OpenRaiser/Intern-Atlas huggingface dataset:
SWAM: SDN-based Wi-Fi Small Cells with Joint Access-Backhaul and Multi-Tenant Capabilities
Venue: arXiv cs.NI 2018
Year: 2018

Dense deployments of Small Cells are required to deliver the capacity promised by 5G networks. In this paper we present SWAM, a system that builds on commodity Wi-Fi routers with multiple wireless interfaces to provide a wireless access infrastructure supporting multi-tenancy, mobility, and integrated wireless access and backhaul. An infrastructure provider can deploy inexpensive SWAM nodes to cover a given geographical area, and re-sell this capacity to provide on-demand connectivity for Mobile Network Operators. Our main contribution is the design of the SWAM datapath and control plane, which are inspired by the overlay techniques used to enable multi-tenancy in data-center networks. We prototype SWAM in an office wireless testbed, and validate experimentally its functionality.
Improved Bounds for Guarding Plane Graphs with Edges
Venue: arXiv cs.CG 2018
Year: 2018

An "edge guard set" of a plane graph $G$ is a subset $Γ$ of edges of $G$ such that each face of $G$ is incident to an endpoint of an edge in $Γ$. Such a set is said to guard $G$. We improve the known upper bounds on the number of edges required to guard any $n$-vertex embedded planar graph $G$: 1- We present a simple inductive proof for a theorem of Everett and Rivera-Campo (1997) that $G$ can be guarded with at most $ \frac{2n}{5}$ edges, then extend this approach with a deeper analysis to yield an improved bound of $\frac{3n}{8}$ edges for any plane graph. 2- We prove that there exists an edge guard set of $G$ with at most $\frac{n}{3}+\fracα{9}$ edges, where $α$ is the number of quadrilateral faces in $G$. This improves the previous bound of $\frac{n}{3} + α$ by Bose, Kirkpatrick, and Li (2003). Moreover, if there is no short path between any two quadrilateral faces in $G$, we show that $\frac{n}{3}$ edges suffice, removing the dependence on $α$.
Ambient Assisted Living technologies from the perspectives of older people and professionals
Venue: arXiv cs.CY 2018
Year: 2018

Ambient Assisted Living (AAL) and Ambient Intelligence technologies are providing support to older people in living an independent and confident life by developing innovative ICT-based products, services, and systems. Despite significant advancement in AAL technologies and smart systems, they have still not found the way into the nursing home of the older people. The reasons are manifold. On one hand, the development of such systems lack in addressing the requirements of the older people and caregivers of the organization and the other is the unwillingness of the older people to make use of assistive systems. A qualitative study was performed at a nursing home to understand the needs and requirements of the residents and caregivers and their perspectives about the existing AAL technologies.
Recognizing Birds from Sound - The 2018 BirdCLEF Baseline System
Venue: arXiv cs.CV 2018
Year: 2018

Reliable identification of bird species in recorded audio files would be a transformative tool for researchers, conservation biologists, and birders. In recent years, artificial neural networks have greatly improved the detection quality of machine learning systems for bird species recognition. We present a baseline system using convolutional neural networks. We publish our code base as reference for participants in the 2018 LifeCLEF bird identification task and discuss our experiments and potential improvements.
Evaluation of IEEE 802.11ad for mmWave V2V Communications
Venue: arXiv cs.NI 2018
Year: 2018

Autonomous vehicles can construct a more accurate perception of their surrounding environment by exchanging rich sensor data with nearby vehicles. Such exchange can require larger bandwidths than currently provided by ITS-G5/DSRC and Cellular V2X. Millimeter wave (mmWave) communications can provide higher bandwidth and could complement current V2X standards. Recent studies have started investigating the potential of IEEE 802.11ad to support high bandwidth vehicular communications. This paper introduces the first performance evaluation of the IEEE 802.11ad MAC (Medium Access Control) and beamforming mechanism for mmWave V2V communications. The study highlights existing opportunities and shortcomings that should guide the development of mmWave communications for V2V communications.
Deep Transfer Network with Joint Distribution Adaptation: A New Intelligent Fault Diagnosis Framework for Industry Application
Venue: arXiv cs.LG 2018
Year: 2018

In recent years, an increasing popularity of deep learning model for intelligent condition monitoring and diagnosis as well as prognostics used for mechanical systems and structures has been observed. In the previous studies, however, a major assumption accepted by default, is that the training and testing data are taking from same feature distribution. Unfortunately, this assumption is mostly invalid in real application, resulting in a certain lack of applicability for the traditional diagnosis approaches. Inspired by the idea of transfer learning that leverages the knowledge learnt from rich labeled data in source domain to facilitate diagnosing a new but similar target task, a new intelligent fault diagnosis framework, i.e., deep transfer network (DTN), which generalizes deep learning model to domain adaptation scenario, is proposed in this paper. By extending the marginal distribution adaptation (MDA) to joint distribution adaptation (JDA), the proposed framework can exploit the discrimination structures associated with the labeled data in source domain to adapt the conditional distribution of unlabeled target data, and thus guarantee a more accurate distribution matching. Extensive empirical evaluations on three fault datasets validate the applicability and practicability of DTN, while achieving many state-of-the-art transfer results in terms of diverse operating conditions, fault severities and fault types.
Metaclasses and Reflection in Smalltalk
Venue: arXiv cs.SE 2018
Year: 2018

