OpenRaiser/Intern-Atlas huggingface dataset:
Spotting Suspicious Link Behavior with fBox: An Adversarial Perspective
Venue: arXiv cs.LG 2014
Year: 2014

How can we detect suspicious users in large online networks? Online popularity of a user or product (via follows, page-likes, etc.) can be monetized on the premise of higher ad click-through rates or increased sales. Web services and social networks which incentivize popularity thus suffer from a major problem of fake connections from link fraudsters looking to make a quick buck. Typical methods of catching this suspicious behavior use spectral techniques to spot large groups of often blatantly fraudulent (but sometimes honest) users. However, small-scale, stealthy attacks may go unnoticed due to the nature of low-rank eigenanalysis used in practice. In this work, we take an adversarial approach to find and prove claims about the weaknesses of modern, state-of-the-art spectral methods and propose fBox, an algorithm designed to catch small-scale, stealth attacks that slip below the radar. Our algorithm has the following desirable properties: (a) it has theoretical underpinnings, (b) it is shown to be highly effective on real data and (c) it is scalable (linear on the input size). We evaluate fBox on a large, public 41.7 million node, 1.5 billion edge who-follows-whom social graph from Twitter in 2010 and with high precision identify many suspicious accounts which have persisted without suspension even to this day.
A Logic-based Approach to Generatively Defined Discriminative Modeling
Venue: arXiv cs.LG 2014
Year: 2014

Conditional random fields (CRFs) are usually specified by graphical models but in this paper we propose to use probabilistic logic programs and specify them generatively. Our intension is first to provide a unified approach to CRFs for complex modeling through the use of a Turing complete language and second to offer a convenient way of realizing generative-discriminative pairs in machine learning to compare generative and discriminative models and choose the best model. We implemented our approach as the D-PRISM language by modifying PRISM, a logic-based probabilistic modeling language for generative modeling, while exploiting its dynamic programming mechanism for efficient probability computation. We tested D-PRISM with logistic regression, a linear-chain CRF and a CRF-CFG and empirically confirmed their excellent discriminative performance compared to their generative counterparts, i.e.\ naive Bayes, an HMM and a PCFG. We also introduced new CRF models, CRF-BNCs and CRF-LCGs. They are CRF versions of Bayesian network classifiers and probabilistic left-corner grammars respectively and easily implementable in D-PRISM. We empirically showed that they outperform their generative counterparts as expected.
Low-Complexity Hybrid Precoding in Massive Multiuser MIMO Systems
Venue: arXiv cs.IT 2014
Year: 2014

Massive multiple-input multiple-output (MIMO) is envisioned to offer considerable capacity improvement, but at the cost of high complexity of the hardware. In this paper, we propose a low-complexity hybrid precoding scheme to approach the performance of the traditional baseband zero-forcing (ZF) precoding (referred to as full-complexity ZF), which is considered a virtually optimal linear precoding scheme in massive MIMO systems. The proposed hybrid precoding scheme, named phased-ZF (PZF), essentially applies phase-only control at the RF domain and then performs a low-dimensional baseband ZF precoding based on the effective channel seen from baseband. Heavily quantized RF phase control up to $2$ bits of precision is also considered and shown to incur very limited degradation. The proposed scheme is simulated in both ideal Rayleigh fading channels and sparsely scattered millimeter wave (mmWave) channels, both achieving highly desirable performance.
Shape and Color Object Tracking for Real-Time Robotic Navigation
Venue: arXiv cs.CV 2014
Year: 2014

This paper presents a real-time approach for single-colored ball detection and tracking. The approach consists of two main phases. In a first offline calibration phase, the intrinsic parameters of the camera and the radial distortion are estimated, and a classification of colors is learned from a sample image of colored balls. The second phase consists of four main steps: (1) color segmentation of the input image into several regions based on the offline classification, (2) robust estimation of the circle parameters (3) refinement of the circle parameters, and (4) ball tracking. The experimental results showed that the approach presents a good compromise between suitability for real-time navigation and robustness to occlusions, background congestion and colors interference in the scene.
Technical Report: A Methodology for Studying 802.11p VANET Broadcasting Performance with Practical Vehicle Distribution
Venue: arXiv cs.NI 2014
Year: 2014

