OpenRaiser/Intern-Atlas huggingface dataset:
Toward Refactoring of DMARF and GIPSY Case Studies -- a Team 10 SOEN6471-S14 Project Report
Venue: arXiv cs.SE 2014
Year: 2014

The intent of this report is to do a background study of the two given OSS case study systems namely GIPSY and DMARF. It is a wide research area in which different studies are being carried out to get the most out of it. It begins with a formal introduction of the two systems and advance with the complex architecture of both. GIPSY (General Intensional Programming System) is a multi-intensional programming system that delivers as a framework for compiling and executing programs written in Lucid Programming Languages. DMARF (Distributed Modular Audio Recognition Framework) is a Java based research platform that acts as a library in applications. As these systems are in their evolving phase and a lot of research is being done upon these topics, it gives us motivation to be a part of this research to get a deeper look into the architectures of both the systems. For the evaluation of quality of metrics of the two open source systems, we have used a tool namely Logiscope. It is a tool to automate the code reviews by providing information based on software metrics and graphs.
Toward Refactoring of DMARF and GIPSY Case Studies -- a Team 4 SOEN6471-S14 Project Report
Venue: arXiv cs.SE 2014
Year: 2014

Software Quality is a major concern in software engineering development in order to be competitive. Such a quality can be achieved by a possible technique called Refactoring where the systems external behavior of the system is not changed. Initially we present our work by analyzing the case studies of ongoing researches of DMARF and GIPSY by understanding their needs and requirements involving the major components in their respective systems. Later sections illustrate the conceptual architecture of these case studies, for this we have referenced the original architecture to draw the important candidate concepts presented in the system, and analyzing their associations with other concepts in the system and then compared this conceptual architecture with the original architectures. Later the document throws light on identifying the code smells exist in the architectures to find them and resolve to minimize the deeper problems. JDeodorant, SonarQube are the tools which we go across for identification and analyzing the source code quality, both these tools are available as an IDE plugin or as an open source platforms. Next is to identify the design patterns exist in the architectures along with their importance and need for existence in respective systems. Finally, the implication is towards introducing refactoring methods onto the smells which have been identified and possibly refactor them accordingly by applying appropriate refactoring methods and showcasing the respective tests to ensure that changes in the architecture does not change the behavior much.
Resource Allocation for Energy-Efficient Device-to-Device Communication in 4G Networks
Venue: arXiv cs.NI 2014
Year: 2014

Device-to-device (D2D) communications as an underlay of a LTE-A (4G) network can reduce the traffic load as well as power consumption in cellular networks by way of utilizing peer-to-peer links for users in proximity of each other. This would enable other cellular users to increment their traffic, and the aggregate traffic for all users can be significantly increased without requiring additional spectrum. However, D2D communications may increase interference to cellular users (CUs) and force CUs to increase their transmit power levels in order to maintain their required quality-of-service (QoS). This paper proposes an energy-efficient resource allocation scheme for D2D communications as an underlay of a fully loaded LTE-A (4G) cellular network. Simulations show that the proposed scheme allocates cellular uplink resources (transmit power and channel) to D2D pairs while maintaining the required QoS for D2D and cellular users and minimizing the total uplink transmit power for all users.
Efficient Polling Protocol for Decentralized Social Networks
Venue: arXiv cs.DC 2014
Year: 2014

We address the polling problem in social networks where individuals collaborate to choose the most favorite choice amongst some options, without divulging their vote and publicly exposing their potentially malicious actions. Given this social interaction model, Guerraoui et al. recently proposed polling protocols that do not rely on any central authority or cryptography system, using a simple secret sharing scheme along with verification procedures to accurately compute the poll's final result. However, these protocols can be deployed safely and efficiently provided that, inter alia, the social graph structure should be transformed into a ring structure-based overlay and the number of participating users is perfect square. Consequently, designing \emph{secure} and \emph{efficient} polling protocols regardless these constraints remains a challenging issue. In this paper, we present EPol, a simple decentralized polling protocol that relies on the current state of social graphs. More explicitly, we define one family of social graphs that satisfy what we call the $m$-broadcasting property (where $m$ is less than or equal to the minimum node degree) and show their structures enable low communication cost and constitute necessary and sufficient condition to ensure vote privacy and limit the impact of dishonest users on the accuracy of the polling output. Our protocol is effective to compute more precisely the final result. Furthermore, despite the use of richer social graph structures, the communication and spatial complexities of EPol are close to be linear.
Accelerating Correlation Power Analysis Using Graphics Processing Units
Venue: arXiv cs.PF 2014
Year: 2014

