OpenRaiser/Intern-Atlas huggingface dataset:
Disjoint Multi-task Learning between Heterogeneous Human-centric Tasks
Venue: arXiv cs.CV 2018
Year: 2018

Human behavior understanding is arguably one of the most important mid-level components in artificial intelligence. In order to efficiently make use of data, multi-task learning has been studied in diverse computer vision tasks including human behavior understanding. However, multi-task learning relies on task specific datasets and constructing such datasets can be cumbersome. It requires huge amounts of data, labeling efforts, statistical consideration etc. In this paper, we leverage existing single-task datasets for human action classification and captioning data for efficient human behavior learning. Since the data in each dataset has respective heterogeneous annotations, traditional multi-task learning is not effective in this scenario. To this end, we propose a novel alternating directional optimization method to efficiently learn from the heterogeneous data. We demonstrate the effectiveness of our model and show performance improvements on both classification and sentence retrieval tasks in comparison to the models trained on each of the single-task datasets.
'Getting out of the closet': Scientific authorship of literary fiction and knowledge transfer
Venue: arXiv cs.DL 2018
Year: 2018

Some scientists write literary fiction books in their spare time. If these books contain scientific knowledge, literary fiction becomes a mechanism of knowledge transfer. In this case, we could conceptualize literary fiction as non-formal knowledge transfer. We model knowledge transfer via literary fiction as a function of the type of scientist (academic or non-academic) and his/her scientific field. Academic scientists are those employed in academia and public research organizations whereas non-academic scientists are those with a scientific background employed in other sectors. We also distinguish between direct knowledge transfer (the book includes the scientist's research topics), indirect knowledge transfer (scientific authors talk about their research with cultural agents) and reverse knowledge transfer (cultural agents give scientists ideas for future research). Through mixed-methods research and a sample from Spain, we find that scientific authorship accounts for a considerable percentage of all literary fiction authorship. Academic scientists do not transfer knowledge directly so often as non-academic scientists, but the former engage into indirect and reverse transfer knowledge more often than the latter. Scientists from History stand out in direct knowledge transfer. We draw propositions about the role of the academic logic and scientific field on knowledge transfer via literary fiction. We advance some tentative conclusions regarding the consideration of scientific authorship of literary fiction as a valuable knowledge transfer mechanism.
Distributional Term Set Expansion
Venue: arXiv cs.CL 2018
Year: 2018

This paper is a short empirical study of the performance of centrality and classification based iterative term set expansion methods for distributional semantic models. Iterative term set expansion is an interactive process using distributional semantics models where a user labels terms as belonging to some sought after term set, and a system uses this labeling to supply the user with new, candidate, terms to label, trying to maximize the number of positive examples found. While centrality based methods have a long history in term set expansion, we compare them to classification methods based on the the Simple Margin method, an Active Learning approach to classification using Support Vector Machines. Examining the performance of various centrality and classification based methods for a variety of distributional models over five different term sets, we can show that active learning based methods consistently outperform centrality based methods.
PyFml - a Textual Language For Feature Modeling
Venue: arXiv cs.SE 2018
Year: 2018

The Feature model is a typical approach to capture variability in a software product line design and implementation. For that, most works automate feature model using a limited graphical notation represented by propositional logic and implemented by Prolog or Java programming languages. These works do not properly combine the extensions of classical feature models and do not provide scalability to implement large size problem issues. In this work, we propose a textual feature modeling language based on Python programming language (PyFML), that generalizes the classical feature models with instance feature cardinalities and attributes which be extended with highlight of replication and complex logical and mathematical cross-tree constraints. textX Meta-language is used for building PyFML to describe and organize feature model dependencies, and PyConstraint Problem Solver is used to implement feature model variability and its constraints validation. The work provides a textual human-readable language to represent feature model and maps the feature model descriptions directly into the object-oriented representation to be used by Constraint Problem Solver for computation. Furthermore, the proposed PyFML makes the notation of feature modeling more expressive to deal with complex software product line representations and using PyConstraint Problem Solver
Facebook Use of Sensitive Data for Advertising in Europe
Venue: arXiv cs.SI 2018
Year: 2018

The upcoming European General Data Protection Regulation (GDPR) prohibits the processing and exploitation of some categories of personal data (health, political orientation, sexual preferences, religious beliefs, ethnic origin, etc.) due to the obvious privacy risks that may be derived from a malicious use of such type of information. These categories are referred to as sensitive personal data. Facebook has been recently fined EUR 1.2M in Spain for collecting, storing and processing sensitive personal data for advertising purposes. This paper quantifies the portion of Facebook users in the European Union (EU) who are labeled with interests linked to sensitive personal data. The results of our study reveal that Facebook labels 73% EU users with sensitive interests. This corresponds to 40% of the overall EU population. We also estimate that a malicious third-party could unveil the identity of Facebook users that have been assigned a sensitive interest at a cost as low as EUR 0.015 per user. Finally, we propose and implement a web browser extension to inform Facebook users of the sensitive interests Facebook has assigned them.
Using Two Independent Channels with Gateway for FlexRay Static Segment Scheduling
Venue: arXiv cs.SY 2018
Year: 2018

