OpenRaiser/Intern-Atlas huggingface dataset:
Learning monocular depth estimation with unsupervised trinocular assumptions
Venue: arXiv cs.CV 2018
Year: 2018

Obtaining accurate depth measurements out of a single image represents a fascinating solution to 3D sensing. CNNs led to considerable improvements in this field, and recent trends replaced the need for ground-truth labels with geometry-guided image reconstruction signals enabling unsupervised training. Currently, for this purpose, state-of-the-art techniques rely on images acquired with a binocular stereo rig to predict inverse depth (i.e., disparity) according to the aforementioned supervision principle. However, these methods suffer from well-known problems near occlusions, left image border, etc inherited from the stereo setup. Therefore, in this paper, we tackle these issues by moving to a trinocular domain for training. Assuming the central image as the reference, we train a CNN to infer disparity representations pairing such image with frames on its left and right side. This strategy allows obtaining depth maps not affected by typical stereo artifacts. Moreover, being trinocular datasets seldom available, we introduce a novel interleaved training procedure enabling to enforce the trinocular assumption outlined from current binocular datasets. Exhaustive experimental results on the KITTI dataset confirm that our proposal outperforms state-of-the-art methods for unsupervised monocular depth estimation trained on binocular stereo pairs as well as any known methods relying on other cues.
Self-Attention Recurrent Network for Saliency Detection
Venue: arXiv cs.CV 2018
Year: 2018

Feature maps in deep neural network generally contain different semantics. Existing methods often omit their characteristics that may lead to sub-optimal results. In this paper, we propose a novel end-to-end deep saliency network which could effectively utilize multi-scale feature maps according to their characteristics. Shallow layers often contain more local information, and deep layers have advantages in global semantics. Therefore, the network generates elaborate saliency maps by enhancing local and global information of feature maps in different layers. On one hand, local information of shallow layers is enhanced by a recurrent structure which shared convolution kernel at different time steps. On the other hand, global information of deep layers is utilized by a self-attention module, which generates different attention weights for salient objects and backgrounds thus achieve better performance. Experimental results on four widely used datasets demonstrate that our method has advantages in performance over existing algorithms.
Kid on The Phone! Toward Automatic Detection of Children on Mobile Devices
Venue: arXiv cs.HC 2018
Year: 2018

Studies have shown that children can be exposed to smart devices at a very early age. This has important implications on research in children-computer interaction, children online safety and early education. Many systems have been built based on such research. In this work, we present multiple techniques to automatically detect the presence of a child on a smart device, which could be used as the first step on such systems. Our methods distinguish children from adults based on behavioral differences while operating a touch-enabled modern computing device. Behavioral differences are extracted from data recorded by the touchscreen and built-in sensors. To evaluate the effectiveness of the proposed methods, a new data set has been created from 50 children and adults who interacted with off-the-shelf applications on smart phones. Results show that it is possible to achieve 99% accuracy and less than 0.5% error rate after 8 consecutive touch gestures using only touch information or 5 seconds of sensor reading. If information is used from multiple sensors, then only after 3 gestures, similar performance could be achieved.
Error Detection in a Large-Scale Lexical Taxonomy
Venue: arXiv cs.AI 2018
Year: 2018

Knowledge base (KB) is an important aspect in artificial intelligence. One significant challenge faced by KB construction is that it contains many noises, which prevents its effective usage. Even though some KB cleansing algorithms have been proposed, they focus on the structure of the knowledge graph and neglect the relation between the concepts, which could be helpful to discover wrong relations in KB. Motived by this, we measure the relation of two concepts by the distance between their corresponding instances and detect errors within the intersection of the conflicting concept sets. For efficient and effective knowledge base cleansing, we first apply a distance-based Model to determine the conflicting concept sets using two different methods. Then, we propose and analyze several algorithms on how to detect and repairing the errors based on our model, where we use hash method for an efficient way to calculate distance. Experimental results demonstrate that the proposed approaches could cleanse the knowledge bases efficiently and effectively.
Energy-Age Tradeoff in Status Update Communication Systems with Retransmission
Venue: arXiv cs.IT 2018
Year: 2018

Age-of-information is a novel performance metric in communication systems to indicate the freshness of the latest received data, which has wide applications in monitoring and control scenarios. Another important performance metric in these applications is energy consumption, since monitors or sensors are usually energy constrained. In this paper, we study the energy-age tradeoff in a status update system where data transmission from a source to a receiver may encounter failure due to channel error. As the status sensing process consumes energy, when a transmission failure happens, the source may either retransmit the existing data to save energy for sensing, or sense and transmit a new update to minimize age-of-information. A threshold-based retransmission policy is considered where each update is allowed to be transmitted no more than M times. Closed-form average age-of-information and energy consumption is derived and expressed as a function of channel failure probability and maximum number of retransmissions M. Numerical simulations validate our analytical results, and illustrate the tradeoff between average age-of-information and energy consumption.
Correspondences between Privacy and Nondiscrimination: Why They Should Be Studied Together
Venue: arXiv cs.CR 2018
Year: 2018

