OpenRaiser/Intern-Atlas huggingface dataset:
Downlink Resource Allocation for the High-speed Train and Local Users in OFDMA Systems
Venue: arXiv cs.IT 2014
Year: 2014

We consider providing services for passengers in a high-speed train and local users (quasi-static users) in a single OFDMA system. For the train, we apply a two-hop architecture, under which, passengers communicate with base stations (BSs) via a mobile relay (MR) installed in the train cabin. With this architecture, all passengers in the train can be represented by the MR. Since the channels of the MR and local users vary differently, we consider allocating system resources (power and subcarriers) over two time-scales for them. We formulate the problem as a capacity optimization problem for the MR subject to the sum capacity constraint of local users. We treat the inter-carrier interference (ICI) at the MR as additive Gaussian noise and derive an explicit expression for the ICI using the two-path Doppler spread model. Then we discuss the optimization problem and propose an optimal power and subcarrier allocation (OPSA) policy. The capacity obtained using OPSA is compared with that of constant power and subcarrier allocation (CPSA) policies. Simulation results justify the optimality of the OPSA. Besides, by comparing the capacity bounds achieved by OPSA with and without ICI, we find that only in specific regions, where the gap between the capacity bounds is large, do practical ICI cancellation methods provide meaningful rate gain.
Time-Switching Based SWIPT for Network-Coded Two-Way Relay Transmission with Data Rate Fairness
Venue: arXiv cs.IT 2014
Year: 2014

This paper investigates the simultaneous wireless power and information transfer (SWPIT) for network-coded two-way re- lay transmission from an information theoretical viewpoint, where two sources exchange information via an energy har- vesting relay. By considering the time switching (TS) relay receiver architecture, we present the TS-based two-way re- laying (TS-TWR) protocol. In order to explore the system throughput limit with data rate fairness, we formulate an op- timization problem under total power constraint. To solve the problem, we first derive some explicit results and then de- sign an efficient algorithm. Numerical results show that with the same total available power, TS-TWR has a certain per- formance loss compared with conventional non-EH two-way relaying due to the path loss effect on energy transfer, where in relatively low and relatively high SNR regimes, the perfor- mance losses are relatively small.
Students Behavioural Analysis in an Online Learning Environment Using Data Mining (ICIAfS)
Venue: arXiv cs.CY 2014
Year: 2014

The focus of this research was to use Educational Data Mining (EDM) techniques to conduct a quantitative analysis of students interaction with an e-learning system through instructor-led non-graded and graded courses. This exercise is useful for establishing a guideline for a series of online short courses for them. A group of 412 students' access behaviour in an e-learning system were analysed and they were grouped into clusters using K-Means clustering method according to their course access log records. The results explained that more than 40% from the student group are passive online learners in both graded and non-graded learning environments. The result showed that the difference in the learning environments could change the online access behaviour of a student group. Clustering divided the student population into five access groups based on their course access behaviour. Among these groups, the least access group (NG-41% and G-42%) and the highest access group (NG-9% and G-5%) could be identified very clearly due to their access variation from the rest of the groups.
Carbon Nanotube Based Delay Model For High Speed Energy Efficient on Chip Data Transmission Using: Current Mode Technique
Venue: arXiv cs.ET 2014
Year: 2014

Speed is a major concern for high density VLSI networks. In this paper the closed form delay model for current mode signalling in VLSI interconnects has been proposed with resistive load termination. RLC interconnect line is modelled using characteristic impedance of transmission line and inductive effect. The inductive effect is dominant at lower technology node is modelled into an equivalent resistance. In this model first order transfer function is designed using finite difference equation, and by applying the boundary conditions at the source and load termination. It has been observed that the dominant pole determines system response and delay in the proposed model. Using CNIA tool (carbon nanotube interconnect analyzer) the interconnect line parameters has been estimated at 45nm technology node. The novel proposed current mode model superiority has been validated for CNT type of material. It superiority factor remains to 66.66% as compared to voltage mode signalling. And current mode dissipates 0.015pJ energy where as VM consume 0.045pJ for a single bit transmission across the interconnect over CNT material. Secondly the damping factor of a lumped RLC circuit is shown to be a useful figure of merit.
Polyphonic Music Generation by Modeling Temporal Dependencies Using a RNN-DBN
Venue: arXiv cs.LG 2014
Year: 2014