The FlexRay bus is a communication standard used in the automotive industry. It offers a deterministic message transmission in the static segment following a time-triggered schedule. Even if its bandwidth is ten times higher than the bandwidth of CAN, its throughput limits are going to be reached in high-class car models soon. A solution that could postpone this problem is to use an efficient scheduling algorithm that exploits both channels of the FlexRay. The significant and often neglected feature that can theoretically double the bandwidth is the possibility to use two independent communication channels that can intercommunicate through the gateway. In this paper, we propose a heuristic algorithm that decomposes the scheduling problem to the ECU-to-channel assignment subproblem which decides which channel the ECUs (Electronic Control Units) should be connected to and the channel scheduling subproblem which creates static segment communication schedules for both channels. The algorithm is able to create a schedule for cases where channels are configured in the independent mode as well as in the fault-tolerant mode or in cases where just part of the signals are fault-tolerant. Finally, the algorithm is evaluated on real data and synthesized data, and the relation between the portion of fault-tolerant signals and the number of allocated slots is presented.
Multi-Criteria Virtual Machine Placement in Cloud Computing Environments: A literature Review
Venue: arXiv cs.NI 2018
Year: 2018

Cloud computing is a revolutionary process that has impacted the manner of using networks. It allows a high level of flexibility as Virtual Machines (VMs) run elastically workloads on physical machines in data centers. The issue of placing virtual machines (VMP) in cloud environments is an important challenge that has been thoroughly addressed, although not yet completely resolved. This article discusses the different problems that may disrupt the placement of VMs and Virtual Network Functions (VNFs), and classifies the existing solutions into five major objective functions based on multiple performance metrics such as energy consumption, Quality of Service, Service Level Agreement, and incurred cost. The existing solutions are also classified based on whether they adopt heuristic, deterministic, meta-heuristic or approximation algorithms. The VNF placement in 5G network is also discussed to highlight the convergence toward optimal usage of mobile services by including NFV/Software-Defined-Network technologies.
Key Lessons from Tailoring Agile Methods for Large-Scale Software Development
Venue: arXiv cs.SE 2018
Year: 2018

We describe advice derived from one of the largest development programs in Norway, where twelve Scrum teams combined agile practices with traditional project management. The Perform program delivered 12 releases over a four-year period, and finished on budget and on time. In this article, we summarize 12 key lessons on five crucial topics, relevant to other large development projects seeking to combine Scrum with traditional project management.
Clustering Properties of Spatial Preferential Attachment Model
Venue: arXiv cs.SI 2018
Year: 2018

In this paper, we study the clustering properties of the Spatial Preferential Attachment (SPA) model introduced by Aiello et al. in 2009. This model naturally combines geometry and preferential attachment using the notion of spheres of influence. It was previously shown in several research papers that graphs generated by the SPA model are similar to real-world networks in many aspects. For example, the vertex degree distribution was shown to follow a power law. In the current paper, we study the behaviour of C(d), which is the average local clustering coefficient for the vertices of degree d. This characteristic was not previously analyzed in the SPA model. However, it was empirically shown that in real-world networks C(d) usually decreases as d^{-a} for some a>0 and it was often observed that a=1. We prove that in the SPA model C(d) decreases as 1/d. Furthermore, we are also able to prove that not only the average but the individual local clustering coefficient of a vertex v of degree d behaves as 1/d if d is large enough. The obtained results are illustrated by numerous experiments with simulated graphs.
Maximum Total Correntropy Diffusion Adaptation over Networks with Noisy Links
Venue: arXiv cs.IT 2018
Year: 2018

Distributed estimation over networks draws much attraction in recent years. In many situations, due to imperfect information communication among nodes, the performance of traditional diffusion adaptive algorithms such as the diffusion LMS (DLMS) may degrade. To deal with this problem, several modified DLMS algorithms have been proposed. However, these DLMS based algorithms still suffer from biased estimation and are not robust to impulsive link noise. In this paper, we focus on improving the performance of diffusion adaptation with noisy links from two aspects: accuracy and robustness. A new algorithm called diffusion maximum total correntropy (DMTC) is proposed. The new algorithm is theoretically unbiased in Gaussian noise, and can efficiently handle the link noises in the presence of large outliers. The adaptive combination rule is applied to further improve the performance. The stability analysis of the proposed algorithm is given. Simulation results show that the DMTC algorithm can achieve good performance in both Gaussian and non-Gaussian noise environments.
Cognitive Deficit of Deep Learning in Numerosity
Venue: arXiv cs.CV 2018
Year: 2018

