OpenRaiser/Intern-Atlas huggingface dataset:
Qualitative Relational Mapping and Navigation for Planetary Rovers
Venue: arXiv cs.RO 2014
Year: 2014

This paper presents a novel method for qualitative mapping of large scale spaces. The proposed framework makes use of a graphical representation of the world in order to build a map consisting of qualitative constraints on the geometric relationships between landmark triplets. A novel measurement method based on camera imagery is presented which extends previous work from the field of Qualitative Spatial Reasoning. Measurements are fused into the map using a deterministic, iterative graph update. A Branch-and-Bound approach is taken to solve a set of non-convex feasibility problems required for generating on-line measurements and off-line operator lookup tables. A navigation approach for travel between distant landmarks is developed, using estimates of the Relative Neighborhood Graph extracted from the qualitative map in order to generate a sequence of landmark objectives based on proximity. Average and asymptotic performance of the mapping algorithm is evaluated using Monte Carlo tests on randomly generated maps, and data-driven simulation results are presented for a robot traversing the Jet Propulsion Laboratory Mars Yard while building a relational map. Simulation results demonstrate an initial rapid convergence of qualitative state estimates for visible landmarks, followed by a slow tapering as the remaining ambiguous states are removed from the map.
Fuzzy Decision Analysis in Negotiation between the System of Systems Agent and the System Agent in an Agent-Based Model
Venue: arXiv cs.MA 2014
Year: 2014

Previous papers have described a computational approach to System of Systems (SoS) development using an Agent-Based Model (ABM). This paper describes the Fuzzy Decision Analysis used in the negotiation between the SoS agent and a System agent in the ABM of an Acknowledged SoS development. An Acknowledged SoS has by definition a limited influence on the development of the individual Systems. The individual Systems have their own priorities, pressures, and agenda which may or may not align with the goals of the SoS. The SoS has some funding and deadlines which can be used to negotiate with the individual System in order to illicit the required capability from that System. The Fuzzy Decision Analysis determines how the SoS agent will adjust the funding and deadlines for each of the Systems in order to achieve the desired SoS architecture quality. The Fuzzy Decision Analysis has inputs of performance, funding, and deadlines as well as weights for each capability. The performance, funding, and deadlines are crisp values which are fuzzified. The fuzzified values are then used with a Fuzzy Inference Engine to get the fuzzy outputs of funding adjustment and deadline adjustment which must then be defuzzified before being passed to the System agent. The first contribution of this paper is the fuzzy decision analysis that represents the negotiation between the SoS agent and the System agent. A second contribution of this paper is the method of implementing the fuzzy decision analysis which provides a generalized fuzzy decision analysis.
Radiation Pattern of Patch Antenna with Slits
Venue: arXiv cs.IT 2014
Year: 2014

The Microstrip antenna has been commercially used in many applications, such as direct broadcast satellite service, mobile satellite communications, global positioning system, medical hyperthermia usage, etc. The patch antenna of the size reduction at a given operating frequency is obtained. Mobile personal communication systems and wireless computer networks are most commonly used nowadays and they are in need of antennas in different frequency bands. In regulate to without difficulty incorporate these antennas into individual systems, a micro strip scrap transmitter have been preferred and intended for a convinced divergence. There is also an analysis of radiation pattern, Gain of the antenna, Directivity of the antenna, Electric Far Field. The simulations results are obtained by using electromagnetic simulation software called feko software are presented and discussed
Proof Pattern Search in Coq/SSReflect
Venue: arXiv cs.LO 2014
Year: 2014

ML4PG is an extension of the Proof General interface, allowing the user to invoke machine-learning algorithms and find proof similarities in Coq/SSReect libraries. In this paper, we present three new improvements to ML4PG. First, a new method of "recurrent clustering" is introduced to collect statistical features from Coq terms. Now the user can receive suggestions about similar definitions, types and lemma statements, in addition to proof strategies. Second, Coq proofs are split into patches to capture proof strategies that could arise at different stages of a proof. Finally, we improve ML4PG's output introducing an automaton-shape representation for proof patterns.
A novel datatype architecture support for programming languages
Venue: arXiv cs.PL 2014
Year: 2014

In programmers point of view, Datatypes in programming language level have a simple description but inside hardware, huge machine codes are responsible to describe type features. Datatype architecture design is a novel approach to match programming features along with hardware design. In this paper a novel Data type-Based Code Reducer (TYPELINE) architecture is proposed and implemented according to significant data types (SDT) of programming languages. TYPELINE uses TEUs for processing various SDT operations. This architecture design leads to reducing the number of machine codes, and increases execution speed, and also improves some parallelism level. This is because this architecture supports some operation for the execution of Abstract Data Types in parallel. Also it ensures to maintain data type features and entire application level specifications using the proposed type conversion unit. This framework includes compiler level identifying execution modes and memory management unit for decreasing object read/write in heap memory by ISA support. This energy-efficient architecture is completely compatible with object oriented programming languages and in combination mode it can process complex C++ data structures with respect to parallel TYPELINE architecture support.
A Probabilistic Method for Nonlinear Robustness Analysis of F-16 Controllers
Venue: arXiv cs.SY 2014
Year: 2014

