OpenRaiser/Intern-Atlas huggingface dataset:
Simulation and Analysis of Container Freight Train Operations at Port Botany
Venue: arXiv cs.OH 2015
Year: 2015

Over two million containers crossed the docks at Sydney's Port Botany in 2011/12; a figure that is forecast increase more than threefold by the end of the next decade. To cope with such large growth in volumes the NSW Government plans to double rail mode share at the port by the year 2020. Conventional wisdom from industry and the media says that existing infrastructure cannot handle such volumes. In this paper we use a combination of data analytics and simulation to examine operations at the port and evaluate the efficacy of current infrastructure to handle projected growth in volumes. Contrary to conventional wisdom we find that current rail resources appear distinctly under-utilised. Moreover: (i) the peak rail capacity of Port Botany is 1.78 million TEU per annum; over six times higher than 2011/12 rail volumes; (ii) there are no infrastructural impediments to the achievement of peak rail capacity; (iii) operational changes, not infrastructural investment, are the key to unlocking the potential of the port; (iv) Port Botany is well positioned to handle projected increases in container volumes over the next decade and beyond, including the 28% rail mode share target established by the New South Wales State Government.
Improving Human Activity Recognition Through Ranking and Re-ranking
Venue: arXiv cs.CV 2015
Year: 2015

We propose two well-motivated ranking-based methods to enhance the performance of current state-of-the-art human activity recognition systems. First, as an improvement over the classic power normalization method, we propose a parameter-free ranking technique called rank normalization (RaN). RaN normalizes each dimension of the video features to address the sparse and bursty distribution problems of Fisher Vectors and VLAD. Second, inspired by curriculum learning, we introduce a training-free re-ranking technique called multi-class iterative re-ranking (MIR). MIR captures relationships among action classes by separating easy and typical videos from difficult ones and re-ranking the prediction scores of classifiers accordingly. We demonstrate that our methods significantly improve the performance of state-of-the-art motion features on six real-world datasets.
Decomposition by tree dimension in Horn clause verification
Venue: arXiv cs.LO 2015
Year: 2015

In this paper we investigate the use of the concept of tree dimension in Horn clause analysis and verification. The dimension of a tree is a measure of its non-linearity - for example a list of any length has dimension zero while a complete binary tree has dimension equal to its height. We apply this concept to trees corresponding to Horn clause derivations. A given set of Horn clauses P can be transformed into a new set of clauses P=<k, whose derivation trees are the subset of P's derivation trees with dimension at most k. Similarly, a set of clauses P>k can be obtained from P whose derivation trees have dimension at least k + 1. In order to prove some property of all derivations of P, we systematically apply these transformations, for various values of k, to decompose the proof into separate proofs for P=<k and P>k (which could be executed in parallel). We show some preliminary results indicating that decomposition by tree dimension is a potentially useful proof technique. We also investigate the use of existing automatic proof tools to prove some interesting properties about dimension(s) of feasible derivation trees of a given program.
A Hidden Markov Model Based System for Entity Extraction from Social Media English Text at FIRE 2015
Venue: arXiv cs.CL 2015
Year: 2015

This paper presents the experiments carried out by us at Jadavpur University as part of the participation in FIRE 2015 task: Entity Extraction from Social Media Text - Indian Languages (ESM-IL). The tool that we have developed for the task is based on Trigram Hidden Markov Model that utilizes information like gazetteer list, POS tag and some other word level features to enhance the observation probabilities of the known tokens as well as unknown tokens. We submitted runs for English only. A statistical HMM (Hidden Markov Models) based model has been used to implement our system. The system has been trained and tested on the datasets released for FIRE 2015 task: Entity Extraction from Social Media Text - Indian Languages (ESM-IL). Our system is the best performer for English language and it obtains precision, recall and F-measures of 61.96, 39.46 and 48.21 respectively.
Stack Exchange Tagger
Venue: arXiv cs.CL 2015
Year: 2015

