OpenRaiser/Intern-Atlas huggingface dataset:
TomoReal: Tomographic Displays
Venue: arXiv cs.GR 2018
Year: 2018

Since the history of display technologies began, people have dreamed an ultimate 3D display system. In order to get close to the dream, 3D displays should provide both of psychological and physiological cues for recognition of depth information. However, it is challenging to satisfy the essential features without sacrifice in conventional technical values including resolution, frame rate, and eye-box. Here, we present a new type of 3D displays: tomographic displays. We claim that tomographic displays may support extremely wide depth of field, quasi-continuous accommodation, omni-directional motion parallax, preserved resolution, full frame, and moderate field of view within enough eye-box. Tomographic displays consist of focus-tunable optics, 2D display panel, and fast spatially adjustable backlight. The synchronization of the focus-tunable optics and the backlight enables the 2D display panel to express the depth information. Tomographic displays have various applications including tabletop 3D displays, head-up displays, and near-eye stereoscopes. In this study, we implement a near-eye display named TomoReal, which is one of the most promising application of tomographic displays. We conclude with the detailed analysis and thorough discussion for tomographic displays, which would open a new research field.
CERES: Distantly Supervised Relation Extraction from the Semi-Structured Web
Venue: arXiv cs.AI 2018
Year: 2018

The web contains countless semi-structured websites, which can be a rich source of information for populating knowledge bases. Existing methods for extracting relations from the DOM trees of semi-structured webpages can achieve high precision and recall only when manual annotations for each website are available. Although there have been efforts to learn extractors from automatically-generated labels, these methods are not sufficiently robust to succeed in settings with complex schemas and information-rich websites. In this paper we present a new method for automatic extraction from semi-structured websites based on distant supervision. We automatically generate training labels by aligning an existing knowledge base with a web page and leveraging the unique structural characteristics of semi-structured websites. We then train a classifier based on the potentially noisy and incomplete labels to predict new relation instances. Our method can compete with annotation-based techniques in the literature in terms of extraction quality. A large-scale experiment on over 400,000 pages from dozens of multi-lingual long-tail websites harvested 1.25 million facts at a precision of 90%.
EMBER: An Open Dataset for Training Static PE Malware Machine Learning Models
Venue: arXiv cs.CR 2018
Year: 2018

This paper describes EMBER: a labeled benchmark dataset for training machine learning models to statically detect malicious Windows portable executable files. The dataset includes features extracted from 1.1M binary files: 900K training samples (300K malicious, 300K benign, 300K unlabeled) and 200K test samples (100K malicious, 100K benign). To accompany the dataset, we also release open source code for extracting features from additional binaries so that additional sample features can be appended to the dataset. This dataset fills a void in the information security machine learning community: a benign/malicious dataset that is large, open and general enough to cover several interesting use cases. We enumerate several use cases that we considered when structuring the dataset. Additionally, we demonstrate one use case wherein we compare a baseline gradient boosted decision tree model trained using LightGBM with default settings to MalConv, a recently published end-to-end (featureless) deep learning model for malware detection. Results show that even without hyper-parameter optimization, the baseline EMBER model outperforms MalConv. The authors hope that the dataset, code and baseline model provided by EMBER will help invigorate machine learning research for malware detection, in much the same way that benchmark datasets have advanced computer vision research.
Efficient algorithms for tensor scaling, quantum marginals and moment polytopes
Venue: arXiv cs.DS 2018
Year: 2018

We present a polynomial time algorithm to approximately scale tensors of any format to arbitrary prescribed marginals (whenever possible). This unifies and generalizes a sequence of past works on matrix, operator and tensor scaling. Our algorithm provides an efficient weak membership oracle for the associated moment polytopes, an important family of implicitly-defined convex polytopes with exponentially many facets and a wide range of applications. These include the entanglement polytopes from quantum information theory (in particular, we obtain an efficient solution to the notorious one-body quantum marginal problem) and the Kronecker polytopes from representation theory (which capture the asymptotic support of Kronecker coefficients). Our algorithm can be applied to succinct descriptions of the input tensor whenever the marginals can be efficiently computed, as in the important case of matrix product states or tensor-train decompositions, widely used in computational physics and numerical mathematics. We strengthen and generalize the alternating minimization approach of previous papers by introducing the theory of highest weight vectors from representation theory into the numerical optimization framework. We show that highest weight vectors are natural potential functions for scaling algorithms and prove new bounds on their evaluations to obtain polynomial-time convergence. Our techniques are general and we believe that they will be instrumental to obtain efficient algorithms for moment polytopes beyond the ones consider here, and more broadly, for other optimization problems possessing natural symmetries.
CUBA: Interprocedural Context-UnBounded Analysis of Concurrent Programs (Extended Manuscript)
Venue: arXiv cs.PL 2018
Year: 2018

