Where design and law meet - An empirical study for understanding legal design and its implication for research and practice

This thesis explores the notion of legal design and proposes its process. The research, conducted through a literature review and expert interviews, provides both academic and empirical findings regarding the subject. Through the literature review, this thesis argues that legal design is an evolving discipline that incorporates many design methods into its framework. First, this study illustrates how the influence of design in the field of law led to the exploration of legal design. Regarding legal design, designerly thinking and doing, including visualization, human-centered design, the design thinking process, and participatory design methods, have been utilized to improve communication of legal documents as well as the user experience of legal services and legal systems. The changed perspective of viewing the mass as the end-user of the law rather than only legal experts and the collaborative, multidisciplinary exploration drives law and design professionals to explore a more radical form of collaboration—legal design—as a new research topic. In the process, the designer who becomes familiar with the legal context and the lawyer who learns designerly think-ing and doing can each claim to be a legal designer. Expert interviews offer empirical knowledge of real-world legal design practices. For instance, through the analysis of the seven collected case projects and expert interviews, this thesis illustrates the understanding of legal design from the standpoint of two parties—designers and lawyers. The insight being is to create a space for legal design at the intersection of design, law, and technology. To explore the space, a legal design process generated from the interviews is proposed. Based on the legal design process, this study suggests three roles designers should assume when engaged in a project with law stakeholders. This thesis also suggests embedding the system thinking within the legal design framework and encouraging the use of more service design methods in the legal design process in order to tackle the complexity of the legal challenges. This thesis conducts explorative research to understand legal design from a design researcher’s perspective. Thus, its limitations include the research methods, the selection of the interviewees, and the scope of the collected case projects. The legal design framework proposed in this study, therefore, needs to be further validated and supported by more comprehensive data

How does rising house price influence stock market participation in China? A micro-household perspective

This is an empirical study on the effect of house price on stock-market participation and its depths based on unique China Household Finance Survey (CHFS) data in 2011 and 2013 including 36213 sample households. We mainly found that, with an increase of one thousand RMB per square meter in macro house price, the probability to participate in the stock market will increase by 5.4% before controlling for wealth effect and 2.84% afterwards, indicating the existence of wealth effect. The participation depths of the stock-total asset ratio is expected to decrease by 0.23% and absolute stock asset is observed to decrease by 5.8 thousand RMB in response to one thousand RMB increase of per square meter house price. The effect of house price on participation decision is also related to housing area, and the negative effect of house price on stock market participation depths gets more intense with the increase of the stock-total asset ratio

Discussion on Emergency Renewal Design of Indoor Space of Multi-Storey Residential Building under Public Health Emergency

In order to deal with public health emergencies, five residential areas in the main urban area of Handan were selected for on-the-spot measurement and questionnaire survey, and the survey data were sorted out and sequenced regression analysis. it is summarized that the main problems of indoor space in emergency are poor spatial independence, unreasonable layout, low flexibility and poor natural lighting effect. On the basis of this, the corresponding optimization strategy is put forward in order to help to update and improve the emergency design of interior space in multi-storey residential buildings

Correlation between mobile phone addiction tendency and its related risk factor among Chinese college students: A cross-sectional study

Purpose: Mobile phone addiction prevalence is a global concern which has attracted great attention. It is now considered that excessive mobile phone usage is associated with potentially harmful and/or disturbing behaviors. The present study was aimed at exploring the current situation and related factors of mobile phone addiction tendency and providing a scientific suggestion for its prevention among college students. Methods: A cross-sectional study was applied for stratified cluster random sampling among college students, including five survey tools: the basic information questionnaire, UCLA loneliness scale, college students’ interpersonal comprehensive diagnostic scale, the Pittsburgh sleep quality index scale and mobile phone addiction tendency scale (MPATS). SPSS v 17.0 statistical tool was applied to analyze data from the survey. Results: A total of 760 questionnaires were administered of which 735 questionnaires were retrieved and the valid questionnaires were 730. Classification of mobile phone addiction tendency has statistical significance with grade. Also, classification of loneliness has statistical significance with major, grade and home address. Furthermore, classification of interpersonal relationship has statistical significance with romance status and grade. Additionally, classification of MPATS was positively correlated with classification of UCLA loneliness scale, Pittsburgh sleep quality index scale and interpersonal relationship scale. Interpersonal relationship, sleep quality, and loneliness were linearly correlated with mobile phone addiction tendency. Conclusion: Grade, interpersonal relationship, sleep quality and loneliness were positively correlated with mobile phone addiction tendency, which are the associated risk factors. Therefore, concerns and interventions are required to decrease the risk factor for the sake of college students’ health

