OpenRaiser/Intern-Atlas huggingface dataset:
Classification of Research Citations (CRC)
Venue: arXiv cs.IR 2015
Year: 2015

Research is a continuous phenomenon. It is recursive in nature. Every research is based on some earlier research outcome. A general approach in reviewing the literature for a problem is to categorize earlier work for the same problem as positive and negative citations. In this paper, we propose a novel automated technique, which classifies whether an earlier work is cited as sentiment positive or sentiment negative. Our approach first extracted the portion of the cited text from citing paper. Using a sentiment lexicon we classify the citation as positive or negative by picking a window of at most five (5) sentences around the cited place (corpus). We have used Naïve-Bayes Classifier for sentiment analysis. The algorithm is evaluated on a manually annotated and class labelled collection of 150 research papers from the domain of computer science. Our preliminary results show an accuracy of 80%. We assert that our approach can be generalized to classification of scientific research papers in different disciplines.
Big Data Technology Literature Review
Venue: arXiv cs.DC 2015
Year: 2015

A short overview of various algorithms and technologies that are helpful for big data storage and manipulation. Includes pointers to papers for further reading, and, where applicable, pointers to open source projects implementing a described storage type.
Artificial Catalytic Reactions in 2D for Combinatorial Optimization
Venue: arXiv cs.ET 2015
Year: 2015

Presented in this paper is a derivation of a 2D catalytic reaction-based model to solve combinatorial optimization problems (COPs). The simulated catalytic reactions, a computational metaphor, occurs in an artificial chemical reactor that finds near-optimal solutions to COPs. The artificial environment is governed by catalytic reactions that can alter the structure of artificial molecular elements. Altering the molecular structure means finding new solutions to the COP. The molecular mass of the elements was considered as a measure of goodness of fit of the solutions. Several data structures and matrices were used to record the directions and locations of the molecules. These provided the model the 2D topology. The Traveling Salesperson Problem (TSP) was used as a working example. The performance of the model in finding a solution for the TSP was compared to the performance of a topology-less model. Experimental results show that the 2D model performs better than the topology-less one.
Organization of Multi-Agent Systems: An Overview
Venue: arXiv cs.MA 2015
Year: 2015

In complex, open, and heterogeneous environments, agents must be able to reorganize towards the most appropriate organizations to adapt unpredictable environment changes within Multi-Agent Systems (MAS). Types of reorganization can be seen from two different levels. The individual agents level (micro-level) in which an agent changes its behaviors and interactions with other agents to adapt its local environment. And the organizational level (macro-level) in which the whole system changes it structure by adding or removing agents. This chapter is dedicated to overview different aspects of what is called MAS Organization including its motivations, paradigms, models, and techniques adopted for statically or dynamically organizing agents in MAS.
Boolean gates on actin filaments
Venue: arXiv cs.ET 2015
Year: 2015

Actin is a globular protein which forms long polar filaments in the eukaryotic cytoskeleton. Actin networks play a key role in cell mechanics and cell motility. They have also been implicated in information transmission and processing, memory and learning in neuronal cells. The acting filaments have been shown to support propagation of voltage pulses. Here we apply a coupled nonlinear transmission line model of actin filaments to study interactions between voltage pulses. By assigning a logical {\sc Truth} to the presence of a voltage pulses in a given location of the actin filament, and {\sc False} to the pulse's absence we represent digital information transmission along these filaments. When two pulses, representing Boolean values of input variables, interact, then they can facilitate or inhibit further propagation of each other. We explore this phenomenon to construct Boolean logical gates and a one-bit half-adder with interacting voltage pulses. We discuss implications of these findings on cellular process and technological applications.
Optimal time sharing in underlay cognitive radio systems with RF energy harvesting
Venue: arXiv cs.IT 2015
Year: 2015

Due to the fundamental tradeoffs, achieving spectrum efficiency and energy efficiency are two contending design challenges for the future wireless networks. However, applying radio-frequency (RF) energy harvesting (EH) in a cognitive radio system could potentially circumvent this tradeoff, resulting in a secondary system with limitless power supply and meaningful achievable information rates. This paper proposes an online solution for the optimal time allocation (time sharing) between the EH phase and the information transmission (IT) phase in an underlay cognitive radio system, which harvests the RF energy originating from the primary system. The proposed online solution maximizes the average achievable rate of the cognitive radio system, subject to the $\varepsilon$-percentile protection criteria for the primary system. The optimal time sharing achieves significant gains compared to equal time allocation between the EH and IT phases.
Specifying Concurrent Problems: Beyond Linearizability
Venue: arXiv cs.DC 2015
Year: 2015

