OpenRaiser/Intern-Atlas huggingface dataset:
DASS: Detail Aware Sketch-Based Surface Modeling
Venue: arXiv cs.GR 2014
Year: 2014

We present a sketch-based modeling system suitable for detail editing, based on a multilevel representation for surfaces. The main advantage of this representation allowing for the control of local (details) and global changes of the model. We used an adaptive mesh (4-8 mesh) and developed a label theory to construct a manifold structure, which is responsible for controlling local editing of the model. The overall shape and global modifications are defined by a variational implicit surface (Hermite RBF). Our system assembles the manifold structures to allow the user to add details without changing the overall shape, as well as edit the overall shape while repositioning details coherently.
A Scalable Mobility-Centric Architecture for Named Data Networking
Venue: arXiv cs.NI 2014
Year: 2014

Information-centric networking (ICN) proposes to redesign the Internet by replacing its host centric design with an information centric one, by establishing communication at the naming level, with the receiver side acting as the driving force behind content delivery. Such design promises great advantages for the delivery of content to and from mobile hosts. This, however, is at the expense of increased networking overhead, specifically in the case of Named-data Networking (NDN) due to use of flooding for path recovery. In this paper, we propose a mobility centric solution to address the overhead and scalability problems in NDN by introducing a novel forwarding architecture that leverages decentralized server-assisted routing over flooding based strategies. We present an in-depth study of the proposed architecture and provide demonstrative results on its throughput and overhead performance at different levels of mobility proving its scalability and effectiveness, when compared to the current NDN based forwarding strategies.
Verifying Component and Connector Models against Crosscutting Structural Views
Venue: arXiv cs.SE 2014
Year: 2014

The structure of component and connector (C&C) models, which are used in many application domains of software engineering, consists of components at different containment levels, their typed input and output ports, and the connectors between them. C&C views, presented in [24], can be used to specify structural properties of C&C models in an expressive and intuitive way. In this work we address the verification of a C&C model against a C&C view and present efficient (polynomial) algorithms to decide satisfaction. A unique feature of our work, not present in existing approaches to checking structural properties of C&C models, is the generation of witnesses for satisfaction/non-satisfaction and of short naturallanguage texts, which serve to explain and formally justify the verification results and point the engineer to its causes. A prototype tool and an evaluation over four example systems with multiple views, performance and scalability experiments, as well as a user study of the usefulness of the witnesses for engineers, demonstrate the contribution of our work to the state-of-the-art in component and connector modeling and analysis.
Set Constraint Model and Automated Encoding into SAT: Application to the Social Golfer Problem
Venue: arXiv cs.AI 2014
Year: 2014

On the one hand, Constraint Satisfaction Problems allow one to declaratively model problems. On the other hand, propositional satisfiability problem (SAT) solvers can handle huge SAT instances. We thus present a technique to declaratively model set constraint problems and to encode them automatically into SAT instances. We apply our technique to the Social Golfer Problem and we also use it to break symmetries of the problem. Our technique is simpler, more declarative, and less error-prone than direct and improved hand modeling. The SAT instances that we automatically generate contain less clauses than improved hand-written instances such as in [20], and with unit propagation they also contain less variables. Moreover, they are well-suited for SAT solvers and they are solved faster as shown when solving difficult instances of the Social Golfer Problem.
On the Synchronization Rate for e-machines
Venue: arXiv cs.IT 2014
Year: 2014

It is known, that an $ε$-machine is either exactly or asymptotically synchronizing. In the exact case, the observer can infer the current machine state after observing $L$ generated symbols with probability $1-a^L$ where $0 \leq a<1$ is a so-called synchronization rate constant. In the asymptotic case, the probability of the correct prediction the current machine state after observing $L$ generated symbols tends to $1$ exponentially fast as $1-b^L$ for $0<b<1$ and the infimum of such $b$ is a so-called prediction rate constant. Hence the synchronization and prediction rate constants serve as natural measures of synchronization for $ε$-machines. In the present work we show how to approximate these constants in polynomial time in terms of the number of machine states.
Contrastive Feature Induction for Efficient Structure Learning of Conditional Random Fields
Venue: arXiv cs.LG 2014
Year: 2014

Structure learning of Conditional Random Fields (CRFs) can be cast into an L1-regularized optimization problem. To avoid optimizing over a fully linked model, gain-based or gradient-based feature selection methods start from an empty model and incrementally add top ranked features to it. However, for high-dimensional problems like statistical relational learning, training time of these incremental methods can be dominated by the cost of evaluating the gain or gradient of a large collection of candidate features. In this study we propose a fast feature evaluation algorithm called Contrastive Feature Induction (CFI), which only evaluates a subset of features that involve both variables with high signals (deviation from mean) and variables with high errors (residue). We prove that the gradient of candidate features can be represented solely as a function of signals and errors, and that CFI is an efficient approximation of gradient-based evaluation methods. Experiments on synthetic and real data sets show competitive learning speed and accuracy of CFI on pairwise CRFs, compared to state-of-the-art structure learning methods such as full optimization over all features, and Grafting.
Speedup of Micromagnetic Simulations with C++ AMP On Graphics Processing Units
Venue: arXiv cs.CE 2014
Year: 2014