Subitizing, or the sense of small natural numbers, is an innate cognitive function of humans and primates; it responds to visual stimuli prior to the development of any symbolic skills, language or arithmetic. Given successes of deep learning (DL) in tasks of visual intelligence and given the primitivity of number sense, a tantalizing question is whether DL can comprehend numbers and perform subitizing. But somewhat disappointingly, extensive experiments of the type of cognitive psychology demonstrate that the examples-driven black box DL cannot see through superficial variations in visual representations and distill the abstract notion of natural number, a task that children perform with high accuracy and confidence. The failure is apparently due to the learning method not the CNN computational machinery itself. A recurrent neural network capable of subitizing does exist, which we construct by encoding a mechanism of mathematical morphology into the CNN convolutional kernels. Also, we investigate, using subitizing as a test bed, the ways to aid the black box DL by cognitive priors derived from human insight. Our findings are mixed and interesting, pointing to both cognitive deficit of pure DL, and some measured successes of boosting DL by predetermined cognitive implements. This case study of DL in cognitive computing is meaningful for visual numerosity represents a minimum level of human intelligence.
BachProp: Learning to Compose Music in Multiple Styles
Venue: arXiv cs.SD 2018
Year: 2018

Hand in hand with deep learning advancements, algorithms of music composition increase in performance. However, most of the successful models are designed for specific musical structures. Here, we present BachProp, an algorithmic composer that can generate music scores in any style given sufficient training data. To adapt BachProp to a broad range of musical styles, we propose a novel normalized representation of music and train a deep network to predict the note transition probabilities of a given music corpus. In this paper, new music scores sampled by BachProp are compared with the original corpora via crowdsourcing. This evaluation indicates that the music scores generated by BachProp are not less preferred than the original music corpus the algorithm was provided with.
Sampling Superquadric Point Clouds with Normals
Venue: arXiv cs.CV 2018
Year: 2018

Superquadrics provide a compact representation of common shapes and have been used both for object/surface modelling in computer graphics and as object-part representation in computer vision and robotics. Superquadrics refer to a family of shapes: here we deal with the superellipsoids and superparaboloids. Due to the strong non-linearities involved in the equations, uniform or close-to-uniform sampling is not attainable through a naive approach of direct sampling from the parametric formulation. This is specially true for more `cubic' superquadrics (with shape parameters close to $0.1$). We extend a previous solution of 2D close-to-uniform uniform sampling of superellipses to the superellipsoid (3D) case and derive our own for the superparaboloid. Additionally, we are able to provide normals for each sampled point. To the best of our knowledge, this is the first complete approach for close-to-uniform sampling of superellipsoids and superparaboloids in one single framework. We present derivations, pseudocode and qualitative and quantitative results using our code, which is available online.
Fully Convolutional Network Ensembles for White Matter Hyperintensities Segmentation in MR Images
Venue: arXiv cs.CV 2018
Year: 2018

White matter hyperintensities (WMH) are commonly found in the brains of healthy elderly individuals and have been associated with various neurological and geriatric disorders. In this paper, we present a study using deep fully convolutional network and ensemble models to automatically detect such WMH using fluid attenuation inversion recovery (FLAIR) and T1 magnetic resonance (MR) scans. The algorithm was evaluated and ranked 1 st in the WMH Segmentation Challenge at MICCAI 2017. In the evaluation stage, the implementation of the algorithm was submitted to the challenge organizers, who then independently tested it on a hidden set of 110 cases from 5 scanners. Averaged dice score, precision and robust Hausdorff distance obtained on held-out test datasets were 80%, 84% and 6.30mm respectively. These were the highest achieved in the challenge, suggesting the proposed method is the state-of-the-art. In this paper, we provide detailed descriptions and quantitative analysis on key components of the system. Furthermore, a study of cross-scanner evaluation is presented to discuss how the combination of modalities and data augmentation affect the generalization capability of the system. The adaptability of the system to different scanners and protocols is also investigated. A quantitative study is further presented to test the effect of ensemble size. Additionally, software and models of our method are made publicly available. The effectiveness and generalization capability of the proposed system show its potential for real-world clinical practice.
Differentially Private Empirical Risk Minimization Revisited: Faster and More General
Venue: arXiv cs.LG 2018
Year: 2018

In this paper we study the differentially private Empirical Risk Minimization (ERM) problem in different settings. For smooth (strongly) convex loss function with or without (non)-smooth regularization, we give algorithms that achieve either optimal or near optimal utility bounds with less gradient complexity compared with previous work. For ERM with smooth convex loss function in high-dimensional ($p\gg n$) setting, we give an algorithm which achieves the upper bound with less gradient complexity than previous ones. At last, we generalize the expected excess empirical risk from convex loss functions to non-convex ones satisfying the Polyak-Lojasiewicz condition and give a tighter upper bound on the utility than the one in \cite{ijcai2017-548}.
Influential User Subscription on Time-Decaying Social Streams
Venue: arXiv cs.SI 2018
Year: 2018

