OpenRaiser/Intern-Atlas huggingface dataset:
Service on Demand: Drone Base Stations Cruising in the Cellular Network
Venue: arXiv cs.NI 2017
Year: 2017

In this paper, the deployment of drone base stations to provide higher performance in the cellular networks is analyzed. In particular, we investigate a new mobility model for drone base stations where they can move freely in the network, ignoring the cell boundaries. Free movement model for drones bring out new challenges such as user association and physical collision among drones. We consider two user association schemes and evaluate their performance through simulation. We show that by deploying a smart user association scheme in the free movement model, the obtained results are greatly better than those restricting each drone base station to fly over a certain small cell area, and serving local users. Additionally, the impact of drones' movement on the load balance, signal strength and interference are studied. Moreover, we show that our proposed algorithm can maintain a comfortable distance among the drones to avoid physical collision.
Analysis of the Leakage Queue: A Queueing Model for Energy Storage Systems with Self-discharge
Venue: arXiv cs.PF 2017
Year: 2017

Energy storage is a crucial component of the smart grid, since it provides the ability to buffer transient fluctuations of the energy supply from renewable sources. Even without a load, energy storage systems experience a reduction of the stored energy through self-discharge. In some storage technologies, the rate of self-discharge can exceed 50% of the stored energy per day. In this paper, we investigate the self-discharge phenomenon in energy storage using a queueing system model, which we refer to as leakage queue. When the average net charge is positive, we discover that the leakage queue operates in one of two regimes: a leakage-dominated regime and a capacity-dominated regime. We find that in the leakage-dominated regime, the stored energy stabilizes at a point that is below the storage capacity. Under suitable independence assumptions for energy supply and demand, the stored energy in this regime closely follows a normal distribution. We present two methods for computing probabilities of underflow and overflow at a leakage queue. The methods are validated in a numerical example where the energy supply resembles a wind energy source.
A Note on Parallel Asynchronous Channels with Arbitrary Skews
Venue: arXiv cs.IT 2017
Year: 2017

A zero-error coding scheme of asymptotic rate $ \log_2 (1+\sqrt{5}) - 1 $ was recently described for a communication channel composed of parallel asynchronous lines satisfying the so-called no switch assumption. We prove that this is in fact the highest rate attainable, i.e., the zero-error capacity of this channel.
Privacy Preserving Internet Browsers: Forensic Analysis of Browzar
Venue: arXiv cs.CR 2017
Year: 2017

With the advance of technology, Criminal Justice agencies are being confronted with an increased need to investigate crimes perpetuated partially or entirely over the Internet. These types of crime are known as cybercrimes. In order to conceal illegal online activity, criminals often use private browsing features or browsers designed to provide total browsing privacy. The use of private browsing is a common challenge faced in for example child exploitation investigations, which usually originate on the Internet. Although private browsing features are not designed specifically for criminal activity, they have become a valuable tool for criminals looking to conceal their online activity. As such, Technological Crime units often focus their forensic analysis on thoroughly examining the web history on a computer. Private browsing features and browsers often require a more in-depth, post mortem analysis. This often requires the use of multiple tools, as well as different forensic approaches to uncover incriminating evidence. This evidence may be required in a court of law, where analysts are often challenged both on their findings and on the tools and approaches used to recover evidence. However, there are very few research on evaluating of private browsing in terms of privacy preserving as well as forensic acquisition and analysis of privacy preserving internet browsers. Therefore in this chapter, we firstly review the private mode of popular internet browsers. Next, we describe the forensic acquisition and analysis of Browzar, a privacy preserving internet browser and compare it with other popular internet browsers
Reconstruct the Logical Network from the Transition Matrix
Venue: arXiv cs.SY 2017
Year: 2017

Reconstructing the logical network from the transition matrix is benefit for learning the logical meaning of the algebraic result from the algebraic representation of a BN. And so far there has no method to convert the matrix expression back to the logic expression for a BN with an arbitrary topology structure. Based on the canonical form and Karnaugh map, we propose a method for reconstructing the logical network from the transition matrix of a Boolean network in this paper.
Learning Approximate Stochastic Transition Models
Venue: arXiv cs.LG 2017
Year: 2017

We examine the problem of learning mappings from state to state, suitable for use in a model-based reinforcement-learning setting, that simultaneously generalize to novel states and can capture stochastic transitions. We show that currently popular generative adversarial networks struggle to learn these stochastic transition models but a modification to their loss functions results in a powerful learning algorithm for this class of problems.
Topological characteristics of oil and gas reservoirs and their applications
Venue: arXiv cs.CG 2017
Year: 2017

