Belief Decay or Persistence? A Mixed-method Study on Belief Movement Over Time

When individuals encounter new information (data), that information is incorporated with their existing beliefs (prior) to form a new belief (posterior) in a process referred to as belief updating. While most studies on rational belief updating in visual data analysis elicit beliefs immediately after data is shown, we posit that there may be critical movement in an individual's beliefs when elicited immediately after data is shown v. after a temporal delay (e.g., due to forgetfulness or weak incorporation of the data). Our paper investigates the hypothesis that posterior beliefs elicited after a time interval will “decay” back towards the prior beliefs compared to the posterior beliefs elicited immediately after new data is presented. In this study, we recruit 101 participants to complete three tasks where beliefs are elicited immediately after seeing new data and again after a brief distractor task. We conduct (1) a quantitative analysis of the results to understand if there are any systematic differences in beliefs elicited immediately after seeing new data or after a distractor task and (2) a qualitative analysis of participants' reflections on the reasons for their belief update. While we find no statistically significant global trends across the participants beliefs elicited immediately v. after the delay, the qualitative analysis provides rich insight into the reasons for an individual's belief movement across 9 prototypical scenarios, which includes (i) decay of beliefs as a result of either forgetting the information shown or strongly held prior beliefs, (ii) strengthening of confidence in updated beliefs by positively integrating the new data and (iii) maintaining a consistently updated belief over time, among others. These results can guide subsequent experiments to disambiguate when and by what mechanism new data is truly incorporated into one's belief system.     

Fast Ion Transport in Li-Rich Alloy Anode for High-Energy-Density All Solid-State Lithium Metal Batteries

All-solid-state Li batteries (ASSLBs) with solid-polymer electrolytes are considered promising battery systems to achieve improved safety and high energy density. However, Li dendrite formation at the Li anode under high charging current density/capacity has limited their development. To tackle the issue, Li-metal alloying has been proposed as an alternative strategy to suppress Li dendrite growth in ASSLBs. One drawback of alloying is the relatively lower operating cell voltages, which will inevitably lower energy density compared to cells with pure Li anode. Herein, a Li-rich Li₁₃In₃ alloy electrode (LiRLIA) is proposed, where the Li₁₃In₃ alloy scaffold guides Li nucleation and hinders Li dendrite formation. Meanwhile, the free Li can recover Li's potential and facilitate fast charge transfer kinetics to realize high-energy-density ASSLBs. Benefitting from the stronger adsorption energy and lower diffusion energy barrier of Li on a Li₁₃In₃ substrate, Li prefers to deposit in the 3D Li₁₃In₃ scaffold selectively. Therefore, the Li–Li symmetric cell constructed with LiRLIA can operate at a high current density/capacity of 5 mA cm⁻²/5 mAh cm⁻² for almost 1000 h.     

Formation Theory and Printability of Photocurable Hydrogel for 3D Bioprinting

Bio-ink has gradually transited from ionic-crosslinking to photocrosslinking due to photocurable bio-hydrogel having good formability and biocompatibility. It is very important to understand and quantify the crosslinking process of photocurable hydrogels, otherwise, bioprinting cannot be standardized and scalable. However, there are few studies on hydrogel formation process and its photocrosslinking behavior which cannot be accurately predicted. Herein, the photoinitiated radical polymerized bio-hydrogels are taken as an example to establish the formation theory. Three typical crosslinking reactions are first distinguished. It is further proposed that not all double-bonds consumed during crosslinking contributeequally to polymerization. Then the concept of effective double-bond conversion (EDBC) is elicited. Deriving from EDBC, several important formation indices are defined. According to theory, it is predicted that slow crosslinking can improve the crosslinking degree. Furthermore, based on the slow crosslinking effect, a new strategy of projection-based 3D printing (PBP) is proposed, which significantly improved printing quality and efficiency. Overall, this work will fill the gap in hydrogel's formation theory, making it possible to accurately quantify the formation process.     

