OpenRaiser/Intern-Atlas huggingface dataset:
Ontology Based Information Extraction for Disease Intelligence
Venue: arXiv cs.AI 2012
Year: 2012

Disease Intelligence (DI) is based on the acquisition and aggregation of fragmented knowledge of diseases at multiple sources all over the world to provide valuable information to doctors, researchers and information seeking community. Some diseases have their own characteristics changed rapidly at different places of the world and are reported on documents as unrelated and heterogeneous information which may be going unnoticed and may not be quickly available. This research presents an Ontology based theoretical framework in the context of medical intelligence and country/region. Ontology is designed for storing information about rapidly spreading and changing diseases with incorporating existing disease taxonomies to genetic information of both humans and infectious organisms. It further maps disease symptoms to diseases and drug effects to disease symptoms. The machine understandable disease ontology represented as a website thus allows the drug effects to be evaluated on disease symptoms and exposes genetic involvements in the human diseases. Infectious agents which have no known place in an existing classification but have data on genetics would still be identified as organisms through the intelligence of this system. It will further facilitate researchers on the subject to try out different solutions for curing diseases.
Color scales that are effective in both color and grayscale
Venue: arXiv cs.GR 2012
Year: 2012

We consider the problem of finding a color scale which performs well when converted to a grayscale. We assume that each color is converted to a shade of gray with the same intensity as the color. We also assume that the color scales have a linear variation of intensity and hue, and find scales which maximize the average chroma (or "colorfulness") of the colors. We find two classes of solutions, which traverse the color wheel in opposite directions. The two classes of scales start with hues near cyan and red. The average chroma of the scales are 65-77% those of the pure colors.
Maximizing System Throughput Using Cooperative Sensing in Multi-Channel Cognitive Radio Networks
Venue: arXiv cs.NI 2012
Year: 2012

In Cognitive Radio Networks (CRNs), unlicensed users are allowed to access the licensed spectrum when it is not currently being used by primary users (PUs). In this paper, we study the throughput maximization problem for a multi-channel CRN where each SU can only sense a limited number of channels. We show that this problem is strongly NP-hard, and propose an approximation algorithm with a factor at least $1/2μ$ where $μ\in [1,2]$ is a system parameter reflecting the sensing capability of SUs across channels and their sensing budgets. This performance guarantee is achieved by exploiting a nice structural property of the objective function and constructing a particular matching. Our numerical results demonstrate the advantage of our algorithm compared with both a random and a greedy sensing assignment algorithm.
Pricing and Intervention in Slotted-Aloha: Technical Report
Venue: arXiv cs.GT 2012
Year: 2012

In many wireless communication networks a common channel is shared by multiple users who must compete to gain access to it. The operation of the network by self-interested and strategic users usually leads to the overuse of the channel resources and to substantial inefficiencies. Hence, incentive schemes are needed to overcome the inefficiencies of non-cooperative equilibrium. In this work we consider a slotted-Aloha like random access protocol and two incentive schemes: pricing and intervention. We provide some criteria for the designer of the protocol to choose one scheme between them and to design the best policy for the selected scheme, depending on the system parameters. Our results show that intervention can achieve the maximum efficiency in the perfect monitoring scenario. In the imperfect monitoring scenario, instead, the performance of the system depends on the information held by the different entities and, in some cases, there exists a threshold for the number of users such that, for a number of users lower than the threshold, intervention outperforms pricing, whereas, for a number of users higher than the threshold pricing outperforms intervention.
Fuzzy Keyword Search over Encrypted Data using Symbol-Based Trie-traverse Search Scheme in Cloud Computing
Venue: arXiv cs.CR 2012
Year: 2012

We exploit edit distance to quantify keywords similarity and develop two advanced techniques on constructing fuzzy keyword sets, which achieve optimized storage and representation overheads. We further propose a brand new symbol-based trie-traverse searching scheme, where a multi-way tree structure is built up using symbols transformed from the resulted fuzzy keyword sets. Through rigorous security analysis, we show that our proposed solution is secure and privacy-preserving, while correctly realizing the goal of fuzzy keyword search. Extensive experimental results demonstrate the efficiency of the proposed solution.
A Bayesian Interpretation of the Particle Swarm Optimization and Its Kernel Extension
Venue: arXiv cs.NE 2012
Year: 2012

