OpenRaiser/Intern-Atlas huggingface dataset:
Progressive Learning for Systematic Design of Large Neural Networks
Venue: arXiv cs.NE 2017
Year: 2017

We develop an algorithm for systematic design of a large artificial neural network using a progression property. We find that some non-linear functions, such as the rectifier linear unit and its derivatives, hold the property. The systematic design addresses the choice of network size and regularization of parameters. The number of nodes and layers in network increases in progression with the objective of consistently reducing an appropriate cost. Each layer is optimized at a time, where appropriate parameters are learned using convex optimization. Regularization parameters for convex optimization do not need a significant manual effort for tuning. We also use random instances for some weight matrices, and that helps to reduce the number of parameters we learn. The developed network is expected to show good generalization power due to appropriate regularization and use of random weights in the layers. This expectation is verified by extensive experiments for classification and regression problems, using standard databases.
Democratizing online controlled experiments at Booking.com
Venue: arXiv cs.HC 2017
Year: 2017

There is an extensive literature about online controlled experiments, both on the statistical methods available to analyze experiment results as well as on the infrastructure built by several large scale Internet companies but also on the organizational challenges of embracing online experiments to inform product development. At Booking.com we have been conducting evidenced based product development using online experiments for more than ten years. Our methods and infrastructure were designed from their inception to reflect Booking.com culture, that is, with democratization and decentralization of experimentation and decision making in mind. In this paper we explain how building a central repository of successes and failures to allow for knowledge sharing, having a generic and extensible code library which enforces a loose coupling between experimentation and business logic, monitoring closely and transparently the quality and the reliability of the data gathering pipelines to build trust in the experimentation infrastructure, and putting in place safeguards to enable anyone to have end to end ownership of their experiments have allowed such a large organization as Booking.com to truly and successfully democratize experimentation.
A JSON Token-Based Authentication and Access Management Schema for Cloud SaaS Applications
Venue: arXiv cs.CR 2017
Year: 2017

Cloud computing is significantly reshaping the computing industry built around core concepts such as virtualization, processing power, connectivity and elasticity to store and share IT resources via a broad network. It has emerged as the key technology that unleashes the potency of Big Data, Internet of Things, Mobile and Web Applications, and other related technologies, but it also comes with its challenges - such as governance, security, and privacy. This paper is focused on the security and privacy challenges of cloud computing with specific reference to user authentication and access management for cloud SaaS applications. The suggested model uses a framework that harnesses the stateless and secure nature of JWT for client authentication and session management. Furthermore, authorized access to protected cloud SaaS resources have been efficiently managed. Accordingly, a Policy Match Gate (PMG) component and a Policy Activity Monitor (PAM) component have been introduced. In addition, other subcomponents such as a Policy Validation Unit (PVU) and a Policy Proxy DB (PPDB) have also been established for optimized service delivery. A theoretical analysis of the proposed model portrays a system that is secure, lightweight and highly scalable for improved cloud resource security and management.
Building Efficient Concurrent Graph Object through Composition of List-based Set
Venue: arXiv cs.DC 2017
Year: 2017

In this paper, we propose a generic concurrent directed graph (for shared memory architecture) that is concurrently being updated by threads adding/deleting vertices and edges. The graph is constructed by the composition of the well known concurrent list-based set data-structure from the literature. Our construction is generic, in the sense that it can be used to obtain various progress guarantees, depending on the granularity of the underlying concurrent set implementation - either blocking or non-blocking. We prove that the proposed construction is linearizable by identifying its linearization points. Finally, we compare the performance of all the variants of the concurrent graph data-structure along with its sequential implementation. We observe that our concurrent graph data-structure mimics the performance of the concurrent list based set.
CollabLoc: Privacy-Preserving Multi-Modal Localization via Collaborative Information Fusion
Venue: arXiv cs.NI 2017
Year: 2017

Mobile phones provide an excellent opportunity for building context-aware applications. In particular, location-based services are important context-aware services that are more and more used for enforcing security policies, for supporting indoor room navigation, and for providing personalized assistance. However, a major problem still remains unaddressed---the lack of solutions that work across buildings while not using additional infrastructure and also accounting for privacy and reliability needs. In this paper, a privacy-preserving, multi-modal, cross-building, collaborative localization platform is proposed based on Wi-Fi RSSI (existing infrastructure), Cellular RSSI, sound and light levels, that enables room-level localization as main application (though sub room level granularity is possible). The privacy is inherently built into the solution based on onion routing, and perturbation/randomization techniques, and exploits the idea of weighted collaboration to increase the reliability as well as to limit the effect of noisy devices (due to sensor noise/privacy). The proposed solution has been analyzed in terms of privacy, accuracy, optimum parameters, and other overheads on location data collected at multiple indoor and outdoor locations using an Android app.
NileTMRG at SemEval-2017 Task 4: Arabic Sentiment Analysis
Venue: arXiv cs.CL 2017
Year: 2017