A classical result by Ramalingam about synchronization-sensitive interprocedural program analysis implies that reachability for concurrent threads running recursive procedures is undecidable. A technique proposed by Qadeer and Rehof, to bound the number of context switches allowed between the threads, leads to an incomplete solution that is, however, believed to catch "most bugs" in practice. The question whether the technique can also prove the absence of bugs at least in some cases has remained largely open. In this paper we introduce a broad verification methodology for resource-parameterized programs that observes how changes to the resource parameter affect the behavior of the program. Applied to the context-unbounded analysis problem (CUBA), the methodology results in partial verification techniques for procedural concurrent programs. Our solutions may not terminate, but are able to both refute and prove context-unbounded safety for concurrent recursive threads. We demonstrate the effectiveness of our method using a variety of examples, the safe of which cannot be proved safe by earlier, context-bounded methods.
Mining actionable information from security forums: the case of malicious IP addresses
Venue: arXiv cs.SI 2018
Year: 2018

The goal of this work is to systematically extract information from hacker forums, whose information would be in general described as unstructured: the text of a post is not necessarily following any writing rules. By contrast, many security initiatives and commercial entities are harnessing the readily public information, but they seem to focus on structured sources of information. Here, we focus on the problem of identifying malicious IP addresses, among the IP addresses which are reported in the forums. We develop a method to automate the identification of malicious IP addresses with the design goal of being independent of external sources. A key novelty is that we use a matrix decomposition method to extract latent features of the behavioral information of the users, which we combine with textual information from the related posts. A key design feature of our technique is that it can be readily applied to different language forums, since it does not require a sophisticated Natural Language Processing approach. In particular, our solution only needs a small number of keywords in the new language plus the users behavior captured by specific features. We also develop a tool to automate the data collection from security forums. Using our tool, we collect approximately 600K posts from 3 different forums. Our method exhibits high classification accuracy, while the precision of identifying malicious IP in post is greater than 88% in all three forums. We argue that our method can provide significantly more information: we find up to 3 times more potentially malicious IP address compared to the reference blacklist VirusTotal. As the cyber-wars are becoming more intense, having early accesses to useful information becomes more imperative to remove the hackers first-move advantage, and our work is a solid step towards this direction.
Offline and Online calibration of Mobile Robot and SLAM Device for Navigation
Venue: arXiv cs.CV 2018
Year: 2018

Robot navigation technology is required to accomplish difficult tasks in various environments. In navigation, it is necessary to know the information of the external environments and the state of the robot under the environment. On the other hand, various studies have been done on SLAM technology, which is also used for navigation, but also applied to devices for Mixed Reality and the like. In this paper, we propose a robot-device calibration method for navigation with a device using SLAM technology on a robot. The calibration is performed by using the position and orientation information given by the robot and the device. In the calibration, the most efficient way of movement is clarified according to the restriction of the robot movement. Furthermore, we also show a method to dynamically correct the position and orientation of the robot so that the information of the external environment and the shape information of the robot maintain consistency in order to reduce the dynamic error occurring during navigation. Our method can be easily used for various kinds of robots and localization with sufficient precision for navigation is possible with offline calibration and online position correction. In the experiments, we confirm the parameters obtained by two types of offline calibration according to the degree of freedom of robot movement and validate the effectiveness of online correction method by plotting localized position error during robot's intense movement. Finally, we show the demonstration of navigation using SLAM device.
Cooperative Strategies for {UAV}-Enabled Small Cell Networks Sharing Unlicensed Spectrum
Venue: arXiv cs.IT 2018
Year: 2018

In this paper, we study an aerial drone base station (DBS) assisted cellular network that consists of a single ground macro base station (MBS), multiple DBSs, and multiple ground terminals (GT). We assume that the MBS transmits to the DBSs and the GTs in the licensed band while the DBSs use a separate unlicensed band (e.g. Wi-Fi) to transmit to the GTs. For the utilization of the DBSs, we propose a cooperative decode--forward (DF) protocol in which multiple DBSs assist the terminals simultaneously while maintaining a predetermined interference level on the coexisting unlicensed band users. For our network setup, we formulate a joint optimization problem for minimizing the aggregate gap between the target rates and the throughputs of terminals by optimizing over the 3D positions of the DBSs and the resources (power, time, bandwidth) of the network. To solve the optimization problem, we propose an efficient nested structured algorithm based on particle swarm optimization and convex optimization methods. Extensive numerical evaluations of the proposed algorithm is performed considering various aspects to demonstrate the performance of our algorithm and the gain for utilizing DBSs.
Erasure Correcting Codes by Using Shift Operation and Exclusive OR
Venue: arXiv cs.IT 2018
Year: 2018

