OpenRaiser/Intern-Atlas huggingface dataset:
SyncGAN: Synchronize the Latent Space of Cross-modal Generative Adversarial Networks
Venue: arXiv cs.CV 2018
Year: 2018

Generative adversarial network (GAN) has achieved impressive success on cross-domain generation, but it faces difficulty in cross-modal generation due to the lack of a common distribution between heterogeneous data. Most existing methods of conditional based cross-modal GANs adopt the strategy of one-directional transfer and have achieved preliminary success on text-to-image transfer. Instead of learning the transfer between different modalities, we aim to learn a synchronous latent space representing the cross-modal common concept. A novel network component named synchronizer is proposed in this work to judge whether the paired data is synchronous/corresponding or not, which can constrain the latent space of generators in the GANs. Our GAN model, named as SyncGAN, can successfully generate synchronous data (e.g., a pair of image and sound) from identical random noise. For transforming data from one modality to another, we recover the latent code by inverting the mappings of a generator and use it to generate data of different modality. In addition, the proposed model can achieve semi-supervised learning, which makes our model more flexible for practical applications.
Deep Residual Learning for Accelerated MRI using Magnitude and Phase Networks
Venue: arXiv cs.CV 2018
Year: 2018

Accelerated magnetic resonance (MR) scan acquisition with compressed sensing (CS) and parallel imaging is a powerful method to reduce MR imaging scan time. However, many reconstruction algorithms have high computational costs. To address this, we investigate deep residual learning networks to remove aliasing artifacts from artifact corrupted images. The proposed deep residual learning networks are composed of magnitude and phase networks that are separately trained. If both phase and magnitude information are available, the proposed algorithm can work as an iterative k-space interpolation algorithm using framelet representation. When only magnitude data is available, the proposed approach works as an image domain post-processing algorithm. Even with strong coherent aliasing artifacts, the proposed network successfully learned and removed the aliasing artifacts, whereas current parallel and CS reconstruction methods were unable to remove these artifacts. Comparisons using single and multiple coil show that the proposed residual network provides good reconstruction results with orders of magnitude faster computational time than existing compressed sensing methods. The proposed deep learning framework may have a great potential for accelerated MR reconstruction by generating accurate results immediately.
Under the Shadow of Sunshine: Characterizing Spam Campaigns Abusing Phone Numbers Across Online Social Networks
Venue: arXiv cs.CY 2018
Year: 2018

Cybercriminals abuse Online Social Networks (OSNs) to lure victims into a variety of spam. Among different spam types, a less explored area is OSN abuse that leverages the telephony channel to defraud users. Phone numbers are advertized via OSNs, and users are tricked into calling these numbers. To expand the reach of such scam / spam campaigns, phone numbers are advertised across multiple platforms like Facebook, Twitter, GooglePlus, Flickr, and YouTube. In this paper, we present the first data-driven characterization of cross-platform campaigns that use multiple OSN platforms to reach their victims and use phone numbers for monetization. We collect 23M posts containing 1.8M unique phone numbers from Twitter, Facebook, GooglePlus, Youtube, and Flickr over a period of six months. Clustering these posts helps us identify 202 campaigns operating across the globe with Indonesia, United States, India, and United Arab Emirates being the most prominent originators. We find that even though Indonesian campaigns generate highest volume (3.2M posts), only 1.6% of the accounts propagating Indonesian campaigns have been suspended so far. By examining campaigns running across multiple OSNs, we discover that Twitter detects and suspends 93% more accounts than Facebook. Therefore, sharing intelligence about abuse-related user accounts across OSNs can aid in spam detection. According to our dataset, around 35K victims and 8.8M USD could have been saved if intelligence was shared across the OSNs. By analyzing phone number based spam campaigns running on OSNs, we highlight the unexplored variety of phone-based attacks surfacing on OSNs.
Curiosity-driven Exploration for Mapless Navigation with Deep Reinforcement Learning
Venue: arXiv cs.RO 2018
Year: 2018

This paper investigates exploration strategies of Deep Reinforcement Learning (DRL) methods to learn navigation policies for mobile robots. In particular, we augment the normal external reward for training DRL algorithms with intrinsic reward signals measured by curiosity. We test our approach in a mapless navigation setting, where the autonomous agent is required to navigate without the occupancy map of the environment, to targets whose relative locations can be easily acquired through low-cost solutions (e.g., visible light localization, Wi-Fi signal localization). We validate that the intrinsic motivation is crucial for improving DRL performance in tasks with challenging exploration requirements. Our experimental results show that our proposed method is able to more effectively learn navigation policies, and has better generalization capabilities in previously unseen environments. A video of our experimental results can be found at https://goo.gl/pWbpcF.
Learnable Image Encryption
Venue: arXiv cs.CV 2018
Year: 2018