We demonstrate applications of topological characteristics of oil and gas reservoirs considered as three-dimensional bodies to geological modeling.
Linear Haskell: practical linearity in a higher-order polymorphic language
Venue: arXiv cs.PL 2017
Year: 2017

Linear type systems have a long and storied history, but not a clear path forward to integrate with existing languages such as OCaml or Haskell. In this paper, we study a linear type system designed with two crucial properties in mind: backwards-compatibility and code reuse across linear and non-linear users of a library. Only then can the benefits of linear types permeate conventional functional programming. Rather than bifurcate types into linear and non-linear counterparts, we instead attach linearity to function arrows. Linear functions can receive inputs from linearly-bound values, but can also operate over unrestricted, regular values. To demonstrate the efficacy of our linear type system - both how easy it can be integrated in an existing language implementation and how streamlined it makes it to write programs with linear types - we implemented our type system in GHC, the leading Haskell compiler, and demonstrate two kinds of applications of linear types: mutable data with pure interfaces; and enforcing protocols in I/O-performing functions.
Deep Spatial Regression Model for Image Crowd Counting
Venue: arXiv cs.CV 2017
Year: 2017

Computer vision techniques have been used to produce accurate and generic crowd count estimators in recent years. Due to severe occlusions, appearance variations, perspective distortions and illumination conditions, crowd counting is a very challenging task. To this end, we propose a deep spatial regression model(DSRM) for counting the number of individuals present in a still image with arbitrary perspective and arbitrary resolution. Our proposed model is based on Convolutional Neural Network (CNN) and long short term memory (LSTM). First, we put the images into a pretrained CNN to extract a set of high-level features. Then the features in adjacent regions are used to regress the local counts with a LSTM structure which takes the spatial information into consideration. The final global count is obtained by a sum of the local patches. We apply our framework on several challenging crowd counting datasets, and the experiment results illustrate that our method on the crowd counting and density estimation problem outperforms state-of-the-art methods in terms of reliability and effectiveness.
Impact of Random Receiver Orientation on Visible Light Communications Channel
Venue: arXiv cs.ET 2017
Year: 2017

Visible Light Communications (VLC) has been studied thoroughly in recent years as an alternative or complementary technology to radio frequency communications. The reliability of VLC channels highly depends on the availability and alignment of line of sight links. In this work, we study the effect of random receiver orientation for mobile users over VLC downlink channels, which affects the existence of line of sight links and the receiver field of view. Based on the statistics of vertical receiver orientation and user mobility, we develop a unified analytical framework to characterize the statistical distribution of VLC downlink channels, which is then utilized to obtain the outage probability and the bit error rate. Our analysis is generalized for arbitrary distributions of receiver orientation/location for a single transmitter, and extended to multiple transmitter case for certain scenarios. Extensive Monte Carlo simulations show a perfect match between the analytical and the simulation data in terms of both the statistical channel distribution and the resulting bit error rate. Our results also characterize the channel attenuation due to random receiver orientation/location for various scenarios of interest.
FashionBrain Project: A Vision for Understanding Europe's Fashion Data Universe
Venue: arXiv cs.AI 2017
Year: 2017

A core business in the fashion industry is the understanding and prediction of customer needs and trends. Search engines and social networks are at the same time a fundamental bridge and a costly middleman between the customer's purchase intention and the retailer. To better exploit Europe's distinctive characteristics e.g., multiple languages, fashion and cultural differences, it is pivotal to reduce retailers' dependence to search engines. This goal can be achieved by harnessing various data channels (manufacturers and distribution networks, online shops, large retailers, social media, market observers, call centers, press/magazines etc.) that retailers can leverage in order to gain more insight about potential buyers, and on the industry trends as a whole. This can enable the creation of novel on-line shopping experiences, the detection of influencers, and the prediction of upcoming fashion trends. In this paper, we provide an overview of the main research challenges and an analysis of the most promising technological solutions that we are investigating in the FashionBrain project.
Sparse Diffusion-Convolutional Neural Networks
Venue: arXiv cs.LG 2017
Year: 2017

The predictive power and overall computational efficiency of Diffusion-convolutional neural networks make them an attractive choice for node classification tasks. However, a naive dense-tensor-based implementation of DCNNs leads to $\mathcal{O}(N^2)$ memory complexity which is prohibitive for large graphs. In this paper, we introduce a simple method for thresholding input graphs that provably reduces memory requirements of DCNNs to O(N) (i.e. linear in the number of nodes in the input) without significantly affecting predictive performance.
Spiking Optical Flow for Event-based Sensors Using IBM's TrueNorth Neurosynaptic System
Venue: arXiv cs.CV 2017
Year: 2017