In this paper, we propose a generic technique to model temporal dependencies and sequences using a combination of a recurrent neural network and a Deep Belief Network. Our technique, RNN-DBN, is an amalgamation of the memory state of the RNN that allows it to provide temporal information and a multi-layer DBN that helps in high level representation of the data. This makes RNN-DBNs ideal for sequence generation. Further, the use of a DBN in conjunction with the RNN makes this model capable of significantly more complex data representation than an RBM. We apply this technique to the task of polyphonic music generation.
A Novel Feature Selection and Extraction Technique for Classification
Venue: arXiv cs.LG 2014
Year: 2014

This paper presents a versatile technique for the purpose of feature selection and extraction - Class Dependent Features (CDFs). We use CDFs to improve the accuracy of classification and at the same time control computational expense by tackling the curse of dimensionality. In order to demonstrate the generality of this technique, it is applied to handwritten digit recognition and text categorization.
Reasoning for Improved Sensor Data Interpretation in a Smart Home
Venue: arXiv cs.AI 2014
Year: 2014

In this paper an ontological representation and reasoning paradigm has been proposed for interpretation of time-series signals. The signals come from sensors observing a smart environment. The signal chosen for the annotation process is a set of unintuitive and complex gas sensor data. The ontology of this paradigm is inspired form the SSN ontology (Semantic Sensor Network) and used for representation of both the sensor data and the contextual information. The interpretation process is mainly done by an incremental ASP solver which as input receives a logic program that is generated from the contents of the ontology. The contextual information together with high level domain knowledge given in the ontology are used to infer explanations (answer sets) for changes in the ambient air detected by the gas sensors.
On the Lattice Smoothing Parameter Problem
Venue: arXiv cs.CC 2014
Year: 2014

The smoothing parameter $η_ε(\mathcal{L})$ of a Euclidean lattice $\mathcal{L}$, introduced by Micciancio and Regev (FOCS'04; SICOMP'07), is (informally) the smallest amount of Gaussian noise that "smooths out" the discrete structure of $\mathcal{L}$ (up to error $ε$). It plays a central role in the best known worst-case/average-case reductions for lattice problems, a wealth of lattice-based cryptographic constructions, and (implicitly) the tightest known transference theorems for fundamental lattice quantities. In this work we initiate a study of the complexity of approximating the smoothing parameter to within a factor $γ$, denoted $γ$-${\rm GapSPP}$. We show that (for $ε= 1/{\rm poly}(n)$): $(2+o(1))$-${\rm GapSPP} \in {\rm AM}$, via a Gaussian analogue of the classic Goldreich-Goldwasser protocol (STOC'98); $(1+o(1))$-${\rm GapSPP} \in {\rm coAM}$, via a careful application of the Goldwasser-Sipser (STOC'86) set size lower bound protocol to thin spherical shells; $(2+o(1))$-${\rm GapSPP} \in {\rm SZK} \subseteq {\rm AM} \cap {\rm coAM}$ (where ${\rm SZK}$ is the class of problems having statistical zero-knowledge proofs), by constructing a suitable instance-dependent commitment scheme (for a slightly worse $o(1)$-term); $(1+o(1))$-${\rm GapSPP}$ can be solved in deterministic $2^{O(n)} {\rm polylog}(1/ε)$ time and $2^{O(n)}$ space. As an application, we demonstrate a tighter worst-case to average-case reduction for basing cryptography on the worst-case hardness of the ${\rm GapSPP}$ problem, with $\tilde{O}(\sqrt{n})$ smaller approximation factor than the ${\rm GapSVP}$ problem. Central to our results are two novel, and nearly tight, characterizations of the magnitude of discrete Gaussian sums.
Construction of Vietnamese SentiWordNet by using Vietnamese Dictionary
Venue: arXiv cs.CL 2014
Year: 2014

