OpenRaiser/Intern-Atlas huggingface dataset:
Joint Power and Resource Allocation for Block-Fading Relay-Assisted Broadcast Channels
Venue: arXiv cs.IT 2012
Year: 2012

We provide the solution for optimizing the power and resource allocation over block-fading relay-assisted broadcast channels in order to maximize the long term average achievable rates region of the users. The problem formulation assumes regenerative (repetition coding) decode-and-forward (DF) relaying strategy, long-term average total transmitted power constraint, orthogonal multiplexing of the users messages within the channel blocks, possibility to use a direct transmission (DT) mode from the base station to the user terminal directly or a relaying (DF) transmission mode, and partial channel state information. We show that our optimization problem can be transformed into an equivalent "no-relaying" broadcast channel optimization problem with each actual user substituted by two virtual users having different channel qualities and multiplexing weights. The proposed power and resource allocation strategies are expressed in closed-form that can be applied practically in centralized relay-assisted wireless networks. Furthermore, we show by numerical examples that our scheme enlarges the achievable rates region significantly.
Approximating Sparse Covering Integer Programs Online
Venue: arXiv cs.DS 2012
Year: 2012

A covering integer program (CIP) is a mathematical program of the form: min {c^T x : Ax >= 1, 0 <= x <= u, x integer}, where A is an m x n matrix, and c and u are n-dimensional vectors, all having non-negative entries. In the online setting, the constraints (i.e., the rows of the constraint matrix A) arrive over time, and the algorithm can only increase the coordinates of vector x to maintain feasibility. As an intermediate step, we consider solving the covering linear program (CLP) online, where the integrality requirement on x is dropped. Our main results are (a) an O(log k)-competitive online algorithm for solving the CLP, and (b) an O(log k log L)-competitive randomized online algorithm for solving the CIP. Here k<=n and L<=m respectively denote the maximum number of non-zero entries in any row and column of the constraint matrix A. By a result of Feige and Korman, this is the best possible for polynomial-time online algorithms, even in the special case of set cover.
Minimax Classifier for Uncertain Costs
Venue: arXiv cs.LG 2012
Year: 2012

Many studies on the cost-sensitive learning assumed that a unique cost matrix is known for a problem. However, this assumption may not hold for many real-world problems. For example, a classifier might need to be applied in several circumstances, each of which associates with a different cost matrix. Or, different human experts have different opinions about the costs for a given problem. Motivated by these facts, this study aims to seek the minimax classifier over multiple cost matrices. In summary, we theoretically proved that, no matter how many cost matrices are involved, the minimax problem can be tackled by solving a number of standard cost-sensitive problems and sub-problems that involve only two cost matrices. As a result, a general framework for achieving minimax classifier over multiple cost matrices is suggested and justified by preliminary empirical studies.
Time-Varying Space-Only Codes for Coded MIMO
Venue: arXiv cs.IT 2012
Year: 2012

Multiple antenna (MIMO) devices are widely used to increase reliability and information bit rate. Optimal error rate performance (full diversity and large coding gain), for unknown channel state information at the transmitter and for maximal rate, can be achieved by approximately universal space-time codes, but comes at a price of large detection complexity, infeasible for most practical systems. We propose a new coded modulation paradigm: error-correction outer code with space-only but time-varying precoder (as inner code). We refer to the latter as Ergodic Mutual Information (EMI) code. The EMI code achieves the maximal multiplexing gain and full diversity is proved in terms of the outage probability. Contrary to most of the literature, our work is not based on the elegant but difficult classical algebraic MIMO theory. Instead, the relation between MIMO and parallel channels is exploited. The theoretical proof of full diversity is corroborated by means of numerical simulations for many MIMO scenarios, in terms of outage probability and word error rate of LDPC coded systems. The full-diversity and full-rate at low detection complexity comes at a price of a small coding gain loss for outer coding rates close to one, but this loss vanishes with decreasing coding rate.
African Trypanosomiasis Detection using Dempster-Shafer Theory
Venue: arXiv cs.AI 2012
Year: 2012

World Health Organization reports that African Trypanosomiasis affects mostly poor populations living in remote rural areas of Africa that can be fatal if properly not treated. This paper presents Dempster-Shafer Theory for the detection of African trypanosomiasis. Sustainable elimination of African trypanosomiasis as a public-health problem is feasible and requires continuous efforts and innovative approaches. In this research, we implement Dempster-Shafer theory for detecting African trypanosomiasis and displaying the result of detection process. We describe eleven symptoms as major symptoms which include fever, red urine, skin rash, paralysis, headache, bleeding around the bite, joint the paint, swollen lymph nodes, sleep disturbances, meningitis and arthritis. Dempster-Shafer theory to quantify the degree of belief, our approach uses Dempster-Shafer theory to combine beliefs under conditions of uncertainty and ignorance, and allows quantitative measurement of the belief and plausibility in our identification result.
An Energy-Efficient MIMO Algorithm with Receive Power Constraint
Venue: arXiv cs.IT 2012
Year: 2012

