OpenRaiser/Intern-Atlas huggingface dataset:
Foundations of Total Functional Data-Flow Programming
Venue: arXiv cs.PL 2014
Year: 2014

The field of declarative stream programming (discrete time, clocked synchronous, modular, data-centric) is divided between the data-flow graph paradigm favored by domain experts, and the functional reactive paradigm favored by academics. In this paper, we describe the foundations of a framework for unifying functional and data-flow styles that differs from FRP proper in significant ways: It is based on set theory to match the expectations of domain experts, and the two paradigms are reduced symmetrically to a low-level middle ground, with strongly compositional semantics. The design of the framework is derived from mathematical first principles, in particular coalgebraic coinduction and a standard relational model of stateful computation. The abstract syntax and semantics introduced here constitute the full core of a novel stream programming language.
MeGARA: Menu-based Game Abstraction and Abstraction Refinement of Markov Automata
Venue: arXiv cs.LO 2014
Year: 2014

Markov automata combine continuous time, probabilistic transitions, and nondeterminism in a single model. They represent an important and powerful way to model a wide range of complex real-life systems. However, such models tend to be large and difficult to handle, making abstraction and abstraction refinement necessary. In this paper we present an abstraction and abstraction refinement technique for Markov automata, based on the game-based and menu-based abstraction of probabilistic automata. First experiments show that a significant reduction in size is possible using abstraction.
Doppler Tolerance, Complementary Code Sets and the Generalized Thue-Morse Sequence
Venue: arXiv cs.IT 2014
Year: 2014

We generalize the construction of Doppler-tolerant Golay complementary waveforms by Pezeshki-Calderbank-Moran-Howard to complementary code sets having more than two codes. This is accomplished by exploiting number-theoretic results involving the sum-of-digits function, equal sums of like powers, and a generalization to more than two symbols of the classical two-symbol Prouhet-Thue-Morse sequence.
How Easy is it to Learn a Controlled Natural Language for Building a Knowledge Base?
Venue: arXiv cs.CL 2014
Year: 2014

Recent developments in controlled natural language editors for knowledge engineering (KE) have given rise to expectations that they will make KE tasks more accessible and perhaps even enable non-engineers to build knowledge bases. This exploratory research focussed on novices and experts in knowledge engineering during their attempts to learn a controlled natural language (CNL) known as OWL Simplified English and use it to build a small knowledge base. Participants' behaviours during the task were observed through eye-tracking and screen recordings. This was an attempt at a more ambitious user study than in previous research because we used a naturally occurring text as the source of domain knowledge, and left them without guidance on which information to select, or how to encode it. We have identified a number of skills (competencies) required for this difficult task and key problems that authors face.
The Chemistry Between High School Students and Computer Science
Venue: arXiv cs.CY 2014
Year: 2014

Computer science enrollments have started to rise again, but the percentage of women undergraduates in computer science is still low. Some studies indicate this might be due to a lack of awareness of computer science at the high school level. We present our experiences running a 5-year, high school outreach program that introduces information about computer science within the context of required chemistry courses. We developed interactive worksheets using Molecular Workbench that help the students learn chemistry and computer science concepts related to relevant events such as the gulf oil spill. Our evaluation of the effectiveness of this approach indicates that the students do become more aware of computer science as a discipline, but system support issues in the classroom can make the approach difficult for teachers and discouraging for the students.
Explaining Violation Traces with Finite State Natural Language Generation Models
Venue: arXiv cs.SE 2014
Year: 2014

An essential element of any verification technique is that of identifying and communicating to the user, system behaviour which leads to a deviation from the expected behaviour. Such behaviours are typically made available as long traces of system actions which would benefit from a natural language explanation of the trace and especially in the context of business logic level specifications. In this paper we present a natural language generation model which can be used to explain such traces. A key idea is that the explanation language is a CNL that is, formally speaking, regular language susceptible transformations that can be expressed with finite state machinery. At the same time it admits various forms of abstraction and simplification which contribute to the naturalness of explanations that are communicated to the user.
Conjunction and Negation of Natural Concepts: A Quantum-theoretic Modeling
Venue: arXiv cs.AI 2014
Year: 2014