The goal of our project is to develop an accurate tagger for questions posted on Stack Exchange. Our problem is an instance of the more general problem of developing accurate classifiers for large scale text datasets. We are tackling the multilabel classification problem where each item (in this case, question) can belong to multiple classes (in this case, tags). We are predicting the tags (or keywords) for a particular Stack Exchange post given only the question text and the title of the post. In the process, we compare the performance of Support Vector Classification (SVC) for different kernel functions, loss function, etc. We found linear SVC with Crammer Singer technique produces best results.
Attitudes towards Refugees in Light of the Paris Attacks
Venue: arXiv cs.SI 2015
Year: 2015

The Paris attacks prompted a massive response on social media including Twitter. This paper explores the immediate response of English speakers on Twitter towards Middle Eastern refugees in Europe. We show that antagonism towards refugees is mostly coming from the United States and is mostly partisan.
'Context, Content, Process' Approach to Align Information Security Investments with Overall Organizational Strategy
Venue: arXiv cs.CY 2015
Year: 2015

Today business environment is highly dependent on complex technologies, and information is considered an important asset. Organizations are therefore required to protect their information infrastructure and follow an inclusive risk management approach. One way to achieve this is by aligning the information security investment decisions with respect to organizational strategy. A large number of information security investment models have are in the literature. These models are useful for optimal and cost-effective investments in information security. However, it is extremely challenging for a decision maker to select one or combination of several models to decide on investments in information security controls. We propose a framework to simplify the task of selecting information security investment model(s). The proposed framework follows the 'Context, Content, Process' approach, and this approach is useful in evaluation and prioritization of investments in information security controls in alignment with the overall organizational strategy.
Limits on the Capacity of In-Band Full Duplex Communication in Uplink Cellular Networks
Venue: arXiv cs.NI 2015
Year: 2015

Simultaneous co-channel transmission and reception, denoted as in-band full duplex (FD) communication, has been promoted as an attractive solution to improve the spectral efficiency of cellular networks. However, in addition to the self-interference problem, cross-mode interference (i.e., between uplink and downlink) imposes a major obstacle for the deployment of FD communication in cellular networks. More specifically, the downlink to uplink interference represents the performance bottleneck for FD operation due to the uplink limited transmission power and venerable operation when compared to the downlink counterpart. While the positive impact of FD communication to the downlink performance has been proved in the literature, its effect on the uplink transmission has been neglected. This paper focuses on the effect of downlink interference on the uplink transmission in FD cellular networks in order to see whether FD communication is beneficial for the uplink transmission or not, and if yes for which type of network. To quantify the expected performance gains, we derive a closed form expression of the maximum achievable uplink capacity in FD cellular networks. In contrast to the downlink capacity which always improves with FD communication, our results show that the uplink performance may improve or degrade depending on the associated network parameters. Particularly, we show that the intensity of base stations (BSs) has a more prominent effect on the uplink performance than their transmission power.
Sampling and Reconstruction of Shapes with Algebraic Boundaries
Venue: arXiv cs.CG 2015
Year: 2015

We present a sampling theory for a class of binary images with finite rate of innovation (FRI). Every image in our model is the restriction of $\mathds{1}_{\{p\leq0\}}$ to the image plane, where $\mathds{1}$ denotes the indicator function and $p$ is some real bivariate polynomial. This particularly means that the boundaries in the image form a subset of an algebraic curve with the implicit polynomial $p$. We show that the image parameters --i.e., the polynomial coefficients-- satisfy a set of linear annihilation equations with the coefficients being the image moments. The inherent sensitivity of the moments to noise makes the reconstruction process numerically unstable and narrows the choice of the sampling kernels to polynomial reproducing kernels. As a remedy to these problems, we replace conventional moments with more stable \emph{generalized moments} that are adjusted to the given sampling kernel. The benefits are threefold: (1) it relaxes the requirements on the sampling kernels, (2) produces annihilation equations that are robust at numerical precision, and (3) extends the results to images with unbounded boundaries. We further reduce the sensitivity of the reconstruction process to noise by taking into account the sign of the polynomial at certain points, and sequentially enforcing measurement consistency. We consider various numerical experiments to demonstrate the performance of our algorithm in reconstructing binary images, including low to moderate noise levels and a range of realistic sampling kernels.
Instance-aware Semantic Segmentation via Multi-task Network Cascades
Venue: arXiv cs.CV 2015
Year: 2015

