OpenRaiser/Intern-Atlas huggingface dataset:
Semantic Similarity Strategies for Job Title Classification
Venue: arXiv cs.AI 2016
Year: 2016

Automatic and accurate classification of items enables numerous downstream applications in many domains. These applications can range from faceted browsing of items to product recommendations and big data analytics. In the online recruitment domain, we refer to classifying job ads to pre-defined or custom occupation categories as job title classification. A large-scale job title classification system can power various downstream applications such as semantic search, job recommendations and labor market analytics. In this paper, we discuss experiments conducted to improve our in-house job title classification system. The classification component of the system is composed of a two-stage coarse and fine level classifier cascade that classifies input text such as job title and/or job ads to one of the thousands of job titles in our taxonomy. To improve classification accuracy and effectiveness, we experiment with various semantic representation strategies such as average W2V vectors and document similarity measures such as Word Movers Distance (WMD). Our initial results show an overall improvement in accuracy of Carotene[1].
Can NDN Perform Better than OLSR in Wireless Ad Hoc Networks?
Venue: arXiv cs.NI 2016
Year: 2016

The emerging paradigm of Information-Centric Networking is an exciting field of research, opening opportunities in many areas, such as forwarding strategies, caching placement policies, applications (e.g. video streaming and instant messaging), to name a few. In this paper, we address the mobility aspect of ICN, as well as how it performs in tactical wireless ad hoc environments. In this paper, we present results from a simulation study that investigates the performance of Named Data Networking, an instantiation of ICN, in such environments. We perform a series of simulations based on ndnSIM studying different mobility scenarios. Our simulations show that even in the short-term absence of the producer, consumers can still achieve better file retrieval when caches are used. As an effort to increase the cache diversity and have a better utilization of the Content Store we study probabilistic LRU. Furthermore, we compare the performance of our NDN network with a TCP based approach, using OLSR routing protocol, discussing advantages and disadvantages of each approach.
Temporal Map Labeling: A New Unified Framework with Experiments
Venue: arXiv cs.CG 2016
Year: 2016

The increased availability of interactive maps on the Internet and on personal mobile devices has created new challenges in computational cartography and, in particular, for label placement in maps. Operations like rotation, zoom, and translation dynamically change the map over time and make a consistent adaptation of the map labeling necessary. In this paper, we consider map labeling for the case that a map undergoes a sequence of operations over a specified time span. We unify and generalize several preceding models for dynamic map labeling into one versatile and flexible model. In contrast to previous research, we completely abstract from the particular operations (e.g., zoom, rotation, etc.) and express the labeling problem as a set of time intervals representing the labels' presences, activities, and conflicts. The model's strength is manifested in its simplicity and broad range of applications. In particular, it supports label selection both for map features with fixed position as well as for moving entities (e.g., for tracking vehicles in logistics or air traffic control). Through extensive experiments on OpenStreetMap data, we evaluate our model using algorithms of varying complexity as a case study for navigation systems. Our experiments show that even simple (and thus, fast) algorithms achieve near-optimal solutions in our model with respect to an intuitive objective function.
Analyzing Dependent Placements of Small Cells in a Two-Layer Heterogeneous Network with a Rate Coverage Constraint
Venue: arXiv cs.IT 2016
Year: 2016

We consider the downlink of a two-layer heterogeneous network, comprising macro cells (MCs) and small cells (SCs). The existing literature generally assumes independent placements of the access points (APs) in different layers; in contrast, we analyze a dependent placement where SC APs are placed at locations with poor service from the MC layer. Our goal is to obtain an estimate of the number of SCs required to maintain a target outage rate. Such an analysis is trivial if the MCs are located according to a Poisson point process (PPP), which provides a lower bound on performance. Here, we consider MCs placed on a hexagonal grid, which complements the PPP model by providing an upper bound on performance. We first provide accurate bounds for the average interference within a MC when SCs are not used. Then, by obtaining the outage areas, we estimate the number of SCs required within an MC to overcome outage. If resource allocation amongst SCs is not used, we show that the problem of outage is not solved completely, and the residual outage area depends on whether co-channel or orthogonal SCs are used. Simulations show that a much smaller residual outage area is obtained with orthogonal SCs.
Exact rule-based stochastic simulations for systems with unlimited number of molecular species
Venue: arXiv cs.DS 2016
Year: 2016