We perform two experiments with the aim to investigate the effects of negation on the combination of natural concepts. In the first experiment, we test the membership weights of a list of exemplars with respect to two concepts, e.g., {\it Fruits} and {\it Vegetables}, and their conjunction {\it Fruits And Vegetables}. In the second experiment, we test the membership weights of the same list of exemplars with respect to the same two concepts, but negating the second, e.g., {\it Fruits} and {\it Not Vegetables}, and again their conjunction {\it Fruits And Not Vegetables}. The collected data confirm existing results on conceptual combination, namely, they show dramatic deviations from the predictions of classical (fuzzy set) logic and probability theory. More precisely, they exhibit conceptual vagueness, gradeness of membership, overextension and double overextension of membership weights with respect to the given conjunctions. Then, we show that the quantum probability model in Fock space recently elaborated to model Hampton's data on concept conjunction (Hampton, 1988a) and disjunction (Hampton, 1988b) faithfully accords with the collected data. Our quantum-theoretic modeling enables to describe these non-classical effects in terms of genuine quantum effects, namely `contextuality', `superposition', `interference' and `emergence'. The obtained results confirm and strenghten the analysis in Aerts (2009a) and Sozzo (2014) on the identification of quantum aspects in experiments on conceptual vagueness. Our results can be inserted within the general research on the identification of quantum structures in cognitive and decision processes.
Distributed consensus on minimum time rendezvous via cyclic alternating projection
Venue: arXiv cs.SY 2014
Year: 2014

In this paper, we propose a distributed algorithm to solve planar minimum time multi-vehicle rendezvous problem with non-identical velocity constraints on cyclic digraph (topology). Motivated by the cyclic alternating projection method that can compute a point's projection on the intersection of some convex sets, we transform the minimum time rendezvous problem into finding the distance between the position plane and the intersection of several second-order cones in position-time space. The distance can be achieved by metric projecting onto the plane and the intersection persistently from any initial point, where the projection onto the intersection is obtained by Dykstra's alternating projection algorithm. It is shown that during the procedure, vehicles use only the information from neighbors and can apply the projection onto the plane asynchronously. Demonstrations are worked out to illustrate the effectiveness of the proposed algorithm.
Equivalence of Learning Algorithms
Venue: arXiv cs.LG 2014
Year: 2014

The purpose of this paper is to introduce a concept of equivalence between machine learning algorithms. We define two notions of algorithmic equivalence, namely, weak and strong equivalence. These notions are of paramount importance for identifying when learning prop erties from one learning algorithm can be transferred to another. Using regularized kernel machines as a case study, we illustrate the importance of the introduced equivalence concept by analyzing the relation between kernel ridge regression (KRR) and m-power regularized least squares regression (M-RLSR) algorithms.
A Utility Proportional Fairness Resource Allocation in Spectrally Radar-Coexistent Cellular Networks
Venue: arXiv cs.NI 2014
Year: 2014

Spectrum sharing is an elegant solution to addressing the scarcity of the bandwidth for wireless communications systems. This research studies the feasibility of sharing the spectrum between sectorized cellular systems and stationary radars interfering with certain sectors of the communications infrastructure. It also explores allocating optimal resources to mobile devices in order to provide with the quality of service for all running applications whilst growing the communications network spectrally coexistent with the radar systems. The rate allocation problem is formulated as two convex optimizations, where the radar-interfering sector assignments are extracted from the portion of the spectrum non-overlapping with the radar operating frequency. Such a double-stage resource allocation procedure inherits the fairness into the rate allocation scheme by first assigning the spectrally radar-overlapping resources.
Conceptors: an easy introduction
Venue: arXiv cs.NE 2014
Year: 2014

Conceptors provide an elementary neuro-computational mechanism which sheds a fresh and unifying light on a diversity of cognitive phenomena. A number of demanding learning and processing tasks can be solved with unprecedented ease, robustness and accuracy. Some of these tasks were impossible to solve before. This entirely informal paper introduces the basic principles of conceptors and highlights some of their usages.
Rapid Control Selection through Hill-Climbing Methods
Venue: arXiv cs.SY 2014
Year: 2014

Consider the problem of control selection in complex dynamical and environmental scenarios where model predictive control (MPC) proves particularly effective. As the performance of MPC is highly dependent on the efficiency of its incorporated search algorithm, this work examined hill climbing as an alternative to traditional systematic or random search algorithms. The relative performance of a candidate hill climbing algorithm was compared to representative systematic and random algorithms in a set of systematic tests and in a real-world control scenario. These tests indicated that hill climbing can provide significantly improved search efficiency when the control space has a large number of dimensions or divisions along each dimension. Furthermore, this demonstrated that there was little increase in search times associated with a significant increase in the number of control configurations considered.
Realization and design of a pilot assist decision-making system based on speech recognition
Venue: arXiv cs.HC 2014
Year: 2014

