OpenRaiser/Intern-Atlas huggingface dataset:
Affordances and Safe Design of Assistance Wearable Virtual Environment of Gesture
Venue: arXiv cs.HC 2015
Year: 2015

Safety and reliability are the main issues for designing assistance wearable virtual environment of technical gesture in aerospace, or health application domains. That needs the integration in the same isomorphic engineering framework of human requirements, systems requirements and the rationale of their relation to the natural and artifactual environment.To explore coupling integration and design functional organization of support technical gesture systems, firstly ecological psychologyprovides usa heuristicconcept: the affordance. On the other hand mathematical theory of integrative physiology provides us scientific concepts: the stabilizing auto-association principle and functional interaction.After demonstrating the epistemological consistence of these concepts, we define an isomorphic framework to describe and model human systems integration dedicated to human in-the-loop system engineering.We present an experimental approach of safe design of assistance wearable virtual environment of gesture based in laboratory and parabolic flights. On the results, we discuss the relevance of our conceptual approach and the applications to future assistance of gesture wearable systems engineering.
Towards Dynamic Updates in Service Composition
Venue: arXiv cs.LO 2015
Year: 2015

We survey our results about verification of adaptable processes. We present adaptable processes as a way of overcoming the limitations that process calculi have for describing patterns of dynamic process evolution. Such patterns rely on direct ways of controlling the behavior and location of running processes, and so they are at the heart of the adaptation capabilities present in many modern concurrent systems. Adaptable processes have named scopes and are sensible to actions of dynamic update at runtime; this allows to express dynamic and static topologies of adaptable processes as well as different evolvability patterns for concurrent processes. We introduce a core calculus of adaptable processes and consider verification problems for them: first based on specific properties related to error occurrence, that we call bounded and eventual adaptation, and then by considering a simple yet expressive temporal logic over adaptable processes. We provide (un)decidability results of such verification problems over adaptable processes considering the spectrum of topologies/evolvability patterns introduced. We then consider distributed adaptability, where a process can update part of a protocol by performing dynamic distributed updates over a set of protocol participants. Dynamic updates in this context are presented as an extension of our work on choreographies and behavioural contracts in multiparty interactions. We show how update mechanisms considered for adaptable processes can be used to extend the theory of choreography and orchestration/contracts, allowing them to be modified at run-time by internal (self-adaptation) or external intervention.
A Constraint-based Approach for Generating Transformation Patterns
Venue: arXiv cs.DC 2015
Year: 2015

Undoing operations is an indispensable feature for many collaborative applications, mainly collaborative editors. It provides the ability to restore a correct state of shared data after erroneous operations. In particular, selective undo allows to undo any operation and is based on rearranging operations in the history thanks to the Operational Transformation (OT) approach. OT is an optimistic replication technique allowing for updating the shared data concurrently while maintaining convergence. It is a challenging task how to meaningfully combine OT and undo approaches. Indeed, undoing operations that are received and executed out-of-order at different sites leads to divergence cases. Even though various undo solutions have been proposed over the recent years, they are either limited or erroneous. In this paper, we propose a constraint-based approach to address the undo problem. We use Constraint Satisfaction Problem (CSP) theory to devise correct and undoable transformation patterns (w.r.t OT and undo properties) which considerably simplifies the design of collaborative objects.
Analysis and Synthesis Prior Greedy Algorithms for Non-linear Sparse Recovery
Venue: arXiv cs.IT 2015
Year: 2015

In this work we address the problem of recovering sparse solutions to non linear inverse problems. We look at two variants of the basic problem, the synthesis prior problem when the solution is sparse and the analysis prior problem where the solution is cosparse in some linear basis. For the first problem, we propose non linear variants of the Orthogonal Matching Pursuit (OMP) and CoSamp algorithms; for the second problem we propose a non linear variant of the Greedy Analysis Pursuit (GAP) algorithm. We empirically test the success rates of our algorithms on exponential and logarithmic functions. We model speckle denoising as a non linear sparse recovery problem and apply our technique to solve it. Results show that our method outperforms state of the art methods in ultrasound speckle denoising.
Sparse Reconstruction of Compressive Sensing MRI using Cross-Domain Stochastically Fully Connected Conditional Random Fields
Venue: arXiv cs.CV 2015
Year: 2015