This paper describes a fully spike-based neural network for optical flow estimation from Dynamic Vision Sensor data. A low power embedded implementation of the method which combines the Asynchronous Time-based Image Sensor with IBM's TrueNorth Neurosynaptic System is presented. The sensor generates spikes with sub-millisecond resolution in response to scene illumination changes. These spike are processed by a spiking neural network running on TrueNorth with a 1 millisecond resolution to accurately determine the order and time difference of spikes from neighboring pixels, and therefore infer the velocity. The spiking neural network is a variant of the Barlow Levick method for optical flow estimation. The system is evaluated on two recordings for which ground truth motion is available, and achieves an Average Endpoint Error of 11% at an estimated power budget of under 80mW for the sensor and computation.
Klout Topics for Modeling Interests and Expertise of Users Across Social Networks
Venue: arXiv cs.IR 2017
Year: 2017

This paper presents Klout Topics, a lightweight ontology to describe social media users' topics of interest and expertise. Klout Topics is designed to: be human-readable and consumer-friendly; cover multiple domains of knowledge in depth; and promote data extensibility via knowledge base entities. We discuss why this ontology is well-suited for text labeling and interest modeling applications, and how it compares to available alternatives. We show its coverage against common social media interest sets, and examples of how it is used to model the interests of over 780M social media users on Klout.com. Finally, we open the ontology for external use.
Software Engineering Modeling Applied to English Verb Classification (and Poetry)
Venue: arXiv cs.SE 2017
Year: 2017

In requirements specification, software engineers create a textual description of the envisioned system as well as develop conceptual models using such tools as Universal Modeling Language (UML) and System Modeling Language (SysML). One such tool, called FM, has recently been developed as an extension of the INPUT-PROCESS-OUTPUT (IPO) model. IPO has been used extensively in many interdisciplinary applications and is described as one of the most fundamental and important of all descriptive tools. This paper is an attempt to understanding the PROCESS in IPO. The fundamental way to describe PROCESS is in verbs. This use of language has an important implication for systems modeling since verbs express the vast range of actions and movements of all things. It is clear that modeling needs to examine verbs. Accordingly, this paper involves a study of English verbs as a bridge to learn about processes, not as linguistic analysis but rather to reveal the semantics of processes, particularly the five verbs that form the basis of FM states: create, process, receive, release, and transfer. The paper focuses on verb classification, and specifically on how to model the action of verbs diagrammatically. From the linguistics point of view, according to some researchers, further exploration of the notion of verb classes is needed for real-world tasks such as machine translation, language generation, and document classification. Accordingly, this non-linguistics study may benefit linguistics.
EduCTX: A blockchain-based higher education credit platform
Venue: arXiv cs.CY 2017
Year: 2017

Blockchain technology enables the creation of a decentralized environment where transactions and data are not under the control of any third party organization. Any transaction ever completed is recorded in a public ledger in a verifiable and permanent way. Based on blockchain technology, we propose a global higher education credit platform, named EduCTX. This platform is based on the concept of the European Credit Transfer and Accumulation System (ECTS). It constitutes a globally trusted, decentralized higher education credit and grading system that can offer a globally unified viewpoint for students and higher education institutions (HEIs), as well as for other potential stakeholders such as companies, institutions, and organizations. As a proof of concept, we present a prototype implementation of the environment, based on the open-source Ark Blockchain Platform. Based on a globally distributed peer-to-peer network, EduCTX will process, manage and control ECTX tokens, which represent credits that students gain for completed courses such as ECTS. HEIs are the peers of the blockchain network. The platform is a first step towards a more transparent and technologically advanced form of higher education systems. The EduCTX platform represents the basis of the EduCTX initiative which anticipates that various HEIs would join forces in order to create a globally efficient, simplified and ubiquitous environment in order to avoid language and administrative barriers. Therefore we invite and encourage HEIs to join the EduCTX initiative and the EduCTX blockchain network.
Probabilistic Couplings for Probabilistic Reasoning
Venue: arXiv cs.LO 2017
Year: 2017

