Predicting the Quality of Short Narratives from Social Media

An important and difficult challenge in building computational models for narratives is the automatic evaluation of narrative quality. Quality evaluation connects narrative understanding and generation as generation systems need to evaluate their own products. To circumvent difficulties in acquiring annotations, we employ upvotes in social media as an approximate measure for story quality. We collected 54,484 answers from a crowd-powered question-and-answer website, Quora, and then used active learning to build a classifier that labeled 28,320 answers as stories. To predict the number of upvotes without the use of social network features, we create neural networks that model textual regions and the interdependence among regions, which serve as strong benchmarks for future research. To our best knowledge, this is the first large-scale study for automatic evaluation of narrative quality.Comment: 7 pages, 2 figures. Accepted at the 2017 IJCAI conferenc

Learning to Segment and Represent Motion Primitives from Driving Data for Motion Planning Applications

Developing an intelligent vehicle which can perform human-like actions requires the ability to learn basic driving skills from a large amount of naturalistic driving data. The algorithms will become efficient if we could decompose the complex driving tasks into motion primitives which represent the elementary compositions of driving skills. Therefore, the purpose of this paper is to segment unlabeled trajectory data into a library of motion primitives. By applying a probabilistic inference based on an iterative Expectation-Maximization algorithm, our method segments the collected trajectories while learning a set of motion primitives represented by the dynamic movement primitives. The proposed method utilizes the mutual dependencies between the segmentation and representation of motion primitives and the driving-specific based initial segmentation. By utilizing this mutual dependency and the initial condition, this paper presents how we can enhance the performance of both the segmentation and the motion primitive library establishment. We also evaluate the applicability of the primitive representation method to imitation learning and motion planning algorithms. The model is trained and validated by using the driving data collected from the Beijing Institute of Technology intelligent vehicle platform. The results show that the proposed approach can find the proper segmentation and establish the motion primitive library simultaneously

Characterizing Location-based Mobile Tracking in Mobile Ad Networks

Mobile apps nowadays are often packaged with third-party ad libraries to monetize user data

Defending Against Local Adversarial Attacks through Empirical Gradient Optimization

Deep neural networks (DNNs) are susceptible to adversarial attacks, including the recently introduced locally visible adversarial patch attack, which achieves a success rate exceeding 96%. These attacks pose significant challenges to DNN security. Various defense methods, such as adversarial training, robust attention modules, watermarking, and gradient smoothing, have been proposed to enhance empirical robustness against patch attacks. However, these methods often have limitations concerning patch location requirements, randomness, and their impact on recognition accuracy for clean images.To address these challenges, we propose a novel defense algorithm called Local Adversarial Attack Empirical Defense using Gradient Optimization (LAAGO). The algorithm incorporates a low-pass filter before noise suppression to effectively mitigate the interference of high-frequency noise on the classifier while preserving the low-frequency areas of the images. Additionally, it emphasizes the original target features by enhancing the image gradients. Extensive experimental results demonstrate that the proposed method improves defense performance by 3.69% for 80 × 80 noise patches (representing approximately 4% of the images), while experiencing only a negligible 0.3% accuracy drop on clean images. The LAAGO algorithm provides a robust defense mechanism against local adversarial attacks, overcoming the limitations of previous methods. Our approach leverages gradient optimization, noise suppression, and feature enhancement, resulting in significant improvements in defense performance while maintaining high accuracy for clean images. This work contributes to the advancement of defense strategies against emerging adversarial attacks, thereby enhancing the security and reliability of deep neural networks

The Practice and Innovation of Energizing the Competitiveness of Brand of County by the IP of Culture and Tourism at Zigui

The integration of culture and tourism makes the interaction between culture and tourism deeper and closer. After years of vigorous development, the tourism of county is no longer like before building infrastructure in the entire scenic area, and the economy of county no longer relies on hardware construction and a large investment. And now a new focus is needed to promote the economy and brand competitiveness of the county. Combining the IP (intellectual property) construction method in the Internet era with regional brands with local cultural characteristics, an innovative form of IP for county cultural and tourism brands at present is created, the Zigui County of Yichang City is the practical example of the innovative form. Combine with the unique culture of Qu Yuan, the Dragon Boat Festival, and navel orange specialty of Zigui, the IPs of brand of the county that are called “one da three xiao”, which are Qudafu, Chengxiaozi, Zongxiaogui, and Zhouxiaolong, were created. The IPs are deeply loved by tourists, and quickly stand out in the competition of tourism spread in the surrounding counties and cities. By energizing the competitiveness of brand of the county through IP, the new appearance of county brand of the Zigui, which effectively attracts traffic and drives the economic promotion of Zigui County, is displayed with affinity, sustainability and influence

LumiGAN: Unconditional Generation of Relightable 3D Human Faces

Unsupervised learning of 3D human faces from unstructured 2D image data is an active research area. While recent works have achieved an impressive level of photorealism, they commonly lack control of lighting, which prevents the generated assets from being deployed in novel environments. To this end, we introduce LumiGAN, an unconditional Generative Adversarial Network (GAN) for 3D human faces with a physically based lighting module that enables relighting under novel illumination at inference time. Unlike prior work, LumiGAN can create realistic shadow effects using an efficient visibility formulation that is learned in a self-supervised manner. LumiGAN generates plausible physical properties for relightable faces, including surface normals, diffuse albedo, and specular tint without any ground truth data. In addition to relightability, we demonstrate significantly improved geometry generation compared to state-of-the-art non-relightable 3D GANs and notably better photorealism than existing relightable GANs.Comment: Project page: https://boyangdeng.com/projects/lumiga

Direct Generation of Electric Currents from Flowing Neutral Ionic Solutions

We have discovered a new method of generating electric currents, directly from high pressure-induced flow of neutral ionic solutions. The mechanism is that the cations and anions have different flow velocities, if their atomic masses are dramatically different, due to different accelerations generated from the high applied pressure. The generated electric current is very sensitive to the strengths of the applied pressure, and it might be potentially used for detection of atomic masses and pressures. Production and storage of electricity are among the most important activities in modern industrial processes. Conventional methods of generating electric current are electromagnetic induction, photovoltaic In this work, we have discovered that electric currents can be directly generated from fast-flowing neutral ionic solutions under external pressures, using molecular dynamics (MD) simulations. The current is the result of dramatically different atomic masses, and hence very different accelerations, of the counterions in the flowing ionic solutions. This method represents a new type of one-step electric current generation, with possible applications in convenient electric energy storage, and highly sensitive detection of very large pressures. In our MD simulations using the NAMD software package We have estimated the influence of the anion's atomic mass on its flow velocity. In the simulations, we only changed the atomic mass of the anion from 35.5 Dalton, to 62.5, 125, 200, and 250 Dalton and left all the other parameters unchanged. The velocities of the cation were very close to the average velocities of the water molecules, since the atomic masses of the atoms in the cation were close to those of the atoms in water molecules. In To study the generation of electric current in a nanoscale confinement, we also simulated the flow of the ionic solution with the same counterions and 6659 water molecules, inside a 6.2 nm long (76,0) carbon nanotube (diameter of 6.03 nm). The system was also positioned inside a 6.2 × 6.2 × 6.2 nm simulation box, with periodic boundary conditions applied. We also only changed the values of and plotted the velocities of the cation and the anion depending on the values of in In summary, we have discovered a method to directly generate electric currents from fast-flowing neutral ionic solutions. This method is very convenient and has a simple mechanism. It could have potential applications in power generation and storage, as well as highly sensitive nanoscale detection of pressures and atomic masse