OpenRaiser/Intern-Atlas huggingface dataset:
Analytical modeling and analysis of interleaving on correlated wireless channels
Venue: arXiv cs.IT 2018
Year: 2018

Interleaving is a mechanism universally used in wireless access technologies to alleviate the effect of channel correlation. In spite of its wide adoption, to the best of our knowledge, there are no analytical models proposed so far. In this paper we fill this void proposing three different models of interleaving. Two of these models are based on numerical algorithms while one of them allows for closed-form expression for packet error probability. Although we use block codes with hard decoding to specify the models our modeling principles are applicable to all forward error correction codes as long as there exists a functional relationship (possibly, probabilistic) between the number of incorrectly received bits in a codeword and the codeword error probability. We evaluate accuracy of our models showing that the worst case prediction is limited by 50\% across a wide range of input parameters. Finally, we study the effect of interleaving in detail demonstrating how it varies with channel correlation, bit error rate and error correction capability. Numerical results reported in this paper allows to identify the optimal value of the interleaving depth that need to be used for a channel with a given degree of correlation. The reference implementations of the models are available [1].
"23andMe confirms: I'm super white" -- Analyzing Twitter Discourse On Genetic Testing
Venue: arXiv cs.SI 2018
Year: 2018

Recent progress in genomics is bringing genetic testing to the masses. Participatory public initiatives are underway to sequence the genome of millions of volunteers, and a new market is booming with a number of companies like 23andMe and AncestryDNA offering affordable tests directly to consumers. Consequently, news, experiences, and views on genetic testing are increasingly shared and discussed online and on social networks like Twitter. In this paper, we present a large-scale analysis of Twitter discourse on genetic testing. We collect 302K tweets from 113K users, posted over 2.5 years, by using thirteen keywords related to genetic testing companies and public initiatives as search keywords. We study both the tweets and the users posting them along several axes, aiming to understand who tweets about genetic testing, what they talk about, and how they use Twitter for that. Among other things, we find that tweets about genetic testing originate from accounts that overall appear to be interested in digital health and technology. Also, marketing efforts as well as announcements, such as the FDA's suspension of 23andMe's health reports, influence the type and the nature of user engagement.Finally, we report on users who share screenshots of their results, and raise a few ethical and societal questions as we find evidence of groups associating genetic testing to racist ideologies.
COBRA: A Fast and Simple Method for Active Clustering with Pairwise Constraints
Venue: arXiv cs.AI 2018
Year: 2018

Clustering is inherently ill-posed: there often exist multiple valid clusterings of a single dataset, and without any additional information a clustering system has no way of knowing which clustering it should produce. This motivates the use of constraints in clustering, as they allow users to communicate their interests to the clustering system. Active constraint-based clustering algorithms select the most useful constraints to query, aiming to produce a good clustering using as few constraints as possible. We propose COBRA, an active method that first over-clusters the data by running K-means with a $K$ that is intended to be too large, and subsequently merges the resulting small clusters into larger ones based on pairwise constraints. In its merging step, COBRA is able to keep the number of pairwise queries low by maximally exploiting constraint transitivity and entailment. We experimentally show that COBRA outperforms the state of the art in terms of clustering quality and runtime, without requiring the number of clusters in advance.
Sliding Line Point Regression for Shape Robust Scene Text Detection
Venue: arXiv cs.CV 2018
Year: 2018

Traditional text detection methods mostly focus on quadrangle text. In this study we propose a novel method named sliding line point regression (SLPR) in order to detect arbitrary-shape text in natural scene. SLPR regresses multiple points on the edge of text line and then utilizes these points to sketch the outlines of the text. The proposed SLPR can be adapted to many object detection architectures such as Faster R-CNN and R-FCN. Specifically, we first generate the smallest rectangular box including the text with region proposal network (RPN), then isometrically regress the points on the edge of text by using the vertically and horizontally sliding lines. To make full use of information and reduce redundancy, we calculate x-coordinate or y-coordinate of target point by the rectangular box position, and just regress the remaining y-coordinate or x-coordinate. Accordingly we can not only reduce the parameters of system, but also restrain the points which will generate more regular polygon. Our approach achieved competitive results on traditional ICDAR2015 Incidental Scene Text benchmark and curve text detection dataset CTW1500.
Preparation of Improved Turkish DataSet for Sentiment Analysis in Social Media
Venue: arXiv cs.CL 2018
Year: 2018