A finite-difference Micromagnetic solver is presented utilizing the C++ Accelerated Massive Parallelism (C++ AMP). The high speed performance of a single Graphics Processing Unit (GPU) is demonstrated compared to a typical CPU-based solver. The speed-up of GPU to CPU is shown to be greater than 100 for problems with larger sizes. This solver is based on C++ AMP and can run on GPUs from various hardware vendors, such as NVIDIA, AMD and Intel, regardless of whether it is dedicated or integrated graphics processor.
Balancing Weighted Substreams in MIMO Interference Channels
Venue: arXiv cs.IT 2014
Year: 2014

Substreams refer to the streams of each user in a system. Substream weighting, where the weights determine the prioritization order, can be important in multiple-input multiple-output interference channels. In this letter, a distributed algorithm is proposed for the problem of power minimization subject to weighted SINR constraint. The algorithm is based on two basic features, the well known distributed power control algorithm by Yates in 1995 and a simple linear search to find feasible SINR targets. The power control law used in the proposed algorithm is proven to linearly converge to a unique fixed-point.
Domain Theory for Modeling OOP: A Summary
Venue: arXiv cs.PL 2014
Year: 2014

Domain theory is `a mathematical theory that serves as a foundation for the semantics of programming languages'. Domains form the basis of a theory of partial information, which extends the familiar notion of partial function to encompass a whole spectrum of "degrees of definedness", so as to model incremental higher-order computation (i.e., computing with infinite data values, such as functions defined over an infinite domain like the domain of integers, infinite trees, and such as objects of object-oriented programming). General considerations from recursion theory dictate that partial functions are unavoidable in any discussion of computability. Domain theory provides an appropriately abstract setting in which the notion of a partial function can be lifted and used to give meaning to higher types, recursive types, etc. NOOP is a domain-theoretic model of nominally-typed OOP. NOOP was used to prove the identification of inheritance and subtyping in mainstream nominally-typed OO programming languages and the validity of this identification. In this report we first present the definitions of basic domain theoretic notions and domain constructors used in the construction of NOOP, then we present the construction of a simple structural model of OOP called COOP as a step towards the construction of NOOP. Like the construction of NOOP, the construction of COOP uses earlier presented domain constructors.
Facts and Figuring: An Experimental Investigation of Network Structure and Performance in Information and Solution Spaces
Venue: arXiv cs.SI 2014
Year: 2014

Using data from a large laboratory experiment on problem solving in which we varied the structure of 16-person networks we investigate how an organization's network structure may be constructed to optimize performance in complex problem-solving tasks. Problem solving involves both search for information and search for theories to make sense of that information. We show that the effect of network structure is opposite for these two equally important forms of search. Dense clustering encourages members of a network to generate more diverse information, but it also has the power to discourage the generation of diverse theories: clustering promotes exploration in information space, but decreases exploration in solution space. Previous research, tending to focus on only one of those two spaces, had produced inconsistent conclusions about the value of network clustering. By adopting an experimental platform on which information was measured separately from solutions, we were able to reconcile past contradictions and clarify the effects of network clustering on performance. The finding both provides a sharper tool for structuring organizations for knowledge work and reveals the challenges inherent in manipulating network structure to enhance performance, as the communication structure that helps one aspect of problem solving may harm the other.
Learning from Others, Together: Brokerage, Closure and Team Performance
Venue: arXiv cs.SI 2014
Year: 2014

Scholarship on teams has focused on the relationship between a team's performance, however defined, and the network structure among team members. For example, Uzzi and Spiro (2005) find that the creative performance of Broadway musical teams depends heavily on the internal cohesion of team members and their past collaborative experience with individuals outside their immediate teams. In other words, team members' internal cohesion and external ties are crucial to the team's success. How, then, do they interact to produce positive performance outcomes? In our work, we separate the proximal causes of tie formation from the proximal determinants of outcomes to determine the mechanism behind this interaction. To examine this puzzle, we examine the performance of national soccer squads over time as a function of changing levels and configurations of brokerage and closure ties formed by players working for professional soccer clubs.
Joint Optimization of Spectrum Sensing and Accessing in Multiuser MISO Cognitive Networks
Venue: arXiv cs.IT 2014
Year: 2014

