OpenRaiser/Intern-Atlas huggingface dataset:
Building Healthcare - Patient Relationship with CRM 2.0: Lesson Learnt from Prita Mulyasari's Case
Venue: arXiv cs.OH 2012
Year: 2012

Healthcare is implementing CRM as a strategy for managing interactions and communication with patients which involves using Information and Communication Technology (ICT) to organize, automate, and coordinate business processes. CRM with the Web technology provides healthcare the ability to broaden service beyond its usual practices, and thus provides a particular advantageous environment for them that want to use ICT to achieve complex healthcare goal. This paper we will discuss and demonstrate how a new approach in CRM will help the healthcare increasing their customer support, and promoting better health to patient. The patients benefited from the customized personal service so that they have full information access to perform self managed their own health and the healthcare provider will have a loyal and retains the right customer. A conceptual framework of approach will be highlighted. Customer centric paradigm in social network's era and value creation of healthcare's business process will be taken into consideration.
Health Information Systems (HIS): Concept and Technology
Venue: arXiv cs.OH 2012
Year: 2012

A health information system (HIS) is the intersection of between healthcare's business process, and information systems to deliver better healthcare services. The nature of healthcare industry, which is highly influenced by economic, social, politic, and technological factors, has changed over time. This paper will address some important concepts of healthcare and related terminologies to provide a holistic view for HIS. Related technological milestones and major events are briefly summarized. The trends and rapid development of health information technologies are also discussed.
Distributed Cooperative Q-learning for Power Allocation in Cognitive Femtocell Networks
Venue: arXiv cs.LG 2012
Year: 2012

In this paper, we propose a distributed reinforcement learning (RL) technique called distributed power control using Q-learning (DPC-Q) to manage the interference caused by the femtocells on macro-users in the downlink. The DPC-Q leverages Q-Learning to identify the sub-optimal pattern of power allocation, which strives to maximize femtocell capacity, while guaranteeing macrocell capacity level in an underlay cognitive setting. We propose two different approaches for the DPC-Q algorithm: namely, independent, and cooperative. In the former, femtocells learn independently from each other while in the latter, femtocells share some information during learning in order to enhance their performance. Simulation results show that the independent approach is capable of mitigating the interference generated by the femtocells on macro-users. Moreover, the results show that cooperation enhances the performance of the femtocells in terms of speed of convergence, fairness and aggregate femtocell capacity.
Adaptive Network Coding for Scheduling Real-time Traffic with Hard Deadlines
Venue: arXiv cs.SY 2012
Year: 2012

We study adaptive network coding (NC) for scheduling real-time traffic over a single-hop wireless network. To meet the hard deadlines of real-time traffic, it is critical to strike a balance between maximizing the throughput and minimizing the risk that the entire block of coded packets may not be decodable by the deadline. Thus motivated, we explore adaptive NC, where the block size is adapted based on the remaining time to the deadline, by casting this sequential block size adaptation problem as a finite-horizon Markov decision process. One interesting finding is that the optimal block size and its corresponding action space monotonically decrease as the deadline approaches, and the optimal block size is bounded by the "greedy" block size. These unique structures make it possible to narrow down the search space of dynamic programming, building on which we develop a monotonicity-based backward induction algorithm (MBIA) that can solve for the optimal block size in polynomial time. Since channel erasure probabilities would be time-varying in a mobile network, we further develop a joint real-time scheduling and channel learning scheme with adaptive NC that can adapt to channel dynamics. We also generalize the analysis to multiple flows with hard deadlines and long-term delivery ratio constraints, devise a low-complexity online scheduling algorithm integrated with the MBIA, and then establish its asymptotical throughput-optimality. In addition to analysis and simulation results, we perform high fidelity wireless emulation tests with real radio transmissions to demonstrate the feasibility of the MBIA in finding the optimal block size in real time.
Designing a WISHBONE Protocol Network Adapter for an Asynchronous Network-on-Chip
Venue: arXiv cs.AR 2012
Year: 2012

The Scaling of microchip technologies, from micron to submicron and now to deep sub-micron (DSM) range, has enabled large scale systems-on-chip (SoC). In future deep submicron (DSM) designs, the interconnect effect will definitely dominate performance. Network-on-Chip (NoC) has become a promising solution to bus-based communication infrastructure limitations. NoC designs usually targets Application Specific Integrated Circuits (ASICs), however, the fabrication process costs a lot. Implementing a NoC on an FPGA does not only reduce the cost but also decreases programming and verification cycles. In this paper, an Asynchronous NoC has been implemented on a SPARTAN-3E\textregistered device. The NoC supports basic transactions of both widely used on-chip interconnection standards, the Open Core Protocol (OCP) and the WISHBONE Protocol. Although, FPGA devices are synchronous in nature, it has been shown that they can be used to prototype a Global Asynchronous Local Synchronous (GALS) systems, comprising an Asynchronous NoC connecting IP cores operating in different clock domains.
A Novel Robust Approach to Least Squares Problems with Bounded Data Uncertainties
Venue: arXiv cs.SY 2012
Year: 2012