This paper describes two systems that were used by the authors for addressing Arabic Sentiment Analysis as part of SemEval-2017, task 4. The authors participated in three Arabic related subtasks which are: Subtask A (Message Polarity Classification), Sub-task B (Topic-Based Message Polarity classification) and Subtask D (Tweet quantification) using the team name of NileTMRG. For subtask A, we made use of our previously developed sentiment analyzer which we augmented with a scored lexicon. For subtasks B and D, we used an ensemble of three different classifiers. The first classifier was a convolutional neural network for which we trained (word2vec) word embeddings. The second classifier consisted of a MultiLayer Perceptron, while the third classifier was a Logistic regression model that takes the same input as the second classifier. Voting between the three classifiers was used to determine the final outcome. The output from task B, was quantified to produce the results for task D. In all three Arabic related tasks in which NileTMRG participated, the team ranked at number one.
Bootstrapping Active IPv6 Measurement with IPv4 and Public DNS
Venue: arXiv cs.NI 2017
Year: 2017

The IPv4 address space is small enough to allow exhaustive active measurement, permitting important insight into Internet growth, policy, and evolution. The IPv6 address space, on the other hand, presents the problem that we can no longer perform exhaustive measurements in the same way, inhibiting our ability to continue studying Internet growth. Access to private datasets (e.g., HTTP access logs on content servers, flow data in ISP networks, or passive DNS traces) solves some problems but may not be feasible or desirable. This paper describes IPv6 address collection by exhaustively sweeping the reverse DNS domain for the IPv4 address space and performing AAAA queries on the results. Subsequent ICMP and TCP measurements are conducted to measure the responsiveness of the resulting set. Key outcomes include: the PTR sweep discovers 965,304 unique, globally routable IPv6 addresses originating from 5,531 ASNs. 56% of the addresses are responsive, across 4,571 ASNs. Upon inferring pairs of IPv4 and IPv6 addresses that are likely associated with the same device, the data indicates a trend toward IPv4 addresses being more responsive than their IPv6 counterparts, with a higher incidence rate of TCP connections being refused, and wide disparity on where TCP connections or ICMP echo requests fail silently when comparing IPv4 and IPv6. The disparity in IPv4 and IPv6 responsiveness is highly variable, and indicative of distinct host configuration and network policies across the two networks, presenting potential policy or security gaps as the IPv6 network matures.
Max-Margin Invariant Features from Transformed Unlabeled Data
Venue: arXiv cs.LG 2017
Year: 2017

The study of representations invariant to common transformations of the data is important to learning. Most techniques have focused on local approximate invariance implemented within expensive optimization frameworks lacking explicit theoretical guarantees. In this paper, we study kernels that are invariant to a unitary group while having theoretical guarantees in addressing the important practical issue of unavailability of transformed versions of labelled data. A problem we call the Unlabeled Transformation Problem which is a special form of semi-supervised learning and one-shot learning. We present a theoretically motivated alternate approach to the invariant kernel SVM based on which we propose Max-Margin Invariant Features (MMIF) to solve this problem. As an illustration, we design an framework for face recognition and demonstrate the efficacy of our approach on a large scale semi-synthetic dataset with 153,000 images and a new challenging protocol on Labelled Faces in the Wild (LFW) while out-performing strong baselines.
Quicker reaction, lower variability: The effect of transient time in flow variability of project-driven production
Venue: arXiv cs.SY 2017
Year: 2017

Based on the knowledge of dynamic systems, the shorter the transient response, or the faster a system reaches the steady-state after the introduction of the change, the smaller will be the output variability. In lean manufacturing, the principle of reducing set-up times has the same purpose: reduce the transient time and improve production flow. Analogously, the analysis of the transient response of project-driven systems may provide crucial information about how fast these systems react to a change and how that change affects their production output. Although some studies have investigated flow variability in projects, few have looked at variability from the perspective that the transient state represents the changeovers on project-driven production systems and how the transient state affects the process' flow variability. The purpose of this study is to investigate the effect of changes in project-driven production systems from a conceptual point of view, furthermore, measuring and correlating the transient response of five cases to their flow variability. Results showed a proportional relationship between the percentile transient time and flow variability of a process. That means that the quicker the production system reacts to change; the less the distress in the production output, consequently, lower levels of flow variability. As practical implications, lean practices focusing on reducing set-up times (transient time) can have their effects measured on project-driven production flow.
DDoS Attacks: Tools, Mitigation Approaches, and Probable Impact on Private Cloud Environment
Venue: arXiv cs.NI 2017
Year: 2017

