Accurate mapping of crop distribution on Earth's surface aids in predicting grain production. Pattern classification along with remote sensing imagery can facilitate traditional manual field measurement techniques using machine learning. With the rapid increase in satellite sensor resolution, the object-based classification paradigm has increasingly been applied. However, scale parameter selection is always the difficult part of object-based classification. Based on ensemble learning, this study proposes a classification method using the multiscale object-based weighted method which includes manual digitizing of crop distribution in the southern region of Jishan County, Shanxi Province, China, applying Gaofen-2 (GF-2) images. This method initially uses estimations of the scale parameter (ESP) tool to select “good” scales, defined here as “preferred” scales, after which feature subsets are screened by each preferred scale as the input of multiple classifiers and classifies. Finally, all classification results are then fused. Our research results indicate that: 1) Feature importance values are sorted differently at different preferred scales; 2) accuracy differences become clear when different preferred scales are combined with different classifiers, and determining the “best” single appropriate scale is generally difficult; 3) accuracy of the multiscale weighted classification method is higher compared to the single preferred scale approach. Furthermore, ensemble learning can be achieved using this method on multiple scales and on multiple classifiers. With this method, procedures that necessitate the selection of segmentation scales and the selection and optimization of classifiers can be skipped altogether.

Image captioning is the task of generating textual descriptions of images. In order to obtain a better image representation, attention mechanisms have been widely adopted in image captioning. However, in existing models with detection-based attention, the rectangular attention regions are not fine-grained, as they contain irrelevant regions (e.g., background or overlapped regions) around the object, making the model generate inaccurate captions. To address this issue, we propose panoptic segmentation-based attention that performs attention at a mask-level (i.e., the shape of the main part of an instance). Our approach extracts feature vectors from the corresponding segmentation regions, which is more fine-grained than current attention mechanisms. Moreover, in order to process features of different classes independently, we propose a dual-attention module which is generic and can be applied to other frameworks. Experimental results showed that our model could recognize the overlapped objects and understand the scene better. Our approach achieved competitive performance against state-of-the-art methods. We made our code available.

Different choices of living space for urban residents are concrete reflections of a social class divide. Emerging residential space differentiation is a critical issue in Chinese cities. This paper aims to explore the current situations and mechanisms of residential spatial differentiation, and reveal its social responses in Chinese cities. Taking Wuhan, the largest city in central China, as an example, this research divides the residential space based on social resource structures. It analyzes and compares spatial differentiation and influencing factors of different residential areas and houses with different prices in Wuhan by spatial differentiation indicators and geographic detector. The results show that residential areas are divergent due to differences in urban resource spatial structure, with few and concentrated high-quality residential areas. The spatial differentiation level of different residential areas also varies, with central location, landscape, educational resources, and other dominant scarce resources being the key to residential spatial differentiation in most of China’s big cities. In addition, residential spatial differentiation can lead to issues including housing wealth inequality, resource deprivation, and class identity solidification. In the end, this paper puts forward policy implications on alleviating residential spatial differentiation and optimizing allocation of residential resources.

The increasing residential space differentiation is an important practical problem for Chinese cities. The different choice of living space for urban residents is a concrete reflection of social class divide, which reflects the differences in the choices and demands of different social groups for urban housing. Taking Wuhan, an important city in central China, as an example, this research divides the residential space based on the social resource structure, and analyzes and compares the spatial differentiation and influencing factors of different residential areas and houses with different prices in Wuhan by spatial differentiation indicators and geographic detector. The results show clear differences in resource structure among residential areas, and there are few high-quality residential areas with a concentrated distribution. The spatial differentiation levels of different residential areas are distinct, and the pursuit of high-quality and scarce resources is the key to the residential space differentiation of most large cities in China. In addition, residential space differentiation can lead to inter-generational problems such as housing wealth inequality, resource deprivation and solidified class identity. Exploring the mechanisms and social effect of residential space differentiation in most large cities in China can provide some reference for China's urban space planning and real estate market regulation in the new era.

The physical unclonable function (PUF) is a widely used hardware security primitive. Before hacking into a PUF-protected system, intruders typically initiate attacks on the PUF as the first step. Many strong PUF designs have been proposed to thwart non-invasive attacks that exploit acquired CRPs. In this work, we propose a general framework for efficient attacks on strong PUFs by investigating from two perspectives, namely, statistical covariances in the challenge space and the design dependency among PUF compositions. The framework consists of two novel attack methods against a wide range of PUF families, including XOR APUFs, interpose PUFs, and bistable ring (BR)-PUFs. It can also exploit the knowledge of reliability information to improve attack efficiency with gradient optimization. We evaluate our proposed attacks through extensive experiments, running both software-based simulation and hardware implementations on FPGAs to compare with corresponding SOTA works. Considerable effort has been made in ensuring identical software/hardware conditions for a fair comparison. The results demonstrate that our framework significantly outperforms SOTA results. Moreover, we show that our framework can efficiently attack diverse PUF families built from entirely different types, while almost all existing works solely focused on attacking one or very limited number of PUF designs.

As online e-learning systems become more prevalent, there is a growing need for them to accommodate individual differences among students. According to the concept of zone of proximal development (ZPD), it is imperative to provide online students with educational contents that are neither too easy nor too difficult, but are slightly beyond their current abilities. However, following ZPD rule is challenging in online e-learning system, due to the following reasons: the system does not know a priori the ability of the online students, especially for the newly arrived student; the exact relationship between student feedback on teaching and their abilities (i.e., reward/gain function) is extremely complicated, and even unknown to each student. Aiming at solving the issue above, this paper proposes a personalized educational scheme to students, POEM, in order to maximize their accumulative learning gains over multiple rounds. Specifically, instead of assuming any specific formal reward function, we first estimate any unknown reward function from noisy samples using Gaussian process (GP) model. Then, the multi-arm bandit based algorithm is used to select the teaching content with the adaptive difficulty level to balance the effect of exploration and exploitation. The simulation results demonstrate the effectiveness of our proposed method.