This thesis explores proofs by coupling from the perspective of formal verification. Long employed in probability theory and theoretical computer science, these proofs construct couplings between the output distributions of two probabilistic processes. Couplings can imply various guarantees comparing two runs of a probabilistic computation. We first show that proofs in the program logic pRHL describe couplings. We formalize couplings that establish various probabilistic properties, including distribution equivalence, convergence, and stochastic domination. Then we give a proofs-as-programs interpretation: a coupling proof encodes a probabilistic product program, whose properties imply relational properties of the original programs. We design the logic xpRHL to construct the product, with extensions to model shift coupling and path coupling. We then propose an approximate version of probabilistic coupling and a corresponding proof technique---proof by approximate coupling---inspired by the logic apRHL, a version of pRHL for building approximate liftings. Drawing on ideas from existing privacy proofs, we extend apRHL with novel proof rules for constructing new approximate couplings. We give an approximate coupling proof of privacy for the Sparse Vector mechanism, a well-known algorithm from the privacy literature whose privacy proof is notoriously subtle, and produce the first formalized proof of privacy for Sparse Vector in apRHL. Finally, we propose several more sophisticated constructions for approximate couplings: a principle for showing accuracy-dependent privacy, a generalization of the advanced composition theorem, and an optimal approximate coupling relating two subsets. We also show equivalences between our approximate couplings and other existing definitions. These ingredients support the first formalized proof of privacy for the Between Thresholds mechanism.
Audiovisual Analytics Vocabulary and Ontology (AAVO): initial core and example expansion
Venue: arXiv cs.CY 2017
Year: 2017

Visual Analytics might be defined as data mining assisted by interactive visual interfaces. The field has been receiving prominent consideration by researchers, developers and the industry. The literature, however, is complex because it involves multiple fields of knowledge and is considerably recent. In this article we describe an initial tentative organization of the knowledge in the field as an OWL ontology and a SKOS vocabulary. This effort might be useful in many ways that include conceptual considerations and software implementations. Within the results and discussions, we expose a core and an example expansion of the conceptualization, and incorporate design issues that enhance the expressive power of the abstraction.
Near-Optimal Straggler Mitigation for Distributed Gradient Methods
Venue: arXiv cs.IT 2017
Year: 2017

Modern learning algorithms use gradient descent updates to train inferential models that best explain data. Scaling these approaches to massive data sizes requires proper distributed gradient descent schemes where distributed worker nodes compute partial gradients based on their partial and local data sets, and send the results to a master node where all the computations are aggregated into a full gradient and the learning model is updated. However, a major performance bottleneck that arises is that some of the worker nodes may run slow. These nodes a.k.a. stragglers can significantly slow down computation as the slowest node may dictate the overall computational time. We propose a distributed computing scheme, called Batched Coupon's Collector (BCC) to alleviate the effect of stragglers in gradient methods. We prove that our BCC scheme is robust to a near optimal number of random stragglers. We also empirically demonstrate that our proposed BCC scheme reduces the run-time by up to 85.4% over Amazon EC2 clusters when compared with other straggler mitigation strategies. We also generalize the proposed BCC scheme to minimize the completion time when implementing gradient descent-based algorithms over heterogeneous worker nodes.
Fine-grained Pattern Matching Over Streaming Time Series
Venue: arXiv cs.CV 2017
Year: 2017

Pattern matching of streaming time series with lower latency under limited computing resource comes to a critical problem, especially as the growth of Industry 4.0 and Industry Internet of Things. However, against traditional single pattern matching problem, a pattern may contain multiple segments representing different statistical properties or physical meanings for more precise and expressive matching in real world. Hence, we formulate a new problem, called "fine-grained pattern matching", which allows users to specify varied granularities of matching deviation to different segments of a given pattern, and fuzzy regions for adaptive breakpoints determination between consecutive segments. In this paper, we propose a novel two-phase approach. In the pruning phase, we introduce Equal-Length Block (ELB) representation together with Block-Skipping Pruning (BSP) policy, which guarantees low cost feature calculation, effective pruning and no false dismissals. In the post-processing phase, a delta-function is proposed to enable us to conduct exact matching in linear complexity. Extensive experiments are conducted to evaluate on synthetic and real-world datasets, which illustrates that our algorithm outperforms the brute-force method and MSM, a multi-step filter mechanism over the multi-scaled representation.
A Decomposition-Based Approach to Reasoning about Free Space Path-Connectivity for Rigid Objects in 2D
Venue: arXiv cs.RO 2017
Year: 2017

In this paper, we compute a conservative approximation of the path-connected components of the free space of a rigid object in a 2D workspace in order to solve two closely related problems: to determine whether there exists a collision-free path between two given configurations, and to verify whether an object can escape arbitrarily far from its initial configuration -- i.e., whether the object is caged. Furthermore, we consider two quantitative characteristics of the free space: the volume of path-connected components and the width of narrow passages. To address these problems, we decompose the configuration space into a set of two-dimensional slices, approximate them as two-dimensional alpha-complexes, and then study the relations between them. This significantly reduces the computational complexity compared to a direct approximation of the free space. We implement our algorithm and run experiments in a three-dimensional configuration space of a simple object showing runtime of less than 2 seconds.