XNA-like 3D Graphics Programming on the Raspberry Pi

The Raspberry Pi is a credit-card sized computing device created by Broadcom in 2012. This device is a kind of mini PC, and it is capable of doing things that desktop PC can do. The goal of the Raspberry Pi Foundation is to allow people all over the world to learn programming. Therefore, the Raspberry Pi is designed as a small sized, low cost device that can provide reasonable data processing capability. However, because of its goal is to keep the price down to maximize openness for learning, Raspberry Pi can only run the Linux operating system. XNA is a set of libraries developed by Microsoft to facilitate the creation and management of video games. It provides a large number of underlying functions to help the development of systems that based on runtime. Therefore, programmers may focus on programming their own code. XNA is built on Microsoft's .NET framework, and it is designed to be used with DirectX. However, as no drivers are developed to provide the low level API defined by DirectX on Linux, it is currently impossible to program with XNA on a Raspberry Pi. This thesis investigates the possibility of developing XNA like programs directly on the Raspberry Pi. Instead of using DirectX, OpenGL ES is used to provide the low level graphics APIs. The code of a project named "JBBRXG11", which is an open source project extending XNA classes on Windows to access DirectX 10 and DirectX 11 graphics features is used as a reference for this project. The project successfully built a library that allows an XNA like program to produce moving, textured 3D models on screen

Fucoxanthin improves functional recovery of orbitopathy in Graves’ disease by downregulating IL-17 mRNA expression in a mouse model

Purpose: To explore the efficacy of fucoxanthin (FX), a carotenoid, against inflammation via inhibition of IL-17 mRNA expression, and its anti-oxidant activity in Graves’ orbitopathy (GO)-induced mice model.Methods: The effects of FX on IL-6, IL-8, IL-17, MCP-1, and TNF-α, in orbital fibroblast tissues extracted from GO-induced BALB/c mice was  investigated. Anti-oxidative stress markers, 8-hydroxy-2’- deoxyguanosine (8-OHdG) and malondialdehyde (MDA) levels were quantified in tear samples collected from GO-induced FX treated mice.Results: FX administration in cultured human orbital fibroblast cells revealed almost complete cell viability and no cell apoptosis. FX resulted in IL-1β induced Beclin-1 and Atg-5 silencing, in cultured human orbital fibroblasts. BALB/c mice immunized with Ad-TSHR289 indicated elevated levels ofthyroid peroxidase and thyroglobulin antibodies in the serum sample. FX predominantly downregulated the mRNA expression of IL-17, and also reduced increased 8-OHdG and MDA in the tear secretion of GO-induced mice.Conclusion: FX may be an effective and useful molecule for the treatment of GO, through its antiinflammatory and anti-oxidative potential, but it requires further investigation to ascertain its therapeutic effectiveness. Keywords: Anti-inflammatory, Anti-oxidant, Fucoxanthin, Graves’ disease, Graves’ orbitopathy, IL-1

Private Model Compression via Knowledge Distillation

The soaring demand for intelligent mobile applications calls for deploying powerful deep neural networks (DNNs) on mobile devices. However, the outstanding performance of DNNs notoriously relies on increasingly complex models, which in turn is associated with an increase in computational expense far surpassing mobile devices' capacity. What is worse, app service providers need to collect and utilize a large volume of users' data, which contain sensitive information, to build the sophisticated DNN models. Directly deploying these models on public mobile devices presents prohibitive privacy risk. To benefit from the on-device deep learning without the capacity and privacy concerns, we design a private model compression framework RONA. Following the knowledge distillation paradigm, we jointly use hint learning, distillation learning, and self learning to train a compact and fast neural network. The knowledge distilled from the cumbersome model is adaptively bounded and carefully perturbed to enforce differential privacy. We further propose an elegant query sample selection method to reduce the number of queries and control the privacy loss. A series of empirical evaluations as well as the implementation on an Android mobile device show that RONA can not only compress cumbersome models efficiently but also provide a strong privacy guarantee. For example, on SVHN, when a meaningful $(9.83,10^{-6})$-differential privacy is guaranteed, the compact model trained by RONA can obtain 20$\times$ compression ratio and 19$\times$ speed-up with merely 0.97% accuracy loss.Comment: Conference version accepted by AAAI'1

Adversarial Attack and Defense on Graph Data: A Survey

Deep neural networks (DNNs) have been widely applied to various applications including image classification, text generation, audio recognition, and graph data analysis. However, recent studies have shown that DNNs are vulnerable to adversarial attacks. Though there are several works studying adversarial attack and defense strategies on domains such as images and natural language processing, it is still difficult to directly transfer the learned knowledge to graph structure data due to its representation challenges. Given the importance of graph analysis, an increasing number of works start to analyze the robustness of machine learning models on graph data. Nevertheless, current studies considering adversarial behaviors on graph data usually focus on specific types of attacks with certain assumptions. In addition, each work proposes its own mathematical formulation which makes the comparison among different methods difficult. Therefore, in this paper, we aim to survey existing adversarial learning strategies on graph data and first provide a unified formulation for adversarial learning on graph data which covers most adversarial learning studies on graph. Moreover, we also compare different attacks and defenses on graph data and discuss their corresponding contributions and limitations. In this work, we systemically organize the considered works based on the features of each topic. This survey not only serves as a reference for the research community, but also brings a clear image researchers outside this research domain. Besides, we also create an online resource and keep updating the relevant papers during the last two years. More details of the comparisons of various studies based on this survey are open-sourced at https://github.com/YingtongDou/graph-adversarial-learning-literature.Comment: In submission to Journal. For more open-source and up-to-date information, please check our Github repository: https://github.com/YingtongDou/graph-adversarial-learning-literatur

Ambient Sound Helps: Audiovisual Crowd Counting in Extreme Conditions

Visual crowd counting has been recently studied as a way to enable people counting in crowd scenes from images. Albeit successful, vision-based crowd counting approaches could fail to capture informative features in extreme conditions, e.g., imaging at night and occlusion. In this work, we introduce a novel task of audiovisual crowd counting, in which visual and auditory information are integrated for counting purposes. We collect a large-scale benchmark, named auDiovISual Crowd cOunting (DISCO) dataset, consisting of 1,935 images and the corresponding audio clips, and 170,270 annotated instances. In order to fuse the two modalities, we make use of a linear feature-wise fusion module that carries out an affine transformation on visual and auditory features. Finally, we conduct extensive experiments using the proposed dataset and approach. Experimental results show that introducing auditory information can benefit crowd counting under different illumination, noise, and occlusion conditions. The dataset and code will be released. Code and data have been made availabl

Sequential Keystroke Behavioral Biometrics for Mobile User Identification via Multi-view Deep Learning

With the rapid growth in smartphone usage, more organizations begin to focus on providing better services for mobile users. User identification can help these organizations to identify their customers and then cater services that have been customized for them. Currently, the use of cookies is the most common form to identify users. However, cookies are not easily transportable (e.g., when a user uses a different login account, cookies do not follow the user). This limitation motivates the need to use behavior biometric for user identification. In this paper, we propose DEEPSERVICE, a new technique that can identify mobile users based on user's keystroke information captured by a special keyboard or web browser. Our evaluation results indicate that DEEPSERVICE is highly accurate in identifying mobile users (over 93% accuracy). The technique is also efficient and only takes less than 1 ms to perform identification.Comment: 2017 Joint European Conference on Machine Learning and Knowledge Discovery in Database