OpenRaiser/Intern-Atlas huggingface dataset:
Formal Probabilistic Analysis of Dynamic Fault Trees in HOL4
Venue: arXiv cs.LO 2018
Year: 2018

Dynamic Fault Trees (DFTs) is a widely used failure modeling technique that allows capturing the dynamic failure characteristics of systems in a very effective manner. Simulation and model checking have been traditionally used for the probabilistic analysis of DFTs. Simulation is usually based on sampling and thus its results are not guaranteed to be complete, whereas model checking employs computer arithmetic and numerical algorithms to compute the exact values of probabilities, which contain many round-off errors. Leveraging upon the expressive and sound nature of higher-order-logic (HOL) theorem proving, we propose, in this work, a formalization of DFT gates and their probabilistic behavior as well as some of their simplification properties in HOL. This formalization would allow us to conduct the probabilistic analysis of DFTs by verifying generic mathematical expressions about their behavior in HOL. In particular, we formalize the AND, OR, Priority-AND, Functional DEPendency, Hot SPare, Cold SPare and the Warm SPare gates and also verify their corresponding probabilistic expressions in HOL. Moreover, we formally verify an important property, Pr(X<Y), using the Lebesgue integral as this relationship allows us to reason about the probabilistic properties of Priority-AND gate and the Before operator. We also formalize the notion of conditional densities in order to formally verify the probabilistic expressions of the Cold SPare and the Warm SPare gates. For illustrating the usefulness of our formalization, we use it to formally analyze the DFT of a Cardiac Assist System.
A Hierarchical Approach to Neural Context-Aware Modeling
Venue: arXiv cs.CL 2018
Year: 2018

We present a new recurrent neural network topology to enhance state-of-the-art machine learning systems by incorporating a broader context. Our approach overcomes recent limitations with extended narratives through a multi-layered computational approach to generate an abstract context representation. Therefore, the developed system captures the narrative on word-level, sentence-level, and context-level. Through the hierarchical set-up, our proposed model summarizes the most salient information on each level and creates an abstract representation of the extended context. We subsequently use this representation to enhance neural language processing systems on the task of semantic error detection. To show the potential of the newly introduced topology, we compare the approach against a context-agnostic set-up including a standard neural language model and a supervised binary classification network. The performance measures on the error detection task show the advantage of the hierarchical context-aware topologies, improving the baseline by 12.75% relative for unsupervised models and 20.37% relative for supervised models.
UH-PRHLT at SemEval-2016 Task 3: Combining Lexical and Semantic-based Features for Community Question Answering
Venue: arXiv cs.CL 2018
Year: 2018

In this work we describe the system built for the three English subtasks of the SemEval 2016 Task 3 by the Department of Computer Science of the University of Houston (UH) and the Pattern Recognition and Human Language Technology (PRHLT) research center - Universitat Polit`ecnica de Val`encia: UH-PRHLT. Our system represents instances by using both lexical and semantic-based similarity measures between text pairs. Our semantic features include the use of distributed representations of words, knowledge graphs generated with the BabelNet multilingual semantic network, and the FrameNet lexical database. Experimental results outperform the random and Google search engine baselines in the three English subtasks. Our approach obtained the highest results of subtask B compared to the other task participants.
AniCode: Authoring Coded Artifacts for Network-Free Personalized Animations
Venue: arXiv cs.GR 2018
Year: 2018

Time-based media (videos, synthetic animations, and virtual reality experiences) are used for communication, in applications such as manufacturers explaining the operation of a new appliance to consumers and scientists illustrating the basis of a new conclusion. However, authoring time-based media that are effective and personalized for the viewer remains a challenge. We introduce AniCode, a novel framework for authoring and consuming time-based media. An author encodes a video animation in a printed code, and affixes the code to an object. A consumer uses a mobile application to capture an image of the object and code, and to generate a video presentation on the fly. Importantly, AniCode presents the video personalized in the consumer's visual context. Our system is designed to be low cost and easy to use. By not requiring an internet connection, and through animations that decode correctly only in the intended context, AniCode enhances privacy of communication using time-based media. Animation schemes in the system include a series of 2D and 3D geometric transformations, color transformation, and annotation. We demonstrate the AniCode framework with sample applications from a wide range of domains, including product "how to" examples, cultural heritage, education, creative art, and design. We evaluate the ease of use and effectiveness of our system with a user study.
Deep Learning-based CSI Feedback Approach for Time-varying Massive MIMO Channels
Venue: arXiv cs.IT 2018
Year: 2018