SentiWordNet is an important lexical resource supporting sentiment analysis in opinion mining applications. In this paper, we propose a novel approach to construct a Vietnamese SentiWordNet (VSWN). SentiWordNet is typically generated from WordNet in which each synset has numerical scores to indicate its opinion polarities. Many previous studies obtained these scores by applying a machine learning method to WordNet. However, Vietnamese WordNet is not available unfortunately by the time of this paper. Therefore, we propose a method to construct VSWN from a Vietnamese dictionary, not from WordNet. We show the effectiveness of the proposed method by generating a VSWN with 39,561 synsets automatically. The method is experimentally tested with 266 synsets with aspect of positivity and negativity. It attains a competitive result compared with English SentiWordNet that is 0.066 and 0.052 differences for positivity and negativity sets respectively.
Multiple alignment of structures using center of proteins
Venue: arXiv cs.CE 2014
Year: 2014

In this paper we report on an algorithm for aligning multiple protein structures. The algorithm has been tested on a variety of inputs and it performs well in comparison to well-known algorithms for this problem.
Rigid and Non-rigid Shape Evolutions for Shape Alignment and Recovery in Images
Venue: arXiv cs.CV 2014
Year: 2014

The same type of objects in different images may vary in their shapes because of rigid and non-rigid shape deformations, occluding foreground as well as cluttered background. The problem concerned in this work is the shape extraction in such challenging situations. We approach the shape extraction through shape alignment and recovery. This paper presents a novel and general method for shape alignment and recovery by using one example shapes based on deterministic energy minimization. Our idea is to use general model of shape deformation in minimizing active contour energies. Given \emph{a priori} form of the shape deformation, we show how the curve evolution equation corresponding to the shape deformation can be derived. The curve evolution is called the prior variation shape evolution (PVSE). We also derive the energy-minimizing PVSE for minimizing active contour energies. For shape recovery, we propose to use the PVSE that deforms the shape while preserving its shape characteristics. For choosing such shape-preserving PVSE, a theory of shape preservability of the PVSE is established. Experimental results validate the theory and the formulations, and they demonstrate the effectiveness of our method.
Online Packing and Covering Framework with Convex Objectives
Venue: arXiv cs.DS 2014
Year: 2014

We consider online fractional covering problems with a convex objective, where the covering constraints arrive over time. Formally, we want to solve $\min\,\{f(x) \mid Ax\ge \mathbf{1},\, x\ge 0\},$ where the objective function $f:\mathbb{R}^n\rightarrow \mathbb{R}$ is convex, and the constraint matrix $A_{m\times n}$ is non-negative. The rows of $A$ arrive online over time, and we wish to maintain a feasible solution $x$ at all times while only increasing coordinates of $x$. We also consider "dual" packing problems of the form $\max\,\{c^\intercal y - g(μ) \mid A^\intercal y \le μ,\, y\ge 0\}$, where $g$ is a convex function. In the online setting, variables $y$ and columns of $A^\intercal$ arrive over time, and we wish to maintain a non-decreasing solution $(y,μ)$. We provide an online primal-dual framework for both classes of problems with competitive ratio depending on certain "monotonicity" and "smoothness" parameters of $f$; our results match or improve on guarantees for some special classes of functions $f$ considered previously. Using this fractional solver with problem-dependent randomized rounding procedures, we obtain competitive algorithms for the following problems: online covering LPs minimizing $\ell_p$-norms of arbitrary packing constraints, set cover with multiple cost functions, capacity constrained facility location, capacitated multicast problem, set cover with set requests, and profit maximization with non-separable production costs. Some of these results are new and others provide a unified view of previous results, with matching or slightly worse competitive ratios.
Cross-Layer Scheduling for OFDMA-based Cognitive Radio Systems with Delay and Security Constraints
Venue: arXiv cs.NI 2014
Year: 2014