We consider the energy-efficiency of Multiple-Input Multiple-Output (MIMO) systems with constrained received power rather than constrained transmit power. A Energy-Efficient Water-Filling (EEWF) algorithm that maximizes the ratio of the transmission rate to the total transmit power has been derived. The EEWF power allocation policy establishes a trade-off between the transmission rate and the total transmit power under the total receive power constraint. The static and the uncorrelated fast fading Rayleigh channels have been considered, where the maximization is performed on the instantaneous realization of the channel assuming perfect information at both the transmitter and the receiver with equal number of antennas. We show, based on Monte Carlo simulations that the energy-efficiency provided by the EEWF algorithm can be more than an order of magnitude greater than the energy-efficiency corresponding to capacity achieving Water-Filling (WF) algorithm. We also show that the energy-efficiency increases with both the number of antennas and the signal-to-noise ratio. The corresponding transmission rate also increases but at a slower rate than the Shannon capacity, while the corresponding total transmit power decreases with the number of antennas.
Robot Navigation using Reinforcement Learning and Slow Feature Analysis
Venue: arXiv cs.AI 2012
Year: 2012

The application of reinforcement learning algorithms onto real life problems always bears the challenge of filtering the environmental state out of raw sensor readings. While most approaches use heuristics, biology suggests that there must exist an unsupervised method to construct such filters automatically. Besides the extraction of environmental states, the filters have to represent them in a fashion that support modern reinforcement algorithms. Many popular algorithms use a linear architecture, so one should aim at filters that have good approximation properties in combination with linear functions. This thesis wants to propose the unsupervised method slow feature analysis (SFA) for this task. Presented with a random sequence of sensor readings, SFA learns a set of filters. With growing model complexity and training examples, the filters converge against trigonometric polynomial functions. These are known to possess excellent approximation capabilities and should therfore support the reinforcement algorithms well. We evaluate this claim on a robot. The task is to learn a navigational control in a simple environment using the least square policy iteration (LSPI) algorithm. The only accessible sensor is a head mounted video camera, but without meaningful filtering, video images are not suited as LSPI input. We will show that filters learned by SFA, based on a random walk video of the robot, allow the learned control to navigate successfully in ca. 80% of the test trials.
Communication Analysis modelling techniques
Venue: arXiv cs.SE 2012
Year: 2012

This report describes and illustrates several modelling techniques proposed by Communication Analysis; namely Communicative Event Diagram, Message Structures and Event Specification Templates. The Communicative Event Diagram is a business process modelling technique that adopts a communicational perspective by focusing on communicative interactions when describing the organizational work practice, instead of focusing on physical activities1; at this abstraction level, we refer to business activities as communicative events. Message Structures is a technique based on structured text that allows specifying the messages associated to communicative events. Event Specification Templates are a means to organise the requirements concerning a communicative event. This report can be useful to analysts and business process modellers in general, since, according to our industrial experience, it is possible to apply many Communication Analysis concepts, guidelines and criteria to other business process modelling notations such as BPMN. Also, Message Structures can complement business process models created with other notations different than Communicative Event Diagram.
Recommendation on Academic Networks using Direction Aware Citation Analysis
Venue: arXiv cs.IR 2012
Year: 2012

The literature search has always been an important part of an academic research. It greatly helps to improve the quality of the research process and output, and increase the efficiency of the researchers in terms of their novel contribution to science. As the number of published papers increases every year, a manual search becomes more exhaustive even with the help of today's search engines since they are not specialized for this task. In academics, two relevant papers do not always have to share keywords, cite one another, or even be in the same field. Although a well-known paper is usually an easy pray in such a hunt, relevant papers using a different terminology, especially recent ones, are not obvious to the eye. In this work, we propose paper recommendation algorithms by using the citation information among papers. The proposed algorithms are direction aware in the sense that they can be tuned to find either recent or traditional papers. The algorithms require a set of papers as input and recommend a set of related ones. If the user wants to give negative or positive feedback on the suggested paper set, the recommendation is refined. The search process can be easily guided in that sense by relevance feedback. We show that this slight guidance helps the user to reach a desired paper in a more efficient way. We adapt our models and algorithms also for the venue and reviewer recommendation tasks. Accuracy of the models and algorithms is thoroughly evaluated by comparison with multiple baselines and algorithms from the literature in terms of several objectives specific to citation, venue, and reviewer recommendation tasks. All of these algorithms are implemented within a publicly available web-service framework (http://theadvisor.osu.edu/) which currently uses the data from DBLP and CiteSeer to construct the proposed citation graph.
An Approach For Robots To Deal With Objects
Venue: arXiv cs.RO 2012
Year: 2012

