OpenRaiser/Intern-Atlas huggingface dataset:
Personalized Classifier Ensemble Pruning Framework for Mobile Crowdsourcing
Venue: arXiv cs.DC 2017
Year: 2017

Ensemble learning has been widely employed by mobile applications, ranging from environmental sensing to activity recognitions. One of the fundamental issue in ensemble learning is the trade-off between classification accuracy and computational costs, which is the goal of ensemble pruning. During crowdsourcing, the centralized aggregator releases ensemble learning models to a large number of mobile participants for task evaluation or as the crowdsourcing learning results, while different participants may seek for different levels of the accuracy-cost trade-off. However, most of existing ensemble pruning approaches consider only one identical level of such trade-off. In this study, we present an efficient ensemble pruning framework for personalized accuracy-cost trade-offs via multi-objective optimization. Specifically, for the commonly used linear-combination style of the trade-off, we provide an objective-mixture optimization to further reduce the number of ensemble candidates. Experimental results show that our framework is highly efficient for personalized ensemble pruning, and achieves much better pruning performance with objective-mixture optimization when compared to state-of-art approaches.
Learn&Fuzz: Machine Learning for Input Fuzzing
Venue: arXiv cs.AI 2017
Year: 2017

Fuzzing consists of repeatedly testing an application with modified, or fuzzed, inputs with the goal of finding security vulnerabilities in input-parsing code. In this paper, we show how to automate the generation of an input grammar suitable for input fuzzing using sample inputs and neural-network-based statistical machine-learning techniques. We present a detailed case study with a complex input format, namely PDF, and a large complex security-critical parser for this format, namely, the PDF parser embedded in Microsoft's new Edge browser. We discuss (and measure) the tension between conflicting learning and fuzzing goals: learning wants to capture the structure of well-formed inputs, while fuzzing wants to break that structure in order to cover unexpected code paths and find bugs. We also present a new algorithm for this learn&fuzz challenge which uses a learnt input probability distribution to intelligently guide where to fuzz inputs.
Optimal Binary $(5,3)$ Projective Space Codes from Maximal Partial Spreads
Venue: arXiv cs.IT 2017
Year: 2017

Recently a construction of optimal non-constant dimension subspace codes, also termed projective space codes, has been reported in a paper of Honold-Kiermaier-Kurz. Restricted to binary codes in a 5-dimensional ambient space with minimum subspace distance 3, these optimal codes were interpreted in terms of maximal partial spreads of 2-dimensional subspaces. In a parallel development, an optimal binary (5,3) code was obtained by a minimal change strategy on a nearly optimal example of Etzion and Vardy. In this article, we report several examples of optimal binary (5,3) codes obtained by the application of this strategy combined with changes to the spread structure of existing codes. We also establish that, based on the types of constituent spreads, our examples lie outside the framework of the existing construction.
Weak Coverage of a Rectangular Barrier
Venue: arXiv cs.DC 2017
Year: 2017

Assume n wireless mobile sensors are initially dispersed in an ad hoc manner in a rectangular region. They are required to move to final locations so that they can detect any intruder crossing the region in a direction parallel to the sides of the rectangle, and thus provide weak barrier coverage of the region. We study three optimization problems related to the movement of sensors to achieve weak barrier coverage: minimizing the number of sensors moved (MinNum), minimizing the average distance moved by the sensors (MinSum), and minimizing the maximum distance moved by the sensors (MinMax). We give an O(n^{3/2}) time algorithm for the MinNum problem for sensors of diameter 1 that are initially placed at integer positions; in contrast we show that the problem is NP-hard even for sensors of diameter 2 that are initially placed at integer positions. We show that the MinSum problem is solvable in O(n log n) time for homogeneous range sensors in arbitrary initial positions, while it is NP-hard for heterogeneous sensor ranges. Finally, we prove that even very restricted homogeneous versions of the MinMax problem are NP-hard.
Photographic dataset: playing cards
Venue: arXiv cs.CV 2017
Year: 2017

