Recommending Privacy Settings for Internet-of-Things

Privacy concerns have been identified as an important barrier to the growth of IoT. These concerns are exacerbated by the complexity of manually setting privacy preferences for numerous different IoT devices. Hence, there is a demand to solve the following, urgent research question: How can we help users simplify the task of managing privacy settings for IoT devices in a user-friendly manner so that they can make good privacy decisions? To solve this problem in the IoT domain, a more fundamental understanding of the logic behind IoT users’ privacy decisions in different IoT contexts is needed. We, therefore, conducted a series of studies to contextualize the IoT users’ decision-making characteristics and designed a set of privacy-setting interfaces to help them manage their privacy settings in various IoT contexts based on the deeper understanding of users’ privacy decision behaviors. In this dissertation, we first present three studies on recommending privacy settings for different IoT environments, namely general/public IoT, household IoT, and fitness IoT, respectively. We developed and utilized a “data-driven” approach in these three studies—We first use statistical analysis and machine learning techniques on the collected user data to gain the underlying insights of IoT users’ privacy decision behavior and then create a set of “smart” privacy defaults/profiles based on these insights. Finally, we design a set of interfaces to incorporate these privacy default/profiles. Users can apply these smart defaults/profiles by either a single click or by answering a few related questions. The biggest limitation of these three studies is that the proposed interfaces have not been tested, so we do not know what level of complexity (both in terms of the user interface and the in terms of the profiles) is most suitable. Thus, in the last study, we address this limitation by conducting a user study to evaluate the new interfaces of recommending privacy settings for household IoT users. The results show that our proposed user interfaces for setting household IoT privacy settings can improve users’ satisfaction. Our research can benefit IoT users, manufacturers, and researchers, privacy-setting interface designers and anyone who wants to adopt IoT devices by providing interfaces that put their most prominent concerns in the forefront and that make it easier to set settings that match their preferences

Procedural Generation and Rendering of Ink Bamboo Painting

This thesis describes an algorithm that generates various ink bamboo paintings. First, a completely procedural model is used to generate the geometric shape of bamboos. The model uses a grammar-like approach that recursively generates new parts of the bamboo in a randomized manner. The random parameters are bounded by rules that simulate the natural form of bamboo. The structure of the bamboo is represented line segments with directions. Various ink stroke sprites of stalk, branch, or leaf shapes are mapped to line segments, using reverse mapping and bilinear sampling to eliminate aliasing effects. The sprites are mapped in different degrees of transparency to simulate the effect of various shades of ink produced by changes in forces when using an ink brush. Finally, a seal is applied to sign the work and enhance the visual effect. The algorithm is implemented in Python 3 and can be run on any computer with the imageio library installed. The output of the program is saved in a PNG image file, which can be used for various types of illustrations. This model is able to produce unique images during every run, and would significantly reduce human labor in painting stylistically similar artworks of ink bamboo paintings

Correlational Research on Mobile Phone Addiction and the Interpersonal Relationship Distress of Chinese College Students

In this essay, we utilized the following scales: Mobile and Internet Addiction Test, Basic Psychological Needs Scale, Negative Coping Style Questionnaire, and Interpersonal Relationships Assessment Scale. With those, we surveyed 1,730 college students, investigating the influence of mobile phone addiction on their interpersonal relationship distress and the mediating chain effect of basic psychological needs and negative coping styles on mobile phone addiction and interpersonal relationship distress. The results indicate that: (1) Mobile phone addiction can predict interpersonal relationship distress in college students; (2) Basic psychological needs serve as the mediating variables between mobile phone addiction and interpersonal relationship distress; (3) Negative coping styles prove to be the mediator between mobile phone addiction and interpersonal relationship distress; and (4) Basic psychological needs and negative coping styles establish a mediating chain effect between mobile phone addiction and interpersonal relationship distress