We introduce expandable partial propensity direct method (EPDM) - a new exact stochastic simulation algorithm suitable for systems involving many interacting molecular species. The algorithm is especially efficient for sparsely populated systems, where the number of species that may potentially be generated is much greater than the number of species actually present in the system at any given time. The number of operations per reaction scales linearly with the number of species, but only those which have one or more molecules. To achieve this kind of performance we are employing a data structure which allows to add and remove species and their interactions on the fly. When a new specie is added, its interactions with every other specie are generated dynamically by a set of user-defined rules. By removing the records involving the species with zero molecules, we keep the number of species as low as possible. This enables simulations of systems for which listing all species is not practical. The algorithm is based on partial propensities direct method (PDM) by Ramaswamy et al. for sampling trajectories of the chemical master equation.
Bibliographic Analysis on Research Publications using Authors, Categorical Labels and the Citation Network
Venue: arXiv cs.DL 2016
Year: 2016

Bibliographic analysis considers the author's research areas, the citation network and the paper content among other things. In this paper, we combine these three in a topic model that produces a bibliographic model of authors, topics and documents, using a nonparametric extension of a combination of the Poisson mixed-topic link model and the author-topic model. This gives rise to the Citation Network Topic Model (CNTM). We propose a novel and efficient inference algorithm for the CNTM to explore subsets of research publications from CiteSeerX. The publication datasets are organised into three corpora, totalling to about 168k publications with about 62k authors. The queried datasets are made available online. In three publicly available corpora in addition to the queried datasets, our proposed model demonstrates an improved performance in both model fitting and document clustering, compared to several baselines. Moreover, our model allows extraction of additional useful knowledge from the corpora, such as the visualisation of the author-topics network. Additionally, we propose a simple method to incorporate supervision into topic modelling to achieve further improvement on the clustering task.
Imbalanced-learn: A Python Toolbox to Tackle the Curse of Imbalanced Datasets in Machine Learning
Venue: arXiv cs.LG 2016
Year: 2016

Imbalanced-learn is an open-source python toolbox aiming at providing a wide range of methods to cope with the problem of imbalanced dataset frequently encountered in machine learning and pattern recognition. The implemented state-of-the-art methods can be categorized into 4 groups: (i) under-sampling, (ii) over-sampling, (iii) combination of over- and under-sampling, and (iv) ensemble learning methods. The proposed toolbox only depends on numpy, scipy, and scikit-learn and is distributed under MIT license. Furthermore, it is fully compatible with scikit-learn and is part of the scikit-learn-contrib supported project. Documentation, unit tests as well as integration tests are provided to ease usage and contribution. The toolbox is publicly available in GitHub: https://github.com/scikit-learn-contrib/imbalanced-learn.
Compressive imaging using fast transform coding
Venue: arXiv cs.IT 2016
Year: 2016

We propose deterministic sampling strategies for compressive imaging based on Delsarte-Goethals frames. We show that these sampling strategies result in multi-scale measurements which can be related to the 2D Haar wavelet transform. We demonstrate the effectiveness of our proposed strategies through numerical experiments.
The Color of the Cat is Gray: 1 Million Full-Sentences Visual Question Answering (FSVQA)
Venue: arXiv cs.CV 2016
Year: 2016

Visual Question Answering (VQA) task has showcased a new stage of interaction between language and vision, two of the most pivotal components of artificial intelligence. However, it has mostly focused on generating short and repetitive answers, mostly single words, which fall short of rich linguistic capabilities of humans. We introduce Full-Sentence Visual Question Answering (FSVQA) dataset, consisting of nearly 1 million pairs of questions and full-sentence answers for images, built by applying a number of rule-based natural language processing techniques to original VQA dataset and captions in the MS COCO dataset. This poses many additional complexities to conventional VQA task, and we provide a baseline for approaching and evaluating the task, on top of which we invite the research community to build further improvements.
Why Johnny Can't Use Stego: a Human-oriented Perspective on the Application of Steganography
Venue: arXiv cs.CR 2016
Year: 2016

Steganography is the discipline that deals with concealing the existence of secret communications. Existing research already provided several fundamentals for defining steganography and presented a multitude of hiding methods and countermeasures for this research discipline. We identified that no work exists that discusses the process of applying steganography from an individual's perspective. This paper presents a phase model that explains pre-conditions of applying steganography as well as the decision-making process and the final termination of a steganographic communication. The model can be used to explain whether an individual can use steganography and to explain whether and why an individual desires to use steganography. Moreover, the model can be used in research publications to indicate the addressed model's phase of scientific contributions. Furthermore, our model can be used to teach the process of steganography-application to students.
Fast and reliable stereopsis measurement at multiple distances with iPad
Venue: arXiv cs.CV 2016
Year: 2016