In a Vehicular Ad-hoc Network (VANET), the performance of the communication protocol is influenced heavily by the vehicular density dynamics. However, most of the previous works on VANET performance modeling paid little attention to vehicle distribution, or simply assumed homogeneous car distribution. It is obvious that vehicles are distributed non-homogeneously along a road segment due to traffic signals and speed limits at different portions of the road, as well as vehicle interactions that are significant on busy streets. In light of the inadequacy, we present in this paper an original methodology to study the broadcasting performance of 802.11p VANETs with practical vehicle distribution in urban environments. Firstly, we adopt the empirically verified stochastic traffic models, which incorporates the effect of urban settings (such as traffic lights and vehicle interactions) on car distribution and generates practical vehicular density profiles. Corresponding 802.11p protocol and performance models are then developed. When coupled with the traffic models, they can predict broadcasting efficiency, delay, as well as throughput performance of 802.11p VANETs based on the knowledge of car density at each location on the road. Extensive simulation is conducted to verify the accuracy of the developed mathematical models with the consideration of vehicle interaction. In general, our results demonstrate the applicability of the proposed methodology on modeling protocol performance in practical signalized road networks, and shed insights into the design and development of future communication protocols and networking functions for VANETs.
Online Tracking of Skin Colour Regions Against a Complex Background
Venue: arXiv cs.CV 2014
Year: 2014

Online tracking of human activity against a complex background is a challenging task for many applications. In this paper, we have developed a robust technique for localizing skin colour regions from unconstrained image frames. A simple and fast segmentation algorithm is used to train a multiplayer perceptron (MLP) for detection of skin colours. Stepper motors are synchronized with the MLP to track the movement of the skin colour regions.
Sturmian words and the Stern sequence
Venue: arXiv cs.DM 2014
Year: 2014

Central, standard, and Christoffel words are three strongly interrelated classes of binary finite words which represent a finite counterpart of characteristic Sturmian words. A natural arithmetization of the theory is obtained by representing central and Christoffel words by irreducible fractions labeling respectively two binary trees, the Raney (or Calkin-Wilf) tree and the Stern-Brocot tree. The sequence of denominators of the fractions in Raney's tree is the famous Stern diatomic numerical sequence. An interpretation of the terms $s(n)$ of Stern's sequence as lengths of Christoffel words when $n$ is odd, and as minimal periods of central words when $n$ is even, allows one to interpret several results on Christoffel and central words in terms of Stern's sequence and, conversely, to obtain a new insight in the combinatorics of Christoffel and central words by using properties of Stern's sequence. One of our main results is a non-commutative version of the "alternating bit sets theorem" by Calkin and Wilf. We also study the length distribution of Christoffel words corresponding to nodes of equal height in the tree, obtaining some interesting bounds and inequalities.
Mobile Agent Systems, Recent Security Threats and Counter Measures
Venue: arXiv cs.CR 2014
Year: 2014

Mobile agent technology offers a dramatically evolving computing paradigm in which a program, in the form of a software agent, can suspend its execution on a host computer, transfers itself to another agent-enabled host on the network, and resumes execution on the new host. It is 1960's since mobile code has been used in the form of remote job entry systems. Today's mobile agents can be characterized in a number of ways ranging from simple distributed objects to highly organized intelligent softwares. As a result of this rapid evolvement of mobile agents, plenty of critical security issues has risen and plenty of work is being done to address these problems. The aim is to provide trusted mobile agent systems that can be easily deployed and widely adopted. In this paper, we provide an overview of the most recent threats facing the designers of agent platforms and the developers of agent-based applications. The paper also identifies security objectives, and measures for countering the identified threats and fulfilling those security objectives.
Learning Distributed Word Representations for Natural Logic Reasoning
Venue: arXiv cs.CL 2014
Year: 2014

