OpenRaiser/Intern-Atlas huggingface dataset:
Exploratory Study of the Privacy Extension for System Theoretic Process Analysis (STPA-Priv) to elicit Privacy Risks in eHealth
Venue: arXiv cs.CY 2017
Year: 2017

Context: System Theoretic Process Analysis for Privacy (STPA-Priv) is a novel privacy risk elicitation method using a top down approach. It has not gotten very much attention but may offer a convenient structured approach and generation of additional artifacts compared to other methods. Aim: The aim of this exploratory study is to find out what benefits the privacy risk elicitation method STPA-Priv has and to explain how the method can be used. Method: Therefore we apply STPA-Priv to a real world health scenario that involves a smart glucose measurement device used by children. Different kinds of data from the smart device including location data should be shared with the parents, physicians, and urban planners. This makes it a sociotechnical system that offers adequate and complex privacy risks to be found. Results: We find out that STPA-Priv is a structured method for privacy analysis and finds complex privacy risks. The method is supported by a tool called XSTAMPP which makes the analysis and its results more profound. Additionally, we learn that an iterative application of the steps might be necessary to find more privacy risks when more information about the system is available later. Conclusions: STPA-Priv helps to identify complex privacy risks that are derived from sociotechnical interactions in a system. It also outputs privacy constraints that are to be enforced by the system to ensure privacy.
Cellular-Enabled UAV Communication: Trajectory Optimization Under Connectivity Constraint
Venue: arXiv cs.IT 2017
Year: 2017

In this paper, we study a cellular-enabled unmanned aerial vehicle (UAV) communication system consisting of one UAV and multiple ground base stations (GBSs). The UAV has a mission of flying from an initial location to a final location, during which it needs to maintain reliable wireless connection with the cellular network by associating with one of the GBSs at each time instant. We aim to minimize the UAV mission completion time by optimizing its trajectory, subject to a quality of connectivity constraint of the GBS-UAV link specified by a minimum received signal-to-noise ratio (SNR) target, which needs to be satisfied throughout the mission. This problem is non-convex and difficult to be optimally solved. We first propose an effective approach to check its feasibility based on graph connectivity verification. Then, by examining the GBS-UAV association sequence during the UAV mission, we obtain useful insights on the optimal UAV trajectory, based on which an efficient algorithm is proposed to find an approximate solution to the trajectory optimization problem by leveraging techniques in convex optimization and graph theory. Numerical results show that our proposed trajectory design achieves near-optimal performance.
Replace or Retrieve Keywords In Documents at Scale
Venue: arXiv cs.DS 2017
Year: 2017

In this paper we introduce, the FlashText algorithm for replacing keywords or finding keywords in a given text. FlashText can search or replace keywords in one pass over a document. The time complexity of this algorithm is not dependent on the number of terms being searched or replaced. For a document of size N (characters) and a dictionary of M keywords, the time complexity will be O(N). This algorithm is much faster than Regex, because regex time complexity is O(MxN). It is also different from Aho Corasick Algorithm, as it doesn't match substrings. FlashText is designed to only match complete words (words with boundary characters on both sides). For an input dictionary of {Apple}, this algorithm won't match it to 'I like Pineapple'. This algorithm is also designed to go for the longest match first. For an input dictionary {Machine, Learning, Machine learning} on a string 'I like Machine learning', it will only consider the longest match, which is Machine Learning. We have made python implementation of this algorithm available as open-source on GitHub, released under the permissive MIT License.
User Environment Detection with Acoustic Sensors Embedded on Mobile Devices for the Recognition of Activities of Daily Living
Venue: arXiv cs.SD 2017
Year: 2017

The detection of the environment where user is located, is of extreme use for the identification of Activities of Daily Living (ADL). ADL can be identified by use of the sensors available in many off-the-shelf mobile devices, including magnetic and motion, and the environment can be also identified using acoustic sensors. The study presented in this paper is divided in two parts: firstly, we discuss the recognition of the environment using acoustic sensors (i.e., microphone), and secondly, we fuse this information with motion and magnetic sensors (i.e., motion and magnetic sensors) for the recognition of standing activities of daily living. The recognition of the environments and the ADL are performed using pattern recognition techniques, in order to develop a system that includes data acquisition, data processing, data fusion, and artificial intelligence methods. The artificial intelligence methods explored in this study are composed by different types of Artificial Neural Networks (ANN), comparing the different types of ANN and selecting the best methods to implement in the different stages of the system developed. Conclusions point to the use of Deep Neural Networks (DNN) with normalized data for the identification of ADL with 85.89% of accuracy, the use of Feedforward neural networks with non-normalized data for the identification of the environments with 86.50% of accuracy, and the use of DNN with normalized data for the identification of standing activities with 100% of accuracy.
Keyword-based Query Comprehending via Multiple Optimized-Demand Augmentation
Venue: arXiv cs.CL 2017
Year: 2017