Semantic segmentation research has recently witnessed rapid progress, but many leading methods are unable to identify object instances. In this paper, we present Multi-task Network Cascades for instance-aware semantic segmentation. Our model consists of three networks, respectively differentiating instances, estimating masks, and categorizing objects. These networks form a cascaded structure, and are designed to share their convolutional features. We develop an algorithm for the nontrivial end-to-end training of this causal, cascaded structure. Our solution is a clean, single-step training framework and can be generalized to cascades that have more stages. We demonstrate state-of-the-art instance-aware semantic segmentation accuracy on PASCAL VOC. Meanwhile, our method takes only 360ms testing an image using VGG-16, which is two orders of magnitude faster than previous systems for this challenging problem. As a by product, our method also achieves compelling object detection results which surpass the competitive Fast/Faster R-CNN systems. The method described in this paper is the foundation of our submissions to the MS COCO 2015 segmentation competition, where we won the 1st place.
On non-iterative training of a neural classifier
Venue: arXiv cs.CV 2015
Year: 2015

Recently an algorithm, was discovered, which separates points in n-dimension by planes in such a manner that no two points are left un-separated by at least one plane{[}1-3{]}. By using this new algorithm we show that there are two ways of classification by a neural network, for a large dimension feature space, both of which are non-iterative and deterministic. To demonstrate the power of both these methods we apply them exhaustively to the classical pattern recognition problem: The Fisher-Anderson's, IRIS flower data set and present the results. It is expected these methods will now be widely used for the training of neural networks for Deep Learning not only because of their non-iterative and deterministic nature but also because of their efficiency and speed and will supersede other classification methods which are iterative in nature and rely on error minimization.
Algorithmic statistics: normal objects and universal models
Venue: arXiv cs.IT 2015
Year: 2015

Kolmogorov suggested to measure quality of a statistical hypothesis $P$ for a data $x$ by two parameters: Kolmogorov complexity $C(P)$ of the hypothesis and the probability $P(x)$ of $x$ with respect to $P$. P. Gács, J. Tromp, P.M.B. Vitányi discovered a small class of models that are universal in the following sense. Each hypothesis $S_{ij}$ from that class is identified by two integer parameters $i,j$ and for every data $x$ and for each complexity level $α$ there is a hypothesis $S_{ij}$ with $j\le i\le l(x)$ of complexity at most $α$ that has almost the best fit among all hypotheses of complexity at most $α$. The hypothesis $S_{ij}$ is identified by $i$ and the leading $i-j$ bits of the binary representation of the number of strings of complexity at most $i$. On the other hand, the initial data $x$ might be completely irrelevant to the the number of strings of complexity at most $i$. Thus $S_{ij}$ seems to have some information irrelevant to the data, which undermines Kolmogorov's approach: the best hypotheses should not have irrelevant information. To restrict the class of hypotheses for a data $x$ to those that have only relevant information, Vereshchagin introduced a notion of a strong model for $x$: those are models for $x$ whose total conditional complexity conditional to $x$ is negligible. An object $x$ is called normal if for each complexity level $α$ at least one its best fitting model of that complexity is strong. In this paper we show that there are "many types" of normal strings. Our second result states that there is a normal object $x$ such that all its best fitting models $S_{ij}$ are not strong for $x$. Our last result states that every best fit strong model for a normal object is again a normal object.
Linear Models of Computation and Program Learning
Venue: arXiv cs.LO 2015
Year: 2015