This paper proposes an erasure correcting code and its systematic form for the distributed storage system. The proposed codes are encoded by exclusive OR and bit-level shift operation. By the shift operation, the encoded packets are slightly longer than the source packets. This paper evaluates the extra length of encoded packets, called overhead, and shows that the proposed codes have smaller overheads than the zigzag decodable code, which is an existing code using exclusive OR and bit-level shift operation.
Living Without a Mobile Phone: An Autoethnography
Venue: arXiv cs.HC 2018
Year: 2018

This paper presents an autoethnography of my experiences living without a mobile phone. What started as an experiment motivated by a personal need to reduce stress, has resulted in two voluntary mobile phone breaks spread over nine years (i.e., 2002-2008 and 2014-2017). Conducting this autoethnography is the means to assess if the lack of having a phone has had any real impact in my life. Based on formative and summative analyses, four meaningful units or themes were identified (i.e., social relationships, everyday work, research career, and location and security), and judged using seven criteria for successful ethnography from existing literature. Furthermore, I discuss factors that allow me to make the choice of not having a mobile phone, as well as the relevance that the lessons gained from not having a mobile phone have on the lives of people who are involuntarily disconnected from communication infrastructures.
Tightly-coupled Monocular Visual-odometric SLAM using Wheels and a MEMS Gyroscope
Venue: arXiv cs.RO 2018
Year: 2018

In this paper, we present a novel tightly-coupled probabilistic monocular visual-odometric Simultaneous Localization and Mapping algorithm using wheels and a MEMS gyroscope, which can provide accurate, robust and long-term localization for the ground robot moving on a plane. Firstly, we present an odometer preintegration theory that integrates the wheel encoder measurements and gyroscope measurements to a local frame. The preintegration theory properly addresses the manifold structure of the rotation group SO(3) and carefully deals with uncertainty propagation and bias correction. Then the novel odometer error term is formulated using the odometer preintegration model and it is tightly integrated into the visual optimization framework. Furthermore, we introduce a complete tracking framework to provide different strategies for motion tracking when (1) both measurements are available, (2) visual measurements are not available, and (3) wheel encoder experiences slippage, which leads the system to be accurate and robust. Finally, the proposed algorithm is evaluated by performing extensive experiments, the experimental results demonstrate the superiority of the proposed system.
The PS-Battles Dataset - an Image Collection for Image Manipulation Detection
Venue: arXiv cs.MM 2018
Year: 2018

The boost of available digital media has led to a significant increase in derivative work. With tools for manipulating objects becoming more and more mature, it can be very difficult to determine whether one piece of media was derived from another one or tampered with. As derivations can be done with malicious intent, there is an urgent need for reliable and easily usable tampering detection methods. However, even media considered semantically untampered by humans might have already undergone compression steps or light post-processing, making automated detection of tampering susceptible to false positives. In this paper, we present the PS-Battles dataset which is gathered from a large community of image manipulation enthusiasts and provides a basis for media derivation and manipulation detection in the visual domain. The dataset consists of 102'028 images grouped into 11'142 subsets, each containing the original image as well as a varying number of manipulated derivatives.
The Users' Perspective on the Privacy-Utility Trade-offs in Health Recommender Systems
Venue: arXiv cs.CY 2018
Year: 2018

Privacy is a major good for users of personalized services such as recommender systems. When applied to the field of health informatics, privacy concerns of users may be amplified, but the possible utility of such services is also high. Despite availability of technologies such as k-anonymity, differential privacy, privacy-aware recommendation, and personalized privacy trade-offs, little research has been conducted on the users' willingness to share health data for usage in such systems. In two conjoint-decision studies (sample size n=521), we investigate importance and utility of privacy-preserving techniques related to sharing of personal health data for k-anonymity and differential privacy. Users were asked to pick a preferred sharing scenario depending on the recipient of the data, the benefit of sharing data, the type of data, and the parameterized privacy. Users disagreed with sharing data for commercial purposes regarding mental illnesses and with high de-anonymization risks but showed little concern when data is used for scientific purposes and is related to physical illnesses. Suggestions for health recommender system development are derived from the findings.
Mechanical design of a distal scanner for confocal microlaparoscope: A conic solution
Venue: arXiv cs.RO 2018
Year: 2018

This paper presents the mechanical design of a distal scanner to perform a spiral scan for mosaic-imaging with a confocal microlaparoscope. First, it is demonstrated with ex vivo experiments that a spiral scan performs better than a raster scan on soft tissue. Then a mechanical design is developed in order to perform the spiral scan. The design in this paper is based on a conic structure with a particular curved surface. The mechanism is simple to implement and to drive; therefore, it is a low-cost solution. A 5:1 scale prototype is implemented by rapid prototyping and the requirements are validated by experiments. The experiments include manual and motor drive of the system. The manual drive demonstrates the resulting spiral motion by drawing the tip trajectory with an attached pencil. The motor drive demonstrates the speed control of the system with an analysis of video thread capturing the trajectory of a laser beam emitted from the tip.
Convolutional Neural Networks for Skull-stripping in Brain MR Imaging using Consensus-based Silver standard Masks
Venue: arXiv cs.CV 2018
Year: 2018