The future of the Internet is predicted to be on the cloud, resulting in more complex and more intensive computing, but possibly also a more insecure digital world. The presence of a large amount of resources organized densely is a key factor in attracting DDoS attacks. Such attacks are arguably more dangerous in private individual clouds with limited resources. This paper discusses several prominent approaches introduced to counter DDoS attacks in private clouds. We also discuss issues and challenges to mitigate DDoS attacks in private clouds.
REPETITA: Repeatable Experiments for Performance Evaluation of Traffic-Engineering Algorithms
Venue: arXiv cs.NI 2017
Year: 2017

In this paper, we propose a pragmatic approach to improve reproducibility of experimental analyses of traffic engineering (TE) algorithms, whose implementation, evaluation and comparison are currently hard to replicate. Our envisioned goal is to enable universally-checkable experiments of existing and future TE algorithms. We describe the design and implementation of REPETITA, a software framework that implements common TE functions, automates experimental setup, and eases comparisons (in terms of solution quality, execution time, etc.) of TE algorithms. In its current version, REPETITA includes (i) a dataset for repeatable experiments, consisting of more than 250 real network topologies with complete bandwidth and delay information as well as associated traffic matrices; and (ii) the implementation of state-of-the-art algorithms for intra-domain TE with IGP weight tweaking and Segment Routing optimization. We showcase how our framework can successfully reproduce results described in the literature, and ease new analyses of qualitatively-diverse TE algorithms. We publicly release our REPETITA implementation, hoping that the community will consider it as a demonstration of feasibility, an incentive and an initial code basis for improving experiment reproducibility: Its plugin-oriented architecture indeed makes REPETITA easy to extend with new datasets, algorithms, TE primitives and analyses. We therefore invite the research community to use and contribute to our released code and dataset.
A note on the dispersion of admissible lattices
Venue: arXiv cs.CG 2017
Year: 2017

In this note we show that the volume of axis-parallel boxes in $\mathbb{R}^d$ which do not intersect an admissible lattice $\mathbb{L}\subset\mathbb{R}^d$ is uniformly bounded. In particular, this implies that the dispersion of the dilated lattices $N^{-1/d}\mathbb{L}$ restricted to the unit cube is of the (optimal) order $N^{-1}$ as $N$ goes to infinity. This result was obtained independently by V.N. Temlyakov (arXiv:1709.08158).
High-Performance Code Generation though Fusion and Vectorization
Venue: arXiv cs.PF 2017
Year: 2017

We present a technique for automatically transforming kernel-based computations in disparate, nested loops into a fused, vectorized form that can reduce intermediate storage needs and lead to improved performance on contemporary hardware. We introduce representations for the abstract relationships and data dependencies of kernels in loop nests and algorithms for manipulating them into more efficient form; we similarly introduce techniques for determining data access patterns for stencil-like array accesses and show how this can be used to elide storage and improve vectorization. We discuss our prototype implementation of these ideas---named HFAV---and its use of a declarative, inference-based front-end to drive transformations, and we present results for some prominent codes in HPC.
Identification in Dynamic Networks
Venue: arXiv cs.SY 2017
Year: 2017

System identification is a common tool for estimating (linear) plant models as a basis for model-based predictive control and optimization. The current challenges in process industry, however, ask for data-driven modelling techniques that go beyond the single unit/plant models. While optimization and control problems become more and more structured in the form of decentralized and/or distributed solutions, the related modelling problems will need to address structured and interconnected systems. An introduction will be given to the current state of the art and related developments in the identification of linear dynamic networks. Starting from classical prediction error methods for open-loop and closed-loop systems, several consequences for the handling of network situations will be presented and new research questions will be highlighted.
Wildbook: Crowdsourcing, computer vision, and data science for conservation
Venue: arXiv cs.CY 2017
Year: 2017

Photographs, taken by field scientists, tourists, automated cameras, and incidental photographers, are the most abundant source of data on wildlife today. Wildbook is an autonomous computational system that starts from massive collections of images and, by detecting various species of animals and identifying individuals, combined with sophisticated data management, turns them into high resolution information database, enabling scientific inquiry, conservation, and citizen science. We have built Wildbooks for whales (flukebook.org), sharks (whaleshark.org), two species of zebras (Grevy's and plains), and several others. In January 2016, Wildbook enabled the first ever full species (the endangered Grevy's zebra) census using photographs taken by ordinary citizens in Kenya. The resulting numbers are now the official species census used by IUCN Red List: http://www.iucnredlist.org/details/7950/0. In 2016, Wildbook partnered up with WWF to build Wildbook for Sea Turtles, Internet of Turtles (IoT), as well as systems for seals and lynx. Most recently, we have demonstrated that we can now use publicly available social media images to count and track wild animals. In this paper we present and discuss both the impact and challenges that the use of crowdsourced images can have on wildlife conservation.
Blackchain: Scalability for Resource-Constrained Accountable Vehicle-to-X Communication
Venue: arXiv cs.CR 2017
Year: 2017