In this correspondence, we introduce a minimax regret criteria to the least squares problems with bounded data uncertainties and solve it using semi-definite programming. We investigate a robust minimax least squares approach that minimizes a worst case difference regret. The regret is defined as the difference between a squared data error and the smallest attainable squared data error of a least squares estimator. We then propose a robust regularized least squares approach to the regularized least squares problem under data uncertainties by using a similar framework. We show that both unstructured and structured robust least squares problems and robust regularized least squares problem can be put in certain semi-definite programming forms. Through several simulations, we demonstrate the merits of the proposed algorithms with respect to the the well-known alternatives in the literature.
Low Complexity Turbo-Equalization: A Clustering Approach
Venue: arXiv cs.SY 2012
Year: 2012

We introduce a low complexity approach to iterative equalization and decoding, or "turbo equalization", that uses clustered models to better match the nonlinear relationship that exists between likelihood information from a channel decoder and the symbol estimates that arise in soft-input channel equalization. The introduced clustered turbo equalizer uses piecewise linear models to capture the nonlinear dependency of the linear minimum mean square error (MMSE) symbol estimate on the symbol likelihoods produced by the channel decoder and maintains a computational complexity that is only linear in the channel memory. By partitioning the space of likelihood information from the decoder, based on either hard or soft clustering, and using locally-linear adaptive equalizers within each clustered region, the performance gap between the linear MMSE equalizer and low-complexity, LMS-based linear turbo equalizers can be dramatically narrowed.
DiscopFlow: A new Tool for Discovering Organizational Structures and Interaction Protocols in WorkFlow
Venue: arXiv cs.DC 2012
Year: 2012

This work deals with Workflow Mining (WM) a very active and promising research area. First, in this paper we give a critical and comparative study of three representative WM systems of this area: the ProM, InWolve and WorkflowMiner systems. The comparison is made according to quality criteria that we have defined such as the capacity to filter and convert a Workflow log, the capacity to discover workflow perspectives and the capacity to support Multi-Analysis of processes. The major drawback of these systems is the non possibility to deal with organizational perspective discovering issue. We mean by organizational perspective, the organizational structures (federation, coalition, market or hierarchy) and interaction protocols (contract net, auction or vote). This paper defends the idea that organizational dimension in Multi-Agent System is an appropriate approach to support the discovering of this organizational perspective. Second, the paper proposes a Workflow log meta-model which extends the classical one by considering the interactions among actors thanks to the FIPA-ACL Performatives. Third, it describes in details our DiscopFlow tool which validates our contribution.
Estimation with a helper who knows the interference
Venue: arXiv cs.IT 2012
Year: 2012

We consider the problem of estimating a signal corrupted by independent interference with the assistance of a cost-constrained helper who knows the interference causally or noncausally. When the interference is known causally, we characterize the minimum distortion incurred in estimating the desired signal. In the noncausal case, we present a general achievable scheme for discrete memoryless systems and novel lower bounds on the distortion for the binary and Gaussian settings. Our Gaussian setting coincides with that of assisted interference suppression introduced by Grover and Sahai. Our lower bound for this setting is based on the relation recently established by Verdú between divergence and minimum mean squared error. We illustrate with a few examples that this lower bound can improve on those previously developed. Our bounds also allow us to characterize the optimal distortion in several interesting regimes. Moreover, we show that causal and noncausal estimation are not equivalent for this problem. Finally, we consider the case where the desired signal is also available at the helper. We develop new lower bounds for this setting that improve on those previously developed, and characterize the optimal distortion up to a constant multiplicative factor for some regimes of interest.
Correlated Node Behavior Model based on Semi Markov Process for MANETS
Venue: arXiv cs.CR 2012
Year: 2012

This paper introduces a new model for node behavior namely Correlated Node Behavior Model which is an extension of Node Behavior Model. The model adopts semi Markov process in continuous time which clusters the node that has correlation. The key parameter of the process is determined by five probabilistic parameters based on the Markovian model. Computed from the transition probabilities of the semi-Markov process, the node correlation impact on network survivability and resilience can be measure quantitatively. From the result, the quantitative analysis of correlated node behavior on the survivability is obtained through mathematical description, and the effectiveness and rationality of the proposed model are verified through numerical analysis. The analytical results show that the effect from correlated failure nodes on network survivability is much severer than other misbehaviors.
Teacher Module in an Assistance Tool - Adaptating a device to a teaching context and and teacher's preferences
Venue: arXiv cs.CY 2012
Year: 2012