In this paper, a joint spectrum sensing and accessing optimization framework for a multiuser cognitive network is proposed to significantly improve spectrum efficiency. For such a cognitive network, there are two important and limited resources that should be distributed in a comprehensive manner, namely feedback bits and time duration. First, regarding the feedback bits, there are two components: sensing component (used to convey various users' sensing results) and accessing component (used to feedback channel state information). A large sensing component can support more users to perform cooperative sensing, which results in high sensing precision. However, a large accessing component is preferred as well, as it has a direct impact on the performance in the multiuser cognitive network when multi-antenna technique, such as zero-forcing beamforming (ZFBF), is utilized. Second, the tradeoff of sensing and accessing duration in a transmission interval needs to be determined, so that the sum transmission rate is optimized while satisfying the interference constraint. In addition, the above two resources are interrelated and inversive under some conditions. Specifically, sensing time can be saved by utilizing more sensing feedback bits for a given performance objective. Hence, the resources should be allocation in a jointly manner. Based on the joint optimization framework and the intrinsic relationship between the two resources, we propose two joint resource allocation schemes by maximizing the average sum transmission rate in a multiuser multi-antenna cognitive network. Simulation results show that, by adopting the joint resource allocation schemes, obvious performance gain can be obtained over the traditional fixed strategies.
Selective Match-Line Energizer Content Addressable Memory(SMLE -CAM)
Venue: arXiv cs.AR 2014
Year: 2014

A Content Addressable Memory (CAM) is a memory primarily designed for high speed search operation. Parallel search scheme forms the basis of CAM, thus power reduction is the challenge associated with a large amount of parallel active circuits. We are presenting a novel algorithm and architecture described as Selective Match-Line Energizer Content Addressable Memory (SMLE-CAM) which energizes only those MLs (Match-Line) whose first three bits are conditionally matched with corresponding first three search bit using special architecture which comprises of novel XNOR-CAM cell and novel XOR-CAM cell. The rest of the CAM chain is followed by NOR-CAM cell. The 256 X 144 bit SMLE-CAM is implemented in TSMC 90 nm technology and its robustness across PVT variation is verified. The post-layout simulation result shows, it has energy metric of 0.115 fJ/bit/search with search time 361.6 ps, the best reported so far. The maximum operating frequency is 1GHz.
E-Learning Quality Criteria and Aspects
Venue: arXiv cs.OH 2014
Year: 2014

As IT grows the impact of new technology reflects in more or less every field. Education also gets new dimensions with the advancement in IT sector. Nowadays education is not limited to books and black boards only it gets a new way i.e. electronic media. Although with e-learning, the education having broader phenomena, yet it is in budding stage. Quality is a crucial issue for education as well as e-learning. It is required to serve qualitative and standardization education. Quality cannot be expressed and set by a simple definition, since in itself quality is a very abstract notion. The specified context and the perspectives of users need to be taken into account when defining quality in e-learning. It is also essential to classify suitable criteria to address quality.
Using Prediction Markets to Incentivize and Measure Collective Knowledge Production
Venue: arXiv cs.GT 2014
Year: 2014

We present a mechanism design, coupling an online collaboration software and a prediction market, which allows tracking down the very roots of individual incentives, actions and how these behaviors influence collective intelligence in terms of knowledge production as a public good. We show that the incentive mechanism efficiently engages users without further governance structure, and doesn't crowd out intrinsic motivation. Furthermore, it enables a powerful and robust creative destruction process, which helps quickly filter out irrelevant knowledge. While still at an early stage, this mechanism design can not only bring insights for knowledge production organization design, but also has the potential to illuminate the fundamental mechanisms underlying the emergence of collective intelligence.
Multicast Multigroup Beamforming under Per-antenna Power Constraints
Venue: arXiv cs.IT 2014
Year: 2014

Linear precoding exploits the spatial degrees of freedom offered by multi-antenna transmitters to serve multiple users over the same frequency resources. The present work focuses on simultaneously serving multiple groups of users, each with its own channel, by transmitting a stream of common symbols to each group. This scenario is known as physical layer multicasting to multiple co-channel groups. Extending the current state of the art in multigroup multicasting, the practical constraint of a maximum permitted power level radiated by each antenna is tackled herein. The considered per antenna power constrained system is optimized in a maximum fairness sense. In other words, the optimization aims at favoring the worst user by maximizing the minimum rate. This Max-Min Fair criterion is imperative in multicast systems, where the performance of all the receivers listening to the same multicast is dictated by the worst rate in the group. An analytic framework to tackle the Max-Min Fair multigroup multicasting scenario under per antenna power constraints is therefore derived. Numerical results display the accuracy of the proposed solution and provide insights to the performance of a per antenna power constrained system.
Information Transfer in Swarms with Leaders
Venue: arXiv cs.NE 2014
Year: 2014