TPatch: A Triggered Physical Adversarial Patch

Autonomous vehicles increasingly utilize the vision-based perception module to acquire information about driving environments and detect obstacles. Correct detection and classification are important to ensure safe driving decisions. Existing works have demonstrated the feasibility of fooling the perception models such as object detectors and image classifiers with printed adversarial patches. However, most of them are indiscriminately offensive to every passing autonomous vehicle. In this paper, we propose TPatch, a physical adversarial patch triggered by acoustic signals. Unlike other adversarial patches, TPatch remains benign under normal circumstances but can be triggered to launch a hiding, creating or altering attack by a designed distortion introduced by signal injection attacks towards cameras. To avoid the suspicion of human drivers and make the attack practical and robust in the real world, we propose a content-based camouflage method and an attack robustness enhancement method to strengthen it. Evaluations with three object detectors, YOLO V3/V5 and Faster R-CNN, and eight image classifiers demonstrate the effectiveness of TPatch in both the simulation and the real world. We also discuss possible defenses at the sensor, algorithm, and system levels.Comment: Appeared in 32nd USENIX Security Symposium (USENIX Security 23

In Defense of Clip-based Video Relation Detection

Video Visual Relation Detection (VidVRD) aims to detect visual relationship triplets in videos using spatial bounding boxes and temporal boundaries. Existing VidVRD methods can be broadly categorized into bottom-up and top-down paradigms, depending on their approach to classifying relations. Bottom-up methods follow a clip-based approach where they classify relations of short clip tubelet pairs and then merge them into long video relations. On the other hand, top-down methods directly classify long video tubelet pairs. While recent video-based methods utilizing video tubelets have shown promising results, we argue that the effective modeling of spatial and temporal context plays a more significant role than the choice between clip tubelets and video tubelets. This motivates us to revisit the clip-based paradigm and explore the key success factors in VidVRD. In this paper, we propose a Hierarchical Context Model (HCM) that enriches the object-based spatial context and relation-based temporal context based on clips. We demonstrate that using clip tubelets can achieve superior performance compared to most video-based methods. Additionally, using clip tubelets offers more flexibility in model designs and helps alleviate the limitations associated with video tubelets, such as the challenging long-term object tracking problem and the loss of temporal information in long-term tubelet feature compression. Extensive experiments conducted on two challenging VidVRD benchmarks validate that our HCM achieves a new state-of-the-art performance, highlighting the effectiveness of incorporating advanced spatial and temporal context modeling within the clip-based paradigm

SynFog: A Photo-realistic Synthetic Fog Dataset based on End-to-end Imaging Simulation for Advancing Real-World Defogging in Autonomous Driving

To advance research in learning-based defogging algorithms, various synthetic fog datasets have been developed. However, existing datasets created using the Atmospheric Scattering Model (ASM) or real-time rendering engines often struggle to produce photo-realistic foggy images that accurately mimic the actual imaging process. This limitation hinders the effective generalization of models from synthetic to real data. In this paper, we introduce an end-to-end simulation pipeline designed to generate photo-realistic foggy images. This pipeline comprehensively considers the entire physically-based foggy scene imaging process, closely aligning with real-world image capture methods. Based on this pipeline, we present a new synthetic fog dataset named SynFog, which features both sky light and active lighting conditions, as well as three levels of fog density. Experimental results demonstrate that models trained on SynFog exhibit superior performance in visual perception and detection accuracy compared to others when applied to real-world foggy images