Effects of Design Features on Visitors' Behavior in a Museum Setting

Effects of Design Features on Visitors' Behavior in a Museum Setting Ting-Jui Chang, M.A. in Interaction Design The study aims to define possible interplay between display design and visitors' behavior in a museum setting. It asks, "What environmental features related to design have impacts on visitors' behavior?" The researcher observed single adult visitors in the 20/21 gallery of the Spencer Museum of Art during two weeks period. Research methods included: 1) measuring the physical setting of the display; 2) ranking art pieces by the curator on three scales: canonical value, popularity, and the Museum goals; and 3) tracking visitors' paths, stops and time-spent. Correlation analysis was used to discover the relationships between design features and visitors' behavior. Graphs/visual representations of the setting, viewing paths and stop locations were studied to identify the patterns of behavior. Findings include: 1) how display designs reflected the importance of the art pieces in the mind of the curator; and 2) patterns of visitors' behavior related to display of art and space

Efficient Two-Step Adversarial Defense for Deep Neural Networks

In recent years, deep neural networks have demonstrated outstanding performance in many machine learning tasks. However, researchers have discovered that these state-of-the-art models are vulnerable to adversarial examples: legitimate examples added by small perturbations which are unnoticeable to human eyes. Adversarial training, which augments the training data with adversarial examples during the training process, is a well known defense to improve the robustness of the model against adversarial attacks. However, this robustness is only effective to the same attack method used for adversarial training. Madry et al.(2017) suggest that effectiveness of iterative multi-step adversarial attacks and particularly that projected gradient descent (PGD) may be considered the universal first order adversary and applying the adversarial training with PGD implies resistance against many other first order attacks. However, the computational cost of the adversarial training with PGD and other multi-step adversarial examples is much higher than that of the adversarial training with other simpler attack techniques. In this paper, we show how strong adversarial examples can be generated only at a cost similar to that of two runs of the fast gradient sign method (FGSM), allowing defense against adversarial attacks with a robustness level comparable to that of the adversarial training with multi-step adversarial examples. We empirically demonstrate the effectiveness of the proposed two-step defense approach against different attack methods and its improvements over existing defense strategies.Comment: 12 page

Preparing random state for quantum financing with quantum walks

In recent years, there has been an emerging trend of combining two innovations in computer science and physics to achieve better computation capability. Exploring the potential of quantum computation to achieve highly efficient performance in various tasks is a vital development in engineering and a valuable question in sciences, as it has a significant potential to provide exponential speedups for technologically complex problems that are specifically advantageous to quantum computers. However, one key issue in unleashing this potential is constructing an efficient approach to load classical data into quantum states that can be executed by quantum computers or quantum simulators on classical hardware. Therefore, the split-step quantum walks (SSQW) algorithm was proposed to address this limitation. We facilitate SSQW to design parameterized quantum circuits (PQC) that can generate probability distributions and optimize the parameters to achieve the desired distribution using a variational solver. A practical example of implementing SSQW using Qiskit has been released as open-source software. Showing its potential as a promising method for generating desired probability amplitude distributions highlights the potential application of SSQW in option pricing through quantum simulation.Comment: 11 pages, 7 figure

Distributed Training Large-Scale Deep Architectures

Scale of data and scale of computation infrastructures together enable the current deep learning renaissance. However, training large-scale deep architectures demands both algorithmic improvement and careful system configuration. In this paper, we focus on employing the system approach to speed up large-scale training. Via lessons learned from our routine benchmarking effort, we first identify bottlenecks and overheads that hinter data parallelism. We then devise guidelines that help practitioners to configure an effective system and fine-tune parameters to achieve desired speedup. Specifically, we develop a procedure for setting minibatch size and choosing computation algorithms. We also derive lemmas for determining the quantity of key components such as the number of GPUs and parameter servers. Experiments and examples show that these guidelines help effectively speed up large-scale deep learning training

THE DYNAMICAL ANALYSIS OF TABLE TENNIS FOREHAND AND BACKHAND DRIVES

The purpose of this study was to analyze the dynamics parameters of table tennis drives by Taiwan collegiate first class table tennis players when they were performing straight and cross court forehand and backhand drives from receiving topspin and backspin serves. Ten Vicon MX-13+ high-speed cameras (250Hz) and two Kistler force plates (1500 Hz) were used to collect the kinematics and kinetics data. The Wilcoxon matched-pairs signed-rank nonparametric statistical test was to compare the differences between forehand and backhand drives. The results showed that there were significant differences between forehand and backhand drives were in the ball initial velocity and the kinetics variables. The GRF data of the players were different between forehand and backhand drives when they performed four different paths of drive

Risk Analysis of Cargos Damages for Aquatic Products of Refrigerated Containers: Shipping Operators’ Perspective in Taiwan

As the development of refrigerated container, transportation of aquatic products is growing rapidly in recent years. It is very important to avoid cargos damages for aquatic products of refrigerated containers, while the shipping operators are running this scope of business. Hence, the risk issue of adopting various improvement strategies would be important for the container shipping operators. In the light of this, the main purpose of this paper is to analyze the risks of cargos damages for aquatic products of refrigerated containers based on the container shipping operators’ perspective in Taiwan. We use four risk assessment procedures - risk identification, risk analysis and evaluation, risk strategies, and risk treatment - as the research method in this paper. The risk factors are generated from literature review and experts interviewing. Then, three dimensions with nineteen risk factors are preliminary identified. We used these risk factors to proceed with the empirical study via questionnaires. Three points of empirical results are presented. At first, the top factor of perceived risk as well as of risk severity is ‘container data setting errors.’ Secondly, the top factor of risk frequency is ‘lack of the goods’ pre-cooling themselves.’ Thirdly, three risk factors are classified into the low-risk area, whereas sixteen risk factors are placed on the medium-risk area. There is no risk factor fix on the high-risk area. Furthermore, three risk strategies - risk prevention, risk reduction, and risk transfer - are suggested to adopt by different risk factors