Particle swarm optimization is a popular method for solving difficult optimization problems. There have been attempts to formulate the method in formal probabilistic or stochastic terms (e.g. bare bones particle swarm) with the aim to achieve more generality and explain the practical behavior of the method. Here we present a Bayesian interpretation of the particle swarm optimization. This interpretation provides a formal framework for incorporation of prior knowledge about the problem that is being solved. Furthermore, it also allows to extend the particle optimization method through the use of kernel functions that represent the intermediary transformation of the data into a different space where the optimization problem is expected to be easier to be resolved, such transformation can be seen as a form of prior knowledge about the nature of the optimization problem. We derive from the general Bayesian formulation the commonly used particle swarm methods as particular cases.
Trust Management in Cloud Computing: A Critical Review
Venue: arXiv cs.DC 2012
Year: 2012

Cloud computing has been attracting the attention of several researchers both in the academia and the industry as it provides many opportunities for organizations by offering a range of computing services. For cloud computing to become widely adopted by both the enterprises and individuals, several issues have to be solved. A key issue that needs special attention is security of clouds, and trust management is an important component of cloud security. In this paper, the authors look at what trust is and how trust has been applied in distributed computing. Trust models proposed for various distributed system has then been summarized. The trust management systems proposed for cloud computing have been investigated with special emphasis on their capability, applicability in practical heterogonous cloud environment and implementabilty. Finally, the proposed models/systems have been compared with each other based on a selected set of cloud computing parameters in a table.
Extending the Interaction Nets Calculus by Generic Rules
Venue: arXiv cs.LO 2012
Year: 2012

We extend the textual calculus for interaction nets by generic rules and propose constraints to preserve uniform confluence. Furthermore, we discuss the implementation of generic rules in the language inets, which is based on the lightweight interaction nets calculus.
Interaction-Oriented Software Engineering: Concepts and Principles
Venue: arXiv cs.SE 2012
Year: 2012

Following established tradition, software engineering today is rooted in a conceptually centralized way of thinking. The primary SE artifact is a specification of a machine -- a computational artifact -- that would meet the (elicited and) stated requirements. Therein lies a fundamental mismatch with (open) sociotechnical systems, which involve multiple autonomous social participants or principals who interact with each other to further their individual goals. No central machine governs the behaviors of the various principals. We introduce Interaction-Oriented Software Engineering (IOSE) as an approach expressly suited to the needs of open sociotechnical systems. In IOSE, specifying a system amounts to specifying the interactions among the principals as protocols. IOSE reinterprets the classical software engineering principles of modularity, abstraction, separation of concerns, and encapsulation in a manner that accords with the realities of sociotechnical systems. To highlight the novelty of IOSE, we show where well-known SE methodologies, especially those that explicitly aim to address either sociotechnical systems or the modeling of interactions among autonomous principals, fail to satisfy the IOSE principles.
Modeling Earthen Dike Stability: Sensitivity Analysis and Automatic Calibration of Diffusivities Based on Live Sensor Data
Venue: arXiv cs.CE 2012
Year: 2012

The paper describes concept and implementation details of integrating a finite element module for dike stability analysis Virtual Dike into an early warning system for flood protection. The module operates in real-time mode and includes fluid and structural sub-models for simulation of porous flow through the dike and for dike stability analysis. Real-time measurements obtained from pore pressure sensors are fed into the simulation module, to be compared with simulated pore pressure dynamics. Implementation of the module has been performed for a real-world test case - an earthen levee protecting a sea-port in Groningen, the Netherlands. Sensitivity analysis and calibration of diffusivities have been performed for tidal fluctuations. An algorithm for automatic diffusivities calibration for a heterogeneous dike is proposed and studied. Analytical solutions describing tidal propagation in one-dimensional saturated aquifer are employed in the algorithm to generate initial estimates of diffusivities.
Education for All: Remote testing system with gesture recognition and recording
Venue: arXiv cs.CY 2012
Year: 2012

Etymologically, in Latin expresses "educare", that means to bring out, or be engaged in the infinite process of learning to present to the society as a valuable citizen. However, unfortunately especially in third world countries, education cannot be achieved due to, lack of inorganic and organic resources. However, many third world countries have embraced the concepts such as One Laptop per Child, facilitating the students to learn. The effective adaptation of these concepts has being launched through many government and non-government projects, providing inorganic resources. However, inorganic resources alone cannot provide quality education, as learning needs assessment procedures, feedback generators and trainers who could guide the students to gain knowledge. This paper attempts to introduce an acceptable solution that can be used to address facilitating resources to enhance the learning experience through enabling organic resources such as teachers, instructors and trainers on a remote mode through technology. This paper introduces a software system that is used to design and distribute examinations and detect gestures of students while answering remotely. The feature enables the teacher or instructor to gain a better understanding about the learner's attitude when taking the assessment. The content of the paper is organized to give the basic idea of the system and it includes description of the system and practical effectiveness of the system with evaluations from different views. A java enabled computer with a webcam and internet access is the minimum requirements to be able to use the proposed system. The development platform is based on java, with the use of "Chilkat" to maintain an asynchronous connection with the FTP server. "iGesture" and "Yuille" approach play major role in gesture detection and recognition.
Close-Form Design of Antenna-Constrained Multi-Cell Multi-User Downlink Interference Alignment
Venue: arXiv cs.IT 2012
Year: 2012