Massive multiple-input multiple-output (MIMO) systems rely on channel state information (CSI) feedback to perform precoding and achieve performance gain in frequency division duplex (FDD) networks. However, the huge number of antennas poses a challenge to conventional CSI feedback reduction methods and leads to excessive feedback overhead. In this article, we develop a real-time CSI feedback architecture, called CsiNet-long short-term memory (LSTM), by extending a novel deep learning (DL)-based CSI sensing and recovery network. CsiNet-LSTM considerably enhances recovery quality and improves trade-off between compression ratio (CR) and complexity by directly learning spatial structures combined with time correlation from training samples of time-varying massive MIMO channels. Simulation results demonstrate that CsiNet- LSTM outperforms existing compressive sensing-based and DLbased methods and is remarkably robust to CR reduction.
Deep Cross Modal Learning for Caricature Verification and Identification(CaVINet)
Venue: arXiv cs.CV 2018
Year: 2018

Learning from different modalities is a challenging task. In this paper, we look at the challenging problem of cross modal face verification and recognition between caricature and visual image modalities. Caricature have exaggerations of facial features of a person. Due to the significant variations in the caricatures, building vision models for recognizing and verifying data from this modality is an extremely challenging task. Visual images with significantly lesser amount of distortions can act as a bridge for the analysis of caricature modality. We introduce a publicly available large Caricature-VIsual dataset [CaVI] with images from both the modalities that captures the rich variations in the caricature of an identity. This paper presents the first cross modal architecture that handles extreme distortions of caricatures using a deep learning network that learns similar representations across the modalities. We use two convolutional networks along with transformations that are subjected to orthogonality constraints to capture the shared and modality specific representations. In contrast to prior research, our approach neither depends on manually extracted facial landmarks for learning the representations, nor on the identities of the person for performing verification. The learned shared representation achieves 91% accuracy for verifying unseen images and 75% accuracy on unseen identities. Further, recognizing the identity in the image by knowledge transfer using a combination of shared and modality specific representations, resulted in an unprecedented performance of 85% rank-1 accuracy for caricatures and 95% rank-1 accuracy for visual images.
A Two-Stream Mutual Attention Network for Semi-supervised Biomedical Segmentation with Noisy Labels
Venue: arXiv cs.CV 2018
Year: 2018

\begin{abstract} Learning-based methods suffer from a deficiency of clean annotations, especially in biomedical segmentation. Although many semi-supervised methods have been proposed to provide extra training data, automatically generated labels are usually too noisy to retrain models effectively. In this paper, we propose a Two-Stream Mutual Attention Network (TSMAN) that weakens the influence of back-propagated gradients caused by incorrect labels, thereby rendering the network robust to unclean data. The proposed TSMAN consists of two sub-networks that are connected by three types of attention models in different layers. The target of each attention model is to indicate potentially incorrect gradients in a certain layer for both sub-networks by analyzing their inferred features using the same input. In order to achieve this purpose, the attention models are designed based on the propagation analysis of noisy gradients at different layers. This allows the attention models to effectively discover incorrect labels and weaken their influence during the parameter updating process. By exchanging multi-level features within the two-stream architecture, the effects of noisy labels in each sub-network are reduced by decreasing the updating gradients. Furthermore, a hierarchical distillation is developed to provide more reliable pseudo labels for unlabelded data, which further boosts the performance of our retrained TSMAN. The experiments using both the HVSMR 2016 and BRATS 2015 benchmarks demonstrate that our semi-supervised learning framework surpasses the state-of-the-art fully-supervised results.
Regional Multi-scale Approach for Visually Pleasing Explanations of Deep Neural Networks
Venue: arXiv cs.CV 2018
Year: 2018

Recently, many methods to interpret and visualize deep neural network predictions have been proposed and significant progress has been made. However, a more class-discriminative and visually pleasing explanation is required. Thus, this paper proposes a region-based approach that estimates feature importance in terms of appropriately segmented regions. By fusing the saliency maps generated from multi-scale segmentations, a more class-discriminative and visually pleasing map is obtained. We incorporate this regional multi-scale concept into a prediction difference method that is model-agnostic. An input image is segmented in several scales using the super-pixel method, and exclusion of a region is simulated by sampling a normal distribution constructed using the boundary prior. The experimental results demonstrate that the regional multi-scale method produces much more class-discriminative and visually pleasing saliency maps.
A First Experiment on Including Text Literals in KGloVe
Venue: arXiv cs.AI 2018
Year: 2018

Graph embedding models produce embedding vectors for entities and relations in Knowledge Graphs, often without taking literal properties into account. We show an initial idea based on the combination of global graph structure with additional information provided by textual information in properties. Our initial experiment shows that this approach might be useful, but does not clearly outperform earlier approaches when evaluated on machine learning tasks.
Scale equivariance in CNNs with vector fields
Venue: arXiv cs.CV 2018
Year: 2018