A public dataset, with a variety of properties suitable for sentiment analysis [1], event prediction, trend detection and other text mining applications, is needed in order to be able to successfully perform analysis studies. The vast majority of data on social media is text-based and it is not possible to directly apply machine learning processes into these raw data, since several different processes are required to prepare the data before the implementation of the algorithms. For example, different misspellings of same word enlarge the word vector space unnecessarily, thereby it leads to reduce the success of the algorithm and increase the computational power requirement. This paper presents an improved Turkish dataset with an effective spelling correction algorithm based on Hadoop [2]. The collected data is recorded on the Hadoop Distributed File System and the text based data is processed by MapReduce programming model. This method is suitable for the storage and processing of large sized text based social media data. In this study, movie reviews have been automatically recorded with Apache ManifoldCF (MCF) [3] and data clusters have been created. Various methods compared such as Levenshtein and Fuzzy String Matching have been proposed to create a public dataset from collected data. Experimental results show that the proposed algorithm, which can be used as an open source dataset in sentiment analysis studies, have been performed successfully to the detection and correction of spelling errors.
Joint Orchestration of Cloud-Based Microservices and Virtual Network Functions
Venue: arXiv cs.NI 2018
Year: 2018

Recent studies show the increasing popularity of distributed cloud applications, which are composed of multiple microservices. Besides their known benefits, microservice architecture also enables to mix and match cloud applications and Network Function Virtualization (NFV) services (service chains), which are composed of Virtual Network Functions (VNFs). Provisioning complex services containing VNFs and microservices in a combined NFV/cloud platform can enhance service quality and optimise cost. Such a platform can be based on the multi-cloud concept. However, current multi-cloud solutions do not support NFV requirements, making them inadequate to support complex services. In this paper, we investigate these challenges and propose a solution for jointly managing and orchestrating microservices and virtual network functions.
D2.1 Models for energy consumption of data structures and algorithms
Venue: arXiv cs.DC 2018
Year: 2018

This deliverable reports our early energy models for data structures and algorithms based on both micro-benchmarks and concurrent algorithms. It reports the early results of Task 2.1 on investigating and modeling the trade-off between energy and performance in concurrent data structures and algorithms, which forms the basis for the whole work package 2 (WP2). The work has been conducted on the two main EXCESS platforms: (1) Intel platform with recent Intel multi-core CPUs and (2) Movidius embedded platform.
Animation-by-Demonstration Computer Puppetry Authoring Framework
Venue: arXiv cs.GR 2018
Year: 2018

This paper presents Master of Puppets (MOP), an animation-by-demonstration framework that allows users to control the motion of virtual characters (puppets) in real time. In the first step, the user is asked to perform the necessary actions that correspond to the character's motions. The user's actions are recorded, and a hidden Markov model (HMM) is used to learn the temporal profile of the actions. During the runtime of the framework, the user controls the motions of the virtual character based on the specified activities. The advantage of the MOP framework is that it recognizes and follows the progress of the user's actions in real time. Based on the forward algorithm, the method predicts the evolution of the user's actions, which corresponds to the evolution of the character's motion. This method treats characters as puppets that can perform only one motion at a time. This means that combinations of motion segments (motion synthesis), as well as the interpolation of individual motion sequences, are not provided as functionalities. By implementing the framework and presenting several computer puppetry scenarios, its efficiency and flexibility in animating virtual characters is demonstrated.
An Iterative Spanning Forest Framework for Superpixel Segmentation
Venue: arXiv cs.CV 2018
Year: 2018

Superpixel segmentation has become an important research problem in image processing. In this paper, we propose an Iterative Spanning Forest (ISF) framework, based on sequences of Image Foresting Transforms, where one can choose i) a seed sampling strategy, ii) a connectivity function, iii) an adjacency relation, and iv) a seed pixel recomputation procedure to generate improved sets of connected superpixels (supervoxels in 3D) per iteration. The superpixels in ISF structurally correspond to spanning trees rooted at those seeds. We present five ISF methods to illustrate different choices of its components. These methods are compared with approaches from the state-of-the-art in effectiveness and efficiency. The experiments involve 2D and 3D datasets with distinct characteristics, and a high level application, named sky image segmentation. The theoretical properties of ISF are demonstrated in the supplementary material and the results show that some of its methods are competitive with or superior to the best baselines in effectiveness and efficiency.
A Machine Learning Approach to Quantitative Prosopography
Venue: arXiv cs.DL 2018
Year: 2018

Prosopography is an investigation of the common characteristics of a group of people in history, by a collective study of their lives. It involves a study of biographies to solve historical problems. If such biographies are unavailable, surviving documents and secondary biographical data are used. Quantitative prosopography involves analysis of information from a wide variety of sources about "ordinary people". In this paper, we present a machine learning framework for automatically designing a people gazetteer which forms the basis of quantitative prosopographical research. The gazetteer is learnt from the noisy text of newspapers using a Named Entity Recognizer (NER). It is capable of identifying influential people from it by making use of a custom designed Influential Person Index (IPI). Our corpus comprises of 14020 articles from a local newspaper, "The Sun", published from New York in 1896. Some influential people identified by our algorithm include Captain Donald Hankey (an English soldier), Dame Nellie Melba (an Australian operatic soprano), Hugh Allan (a Canadian shipping magnate) and Sir Hugh John McDonald (the first Prime Minister of Canada).
The Benefits of Population Diversity in Evolutionary Algorithms: A Survey of Rigorous Runtime Analyses
Venue: arXiv cs.NE 2018
Year: 2018