This is a photographic dataset collected for testing image processing algorithms. The idea is to have images that can exploit the properties of total variation, therefore a set of playing cards was distributed on the scene. The dataset is made available at www.fips.fi/photographic_dataset2.php
Design and Implementation of a Semantic Dialogue System for Radiologists
Venue: arXiv cs.HC 2017
Year: 2017

This chapter describes a semantic dialogue system for radiologists in a comprehensive case study within the large-scale MEDICO project. MEDICO addresses the need for advanced semantic technologies in the search for medical image and patient data. The objectives are, first, to enable a seamless integration of medical images and different user applications by providing direct access to image semantics, and second, to design and implement a multimodal dialogue shell for the radiologist. Speech-based semantic image retrieval and annotation of medical images should provide the basis for help in clinical decision support and computer aided diagnosis. We will describe the clinical workflow and interaction requirements and focus on the design and implementation of a multimodal user interface for patient/image search or annotation and its implementation while using a speech-based dialogue shell. Ontology modeling provides the backbone for knowledge representation in the dialogue shell and the specific medical application domain; ontology structures are the communication basis of our combined semantic search and retrieval architecture which includes the MEDICO server, the triple store, the semantic search API, the medical visualization toolkit MITK, and the speech-based dialogue shell, amongst others. We will focus on usability aspects of multimodal applications, our storyboard and the implemented speech and touchscreen interaction design.
LAREX - A semi-automatic open-source Tool for Layout Analysis and Region Extraction on Early Printed Books
Venue: arXiv cs.CV 2017
Year: 2017

A semi-automatic open-source tool for layout analysis on early printed books is presented. LAREX uses a rule based connected components approach which is very fast, easily comprehensible for the user and allows an intuitive manual correction if necessary. The PageXML format is used to support integration into existing OCR workflows. Evaluations showed that LAREX provides an efficient and flexible way to segment pages of early printed books.
Learning Light Transport the Reinforced Way
Venue: arXiv cs.LG 2017
Year: 2017

We show that the equations of reinforcement learning and light transport simulation are related integral equations. Based on this correspondence, a scheme to learn importance while sampling path space is derived. The new approach is demonstrated in a consistent light transport simulation algorithm that uses reinforcement learning to progressively learn where light comes from. As using this information for importance sampling includes information about visibility, too, the number of light transport paths with zero contribution is dramatically reduced, resulting in much less noisy images within a fixed time budget.
Design of Improved Quasi-Cyclic Protograph-Based Raptor-Like LDPC Codes for Short Block-Lengths
Venue: arXiv cs.IT 2017
Year: 2017

Protograph-based Raptor-like low-density parity-check codes (PBRL codes) are a recently proposed family of easily encodable and decodable rate-compatible LDPC (RC-LDPC) codes. These codes have an excellent iterative decoding threshold and performance across all design rates. PBRL codes designed thus far, for both long and short block-lengths, have been based on optimizing the iterative decoding threshold of the protograph of the RC code family at various design rates. In this work, we propose a design method to obtain better quasi-cyclic (QC) RC-LDPC codes with PBRL structure for short block-lengths (of a few hundred bits). We achieve this by maximizing an upper bound on the minimum distance of any QC-LDPC code that can be obtained from the protograph of a PBRL ensemble. The obtained codes outperform the original PBRL codes at short block-lengths by significantly improving the error floor behavior at all design rates. Furthermore, we identify a reduction in complexity of the design procedure, facilitated by the general structure of a PBRL ensemble.
FPGA Architecture for Deep Learning and its application to Planetary Robotics
Venue: arXiv cs.LG 2017
Year: 2017