Tasks and objects are two predominant ways of specifying distributed problems. A task is specified by an input/output relation, defining for each set of processes that may run concurrently, and each assignment of inputs to the processes in the set, the valid outputs of the processes. An object is specified by an automaton describing the outputs the object may produce when it is accessed sequentially. Thus, tasks explicitly state what may happen only when sets of processes run concurrently, while objects only specify what happens when processes access the object sequentially. Each one requires its own implementation notion, to tell when an execution satisfies the specification. For objects linearizability is commonly used, a very elegant and useful consistency condition. For tasks implementation notions are less explored. The paper introduces the notion of interval-sequential object. The corresponding implementation notion of interval-linearizability generalizes linearizability, and allows to associate states along the interval of execution of an operation. Interval-linearizability allows to specify any task, however, there are sequential one-shot objects that cannot be expressed as tasks, under the simplest interpretation of a task. It also shows that a natural extension of the notion of a task is expressive enough to specify any interval-sequential object.
Simultaneous Wireless Information and Power Transfer in a Two-User OFDM Interference Channel
Venue: arXiv cs.IT 2015
Year: 2015

In this paper, we study the Simultaneous Wireless Information and Power Transfer (SWIPT) in a Single-Input Single-Output (SISO) two-user Orthogonal Frequency Division Multiplexing (OFDM) Interference Channel (IFC). We assume that the transmitters are non-cooperative and have perfect knowledge of the local Channel State Information (CSI). We show that the necessary condition for the optimal transmission strategy at high SNR is for the energy transmitter to transmit its signal by allocating its transmit power on a single subcarrier. Accordingly, we propose a one-subcarrier selection method for the energy transmitter and identify the achievable rate-energy region. In addition, we further enlarge the achievable rate-energy region by enabling a basic form of transmitter cooperation where messages are exchanged to inform the energy transmitter about the subcarriers unutilized by the information transmitter.
TurboMOR: an Efficient Model Order Reduction Technique for RC Networks with Many Ports
Venue: arXiv cs.CE 2015
Year: 2015

Model order reduction (MOR) techniques play a crucial role in the computer-aided design of modern integrated circuits, where they are used to reduce the size of parasitic networks. Unfortunately, the efficient reduction of passive networks with many ports is still an open problem. Existing techniques do not scale well with the number of ports, and lead to dense reduced models that burden subsequent simulations. In this paper, we propose TurboMOR, a novel MOR technique for the efficient reduction of passive RC networks. TurboMOR is based on moment-matching, achieved through efficient congruence transformations based on Householder reflections. A novel feature of TurboMOR is the block-diagonal structure of the reduced models, that makes them more efficient than the dense models produced by existing techniques. Moreover, the model structure allows for an insightful interpretation of the reduction process in terms of system theory. Numerical results show that TurboMOR scales more favourably than existing techniques in terms of reduction time, simulation time and memory consumption.
Partitioning Uncertain Workflows
Venue: arXiv cs.DC 2015
Year: 2015

It is common practice to partition complex workflows into separate channels in order to speed up their completion times. When this is done within a distributed environment, unavoidable fluctuations make individual realizations depart from the expected average gains. We present a method for breaking any complex workflow into several workloads in such a way that once their outputs are joined, their full completion takes less time and exhibit smaller variance than when running in only one channel. We demonstrate the effectiveness of this method in two different scenarios; the optimization of a convex function and the transmission of a large computer file over the Internet.
Convolutional Color Constancy
Venue: arXiv cs.CV 2015
Year: 2015

Color constancy is the problem of inferring the color of the light that illuminated a scene, usually so that the illumination color can be removed. Because this problem is underconstrained, it is often solved by modeling the statistical regularities of the colors of natural objects and illumination. In contrast, in this paper we reformulate the problem of color constancy as a 2D spatial localization task in a log-chrominance space, thereby allowing us to apply techniques from object detection and structured prediction to the color constancy problem. By directly learning how to discriminate between correctly white-balanced images and poorly white-balanced images, our model is able to improve performance on standard benchmarks by nearly 40%.
DC Proximal Newton for Non-Convex Optimization Problems
Venue: arXiv cs.LG 2015
Year: 2015

We introduce a novel algorithm for solving learning problems where both the loss function and the regularizer are non-convex but belong to the class of difference of convex (DC) functions. Our contribution is a new general purpose proximal Newton algorithm that is able to deal with such a situation. The algorithm consists in obtaining a descent direction from an approximation of the loss function and then in performing a line search to ensure sufficient descent. A theoretical analysis is provided showing that the iterates of the proposed algorithm {admit} as limit points stationary points of the DC objective function. Numerical experiments show that our approach is more efficient than current state of the art for a problem with a convex loss functions and non-convex regularizer. We have also illustrated the benefit of our algorithm in high-dimensional transductive learning problem where both loss function and regularizers are non-convex.
Improved Purely Additive Fault-Tolerant Spanners
Venue: arXiv cs.DS 2015
Year: 2015