Unveiling the Synergy of Interfacial Contact and Defects in α-Fe2O3 for Enhanced Photo-Electrochemical Water Splitting

Photo-electrochemical (PEC) water splitting is a promising method for converting solar energy into clean energy, but the mechanism of improving PEC efficiency through the interfacial contact and defect strategy remains highly controversial. Herein, reduced graphene oxide (rGO) and oxygen vacancies are introduced into α-Fe₂O₃ nanorod (NR) arrays using a simple spin-coating method and acid treatment. The resultant oxygen vacancy–α-Fe₂O₃/rGO-integrated system exhibits a higher photocurrent, four times than the pristine α-Fe₂O₃. It is well evidenced that the electronic interface interaction between α-Fe₂O₃ and rGO is boosted with the oxygen vacancies, facilitating electron transfer from α-Fe₂O₃ to rGO. Moreover, the oxygen vacancies not only create interband states in α-Fe₂O₃ that can trap photogenerated holes and thus facilitate charge separation but significantly also strengthen the adsorption of oxidative intermediates and reduce the energy barrier of rate-determining step during oxygen evolution reaction (OER). This study demonstrates an rGO–oxygen vacancy synergistic interfacial contact and defect modification approach to design semiconducting photocatalysts for high-efficiency solar energy capture and conversion. The generated principle is expected to be extendable to another material system.     

Future Event Prediction Based on Temporal Knowledge Graph Embedding

Accurate prediction of future events brings great benefits and reduces losses for society in many domains, such as civil unrest, pandemics, and crimes. Knowledge graph is a general language for describing and modeling complex systems. Different types of events continually occur, which are often related to historical and concurrent events. In this paper, we formalize the future event prediction as a temporal knowledge graph reasoning problem. Most existing studies either conduct reasoning on static knowledge graphs or assume knowledges graphs of all timestamps are available during the training process. As a result, they cannot effectively reason over temporal knowledge graphs and predict events happening in the future. To address this problem, some recent works learn to infer future events based on historical event-based temporal knowledge graphs. However, these methods do not comprehensively consider the latent patterns and influences behind historical events and concurrent events simultaneously. This paper proposes a new graph representation learning model, namely Recurrent Event Graph ATtention Network (RE-GAT), based on a novel historical and concurrent events attention-aware mechanism by modeling the event knowledge graph sequence recurrently. More specifically, our RE-GAT uses an attention-based historical events embedding module to encode past events, and employs an attention-based concurrent events embedding module to model the associations of events at the same timestamp. A translation-based decoder module and a learning objective are developed to optimize the embeddings of entities and relations. We evaluate our proposed method on four benchmark datasets. Extensive experimental results demonstrate the superiority of our RE-GAT model comparing to various baselines, which proves that our method can more accurately predict what events are going to happen.     

Blockchain Based Consensus Algorithm and Trustworthy Evaluation of Authenticated Subgraph Queries

Over the past era, subgraph mining from a large collection of graph database is a crucial problem. In addition, scalability is another big problem due to insufficient storage. There are several security challenges associated with subgraph mining in today’s on-demand system. To address this downside, our proposed work introduces a Blockchain-based Consensus algorithm for Authenticated query search in the Large-Scale Dynamic Graphs (BCCA-LSDG). The two-fold process is handled in the proposed BCCA-LSDG: graph indexing and authenticated query search (query processing). A blockchain-based reputation system is meant to maintain the trust blockchain and cloud server of the proposed architecture. To resolve the issues and provide safe big data transmission, the proposed technique also combines blockchain with a consensus algorithm architecture. Security of the big data is ensured by dividing the BC network into distinct networks, each with a restricted number of allowed entities, data kept in the cloud gate server, and data analysis in the blockchain. The consensus algorithm is crucial for maintaining the speed, performance and security of the blockchain. Then Dual Similarity based MapReduce helps in mapping and reducing the relevant subgraphs with the use of optimal feature sets. Finally, the graph index refinement process is undertaken to improve the query results. Concerning query error, fuzzy logic is used to refine the index of the graph dynamically. The proposed technique outperforms advanced methodologies in both blockchain and non-blockchain systems, and the combination of blockchain and subgraph provides a secure communication platform, according to the findings.     

