OpenRaiser/Intern-Atlas huggingface dataset:
Software Reuse in Cardiology Related Medical Database Using K-Means Clustering Technique
Venue: arXiv cs.SE 2013
Year: 2013

Software technology based on reuse is identified as a process of designing software for the reuse purpose. The software reuse is a process in which the existing software is used to build new software. A metric is a quantitative indicator of an attribute of an item or thing. Reusability is the likelihood for a segment of source code that can be used again to add new functionalities with slight or no modification. A lot of research has been projected using reusability in reducing code, domain, requirements, design etc., but very little work is reported using software reuse in medical domain. An attempt is made to bridge the gap in this direction, using the concepts of clustering and classifying the data based on the distance measures. In this paper cardiologic database is considered for study. The developed model will be useful for Doctors or Paramedics to find out the patients level in the cardiologic disease, deduce the medicines required in seconds and propose them to the patient. In order to measure the reusability K means clustering algorithm is used.
Quality Assessment of Pixel-Level ImageFusion Using Fuzzy Logic
Venue: arXiv cs.CV 2013
Year: 2013

Image fusion is to reduce uncertainty and minimize redundancy in the output while maximizing relevant information from two or more images of a scene into a single composite image that is more informative and is more suitable for visual perception or processing tasks like medical imaging, remote sensing, concealed weapon detection, weather forecasting, biometrics etc. Image fusion combines registered images to produce a high quality fused image with spatial and spectral information. The fused image with more information will improve the performance of image analysis algorithms used in different applications. In this paper, we proposed a fuzzy logic method to fuse images from different sensors, in order to enhance the quality and compared proposed method with two other methods i.e. image fusion using wavelet transform and weighted average discrete wavelet transform based image fusion using genetic algorithm (here onwards abbreviated as GA) along with quality evaluation parameters image quality index (IQI), mutual information measure (MIM), root mean square error (RMSE), peak signal to noise ratio (PSNR), fusion factor (FF), fusion symmetry (FS) and fusion index (FI) and entropy. The results obtained from proposed fuzzy based image fusion approach improves quality of fused image as compared to earlier reported methods, wavelet transform based image fusion and weighted average discrete wavelet transform based image fusion using genetic algorithm.
Multiuser SM-MIMO versus Massive MIMO: Uplink Performance Comparison
Venue: arXiv cs.IT 2013
Year: 2013

In this paper, we propose algorithms for signal detection in large-scale multiuser {\em spatial modulation multiple-input multiple-output (SM-MIMO)} systems. In large-scale SM-MIMO, each user is equipped with multiple transmit antennas (e.g., 2 or 4 antennas) but only one transmit RF chain, and the base station (BS) is equipped with tens to hundreds of (e.g., 128) receive antennas. In SM-MIMO, in a given channel use, each user activates any one of its multiple transmit antennas and the index of the activated antenna conveys information bits in addition to the information bits conveyed through conventional modulation symbols (e.g., QAM). We propose two different algorithms for detection of large-scale SM-MIMO signals at the BS; one is based on {\em message passing} and the other is based on {\em local search}. The proposed algorithms are shown to achieve very good performance and scale well. Also, for the same spectral efficiency, multiuser SM-MIMO outperforms conventional multiuser MIMO (recently being referred to as massive MIMO) by several dBs; for e.g., with 16 users, 128 antennas at the BS and 4 bpcu per user, SM-MIMO with 4 transmit antennas per user and 4-QAM outperforms massive MIMO with 1 transmit antenna per user and 16-QAM by about 4 to 5 dB at $10^{-3}$ uncoded BER. The SNR advantage of SM-MIMO over massive MIMO can be attributed to the following reasons: (i) because of the spatial index bits, SM-MIMO can use a lower-order QAM alphabet compared to that in massive MIMO to achieve the same spectral efficiency, and (ii) for the same spectral efficiency and QAM size, massive MIMO will need more spatial streams per user which leads to increased spatial interference.
On Unconditionally Secure Multiparty Computation for Realizing Correlated Equilibria in Games
Venue: arXiv cs.CR 2013
Year: 2013

In game theory, a trusted mediator acting on behalf of the players can enable the attainment of correlated equilibria, which may provide better payoffs than those available from the Nash equilibria alone. We explore the approach of replacing the trusted mediator with an unconditionally secure sampling protocol that jointly generates the players' actions. We characterize the joint distributions that can be securely sampled by malicious players via protocols using error-free communication. This class of distributions depends on whether players may speak simultaneously ("cheap talk") or must speak in turn ("polite talk"). In applying sampling protocols toward attaining correlated equilibria with rational players, we observe that security against malicious parties may be much stronger than necessary. We propose the concept of secure sampling by rational players, and show that many more distributions are feasible given certain utility functions. However, the payoffs attainable via secure sampling by malicious players are a dominant subset of the rationally attainable payoffs.
Category-Theoretic Quantitative Compositional Distributional Models of Natural Language Semantics
Venue: arXiv cs.CL 2013
Year: 2013