This paper considers the resource allocation problem in an Orthogonal Frequency Division Multiple Access (OFDMA) based cognitive radio (CR) network, where the CR base station adopts full overlay scheme to transmit both private and open information to multiple users with average delay and power constraints. A stochastic optimization problem is formulated to develop flow control and radio resource allocation in order to maximize the long-term system throughput of open and private information in CR system and ensure the stability of primary system. The corresponding optimal condition for employing full overlay is derived in the context of concurrent transmission of open and private information. An online resource allocation scheme is designed to adapt the transmission of open and private information based on monitoring the status of primary system as well as the channel and queue states in the CR network. The scheme is proven to be asymptotically optimal in solving the stochastic optimization problem without knowing any statistical information. Simulations are provided to verify the analytical results and efficiency of the scheme.
Return on citation: a consistent metric to evaluate papers, journals and researchers
Venue: arXiv cs.DL 2014
Year: 2014

Evaluating and comparing the academic performance of a journal, a researcher or a single paper has long remained a critical, necessary but also controversial issue. Most of existing metrics invalidate comparison across different fields of science or even between different types of papers in the same field. This paper proposes a new metric, called return on citation (ROC), which is simply a citation ratio but applies to evaluating the paper, the journal and the researcher in a consistent way, allowing comparison across different fields of science and between different types of papers and discouraging unnecessary and coercive/self-citation.
Implementation of a Hierarchical fuzzy controller for a biped robot
Venue: arXiv cs.RO 2014
Year: 2014

In this paper the design of a control system for a biped robot is described. Control is specified for a walk cycle of the robot. The implementation of the control system was done on Matlab Simulink. In this paper a hierarchical fuzzy logic controller (HFLC) is proposed to control a planar biped walk. The HFLC design is bio-inspired from human locomotion system. The proposed method is applied to control five links planar biped into free area and without obstacles.
QPEL: Quantum Program and Effect Language
Venue: arXiv cs.LO 2014
Year: 2014

We present the syntax and rules of deduction of QPEL (Quantum Program and Effect Language), a language for describing both quantum programs, and properties of quantum programs - effects on the appropriate Hilbert space. We show how semantics may be given in terms of state-and-effect triangles, a categorical setting that allows semantics in terms of Hilbert spaces, C*-algebras, and other categories. We prove soundness and completeness results that show the derivable judgements are exactly those provable in all state-and-effect triangles.
Tensors, !-graphs, and non-commutative quantum structures
Venue: arXiv cs.LO 2014
Year: 2014

Categorical quantum mechanics (CQM) and the theory of quantum groups rely heavily on the use of structures that have both an algebraic and co-algebraic component, making them well-suited for manipulation using diagrammatic techniques. Diagrams allow us to easily form complex compositions of (co)algebraic structures, and prove their equality via graph rewriting. One of the biggest challenges in going beyond simple rewriting-based proofs is designing a graphical language that is expressive enough to prove interesting properties (e.g. normal form results) about not just single diagrams, but entire families of diagrams. One candidate is the language of !-graphs, which consist of graphs with certain subgraphs marked with boxes (called !-boxes) that can be repeated any number of times. New !-graph equations can then be proved using a powerful technique called !-box induction. However, previously this technique only applied to commutative (or cocommutative) algebraic structures, severely limiting its applications in some parts of CQM and (especially) quantum groups. In this paper, we fix this shortcoming by offering a new semantics for non-commutative !-graphs using an enriched version of Penrose's abstract tensor notation.
Bewelcome.org -- a non-profit democratic hospex service set up for growth
Venue: arXiv cs.SI 2014
Year: 2014

This paper presents an extensive data-based analysis of the non-profit democratic hospitality exchange service bewelcome.org. We hereby pursuit the goal of determining the factors influencing its growth. It also provides general insights on internet-based hospitality exchange services. The other investigated services are hospitalityclub.org and couchsurfing.org. Communities using the three services are interconnected -- comparing their data provides additional information.
An OFDM Signal Identification Method for Wireless Communications Systems
Venue: arXiv cs.IT 2014
Year: 2014