Privacy and nondiscrimination are related but different. We make this observation precise in two ways. First, we show that both privacy and nondiscrimination have two versions, a causal version and a statical associative version, with each version corresponding to a competing view of the proper goal of privacy or nondiscrimination. Second, for each version, we show that a difference between the privacy edition of the version and the nondiscrimination edition of the version is related to the difference between Bayesian probabilities and frequentist probabilities. In particular, privacy admits both Bayesian and frequentist interpretations whereas nondiscrimination is limited to the frequentist interpretation. We show how the introduced correspondence allows results from one area of research to be used for the other.
Concentration bounds for empirical conditional value-at-risk: The unbounded case
Venue: arXiv cs.LG 2018
Year: 2018

In several real-world applications involving decision making under uncertainty, the traditional expected value objective may not be suitable, as it may be necessary to control losses in the case of a rare but extreme event. Conditional Value-at-Risk (CVaR) is a popular risk measure for modeling the aforementioned objective. We consider the problem of estimating CVaR from i.i.d. samples of an unbounded random variable, which is either sub-Gaussian or sub-exponential. We derive a novel one-sided concentration bound for a natural sample-based CVaR estimator in this setting. Our bound relies on a concentration result for a quantile-based estimator for Value-at-Risk (VaR), which may be of independent interest.
An Efficient Approach to Learning Chinese Judgment Document Similarity Based on Knowledge Summarization
Venue: arXiv cs.AI 2018
Year: 2018

A previous similar case in common law systems can be used as a reference with respect to the current case such that identical situations can be treated similarly in every case. However, current approaches for judgment document similarity computation failed to capture the core semantics of judgment documents and therefore suffer from lower accuracy and higher computation complexity. In this paper, a knowledge block summarization based machine learning approach is proposed to compute the semantic similarity of Chinese judgment documents. By utilizing domain ontologies for judgment documents, the core semantics of Chinese judgment documents is summarized based on knowledge blocks. Then the WMD algorithm is used to calculate the similarity between knowledge blocks. At last, the related experiments were made to illustrate that our approach is very effective and efficient in achieving higher accuracy and faster computation speed in comparison with the traditional approaches.
Data-driven modeling for boiling heat transfer: using deep neural networks and high-fidelity simulation results
Venue: arXiv cs.CE 2018
Year: 2018

Boiling heat transfer occurs in many situations and can be used for thermal management in various engineered systems with high energy density, from power electronics to heat exchangers in power plants and nuclear reactors. Essentially, boiling is a complex physical process that involves interactions between heating surface, liquid, and vapor. For engineering applications, the boiling heat transfer is usually predicted by empirical correlations or semi-empirical models, which has relatively large uncertainty. In this paper, a data-driven approach based on deep feedforward neural networks is studied. The proposed networks use near wall local features to predict the boiling heat transfer. The inputs of networks include the local momentum and energy convective transport, pressure gradients, turbulent viscosity, and surface information. The outputs of the networks are the quantities of interest of a typical boiling system, including heat transfer components, wall superheat, and near wall void fraction. The networks are trained by the high-fidelity data processed from first principle simulation of pool boiling under varying input heat fluxes. State-of-the-art algorithms are applied to prevent the overfitting issue when training the deep networks. The trained networks are tested in interpolation cases and extrapolation cases which both demonstrate good agreement with the original high-fidelity simulation results.
Reasoning with Justifiable Exceptions in Contextual Hierarchies (Appendix)
Venue: arXiv cs.AI 2018
Year: 2018

This paper is an appendix to the paper "Reasoning with Justifiable Exceptions in Contextual Hierarchies" by Bozzato, Serafini and Eiter, 2018. It provides further details on the language, the complexity results and the datalog translation introduced in the main paper.
Query Answering for Rough EL Ontologies (Extended Technical Report)
Venue: arXiv cs.LO 2018
Year: 2018

Querying large datasets with incomplete and vague data is still a challenge. Ontology-based query answering extends standard database query answering by background knowledge from an ontology to augment incomplete data. We focus on ontologies written in rough description logics (DLs), which allow to represent vague knowledge by partitioning the domain of discourse into classes of indiscernible elements. In this paper, we extend the combined approach for ontology-based query answering to a variant of the DL EL augmented with rough concept constructors. We show that this extension preserves the good computational properties of classical EL and can be implemented by standard database systems.
GLSE Precoders for Massive MIMO Systems: Analysis and Applications
Venue: arXiv cs.IT 2018
Year: 2018