Correlation Power Analysis (CPA) is a type of power analysis based side channel attack that can be used to derive the secret key of encryption algorithms including DES (Data Encryption Standard) and AES (Advanced Encryption Standard). A typical CPA attack on unprotected AES is performed by analysing a few thousand power traces that requires about an hour of computational time on a general purpose CPU. Due to the severity of this situation, a large number of researchers work on countermeasures to such attacks. Verifying that a proposed countermeasure works well requires performing the CPA attack on about 1.5 million power traces. Such processing, even for a single attempt of verification on commodity hardware would run for several days making the verification process infeasible. Modern Graphics Processing Units (GPUs) have support for thousands of light weight threads, making them ideal for parallelizable algorithms like CPA. While the cost of a GPU being lesser than a high performance multicore server, still the GPU performance for this algorithm is many folds better than that of a multicore server. We present an algorithm and its implementation on GPU for CPA on 128-bit AES that is capable of executing 1300x faster than that on a single threaded CPU and more than 60x faster than that on a 32 threaded multicore server. We show that an attack that would take hours on the multicore server would take even less than a minute on a much cost effective GPU.
To Use or Not to Use: Graphics Processing Units for Pattern Matching Algorithms
Venue: arXiv cs.DC 2014
Year: 2014

String matching is an important part in today's computer applications and Aho-Corasick algorithm is one of the main string matching algorithms used to accomplish this. This paper discusses that when can the GPUs be used for string matching applications using the Aho-Corasick algorithm as a benchmark. We have to identify the best unit to run our string matching algorithm according to the performance of our devices and the applications. Sometimes CPU gives better performance than GPU and sometimes GPU gives better performance than CPU. Therefore, identifying this critical point is significant task for researchers who are using GPUs to improve the performance of their string matching applications based on string matching algorithms.
A Structured Hardware Software Architecture for Peptide Based Diagnosis of Baylisascaris Procyonis Infection (ICIAfS14)
Venue: arXiv cs.CE 2014
Year: 2014

The problem of inferring proteins from complex peptide cocktails (digestion products of biological samples) in shotgun proteomic workflow sets extreme demands on computational resources in respect of the required very high processing throughputs, rapid processing rates and reliability of results. This is exacerbated by the fact that, in general, a given protein cannot be defined by a fixed sequence of amino acids due to the existence of splice variants and isoforms of that protein. Therefore, the problem of protein inference could be considered as one of identifying sequences of amino acids with some limited tolerance. In the current paper a model-based hardware acceleration of a structured and practical inference approach is developed and validated on a mass spectrometry experiment of realistic size. We have achieved 10 times maximum speed-up in the co-designed workflow compared to a similar software-only workflow run on the processor used for co-design.
Models of robotic feeding, choice, and the survival mechanism
Venue: arXiv cs.RO 2014
Year: 2014

Diagrammatic models of feeding choices reveal fundamental robotic behaviors. Successful choices are reinforced by positive feedback, while unsuccessful ones by negative feedback. This paper will address robotic feeding by casually relating consequential behavior subtended by a strong dependence upon survival.
Improved texture image classification through the use of a corrosion-inspired cellular automaton
Venue: arXiv cs.CV 2014
Year: 2014