Deposition of Diamond-like Carbon Films by Liquid Electrochemical Technique

Diamond-like carbon (DLC) films are amorphous carbon films that can be produced with or without hydrogen, depending on the deposition techniques and conditions. Besides the conventional vapor deposition, DLC films can be deposited by liquid electrochemical technique that utilizes the electrolysis of organic solution. Liquid electrochemical deposition of DLC films has gained growing interest because of the simplicity of experimental setup, the scalability of the process, low process temperature and the possibility of deposition on substrates with complex shape. Although some work has already been published, some important aspects are still missing. The aim of this dissertation is to investigate the influence of the experimental set-up and deposition parameters, as well as to evaluate possible technological applications of the process.Diamantähnliche Kohlenstoffschichten (DLC) sind amorphe Kohlenstoffschichten, die abhängig von der Abscheidetechnik und den Abscheidebedingungen, sowohl mit als auch ohne Wasserstoff hergestellt werden können. Neben der konventionellen Gasphasenabscheidung können DLC Schichten auch mittels elektrochemischer Abscheidung aus der flüssigen Phase hergestellt werden. Unter der angelegten Spannung reagieren die organischen Moleküle und werden zu DLC-Schichten auf dem Substrat. Die elektrochemische Abscheidung von DLC Schichten gewinnt aufgrund der Einfachheit des experimentellen Aufbaus, der Skalierbarkeit des Prozesses, der niedrigen Prozesstemperatur und der Möglichkeit auch geometrisch komplexe Strukturen zu beschichten zunehmend an Interesse. Obwohl bereits einige Arbeiten zu diesem Thema veröffentlicht wurden, fehlen noch entscheidende Aspekte. Das Ziel dieser Dissertation ist es daher, den Einfluss des experimentellen Aufbaus und der Abscheidebedingungen zu untersuchen und das technologische Potential des Verfahrens einzuschätzen

PLE-SLAM: A Visual-Inertial SLAM Based on Point-Line Features and Efficient IMU Initialization

Visual-inertial SLAM is crucial in various fields, such as aerial vehicles, industrial robots, and autonomous driving. The fusion of camera and inertial measurement unit (IMU) makes up for the shortcomings of a signal sensor, which significantly improves the accuracy and robustness of localization in challenging environments. This article presents PLE-SLAM, an accurate and real-time visual-inertial SLAM algorithm based on point-line features and efficient IMU initialization. First, we use parallel computing methods to extract features and compute descriptors to ensure real-time performance. Adjacent short line segments are merged into long line segments, and isolated short line segments are directly deleted. Second, a rotation-translation-decoupled initialization method is extended to use both points and lines. Gyroscope bias is optimized by tightly coupling IMU measurements and image observations. Accelerometer bias and gravity direction are solved by an analytical method for efficiency. To improve the system's intelligence in handling complex environments, a scheme of leveraging semantic information and geometric constraints to eliminate dynamic features and A solution for loop detection and closed-loop frame pose estimation using CNN and GNN are integrated into the system. All networks are accelerated to ensure real-time performance. The experiment results on public datasets illustrate that PLE-SLAM is one of the state-of-the-art visual-inertial SLAM systems

Surface defects repairing of sprayed Ca-P coating by the microwave-hydrothermal method

The increasing interest in decreasing the surface defects of sprayed Ca-P coating deposited on carbon/carbon (C/C) composites to enhance the bonding strength, bioactivity and corrosion resistance of the coating is justified by the growing evidence of its beneficial effect on the bone replacement fields. Microwave-hydrothermal (MH) method detailed in the previous study is successfully used to reduce the above coating defects and the MH mechanism is well studied here. Hence, five different treatment reagents involving calcium and phosphorus solution, sulfuric acid (H2SO4) solution, ammonium hydroxide (NH3·H2O) solution, only Ca2+ solution and deionized water are selected as the precursor solution. The surface, cross-sectional morphologies, phase and composition of the coatings are characterized by the scanning electron microscopy (SEM), X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FTIR), microscopy Raman spectroscopy and X-ray photoelectron spectroscopy (XPS) spectra. Elastic modulus and coating hardness are measured by nanoindentation. Results reveal that the presence of calcium and phosphorus ions, as well as the H2SO4 in the precursor solution during the MH process, have a positive influence on the reduction of sprayed Ca-P coating surface defects. However, the coating treated by other three solutions cannot produce new phases on the basis of sprayed Ca-P coating and the surface defects of it are not decreased. Nevertheless, the elastic modulus and hardness of the coating treated by H2SO4 solution are very weak. MH treated coating by calcium and phosphorus ions in the precursor solution and in NH3·H2O solution, only Ca2+ solution and deionized water own the similar elastic modulus and hardness to that of the sprayed Ca-P coating. To conclude, in the MH process, the surface defects of the sprayed Ca-P coating are only lowered in calcium and phosphorus precursor solution and the coating strength is not dropped, which demonstrates the promoting mechanism of MH process