Many Object Oriented Programming Languages provide reflective features which may be used to control the interpretive mechanism of the language. Often these features are defined with respect to a golden braid consisting of objects classes and meta-classes. This report reviews the Smalltalk golden braid and generalize it for multiple inheritance leading to choices between many different inheritance strategies. The reflective features of Smalltalk cannot affect the basic mechanisms of inheritance and so an arbitrary choice must be made for multiple inheritance. A language is described in which the reflective features of Smalltalk are extended so as to allow programmer defined inheritance strategies.
Deep Triplet Ranking Networks for One-Shot Recognition
Venue: arXiv cs.LG 2018
Year: 2018

Despite the breakthroughs achieved by deep learning models in conventional supervised learning scenarios, their dependence on sufficient labeled training data in each class prevents effective applications of these deep models in situations where labeled training instances for a subset of novel classes are very sparse -- in the extreme case only one instance is available for each class. To tackle this natural and important challenge, one-shot learning, which aims to exploit a set of well labeled base classes to build classifiers for the new target classes that have only one observed instance per class, has recently received increasing attention from the research community. In this paper we propose a novel end-to-end deep triplet ranking network to perform one-shot learning. The proposed approach learns class universal image embeddings on the well labeled base classes under a triplet ranking loss, such that the instances from new classes can be categorized based on their similarity with the one-shot instances in the learned embedding space. Moreover, our approach can naturally incorporate the available one-shot instances from the new classes into the embedding learning process to improve the triplet ranking model. We conduct experiments on two popular datasets for one-shot learning. The results show the proposed approach achieves better performance than the state-of-the- art comparison methods.
Task-centric Optimization of Configurations for Assistive Robots
Venue: arXiv cs.RO 2018
Year: 2018

Robots can provide assistance to a human by moving objects to locations around the person's body. With a well chosen initial configuration, a robot can better reach locations important to an assistive task despite model error, pose uncertainty and other sources of variation. However, finding effective configurations can be challenging due to complex geometry, a large number of degrees of freedom, task complexity and other factors. We present task-centric optimization of robot configurations (TOC), which is an algorithm that finds configurations from which the robot can better reach task-relevant locations and handle task variation. Notably, TOC can return more than one configuration that when used sequentially enable a simulated assistive robot to reach more task-relevant locations. TOC performs substantial offline computation to generate a function that can be applied rapidly online to select robot configurations based on current observations. TOC explicitly models the task, environment, and user, and implicitly handles error using representations of robot dexterity. We evaluated TOC in simulation with a PR2 assisting a user with 9 assistive tasks in both a wheelchair and a robotic bed. TOC had an overall average success rate of 90.6\% compared to 50.4\%, 43.5\%, and 58.9\% for three baseline methods from literature. We additionally demonstrate how TOC can find configurations for more than one robot and can be used to assist in designing or optimizing environments.
Stylistic Variation in Social Media Part-of-Speech Tagging
Venue: arXiv cs.CL 2018
Year: 2018

Social media features substantial stylistic variation, raising new challenges for syntactic analysis of online writing. However, this variation is often aligned with author attributes such as age, gender, and geography, as well as more readily-available social network metadata. In this paper, we report new evidence on the link between language and social networks in the task of part-of-speech tagging. We find that tagger error rates are correlated with network structure, with high accuracy in some parts of the network, and lower accuracy elsewhere. As a result, tagger accuracy depends on training from a balanced sample of the network, rather than training on texts from a narrow subcommunity. We also describe our attempts to add robustness to stylistic variation, by building a mixture-of-experts model in which each expert is associated with a region of the social network. While prior work found that similar approaches yield performance improvements in sentiment analysis and entity linking, we were unable to obtain performance improvements in part-of-speech tagging, despite strong evidence for the link between part-of-speech error rates and social network structure.
The Aftermath of Disbanding an Online Hateful Community
Venue: arXiv cs.SI 2018
Year: 2018

