Text-based personality computing (TPC) has gained many research interests in NLP. In this paper, we describe 15 challenges that we consider deserving the attention of the research community. These challenges are organized by the following topics: personality taxonomies, measurement quality, datasets, performance evaluation, modelling choices, as well as ethics and fairness. When addressing each challenge, not only do we combine perspectives from both NLP and social sciences, but also offer concrete suggestions towards more valid and reliable TPC research.

Stance detection (SD) can be considered a special case of textual entailment recognition (TER), a generic natural language task. Modelling SD as TER may offer benefits like more training data and a more general learning scheme. In this paper, we present an initial empirical analysis of this approach. We apply it to a difficult but relevant test case where no existing labelled SD dataset is available, because this is where modelling SD as TER may be especially helpful. We also leverage measurement knowledge from social sciences to improve model performance. We discuss our findings and suggest future research directions.

A default assumption in reinforcement learning and optimal control is that experience arrives at discrete time points on a fixed clock cycle. Many applications, however, involve continuous systems where the time discretization is not fixed but instead can be managed by a learning algorithm. By analyzing Monte-Carlo value estimation for LQR systems in both finite-horizon and infinite-horizon settings, we uncover a fundamental trade-off between approximation and statistical error in value estimation. Importantly, these two errors behave differently with respect to time discretization, which implies that there is an optimal choice for the temporal resolution that depends on the data budget. These findings show how adapting the temporal resolution can provably improve value estimation quality in LQR systems from finite data. Empirically, we demonstrate the trade-off in numerical simulations of LQR instances and several non-linear environments.

Synergetic use of sensors for soil moisture retrieval is attracting considerable interest due to the different advantages of different sensors. Active, passive, and optic data integration could be a comprehensive solution for exploiting the advantages of different sensors aimed at preparing soil moisture maps. Typically, pixel-based methods are used for multi-sensor fusion. Since, different applications need different scales of soil moisture maps, pixel-based approaches are limited for this purpose. Object-based image analysis employing an image object instead of a pixel could help us to meet this need. This paper proposes a segment-based image fusion framework to evaluate the possibility of preparing a multi-scale soil moisture map through integrated Sentinel-1, Sentinel-2, and Soil Moisture Active Passive (SMAP) data. The results confirmed that the proposed methodology was able to improve soil moisture estimation in different scales up to 20% better compared to pixel-based fusion approach.

Machine Learning (ML) technologies have been increasingly adopted in Medical Cyber-Physical Systems (MCPS) to enable smart healthcare. Assuring the safety and effectiveness of learning-enabled MCPS is challenging, as such systems must account for diverse patient profiles and physiological dynamics and handle operational uncertainties. In this paper, we develop a safety assurance case for ML controllers in learning-enabled MCPS, with an emphasis on establishing confidence in the ML-based predictions. We present the safety assurance case in detail for Artificial Pancreas Systems (APS) as a representative application of learning-enabled MCPS, and provide a detailed analysis by implementing a deep neural network for the prediction in APS. We check the sufficiency of the ML data and analyze the correctness of the ML-based prediction using formal verification. Finally, we outline open research problems based on our experience in this paper.

Current technological advances open up new opportunities for bringing human-machine interaction to a new level of human-centered cooperation. In this context, a key issue is the semantic understanding of the environment in order to enable mobile robots more complex interactions and a facilitated communication with humans. Prerequisites are the vision-based registration of semantic objects and humans, where the latter are further analyzed for potential interaction partners. Despite significant research achievements, the reliable and fast registration of semantic information still remains a challenging task for mobile robots in real-world scenarios. In this paper, we present a vision-based system for mobile assistive robots to enable a semantic-aware environment perception without additional a-priori knowledge. We deploy our system on a mobile humanoid robot that enables us to test our methods in real-world applications.