This paper investigates the downlink channels in multi-cell multi-user interfering networks. The goal is to propose close-form designs to obtain degrees of freedom (DoF) in high SNR region for the network composed of base stations (BS) as transmitters and mobile stations (MS) as receivers. Consider the realistic system, both BS and MS have finite antennas, so that the design of interference alignment is highly constrained by the feasibility conditions. The focus of design is to explore potential opportunities of alignment in the subspace both from the BS transmit side and from the MS receive side. The new IA schemes for cellular downlink channels are in the form of causal dynamic processes in contrary to conventional static IA schemes. For different implementations, system conditions are compared from all aspects, which include antenna usage, CSI overhead and computational complexity. This research scope covers a wide range of typical multi-cell multi-user network models. The first one is a $K$-cell fully connected cellular network; the second one is a Wyner cyclic cellular network with two adjacent interfering links; the third one is a Wyner cyclic cellular network with single adjacent interfering link considering cell-edge and cell-interior users respectively.
Efficient Superimposition Recovering Algorithm
Venue: arXiv cs.CV 2012
Year: 2012

In this article, we address the issue of recovering latent transparent layers from superimposition images. Here, we assume we have the estimated transformations and extracted gradients of latent layers. To rapidly recover high-quality image layers, we propose an Efficient Superimposition Recovering Algorithm (ESRA) by extending the framework of accelerated gradient method. In addition, a key building block (in each iteration) in our proposed method is the proximal operator calculating. Here we propose to employ a dual approach and present our Parallel Algorithm with Constrained Total Variation (PACTV) method. Our recovering method not only reconstructs high-quality layers without color-bias problem, but also theoretically guarantees good convergence performance.
I Have the Proof: Providing Proofs of Past Data Possession in Cloud Forensics
Venue: arXiv cs.CR 2012
Year: 2012

Cloud computing has emerged as a popular computing paradigm in recent years. However, today's cloud computing architectures often lack support for computer forensic investigations. A key task of digital forensics is to prove the presence of a particular file in a given storage system. Unfortunately, it is very hard to do so in a cloud given the black-box nature of clouds and the multi-tenant cloud models. In clouds, analyzing the data from a virtual machine instance or data stored in a cloud storage only allows us to investigate the current content of the cloud storage, but not the previous contents. In this paper, we introduce the idea of building proofs of past data possession in the context of a cloud storage service. We present a scheme for creating such proofs and evaluate its performance in a real cloud provider. We also discuss how this proof of past data possession can be used effectively in cloud forensics.
A Sensing Policy Based on Confidence Bounds and a Restless Multi-Armed Bandit Model
Venue: arXiv cs.IT 2012
Year: 2012

A sensing policy for the restless multi-armed bandit problem with stationary but unknown reward distributions is proposed. The work is presented in the context of cognitive radios in which the bandit problem arises when deciding which parts of the spectrum to sense and exploit. It is shown that the proposed policy attains asymptotically logarithmic weak regret rate when the rewards are bounded independent and identically distributed or finite state Markovian. Simulation results verifying uniformly logarithmic weak regret are also presented. The proposed policy is a centrally coordinated index policy, in which the index of a frequency band is comprised of a sample mean term and a confidence term. The sample mean term promotes spectrum exploitation whereas the confidence term encourages exploration. The confidence term is designed such that the time interval between consecutive sensing instances of any suboptimal band grows exponentially. This exponential growth between suboptimal sensing time instances leads to logarithmically growing weak regret. Simulation results demonstrate that the proposed policy performs better than other similar methods in the literature.
Free-surface flow simulations for discharge-based operation of hydraulic structure gates
Venue: arXiv cs.CE 2012
Year: 2012

We combine non-hydrostatic flow simulations of the free surface with a discharge model based on elementary gate flow equations for decision support in operation of hydraulic structure gates. A water level-based gate control used in most of today's general practice does not take into account the fact that gate operation scenarios producing similar total discharged volumes and similar water levels may have different local flow characteristics. Accurate and timely prediction of local flow conditions around hydraulic gates is important for several aspects of structure management: ecology, scour, flow-induced gate vibrations and waterway navigation. The modelling approach is described and tested for a multi-gate sluice structure regulating discharge from a river to the sea. The number of opened gates is varied and the discharge is stabilized with automated control by varying gate openings. The free-surface model was validated for discharge showing a correlation coefficient of 0.994 compared to experimental data. Additionally, we show the analysis of CFD results for evaluating bed stability and gate vibrations.
Rate-Distortion Analysis of Multiview Coding in a DIBR Framework
Venue: arXiv cs.CV 2012
Year: 2012