This thesis is about the problem of compositionality in distributional semantics. Distributional semantics presupposes that the meanings of words are a function of their occurrences in textual contexts. It models words as distributions over these contexts and represents them as vectors in high dimensional spaces. The problem of compositionality for such models concerns itself with how to produce representations for larger units of text by composing the representations of smaller units of text. This thesis focuses on a particular approach to this compositionality problem, namely using the categorical framework developed by Coecke, Sadrzadeh, and Clark, which combines syntactic analysis formalisms with distributional semantic representations of meaning to produce syntactically motivated composition operations. This thesis shows how this approach can be theoretically extended and practically implemented to produce concrete compositional distributional models of natural language semantics. It furthermore demonstrates that such models can perform on par with, or better than, other competing approaches in the field of natural language processing. There are three principal contributions to computational linguistics in this thesis. The first is to extend the DisCoCat framework on the syntactic front and semantic front, incorporating a number of syntactic analysis formalisms and providing learning procedures allowing for the generation of concrete compositional distributional models. The second contribution is to evaluate the models developed from the procedures presented here, showing that they outperform other compositional distributional models present in the literature. The third contribution is to show how using category theory to solve linguistic problems forms a sound basis for research, illustrated by examples of work on this topic, that also suggest directions for future research.
Tasks and architecture of documentation subsystem in multi-level modeling environment MARS
Venue: arXiv cs.OH 2013
Year: 2013

The article describes the automated documentation system designed to generate reports on research conducted by computer complex technical objects and systems in multi-level modeling environment «MARS». We defined the purposes, tasks and abilities of documentation system and examined the types and structure of documents, and gave an example of its practical use
3D Cache Hierarchy Optimization
Venue: arXiv cs.AR 2013
Year: 2013

3D integration has the potential to improve the scalability and performance of Chip Multiprocessors (CMP). A closed form analytical solution for optimizing 3D CMP cache hierarchy is developed. It allows optimal partitioning of the cache hierarchy levels into 3D silicon layers and optimal allocation of area among cache hierarchy levels under constrained area and power budgets. The optimization framework is extended by incorporating the impact of multithreaded data sharing on the private cache miss rate. An analytical model for cache access time as a function of cache size and a number of 3D partitions is proposed and verified using CACTI simulation.
A Survey on the State of the Practice in Distributed Software Development: Criteria for Task Allocation
Venue: arXiv cs.SE 2013
Year: 2013

The allocation of tasks can be seen as a success-critical management activity in distributed development projects. However, such task allocation is still one of the major challenges in global software development due to an insufficient understanding of the criteria that influence task allocation decisions. This article presents a qualitative study aimed at identifying and understanding such criteria that are used in practice. Based on interviews with managers from selected software development organizations, criteria currently applied in industry are identified. One important result is, for instance, that the sourcing strategy and the type of software to be developed have a significant effect on the applied criteria. The article presents the goals, design, and results of the study as well as an overview of related and future work.
Architectural improvements and 28 nm FPGA implementation of the APEnet+ 3D Torus network for hybrid HPC systems
Venue: arXiv cs.AR 2013
Year: 2013

Modern Graphics Processing Units (GPUs) are now considered accelerators for general purpose computation. A tight interaction between the GPU and the interconnection network is the strategy to express the full potential on capability computing of a multi-GPU system on large HPC clusters; that is the reason why an efficient and scalable interconnect is a key technology to finally deliver GPUs for scientific HPC. In this paper we show the latest architectural and performance improvement of the APEnet+ network fabric, a FPGA-based PCIe board with 6 fully bidirectional off-board links with 34 Gbps of raw bandwidth per direction, and X8 Gen2 bandwidth towards the host PC. The board implements a Remote Direct Memory Access (RDMA) protocol that leverages upon peer-to-peer (P2P) capabilities of Fermi- and Kepler-class NVIDIA GPUs to obtain real zero-copy, low-latency GPU-to-GPU transfers. Finally, we report on the development activities for 2013 focusing on the adoption of the latest generation 28 nm FPGAs and the preliminary tests performed on this new platform.
Demand Side Management in Smart Grids using a Repeated Game Framework
Venue: arXiv cs.SY 2013
Year: 2013