We study the effect of injecting local scale equivariance into Convolutional Neural Networks. This is done by applying each convolutional filter at multiple scales. The output is a vector field encoding for the maximally activating scale and the scale itself, which is further processed by the following convolutional layers. This allows all the intermediate representations to be locally scale equivariant. We show that this improves the performance of the model by over $20\%$ in the scale equivariant task of regressing the scaling factor applied to randomly scaled MNIST digits. Furthermore, we find it also useful for scale invariant tasks, such as the actual classification of randomly scaled digits. This highlights the usefulness of allowing for a compact representation that can also learn relationships between different local scales by keeping internal scale equivariance.
Practical Constrained Optimization of Auction Mechanisms in E-Commerce Sponsored Search Advertising
Venue: arXiv cs.GT 2018
Year: 2018

Sponsored search in E-commerce platforms such as Amazon, Taobao and Tmall provides sellers an effective way to reach potential buyers with most relevant purpose. In this paper, we study the auction mechanism optimization problem in sponsored search on Alibaba's mobile E-commerce platform. Besides generating revenue, we are supposed to maintain an efficient marketplace with plenty of quality users, guarantee a reasonable return on investment (ROI) for advertisers, and meanwhile, facilitate a pleasant shopping experience for the users. These requirements essentially pose a constrained optimization problem. Directly optimizing over auction parameters yields a discontinuous, non-convex problem that denies effective solutions. One of our major contribution is a practical convex optimization formulation of the original problem. We devise a novel re-parametrization of auction mechanism with discrete sets of representative instances. To construct the optimization problem, we build an auction simulation system which estimates the resulted business indicators of the selected parameters by replaying the auctions recorded from real online requests. We summarized the experiments on real search traffics to analyze the effects of fidelity of auction simulation, the efficacy under various constraint targets and the influence of regularization. The experiment results show that with proper entropy regularization, we are able to maximize revenue while constraining other business indicators within given ranges.
ABE-Cities: An Attribute-Based Encryption System for Smart Cities
Venue: arXiv cs.CR 2018
Year: 2018

In the near future, a technological revolution will involve our cities, where a variety of smart services based on the Internet of Things will be developed to facilitate the needs of the citizens. Sensing devices are already being deployed in urban environments, and they will generate huge amounts of data. Such data are typically outsourced to some cloud storage because this lowers capital and operating expenses and guarantees high availability. However, cloud storage may have incentives to release stored data to unauthorized entities. In this work we present ABE-Cities, an encryption scheme for urban sensing which solves the above problems while ensuring fine-grained access control on data by means of Attribute-Based Encryption (ABE). Basically, ABE-Cities encrypts data before storing it in the cloud and provides users with keys able to decrypt only those portions of data the user is authorized to access. In ABE-Cities, the sensing devices perform only lightweight symmetric cryptography operations, thus they can also be resource-constrained. ABE-Cities provides planned expiration of keys, as well as their unplanned revocation. We propose methods to make the key revocation efficient, and we show by simulations the overall efficiency of ABE-Cities.
A Parametric Framework for Reversible $π$-Calculi
Venue: arXiv cs.LO 2018
Year: 2018

This paper presents a study of causality in a reversible, concurrent setting. There exist various notions of causality in pi-calculus, which differ in the treatment of parallel extrusions of the same name. In this paper we present a uniform framework for reversible pi-calculi that is parametric with respect to a data structure that stores information about an extrusion of a name. Different data structures yield different approaches to the parallel extrusion problem. We map three well-known causal semantics into our framework. We show that the (parametric) reversibility induced by our framework is causally- consistent and prove a causal correspondence between an appropriate instance of the framework and Boreale and Sangiorgi's causal semantics.
Parallel Optimal Control for Cooperative Automation of Large-scale Connected Vehicles via ADMM
Venue: arXiv cs.SY 2018
Year: 2018

This paper proposes a parallel optimization algorithm for cooperative automation of large-scale connected vehicles. The task of cooperative automation is formulated as a centralized optimization problem taking the whole decision space of all vehicles into account. Considering the uncertainty of the environment, the problem is solved in a receding horizon fashion. Then, we employ the alternating direction method of multipliers (ADMM) to solve the centralized optimization in a parallel way, which scales more favorably to large-scale instances. Also, Taylor series is used to linearize nonconvex constraints caused by coupling collision avoidance constraints among interactive vehicles. Simulations with two typical traffic scenes for multiple vehicles demonstrate the effectiveness and efficiency of our method.
Toward a Tactile Internet Reference Architecture: Vision and Progress of the IEEE P1918.1 Standard
Venue: arXiv cs.NI 2018
Year: 2018