Depth image based rendering techniques for multiview applications have been recently introduced for efficient view generation at arbitrary camera positions. Encoding rate control has thus to consider both texture and depth data. Due to different structures of depth and texture images and their different roles on the rendered views, distributing the available bit budget between them however requires a careful analysis. Information loss due to texture coding affects the value of pixels in synthesized views while errors in depth information lead to shift in objects or unexpected patterns at their boundaries. In this paper, we address the problem of efficient bit allocation between textures and depth data of multiview video sequences. We adopt a rate-distortion framework based on a simplified model of depth and texture images. Our model preserves the main features of depth and texture images. Unlike most recent solutions, our method permits to avoid rendering at encoding time for distortion estimation so that the encoding complexity is not augmented. In addition to this, our model is independent of the underlying inpainting method that is used at decoder. Experiments confirm our theoretical results and the efficiency of our rate allocation strategy.
Hash in a Flash: Hash Tables for Solid State Devices
Venue: arXiv cs.DB 2012
Year: 2012

In recent years, information retrieval algorithms have taken center stage for extracting important data in ever larger datasets. Advances in hardware technology have lead to the increasingly wide spread use of flash storage devices. Such devices have clear benefits over traditional hard drives in terms of latency of access, bandwidth and random access capabilities particularly when reading data. There are however some interesting trade-offs to consider when leveraging the advanced features of such devices. On a relative scale writing to such devices can be expensive. This is because typical flash devices (NAND technology) are updated in blocks. A minor update to a given block requires the entire block to be erased, followed by a re-writing of the block. On the other hand, sequential writes can be two orders of magnitude faster than random writes. In addition, random writes are degrading to the life of the flash drive, since each block can support only a limited number of erasures. TF-IDF can be implemented using a counting hash table. In general, hash tables are a particularly challenging case for the flash drive because this data structure is inherently dependent upon the randomness of the hash function, as opposed to the spatial locality of the data. This makes it difficult to avoid the random writes incurred during the construction of the counting hash table for TF-IDF. In this paper, we will study the design landscape for the development of a hash table for flash storage devices. We demonstrate how to effectively design a hash table with two related hash functions, one of which exhibits a data placement property with respect to the other. Specifically, we focus on three designs based on this general philosophy and evaluate the trade-offs among them along the axes of query performance, insert and update times and I/O time through an implementation of the TF-IDF algorithm.
From the Closed Classical Algorithmic Universe to an Open World of Algorithmic Constellations
Venue: arXiv cs.OH 2012
Year: 2012

In this paper we analyze methodological and philosophical implications of algorithmic aspects of unconventional computation. At first, we describe how the classical algorithmic universe developed and analyze why it became closed in the conventional approach to computation. Then we explain how new models of algorithms turned the classical closed algorithmic universe into the open world of algorithmic constellations, allowing higher flexibility and expressive power, supporting constructivism and creativity in mathematical modeling. As Goedels undecidability theorems demonstrate, the closed algorithmic universe restricts essential forms of mathematical cognition. In contrast, the open algorithmic universe, and even more the open world of algorithmic constellations, remove such restrictions and enable new, richer understanding of computation.
Application of Data mining in Protein sequence Classification
Venue: arXiv cs.CE 2012
Year: 2012

Protein sequence classification involves feature selection for accurate classification. Popular protein sequence classification techniques involve extraction of specific features from the sequences. Researchers apply some well-known classification techniques like neural networks, Genetic algorithm, Fuzzy ARTMAP,Rough Set Classifier etc for accurate classification. This paper presents a review is with three different classification models such as neural network model, fuzzy ARTMAP model and Rough set classifier model. This is followed by a new technique for classifying protein sequences. The proposed model is typically implemented with an own designed tool and tries to reduce the computational overheads encountered by earlier approaches and increase the accuracy of classification
An Effective Method for Fingerprint Classification
Venue: arXiv cs.CV 2012
Year: 2012

This paper presents an effective method for fingerprint classification using data mining approach. Initially, it generates a numeric code sequence for each fingerprint image based on the ridge flow patterns. Then for each class, a seed is selected by using a frequent itemsets generation technique. These seeds are subsequently used for clustering the fingerprint images. The proposed method was tested and evaluated in terms of several real-life datasets and a significant improvement in reducing the misclassification errors has been noticed in comparison to its other counterparts.