The network-based machine learning algorithm is very powerful tools. However, it requires huge training dataset. Researchers often meet privacy issues when they collect image dataset especially for surveillance applications. A learnable image encryption scheme is introduced. The key idea of this scheme is to encrypt images, so that human cannot understand images but the network can be train with encrypted images. This scheme allows us to train the network without the privacy issues. In this paper, a simple learnable image encryption algorithm is proposed. Then, the proposed algorithm is validated with cifar dataset.
Expanding a robot's life: Low power object recognition via FPGA-based DCNN deployment
Venue: arXiv cs.CV 2018
Year: 2018

FPGAs are commonly used to accelerate domain-specific algorithmic implementations, as they can achieve impressive performance boosts, are reprogrammable and exhibit minimal power consumption. In this work, the SqueezeNet DCNN is accelerated using an SoC FPGA in order for the offered object recognition resource to be employed in a robotic application. Experiments are conducted to investigate the performance and power consumption of the implementation in comparison to deployment on other widely-used computational systems.
DeepScores -- A Dataset for Segmentation, Detection and Classification of Tiny Objects
Venue: arXiv cs.CV 2018
Year: 2018

We present the DeepScores dataset with the goal of advancing the state-of-the-art in small objects recognition, and by placing the question of object recognition in the context of scene understanding. DeepScores contains high quality images of musical scores, partitioned into 300,000 sheets of written music that contain symbols of different shapes and sizes. With close to a hundred millions of small objects, this makes our dataset not only unique, but also the largest public dataset. DeepScores comes with ground truth for object classification, detection and semantic segmentation. DeepScores thus poses a relevant challenge for computer vision in general, beyond the scope of optical music recognition (OMR) research. We present a detailed statistical analysis of the dataset, comparing it with other computer vision datasets like Caltech101/256, PASCAL VOC, SUN, SVHN, ImageNet, MS-COCO, smaller computer vision datasets, as well as with other OMR datasets. Finally, we provide baseline performances for object classification and give pointers to future research based on this dataset.
Predictions of short-term driving intention using recurrent neural network on sequential data
Venue: arXiv cs.AI 2018
Year: 2018

Predictions of driver's intentions and their behaviors using the road is of great importance for planning and decision making processes of autonomous driving vehicles. In particular, relatively short-term driving intentions are the fundamental units that constitute more sophisticated driving goals, behaviors, such as overtaking the slow vehicle in front, exit or merge onto a high way, etc. While it is not uncommon that most of the time human driver can rationalize, in advance, various on-road behaviors, intentions, as well as the associated risks, aggressiveness, reciprocity characteristics, etc., such reasoning skills can be challenging and difficult for an autonomous driving system to learn. In this article, we demonstrate a disciplined methodology that can be used to build and train a predictive drive system, therefore to learn the on-road characteristics aforementioned.
On the structure of C-algebras through atomicity and if-then-else
Venue: arXiv cs.LO 2018
Year: 2018

This paper introduces the notions of atoms and atomicity in $C$-algebras and obtains a characterisation of atoms in the $C$-algebra of transformations. Further, this work presents some necessary conditions and sufficient conditions for the atomicity of $C$-algebras and shows that the class of finite atomic $C$-algebras is precisely that of finite adas. This paper also uses the if-then-else action to study the structure of $C$-algebras and classify the elements of the $C$-algebra of transformations.
Universal Planning Networks
Venue: arXiv cs.LG 2018
Year: 2018

A key challenge in complex visuomotor control is learning abstract representations that are effective for specifying goals, planning, and generalization. To this end, we introduce universal planning networks (UPN). UPNs embed differentiable planning within a goal-directed policy. This planning computation unrolls a forward model in a latent space and infers an optimal action plan through gradient descent trajectory optimization. The plan-by-gradient-descent process and its underlying representations are learned end-to-end to directly optimize a supervised imitation learning objective. We find that the representations learned are not only effective for goal-directed visual imitation via gradient-based trajectory optimization, but can also provide a metric for specifying goals using images. The learned representations can be leveraged to specify distance-based rewards to reach new target states for model-free reinforcement learning, resulting in substantially more effective learning when solving new tasks described via image-based goals. We were able to achieve successful transfer of visuomotor planning strategies across robots with significantly different morphologies and actuation capabilities.
Persistent Memory Transactions
Venue: arXiv cs.DC 2018
Year: 2018