Harassing and hateful speech in online spaces has become a common problem for platform maintainers and their users. The toxicity created by such content can discourage user participation and engagement. Therefore, it is crucial for and a common goal of platform managers to diminish hateful and harmful content. Over the last year, Reddit, a major online platform, enacted a policy of banning sub-communities (subreddits) that they deem harassing, with the goal of diminishing such activities. We studied the effects of banning the largest hateful subreddit (r/fatpeoplehate or FPH) on the users and other subreddits that were associated with it. We found that, while a number of outcomes were possible --- in this case the subreddit ban led to a sustained reduced interaction of its members (FPH users) with the Reddit platform. We also found that the many counter-actions taken by FPH users were short-lived and promptly neutralized by both Reddit administrators and the admins of individual subreddits. Our findings show that forum-banning can be an effective means by which to diminish objectionable content. Moreover, our detailed analysis of the post-banning behavior of FPH users highlights a number of the behavioral patterns that banning can create.
QoS Provisioning in Large Wireless Networks
Venue: arXiv cs.IT 2018
Year: 2018

Quality of service (QoS) provisioning in next-generation mobile communications systems entails a deep understanding of the delay performance. The delay in wireless networks is strongly affected by the traffic arrival process and the service process, which in turn depends on the medium access protocol and the signal-to-interference-plus-noise ratio (SINR) distribution. In this work, we characterize the conditional distribution of the service process given the point process in Poisson bipolar networks. We then provide an upper bound on the delay violation probability combining tools from stochastic network calculus and stochastic geometry. Furthermore, we analyze the delay performance under statistical queueing constraints using the effective capacity formulation. The impact of QoS requirements, network geometry and link distance on the delay performance is identified. Our results provide useful insights for guaranteeing stringent delay requirements in large wireless networks.
Light Spanners for High Dimensional Norms via Stochastic Decompositions
Venue: arXiv cs.DS 2018
Year: 2018

Spanners for low dimensional spaces (e.g. Euclidean space of constant dimension, or doubling metrics) are well understood. This lies in contrast to the situation in high dimensional spaces, where except for the work of Har-Peled, Indyk and Sidiropoulos (SODA 2013), who showed that any $n$-point Euclidean metric has an $O(t)$-spanner with $\tilde{O}(n^{1+1/t^2})$ edges, little is known. In this paper we study several aspects of spanners in high dimensional normed spaces. First, we build spanners for finite subsets of $\ell_p$ with $1<p\le 2$. Second, our construction yields a spanner which is both sparse and also {\em light}, i.e., its total weight is not much larger than that of the minimum spanning tree. In particular, we show that any $n$-point subset of $\ell_p$ for $1<p\le 2$ has an $O(t)$-spanner with $n^{1+\tilde{O}(1/t^p)}$ edges and lightness $n^{\tilde{O}(1/t^p)}$. In fact, our results are more general, and they apply to any metric space admitting a certain low diameter stochastic decomposition. It is known that arbitrary metric spaces have an $O(t)$-spanner with lightness $O(n^{1/t})$. We exhibit the following tradeoff: metrics with decomposability parameter $ν=ν(t)$ admit an $O(t)$-spanner with lightness $\tilde{O}(ν^{1/t})$. For example, $n$-point Euclidean metrics have $ν\le n^{1/t}$, metrics with doubling constant $λ$ have $ν\leλ$, and graphs of genus $g$ have $ν\le g$. While these families do admit a ($1+ε$)-spanner, its lightness depend exponentially on the dimension (resp. $\log g$). Our construction alleviates this exponential dependency, at the cost of incurring larger stretch.
A Complementary Tracking Model with Multiple Features
Venue: arXiv cs.CV 2018
Year: 2018

Discriminative Correlation Filters based tracking algorithms exploiting conventional handcrafted features have achieved impressive results both in terms of accuracy and robustness. Template handcrafted features have shown excellent performance, but they perform poorly when the appearance of target changes rapidly such as fast motions and fast deformations. In contrast, statistical handcrafted features are insensitive to fast states changes, but they yield inferior performance in the scenarios of illumination variations and background clutters. In this work, to achieve an efficient tracking performance, we propose a novel visual tracking algorithm, named MFCMT, based on a complementary ensemble model with multiple features, including Histogram of Oriented Gradients (HOGs), Color Names (CNs) and Color Histograms (CHs). Additionally, to improve tracking results and prevent targets drift, we introduce an effective fusion method by exploiting relative entropy to coalesce all basic response maps and get an optimal response. Furthermore, we suggest a simple but efficient update strategy to boost tracking performance. Comprehensive evaluations are conducted on two tracking benchmarks demonstrate and the experimental results demonstrate that our method is competitive with numerous state-of-the-art trackers. Our tracker achieves impressive performance with faster speed on these benchmarks.
Delegating via Quitting Games
Venue: arXiv cs.AI 2018
Year: 2018