This paper proposes the class of Generalized Least-Square-Error (GLSE) precoders for multiuser massive MIMO systems. For a generic transmit constellation, GLSE precoders minimize the interference at user terminals assuring that given constraints on the transmit signals are satisfied. The general form of these precoders enables us to impose multiple restrictions at the transmit signal such as limited peak power and restricted number of active transmit antennas. Invoking the replica method from statistical mechanics, we study the performance of GLSE precoders in the large-system limit. We show that the output symbols of these precoders are identically distributed and their statistics are described with an equivalent scalar GLSE precoder. Using the asymptotic results, we further address some applications of the GLSE precoders; namely forming transmit signals over a restricted alphabet and transmit antenna selection. Our investigations demonstrate that a computationally efficient GLSE precoder requires $41\%$ fewer active transmit antennas than conventional selection protocols in order to achieve a desired level of input-output distortion.
Residual Memory Networks: Feed-forward approach to learn long temporal dependencies
Venue: arXiv cs.CL 2018
Year: 2018

Training deep recurrent neural network (RNN) architectures is complicated due to the increased network complexity. This disrupts the learning of higher order abstracts using deep RNN. In case of feed-forward networks training deep structures is simple and faster while learning long-term temporal information is not possible. In this paper we propose a residual memory neural network (RMN) architecture to model short-time dependencies using deep feed-forward layers having residual and time delayed connections. The residual connection paves way to construct deeper networks by enabling unhindered flow of gradients and the time delay units capture temporal information with shared weights. The number of layers in RMN signifies both the hierarchical processing depth and temporal depth. The computational complexity in training RMN is significantly less when compared to deep recurrent networks. RMN is further extended as bi-directional RMN (BRMN) to capture both past and future information. Experimental analysis is done on AMI corpus to substantiate the capability of RMN in learning long-term information and hierarchical information. Recognition performance of RMN trained with 300 hours of Switchboard corpus is compared with various state-of-the-art LVCSR systems. The results indicate that RMN and BRMN gains 6 % and 3.8 % relative improvement over LSTM and BLSTM networks.
Discovering Latent Information By Spreading Activation Algorithm For Document Retrieval
Venue: arXiv cs.AI 2018
Year: 2018

Syntactic search relies on keywords contained in a query to find suitable documents. So, documents that do not contain the keywords but contain information related to the query are not retrieved. Spreading activation is an algorithm for finding latent information in a query by exploiting relations between nodes in an associative network or semantic network. However, the classical spreading activation algorithm uses all relations of a node in the network that will add unsuitable information into the query. In this paper, we propose a novel approach for semantic text search, called query-oriented-constrained spreading activation that only uses relations relating to the content of the query to find really related information. Experiments on a benchmark dataset show that, in terms of the MAP measure, our search engine is 18.9% and 43.8% respectively better than the syntactic search and the search using the classical constrained spreading activation. KEYWORDS: Information Retrieval, Ontology, Semantic Search, Spreading Activation
A Survey on Surrogate Approaches to Non-negative Matrix Factorization
Venue: arXiv cs.LG 2018
Year: 2018

Motivated by applications in hyperspectral imaging we investigate methods for approximating a high-dimensional non-negative matrix $\mathbf{\mathit{Y}}$ by a product of two lower-dimensional, non-negative matrices $\mathbf{\mathit{K}}$ and $\mathbf{\mathit{X}}.$ This so-called non-negative matrix factorization is based on defining suitable Tikhonov functionals, which combine a discrepancy measure for $\mathbf{\mathit{Y}}\approx\mathbf{\mathit{KX}}$ with penalty terms for enforcing additional properties of $\mathbf{\mathit{K}}$ and $\mathbf{\mathit{X}}$. The minimization is based on alternating minimization with respect to $\mathbf{\mathit{K}}$ or $\mathbf{\mathit{X}}$, where in each iteration step one replaces the original Tikhonov functional by a locally defined surrogate functional. The choice of surrogate functionals is crucial: It should allow a comparatively simple minimization and simultaneously its first order optimality condition should lead to multiplicative update rules, which automatically preserve non-negativity of the iterates. We review the most standard construction principles for surrogate functionals for Frobenius-norm and Kullback-Leibler discrepancy measures. We extend the known surrogate constructions by a general framework, which allows to add a large variety of penalty terms. The paper finishes by deriving the corresponding alternating minimization schemes explicitely and by applying these methods to MALDI imaging data.
Multi-Message Private Information Retrieval using Product-Matrix MSR and MBR Codes
Venue: arXiv cs.IT 2018
Year: 2018