This paper presents a comprehensive analysis of performance trade offs between implementation choices for transaction runtime systems on persistent memory. We compare three implementations of transaction runtimes: undo logging, redo logging, and copy-on-write. We also present a memory allocator that plugs into these runtimes. Our microbenchmark based evaluation focuses on understanding the interplay between various factors that contribute to performance differences between the three runtimes -- read/write access patterns of workloads, size of the persistence domain (portion of the memory hierarchy where the data is effectively persistent), cache locality, and transaction runtime bookkeeping overheads. No single runtime emerges as a clear winner. We confirm our analysis in more realistic settings of three "real world" applications we developed with our transactional API: (i) a key-value store we implemented from scratch, (ii) a SQLite port, and (iii) a persistified version of memcached, a popular key-value store. These findings are not only consistent with our microbenchmark analysis, but also provide additional interesting insights into other factors (e.g. effects of multithreading and synchronization) that affect application performance.
A study of coordination logic description and execution for dynamic device coordination services
Venue: arXiv cs.DC 2018
Year: 2018

Recently, IoT technologies have been progressed, and many devices are connected to networks. Previously, IoT services were developed by vertical integration style. But now Open IoT concept has attracted attentions which achieves various IoT services by integrating horizontal separated devices and services. For Open IoT era, we have proposed the Tacit Computing technology to discover the devices with necessary data for users on demand and use them dynamically. Although Tacit Computing can discover and use the device based on the situation, a study of coordination logic description is insufficient when multiple devices are coordinated. In this paper, we study coordination logic description and execution to coordinate multiple devices dynamically. We compare methods with ideas to convert abstract description to specific interface access means at execution time, study merits and demerits and propose an appropriate method.
Deep Spatiotemporal Models for Robust Proprioceptive Terrain Classification
Venue: arXiv cs.RO 2018
Year: 2018

Terrain classification is a critical component of any autonomous mobile robot system operating in unknown real-world environments. Over the years, several proprioceptive terrain classification techniques have been introduced to increase robustness or act as a fallback for traditional vision based approaches. However, they lack widespread adaptation due to various factors that include inadequate accuracy, robustness and slow run-times. In this paper, we use vehicle-terrain interaction sounds as a proprioceptive modality and propose a deep Long-Short Term Memory (LSTM) based recurrent model that captures both the spatial and temporal dynamics of such a problem, thereby overcoming these past limitations. Our model consists of a new Convolution Neural Network (CNN) architecture that learns deep spatial features, complemented with LSTM units that learn complex temporal dynamics. Experiments on two extensive datasets collected with different microphones on various indoor and outdoor terrains demonstrate state-of-the-art performance compared to existing techniques. We additionally evaluate the performance in adverse acoustic conditions with high ambient noise and propose a noise-aware training scheme that enables learning of more generalizable models that are essential for robust real-world deployments.
DeepSigns: A Generic Watermarking Framework for IP Protection of Deep Learning Models
Venue: arXiv cs.CR 2018
Year: 2018

Deep Learning (DL) models have caused a paradigm shift in our ability to comprehend raw data in various important fields, ranging from intelligence warfare and healthcare to autonomous transportation and automated manufacturing. A practical concern, in the rush to adopt DL models as a service, is protecting the models against Intellectual Property (IP) infringement. The DL models are commonly built by allocating significant computational resources that process vast amounts of proprietary training data. The resulting models are therefore considered to be the IP of the model builder and need to be protected to preserve the owner's competitive advantage. This paper proposes DeepSigns, a novel end-to-end IP protection framework that enables insertion of coherent digital watermarks in contemporary DL models. DeepSigns, for the first time, introduces a generic watermarking methodology that can be used for protecting DL owner's IP rights in both white-box and black-box settings, where the adversary may or may not have the knowledge of the model internals. The suggested methodology is based on embedding the owner's signature (watermark) in the probability density function (pdf) of the data abstraction obtained in different layers of a DL model. DeepSigns can demonstrably withstand various removal and transformation attacks, including model compression, model fine-tuning, and watermark overwriting. Proof-of-concept evaluations on MNIST, and CIFAR10 datasets, as well as a wide variety of neural network architectures including Wide Residual Networks, Convolution Neural Networks, and Multi-Layer Perceptrons corroborate DeepSigns' effectiveness and applicability.
Detecting Cross-Site Scripting Vulnerabilities through Automated Unit Testing
Venue: arXiv cs.CR 2018
Year: 2018

The best practice to prevent Cross Site Scripting (XSS) attacks is to apply encoders to sanitize untrusted data. To balance security and functionality, encoders should be applied to match the web page context, such as HTML body, JavaScript, and style sheets. A common programming error is the use of a wrong encoder to sanitize untrusted data, leaving the application vulnerable. We present a security unit testing approach to detect XSS vulnerabilities caused by improper encoding of untrusted data. Unit tests for the XSS vulnerability are automatically constructed out of each web page and then evaluated by a unit test execution framework. A grammar-based attack generator is used to automatically generate test inputs. We evaluate our approach on a large open source medical records application, demonstrating that we can detect many 0-day XSS vulnerabilities with very low false positives, and that the grammar-based attack generator has better test coverage than industry best practices.
Stress-Based Navigation for Microscopic Robots in Viscous Fluids
Venue: arXiv cs.RO 2018
Year: 2018

