的影响。基于分析，我们还提出了一种新型的模糊合成管道，以综合更现实的模糊。我们表明，我们的合成管道可以改善实际模糊图像上的脱毛性能。

Many theories, based on neuroscientific and psychological empirical evidence and on computational concepts, have been elaborated to explain the emergence of consciousness in the central nervous system. These theories propose key fundamental mechanisms to explain consciousness, but they only partially connect such mechanisms to the possible functional and adaptive role of consciousness. Recently, some cognitive and neuroscientific models try to solve this gap by linking consciousness to various aspects of goal-directed behaviour, the pivotal cognitive process that allows mammals to flexibly act in challenging environments. Here we propose the Representation Internal-Manipulation (RIM) theory of consciousness, a theory that links the main elements of consciousness theories to components and functions of goal-directed behaviour, ascribing a central role for consciousness to the goal-directed manipulation of internal representations. This manipulation relies on four specific computational operations to perform the flexible internal adaptation of all key elements of goal-directed computation, from the representations of objects to those of goals, actions, and plans. Finally, we propose the concept of `manipulation agency' relating the sense of agency to the internal manipulation of representations. This allows us to propose that the subjective experience of consciousness is associated to the human capacity to generate and control a simulated internal reality that is vividly perceived and felt through the same perceptual and emotional mechanisms used to tackle the external world.