Population diversity is crucial in evolutionary algorithms to enable global exploration and to avoid poor performance due to premature convergence. This book chapter reviews runtime analyses that have shown benefits of population diversity, either through explicit diversity mechanisms or through naturally emerging diversity. These works show that the benefits of diversity are manifold: diversity is important for global exploration and the ability to find several global optima. Diversity enhances crossover and enables crossover to be more effective than mutation. Diversity can be crucial in dynamic optimization, when the problem landscape changes over time. And, finally, it facilitates search for the whole Pareto front in evolutionary multiobjective optimization. The presented analyses rigorously quantify the performance of evolutionary algorithms in the light of population diversity, laying the foundation for a rigorous understanding of how search dynamics are affected by the presence or absence of population diversity and the introduction of diversity mechanisms.
TransRev: Modeling Reviews as Translations from Users to Items
Venue: arXiv cs.IR 2018
Year: 2018

The text of a review expresses the sentiment a customer has towards a particular product. This is exploited in sentiment analysis where machine learning models are used to predict the review score from the text of the review. Furthermore, the products costumers have purchased in the past are indicative of the products they will purchase in the future. This is what recommender systems exploit by learning models from purchase information to predict the items a customer might be interested in. We propose TransRev, an approach to the product recommendation problem that integrates ideas from recommender systems, sentiment analysis, and multi-relational learning into a joint learning objective. TransRev learns vector representations for users, items, and reviews. The embedding of a review is learned such that (a) it performs well as input feature of a regression model for sentiment prediction; and (b) it always translates the reviewer embedding to the embedding of the reviewed items. This allows TransRev to approximate a review embedding at test time as the difference of the embedding of each item and the user embedding. The approximated review embedding is then used with the regression model to predict the review score for each item. TransRev outperforms state of the art recommender systems on a large number of benchmark data sets. Moreover, it is able to retrieve, for each user and item, the review text from the training set whose embedding is most similar to the approximated review embedding.
Hyperledger Fabric: A Distributed Operating System for Permissioned Blockchains
Venue: arXiv cs.DC 2018
Year: 2018

Fabric is a modular and extensible open-source system for deploying and operating permissioned blockchains and one of the Hyperledger projects hosted by the Linux Foundation (www.hyperledger.org). Fabric is the first truly extensible blockchain system for running distributed applications. It supports modular consensus protocols, which allows the system to be tailored to particular use cases and trust models. Fabric is also the first blockchain system that runs distributed applications written in standard, general-purpose programming languages, without systemic dependency on a native cryptocurrency. This stands in sharp contrast to existing blockchain platforms that require "smart-contracts" to be written in domain-specific languages or rely on a cryptocurrency. Fabric realizes the permissioned model using a portable notion of membership, which may be integrated with industry-standard identity management. To support such flexibility, Fabric introduces an entirely novel blockchain design and revamps the way blockchains cope with non-determinism, resource exhaustion, and performance attacks. This paper describes Fabric, its architecture, the rationale behind various design decisions, its most prominent implementation aspects, as well as its distributed application programming model. We further evaluate Fabric by implementing and benchmarking a Bitcoin-inspired digital currency. We show that Fabric achieves end-to-end throughput of more than 3500 transactions per second in certain popular deployment configurations, with sub-second latency, scaling well to over 100 peers.
The Impact of Class Rebalancing Techniques on the Performance and Interpretation of Defect Prediction Models
Venue: arXiv cs.SE 2018
Year: 2018

Defect prediction models that are trained on class imbalanced datasets (i.e., the proportion of defective and clean modules is not equally represented) are highly susceptible to produce inaccurate prediction models. Prior research compares the impact of class rebalancing techniques on the performance of defect prediction models. Prior research efforts arrive at contradictory conclusions due to the use of different choice of datasets, classification techniques, and performance measures. Such contradictory conclusions make it hard to derive practical guidelines for whether class rebalancing techniques should be applied in the context of defect prediction models. In this paper, we investigate the impact of 4 popularly-used class rebalancing techniques on 10 commonly-used performance measures and the interpretation of defect prediction models. We also construct statistical models to better understand in which experimental design settings that class rebalancing techniques are beneficial for defect prediction models. Through a case study of 101 datasets that span across proprietary and open-source systems, we recommend that class rebalancing techniques are necessary when quality assurance teams wish to increase the completeness of identifying software defects (i.e., Recall). However, class rebalancing techniques should be avoided when interpreting defect prediction models. We also find that class rebalancing techniques do not impact the AUC measure. Hence, AUC should be used as a standard measure when comparing defect prediction models.
The Impact of Automated Parameter Optimization on Defect Prediction Models
Venue: arXiv cs.SE 2018
Year: 2018