A system based on speech recognition is proposed for pilot assist decision-making. It is based on a HIL aircraft simulation platform and uses the microcontroller SPCE061A as the central processor to achieve better reliability and higher cost-effect performance. Technologies of LPCC (linear predictive cepstral coding) and DTW (Dynamic Time Warping) are applied for isolated-word speech recognition to gain a smaller amount of calculation and a better real-time performance. Besides, we adopt the PWM (Pulse Width Modulation) regulation technology to effectively regulate each control surface by speech, and thus to assist the pilot to make decisions. By trial and error, it is proved that we have a satisfactory accuracy rate of speech recognition and control effect. More importantly, our paper provides a creative idea for intelligent human-computer interaction and applications of speech recognition in the field of aviation control. Our system is also very easy to be extended and applied.
A Hitchhiker's Guide to Search-Based Software Engineering for Software Product Lines
Venue: arXiv cs.SE 2014
Year: 2014

Search Based Software Engineering (SBSE) is an emerging discipline that focuses on the application of search-based optimization techniques to software engineering problems. The capacity of SBSE techniques to tackle problems involving large search spaces make their application attractive for Software Product Lines (SPLs). In recent years, several publications have appeared that apply SBSE techniques to SPL problems. In this paper, we present the results of a systematic mapping study of such publications. We identified the stages of the SPL life cycle where SBSE techniques have been used, what case studies have been employed and how they have been analysed. This mapping study revealed potential venues for further research as well as common misunderstanding and pitfalls when applying SBSE techniques that we address by providing a guideline for researchers and practitioners interested in exploiting these techniques.
Improved Secure Address Resolution Protocol
Venue: arXiv cs.CR 2014
Year: 2014

In this paper, an improved secure address resolution protocol is presented where ARP spoofing attack is prevented. The proposed methodology is a centralised methodology for preventing ARP spoofing attack. In the proposed model there is a central server on a network or subnet which prevents ARP spoofing attack.
"Mental Rotation" by Optimizing Transforming Distance
Venue: arXiv cs.LG 2014
Year: 2014

The human visual system is able to recognize objects despite transformations that can drastically alter their appearance. To this end, much effort has been devoted to the invariance properties of recognition systems. Invariance can be engineered (e.g. convolutional nets), or learned from data explicitly (e.g. temporal coherence) or implicitly (e.g. by data augmentation). One idea that has not, to date, been explored is the integration of latent variables which permit a search over a learned space of transformations. Motivated by evidence that people mentally simulate transformations in space while comparing examples, so-called "mental rotation", we propose a transforming distance. Here, a trained relational model actively transforms pairs of examples so that they are maximally similar in some feature space yet respect the learned transformational constraints. We apply our method to nearest-neighbour problems on the Toronto Face Database and NORB.
Learning ELM network weights using linear discriminant analysis
Venue: arXiv cs.NE 2014
Year: 2014

We present an alternative to the pseudo-inverse method for determining the hidden to output weight values for Extreme Learning Machines performing classification tasks. The method is based on linear discriminant analysis and provides Bayes optimal single point estimates for the weight values.
Reaction to New Security Threat Class
Venue: arXiv cs.DL 2014
Year: 2014

Each new identified security threat class triggers new research and development efforts by the scientific and professional communities. In this study, we investigate the rate at which the scientific and professional communities react to new identified threat classes as it is reflected in the number of patents, scientific articles and professional publications over a long period of time. The following threat classes were studied: Phishing; SQL Injection; BotNet; Distributed Denial of Service; and Advanced Persistent Threat. Our findings suggest that in most cases it takes a year for the scientific community and more than two years for industry to react to a new threat class with patents. Since new products follow patents, it is reasonable to expect that there will be a window of approximately two to three years in which no effective product is available to cope with the new threat class.
Impact of Internet Governance
Venue: arXiv cs.CY 2014
Year: 2014

This paper represents the overview of the influence of internet governance in all the transactions, trading, business, social services, educational activities, research etc. occurring through the internet. It is very essential to implement laws and regulations in the field wherever the transaction takes place, either in the form of money, material or services. To avoid any kind of fraud and cheating and to establish a peaceful environment in physical as well as virtual word, it is essential to have some organization which assures safety and security. Here I-governance is the governing body that tries to take care of all those requirements.
Enhanced capacity & coverage by Wi-Fi LTE Integration
Venue: arXiv cs.NI 2014
Year: 2014

Wi-Fi provides cost-effective data capacity at hotspots in conjunction with broadband cellular networks. The hotspots are required to capture a large number of users and provide high data rates. Data rates, over the Wi-Fi interface, are influenced by the media access protocol, which loses throughput due to delays and unintended collisions when a large number of users are active. The hotspot range which determines the number of users, that can associate, is limited by the lower power of the client rather than the access point. By diverting the traffic destined to the access point via another access network, both range and efficiency can be improved. This uplink redirection or diversion is achieved by simultaneous use of the Wi-Fi and LTE radio interfaces. Three options - loose, tight, and hybrid integration are presented towards providing enhanced capacity and coverage.
A Cascade Neural Network Architecture investigating Surface Plasmon Polaritons propagation for thin metals in OpenMP
Venue: arXiv cs.NE 2014
Year: 2014