In this paper, the problem of classifying synthetic and natural texture images is addressed. To tackle this problem, an innovative method is proposed that combines concepts from corrosion modeling and cellular automata to generate a texture descriptor. The core processes of metal (pitting) corrosion are identified and applied to texture images by incorporating the basic mechanisms of corrosion in the transition function of the cellular automaton. The surface morphology of the image is analyzed before and during the application of the transition function of the cellular automaton. In each iteration the cumulative mass of corroded product is obtained to construct each of the attributes of the texture descriptor. In a final step, this texture descriptor is used for image classification by applying Linear Discriminant Analysis. The method was tested on the well-known Brodatz and Vistex databases. In addition, in order to verify the robustness of the method, its invariance to noise and rotation were tested. To that end, different variants of the original two databases were obtained through addition of noise to and rotation of the images. The results showed that the method is effective for texture classification according to the high success rates obtained in all cases. This indicates the potential of employing methods inspired on natural phenomena in other fields.
Impact of Energy Consumption on Multipath TCP Enabled Mobiles
Venue: arXiv cs.NI 2014
Year: 2014

Multiple accesses are common for most mobile devices today. This technological advance opens up a new design space for improving the communication performance of mobile devices. Multipath TCP is a TCP extension that enables using multiple network paths between two end systems for a single TCP connection, increasing performance and reliability. Meanwhile, when operating multiple active interfaces, multipath-TCP also consumes substantial more power and drains out bettery faster than using one interface. Thus, enabling Multipath TCP on mobile devices brings in new challenges. In this paper, we theoretically analyze the underlying design choices given by the Multipath TCP. In particular, we theoretically formulate the rela- tion between performance (throughput) and energy consumption for Multipath TCP. We find that sometime the throughput and energy consumption can be concurrently improved.
Unsupervised Learning through Prediction in a Model of Cortex
Venue: arXiv cs.NE 2014
Year: 2014

We propose a primitive called PJOIN, for "predictive join," which combines and extends the operations JOIN and LINK, which Valiant proposed as the basis of a computational theory of cortex. We show that PJOIN can be implemented in Valiant's model. We also show that, using PJOIN, certain reasonably complex learning and pattern matching tasks can be performed, in a way that involves phenomena which have been observed in cognition and the brain, namely memory-based prediction and downward traffic in the cortical hierarchy.
Analysis of Saturated Belief Propagation Decoding of Low-Density Parity-Check Codes
Venue: arXiv cs.IT 2014
Year: 2014

We consider the effect of log-likelihood ratio saturation on belief propagation decoder low-density parity-check codes. Saturation is commonly done in practice and is known to have a significant effect on error floor performance. Our focus is on threshold analysis and stability of density evolution. We analyze the decoder for standard low-density parity-check code ensembles and show that belief propagation decoding generally degrades gracefully with saturation. Stability of density evolution is, on the other hand, rather strongly effected by saturation and the asymptotic qualitative effect of saturation is similar to reduction by one of variable node degree. We also show under what conditions the block threshold for the saturated belief propagation corresponds with the bit threshold.
Learning from Labeled Features for Document Filtering
Venue: arXiv cs.IR 2014
Year: 2014

Existing document filtering systems learn user profiles based on user relevance feedback on documents. In some cases, users may have prior knowledge about what features are important. For example, a Spanish speaker may only want news written in Spanish, and thus a relevant document should contain the feature "Language: Spanish"; a researcher focusing on HIV knows an article with the medical subject "Subject: AIDS" is very likely to be relevant to him/her. Semi-structured documents with rich metadata are increasingly prevalent on the Internet. Motivated by the well-adopted faceted search interface in e-commerce, we study the exploitation of user prior knowledge on faceted features for semi-structured document filtering. We envision two faceted feedback mechanisms, and propose a novel user profile learning algorithm that can incorporate user feedback on features. To evaluate the proposed work, we use two data sets from the TREC filtering track, and conduct a user study on Amazon Mechanical Turk. Our experiment results show that user feedback on faceted features is useful for filtering. The proposed user profile learning algorithm can effectively learn from user feedback on both documents and features, and performs better than several existing methods.
Hole Detection and Shape-Free Representation and Double Landmarks Based Geographic Routing in Wireless Sensor Networks
Venue: arXiv cs.NI 2014
Year: 2014