Demand side management (DSM) is a key solution for reducing the peak-time power consumption in smart grids. To provide incentives for consumers to shift their consumption to off-peak times, the utility company charges consumers differential pricing for using power at different times of the day. Consumers take into account these differential prices when deciding when and how much power to consume daily. Importantly, while consumers enjoy lower billing costs when shifting their power usage to off-peak times, they also incur discomfort costs due to the altering of their power consumption patterns. Existing works propose stationary strategies for the myopic consumers to minimize their short-term billing and discomfort costs. In contrast, we model the interaction emerging among self-interested, foresighted consumers as a repeated energy scheduling game and prove that the stationary strategies are suboptimal in terms of long-term total billing and discomfort costs. Subsequently, we propose a novel framework for determining optimal nonstationary DSM strategies, in which consumers can choose different daily power consumption patterns depending on their preferences, routines, and needs. As a direct consequence of the nonstationary DSM policy, different subsets of consumers are allowed to use power in peak times at a low price. The subset of consumers that are selected daily to have their joint discomfort and billing costs minimized is determined based on the consumers' power consumption preferences as well as on the past history of which consumers have shifted their usage previously. Importantly, we show that the proposed strategies are incentive-compatible. Simulations confirm that, given the same peak-to-average ratio, the proposed strategy can reduce the total cost (billing and discomfort costs) by up to 50% compared to existing DSM strategies.
Mining Crash Fix Patterns
Venue: arXiv cs.SE 2013
Year: 2013

During the life cycle of software development, developers have to fix different kinds of bugs reported by testers or end users. The efficiency and effectiveness of fixing bugs have a huge impact on the reliability of the software as well as the productivity of the development team. Software companies usually spend a large amount of money and human resources on the testing and bug fixing departments. As a result, a better and more reliable way to fix bugs is highly desired by them. In order to achieve such goal, in depth studies on the characteristics of bug fixes from well maintained, highly popular software projects are necessary. In this paper, we study the bug fixing histories extracted from the Eclipse project, a well maintained, highly popular open source project. After analyzing more than 36,000 bugs that belongs to three major kinds of exception types, we are able to reveal some common fix types that are frequently used to fix certain kinds of program exceptions. Our analysis shows that almost all of the exceptions that belong to a certain exception can be fixed by less than ten fix types. Our result implies that most of the bugs in software projects can be and should be fixed by only a few common fix patterns.
Unsupervised learning human's activities by overexpressed recognized non-speech sounds
Venue: arXiv cs.AI 2013
Year: 2013

Human activity and environment produces sounds such as, at home, the noise produced by water, cough, or television. These sounds can be used to determine the activity in the environment. The objective is to monitor a person's activity or determine his environment using a single low cost microphone by sound analysis. The purpose is to adapt programs to the activity or environment or detect abnormal situations. Some patterns of over expressed repeatedly in the sequences of recognized sounds inter and intra environment allow to characterize activities such as the entrance of a person in the house, or a tv program watched. We first manually annotated 1500 sounds of daily life activity of old persons living at home recognized sounds. Then we inferred an ontology and enriched the database of annotation with a crowed sourced manual annotation of 7500 sounds to help with the annotation of the most frequent sounds. Using learning sound algorithms, we defined 50 types of the most frequent sounds. We used this set of recognizable sounds as a base to tag sounds and put tags on them. By using over expressed number of motifs of sequences of the tags, we were able to categorize using only a single low-cost microphone, complex activities of daily life of a persona at home as watching TV, entrance in the apartment of a person, or phone conversation including detecting unknown activities as repeated tasks performed by users.
Fast Tracking via Spatio-Temporal Context Learning
Venue: arXiv cs.CV 2013
Year: 2013

In this paper, we present a simple yet fast and robust algorithm which exploits the spatio-temporal context for visual tracking. Our approach formulates the spatio-temporal relationships between the object of interest and its local context based on a Bayesian framework, which models the statistical correlation between the low-level features (i.e., image intensity and position) from the target and its surrounding regions. The tracking problem is posed by computing a confidence map, and obtaining the best target location by maximizing an object location likelihood function. The Fast Fourier Transform is adopted for fast learning and detection in this work. Implemented in MATLAB without code optimization, the proposed tracker runs at 350 frames per second on an i7 machine. Extensive experimental results show that the proposed algorithm performs favorably against state-of-the-art methods in terms of efficiency, accuracy and robustness.
On the Number of Edges of Fan-Crossing Free Graphs
Venue: arXiv cs.CG 2013
Year: 2013

A graph drawn in the plane with n vertices is k-fan-crossing free for k > 1 if there are no k+1 edges $g,e_1,...e_k$, such that $e_1,e_2,...e_k$ have a common endpoint and $g$ crosses all $e_i$. We prove a tight bound of 4n-8 on the maximum number of edges of a 2-fan-crossing free graph, and a tight 4n-9 bound for a straight-edge drawing. For k > 2, we prove an upper bound of 3(k-1)(n-2) edges. We also discuss generalizations to monotone graph properties.
Information dissemination processes in directed social networks
Venue: arXiv cs.SI 2013
Year: 2013