The detection of state-sponsored trolls acting in information operations is an unsolved and critical challenge for the research community, with repercussions that go beyond the online realm. In this paper, we propose a novel AI-based solution for the detection of state-sponsored troll accounts, which consists of two steps. The first step aims at classifying trajectories of accounts' online activities as belonging to either a state-sponsored troll or to an organic user account. In the second step, we exploit the classified trajectories to compute a metric, namely "troll score", which allows us to quantify the extent to which an account behaves like a state-sponsored troll. As a study case, we consider the troll accounts involved in the Russian interference campaign during the 2016 US Presidential election, identified as Russian trolls by the US Congress. Experimental results show that our approach identifies accounts' trajectories with an AUC close to 99\% and, accordingly, classify Russian trolls and organic users with an AUC of 97\%. Finally, we evaluate whether the proposed solution can be generalized to different contexts (e.g., discussions about Covid-19) and generic misbehaving users, showing promising results that will be further expanded in our future endeavors.

在本文中，我们专注于改进二进制2D实例细分，以帮助人类用多边形标记地面真相数据集。人类的标签只需要在物体周围绘制盒子，然后自动生成多边形。为了有用，我们的系统必须实时运行CPU。二进制实例细分的最常见方法涉及编码器折叠网络。本报告评估了最先进的编码器 - 码头网络，并提出了一种使用这些网络改善实例分割质量的方法。除了网络体系结构的改进之外，我们提出的方法还依靠为网络输入，所谓的极端点（即对象轮廓上的最外部点）提供额外的信息。用户可以几乎尽快给它们标记它们，而不是边界框。边界框也可以从极端点推导。与其他最先进的编码器网络相比，此方法可产生更好的IOU，并且在将其部署在CPU上时也足够快。

图形信号处理（GSP）中的基本前提是，将目标信号的成对（反）相关性作为边缘权重以用于图形过滤。但是，现有的快速图抽样方案仅针对描述正相关的正图设计和测试。在本文中，我们表明，对于具有强固有抗相关的数据集，合适的图既包含正边缘和负边缘。作为响应，我们提出了一种以平衡签名图的概念为中心的线性时间签名的图形采样方法。具体而言，给定的经验协方差数据矩阵$ \ bar {\ bf {c}} $，我们首先学习一个稀疏的逆矩阵（Graph laplacian）$ \ MATHCAL {l} $对应于签名图$ \ Mathcal $ \ Mathcal {G} $ 。我们为平衡签名的图形$ \ Mathcal {g} _b $ - 近似$ \ Mathcal {g} $通过Edge Exge Exgement Exgmentation -As Graph频率组件定义Laplacian $ \ Mathcal {L} _b $的特征向量。接下来，我们选择样品以将低通滤波器重建误差分为两个步骤最小化。我们首先将Laplacian $ \ Mathcal {L} _b $的所有Gershgorin圆盘左端对齐，最小的EigenValue $ \ lambda _ {\ min}（\ Mathcal {l} _b）$通过相似性转换$ \ MATHCAL $ \ MATHCAL} s \ Mathcal {l} _b \ s^{ - 1} $，利用最新的线性代数定理，称为gershgorin disc perfect perfect对齐（GDPA）。然后，我们使用以前的快速gershgorin盘式对齐采样（GDAS）方案对$ \ Mathcal {L} _p $进行采样。实验结果表明，我们签名的图形采样方法在各种数据集上明显优于现有的快速采样方案。

在县粒度上预测每年农作物的产量对于国家粮食生产和价格稳定至关重要。在本文中，为了实现更好的作物产量预测，利用最新的图形信号处理（GSP）工具来利用相邻县之间的空间相关性，我们通过图形光谱滤波来证明相关的特征，这些特征是深度学习预测模型的输入。具体而言，我们首先构建一个具有边缘权重的组合图，该图可以通过公制学习编码土壤和位置特征的县对县的相似性。然后，我们通过最大的后验（MAP）配方使用图形laplacian正常化程序（GLR）来定性特征。我们关注的挑战是估算关键的权重参数$ \ mu $，交易忠诚度和GLR，这是噪声差异的函数，以无监督的方式。我们首先使用发现局部恒定区域的图集集合检测（GCD）过程直接从噪声浪费的图形信号估算噪声方差。然后，我们通过通过偏置变化分析来计算最佳$ \ mu $最大程度地减少近似平方误差函数。收集到的USDA数据的实验结果表明，使用DeNo的特征作为输入，可以明显改善作物产量预测模型的性能。