This paper presents a new framework for controller robustness verification with respect to F-16 aircraft's closed-loop performance in longitudinal flight. We compare the state regulation performance of a linear quadratic regulator (LQR) and a gain-scheduled linear quadratic regulator (gsLQR), applied to nonlinear open-loop dynamics of F-16, in presence of stochastic initial condition and parametric uncertainties, as well as actuator disturbance. We show that, in presence of initial condition uncertainties alone, both LQR and gsLQR have comparable immediate and asymptotic performances, but the gsLQR exhibits better transient performance at intermediate times. This remains true in the presence of additional actuator disturbance. Also, gsLQR is shown to be more robust than LQR, against parametric uncertainties. The probabilistic framework proposed here, leverages transfer operator based density computation in exact arithmetic and introduces optimal transport theoretic performance validation and verification (V&V) for nonlinear dynamical systems. Numerical results from our proposed method, are in unison with Monte Carlo simulations.
SPHERE: Meaningful and Inclusive Sensor-Based Home Healthcare
Venue: arXiv cs.HC 2014
Year: 2014

Given current demographic and health trends, and their economic implications, home healthcare technology has become a fertile area for research and development. Motivated by the need for a radical reform of healthcare provision, SPHERE is a large-scale Interdisciplinary Research Collaboration that aims to develop home sensor systems to monitor people's health and wellbeing in the home. This paper outlines the unique circumstances of designing healthcare technology for the home environment, with a particular focus on how to ensure future systems are meaningful to and desirable for the intended users.
Improving Hard Disk Contention-based Covert Channel in Cloud Computing Environment
Venue: arXiv cs.CR 2014
Year: 2014

Steganographic methods allow the covert exchange of secret data between parties aware of the procedure. The cloud computing environment is a new and hot target for steganographers, and currently not many solutions have been proposed. This paper proposes CloudSteg which is a steganographic method that allows the creation of a covert channel based on hard disk contention between the two cloud instances that reside on the same physical machine. Experimental results conducted using open source cloud environment OpenStack, show that CloudSteg is able to achieve a bandwidth of about 0.1 bps which is 1000 times higher than is known from the state-of-the-art version.
A Robust Framework for Moving-Object Detection and Vehicular Traffic Density Estimation
Venue: arXiv cs.CV 2014
Year: 2014

Intelligent machines require basic information such as moving-object detection from videos in order to deduce higher-level semantic information. In this paper, we propose a methodology that uses a texture measure to detect moving objects in video. The methodology is computationally inexpensive, requires minimal parameter fine-tuning and also is resilient to noise, illumination changes, dynamic background and low frame rate. Experimental results show that performance of the proposed approach is higher than those of state-of-the-art approaches. We also present a framework for vehicular traffic density estimation using the foreground object detection technique and present a comparison between the foreground object detection-based framework and the classical density state modelling-based framework for vehicular traffic density estimation.
GQM+Strategies: A Comprehensive Methodology for Aligning Business Strategies with Software Measurement
Venue: arXiv cs.SE 2014
Year: 2014

In software-intensive organizations, an organizational management system will not guarantee organizational success unless the business strategy can be translated into a set of operational software goals. The Goal Question Metric (GQM) approach has proven itself useful in a variety of industrial settings to support quantitative software project management. However, it does not address linking software measurement goals to higher-level goals of the organization in which the software is being developed. This linkage is important, as it helps to justify software measurement efforts and allows measurement data to contribute to higher-level decisions. In this paper, we propose a GQM+Strategies(R) measurement approach that builds on the GQM approach to plan and implement software measurement. GQM+Strategies(R) provides mechanisms for explicitly linking software measurement goals to higher-level goals for the software organization, and further to goals and strategies at the level of the entire business. The proposed method is illustrated in the context of an example application of the method.
Modelling Load Balancing and Carrier Aggregation in Mobile Networks
Venue: arXiv cs.NI 2014
Year: 2014