Swarm dynamics is the study of collections of agents that interact with one another without central control. In natural systems, insects, birds, fish and other large mammals function in larger units to increase the overall fitness of the individuals. Their behavior is coordinated through local interactions to enhance mate selection, predator detection, migratory route identification and so forth [Andersson and Wallander 2003; Buhl et al. 2006; Nagy et al. 2010; Partridge 1982; Sumpter et al. 2008]. In artificial systems, swarms of autonomous agents can augment human activities such as search and rescue, and environmental monitoring by covering large areas with multiple nodes [Alami et al. 2007; Caruso et al. 2008; Ogren et al. 2004; Paley et al. 2007; Sibley et al. 2002]. In this paper, we explore the interplay between swarm dynamics, covert leadership and theoretical information transfer. A leader is a member of the swarm that acts upon information in addition to what is provided by local interactions. Depending upon the leadership model, leaders can use their external information either all the time or in response to local conditions [Couzin et al. 2005; Sun et al. 2013]. A covert leader is a leader that is treated no differently than others in the swarm, so leaders and followers participate equally in whatever interaction model is used [Rossi et al. 2007]. In this study, we use theoretical information transfer as a means of analyzing swarm interactions to explore whether or not it is possible to distinguish between followers and leaders based on interactions within the swarm. We find that covert leaders can be distinguished from followers in a swarm because they receive less transfer entropy than followers.
Relevance Singular Vector Machine for low-rank matrix sensing
Venue: arXiv cs.NA 2014
Year: 2014

In this paper we develop a new Bayesian inference method for low rank matrix reconstruction. We call the new method the Relevance Singular Vector Machine (RSVM) where appropriate priors are defined on the singular vectors of the underlying matrix to promote low rank. To accelerate computations, a numerically efficient approximation is developed. The proposed algorithms are applied to matrix completion and matrix reconstruction problems and their performance is studied numerically.
Dispersion and Line Formation in Artificial Swarm Intelligence
Venue: arXiv cs.NE 2014
Year: 2014

One of the major motifs in collective or swarm intelligence is that, even though individuals follow simple rules, the resulting global behavior can be complex and intelligent. In artificial swarm systems, such as swarm robots, the goal is to use systems that are as simple and cheap as possible, deploy many of them, and coordinate them to conduct complex tasks that each individual cannot accomplish. Shape formation in artificial intelligence systems is usually required for specific task-oriented performance, including 1) forming sensing grids, 2) exploring and mapping in space, underwater, or hazardous environments, and 3) forming a barricade for surveillance or protecting an area or a person. This paper presents a dynamic model of an artificial swarm system based on a virtual spring damper model and algorithms for dispersion without a leader and line formation with an interim leader using only the distance estimation among the neighbors.
Mathematical Language Processing Project
Venue: arXiv cs.DL 2014
Year: 2014

In natural language, words and phrases themselves imply the semantics. In contrast, the meaning of identifiers in mathematical formulae is undefined. Thus scientists must study the context to decode the meaning. The Mathematical Language Processing (MLP) project aims to support that process. In this paper, we compare two approaches to discover identifier-definition tuples. At first we use a simple pattern matching approach. Second, we present the MLP approach that uses part-of-speech tag based distances as well as sentence positions to calculate identifier-definition probabilities. The evaluation of our prototypical system, applied on the Wikipedia text corpus, shows that our approach augments the user experience substantially. While hovering the identifiers in the formula, tool-tips with the most probable definitions occur. Tests with random samples show that the displayed definitions provide a good match with the actual meaning of the identifiers.
Statistical Study On The Number Of Injective Linear Finite Transducers
Venue: arXiv cs.FL 2014
Year: 2014

The notion of linear finite transducer (LFT) plays a crucial role in some cryptographic systems. In this paper we present a way to get an approximate value, by random sampling, for the number of non-equivalent injective LFTs. By introducing a recurrence relation to count canonical LFTs, we show how to estimate the percentage of $τ$-injective LFTs. Several experimental results are presented, which by themselves constitute an important step towards the evaluation of the key space of those systems.
Convergence of maxgeneralized mean-mingeneralized mean powers of intuitionistic fuzzy matrices
Venue: arXiv cs.DM 2014
Year: 2014

Intuitionistic fuzzy relations on finite universes can be represent by intuitionistic fuzzy matrices and the limiting behavior of the power matrices depends on the algebraic operation employed on the matrices. In this paper, the power of intuitionistic fuzzy matrices with maxgeneralized mean-mingeneralized mean operation have been studied. Here it is shown that the power of intuitionistic fuzzy matrices with the said operations are always convergent. The convergence of powers for an intuitionistic fuzzy matrix with convex combination of max-min and maxarithmetic mean-minarithmetic mean are also dicussed here.