Objects moving in fluids experience patterns of stress on their surfaces determined by their motion and the geometry of nearby boundaries. Fish and underwater robots can use these patterns for navigation. This paper extends this stress-based navigation to microscopic robots in tiny vessels, where robots can exploit the physics of fluids at low Reynolds number. This applies, for instance, in vessels with sizes and flow speeds comparable to those of capillaries in biological tissues. We describe how a robot can use simple computations to estimate its motion, orientation and distance to nearby vessel walls from fluid-induced stresses on its surface. Numerically evaluating these estimates for a variety of vessel sizes and robot positions shows they are most accurate when robots are close to vessel walls.
VerdictDB: Universalizing Approximate Query Processing
Venue: arXiv cs.DB 2018
Year: 2018

Despite 25 years of research in academia, approximate query processing (AQP) has had little industrial adoption. One of the major causes of this slow adoption is the reluctance of traditional vendors to make radical changes to their legacy codebases, and the preoccupation of newer vendors (e.g., SQL-on-Hadoop products) with implementing standard features. Additionally, the few AQP engines that are available are each tied to a specific platform and require users to completely abandon their existing databases---an unrealistic expectation given the infancy of the AQP technology. Therefore, we argue that a universal solution is needed: a database-agnostic approximation engine that will widen the reach of this emerging technology across various platforms. Our proposal, called VerdictDB, uses a middleware architecture that requires no changes to the backend database, and thus, can work with all off-the-shelf engines. Operating at the driver-level, VerdictDB intercepts analytical queries issued to the database and rewrites them into another query that, if executed by any standard relational engine, will yield sufficient information for computing an approximate answer. VerdictDB uses the returned result set to compute an approximate answer and error estimates, which are then passed on to the user or application. However, lack of access to the query execution layer introduces significant challenges in terms of generality, correctness, and efficiency. This paper shows how VerdictDB overcomes these challenges and delivers up to 171$\times$ speedup (18.45$\times$ on average) for a variety of existing engines, such as Impala, Spark SQL, and Amazon Redshift, while incurring less than 2.6% relative error. VerdictDB is open-sourced under Apache License.
Automatic Normalization of Word Variations in Code-Mixed Social Media Text
Venue: arXiv cs.CL 2018
Year: 2018

Social media platforms such as Twitter and Facebook are becoming popular in multilingual societies. This trend induces portmanteau of South Asian languages with English. The blend of multiple languages as code-mixed data has recently become popular in research communities for various NLP tasks. Code-mixed data consist of anomalies such as grammatical errors and spelling variations. In this paper, we leverage the contextual property of words where the different spelling variation of words share similar context in a large noisy social media text. We capture different variations of words belonging to same context in an unsupervised manner using distributed representations of words. Our experiments reveal that preprocessing of the code-mixed dataset based on our approach improves the performance in state-of-the-art part-of-speech tagging (POS-tagging) and sentiment analysis tasks.
Prediction and Localization of Student Engagement in the Wild
Venue: arXiv cs.CV 2018
Year: 2018

In this paper, we introduce a new dataset for student engagement detection and localization. Digital revolution has transformed the traditional teaching procedure and a result analysis of the student engagement in an e-learning environment would facilitate effective task accomplishment and learning. Well known social cues of engagement/disengagement can be inferred from facial expressions, body movements and gaze pattern. In this paper, student's response to various stimuli videos are recorded and important cues are extracted to estimate variations in engagement level. In this paper, we study the association of a subject's behavioral cues with his/her engagement level, as annotated by labelers. We then localize engaging/non-engaging parts in the stimuli videos using a deep multiple instance learning based framework, which can give useful insight into designing Massive Open Online Courses (MOOCs) video material. Recognizing the lack of any publicly available dataset in the domain of user engagement, a new `in the wild' dataset is created to study the subject engagement problem. The dataset contains 195 videos captured from 78 subjects which is about 16.5 hours of recording. We present detailed baseline results using different classifiers ranging from traditional machine learning to deep learning based approaches. The subject independent analysis is performed so that it can be generalized to new users. The problem of engagement prediction is modeled as a weakly supervised learning problem. The dataset is manually annotated by different labelers for four levels of engagement independently and the correlation studies between annotated and predicted labels of videos by different classifiers is reported. This dataset creation is an effort to facilitate research in various e-learning environments such as intelligent tutoring systems, MOOCs, and others.