This communication presents the genesis and the implementation of a teacher module, which is included in an Assistance Tool (AT). The teacher module is based on a teacher model for which we did a thorough analysis of the state of the art. The aim of the AT is to help a teacher to design pedagogical devices. Teachers can formulate their needs (assistance in the design) and the AT can relieve them from repetitive tasks related to the deployment of a teaching device (assistance in the deployment).
On Training Deep Boltzmann Machines
Venue: arXiv cs.NE 2012
Year: 2012

The deep Boltzmann machine (DBM) has been an important development in the quest for powerful "deep" probabilistic models. To date, simultaneous or joint training of all layers of the DBM has been largely unsuccessful with existing training methods. We introduce a simple regularization scheme that encourages the weight vectors associated with each hidden unit to have similar norms. We demonstrate that this regularization can be easily combined with standard stochastic maximum likelihood to yield an effective training strategy for the simultaneous training of all layers of the deep Boltzmann machine.
Blind Channel Estimation Enhancement for Mimo- OFDM Systems under High Mobility Conditions
Venue: arXiv cs.NI 2012
Year: 2012

In this paper, we propose an enhancement of a blind channel estimator based on a subspace approach in a MIMO OFDM context (Multi Input Multi Output Orthogonal Frequency Division Multiplexing) in high mobility scenario. As known, the combination between the MIMO context and the OFDM system has stimulated mainly the evolution of the fourth generation broadband wireless communications. The simulations results have demonstrated the effectiveness of the approach for a 16 QAM modulation scheme and had been evaluated in term of bit error rate BER and mean square error MSE versus the signal to noise ratio SNR.
Optimization of Cellular Resources Evading Intra and Inter Tier Interference in Femto cells Equipped Macro cell Networks
Venue: arXiv cs.NI 2012
Year: 2012

Cellular network resources are essential to be optimized in Femto cells equipped macro cell networks. This is achieved by increasing the cellular coverage and channel capacity, and reducing power usage and interference between femto cells and macro cells. In this paper, the optimization approach for cellular resources with installed femto cells in macro cell networks has been addressed by deploying smart antennas applications and effect power adaptation method which significantly optimize the cellular coverage, channel capacity, power usage, and intra and inter tier interference. The simulation results also illustrate the outstanding performance of this optimization methodology.
Recommender systems in industrial contexts
Venue: arXiv cs.IR 2012
Year: 2012

This thesis consists of four parts: - An analysis of the core functions and the prerequisites for recommender systems in an industrial context: we identify four core functions for recommendation systems: Help do Decide, Help to Compare, Help to Explore, Help to Discover. The implementation of these functions has implications for the choices at the heart of algorithmic recommender systems. - A state of the art, which deals with the main techniques used in automated recommendation system: the two most commonly used algorithmic methods, the K-Nearest-Neighbor methods (KNN) and the fast factorization methods are detailed. The state of the art presents also purely content-based methods, hybridization techniques, and the classical performance metrics used to evaluate the recommender systems. This state of the art then gives an overview of several systems, both from academia and industry (Amazon, Google ...). - An analysis of the performances and implications of a recommendation system developed during this thesis: this system, Reperio, is a hybrid recommender engine using KNN methods. We study the performance of the KNN methods, including the impact of similarity functions used. Then we study the performance of the KNN method in critical uses cases in cold start situation. - A methodology for analyzing the performance of recommender systems in industrial context: this methodology assesses the added value of algorithmic strategies and recommendation systems according to its core functions.
Variety Evasive Sets
Venue: arXiv cs.CC 2012
Year: 2012

We give an explicit construction of a large subset of F^n, where F is a finite field, that has small intersection with any affine variety of fixed dimension and bounded degree. Our construction generalizes a recent result of Dvir and Lovett (STOC 2012) who considered varieties of degree one (affine subspaces).
High Speed Compressed Sensing Reconstruction in Dynamic Parallel MRI Using Augmented Lagrangian and Parallel Processing
Venue: arXiv cs.IT 2012
Year: 2012

Magnetic Resonance Imaging (MRI) is one of the fields that the compressed sensing theory is well utilized to reduce the scan time significantly leading to faster imaging or higher resolution images. It has been shown that a small fraction of the overall measurements are sufficient to reconstruct images with the combination of compressed sensing and parallel imaging. Various reconstruction algorithms has been proposed for compressed sensing, among which Augmented Lagrangian based methods have been shown to often perform better than others for many different applications. In this paper, we propose new Augmented Lagrangian based solutions to the compressed sensing reconstruction problem with analysis and synthesis prior formulations. We also propose a computational method which makes use of properties of the sampling pattern to significantly improve the speed of the reconstruction for the proposed algorithms in Cartesian sampled MRI. The proposed algorithms are shown to outperform earlier methods especially for the case of dynamic MRI for which the transfer function tends to be a very large matrix and significantly ill conditioned. It is also demonstrated that the proposed algorithm can be accelerated much further than other methods in case of a parallel implementation with graphics processing units (GPUs).
A Novel Training Algorithm for HMMs with Partial and Noisy Access to the States
Venue: arXiv cs.LG 2012
Year: 2012