In this paper, we consider the problem of machine reading task when the questions are in the form of keywords, rather than natural language. In recent years, researchers have achieved significant success on machine reading comprehension tasks, such as SQuAD and TriviaQA. These datasets provide a natural language question sentence and a pre-selected passage, and the goal is to answer the question according to the passage. However, in the situation of interacting with machines by means of text, people are more likely to raise a query in form of several keywords rather than a complete sentence. The keyword-based query comprehension is a new challenge, because small variations to a question may completely change its semantical information, thus yield different answers. In this paper, we propose a novel neural network system that consists a Demand Optimization Model based on a passage-attention neural machine translation and a Reader Model that can find the answer given the optimized question. The Demand Optimization Model optimizes the original query and output multiple reconstructed questions, then the Reader Model takes the new questions as input and locate the answers from the passage. To make predictions robust, an evaluation mechanism will score the reconstructed questions so the final answer strike a good balance between the quality of both the Demand Optimization Model and the Reader Model. Experimental results on several datasets show that our framework significantly improves multiple strong baselines on this challenging task.
On the complete weight enumerators of some linear codes with a few weights
Venue: arXiv cs.IT 2017
Year: 2017

Linear codes with a few weights have important applications in authentication codes, secret sharing, consumer electronics, etc.. The determination of the parameters such as Hamming weight distributions and complete weight enumerators of linear codes are important research topics. In this paper, we consider some classes of linear codes with a few weights and determine the complete weight enumerators from which the corresponding Hamming weight distributions are derived with help of some sums involving Legendre symbol.
Minimum Energy Quantized Neural Networks
Venue: arXiv cs.NE 2017
Year: 2017

This work targets the automated minimum-energy optimization of Quantized Neural Networks (QNNs) - networks using low precision weights and activations. These networks are trained from scratch at an arbitrary fixed point precision. At iso-accuracy, QNNs using fewer bits require deeper and wider network architectures than networks using higher precision operators, while they require less complex arithmetic and less bits per weights. This fundamental trade-off is analyzed and quantified to find the minimum energy QNN for any benchmark and hence optimize energy-efficiency. To this end, the energy consumption of inference is modeled for a generic hardware platform. This allows drawing several conclusions across different benchmarks. First, energy consumption varies orders of magnitude at iso-accuracy depending on the number of bits used in the QNN. Second, in a typical system, BinaryNets or int4 implementations lead to the minimum energy solution, outperforming int8 networks up to 2-10x at iso-accuracy. All code used for QNN training is available from https://github.com/BertMoons.
Vertex-Context Sampling for Weighted Network Embedding
Venue: arXiv cs.SI 2017
Year: 2017

In recent years, network embedding methods have garnered increasing attention because of their effectiveness in various information retrieval tasks. The goal is to learn low-dimensional representations of vertexes in an information network and simultaneously capture and preserve the network structure. Critical to the performance of a network embedding method is how the edges/vertexes of the network is sampled for the learning process. Many existing methods adopt a uniform sampling method to reduce learning complexity, but when the network is non-uniform (i.e. a weighted network) such uniform sampling incurs information loss. The goal of this paper is to present a generalized vertex sampling framework that works seamlessly with most existing network embedding methods to support weighted instead of uniform vertex/edge sampling. For efficiency, we propose a delicate sequential vertex-to-context graph data structure, such that sampling a training pair for learning takes only constant time. For scalability and memory efficiency, we design the graph data structure in a way that keeps space consumption low without requiring additional space. In addition to implementing existing network embedding methods, the proposed framework can be used to implement extensions that feature high-order proximity modeling and weighted relation modeling. Experiments conducted on three datasets, including a commercial large-scale one, verify the effectiveness and efficiency of the proposed weighted network embedding methods on a variety of tasks, including word similarity search, multi-label classification, and item recommendation.
Re-DPoctor: Real-time health data releasing with w-day differential privacy
Venue: arXiv cs.CR 2017
Year: 2017

Wearable devices enable users to collect health data and share them with healthcare providers for improved health service. Since health data contain privacy-sensitive information, unprotected data release system may result in privacy leakage problem. Most of the existing work use differential privacy for private data release. However, they have limitations in healthcare scenarios because they do not consider the unique features of health data being collected from wearables, such as continuous real-time collection and pattern preservation. In this paper, we propose Re-DPoctor, a real-time health data releasing scheme with $w$-day differential privacy where the privacy of health data collected from any consecutive $w$ days is preserved. We improve utility by using a specially-designed partition algorithm to protect the health data patterns. Meanwhile, we improve privacy preservation by applying newly proposed adaptive sampling technique and budget allocation method. We prove that Re-DPoctor satisfies $w$-day differential privacy. Experiments on real health data demonstrate that our method achieves better utility with strong privacy guarantee than existing state-of-the-art methods.
Acquiring Target Stacking Skills by Goal-Parameterized Deep Reinforcement Learning
Venue: arXiv cs.RO 2017
Year: 2017