In this paper, we study the performance of multicarrier mobile networks. Specifically, we analyze the flow-level performance of two inter-carrier load balancing schemes and the gain engendered by Carrier Aggregation (CA). CA is one of the most important features of HSPA+ and LTE-A networks; it allows devices to be served simultaneously by several carriers. We propose two load balancing schemes, namely Join the Fastest Queue (JFQ) and Volume Balancing (VB), that allow the traffic of CA and non-CA users to be distributed over the aggregated carriers. We then evaluate the performance of these schemes by means of analytical modeling. We show that the proposed schemes achieve quasi-ideal load balancing. We also investigate the impact of mixing traffic of CA and non-CA users in the same cell and show that performance is practically insensitive to the traffic mix.
Approximate Computation of DFT without Performing Any Multiplications: Applications to Radar Signal Processing
Venue: arXiv cs.CC 2014
Year: 2014

In many practical problems it is not necessary to compute the DFT in a perfect manner including some radar problems. In this article a new multiplication free algorithm for approximate computation of the DFT is introduced. All multiplications $(a\times b)$ in DFT are replaced by an operator which computes $sign(a\times b)(|a|+|b|)$. The new transform is especially useful when the signal processing algorithm requires correlations. Ambiguity function in radar signal processing requires high number of multiplications to compute the correlations. This new additive operator is used to decrease the number of multiplications. Simulation examples involving passive radars are presented.
Optimal Rectangle Packing: An Absolute Placement Approach
Venue: arXiv cs.AI 2014
Year: 2014

We consider the problem of finding all enclosing rectangles of minimum area that can contain a given set of rectangles without overlap. Our rectangle packer chooses the x-coordinates of all the rectangles before any of the y-coordinates. We then transform the problem into a perfect-packing problem with no empty space by adding additional rectangles. To determine the y-coordinates, we branch on the different rectangles that can be placed in each empty position. Our packer allows us to extend the known solutions for a consecutive-square benchmark from 27 to 32 squares. We also introduce three new benchmarks, avoiding properties that make a benchmark easy, such as rectangles with shared dimensions. Our third benchmark consists of rectangles of increasingly high precision. To pack them efficiently, we limit the rectangles coordinates and the bounding box dimensions to the set of subset sums of the rectangles dimensions. Overall, our algorithms represent the current state-of-the-art for this problem, outperforming other algorithms by orders of magnitude, depending on the benchmark.
Scheduling a Dynamic Aircraft Repair Shop with Limited Repair Resources
Venue: arXiv cs.AI 2014
Year: 2014

We address a dynamic repair shop scheduling problem in the context of military aircraft fleet management where the goal is to maintain a full complement of aircraft over the long-term. A number of flights, each with a requirement for a specific number and type of aircraft, are already scheduled over a long horizon. We need to assign aircraft to flights and schedule repair activities while considering the flights requirements, repair capacity, and aircraft failures. The number of aircraft awaiting repair dynamically changes over time due to failures and it is therefore necessary to rebuild the repair schedule online. To solve the problem, we view the dynamic repair shop as successive static repair scheduling sub-problems over shorter time periods. We propose a complete approach based on the logic-based Benders decomposition to solve the static sub-problems, and design different rescheduling policies to schedule the dynamic repair shop. Computational experiments demonstrate that the Benders model is able to find and prove optimal solutions on average four times faster than a mixed integer programming model. The rescheduling approach having both aspects of scheduling over a longer horizon and quickly adjusting the schedule increases aircraft available in the long term by 10% compared to the approaches having either one of the aspects alone.
A Refined View of Causal Graphs and Component Sizes: SP-Closed Graph Classes and Beyond
Venue: arXiv cs.AI 2014
Year: 2014

The causal graph of a planning instance is an important tool for planning both in practice and in theory. The theoretical studies of causal graphs have largely analysed the computational complexity of planning for instances where the causal graph has a certain structure, often in combination with other parameters like the domain size of the variables. Chen and Gimand#233;nez ignored even the structure and considered only the size of the weakly connected components. They proved that planning is tractable if the components are bounded by a constant and otherwise intractable. Their intractability result was, however, conditioned by an assumption from parameterised complexity theory that has no known useful relationship with the standard complexity classes. We approach the same problem from the perspective of standard complexity classes, and prove that planning is NP-hard for classes with unbounded components under an additional restriction we refer to as SP-closed. We then argue that most NP-hardness theorems for causal graphs are difficult to apply and, thus, prove a more general result; even if the component sizes grow slowly and the class is not densely populated with graphs, planning still cannot be tractable unless the polynomial hierachy collapses. Both these results still hold when restricted to the class of acyclic causal graphs. We finally give a partial characterization of the borderline between NP-hard and NP-intermediate classes, giving further insight into the problem.
Learning by Observation of Agent Software Images
Venue: arXiv cs.AI 2014
Year: 2014