In wireless sensor networks, an important issue of Geographic Routing is local minimum problem, which is caused by hole that blocks the greedy forwarding process. To avoid the long detour path, recent research focuses on detecting the hole in advance, then the nodes located on the boundary of the hole advertise the hole information to the nodes near the hole.
How to Correctly Deal With Pseudorandom Numbers in Manycore Environments - Application to GPU programming with Shoverand
Venue: arXiv cs.DC 2014
Year: 2014

Stochastic simulations are often sensitive to the source of randomness that character-izes the statistical quality of their results. Consequently, we need highly reliable Random Number Generators (RNGs) to feed such applications. Recent developments try to shrink the computa-tion time by relying more and more General Purpose Graphics Processing Units (GP-GPUs) to speed-up stochastic simulations. Such devices bring new parallelization possibilities, but they also introduce new programming difficulties. Since RNGs are at the base of any stochastic simulation, they also need to be ported to GP-GPU. There is still a lack of well-designed implementations of quality-proven RNGs on GP-GPU platforms. In this paper, we introduce ShoveRand, a frame-work defining common rules to generate random numbers uniformly on GP-GPU. Our framework is designed to cope with any GPU-enabled development platform and to expose a straightfor-ward interface to users. We also provide an existing RNG implementation with this framework to demonstrate its efficiency in both development and ease of use.
Interactive Retrieval Based on Wikipedia Concepts
Venue: arXiv cs.IR 2014
Year: 2014

This paper presents a new user feedback mechanism based on Wikipedia concepts for interactive retrieval. In this mechanism, the system presents to the user a group of Wikipedia concepts, and the user can choose those relevant to refine his/her query. To realize this mechanism, we propose methods to address two problems: 1) how to select a small number of possibly relevant Wikipedia concepts to show the user, and 2) how to re-rank retrieved documents given the user-identified Wikipedia concepts. Our methods are evaluated on three TREC data sets. The experiment results show that our methods can dramatically improve retrieval performances.
Loading Large Sparse Matrices Stored in Files in the Adaptive-Blocking Hierarchical Storage Format
Venue: arXiv cs.DC 2014
Year: 2014

The parallel algorithm for loading large sparse matrices from files into distributed memories of high performance computing (HPC) systems is presented. This algorithm was designed specially for matrices stored in files in the space-effcient adaptive-blocking hierarchical storage format (ABHSF). The algorithm can be used even if matrix storing and loading procedures use a different number of processes, different matrix-processes mapping, or different in-memory storage format. The file format based on the utilization of the HDF5 library is described as well. Finally, the presented experimental study evaluates the proposed algorithm empirically.
The random matrix regime of Maronna's M-estimator for observations corrupted by elliptical noises
Venue: arXiv cs.IT 2014
Year: 2014

This article studies the behavior of the Maronna robust scatter estimator $\hat{C}_N\in \mathbb{C}^{N\times N}$ of a sequence of observations $y_1,...,y_n$ which is composed of a $K$ dimensional signal drown in a heavy tailed noise, i.e $y_i=A_N s_i+x_i$ where $A_N \in \mathbb{C}^{N\times K}$ and $x_i$ is drawn from elliptical distribution. In particular, we prove that as the population dimension $N$, the number of observations $n$ and the rank of $A_N$ grow to infinity at the same pace and under some mild assumptions, the robust scatter matrix can be characterized by a random matrix $\hat{S}_N$ that follows a standard random model. Our analysis can be very useful for many applications of the fields of statistical inference and signal processing.
On Codes based on BCK-algebras
Venue: arXiv cs.IT 2014
Year: 2014

In this paper, we present some new connections between BCK- algebras and binary block codes.
From Logical to Distributional Models
Venue: arXiv cs.LO 2014
Year: 2014