Defect prediction models---classifiers that identify defect-prone software modules---have configurable parameters that control their characteristics (e.g., the number of trees in a random forest). Recent studies show that these classifiers underperform when default settings are used. In this paper, we study the impact of automated parameter optimization on defect prediction models. Through a case study of 18 datasets, we find that automated parameter optimization: (1) improves AUC performance by up to 40 percentage points; (2) yields classifiers that are at least as stable as those trained using default settings; (3) substantially shifts the importance ranking of variables, with as few as 28% of the top-ranked variables in optimized classifiers also being top-ranked in non-optimized classifiers; (4) yields optimized settings for 17 of the 20 most sensitive parameters that transfer among datasets without a statistically significant drop in performance; and (5) adds less than 30 minutes of additional computation to 12 of the 26 studied classification techniques. While widely-used classification techniques like random forest and support vector machines are not optimization-sensitive, traditionally overlooked techniques like C5.0 and neural networks can actually outperform widely-used techniques after optimization is applied. This highlights the importance of exploring the parameter space when using parameter-sensitive classification techniques.
Cataloging the Visible Universe through Bayesian Inference at Petascale
Venue: arXiv cs.DC 2018
Year: 2018

Astronomical catalogs derived from wide-field imaging surveys are an important tool for understanding the Universe. We construct an astronomical catalog from 55 TB of imaging data using Celeste, a Bayesian variational inference code written entirely in the high-productivity programming language Julia. Using over 1.3 million threads on 650,000 Intel Xeon Phi cores of the Cori Phase II supercomputer, Celeste achieves a peak rate of 1.54 DP PFLOP/s. Celeste is able to jointly optimize parameters for 188M stars and galaxies, loading and processing 178 TB across 8192 nodes in 14.6 minutes. To achieve this, Celeste exploits parallelism at multiple levels (cluster, node, and thread) and accelerates I/O through Cori's Burst Buffer. Julia's native performance enables Celeste to employ high-level constructs without resorting to hand-written or generated low-level code (C/C++/Fortran), and yet achieve petascale performance.
Stochastic Optimization and Control Framework for 5G Network Slicing with Effective Isolation
Venue: arXiv cs.IT 2018
Year: 2018

Network slicing is an emerging technique for providing resources to diverse wireless services with heterogeneous quality-of-service needs. However, beyond satisfying end-to-end requirements of network users, network slicing needs to also provide isolation between slices so as to prevent one slice's faults and congestion from affecting other slices. In this paper, the problem of network slicing is studied in the context of a wireless system having a time-varying number of users that require two types of slices: reliable low latency (RLL) and self-managed (capacity limited) slices. To address this problem, a novel control framework for stochastic optimization is proposed based on the Lyapunov drift-plus-penalty method. This new framework enables the system to minimize power, maintain slice isolation, and provide reliable and low latency end-to-end communication for RLL slices. Simulation results show that the proposed approach can maintain the system's reliability while providing effective slice isolation in the event of sudden changes in the network environment.
An Incremental Off-policy Search in a Model-free Markov Decision Process Using a Single Sample Path
Venue: arXiv cs.AI 2018
Year: 2018

In this paper, we consider a modified version of the control problem in a model free Markov decision process (MDP) setting with large state and action spaces. The control problem most commonly addressed in the contemporary literature is to find an optimal policy which maximizes the value function, i.e., the long run discounted reward of the MDP. The current settings also assume access to a generative model of the MDP with the hidden premise that observations of the system behaviour in the form of sample trajectories can be obtained with ease from the model. In this paper, we consider a modified version, where the cost function is the expectation of a non-convex function of the value function without access to the generative model. Rather, we assume that a sample trajectory generated using a priori chosen behaviour policy is made available. In this restricted setting, we solve the modified control problem in its true sense, i.e., to find the best possible policy given this limited information. We propose a stochastic approximation algorithm based on the well-known cross entropy method which is data (sample trajectory) efficient, stable, robust as well as computationally and storage efficient. We provide a proof of convergence of our algorithm to a policy which is globally optimal relative to the behaviour policy. We also present experimental results to corroborate our claims and we demonstrate the superiority of the solution produced by our algorithm compared to the state-of-the-art algorithms under appropriately chosen behaviour policy.