In this paper, we propose a new Blockchain-based message and revocation accountability system called Blackchain. Combining a distributed ledger with existing mechanisms for security in V2X communication systems, we design a distributed event data recorder (EDR) that satisfies traditional accountability requirements by providing a compressed global state. Unlike previous approaches, our distributed ledger solution provides an accountable revocation mechanism without requiring trust in a single misbehavior authority, instead allowing a collaborative and transparent decision making process through Blackchain. This makes Blackchain an attractive alternative to existing solutions for revocation in a Security Credential Management System (SCMS), which suffer from the traditional disadvantages of PKIs, notably including centralized trust. Our proposal becomes scalable through the use of hierarchical consensus: individual vehicles dynamically create clusters, which then provide their consensus decisions as input for road-side units (RSUs), which in turn publish their results to misbehavior authorities. This authority, which is traditionally a single entity in the SCMS, responsible for the integrity of the entire V2X network, is now a set of authorities that transparently perform a revocation, whose result is then published in a global Blackchain state. This state can be used to prevent the issuance of certificates to previously malicious users, and also prevents the authority from misbehaving through the transparency implied by a global system state.
Conformal predictive distributions with kernels
Venue: arXiv cs.LG 2017
Year: 2017

This paper reviews the checkered history of predictive distributions in statistics and discusses two developments, one from recent literature and the other new. The first development is bringing predictive distributions into machine learning, whose early development was so deeply influenced by two remarkable groups at the Institute of Automation and Remote Control. The second development is combining predictive distributions with kernel methods, which were originated by one of those groups, including Emmanuel Braverman.
The Shape of an Image: A Study of Mapper on Images
Venue: arXiv cs.CV 2017
Year: 2017

We study the topological construction called Mapper in the context of simply connected domains, in particular on images. The Mapper construction can be considered as a generalization for contour, split, and joint trees on simply connected domains. A contour tree on an image domain assumes the height function to be a piecewise linear Morse function. This is a rather restrictive class of functions and does not allow us to explore the topology for most real world images. The Mapper construction avoids this limitation by assuming only continuity on the height function allowing this construction to robustly deal with a significant larger set of images. We provide a customized construction for Mapper on images, give a fast algorithm to compute it, and show how to simplify the Mapper structure in this case. Finally, we provide a simple procedure that guarantees the equivalence of Mapper to contour, join, and split trees on a simply connected domain.
The Geodesic $2$-center Problem in a Simple Polygon
Venue: arXiv cs.CG 2017
Year: 2017

The geodesic $k$-center problem in a simple polygon with $n$ vertices consists in the following. Find a set $S$ of $k$ points in the polygon that minimizes the maximum geodesic distance from any point of the polygon to its closest point in $S$. In this paper, we focus on the case where $k=2$ and present an exact algorithm that returns a geodesic $2$-center in $O(n^2\log^2 n)$ time.
Sufficient and necessary causation are dual
Venue: arXiv cs.AI 2017
Year: 2017

Causation has been the issue of philosophic debate since Hippocrates. Recent work defines actual causation in terms of Pearl/Halpern's causality framework, formalizing necessary causes (IJCAI'15). This has inspired causality notions in the security domain (CSF'15), which, perhaps surprisingly, formalize sufficient causes instead. We provide an explicit relation between necessary and sufficient causes.
Capacity Results on Multiple-Input Single-Output Wireless Optical Channels
Venue: arXiv cs.IT 2017
Year: 2017

This paper derives upper and lower bounds on the capacity of the multiple-input single-output free-space optical intensity channel with signal-independent additive Gaussian noise subject to both an average-intensity and a peak-intensity constraint. In the limit where the signal-to-noise ratio (SNR) tends to infinity, the asymptotic capacity is specified, while in the limit where the SNR tends to zero, the exact slope of the capacity is also given.
Recursion Aware Modeling and Discovery For Hierarchical Software Event Log Analysis (Extended)
Venue: arXiv cs.SE 2017
Year: 2017

This extended paper presents 1) a novel hierarchy and recursion extension to the process tree model; and 2) the first, recursion aware process model discovery technique that leverages hierarchical information in event logs, typically available for software systems. This technique allows us to analyze the operational processes of software systems under real-life conditions at multiple levels of granularity. The work can be positioned in-between reverse engineering and process mining. An implementation of the proposed approach is available as a ProM plugin. Experimental results based on real-life (software) event logs demonstrate the feasibility and usefulness of the approach and show the huge potential to speed up discovery by exploiting the available hierarchy.