Let $G$ be an unweighted $n$-node undirected graph. A \emph{$β$-additive spanner} of $G$ is a spanning subgraph $H$ of $G$ such that distances in $H$ are stretched at most by an additive term $β$ w.r.t. the corresponding distances in $G$. A natural research goal related with spanners is that of designing \emph{sparse} spanners with \emph{low} stretch. In this paper, we focus on \emph{fault-tolerant} additive spanners, namely additive spanners which are able to preserve their additive stretch even when one edge fails. We are able to improve all known such spanners, in terms of either sparsity or stretch. In particular, we consider the sparsest known spanners with stretch $6$, $28$, and $38$, and reduce the stretch to $4$, $10$, and $14$, respectively (while keeping the same sparsity). Our results are based on two different constructions. On one hand, we show how to augment (by adding a \emph{small} number of edges) a fault-tolerant additive \emph{sourcewise spanner} (that approximately preserves distances only from a given set of source nodes) into one such spanner that preserves all pairwise distances. On the other hand, we show how to augment some known fault-tolerant additive spanners, based on clustering techniques. This way we decrease the additive stretch without any asymptotic increase in their size. We also obtain improved fault-tolerant additive spanners for the case of one vertex failure, and for the case of $f$ edge failures.
Dynamic Bayesian Networks as Formal Abstractions of Structured Stochastic Processes
Venue: arXiv cs.SY 2015
Year: 2015

We study the problem of finite-horizon probabilistic invariance for discrete-time Markov processes over general (uncountable) state spaces. We compute discrete-time, finite-state Markov chains as formal abstractions of general Markov processes. Our abstraction differs from existing approaches in two ways. First, we exploit the structure of the underlying Markov process to compute the abstraction separately for each dimension. Second, we employ dynamic Bayesian networks (DBN) as compact representations of the abstraction. In contrast, existing approaches represent and store the (exponentially large) Markov chain explicitly, which leads to heavy memory requirements limiting the application to models of dimension less than half, according to our experiments. We show how to construct a DBN abstraction of a Markov process satisfying an independence assumption on the driving process noise. We compute a guaranteed bound on the error in the abstraction w.r.t.\ the probabilistic invariance property; the dimension-dependent abstraction makes the error bounds more precise than existing approaches. Additionally, we show how factor graphs and the sum-product algorithm for DBNs can be used to solve the finite-horizon probabilistic invariance problem. Together, DBN-based representations and algorithms can be significantly more efficient than explicit representations of Markov chains for abstracting and model checking structured Markov processes.
Self-Learning Cloud Controllers: Fuzzy Q-Learning for Knowledge Evolution
Venue: arXiv cs.SY 2015
Year: 2015

Cloud controllers aim at responding to application demands by automatically scaling the compute resources at runtime to meet performance guarantees and minimize resource costs. Existing cloud controllers often resort to scaling strategies that are codified as a set of adaptation rules. However, for a cloud provider, applications running on top of the cloud infrastructure are more or less black-boxes, making it difficult at design time to define optimal or pre-emptive adaptation rules. Thus, the burden of taking adaptation decisions often is delegated to the cloud application. Yet, in most cases, application developers in turn have limited knowledge of the cloud infrastructure. In this paper, we propose learning adaptation rules during runtime. To this end, we introduce FQL4KE, a self-learning fuzzy cloud controller. In particular, FQL4KE learns and modifies fuzzy rules at runtime. The benefit is that for designing cloud controllers, we do not have to rely solely on precise design-time knowledge, which may be difficult to acquire. FQL4KE empowers users to specify cloud controllers by simply adjusting weights representing priorities in system goals instead of specifying complex adaptation rules. The applicability of FQL4KE has been experimentally assessed as part of the cloud application framework ElasticBench. The experimental results indicate that FQL4KE outperforms our previously developed fuzzy controller without learning mechanisms and the native Azure auto-scaling.
New Class of Pseudorandom D-sequences to Generate Cryptographic Keys
Venue: arXiv cs.CR 2015
Year: 2015

This article proposes the use of pseudorandom decimal sequences that have gone through an additional random mapping for the design of cryptographic keys. These sequences are generated by starting with inverse prime expansions in base 3 and then replacing 2 in the expansion with either the string 01 or 10 based on the preceding bit, which represents a general mapping. We show that the resulting pseudorandom sequences have excellent autocorrelation properties. Such a method can be extended to inverse prime expansions to any base.
Optimal and Resilient Pheromone Utilization in Ant Foraging
Venue: arXiv cs.DC 2015
Year: 2015