Natural logic offers a powerful relational conception of meaning that is a natural counterpart to distributed semantic representations, which have proven valuable in a wide range of sophisticated language tasks. However, it remains an open question whether it is possible to train distributed representations to support the rich, diverse logical reasoning captured by natural logic. We address this question using two neural network-based models for learning embeddings: plain neural networks and neural tensor networks. Our experiments evaluate the models' ability to learn the basic algebra of natural logic relations from simulated data and from the WordNet noun graph. The overall positive results are promising for the future of learned distributed representations in the applied modeling of logical semantics.
Models for the k-metric dimension
Venue: arXiv cs.DS 2014
Year: 2014

For an undirected graph G=(V,E), a vertex x \in V separates vertices u and v (where u,v \in V, u \neq v) if their distances to x are not equal. Given an integer parameter k \geq 1, a set of vertices L \subseteq V is a feasible solution if for every pair of distinct vertices, u,v, there are at least k distinct vertices x_1,x_2,...,x_k \in L each separating u and v. Such a feasible solution is called a "landmark set", and the k-metric dimension of a graph is the minimal cardinality of a landmark set for the parameter k. The case k=1 is a classic problem, where in its weighted version, each vertex v has a non-negative weight, and the goal is to find a landmark set with minimal total weight. We generalize the problem for k \geq 2, introducing two models, and we seek for solutions to both the weighted version and the unweighted version of this more general problem. In the model of all-pairs (AP), k separations are needed for every pair of distinct vertices of V, while in the non-landmarks model (NL), such separations are required only for pairs of distinct vertices in V \setminus L. We study the weighted and unweighted versions for both models (AP and NL), for path graphs, complete graphs, complete bipartite graphs, and complete wheel graphs, for all values of k \geq 2. We present algorithms for these cases, thus demonstrating the difference between the two new models, and the differences between the cases k=1 and k \geq 2.
Implicit segmentation of Kannada characters in offline handwriting recognition using hidden Markov models
Venue: arXiv cs.LG 2014
Year: 2014

We describe a method for classification of handwritten Kannada characters using Hidden Markov Models (HMMs). Kannada script is agglutinative, where simple shapes are concatenated horizontally to form a character. This results in a large number of characters making the task of classification difficult. Character segmentation plays a significant role in reducing the number of classes. Explicit segmentation techniques suffer when overlapping shapes are present, which is common in the case of handwritten text. We use HMMs to take advantage of the agglutinative nature of Kannada script, which allows us to perform implicit segmentation of characters along with recognition. All the experiments are performed on the Chars74k dataset that consists of 657 handwritten characters collected across multiple users. Gradient-based features are extracted from individual characters and are used to train character HMMs. The use of implicit segmentation technique at the character level resulted in an improvement of around 10%. This system also outperformed an existing system tested on the same dataset by around 16%. Analysis based on learning curves showed that increasing the training data could result in better accuracy. Accordingly, we collected additional data and obtained an improvement of 4% with 6 additional samples.
MIMO Broadcasting for Simultaneous Wireless Information and Power Transfer: Weighted MMSE Approaches
Venue: arXiv cs.IT 2014
Year: 2014

We consider simultaneous wireless information and power transfer (SWIPT) in MIMO Broadcast networks where one energy harvesting (EH) user and one information decoding (ID) user share the same time and frequency resource. In contrast to previous SWIPT systems based on the information rate, this paper addresses the problem in terms of the weighted minimum mean squared error (WMMSE) criterion. First, we formulate the WMMSE-SWIPT problem which minimizes the weighted sum-MSE of the message signal arrived at the ID user, while satisfying the requirement on the energy that can be harvested from the signal at the EH user. Then, we propose the optimal precoder structure of the problem and identify the best possible MSE-energy tradeoff region through the alternative update of the linear precoder at the transmitter with the linear receiver at the ID user. From the derived solution, several interesting observations are made compared to the conventional SWIPT designs.
Hybrid Mobility Prediction of 802.11 Infrastructure Nodes by Location Tracking and Data Mining
Venue: arXiv cs.NI 2014
Year: 2014

