Predicting the Mobile Consumer Purchase Behavior using Quantified Visual Preferences

Most mobile consumers make a decision about the product in a split second. The decision making in the mobile environment is surely faster than in front of the desktop. This paper claims that the decision-making in the mobile shopping is highly depending on the product’s first impression and their visual preference. By predicting the human’s visual preference based on the image processing model of perceived colorfulness and perceived visual complexity, this study tested an S-O-R path model from visual preference to consumer’s bookmarking and purchase intention via age and gender as moderators. With the controlled laboratory experiment, we substantiated our predicting image preference model. Further, a plan for a real data based analysis is proposed to validate the congruity of our model with the Korean mobile shopping company later

Advancing Adversarial Training by Injecting Booster Signal

Recent works have demonstrated that deep neural networks (DNNs) are highly vulnerable to adversarial attacks. To defend against adversarial attacks, many defense strategies have been proposed, among which adversarial training has been demonstrated to be the most effective strategy. However, it has been known that adversarial training sometimes hurts natural accuracy. Then, many works focus on optimizing model parameters to handle the problem. Different from the previous approaches, in this paper, we propose a new approach to improve the adversarial robustness by using an external signal rather than model parameters. In the proposed method, a well-optimized universal external signal called a booster signal is injected into the outside of the image which does not overlap with the original content. Then, it boosts both adversarial robustness and natural accuracy. The booster signal is optimized in parallel to model parameters step by step collaboratively. Experimental results show that the booster signal can improve both the natural and robust accuracies over the recent state-of-the-art adversarial training methods. Also, optimizing the booster signal is general and flexible enough to be adopted on any existing adversarial training methods.Comment: Accepted at IEEE Transactions on Neural Networks and Learning System

Rediscovery of the Effectiveness of Standard Convolution for Lightweight Face Detection

This paper analyses the design choices of face detection architecture that improve efficiency between computation cost and accuracy. Specifically, we re-examine the effectiveness of the standard convolutional block as a lightweight backbone architecture on face detection. Unlike the current tendency of lightweight architecture design, which heavily utilizes depthwise separable convolution layers, we show that heavily channel-pruned standard convolution layer can achieve better accuracy and inference speed when using a similar parameter size. This observation is supported by the analyses concerning the characteristics of the target data domain, face. Based on our observation, we propose to employ ResNet with a highly reduced channel, which surprisingly allows high efficiency compared to other mobile-friendly networks (e.g., MobileNet-V1,-V2,-V3). From the extensive experiments, we show that the proposed backbone can replace that of the state-of-the-art face detector with a faster inference speed. Also, we further propose a new feature aggregation method maximizing the detection performance. Our proposed detector EResFD obtained 80.4% mAP on WIDER FACE Hard subset which only takes 37.7 ms for VGA image inference in on CPU. Code will be available at https://github.com/clovaai/EResFD

Nonlinear and nonreciprocal transport effects in untwinned thin films of ferromagnetic Weyl metal SrRuO3_3

The identification of distinct charge transport features, deriving from nontrivial bulk band and surface states, has been a challenging subject in the field of topological systems. In topological Dirac and Weyl semimetals, nontrivial conical bands with Fermi-arc surfaces states give rise to negative longitudinal magnetoresistance due to chiral anomaly effect and unusual thickness dependent quantum oscillation from Weyl-orbit effect, which were demonstrated recently in experiments. In this work, we report the experimental observations of large nonlinear and nonreciprocal transport effects for both longitudinal and transverse channels in an untwinned Weyl metal of SrRuO$_3$ thin film grown on a SrTiO$_{3}$ substrate. From rigorous measurements with bias current applied along various directions with respect to the crystalline principal axes, the magnitude of nonlinear Hall signals from the transverse channel exhibits a simple sin$\alpha$ dependent at low temperatures, where $\alpha$ is the angle between bias current direction and orthorhombic [001]$_{\rm o}$, reaching a maximum when current is along orthorhombic [1-10]$_{\rm o}$. On the contrary, the magnitude of nonlinear and nonreciprocal signals in the longitudinal channel attains a maximum for bias current along [001]$_{\rm o}$, and it vanishes for bias current along [1-10]$_{\rm o}$. The observed $\alpha$-dependent nonlinear and nonreciprocal signals in longitudinal and transverse channels reveal a magnetic Weyl phase with an effective Berry curvature dipole along [1-10]$_{\rm o}$ from surface states, accompanied by 1D chiral edge modes along [001]$_{\rm o}$.Comment: 24 pages, 6 figure