Autonomous control systems onboard planetary rovers and spacecraft benefit from having cognitive capabilities like learning so that they can adapt to unexpected situations in-situ. Q-learning is a form of reinforcement learning and it has been efficient in solving certain class of learning problems. However, embedded systems onboard planetary rovers and spacecraft rarely implement learning algorithms due to the constraints faced in the field, like processing power, chip size, convergence rate and costs due to the need for radiation hardening. These challenges present a compelling need for a portable, low-power, area efficient hardware accelerator to make learning algorithms practical onboard space hardware. This paper presents a FPGA implementation of Q-learning with Artificial Neural Networks (ANN). This method matches the massive parallelism inherent in neural network software with the fine-grain parallelism of an FPGA hardware thereby dramatically reducing processing time. Mars Science Laboratory currently uses Xilinx-Space-grade Virtex FPGA devices for image processing, pyrotechnic operation control and obstacle avoidance. We simulate and program our architecture on a Xilinx Virtex 7 FPGA. The architectural implementation for a single neuron Q-learning and a more complex Multilayer Perception (MLP) Q-learning accelerator has been demonstrated. The results show up to a 43-fold speed up by Virtex 7 FPGAs compared to a conventional Intel i5 2.3 GHz CPU. Finally, we simulate the proposed architecture using the Symphony simulator and compiler from Xilinx, and evaluate the performance and power consumption.
Spherical Planetary Robot for Rugged Terrain Traversal
Venue: arXiv cs.RO 2017
Year: 2017

Wheeled planetary rovers such as the Mars Exploration Rovers (MERs) and Mars Science Laboratory (MSL) have provided unprecedented, detailed images of the Mars surface. However, these rovers are large and are of high-cost as they need to carry sophisticated instruments and science laboratories. We propose the development of low-cost planetary rovers that are the size and shape of cantaloupes and that can be deployed from a larger rover. The rover named SphereX is 2 kg in mass, is spherical, holonomic and contains a hopping mechanism to jump over rugged terrain. A small low-cost rover complements a larger rover, particularly to traverse rugged terrain or roll down a canyon, cliff or crater to obtain images and science data. While it may be a one-way journey for these small robots, they could be used tactically to obtain high-reward science data. The robot is equipped with a pair of stereo cameras to perform visual navigation and has room for a science payload. In this paper, we analyze the design and development of a laboratory prototype. The results show a promising pathway towards development of a field system.
Optimized Bucket Wheel Design for Asteroid Excavation
Venue: arXiv cs.RO 2017
Year: 2017

Current spacecraft need to launch with all of their required fuel for travel. This limits the system performance, payload capacity, and mission flexibility. One compelling alternative is to perform In-Situ Resource Utilization (ISRU) by extracting fuel from small bodies in local space such as asteroids or small satellites. Compared to the Moon or Mars, the microgravity on an asteroid demands a fraction of the energy for digging and accessing hydrated regolith just below the surface. Previous asteroid excavation efforts have focused on discrete capture events (an extension of sampling technology) or whole-asteroid capture and processing. This paper proposes an optimized bucket wheel design for surface excavation of an asteroid or small-body. Asteroid regolith is excavated and water extracted for use as rocket propellant. Our initial study focuses on system design, bucket wheel mechanisms, and capture dynamics applied to ponded materials known to exist on asteroids like Itokawa and Eros and small satellites like Phobos and Deimos. For initial evaluation of material-spacecraft dynamics and mechanics, we assume lunar-like regolith for bulk density, particle size and cohesion. We shall present our estimates for the energy balance of excavation and processing versus fuel gained. Conventional electrolysis of water is used to produce hydrogen and oxygen. It is compared with steam for propulsion and both show significant delta-v. We show that a return trip from Deimos to Earth is possible for a 12 kg craft using ISRU processed fuel.
Approximate Capacity of a Class of Partially Connected Interference Channels
Venue: arXiv cs.IT 2017
Year: 2017

We derive inner and outer bounds on the capacity region for a class of three-user partially connected interference channels. We focus on the impact of topology, interference alignment, and interplay between interference and noise. The representative channels we consider are the ones that have clear interference alignment gain. For these channels, Z-channel type outer bounds are tight to within a constant gap from capacity. We present near-optimal achievable schemes based on rate-splitting and lattice alignment.
Understanding how Software Can Support the Needs of Family Caregivers for Patients with Severe Conditions
Venue: arXiv cs.CY 2017
Year: 2017