Distinction of OFDM signals from single carrier signals is highly important for adaptive receiver algorithms and signal identification applications. OFDM signals exhibit Gaussian characteristics in time domain and fourth order cumulants of Gaussian distributed signals vanish in contrary to the cumulants of other signals. Thus fourth order cumulants can be utilized for OFDM signal identification. In this paper, first, formulations of the estimates of the fourth order cumulants for OFDM signals are provided. Then it is shown these estimates are affected significantly from the wireless channel impairments, frequency offset, phase offset and sampling mismatch. To overcome these problems, a general chi-square constant false alarm rate Gaussianity test which employs estimates of cumulants and their covariances is adapted to the specific case of wireless OFDM signals. Estimation of the covariance matrix of the fourth order cumulants are greatly simplified peculiar to the OFDM signals. A measurement setup is developed to analyze the performance of the identification method and for comparison purposes. A parametric measurement analysis is provided depending on modulation order, signal to noise ratio, number of symbols, and degree of freedom of the underlying test. The proposed method outperforms statistical tests which are based on fixed thresholds or empirical values, while a priori information requirement and complexity of the proposed method are lower than the coherent identification techniques.
Time-Symmetric Physics: A Radical Approach to the Decoherence Problem
Venue: arXiv cs.OH 2014
Year: 2014

The most powerful form of quantum learning system possible would somehow learn the parameters W of a quantum system f(X, W), for f representing the largest, most powerful set of possible input-output relations. This paper addresses the issue of how to enlarge the set represented by f, by using a new formulation of time-symmetric physics to model analog quantum computers based on spin and by exploring possible sources of backwards-time free energy so as to address problems of decoherence and dissipation.
Discriminative Clustering with Relative Constraints
Venue: arXiv cs.LG 2014
Year: 2014

We study the problem of clustering with relative constraints, where each constraint specifies relative similarities among instances. In particular, each constraint $(x_i, x_j, x_k)$ is acquired by posing a query: is instance $x_i$ more similar to $x_j$ than to $x_k$? We consider the scenario where answers to such queries are based on an underlying (but unknown) class concept, which we aim to discover via clustering. Different from most existing methods that only consider constraints derived from yes and no answers, we also incorporate don't know responses. We introduce a Discriminative Clustering method with Relative Constraints (DCRC) which assumes a natural probabilistic relationship between instances, their underlying cluster memberships, and the observed constraints. The objective is to maximize the model likelihood given the constraints, and in the meantime enforce cluster separation and cluster balance by also making use of the unlabeled instances. We evaluated the proposed method using constraints generated from ground-truth class labels, and from (noisy) human judgments from a user study. Experimental results demonstrate: 1) the usefulness of relative constraints, in particular when don't know answers are considered; 2) the improved performance of the proposed method over state-of-the-art methods that utilize either relative or pairwise constraints; and 3) the robustness of our method in the presence of noisy constraints, such as those provided by human judgement.
Maximum Margin Clustering for State Decomposition of Metastable Systems
Venue: arXiv cs.LG 2014
Year: 2014

When studying a metastable dynamical system, a prime concern is how to decompose the phase space into a set of metastable states. Unfortunately, the metastable state decomposition based on simulation or experimental data is still a challenge. The most popular and simplest approach is geometric clustering which is developed based on the classical clustering technique. However, the prerequisites of this approach are: (1) data are obtained from simulations or experiments which are in global equilibrium and (2) the coordinate system is appropriately selected. Recently, the kinetic clustering approach based on phase space discretization and transition probability estimation has drawn much attention due to its applicability to more general cases, but the choice of discretization policy is a difficult task. In this paper, a new decomposition method designated as maximum margin metastable clustering is proposed, which converts the problem of metastable state decomposition to a semi-supervised learning problem so that the large margin technique can be utilized to search for the optimal decomposition without phase space discretization. Moreover, several simulation examples are given to illustrate the effectiveness of the proposed method.