We consider two classes of computations which admit taking linear combinations of execution runs: probabilistic sampling and generalized animation. We argue that the task of program learning should be more tractable for these architectures than for conventional deterministic programs. We look at the recent advances in the "sampling the samplers" paradigm in higher-order probabilistic programming. We also discuss connections between partial inconsistency, non-monotonic inference, and vector semantics.
Admissible colourings of 3-manifold triangulations for Turaev-Viro type invariants
Venue: arXiv cs.CG 2015
Year: 2015

Turaev Viro invariants are amongst the most powerful tools to distinguish 3-manifolds: They are implemented in mathematical software, and allow practical computations. The invariants can be computed purely combinatorially by enumerating colourings on the edges of a triangulation T. These edge colourings can be interpreted as embeddings of surfaces in T. We give a characterisation of how these embedded surfaces intersect with the tetrahedra of T. This is done by characterising isotopy classes of simple closed loops in the 3-punctured disk. As a direct result we obtain a new system of coordinates for edge colourings which allows for simpler definitions of the tetrahedron weights incorporated in the Turaev-Viro invariants. Moreover, building on a detailed analysis of the colourings, as well as classical work due to Kirby and Melvin, Matveev, and others, we show that considering a much smaller set of colourings suffices to compute Turaev-Viro invariants in certain significant cases. This results in a substantial improvement of running times to compute the invariants, reducing the number of colourings to consider by a factor of $2^n$. In addition, we present an algorithm to compute Turaev-Viro invariants of degree four -- a problem known to be #P-hard -- which capitalises on the combinatorial structure of the input. The improved algorithms are shown to be optimal in the following sense: There exist triangulations admitting all colourings the algorithms consider. Furthermore, we demonstrate that our new algorithms to compute Turaev-Viro invariants are able to distinguish the majority of $\mathbb{Z}$-homology spheres with complexity up to $11$ in $O(2^n)$ operations in $\mathbb{Q}$.
Predicting links in ego-networks using temporal information
Venue: arXiv cs.SI 2015
Year: 2015

Link prediction appears as a central problem of network science, as it calls for unfolding the mechanisms that govern the micro-dynamics of the network. In this work, we are interested in ego-networks, that is the mere information of interactions of a node to its neighbors, in the context of social relationships. As the structural information is very poor, we rely on another source of information to predict links among egos' neighbors: the timing of interactions. We define several features to capture different kinds of temporal information and apply machine learning methods to combine these various features and improve the quality of the prediction. We demonstrate the efficiency of this temporal approach on a cellphone interaction dataset, pointing out features which prove themselves to perform well in this context, in particular the temporal profile of interactions and elapsed time between contacts.
Hamming distances from a function to all codewords of a Generalized Reed-Muller code of order one
Venue: arXiv cs.IT 2015
Year: 2015

For any finite field ${\mathbb F}_q$ with $q$ elements, we study the set ${\mathcal F}_{(q,m)}$ of functions from ${\mathbb F}_q^m$ into ${\mathbb F}^q$. We introduce a transformation that allows us to determine a linear system of $q^{m+1}$ equations and $q^{m+1}$ unknowns, which has for solution the Hamming distances of a function in ${\mathcal F}_{(q,m)}$ to all the affine functions.
On the Total Number of Bends for Planar Octilinear Drawings
Venue: arXiv cs.CG 2015
Year: 2015

An octilinear drawing of a planar graph is one in which each edge is drawn as a sequence of horizontal, vertical and diagonal at 45 degrees line-segments. For such drawings to be readable, special care is needed in order to keep the number of bends small. As the problem of finding planar octilinear drawings of minimum number of bends is NP-hard, in this paper we focus on upper and lower bounds. From a recent result of Keszegh et al. on the slope number of planar graphs, we can derive an upper bound of 4n-10 bends for 8-planar graphs with n vertices. We considerably improve this general bound and corresponding previous ones for triconnected 4-, 5- and 6-planar graphs. We also derive non-trivial lower bounds for these three classes of graphs by a technique inspired by the network flow formulation of Tamassia.
Portrait of an Online Shopper: Understanding and Predicting Consumer Behavior
Venue: arXiv cs.SI 2015
Year: 2015