Surface plasmon polaritons (SPPs) confined along metal-dielectric interface have attracted a relevant interest in the area of ultracompact photonic circuits, photovoltaic devices and other applications due to their strong field confinement and enhancement. This paper investigates a novel cascade neural network (NN) architecture to find the dependance of metal thickness on the SPP propagation. Additionally, a novel training procedure for the proposed cascade NN has been developed using an OpenMP-based framework, thus greatly reducing training time. The performed experiments confirm the effectiveness of the proposed NN architecture for the problem at hand.
The Role of Large-Scale Fading in Uplink Massive MIMO Systems
Venue: arXiv cs.IT 2014
Year: 2014

In this correspondence, we analyze the ergodic capacity of a large uplink multi-user multiple-input multiple-output (MU-MIMO) system over generalized-$K$ fading channels. In the considered scenario, multiple users transmit their information to a base station equipped with a very large number of antennas. Since the effect of fast fading asymptotically disappears in massive MIMO systems, large-scale fading becomes the most dominant factor for the ergodic capacity of massive MIMO systems. Regarding this fact, in our work we concentrate our attention on the effects of large-scale fading for massive MIMO systems. Specifically, some interesting and novel lower bounds of the ergodic capacity have been derived with both perfect channel state information (CSI) and imperfect CSI. Simulation results assess the accuracy of these analytical expressions.
Event and Anomaly Detection Using Tucker3 Decomposition
Venue: arXiv cs.AI 2014
Year: 2014

Failure detection in telecommunication networks is a vital task. So far, several supervised and unsupervised solutions have been provided for discovering failures in such networks. Among them unsupervised approaches has attracted more attention since no label data is required. Often, network devices are not able to provide information about the type of failure. In such cases the type of failure is not known in advance and the unsupervised setting is more appropriate for diagnosis. Among unsupervised approaches, Principal Component Analysis (PCA) is a well-known solution which has been widely used in the anomaly detection literature and can be applied to matrix data (e.g. Users-Features). However, one of the important properties of network data is their temporal sequential nature. So considering the interaction of dimensions over a third dimension, such as time, may provide us better insights into the nature of network failures. In this paper we demonstrate the power of three-way analysis to detect events and anomalies in time-evolving network data.
A swarm optimization algorithm inspired in the behavior of the social-spider
Venue: arXiv cs.NE 2014
Year: 2014

Swarm intelligence is a research field that models the collective behavior in swarms of insects or animals. Several algorithms arising from such models have been proposed to solve a wide range of complex optimization problems. In this paper, a novel swarm algorithm called the Social Spider Optimization (SSO) is proposed for solving optimization tasks. The SSO algorithm is based on the simulation of cooperative behavior of social-spiders. In the proposed algorithm, individuals emulate a group of spiders which interact to each other based on the biological laws of the cooperative colony. The algorithm considers two different search agents (spiders): males and females. Depending on gender, each individual is conducted by a set of different evolutionary operators which mimic different cooperative behaviors that are typically found in the colony. In order to illustrate the proficiency and robustness of the proposed approach, it is compared to other well-known evolutionary methods. The comparison examines several standard benchmark functions that are commonly considered within the literature of evolutionary algorithms. The outcome shows a high performance of the proposed method for searching a global optimum with several benchmark functions.
Probabilistic Recursion Theory and Implicit Computational Complexity (Long Version)
Venue: arXiv cs.LO 2014
Year: 2014

We show that probabilistic computable functions, i.e., those functions outputting distributions and computed by probabilistic Turing machines, can be characterized by a natural generalization of Church and Kleene's partial recursive functions. The obtained algebra, following Leivant, can be restricted so as to capture the notion of polytime sampleable distributions, a key concept in average-case complexity and cryptography.
On largest volume simplices and sub-determinants
Venue: arXiv cs.CG 2014
Year: 2014

We show that the problem of finding the simplex of largest volume in the convex hull of $n$ points in $\mathbb{Q}^d$ can be approximated with a factor of $O(\log d)^{d/2}$ in polynomial time. This improves upon the previously best known approximation guarantee of $d^{(d-1)/2}$ by Khachiyan. On the other hand, we show that there exists a constant $c>1$ such that this problem cannot be approximated with a factor of $c^d$, unless $P=NP$. % This improves over the $1.09$ inapproximability that was previously known. Our hardness result holds even if $n = O(d)$, in which case there exists a $\bar c\,^{d}$-approximation algorithm that relies on recent sampling techniques, where $\bar c$ is again a constant. We show that similar results hold for the problem of finding the largest absolute value of a subdeterminant of a $d\times n$ matrix.