Social networks can have asymmetric relationships. In the online social network Twitter, a follower receives tweets from a followed person but the followed person is not obliged to subscribe to the channel of the follower. Thus, it is natural to consider the dissemination of information in directed networks. In this work we use the mean-field approach to derive differential equations that describe the dissemination of information in a social network with asymmetric relationships. In particular, our model reflects the impact of the degree distribution on the information propagation process. We further show that for an important subclass of our model, the differential equations can be solved analytically.
The Complexity of Optimal Multidimensional Pricing
Venue: arXiv cs.GT 2013
Year: 2013

We resolve the complexity of revenue-optimal deterministic auctions in the unit-demand single-buyer Bayesian setting, i.e., the optimal item pricing problem, when the buyer's values for the items are independent. We show that the problem of computing a revenue-optimal pricing can be solved in polynomial time for distributions of support size 2, and its decision version is NP-complete for distributions of support size 3. We also show that the problem remains NP-complete for the case of identical distributions.
Large Margin Semi-supervised Structured Output Learning
Venue: arXiv cs.LG 2013
Year: 2013

In structured output learning, obtaining labelled data for real-world applications is usually costly, while unlabelled examples are available in abundance. Semi-supervised structured classification has been developed to handle large amounts of unlabelled structured data. In this work, we consider semi-supervised structural SVMs with domain constraints. The optimization problem, which in general is not convex, contains the loss terms associated with the labelled and unlabelled examples along with the domain constraints. We propose a simple optimization approach, which alternates between solving a supervised learning problem and a constraint matching problem. Solving the constraint matching problem is difficult for structured prediction, and we propose an efficient and effective hill-climbing method to solve it. The alternating optimization is carried out within a deterministic annealing framework, which helps in effective constraint matching, and avoiding local minima which are not very useful. The algorithm is simple to implement and achieves comparable generalization performance on benchmark datasets.
Pattern-Coupled Sparse Bayesian Learning for Recovery of Block-Sparse Signals
Venue: arXiv cs.IT 2013
Year: 2013

We consider the problem of recovering block-sparse signals whose structures are unknown \emph{a priori}. Block-sparse signals with nonzero coefficients occurring in clusters arise naturally in many practical scenarios. However, the knowledge of the block structure is usually unavailable in practice. In this paper, we develop a new sparse Bayesian learning method for recovery of block-sparse signals with unknown cluster patterns. Specifically, a pattern-coupled hierarchical Gaussian prior model is introduced to characterize the statistical dependencies among coefficients, in which a set of hyperparameters are employed to control the sparsity of signal coefficients. Unlike the conventional sparse Bayesian learning framework in which each individual hyperparameter is associated independently with each coefficient, in this paper, the prior for each coefficient not only involves its own hyperparameter, but also the hyperparameters of its immediate neighbors. In doing this way, the sparsity patterns of neighboring coefficients are related to each other and the hierarchical model has the potential to encourage structured-sparse solutions. The hyperparameters, along with the sparse signal, are learned by maximizing their posterior probability via an expectation-maximization (EM) algorithm. Numerical results show that the proposed algorithm presents uniform superiority over other existing methods in a series of experiments.
Analyzing the Optimal Neighborhood: Algorithms for Budgeted and Partial Connected Dominating Set Problems
Venue: arXiv cs.DS 2013
Year: 2013

We study partial and budgeted versions of the well studied connected dominating set problem. In the partial connected dominating set problem, we are given an undirected graph G = (V,E) and an integer n', and the goal is to find a minimum subset of vertices that induces a connected subgraph of G and dominates at least n' vertices. We obtain the first polynomial time algorithm with an O(\ln Δ) approximation factor for this problem, thereby significantly extending the results of Guha and Khuller (Algorithmica, Vol. 20(4), Pages 374-387, 1998) for the connected dominating set problem. We note that none of the methods developed earlier can be applied directly to solve this problem. In the budgeted connected dominating set problem, there is a budget on the number of vertices we can select, and the goal is to dominate as many vertices as possible. We obtain a (1/13)(1 - 1/e) approximation algorithm for this problem. Finally, we show that our techniques extend to a more general setting where the profit function associated with a subset of vertices is a monotone "special" submodular function. This generalization captures the connected dominating set problem with capacities and/or weighted profits as special cases. This implies a O(\ln q) approximation (where q denotes the quota) and an O(1) approximation algorithms for the partial and budgeted versions of these problems. While the algorithms are simple, the results make a surprising use of the greedy set cover framework in defining a useful profit function.
Power Control Protocols in VANET
Venue: arXiv cs.NI 2013
Year: 2013

Vehicular Ad hoc Networks (VANET) is one of the most challenging research area in the field of the Mobile Ad hoc Network (MANET), Power control is a critical issue in VANETwhere is should be managed carefully to help the channel to have high performance. In this paper a comparative study in the published protocols in the field of safety message dynamic power control will be presented and evaluated.