Influence maximization which asks for $k$-size seed set from a social network such that maximizing the influence over all other users (called influence spread) has widely attracted attention due to its significant applications in viral marketing and rumor control. In real world scenarios, people are interested in the most influential users in particular topics, and want to subscribe the topics-of-interests over social networks. In this paper, we formulate the problem of influential users subscription on time-decaying social stream, which asks for maintaining the $k$-size influential users sets for each topic-aware subscription queries. We first analyze the widely adopted sliding window model and propose a newly time-decaying influence model to overcome the shortages when calculating the influence over social stream. Developed from sieve based streaming algorithm, we propose an efficient algorithm to support the calculation of time-decaying influence over dynamically updating social networks. Using information among subscriptions, we then construct the Prefix Tree Structure to allow us minimizing the times of calculating influence of each update and easily maintained. Pruning techniques are also applied to the Prefix Tree to optimize the performance of social stream update. Our approach ensures a $\frac{1}{2}-ε$ approximation ratio. Experimental results show that our approach significantly outperforms the baseline approaches in efficiency and result quality.
Sharkzor: Interactive Deep Learning for Image Triage, Sort and Summary
Venue: arXiv cs.HC 2018
Year: 2018

Sharkzor is a web application for machine-learning assisted image sort and summary. Deep learning algorithms are leveraged to infer, augment, and automate the user's mental model. Initially, images uploaded by the user are spread out on a canvas. The user then interacts with the images to impute their mental model into the application's algorithmic underpinnings. Methods of interaction within Sharkzor's user interface and user experience support three primary user tasks; triage, organize and automate. The user triages the large pile of overlapping images by moving images of interest into proximity. The user then organizes said images into meaningful groups. After interacting with the images and groups, deep learning helps to automate the user's interactions. The loop of interaction, automation, and response by the user allows the system to quickly make sense of large amounts of data.
GapFlyt: Active Vision Based Minimalist Structure-less Gap Detection For Quadrotor Flight
Venue: arXiv cs.RO 2018
Year: 2018

Although quadrotors, and aerial robots in general, are inherently active agents, their perceptual capabilities in literature so far have been mostly passive in nature. Researchers and practitioners today use traditional computer vision algorithms with the aim of building a representation of general applicability: a 3D reconstruction of the scene. Using this representation, planning tasks are constructed and accomplished to allow the quadrotor to demonstrate autonomous behavior. These methods are inefficient as they are not task driven and such methodologies are not utilized by flying insects and birds. Such agents have been solving the problem of navigation and complex control for ages without the need to build a 3D map and are highly task driven. In this paper, we propose this framework of bio-inspired perceptual design for quadrotors. We use this philosophy to design a minimalist sensori-motor framework for a quadrotor to fly though unknown gaps without a 3D reconstruction of the scene using only a monocular camera and onboard sensing. We successfully evaluate and demonstrate the proposed approach in many real-world experiments with different settings and window shapes, achieving a success rate of 85% at 2.5ms$^{-1}$ even with a minimum tolerance of just 5cm. To our knowledge, this is the first paper which addresses the problem of gap detection of an unknown shape and location with a monocular camera and onboard sensing.
Model Generation for Quantified Formulas: A Taint-Based Approach
Venue: arXiv cs.LO 2018
Year: 2018

We focus in this paper on generating models of quantified first-order formulas over built-in theories, which is paramount in software verification and bug finding. While standard methods are either geared toward proving the absence of solution or targeted to specific theories, we propose a generic approach based on a reduction to the quantifier-free case. Our technique allows thus to reuse all the efficient machinery developed for that context. Experiments show a substantial improvement over state-of-the-art methods.
Calculating the similarity between words and sentences using a lexical database and corpus statistics
Venue: arXiv cs.CL 2018
Year: 2018

Calculating the semantic similarity between sentences is a long dealt problem in the area of natural language processing. The semantic analysis field has a crucial role to play in the research related to the text analytics. The semantic similarity differs as the domain of operation differs. In this paper, we present a methodology which deals with this issue by incorporating semantic similarity and corpus statistics. To calculate the semantic similarity between words and sentences, the proposed method follows an edge-based approach using a lexical database. The methodology can be applied in a variety of domains. The methodology has been tested on both benchmark standards and mean human similarity dataset. When tested on these two datasets, it gives highest correlation value for both word and sentence similarity outperforming other similar models. For word similarity, we obtained Pearson correlation coefficient of 0.8753 and for sentence similarity, the correlation obtained is 0.8794.