Multispectral Invisible Coating: Laminated Visible-Thermal Physical Attack against Multispectral Object Detectors Using Transparent Low-E Films

Multispectral object detection plays a vital role in safety-critical vision systems that require an around-the-clock operation and encounter dynamic real-world situations(e.g., self-driving cars and autonomous surveillance systems). Despite its crucial competence in safety-related applications, its security against physical attacks is severely understudied. We investigate the vulnerability of multispectral detectors against physical attacks by proposing a new physical method: Multispectral Invisible Coating. Utilizing transparent Low-e films, we realize a laminated visible-thermal physical attack by attaching Low-e films over a visible attack printing. Moreover, we apply our physical method to manufacture a Multispectral Invisible Suit that hides persons from the multiple view angles of Multispectral detectors. To simulate our attack under various surveillance scenes, we constructed a large-scale multispectral pedestrian dataset which we will release in public. Extensive experiments show that our proposed method effectively attacks the state-of-the-art multispectral detector both in the digital space and the physical world

Modeling and Control of a Soft Robotic Fish with Integrated Soft Sensing

Soft robotics can be used not only as a means of achieving novel, more lifelike forms of locomotion, but also as a tool to understand complex biomechanics through the use of robotic model animals. Herein, the control of the undulation mechanics of an entirely soft robotic subcarangiform fish is presented, using antagonistic fast‐PneuNet actuators and hyperelastic eutectic gallium–indium (eGaIn) embedded in silicone channels for strain sensing. To design a controller, a simple, data‐driven lumped parameter approach is developed, which allows accurate but lightweight simulation, tuned using experimental data and a genetic algorithm. The model accurately predicts the robot's behavior over a range of driving frequencies and a range of pressure amplitudes, including the effect of antagonistic co‐contraction of the soft actuators. An amplitude controller is prototyped using the model and deployed to the robot to reach the setpoint of a tail‐beat amplitude using fully soft and real‐time strain sensing

Potential of Hematologic Parameters in Predicting Mortality of Patients with Traumatic Brain Injury

Traumatic brain injury (TBI) occurs frequently, and acute TBI requiring surgical treatment is closely related to patient survival. Models for predicting the prognosis of patients with TBI do not consider various factors of patient status; therefore, it is difficult to predict the prognosis more accurately. In this study, we created a model that can predict the survival of patients with TBI by adding hematologic parameters along with existing non-hematologic parameters. The best-fitting model was created using the Akaike information criterion (AIC), and hematologic factors including preoperative hematocrit, preoperative C-reactive protein (CRP), postoperative white blood cell (WBC) count, and postoperative hemoglobin were selected to predict the prognosis. Among several prediction models, the model that included age, Glasgow Coma Scale, Injury Severity Score, preoperative hematocrit, preoperative CRP, postoperative WBC count, postoperative hemoglobin, and postoperative CRP showed the highest area under the curve and the lowest corrected AIC for a finite sample size. Our study showed a new prediction model for mortality in patients with TBI using non-hematologic and hematologic parameters. This prediction model could be useful for the management of patients with TBI

Performance Investigation of Superplastic Shape Memory Alloy-Based Vibration Isolator for X-Band Active Small SAR Satellite of S-STEP under Acoustic and Random Vibration Environments

In a launch environment, all satellites are subjected to severe random vibration and acoustic loads owing to rocket separation, airflow, and injection/combustion of the fuel. Structural vibrations induced by mechanical loads cause the malfunction of vibration-sensitive components in a satellite, leading to failures during the launch process or an on-orbit mission. Therefore, in this study, a shape memory alloy-based vibration isolator was used on the connection between the launch vehicle and satellite to reduce the vibration transmission to a satellite. The vibration isolator exhibited a high performance in the vibration isolation, owing to the dynamic properties of super-elasticity and high damping. The vibration-reduction performance of the vibration isolator was experimentally verified using random vibration and acoustic tests in a structural thermal model of the satellite developed in the synthetic aperture radar technology experimental project. Owing to the super-elasticity and high attenuation characteristics of the vibration isolator, it was possible to significantly reduce the random vibration of the satellite in the launch environment. Although the mechanical load of the acoustic test mainly excited the antenna on the upper side of the satellite rather than the bottom side, the results of the acoustic test showed the same trend as the random vibration test. From this perspective, the vibration isolator can contribute to saving the costs required for satellite development. These advantages have made it possible to develop satellites according to the new space paradigm, which is a trend in the space industry worldwide