基于自监督图对比学习的视频问答方法

视频问答作为一种跨模态理解任务,在给定一段视频和与之相关的问题的条件下,需要通过不同模态语义信息之间的交互来产生问题的答案.近年来,由于图神经网络在跨模态信息融合与推理方面强大的能力,其在视频问答任务中取得了显著的进展.但是,大多数现有的图网络方法由于自身固有的过拟合或过平滑、弱鲁棒性和弱泛化性的缺陷使得视频问答模型的性能未能进一步提升.鉴于预训练技术中自监督对比学习方法的有效性和鲁棒性,在视频问答任务中利用图数据增强的思路提出了一种图网络自监督对比学习框架GMC.该框架使用针对节点和边的两种数据增强操作来生成相异子样本,并通过提升原样本与生成子样本图数据预测分布之间的一致性来提高视频问答模型的准确率和鲁棒性.在视频问答公开数据集上通过与现有先进的视频问答模型和不同GMC变体模型的实验对比验证了所提框架的有效性.     

FS-Net: 面向时序知识图谱推理的频次统计网络

时序知识图谱推理吸引了研究人员的极大关注.现有的时序知识图谱推理技术通过建模历史信息取得了巨大的进步.但是,时变性问题和不可见实体(关系)问题仍然是阻碍时序知识图谱推理模型性能进一步提升的两大挑战;而且由于需要对历史子图序列的结构信息和时间依赖信息进行建模,传统的基于嵌入的方法往往在训练和预测过程中具有较高的时间消耗,这极大地限制了推理模型在现实场景中的应用.针对以上困境,提出了一个用于时序知识图谱推理的频次统计网络, FS-Net.一方面, FS-Net不断基于最新的短期历史的事实频次统计,动态地为变化的时间戳上的预测生成时变的得分;另一方面,FS-Net基于当前时间戳上的事实频次统计,为预测补充历史不可见实体(关系);特别地,FS-Net不需要进行训练,而且具有极高的时间效率.在两个时序知识图谱基准数据集上的大量实验,表明了FS-Net相较于基准模型的巨大提升.     

融合知识图谱和评论文本的个性化推荐模型

推荐系统的目的是解决“信息过载”的问题,然而目前的研究方法大多利用用户和商品信息对用户兴趣进行建模,没有同时利用知识图谱构建用户模型来增强推荐系统的性能,因此提出了融合知识图谱和评论文本的个性化推荐模型。首先,通过不同类型的知识图谱分别关联用户项目和用户评论文本,扩展用户的兴趣和提取评论文本中的实体;其次,通过构造用户兴趣网络得到带有用户兴趣偏好的兴趣特征;然后,通过构造画像模块和情感模块的画像网络提取到带有用户情感倾向的画像特征;利用决策层进行点击率预测。最后在Amazon数据集上进行了实验比较,对所提模型的性能进行了评估,并与目前的融合知识图谱和评论文本的推荐模型进行比较,验证了所提模型的有效性。     

Peering through the lens of high-reliability theory: A competencies driven security culture model of high-reliability organisations

To improve organisational safety and enhance security efficiency, organisations seek to establish a culture of security that provides a foundation for how employees should approach security. There are several frameworks and models that provide a set of requirements for forming security cultures; however, for many organisations, the requirements of the frameworks are difficult to meet, if not impossible. In this research, we take a different perspective and focus on the core underlying competencies that high-reliability organisations (HROs) have shown to be effective in achieving levels of risk tolerance consistent with the goals of a security culture. In doing so we draw on high-reliability theory to develop a Security Culture Model that explains how a firm's supportive and practical competencies form its organisational security culture. To refine and test the model, we conducted a developmental mixed-method study using interviews and survey data with professional managers involved in the information security (InfoSec) programs within their respective HROs. Our findings emphasise the importance of an organisation's supportive and practical competencies for developing a culture of security. Our results suggest that organisations' security cultures are a product of their InfoSec practices and that organisational mindfulness, top management involvement and organisational structure are key to the development of those practices.