Convolutional neural networks (CNN) for medical imaging are constrained by the number of annotated data required in the training stage. Usually, manual annotation is considered to be the "gold standard". However, medical imaging datasets that include expert manual segmentation are scarce as this step is time-consuming, and therefore expensive. Moreover, single-rater manual annotation is most often used in data-driven approaches making the network optimal with respect to only that single expert. In this work, we propose a CNN for brain extraction in magnetic resonance (MR) imaging, that is fully trained with what we refer to as silver standard masks. Our method consists of 1) developing a dataset with "silver standard" masks as input, and implementing both 2) a tri-planar method using parallel 2D U-Net-based CNNs (referred to as CONSNet) and 3) an auto-context implementation of CONSNet. The term CONSNet refers to our integrated approach, i.e., training with silver standard masks and using a 2D U-Net-based architecture. Our results showed that we outperformed (i.e., larger Dice coefficients) the current state-of-the-art SS methods. Our use of silver standard masks reduced the cost of manual annotation, decreased inter-intra-rater variability, and avoided CNN segmentation super-specialization towards one specific manual annotation guideline that can occur when gold standard masks are used. Moreover, the usage of silver standard masks greatly enlarges the volume of input annotated data because we can relatively easily generate labels for unlabeled data. In addition, our method has the advantage that, once trained, it takes only a few seconds to process a typical brain image volume using modern hardware, such as a high-end graphics processing unit. In contrast, many of the other competitive methods have processing times in the order of minutes.
Roster Evaluation Based on Classifiers for the Nurse Rostering Problem
Venue: arXiv cs.AI 2018
Year: 2018

The personnel scheduling problem is a well-known NP-hard combinatorial problem. Due to the complexity of this problem and the size of the real-world instances, it is not possible to use exact methods, and thus heuristics, meta-heuristics, or hyper-heuristics must be employed. The majority of heuristic approaches are based on iterative search, where the quality of intermediate solutions must be calculated. Unfortunately, this is computationally highly expensive because these problems have many constraints and some are very complex. In this study, we propose a machine learning technique as a tool to accelerate the evaluation phase in heuristic approaches. The solution is based on a simple classifier, which is able to determine whether the changed solution (more precisely, the changed part of the solution) is better than the original or not. This decision is made much faster than a standard cost-oriented evaluation process. However, the classification process cannot guarantee 100% correctness. Therefore, our approach, which is illustrated using a tabu search algorithm in this study, includes a filtering mechanism, where the classifier rejects the majority of the potentially bad solutions and the remaining solutions are then evaluated in a standard manner. We also show how the boosting algorithms can improve the quality of the final solution compared with a simple classifier. We verified our proposed approach and premises, based on standard and real-world benchmark instances, to demonstrate the significant speedup obtained with comparable solution quality.
On Solving Quantified Bit-Vectors using Invertibility Conditions
Venue: arXiv cs.LO 2018
Year: 2018

We present a novel approach for solving quantified bit-vector formulas in Satisfiability Modulo Theories (SMT) based on computing symbolic inverses of bit-vector operators. We derive conditions that precisely characterize when bit-vector constraints are invertible for a representative set of bit-vector operators commonly supported by SMT solvers. We utilize syntax-guided synthesis techniques to aid in establishing these conditions and verify them independently by using several SMT solvers. We show that invertibility conditions can be embedded into quantifier instantiations using Hilbert choice expressions, and give experimental evidence that a counterexample-guided approach for quantifier instantiation utilizing these techniques leads to performance improvements with respect to state-of-the-art solvers for quantified bit-vector constraints.
Regularized Singular Value Decomposition and Application to Recommender System
Venue: arXiv cs.LG 2018
Year: 2018

Singular value decomposition (SVD) is the mathematical basis of principal component analysis (PCA). Together, SVD and PCA are one of the most widely used mathematical formalism/decomposition in machine learning, data mining, pattern recognition, artificial intelligence, computer vision, signal processing, etc. In recent applications, regularization becomes an increasing trend. In this paper, we present a regularized SVD (RSVD), present an efficient computational algorithm, and provide several theoretical analysis. We show that although RSVD is non-convex, it has a closed-form global optimal solution. Finally, we apply RSVD to the application of recommender system and experimental result show that RSVD outperforms SVD significantly.