In this paper, we report an extensive analysis that we performed in two scenarios where the care relation between doctor and patients are mediated by the relatives of the patients: Pediatric Palliative Care (PPC) and Nursing Homes (NH). When the patients are children or very old adults in the end of life, the provision of care often involve a family caregiver as the main point of contact for the health service. PPC and NH are characterized by emotional complexity, since incurable diseases expose the family caregivers to heavy careload and human distress. In this paper, we discuss our findings with a novel perspective, focusing on: information, coordination and social challenges that arise by dealing with such contexts; the existing technology as it is appropriated today to cope with them; and what we, as software researchers, can do to develop the right solutions.
Minimum-Distance Based Construction of Multi-Kernel Polar Codes
Venue: arXiv cs.IT 2017
Year: 2017

In this paper, we propose a construction for multi-kernel polar codes based on the maximization of the minimum distance. Compared to the original construction based on density evolution, our new design shows particular advantages for short code lengths, where the polarization effect has less impact on the performance than the distances of the code. We introduce and compute the minimum-distance profile and provide a simple greedy algorithm for the code design. Compared to state-of-the-art punctured or shortened Arikan polar codes, multi-kernel polar codes with our new design show significantly improved error-rate performance.
Identifying Consistent Statements about Numerical Data with Dispersion-Corrected Subgroup Discovery
Venue: arXiv cs.AI 2017
Year: 2017

Existing algorithms for subgroup discovery with numerical targets do not optimize the error or target variable dispersion of the groups they find. This often leads to unreliable or inconsistent statements about the data, rendering practical applications, especially in scientific domains, futile. Therefore, we here extend the optimistic estimator framework for optimal subgroup discovery to a new class of objective functions: we show how tight estimators can be computed efficiently for all functions that are determined by subgroup size (non-decreasing dependence), the subgroup median value, and a dispersion measure around the median (non-increasing dependence). In the important special case when dispersion is measured using the average absolute deviation from the median, this novel approach yields a linear time algorithm. Empirical evaluation on a wide range of datasets shows that, when used within branch-and-bound search, this approach is highly efficient and indeed discovers subgroups with much smaller errors.
Riemannian-geometry-based modeling and clustering of network-wide non-stationary time series: The brain-network case
Venue: arXiv cs.LG 2017
Year: 2017

This paper advocates Riemannian multi-manifold modeling in the context of network-wide non-stationary time-series analysis. Time-series data, collected sequentially over time and across a network, yield features which are viewed as points in or close to a union of multiple submanifolds of a Riemannian manifold, and distinguishing disparate time series amounts to clustering multiple Riemannian submanifolds. To support the claim that exploiting the latent Riemannian geometry behind many statistical features of time series is beneficial to learning from network data, this paper focuses on brain networks and puts forth two feature-generation schemes for network-wide dynamic time series. The first is motivated by Granger-causality arguments and uses an auto-regressive moving average model to map low-rank linear vector subspaces, spanned by column vectors of appropriately defined observability matrices, to points into the Grassmann manifold. The second utilizes (non-linear) dependencies among network nodes by introducing kernel-based partial correlations to generate points in the manifold of positive-definite matrices. Capitilizing on recently developed research on clustering Riemannian submanifolds, an algorithm is provided for distinguishing time series based on their geometrical properties, revealed within Riemannian feature spaces. Extensive numerical tests demonstrate that the proposed framework outperforms classical and state-of-the-art techniques in clustering brain-network states/structures hidden beneath synthetic fMRI time series and brain-activity signals generated from real brain-network structural connectivity matrices.
Adaptively Detecting Malicious Queries in Web Attacks
Venue: arXiv cs.CR 2017
Year: 2017