Delegation allows an agent to request that another agent completes a task. In many situations the task may be delegated onwards, and this process can repeat until it is eventually, successfully or unsuccessfully, performed. We consider policies to guide an agent in choosing who to delegate to when such recursive interactions are possible. These policies, based on quitting games and multi-armed bandits, were empirically tested for effectiveness. Our results indicate that the quitting game based policies outperform those which do not explicitly account for the recursive nature of delegation.
Streaming Active Learning Strategies for Real-Life Credit Card Fraud Detection: Assessment and Visualization
Venue: arXiv cs.LG 2018
Year: 2018

Credit card fraud detection is a very challenging problem because of the specific nature of transaction data and the labeling process. The transaction data is peculiar because they are obtained in a streaming fashion, they are strongly imbalanced and prone to non-stationarity. The labeling is the outcome of an active learning process, as every day human investigators contact only a small number of cardholders (associated to the riskiest transactions) and obtain the class (fraud or genuine) of the related transactions. An adequate selection of the set of cardholders is therefore crucial for an efficient fraud detection process. In this paper, we present a number of active learning strategies and we investigate their fraud detection accuracies. We compare different criteria (supervised, semi-supervised and unsupervised) to query unlabeled transactions. Finally, we highlight the existence of an exploitation/exploration trade-off for active learning in the context of fraud detection, which has so far been overlooked in the literature.
IPTV Over ICN
Venue: arXiv cs.NI 2018
Year: 2018

The efficient provision of IPTV services requires support for IP multicasting and IGMP snooping, limiting such services to single operator networks. Information-Centric Networking (ICN), with its native support for multicast seems ideal for such services, but it requires operators and users to overhaul their networks and applications. The POINT project has proposed a hybrid, IP-over-ICN, architecture, preserving IP devices and applications at the edge, but interconnecting them via an SDN-based ICN core. This allows individual operators to exploit the benefits of ICN, without expecting the rest of the Internet to change. In this paper, we first outline the POINT approach and show how it can handle multicast-based IPTV services in a more efficient and resilient manner than IP. We then describe a successful trial of the POINT prototype in a production network, where real users tested actual IPTV services over both IP and POINT under regular and exceptional conditions. Results from the trial show that the POINT prototype matched or improved upon the services offered via plain IP.
An Approximate Shading Model with Detail Decomposition for Object Relighting
Venue: arXiv cs.CV 2018
Year: 2018

We present an object relighting system that allows an artist to select an object from an image and insert it into a target scene. Through simple interactions, the system can adjust illumination on the inserted object so that it appears naturally in the scene. To support image-based relighting, we build object model from the image, and propose a \emph{perceptually-inspired} approximate shading model for the relighting. It decomposes the shading field into (a) a rough shape term that can be reshaded, (b) a parametric shading detail that encodes missing features from the first term, and (c) a geometric detail term that captures fine-scale material properties. With this decomposition, the shading model combines 3D rendering and image-based composition and allows more flexible compositing than image-based methods. Quantitative evaluation and a set of user studies suggest our method is a promising alternative to existing methods of object insertion.
Robust And Scalable Learning Of Complex Dataset Topologies Via Elpigraph
Venue: arXiv cs.LG 2018
Year: 2018

Large datasets represented by multidimensional data point clouds often possess non-trivial distributions with branching trajectories and excluded regions, with the recent single-cell transcriptomic studies of developing embryo being notable examples. Reducing the complexity and producing compact and interpretable representations of such data remains a challenging task. Most of the existing computational methods are based on exploring the local data point neighbourhood relations, a step that can perform poorly in the case of multidimensional and noisy data. Here we present ElPiGraph, a scalable and robust method for approximation of datasets with complex structures which does not require computing the complete data distance matrix or the data point neighbourhood graph. This method is able to withstand high levels of noise and is capable of approximating complex topologies via principal graph ensembles that can be combined into a consensus principal graph. ElPiGraph deals efficiently with large and complex datasets in various fields from biology, where it can be used to infer gene dynamics from single-cell RNA-Seq, to astronomy, where it can be used to explore complex structures in the distribution of galaxies.
Approaches for Enriching and Improving Textual Knowledge Bases
Venue: arXiv cs.CL 2018
Year: 2018

Verifiability is one of the core editing principles in Wikipedia, where editors are encouraged to provide citations for the added statements. Statements can be any arbitrary piece of text, ranging from a sentence up to a paragraph. However, in many cases, citations are either outdated, missing, or link to non-existing references (e.g. dead URL, moved content etc.). In total, 20\% of the cases such citations refer to news articles and represent the second most cited source. Even in cases where citations are provided, there are no explicit indicators for the span of a citation for a given piece of text. In addition to issues related with the verifiability principle, many Wikipedia entity pages are incomplete, with relevant information that is already available in online news sources missing. Even for the already existing citations, there is often a delay between the news publication time and the reference time. In this thesis, we address the aforementioned issues and propose automated approaches that enforce the verifiability principle in Wikipedia, and suggest relevant and missing news references for further enriching Wikipedia entity pages.