Understanding physical phenomena is a key component of human intelligence and enables physical interaction with previously unseen environments. In this paper, we study how an artificial agent can autonomously acquire this intuition through interaction with the environment. We created a synthetic block stacking environment with physics simulation in which the agent can learn a policy end-to-end through trial and error. Thereby, we bypass to explicitly model physical knowledge within the policy. We are specifically interested in tasks that require the agent to reach a given goal state that may be different for every new trial. To this end, we propose a deep reinforcement learning framework that learns policies which are parametrized by a goal. We validated the model on a toy example navigating in a grid world with different target positions and in a block stacking task with different target structures of the final tower. In contrast to prior work, our policies show better generalization across different goals.
Fast Dynamic Arrays
Venue: arXiv cs.DS 2017
Year: 2017

We present a highly optimized implementation of tiered vectors, a data structure for maintaining a sequence of $n$ elements supporting access in time $O(1)$ and insertion and deletion in time $O(n^ε)$ for $ε> 0$ while using $o(n)$ extra space. We consider several different implementation optimizations in C++ and compare their performance to that of vector and multiset from the standard library on sequences with up to $10^8$ elements. Our fastest implementation uses much less space than multiset while providing speedups of $40\times$ for access operations compared to multiset and speedups of $10.000\times$ compared to vector for insertion and deletion operations while being competitive with both data structures for all other operations.
A Coupled Lattice Boltzmann Method and Discrete Element Method for Discrete Particle Simulations of Particulate Flows
Venue: arXiv cs.CE 2017
Year: 2017

Discrete particle simulations are widely used to study large-scale particulate flows in complex geometries where particle-particle and particle-fluid interactions require an adequate representation but the computational cost has to be kept low. In this work, we present a novel coupling approach for such simulations. A lattice Boltzmann formulation of the generalized Navier-Stokes equations is used to describe the fluid motion. This promises efficient simulations suitable for high performance computing and, since volume displacement effects by the solid phase are considered, our approach is also applicable to non-dilute particulate systems. The discrete element method is combined with an explicit evaluation of interparticle lubrication forces to simulate the motion of individual submerged particles. Drag, pressure and added mass forces determine the momentum transfer by fluid-particle interactions. A stable coupling algorithm is presented and discussed in detail. We demonstrate the validity of our approach for dilute as well as dense systems by predicting the settling velocity of spheres over a broad range of solid volume fractions in good agreement with semi-empirical correlations. Additionally, the accuracy of particle-wall interactions in a viscous fluid is thoroughly tested and established. Our approach can thus be readily used for various particulate systems and can be extended straightforward to e.g. non-spherical particles.
CMS Analysis and Data Reduction with Apache Spark
Venue: arXiv cs.DC 2017
Year: 2017

Experimental Particle Physics has been at the forefront of analyzing the world's largest datasets for decades. The HEP community was among the first to develop suitable software and computing tools for this task. In recent times, new toolkits and systems for distributed data processing, collectively called "Big Data" technologies have emerged from industry and open source projects to support the analysis of Petabyte and Exabyte datasets in industry. While the principles of data analysis in HEP have not changed (filtering and transforming experiment-specific data formats), these new technologies use different approaches and tools, promising a fresh look at analysis of very large datasets that could potentially reduce the time-to-physics with increased interactivity. Moreover these new tools are typically actively developed by large communities, often profiting of industry resources, and under open source licensing. These factors result in a boost for adoption and maturity of the tools and for the communities supporting them, at the same time helping in reducing the cost of ownership for the end-users. In this talk, we are presenting studies of using Apache Spark for end user data analysis. We are studying the HEP analysis workflow separated into two thrusts: the reduction of centrally produced experiment datasets and the end-analysis up to the publication plot. Studying the first thrust, CMS is working together with CERN openlab and Intel on the CMS Big Data Reduction Facility. The goal is to reduce 1 PB of official CMS data to 1 TB of ntuple output for analysis. We are presenting the progress of this 2-year project with first results of scaling up Spark-based HEP analysis. Studying the second thrust, we are presenting studies on using Apache Spark for a CMS Dark Matter physics search, comparing Spark's feasibility, usability and performance to the ROOT-based analysis.
Almost instant brain atlas segmentation for large-scale studies
Venue: arXiv cs.CV 2017
Year: 2017