Purpose: To present a new fast and reliable application for iPad (ST) for screening stereopsis at multiple distances. Methods: A new iPad application (app) based on a random dot stereogram was designed for screening stereopsis at multiple distances. Sixty-five subjects with no ocular diseases and wearing their habitual correction were tested at two different distances: 3 m and at 0.4 m. Results were compared with other commercial tests: TNO (at near) and Howard Dolman (at distance) Subjects were cited one week later in order to repeat the same procedures for assessing reproducibility of the tests. Results: Stereopsis at near was better with ST (40 arcsec) than with TNO (60 arcsec), but not significantly (p = 0.36). The agreement was good (k = 0.604) and the reproducibility was better with ST (k = 0.801) than with TNO (k = 0.715), in fact median difference between days was significant only with TNO (p = 0.02). On the other hand, poor agreement was obtained between HD and ST at far distance (k=0.04), obtaining significant differences in medians (p = 0.001) and poorer reliability with HD (k = 0.374) than with ST (k = 0.502). Conclusions: Screening stereopsis at near with a new iPad app demonstrated to be a fast and realiable. Results were in a good agreement with conventional tests as TNO, but it could not be compared at far vision with HD due to the limited resolution of the iPad.
Detecting Anomalous User Behavior Using an Extended Isolation Forest Algorithm: An Enterprise Case Study
Venue: arXiv cs.CR 2016
Year: 2016

Anomalous user behavior detection is the core component of many information security systems, such as intrusion detection, insider threat detection and authentication systems. Anomalous behavior will raise an alarm to the system administrator and can be further combined with other information to determine whether it constitutes an unauthorised or malicious use of a resource. This paper presents an anomalous user behaviour detection framework that applies an extended version of Isolation Forest algorithm. Our method is fast and scalable and does not require example anomalies in the training data set. We apply our method to an enterprise dataset. The experimental results show that the system is able to isolate anomalous instances from the baseline user model using a single feature or combined features.
PixelNet: Towards a General Pixel-level Architecture
Venue: arXiv cs.CV 2016
Year: 2016

We explore architectures for general pixel-level prediction problems, from low-level edge detection to mid-level surface normal estimation to high-level semantic segmentation. Convolutional predictors, such as the fully-convolutional network (FCN), have achieved remarkable success by exploiting the spatial redundancy of neighboring pixels through convolutional processing. Though computationally efficient, we point out that such approaches are not statistically efficient during learning precisely because spatial redundancy limits the information learned from neighboring pixels. We demonstrate that (1) stratified sampling allows us to add diversity during batch updates and (2) sampled multi-scale features allow us to explore more nonlinear predictors (multiple fully-connected layers followed by ReLU) that improve overall accuracy. Finally, our objective is to show how a architecture can get performance better than (or comparable to) the architectures designed for a particular task. Interestingly, our single architecture produces state-of-the-art results for semantic segmentation on PASCAL-Context, surface normal estimation on NYUDv2 dataset, and edge detection on BSDS without contextual post-processing.
Simulation of the IEEE 1588 Precision Time Protocol in OMNeT++
Venue: arXiv cs.NI 2016
Year: 2016

Real-time systems rely on a distributed global time base. As any physical clock device suffers from noise, it is necessary to provide some kind of clock synchronization to establish such a global time base. Different clock synchronization methods have been invented for individual application domains. The Precision Time Protocol (PTP), which is specified in IEEE 1588, is another interesting option. It targets local networks, where it is acceptable to assume small amounts of hardware support, and promises sub-microsecond precision. PTP provides many different implementation and configuration options, and thus the Design Space Exploration (DSE) is challenging. In this paper we discuss the implementation of realistic clock noise and its synchronization via PTP in OMNeT++. The components presented in this paper are intended to assist engineers with the configuration of PTP networks.
PSVR - Self-stabilizing Publish/Subscribe Communication for Ad-hoc Networks
Venue: arXiv cs.DS 2016
Year: 2016

This paper presents the novel routing algorithm PSVR for pub/sub systems in ad-hoc networks. Its focus is on scenarios where communications links are unstable and nodes frequently change subscriptions. PSVR presents a compromise of size and maintenance effort for routing tables due to sub- and unsubscriptions and the length of routing paths. Designed in a self-stabilizing manner it scales well with network size. The evaluation reveals that PSVR only needs slightly more messages than a close to optimal routing structure for publication delivery, and creates shorter routing paths than an existing self-stabilizing algorithm. A real world deployment shows the usability of the approach
How Useful is Region-based Classification of Remote Sensing Images in a Deep Learning Framework?
Venue: arXiv cs.CV 2016
Year: 2016