This paper proposes a new estimation algorithm for the parameters of an HMM as to best account for the observed data. In this model, in addition to the observation sequence, we have \emph{partial} and \emph{noisy} access to the hidden state sequence as side information. This access can be seen as "partial labeling" of the hidden states. Furthermore, we model possible mislabeling in the side information in a joint framework and derive the corresponding EM updates accordingly. In our simulations, we observe that using this side information, we considerably improve the state recognition performance, up to 70%, with respect to the "achievable margin" defined by the baseline algorithms. Moreover, our algorithm is shown to be robust to the training conditions.
Truthfulness, Proportional Fairness, and Efficiency
Venue: arXiv cs.GT 2012
Year: 2012

How does one allocate a collection of resources to a set of strategic agents in a fair and efficient manner without using money? For in many scenarios it is not feasible to use money to compensate agents for otherwise unsatisfactory outcomes. This paper studies this question, looking at both fairness and efficiency measures. We employ the proportionally fair solution, which is a well-known fairness concept for money-free settings. But although finding a proportionally fair solution is computationally tractable, it cannot be implemented in a truthful fashion. Consequently, we seek approximate solutions. We give several truthful mechanisms which achieve proportional fairness in an approximate sense. We use a strong notion of approximation, requiring the mechanism to give each agent a good approximation of its proportionally fair utility. In particular, one of our mechanisms provides a better and better approximation factor as the minimum demand for every good increases. A motivating example is provided by the massive privatization auction in the Czech republic in the early 90s. With regard to efficiency, prior work has shown a lower bound of 0.5 on the approximation factor of any swap-dictatorial mechanism approximating a social welfare measure even for the two agents and multiple goods case. We surpass this lower bound by designing a non-swap-dictatorial mechanism for this case. Interestingly, the new mechanism builds on the notion of proportional fairness.
Estimating a Random Walk First-Passage Time from Noisy or Delayed Observations
Venue: arXiv cs.IT 2012
Year: 2012

A random walk (or a Wiener process), possibly with drift, is observed in a noisy or delayed fashion. The problem considered in this paper is to estimate the first time τthe random walk reaches a given level. Specifically, the p-moment (p\geq 1) optimization problem \inf_η\ex|η-τ|^p is investigated where the infimum is taken over the set of stopping times that are defined on the observation process. When there is no drift, optimal stopping rules are characterized for both types of observations. When there is a drift, upper and lower bounds on \inf_η\ex|η-τ|^p are established for both types of observations. The bounds are tight in the large-level regime for noisy observations and in the large-level-large-delay regime for delayed observations. Noteworthy, for noisy observations there exists an asymptotically optimal stopping rule that is a function of a single observation. Simulation results are provided that corroborate the validity of the results for non-asymptotic settings.
Successive Refinement with Decoder Cooperation and its Channel Coding Duals
Venue: arXiv cs.IT 2012
Year: 2012

We study cooperation in multi terminal source coding models involving successive refinement. Specifically, we study the case of a single encoder and two decoders, where the encoder provides a common description to both the decoders and a private description to only one of the decoders. The decoders cooperate via cribbing, i.e., the decoder with access only to the common description is allowed to observe, in addition, a deterministic function of the reconstruction symbols produced by the other. We characterize the fundamental performance limits in the respective settings of non-causal, strictly-causal and causal cribbing. We use a new coding scheme, referred to as Forward Encoding and Block Markov Decoding, which is a variant of one recently used by Cuff and Zhao for coordination via implicit communication. Finally, we use the insight gained to introduce and solve some dual channel coding scenarios involving Multiple Access Channels with cribbing.
Computational Complexity Analysis of Multi-Objective Genetic Programming
Venue: arXiv cs.NE 2012
Year: 2012

The computational complexity analysis of genetic programming (GP) has been started recently by analyzing simple (1+1) GP algorithms for the problems ORDER and MAJORITY. In this paper, we study how taking the complexity as an additional criteria influences the runtime behavior. We consider generalizations of ORDER and MAJORITY and present a computational complexity analysis of (1+1) GP using multi-criteria fitness functions that take into account the original objective and the complexity of a syntax tree as a secondary measure. Furthermore, we study the expected time until population-based multi-objective genetic programming algorithms have computed the Pareto front when taking the complexity of a syntax tree as an equally important objective.