Large scale studies of group differences in healthy controls and patients and screenings for early stage disease prevention programs require processing and analysis of extensive multisubject datasets. Complexity of the task increases even further when segmenting structural MRI of the brain into an atlas with more than 50 regions. Current automatic approaches are time-consuming and hardly scalable; they often involve many error prone intermediate steps and don't utilize other available modalities. To alleviate these problems, we propose a feedforward fully convolutional neural network trained on the output produced by the state of the art models. Incredible speed due to available powerful GPUs neural network makes this analysis much easier and faster (from $>10$ hours to a minute). The proposed model is more than two orders of magnitudes faster than the state of the art and yet as accurate. We have evaluated the network's performance by comparing it with the state of the art in the task of differentiating region volumes of healthy controls and patients with schizophrenia on a dataset with 311 subjects. This comparison provides a strong evidence that speed did not harm the accuracy. The overall quality may also be increased by utilizing multi-modal datasets (not an easy task for other models) by simple adding more modalities as an input. Our model will be useful in large-scale studies as well as in clinical care solutions, where it can significantly reduce delay between the patient screening and the result.
On the Philosophy of Bitcoin/Blockchain Technology: Is it a Chaotic, Complex System?
Venue: arXiv cs.CR 2017
Year: 2017

The philosophy of blockchain technology is concerned, among other things, with blockchain ontology, how it might be characterised, how it is being created, implemented, and adopted, how it operates in the world, and how it evolves over time. This paper concentrates on whether Bitcoin/blockchain can be considered a complex system and, if so, whether it is a chaotic one. Beyond mere academic curiosity, a positive response would raise concerns about the likelihood of Bitcoin/blockchain entering a 2010-Flash-Crash-type of chaotic regime, with catastrophic consequences for financial systems based on it. The paper starts by highlighting the relevant details of the Bitcoin/blockchain ecosystem formed by the blockchain itself, bitcoin end users (payers and payees), capital gains seekers, miners, full nodes maintainers, and developers, and their interactions. Then the Information Theory of Complex Systems is briefly discussed for later use. Finally, the blockchain is investigated with the help of Crutchfield's Statistical Complexity measure. The low non-null statistical complexity value obtained suggests that the blockchain may be considered algorithmically complicated but hardly a complex system and unlikely to enter a chaotic regime.
A Bio-Inspired Multi-Exposure Fusion Framework for Low-light Image Enhancement
Venue: arXiv cs.CV 2017
Year: 2017

Low-light images are not conducive to human observation and computer vision algorithms due to their low visibility. Although many image enhancement techniques have been proposed to solve this problem, existing methods inevitably introduce contrast under- and over-enhancement. Inspired by human visual system, we design a multi-exposure fusion framework for low-light image enhancement. Based on the framework, we propose a dual-exposure fusion algorithm to provide an accurate contrast and lightness enhancement. Specifically, we first design the weight matrix for image fusion using illumination estimation techniques. Then we introduce our camera response model to synthesize multi-exposure images. Next, we find the best exposure ratio so that the synthetic image is well-exposed in the regions where the original image is under-exposed. Finally, the enhanced result is obtained by fusing the input image and the synthetic image according to the weight matrix. Experiments show that our method can obtain results with less contrast and lightness distortion compared to that of several state-of-the-art methods.
Security Against Impersonation Attacks in Distributed Systems
Venue: arXiv cs.GT 2017
Year: 2017

In a multi-agent system, transitioning from a centralized to a distributed decision-making strategy can introduce vulnerability to adversarial manipulation. We study the potential for adversarial manipulation in a class of graphical coordination games where the adversary can pose as a friendly agent in the game, thereby influencing the decision-making rules of a subset of agents. The adversary's influence can cascade throughout the system, indirectly influencing other agents' behavior and significantly impacting the emergent collective behavior. The main results in this paper focus on characterizing conditions under which the adversary's local influence can dramatically impact the emergent global behavior, e.g., destabilize efficient Nash equilibria.
Decentralized Deep Scheduling for Interference Channels
Venue: arXiv cs.IT 2017
Year: 2017

In this paper, we study the problem of decentralized scheduling in Interference Channels (IC). In this setting, each Transmitter (TX) receives an arbitrary amount of feedback regarding the global multi-user channel state based on which it decides whether to transmit or to stay silent without any form of communication with the other TXs. While many methods have been proposed to tackle the problem of link scheduling in the presence of reliable Channel State Information (CSI), finding the optimally robust transmission strategy in the presence of arbitrary channel uncertainties at each TX has remained elusive for the past years. In this work, we recast the link scheduling problem as a decentralized classification problem and we propose the use of Collaborative Deep Neural Networks (C-DNNs) to solve this problem. After adequate training, the scheduling obtained using the C-DNNs flexibly adapts to the decentralized CSI configuration to outperform other scheduling algorithms.