The term Tactile Internet broadly refers to a communication network which is capable of delivering control, touch, and sensing/actuation information in real-time. The Tactile Internet is currently a topic of interest for various standardization bodies. The emerging IEEE P1918.1 standards working group is focusing on defining a framework for the Tactile Internet. The main objective of this article is to present a reference architecture for the Tactile Internet based on the latest developments within the IEEE P1918.1 standard. The article provides an in-depth treatment of various architectural aspects including the key entities, the interfaces, the functional capabilities and the protocol stack. A case study has been presented as a manifestation of the architecture. Performance evaluation demonstrates the impact of functional capabilities and the underlying enablers on user-level utility pertaining to a generic Tactile Internet application.
Improve3C: Data Cleaning on Consistency and Completeness with Currency
Venue: arXiv cs.DB 2018
Year: 2018

Data quality plays a key role in big data management today. With the explosive growth of data from a variety of sources, the quality of data is faced with multiple problems. Motivated by this, we study the multiple data quality improvement on completeness, consistency and currency in this paper. For the proposed problem, we introduce a 4-step framework, named Improve3C, for detection and quality improvement on incomplete and inconsistent data without timestamps. We compute and achieve a relative currency order among records derived from given currency constraints, according to which inconsistent and incomplete data can be repaired effectively considering the temporal impact. For both effectiveness and efficiency consideration, we carry out inconsistent repair ahead of incomplete repair. Currency-related consistency distance is defined to measure the similarity between dirty records and clean ones more accurately. In addition, currency orders are treated as an important feature in the training process of incompleteness repair. The solution algorithms are introduced in detail with examples. A thorough experiment on one real-life data and a synthetic one verifies that the proposed method can improve the performance of dirty data cleaning with multiple quality problems which are hard to be cleaned by the existing approaches effectively.
Develop the application for learning place value
Venue: arXiv cs.CY 2018
Year: 2018

The objectives of this research were 1) to develop the application for learning place value, 2) to determine the efficiency of developed application, 3) to compare academic achievement to compare academic achievement between pre-lesson and post-lesson of students who learned with the developed application about place value, 4) to compare academic achievement between students who learned place value by the developed application and through traditional method during post-lesson, 5) to compare retention of academic achievement of application for learning place value after 2 weeks, and 6) to find satisfactory of student who learned the application for learning place value. The sample group selected through purposive sampling was 5 content specialists and 400 pratomsuksa 1 students.
Learning Stabilizable Dynamical Systems via Control Contraction Metrics
Venue: arXiv cs.SY 2018
Year: 2018

We propose a novel framework for learning stabilizable nonlinear dynamical systems for continuous control tasks in robotics. The key idea is to develop a new control-theoretic regularizer for dynamics fitting rooted in the notion of stabilizability, which guarantees that the learned system can be accompanied by a robust controller capable of stabilizing any open-loop trajectory that the system may generate. By leveraging tools from contraction theory, statistical learning, and convex optimization, we provide a general and tractable semi-supervised algorithm to learn stabilizable dynamics, which can be applied to complex underactuated systems. We validated the proposed algorithm on a simulated planar quadrotor system and observed notably improved trajectory generation and tracking performance with the control-theoretic regularized model over models learned using traditional regression techniques, especially when using a small number of demonstration examples. The results presented illustrate the need to infuse standard model-based reinforcement learning algorithms with concepts drawn from nonlinear control theory for improved reliability.
FMCode: A 3D In-the-Air Finger Motion Based User Login Framework for Gesture Interface
Venue: arXiv cs.CV 2018
Year: 2018

Applications using gesture-based human-computer interface require a new user login method with gestures because it does not have a traditional input method to type a password. However, due to various challenges, existing gesture-based authentication systems are generally considered too weak to be useful in practice. In this paper, we propose a unified user login framework using 3D in-air-handwriting, called FMCode. We define new types of features critical to distinguish legitimate users from attackers and utilize Support Vector Machine (SVM) for user authentication. The features and data-driven models are specially designed to accommodate minor behavior variations that existing gesture authentication methods neglect. In addition, we use deep neural network approaches to efficiently identify the user based on his or her in-air-handwriting, which avoids expansive account database search methods employed by existing work. On a dataset collected by us with over 100 users, our prototype system achieves 0.1% and 0.5% best Equal Error Rate (EER) for user authentication, as well as 96.7% and 94.3% accuracy for user identification, using two types of gesture input devices. Compared to existing behavioral biometric systems using gesture and in-air-handwriting, our framework achieves the state-of-the-art performance. In addition, our experimental results show that FMCode is capable to defend against client-side spoofing attacks, and it performs persistently in the long run. These results and discoveries pave the way to practical usage of gesture-based user login over the gesture interface.