In an IEEE 802.11 Infrastructure network, as the mobile node is moving from one access point to another, the resource allocation and smooth hand off may be a problem. If some reliable prediction is done on mobile nodes next move, then resources can be allocated optimally as the mobile node moves around. This would increase the performance throughput of wireless network. We plan to investigate on a hybrid mobility prediction scheme that uses location tracking and data mining to predict the future path of the mobile node. We also propose a secure version of the same scheme. Through simulation and analysis, we present the prediction accuracy of our proposal.
On Torsion-Free Semigroups Generated by Invertible Reversible Mealy Automata
Venue: arXiv cs.FL 2014
Year: 2014

This paper addresses the torsion problem for a class of automaton semigroups, defined as semigroups of transformations induced by Mealy automata, aka letter-by-letter transducers with the same input and output alphabet. The torsion problem is undecidable for automaton semigroups in general, but is known to be solvable within the well-studied class of (semi)groups generated by invertible bounded Mealy automata. We focus on the somehow antipodal class of invertible reversible Mealy automata and prove that for a wide subclass the generated semigroup is torsion-free.
Universal compression of Gaussian sources with unknown parameters
Venue: arXiv cs.IT 2014
Year: 2014

For a collection of distributions over a countable support set, the worst case universal compression formulation by Shtarkov attempts to assign a universal distribution over the support set. The formulation aims to ensure that the universal distribution does not underestimate the probability of any element in the support set relative to distributions in the collection. When the alphabet is uncountable and we have a collection $\cal P$ of Lebesgue continuous measures instead, we ask if there is a corresponding universal probability density function (pdf) that does not underestimate the value of the density function at any point in the support relative to pdfs in $\cal P$. Analogous to the worst case redundancy of a collection of distributions over a countable alphabet, we define the \textit{attenuation} of a class to be $A$ when the worst case optimal universal pdf at any point $x$ in the support is always at least the value any pdf in the collection $\cal P$ assigns to $x$ divided by $A$. We analyze the attenuation of the worst optimal universal pdf over length-$n$ samples generated \textit{i.i.d.} from a Gaussian distribution whose mean can be anywhere between $-α/2$ to $α/2$ and variance between $σ_m^2$ and $σ_M^2$. We show that this attenuation is finite, grows with the number of samples as ${\cal O}(n)$, and also specify the attentuation exactly without approximations. When only one parameter is allowed to vary, we show that the attenuation grows as ${\cal O}(\sqrt{n})$, again keeping in line with results from prior literature that fix the order of magnitude as a factor of $\sqrt{n}$ per parameter. In addition, we also specify the attenuation exactly without approximation when only the mean or only the variance is allowed to vary.
Domain-Independent Optimistic Initialization for Reinforcement Learning
Venue: arXiv cs.LG 2014
Year: 2014

In Reinforcement Learning (RL), it is common to use optimistic initialization of value functions to encourage exploration. However, such an approach generally depends on the domain, viz., the scale of the rewards must be known, and the feature representation must have a constant norm. We present a simple approach that performs optimistic initialization with less dependence on the domain.
Human Motion Capture Data Tailored Transform Coding
Venue: arXiv cs.MM 2014
Year: 2014

Human motion capture (mocap) is a widely used technique for digitalizing human movements. With growing usage, compressing mocap data has received increasing attention, since compact data size enables efficient storage and transmission. Our analysis shows that mocap data have some unique characteristics that distinguish themselves from images and videos. Therefore, directly borrowing image or video compression techniques, such as discrete cosine transform, does not work well. In this paper, we propose a novel mocap-tailored transform coding algorithm that takes advantage of these features. Our algorithm segments the input mocap sequences into clips, which are represented in 2D matrices. Then it computes a set of data-dependent orthogonal bases to transform the matrices to frequency domain, in which the transform coefficients have significantly less dependency. Finally, the compression is obtained by entropy coding of the quantized coefficients and the bases. Our method has low computational cost and can be easily extended to compress mocap databases. It also requires neither training nor complicated parameter setting. Experimental results demonstrate that the proposed scheme significantly outperforms state-of-the-art algorithms in terms of compression performance and speed.
mS2GD: Mini-Batch Semi-Stochastic Gradient Descent in the Proximal Setting
Venue: arXiv cs.LG 2014
Year: 2014