The paper relates two variants of semantic models for natural language, logical functional models and compositional distributional vector space models, by transferring the logic and reasoning from the logical to the distributional models. The geometrical operations of quantum logic are reformulated as algebraic operations on vectors. A map from functional models to vector space models makes it possible to compare the meaning of sentences word by word.
Stochastic Relational Presheaves and Dynamic Logic for Contextuality
Venue: arXiv cs.LO 2014
Year: 2014

Presheaf models provide a formulation of labelled transition systems that is useful for, among other things, modelling concurrent computation. This paper aims to extend such models further to represent stochastic dynamics such as shown in quantum systems. After reviewing what presheaf models represent and what certain operations on them mean in terms of notions such as internal and external choices, composition of systems, and so on, I will show how to extend those models and ideas by combining them with ideas from other category-theoretic approaches to relational models and to stochastic processes. It turns out that my extension yields a transitional formulation of sheaf-theoretic structures that Abramsky and Brandenburger proposed to characterize non-locality and contextuality. An alternative characterization of contextuality will then be given in terms of a dynamic modal logic of the models I put forward.
A Kochen-Specker system has at least 22 vectors (extended abstract)
Venue: arXiv cs.DM 2014
Year: 2014

At the heart of the Conway-Kochen Free Will theorem and Kochen and Specker's argument against non-contextual hidden variable theories is the existence of a Kochen-Specker (KS) system: a set of points on the sphere that has no 0,1-coloring such that at most one of two orthogonal points are colored 1 and of three pairwise orthogonal points exactly one is colored 1. In public lectures, Conway encouraged the search for small KS systems. At the time of writing, the smallest known KS system has 31 vectors. Arends, Ouaknine and Wampler have shown that a KS system has at least 18 vectors, by reducing the problem to the existence of graphs with a topological embeddability and non-colorability property. The bottleneck in their search proved to be the sheer number of graphs on more than 17 vertices and deciding embeddability. Continuing their effort, we prove a restriction on the class of graphs we need to consider and develop a more practical decision procedure for embeddability to improve the lower bound to 22.
A 2-Categorical Analysis of Complementary Families, Quantum Key Distribution and the Mean King Problem
Venue: arXiv cs.LO 2014
Year: 2014

This paper explores the use of 2-categorical technology for describing and reasoning about complex quantum procedures. We give syntactic definitions of a family of complementary measurements, and of quantum key distribution, and show that they are equivalent. We then show abstractly that either structure gives a solution to the Mean King problem, which we also formulate 2-categorically.
A VC-dimension-based Outer Bound on the Zero-Error Capacity of the Binary Adder Channel
Venue: arXiv cs.IT 2014
Year: 2014

The binary adder is a two-user multiple access channel whose inputs are binary and whose output is the real sum of the inputs. While the Shannon capacity region of this channel is well known, little is known regarding its zero-error capacity region, and a large gap remains between the best inner and outer bounds. In this paper, we provide an improved outer bound for this problem. To that end, we introduce a soft variation of the Saur-Perles-Shelah Lemma, that is then used in conjunction with an outer bound for the Shannon capacity region with an additional common message.
Quantum Structure in Cognition and the Foundations of Human Reasoning
Venue: arXiv cs.AI 2014
Year: 2014

Traditional cognitive science rests on a foundation of classical logic and probability theory. This foundation has been seriously challenged by several findings in experimental psychology on human decision making. Meanwhile, the formalism of quantum theory has provided an efficient resource for modeling these classically problematical situations. In this paper, we start from our successful quantum-theoretic approach to the modeling of concept combinations to formulate a unifying explanatory hypothesis. In it, human reasoning is the superposition of two processes -- a conceptual reasoning, whose nature is emergence of new conceptuality, and a logical reasoning, founded on an algebraic calculus of the logical type. In most cognitive processes however, the former reasoning prevails over the latter. In this perspective, the observed deviations from classical logical reasoning should not be interpreted as biases but, rather, as natural expressions of emergence in its deepest form.