Understanding object and its context are very important for robots when dealing with objects for completion of a mission. In this paper, an Affordance-based Ontology (ABO) is proposed for easy robot dealing with substantive and non-substantive objects. An ABO is a machine-understandable representation of objects and their relationships by what it's related to and how it's related. By using ABO, when dealing with a substantive object, robots can understand the representation of its object and its relation with other non-substantive objects. When the substantive object is not available, the robots have the understanding ability, in term of objects and their functions to select a non substantive object in order to complete the mission, such as giving raincoat or hat instead of getting stuck due to the unavailability of substantive object, e.g. umbrella. The experiment is done in the Ubiquitous Robotics Technology (u-RT) Space of National Institute of Advanced Industrial Science and Technology (AIST), Tsukuba, Japan.
Anonymous Card Shuffling and its Applications to Parallel Mixnets
Venue: arXiv cs.DS 2012
Year: 2012

We study the question of how to shuffle $n$ cards when faced with an opponent who knows the initial position of all the cards {\em and} can track every card when permuted, {\em except} when one takes $K< n$ cards at a time and shuffles them in a private buffer "behind your back," which we call {\em buffer shuffling}. The problem arises naturally in the context of parallel mixnet servers as well as other security applications. Our analysis is based on related analyses of load-balancing processes. We include extensions to variations that involve corrupted servers and adversarially injected messages, which correspond to an opponent who can peek at some shuffles in the buffer and who can mark some number of the cards. In addition, our analysis makes novel use of a sum-of-squares metric for anonymity, which leads to improved performance bounds for parallel mixnets and can also be used to bound well-known existing anonymity measures.
Parsing of Myanmar sentences with function tagging
Venue: arXiv cs.CL 2012
Year: 2012

This paper describes the use of Naive Bayes to address the task of assigning function tags and context free grammar (CFG) to parse Myanmar sentences. Part of the challenge of statistical function tagging for Myanmar sentences comes from the fact that Myanmar has free-phrase-order and a complex morphological system. Function tagging is a pre-processing step for parsing. In the task of function tagging, we use the functional annotated corpus and tag Myanmar sentences with correct segmentation, POS (part-of-speech) tagging and chunking information. We propose Myanmar grammar rules and apply context free grammar (CFG) to find out the parse tree of function tagged Myanmar sentences. Experiments show that our analysis achieves a good result with parsing of simple sentences and three types of complex sentences.
Performance Measurement of Cloud Computing Services
Venue: arXiv cs.NI 2012
Year: 2012

Cloud computing today has now been growing as new technologies and new business models. In distributed technology perspective, cloud computing most like client-server services like web-based or web-service but it used virtual resources to execute. Currently, cloud computing relies on the use of an elastic virtual machine and the use of network for data exchange. We conduct an experimental setup to measure the quality of service received by cloud computing customers. Experimental setup done by creating a HTTP service that runs in the cloud computing infrastructure. We interest to know about the impact of increasing the number of users on the average quality received by users. The qualities received by user measured within two parameters consist of average response times and the number of requests time out. Experimental results of this study show that increasing the number of users has increased the average response time. Similarly, the number of request time out increasing with increasing number of users. It means that the qualities of service received by user are decreasing also. We found that the impact of the number of users on the quality of service is no longer in linear trend. The results of this study can be used as a reference model for the network operator in performing services in which a certain number of users in order to obtain optimal quality services.
Content based video retrieval systems
Venue: arXiv cs.MM 2012
Year: 2012

With the development of multimedia data types and available bandwidth there is huge demand of video retrieval systems, as users shift from text based retrieval systems to content based retrieval systems. Selection of extracted features play an important role in content based video retrieval regardless of video attributes being under consideration. These features are intended for selecting, indexing and ranking according to their potential interest to the user. Good features selection also allows the time and space costs of the retrieval process to be reduced. This survey reviews the interesting features that can be extracted from video data for indexing and retrieval along with similarity measurement methods. We also identify present research issues in area of content based video retrieval systems.
Compressed Sensing with Nonlinear Observations and Related Nonlinear Optimisation Problems
Venue: arXiv cs.IT 2012
Year: 2012

Non-convex constraints have recently proven a valuable tool in many optimisation problems. In particular sparsity constraints have had a significant impact on sampling theory, where they are used in Compressed Sensing and allow structured signals to be sampled far below the rate traditionally prescribed. Nearly all of the theory developed for Compressed Sensing signal recovery assumes that samples are taken using linear measurements. In this paper we instead address the Compressed Sensing recovery problem in a setting where the observations are non-linear. We show that, under conditions similar to those required in the linear setting, the Iterative Hard Thresholding algorithm can be used to accurately recover sparse or structured signals from few non-linear observations. Similar ideas can also be developed in a more general non-linear optimisation framework. In the second part of this paper we therefore present related result that show how this can be done under sparsity and union of subspaces constraints, whenever a generalisation of the Restricted Isometry Property traditionally imposed on the Compressed Sensing system holds.
A Common Evaluation Setting for Just.Ask, Open Ephyra and Aranea QA systems
Venue: arXiv cs.IR 2012
Year: 2012