We propose a mini-batching scheme for improving the theoretical complexity and practical performance of semi-stochastic gradient descent applied to the problem of minimizing a strongly convex composite function represented as the sum of an average of a large number of smooth convex functions, and simple nonsmooth convex function. Our method first performs a deterministic step (computation of the gradient of the objective function at the starting point), followed by a large number of stochastic steps. The process is repeated a few times with the last iterate becoming the new starting point. The novelty of our method is in introduction of mini-batching into the computation of stochastic steps. In each step, instead of choosing a single function, we sample $b$ functions, compute their gradients, and compute the direction based on this. We analyze the complexity of the method and show that the method benefits from two speedup effects. First, we prove that as long as $b$ is below a certain threshold, we can reach predefined accuracy with less overall work than without mini-batching. Second, our mini-batching scheme admits a simple parallel implementation, and hence is suitable for further acceleration by parallelization.
On Succinct Representations of Binary Trees
Venue: arXiv cs.DS 2014
Year: 2014

We observe that a standard transformation between \emph{ordinal} trees (arbitrary rooted trees with ordered children) and binary trees leads to interesting succinct binary tree representations. There are four symmetric versions of these transformations. Via these transformations we get four succinct representations of $n$-node binary trees that use $2n + n/(\log n)^{O(1)}$ bits and support (among other operations) navigation, inorder numbering, one of pre- or post-order numbering, subtree size and lowest common ancestor (LCA) queries. The ability to support inorder numbering is crucial for the well-known range-minimum query (RMQ) problem on an array $A$ of $n$ ordered values. While this functionality, and more, is also supported in $O(1)$ time using $2n + o(n)$ bits by Davoodi et al.'s (\emph{Phil. Trans. Royal Soc. A} \textbf{372} (2014)) extension of a representation by Farzan and Munro (\emph{Algorithmica} \textbf{6} (2014)), their \emph{redundancy}, or the $o(n)$ term, is much larger, and their approach may not be suitable for practical implementations. One of these transformations is related to the Zaks' sequence (S.~Zaks, \emph{Theor. Comput. Sci.} \textbf{10} (1980)) for encoding binary trees, and we thus provide the first succinct binary tree representation based on Zaks' sequence. Another of these transformations is equivalent to Fischer and Heun's (\emph{SIAM J. Comput.} \textbf{40} (2011)) \minheap\ structure for this problem. Yet another variant allows an encoding of the Cartesian tree of $A$ to be constructed from $A$ using only $O(\sqrt{n} \log n)$ bits of working space.
Pirus: A Web-based File Hosting Service with Object Oriented Logic in Cloud Computing
Venue: arXiv cs.SE 2014
Year: 2014

In this paper a new Web-based File Hosting Service with Object Oriented Logic in Cloud Computing called Pirus was developed. The service will be used by the academic community of the University of Piraeus giving users the ability to remotely store and access their personal files with no security compromises. It also offers the administrators the ability to manage users and roles. The objective was to deliver a fully operational service, using state-of-the-art programming techniques to enable scalability and future development of the existing functionality. The use of technologies such as .NET Framework, C# programming language, CSS and jQuery, MSSQL for database hosting and the support of Virtualization and Cloud Computing will contribute significantly in compatibility, code reuse, reliability and reduce of maintenance costs and resources. The service was installed and tested in a controlled environment to ascertain the required functionality and the offered reliability and safety with complete success. The technologies used and supported, allow future work in upgrading and extending the service. Changes and improvements, in hardware and software, in order to convert the service to a SaaS (Software as a Service) Cloud application is a logical step in order to efficiently offer the service to a wider community. Improved and added functionality offered by further development will leverage the user experience.