Multi-message private information retrieval (MPIR) is an interesting variation of PIR which allows a user to download multiple messages from the database without revealing the identity of the desired messages. Obviously, the user can repeatly use a single-message PIR scheme, but we wish for a more efficient way to reduce the download cost. In [1], Banawan and Ukulus investigate the multi-message PIR problem with replicated database. In this paper, we consider multi-message PIR schemes where the database is stored using minimum storage regenerating (MSR) or minimum bandwidth regenerating (MBR) codes which are classes of optimal regenerating codes providing efficient repair when a node in the system fails. The relationships between the costs of storage, retrieval and repair are analysed, and explicit schemes using the MSR and MBR codes from [2], which both achieve the optimal curve of the trade-off, are given. To the best of our knowledge, our work is the first to explore the multi-message PIR with coded database.
Digital Blues: An Investigation into the Use of Bluetooth Protocols
Venue: arXiv cs.CY 2018
Year: 2018

The proliferation of Bluetooth mobile device communications into all aspects of modern society raises security questions by both academicians and practitioners. This environment prompted an investigation into the real-world use of Bluetooth protocols along with an analysis of documented security attacks. The experiment discussed in this paper collected data for one week in a local coffee shop. The data collection took about an hour each day and identified 478 distinct devices. The contribution of this research is two-fold. First, it provides insight into real-world Bluetooth protocols that are being utilized by the general public. Second, it provides foundational research that is necessary for future Bluetooth penetration testing research.
The Parameterized Complexity of Finding Point Sets with Hereditary Properties
Venue: arXiv cs.CG 2018
Year: 2018

We consider problems where the input is a set of points in the plane and an integer $k$, and the task is to find a subset $S$ of the input points of size $k$ such that $S$ satisfies some property. We focus on properties that depend only on the order type of the points and are monotone under point removals. We show that not all such problems are fixed-parameter tractable parameterized by $k$, by exhibiting a property defined by three forbidden patterns for which finding a $k$-point subset with the property is $\mathrm{W}[1]$-complete and (assuming the exponential time hypothesis) cannot be solved in time $n^{o(k/\log k)}$. However, we show that problems of this type are fixed-parameter tractable for all properties that include all collinear point sets, properties that exclude at least one convex polygon, and properties defined by a single forbidden pattern.
Efficient Fusion of Sparse and Complementary Convolutions
Venue: arXiv cs.CV 2018
Year: 2018

We propose a new method to create compact convolutional neural networks (CNNs) by exploiting sparse convolutions. Different from previous works that learn sparsity in models, we directly employ hand-crafted kernels with regular sparse patterns, which result in the computational gain in practice without sophisticated and dedicated software or hardware. The core of our approach is an efficient network module that linearly combines sparse kernels to yield feature representations as strong as those from regular kernels. We integrate this module into various network architectures and demonstrate its effectiveness on three vision tasks, object classification, localization and detection. For object classification and localization, our approach achieves comparable or better performance than several baselines and related works while providing lower computational costs with fewer parameters (on average, a $2-4\times$ reduction of convolutional parameters and computation). For object detection, our approach leads to a VGG-16-based Faster RCNN detector that is 12.4$\times$ smaller and about 3$\times$ faster than the baseline.
Privacy in Social Media: Identification, Mitigation and Applications
Venue: arXiv cs.CR 2018
Year: 2018

The increasing popularity of social media has attracted a huge number of people to participate in numerous activities on a daily basis. This results in tremendous amounts of rich user-generated data. This data provides opportunities for researchers and service providers to study and better understand users' behaviors and further improve the quality of the personalized services. Publishing user-generated data risks exposing individuals' privacy. Users privacy in social media is an emerging task and has attracted increasing attention in recent years. These works study privacy issues in social media from the two different points of views: identification of vulnerabilities, and mitigation of privacy risks. Recent research has shown the vulnerability of user-generated data against the two general types of attacks, identity disclosure and attribute disclosure. These privacy issues mandate social media data publishers to protect users' privacy by sanitizing user-generated data before publishing it. Consequently, various protection techniques have been proposed to anonymize user-generated social media data. There is a vast literature on privacy of users in social media from many perspectives. In this survey, we review the key achievements of user privacy in social media. In particular, we review and compare the state-of-the-art algorithms in terms of the privacy leakage attacks and anonymization algorithms. We overview the privacy risks from different aspects of social media and categorize the relevant works into five groups 1) graph data anonymization and de-anonymization, 2) author identification, 3) profile attribute disclosure, 4) user location and privacy, and 5) recommender systems and privacy issues. We also discuss open problems and future research directions for user privacy issues in social media.