Question Answering (QA) is not a new research field in Natural Language Processing (NLP). However in recent years, QA has been a subject of growing study. Nowadays, most of the QA systems have a similar pipelined architecture and each system use a set of unique techniques to accomplish its state of the art results. However, many things are not clear in the QA processing. It is not clear the extend of the impact of tasks performed in earlier stages in following stages of the pipelining process. It is not clear, if techniques used in a QA system can be used in another QA system to improve its results. And finally, it is not clear in what setting should be these systems tested in order to properly analyze their results.
A Novel Method For Speech Segmentation Based On Speakers' Characteristics
Venue: arXiv cs.AI 2012
Year: 2012

Speech Segmentation is the process change point detection for partitioning an input audio stream into regions each of which corresponds to only one audio source or one speaker. One application of this system is in Speaker Diarization systems. There are several methods for speaker segmentation; however, most of the Speaker Diarization Systems use BIC-based Segmentation methods. The main goal of this paper is to propose a new method for speaker segmentation with higher speed than the current methods - e.g. BIC - and acceptable accuracy. Our proposed method is based on the pitch frequency of the speech. The accuracy of this method is similar to the accuracy of common speaker segmentation methods. However, its computation cost is much less than theirs. We show that our method is about 2.4 times faster than the BIC-based method, while the average accuracy of pitch-based method is slightly higher than that of the BIC-based method.
Text Steganography using LSB insertion method along with Chaos Theory
Venue: arXiv cs.MM 2012
Year: 2012

The art of information hiding has been around nearly as long as the need for covert communication. Steganography, the concealing of information, arose early on as an extremely useful method for covert information transmission. Steganography is the art of hiding secret message within a larger image or message such that the hidden message or an image is undetectable; this is in contrast to cryptography, where the existence of the message itself is not disguised, but the content is obscure. The goal of a steganographic method is to minimize the visually apparent and statistical differences between the cover data and a steganogram while maximizing the size of the payload. Current digital image steganography presents the challenge of hiding message in a digital image in a way that is robust to image manipulation and attack. This paper explains about how a secret message can be hidden into an image using least significant bit insertion method along with chaos.
Strong Conflict-Free Coloring of Intervals
Venue: arXiv cs.DS 2012
Year: 2012

We consider the k-strong conflict-free coloring of a set of points on a line with respect to a family of intervals: Each point on the line must be assigned a color so that the coloring has to be conflict-free, in the sense that in every interval I there are at least k colors each appearing exactly once in I. In this paper, we present a polynomial algorithm for the general problem; the algorithm has an approximation factor 5-2/k when k\geq2 and approximation factor 2 for k=1. In the special case the family contains all the possible intervals on the given set of points, we show that a 2 approximation algorithm exists, for any k\geq1.
Evolutionary algorithms in genetic regulatory networks model
Venue: arXiv cs.CE 2012
Year: 2012

Genetic Regulatory Networks (GRNs) plays a vital role in the understanding of complex biological processes. Modeling GRNs is significantly important in order to reveal fundamental cellular processes, examine gene functions and understanding their complex relationships. Understanding the interactions between genes gives rise to develop better method for drug discovery and diagnosis of the disease since many diseases are characterized by abnormal behaviour of the genes. In this paper we have reviewed various evolutionary algorithms-based approach for modeling GRNs and discussed various opportunities and challenges.
Analysis of WiMAX Physical Layer Using Spatial Multiplexing
Venue: arXiv cs.OH 2012
Year: 2012

Broadband Wireless Access (BWA) has emerged as a promising solution for providing last mile internet access technology to provide high speed internet access to the users in the residential as well as in the small and medium sized enterprise sectors. IEEE 802.16e is one of the most promising and attractive candidate among the emerging technologies for broadband wireless access. The emergence of WiMAX protocol has attracted various interests from almost all the fields of wireless communications. MIMO systems which are created according to the IEEE 802.16-2005 standard (WiMAX) under different fading channels can be implemented to get the benefits of both the MIMO and WiMAX technologies. In this paper analysis of higher level of modulations (i.e. M-PSK and M-QAM for different values of M) with different code rates and on WiMAX-MIMO system is presented for Rayleigh channel by focusing on spatial multiplexing MIMO technique. Signal-to Noise Ratio (SNR) vs Bit Error Rate (BER) analysis has been done.