Pheromones are a chemical substance produced and released by ants as means of communication. In this work we present the minimum amount of pheromones necessary and sufficient for a colony of ants (identical mobile agents) to deterministically find a food source (treasure), assuming that each ant has the computational capabilities of either a Finite State Machine (FSM) or a Turing Machine (TM). In addition, we provide pheromone-based foraging algorithms capable of handling fail-stop faults. In more detail, we consider the case where $k$ identical ants, initially located at the center (nest) of an infinite two-dimensional grid and communicate only through pheromones, perform a collaborative search for an adversarially hidden treasure placed at an unknown distance $D$. We begin by proving a tight lower bound of $Ω(D)$ on the amount of pheromones required by any number of FSM based ants to complete the search, and continue to reduce the lower bound to $Ω(k)$ for the stronger ants modeled as TM. We provide algorithms which match the aforementioned lower bounds, and still terminate in optimal $\mathcal{O}(D + D^2 / k)$ time, under both the synchronous and asynchronous models. Furthermore, we consider a more realistic setting, where an unknown number $f < k$ of ants may fail-stop at any time; we provide fault-tolerant FSM algorithms (synchronous and asynchronous), that terminate in $\mathcal{O}(D + D^2/(k-f) + Df)$ rounds and emit no more than the same asymptotic minimum number of $\mathcal{O}(D)$ pheromones overall.
Twitter Sentiment Analysis Applied to Finance: A Case Study in the Retail Industry
Venue: arXiv cs.CY 2015
Year: 2015

This paper presents a financial analysis over Twitter sentiment analytics extracted from listed retail brands. We investigate whether there is statistically-significant information between the Twitter sentiment and volume, and stock returns and volatility. Traditional newswires are also considered as a proxy for the market sentiment for comparative purpose. The results suggest that social media is indeed a valuable source in the analysis of the financial dynamics in the retail sector even when compared to mainstream news such as the Wall Street Journal and Dow Jones Newswires.
Node Labels in Local Decision
Venue: arXiv cs.DC 2015
Year: 2015

The role of unique node identifiers in network computing is well understood as far as symmetry breaking is concerned. However, the unique identifiers also leak information about the computing environment - in particular, they provide some nodes with information related to the size of the network. It was recently proved that in the context of local decision, there are some decision problems such that (1) they cannot be solved without unique identifiers, and (2) unique node identifiers leak a sufficient amount of information such that the problem becomes solvable (PODC 2013). In this work we give study what is the minimal amount of information that we need to leak from the environment to the nodes in order to solve local decision problems. Our key results are related to scalar oracles $f$ that, for any given $n$, provide a multiset $f(n)$ of $n$ labels; then the adversary assigns the labels to the $n$ nodes in the network. This is a direct generalisation of the usual assumption of unique node identifiers. We give a complete characterisation of the weakest oracle that leaks at least as much information as the unique identifiers. Our main result is the following dichotomy: we classify scalar oracles as large and small, depending on their asymptotic behaviour, and show that (1) any large oracle is at least as powerful as the unique identifiers in the context of local decision problems, while (2) for any small oracle there are local decision problems that still benefit from unique identifiers.
Robust Mars Atmospheric Entry Integrated Navigation based on Parameter Sensitivity
Venue: arXiv cs.SY 2015
Year: 2015

This paper presented a robust integrated navigation algorithm based on a special robust desensitized extended Kalman filtering with analytical gain (ADEKF) during the Mars atmospheric entry. The robust ADEKF is designed by minimizing a new function penalized by a trace weighted norm of the state error sensitivities and giving a closed-form gain matrix. The uncertainties of the Mars atmospheric density and the lift-to-drag ratio (LDR) percentage are modeled. Sensitivity matrices are defined to character the parameter uncertainties, and corresponding perturbation matrices are proposed to describe the navigation errors respected to the parameter uncertainties. The numerical simulation results show that the robust integrated navigation algorithm based on the robust ADEKF effectively reduces the negative effects of the two parameter uncertainties and has good consistency during the Mars entry.
Lambda-Policy Iteration: A Review and a New Implementation
Venue: arXiv cs.SY 2015
Year: 2015

In this paper we discuss $ł$-policy iteration, a method for exact and approximate dynamic programming. It is intermediate between the classical value iteration (VI) and policy iteration (PI) methods, and it is closely related to optimistic (also known as modified) PI, whereby each policy evaluation is done approximately, using a finite number of VI. We review the theory of the method and associated questions of bias and exploration arising in simulation-based cost function approximation. We then discuss various implementations, which offer advantages over well-established PI methods that use LSPE($ł$), LSTD($ł$), or TD($ł$) for policy evaluation with cost function approximation. One of these implementations is based on a new simulation scheme, called geometric sampling, which uses multiple short trajectories rather than a single infinitely long trajectory.