In this paper, we investigate the impact of segmentation algorithms as a preprocessing step for classification of remote sensing images in a deep learning framework. Especially, we address the issue of segmenting the image into regions to be classified using pre-trained deep neural networks as feature extractors for an SVM-based classifier. An efficient segmentation as a preprocessing step helps learning by adding a spatially-coherent structure to the data. Therefore, we compare algorithms producing superpixels with more traditional remote sensing segmentation algorithms and measure the variation in terms of classification accuracy. We establish that superpixel algorithms allow for a better classification accuracy as a homogenous and compact segmentation favors better generalization of the training samples.
SafeGuardPF: Safety Guaranteed Reactive Potential Fields for Mobile Robots in Unknown and Dynamic Environments
Venue: arXiv cs.RO 2016
Year: 2016

An autonomous navigation with proven collision avoidance in unknown and dynamic environments is still a challenge, particularly when there are moving obstacles. A popular approach to collision avoidance in the face of moving obstacles is based on model predictive algorithms, which, however, may be computationally expensive. Hence, we adopt a reactive potential field approach here. At every cycle, the proposed approach requires only current robot states relative to the closest obstacle point to find the potential field in the current position; thus, it is more computationally efficient and more suitable to scale up for multiple agent scenarios. Our main contribution here is to write the reactive potential field based motion controller as a hybrid automaton, and then formally verify its safety using differential dynamic logic. In particular, we can guarantee a passive safety property, which means that collisions cannot occur if the robot is to blame, namely a collision can occur only if the robot is at rest. The proposed controller and verification results are demonstrated via simulations and implementation on a Pioneer P3-AT robot.
Is the deconvolution layer the same as a convolutional layer?
Venue: arXiv cs.CV 2016
Year: 2016

In this note, we want to focus on aspects related to two questions most people asked us at CVPR about the network we presented. Firstly, What is the relationship between our proposed layer and the deconvolution layer? And secondly, why are convolutions in low-resolution (LR) space a better choice? These are key questions we tried to answer in the paper, but we were not able to go into as much depth and clarity as we would have liked in the space allowance. To better answer these questions in this note, we first discuss the relationships between the deconvolution layer in the forms of the transposed convolution layer, the sub-pixel convolutional layer and our efficient sub-pixel convolutional layer. We will refer to our efficient sub-pixel convolutional layer as a convolutional layer in LR space to distinguish it from the common sub-pixel convolutional layer. We will then show that for a fixed computational budget and complexity, a network with convolutions exclusively in LR space has more representation power at the same speed than a network that first upsamples the input in high resolution space.
Nash Social Welfare, Matrix Permanent, and Stable Polynomials
Venue: arXiv cs.DS 2016
Year: 2016

We study the problem of allocating $m$ items to $n$ agents subject to maximizing the Nash social welfare (NSW) objective. We write a novel convex programming relaxation for this problem, and we show that a simple randomized rounding algorithm gives a $1/e$ approximation factor of the objective. Our main technical contribution is an extension of Gurvits's lower bound on the coefficient of the square-free monomial of a degree $m$-homogeneous stable polynomial on $m$ variables to all homogeneous polynomials. We use this extension to analyze the expected welfare of the allocation returned by our randomized rounding algorithm.
Measuring the match between evaluators and evaluees: Cognitive distances between panel members and research groups at the journal level
Venue: arXiv cs.DL 2016
Year: 2016

When research groups are evaluated by an expert panel, it is an open question how one can determine the match between panel and research groups. In this paper, we outline two quantitative approaches that determine the cognitive distance between evaluators and evaluees, based on the journals they have published in. We use example data from four research evaluations carried out between 2009 and 2014 at the University of Antwerp. While the barycenter approach is based on a journal map, the similarity-adapted publication vector (SAPV) approach is based on the full journal similarity matrix. Both approaches determine an entity's profile based on the journals in which it has published. Subsequently, we determine the Euclidean distance between the barycenter or SAPV profiles of two entities as an indicator of the cognitive distance between them. Using a bootstrapping approach, we determine confidence intervals for these distances. As such, the present article constitutes a refinement of a previous proposal that operates on the level of Web of Science subject categories.
Multilayer Spectral Graph Clustering via Convex Layer Aggregation
Venue: arXiv cs.LG 2016
Year: 2016

Multilayer graphs are commonly used for representing different relations between entities and handling heterogeneous data processing tasks. New challenges arise in multilayer graph clustering for assigning clusters to a common multilayer node set and for combining information from each layer. This paper presents a theoretical framework for multilayer spectral graph clustering of the nodes via convex layer aggregation. Under a novel multilayer signal plus noise model, we provide a phase transition analysis that establishes the existence of a critical value on the noise level that permits reliable cluster separation. The analysis also specifies analytical upper and lower bounds on the critical value, where the bounds become exact when the clusters have identical sizes. Numerical experiments on synthetic multilayer graphs are conducted to validate the phase transition analysis and study the effect of layer weights and noise levels on clustering reliability.