Web request query strings (queries), which pass parameters to the referenced resource, are always manipulated by attackers to retrieve sensitive data and even take full control of victim web servers and web applications. However, existing malicious query detection approaches in the current literature cannot cope with changing web attacks with constant detection models. In this paper, we propose AMODS, an adaptive system that periodically updates the detection model to detect the latest unknown attacks. We also propose an adaptive learning strategy, called SVM HYBRID, leveraged by our system to minimize manual work. In the evaluation, an up-to-date detection model is trained on a ten-day query dataset collected from an academic institute's web server logs. Our system outperforms existing web attack detection methods, with an F-value of 94.79% and FP rate of 0.09%. The total number of malicious queries obtained by SVM HYBRID is 2.78 times that by the popular Support Vector Machine Adaptive Learning (SVM AL) method. The malicious queries obtained can be used to update the Web Application Firewall (WAF) signature library.
Flying, Hopping Pit-Bots for Cave and Lava Tube Exploration on the Moon and Mars
Venue: arXiv cs.RO 2017
Year: 2017

Wheeled ground robots are limited from exploring extreme environments such as caves, lava tubes and skylights. Small robots that utilize unconventional mobility through hopping, flying and rolling can overcome many roughness limitations and thus extend exploration sites of interest on Moon and Mars. In this paper we introduce a network of 3 kg, 0.30 m diameter ball robots (pit-bots) that can fly, hop and roll using an onboard miniature propulsion system. These pit-bots can be deployed from a lander or large rover. Each robot is equipped with a smartphone sized computer, stereo camera and laser rangefinder to per-form navigation and mapping. The ball robot can carry a payload of 1 kg or perform sample return. Our studies show a range of 5 km and 0.7 hours flight time on the Moon.
JSForce: A Forced Execution Engine for Malicious JavaScript Detection
Venue: arXiv cs.CR 2017
Year: 2017

The drastic increase of JavaScript exploitation attacks has led to a strong interest in developing techniques to enable malicious JavaScript analysis. Existing analysis tech- niques fall into two general categories: static analysis and dynamic analysis. Static analysis tends to produce inaccurate results (both false positive and false negative) and is vulnerable to a wide series of obfuscation techniques. Thus, dynamic analysis is constantly gaining popularity for exposing the typical features of malicious JavaScript. However, existing dynamic analysis techniques possess limitations such as limited code coverage and incomplete environment setup, leaving a broad attack surface for evading the detection. To overcome these limitations, we present the design and implementation of a novel JavaScript forced execution engine named JSForce which drives an arbitrary JavaScript snippet to execute along different paths without any input or environment setup. We evaluate JSForce using 220,587 HTML and 23,509 PDF real- world samples. Experimental results show that by adopting our forced execution engine, the malicious JavaScript detection rate can be substantially boosted by 206.29% using same detection policy without any noticeable false positive increase. We also make JSForce publicly available as an online service and will release the source code to the security community upon the acceptance for publication.
Secure SWIPT Networks Based on a Non-linear Energy Harvesting Model
Venue: arXiv cs.IT 2017
Year: 2017

We optimize resource allocation to enable communication security in simultaneous wireless information and power transfer (SWIPT) for internet-of-things (IoT) networks. The resource allocation algorithm design is formulated as a non-convex optimization problem. We aim at maximizing the total harvested power at energy harvesting (EH) receivers via the joint optimization of transmit beamforming vectors and the covariance matrix of the artificial noise injected to facilitate secrecy provisioning. The proposed problem formulation takes into account the non-linearity of energy harvesting circuits and the quality of service requirements for secure communication. To obtain a globally optimal solution of the resource allocation problem, we first transform the resulting non-convex sum-of-ratios objective function into an equivalent objective function in parametric subtractive form, which facilitates the design of a novel iterative resource allocation algorithm. In each iteration, the semidefinite programming (SDP) relaxation approach is adopted to solve a rank-constrained optimization problem optimally. Numerical results reveal that the proposed algorithm can guarantee communication security and provide a significant performance gain in terms of the harvested energy compared to existing designs which are based on the traditional linear EH model.