Consumer spending accounts for a large fraction of the US economic activity. Increasingly, consumer activity is moving to the web, where digital traces of shopping and purchases provide valuable data about consumer behavior. We analyze these data extracted from emails and combine them with demographic information to characterize, model, and predict consumer behavior. Breaking down purchasing by age and gender, we find that the amount of money spent on online purchases grows sharply with age, peaking in late 30s. Men are more frequent online purchasers and spend more money when compared to women. Linking online shopping to income, we find that shoppers from more affluent areas purchase more expensive items and buy them more frequently, resulting in significantly more money spent on online purchases. We also look at dynamics of purchasing behavior and observe daily and weekly cycles in purchasing behavior, similarly to other online activities. More specifically, we observe temporal patterns in purchasing behavior suggesting shoppers have finite budgets: the more expensive an item, the longer the shopper waits since the last purchase to buy it. We also observe that shoppers who email each other purchase more similar items than socially unconnected shoppers, and this effect is particularly evident among women. Finally, we build a model to predict when shoppers will make a purchase and how much will spend on it. We find that temporal features improve prediction accuracy over competitive baselines. A better understanding of consumer behavior can help improve marketing efforts and make online shopping more pleasant and efficient.
Globally Optimal Joint Uplink Base Station Association and Beamforming
Venue: arXiv cs.IT 2015
Year: 2015

The joint base station (BS) association and beamforming problem has been studied extensively in recent years, yet the computational complexity for even the simplest SISO case has not been fully characterized. In this paper, we consider the problems for an uplink SISO/SIMO cellular network under the max-min fairness criterion. We first prove that the problems for both the SISO and SIMO scenarios are polynomial time solvable. Secondly, we present a fixed point based binary search (BS-FP) algorithm for both SISO and SIMO scenarios whereby a QoS (Quality of Service) constrained subproblem is solved at each step by a fixed point method. Thirdly, we propose a normalized fixed point (NFP) iterative algorithm to directly solve the original problem and prove its geometric convergence to global optima. Although it is not known whether the NFP algorithm is a polynomial time algorithm, empirically it converges to the global optima orders of magnitude faster than the polynomial time algorithms, making it suitable for applications in huge-scale networks.
Horn Linear Logic and Minsky Machines
Venue: arXiv cs.LO 2015
Year: 2015

Here we give a detailed proof for the crucial point in our Minsky machine simulation - that any linear logic derivation for a specific Horn sequent can be transformed into a Minsky computation leading from an initial configuration to the halting configuration. Among other things, the presentation advantage of the 3-step program is that the non-trivial tricky points are distributed between the independent parts each of which we justify following its own intrinsic methodology (to say nothing of the induction used in the opposite directions): (1) From LL to HLL - we use purely proof-theoretic arguments. (2) From HLL to Horn programs - we translate trees (HLL derivations) into another trees (Horn programs)of the same shape, almost. (3) From Horn programs to Minsky computations - we use purely computational arguments.
Analysis of the temporal and structural features of threads in a mailing-list
Venue: arXiv cs.SI 2015
Year: 2015

A link stream is a collection of triplets $(t,u,v)$ indicating that an interaction occurred between $u$ and $v$ at time $t$. Link streams model many real-world situations like email exchanges between individuals, connections between devices, and others. Much work is currently devoted to the generalization of classical graph and network concepts to link streams. In this paper, we generalize the existing notions of intra-community density and inter-community density. We focus on emails exchanges in the Debian mailing-list, and show that threads of emails, like communities in graphs, are dense subsets loosely connected from a link stream perspective.