Learning by observation can be of key importance whenever agents sharing similar features want to learn from each other. This paper presents an agent architecture that enables software agents to learn by direct observation of the actions executed by expert agents while they are performing a task. This is possible because the proposed architecture displays information that is essential for observation, making it possible for software agents to observe each other. The agent architecture supports a learning process that covers all aspects of learning by observation, such as discovering and observing experts, learning from the observed data, applying the acquired knowledge and evaluating the agents progress. The evaluation provides control over the decision to obtain new knowledge or apply the acquired knowledge to new problems. We combine two methods for learning from the observed information. The first one, the recall method, uses the sequence on which the actions were observed to solve new problems. The second one, the classification method, categorizes the information in the observed data and determines to which set of categories the new problems belong. Results show that agents are able to learn in conditions where common supervised learning algorithms fail, such as when agents do not know the results of their actions a priori or when not all the effects of the actions are visible. The results also show that our approach provides better results than other learning methods since it requires shorter learning periods.
Linear Network Coding for Multiple Groupcast Sessions: An Interference Alignment Approach
Venue: arXiv cs.IT 2014
Year: 2014

We consider the problem of linear network coding over communication networks, representable by directed acyclic graphs, with multiple groupcast sessions: the network comprises of multiple destination nodes, each desiring messages from multiple sources. We adopt an interference alignment perspective, providing new insights into designing practical network coding schemes as well as the impact of network topology on the complexity of the alignment scheme. In particular, we show that under certain (polynomial-time checkable) constraints on networks with $K$ sources, it is possible to achieve a rate of $1/(L+d+1)$ per source using linear network coding coupled with interference alignment, where each destination receives messages from $L$ sources ($L < K$), and $d$ is a parameter, solely dependent on the network topology, that satisfies $0 \leq d < K-L$.
Axis2UNO: Web Services Enabled Openoffice.org
Venue: arXiv cs.SE 2014
Year: 2014

Openoffice.org is a popular, free and open source office product. This product is used by millions of people and developed, maintained and extended by thousands of developers worldwide. Playing a dominant role in the web, web services technology is serving millions of people every day. Axis2 is one of the most popular, free and open source web service engines. The framework presented in this paper, Axis2UNO, a combination of such two technologies is capable of making a new era in office environment. Two other attempts to enhance web services functionality in office products are Excel Web Services and UNO Web Service Proxy. Excel Web Services is combined with Microsoft SharePoint technology and exposes information sharing in a different perspective within the proprietary Microsoft office products. UNO Web Service Proxy is implemented with Java Web Services Developer Pack and enables basic web services related functionality in Openoffice.org. However, the work presented here is the first one to combine Openoffice.org and Axis2 and we expect it to outperform the other efforts with the community involvement and feature richness in those products.
User Friendly Line CAPTCHAs
Venue: arXiv cs.HC 2014
Year: 2014

CAPTCHAs or reverse Turing tests are real-time assessments used by programs (or computers) to tell humans and machines apart. This is achieved by assigning and assessing hard AI problems that could only be solved easily by human but not by machines. Applications of such assessments range from stopping spammers from automatically filling online forms to preventing hackers from performing dictionary attack. Today, the race between makers and breakers of CAPTCHAs is at a juncture, where the CAPTCHAs proposed are not even answerable by humans. We consider such CAPTCHAs as non user friendly. In this paper, we propose a novel technique for reverse Turing test - we call it the Line CAPTCHAs - that mainly focuses on user friendliness while not compromising the security aspect that is expected to be provided by such a system.
Forgetting the Words but Remembering the Meaning: Modeling Forgetting in a Verbal and Semantic Tag Recommender
Venue: arXiv cs.IR 2014
Year: 2014

We assume that recommender systems are more successful, when they are based on a thorough understanding of how people process information. In the current paper we test this assumption in the context of social tagging systems. Cognitive research on how people assign tags has shown that they draw on two interconnected levels of knowledge in their memory: on a conceptual level of semantic fields or topics, and on a lexical level that turns patterns on the semantic level into words. Another strand of tagging research reveals a strong impact of time dependent forgetting on users' tag choices, such that recently used tags have a higher probability being reused than "older" tags. In this paper, we align both strands by implementing a computational theory of human memory that integrates the two-level conception and the process of forgetting in form of a tag recommender and test it in three large-scale social tagging datasets (drawn from BibSonomy, CiteULike and Flickr). As expected, our results reveal a selective effect of time: forgetting is much more pronounced on the lexical level of tags. Second, an extensive evaluation based on this observation shows that a tag recommender interconnecting both levels and integrating time dependent forgetting on the lexical level results in high accuracy predictions and outperforms other well-established algorithms, such as Collaborative Filtering, Pairwise Interaction Tensor Factorization, FolkRank and two alternative time dependent approaches. We conclude that tag recommenders can benefit from going beyond the manifest level of word co-occurrences, and from including forgetting processes on the lexical level.