RIGID: Recurrent GAN Inversion and Editing of Real Face Videos

GAN inversion is indispensable for applying the powerful editability of GAN to real images. However, existing methods invert video frames individually often leading to undesired inconsistent results over time. In this paper, we propose a unified recurrent framework, named \textbf{R}ecurrent v\textbf{I}deo \textbf{G}AN \textbf{I}nversion and e\textbf{D}iting (RIGID), to explicitly and simultaneously enforce temporally coherent GAN inversion and facial editing of real videos. Our approach models the temporal relations between current and previous frames from three aspects. To enable a faithful real video reconstruction, we first maximize the inversion fidelity and consistency by learning a temporal compensated latent code. Second, we observe incoherent noises lie in the high-frequency domain that can be disentangled from the latent space. Third, to remove the inconsistency after attribute manipulation, we propose an \textit{in-between frame composition constraint} such that the arbitrary frame must be a direct composite of its neighboring frames. Our unified framework learns the inherent coherence between input frames in an end-to-end manner, and therefore it is agnostic to a specific attribute and can be applied to arbitrary editing of the same video without re-training. Extensive experiments demonstrate that RIGID outperforms state-of-the-art methods qualitatively and quantitatively in both inversion and editing tasks. The deliverables can be found in \url{https://cnnlstm.github.io/RIGID}Comment: ICCV202

H2CGL: Modeling Dynamics of Citation Network for Impact Prediction

The potential impact of a paper is often quantified by how many citations it will receive. However, most commonly used models may underestimate the influence of newly published papers over time, and fail to encapsulate this dynamics of citation network into the graph. In this study, we construct hierarchical and heterogeneous graphs for target papers with an annual perspective. The constructed graphs can record the annual dynamics of target papers' scientific context information. Then, a novel graph neural network, Hierarchical and Heterogeneous Contrastive Graph Learning Model (H2CGL), is proposed to incorporate heterogeneity and dynamics of the citation network. H2CGL separately aggregates the heterogeneous information for each year and prioritizes the highly-cited papers and relationships among references, citations, and the target paper. It then employs a weighted GIN to capture dynamics between heterogeneous subgraphs over years. Moreover, it leverages contrastive learning to make the graph representations more sensitive to potential citations. Particularly, co-cited or co-citing papers of the target paper with large citation gap are taken as hard negative samples, while randomly dropping low-cited papers could generate positive samples. Extensive experimental results on two scholarly datasets demonstrate that the proposed H2CGL significantly outperforms a series of baseline approaches for both previously and freshly published papers. Additional analyses highlight the significance of the proposed modules. Our codes and settings have been released on Github (https://github.com/ECNU-Text-Computing/H2CGL)Comment: Accepted by IP&

Investigations on the mechanism of microweld changes during ultrasonic wire bonding by molecular dynamics simulation

Despite the wide and long-term applications of ultrasonic (US) wire bonding and other US metal joining technologies, the mechanism of microweld changes during the bonding process, including formation, deformation and breakage, is rarely known as it is very difficult to be investigated by experiments. In this work, this mechanism under different surface topographies and displacement patterns is studied by molecular dynamics simulation. It is found that microwelds can be formed or broken instantly. Due to the relative motion between the local wire part and the local substrate part, microwelds can be largely deformed or even broken. The impacts of material, surface topography, approaching distance and vibration amplitude on the microweld changes are investigated via the quantification of the shear stress and the equivalent bonded area. It is shown that these four factors significantly influence the final connection and the interface structure. The analysis of the scale influence on the microweld changes shows that the simulation results at a small-scale are able to represent those at a large-scale which is close to the range of the commonly used surface roughness. This deeper understanding on the microweld changes leads to a better control strategy and an enhancement of the bonding process