Magnetic Resonance Imaging (MRI) is a crucial medical imaging technology for the screening and diagnosis of frequently occurring cancers. However image quality may suffer by long acquisition times for MRIs due to patient motion, as well as result in great patient discomfort. Reducing MRI acquisition time can reduce patient discomfort and as a result reduces motion artifacts from the acquisition process. Compressive sensing strategies, when applied to MRI, have been demonstrated to be effective at decreasing acquisition times significantly by sparsely sampling the \emph{k}-space during the acquisition process. However, such a strategy requires advanced reconstruction algorithms to produce high quality and reliable images from compressive sensing MRI. This paper proposes a new reconstruction approach based on cross-domain stochastically fully connected conditional random fields (CD-SFCRF) for compressive sensing MRI. The CD-SFCRF introduces constraints in both \emph{k}-space and spatial domains within a stochastically fully connected graphical model to produce improved MRI reconstruction. Experimental results using T2-weighted (T2w) imaging and diffusion-weighted imaging (DWI) of the prostate show strong performance in preserving fine details and tissue structures in the reconstructed images when compared to other tested methods even at low sampling rates.
On Temporal Regularity in Social Interactions: Predicting Mobile Phone Calls
Venue: arXiv cs.SI 2015
Year: 2015

In this paper we predict outgoing mobile phone calls using a machine learning approach. We analyze to which extent the activity of mobile phone users is predictable. The premise is that mobile phone users exhibit temporal regularity in their interactions with majority of their contacts. In the sociological context, most social interactions have fairly reliable temporal regularity. If we quantify the extension of this behavior to interactions on mobile phones we expect that caller-callee interaction is not merely a result of randomness, rather it exhibits a temporal pattern. To this end, we tested our approach on an anonymized mobile phone usage dataset collected specifically for analyzing temporal patterns in mobile phone communication. The data consists of 783 users and more than 12,000 caller-callee pairs. The results show that users' historic calling patterns can predict future calls with reasonable accuracy.
Time delay estimator for predetermined repeated signal robust to narrowband interference
Venue: arXiv cs.IT 2015
Year: 2015

In this paper, time delay estimation techniques robust to narrowband interference (NBI) are proposed. Owing to the deluge of wireless signal interference these days, narrowband interference is a common problem for communication and positioning systems. To mitigate the effect of this narrow band interference, we propose a robust time delay estimator for a predetermined repeated synchronization signal in an NBI environment. We exploit an ensemble of average and sample covariance matrices to estimate the noise profile. In addition, to increase the detection probability, we suppress the variance of likelihood value by employing a von-Mises distribution in the time-delay estimator. Our proposed time delay estimator shows a better performance in an NBI environment compared to a typical time delay estimator.
Ramanujan subspace pursuit for signal periodic decomposition
Venue: arXiv cs.IT 2015
Year: 2015

The period estimation and periodic decomposition of a signal are the long-standing problems in the field of signal processing and biomolecular sequence analysis. To address such problems, we introduce the Ramanujan subspace pursuit (RSP) based on the Ramanujan subspace. As a greedy iterative algorithm, the RSP can uniquely decompose any signal into a sum of exactly periodic components, by selecting and removing the most dominant periodic component from the residual signal in each iteration. In the RSP, a novel periodicity metric is derived based on the energy of the exactly periodic component obtained by orthogonally projecting the residual signal into the Ramanujan subspace, and is then used to select the most dominant periodic component in each iteration. To reduce the computational cost of the RSP, we also propose the fast RSP (FRSP) based on the relationship between the periodic subspace and the Ramanujan subspace, and based on the maximum likelihood estimation of the energy of the periodic component in the periodic subspace. The fast RSP has a lower computational cost and can decompose a signal of length $N$ into the sum of $K$ exactly periodic components in $ \mathcal{O}(K N\log N)$. In addition, our results show that the RSP outperforms the current algorithms for period estimation.
Electricity Demand Forecasting by Multi-Task Learning
Venue: arXiv cs.LG 2015
Year: 2015

We explore the application of kernel-based multi-task learning techniques to forecast the demand of electricity in multiple nodes of a distribution network. We show that recently developed output kernel learning techniques are particularly well suited to solve this problem, as they allow to flexibly model the complex seasonal effects that characterize electricity demand data, while learning and exploiting correlations between multiple demand profiles. We also demonstrate that kernels with a multiplicative structure yield superior predictive performance with respect to the widely adopted (generalized) additive models. Our study is based on residential and industrial smart meter data provided by the Irish Commission for Energy Regulation (CER).
The Proficiency-Congruency Dilemma: Virtual Team Design and Performance in Multiplayer Online Games
Venue: arXiv cs.HC 2015
Year: 2015

Multiplayer online battle arena games provide an excellent opportunity to study team performance. When designing a team, players must negotiate a \textit{proficiency-congruency dilemma} between selecting roles that best match their experience and roles that best complement the existing roles on the team. We adopt a mixed-methods approach to explore how users negotiate this dilemma. Using data from \textit{League of Legends}, we define a similarity space to operationalize team design constructs about role proficiency, generality, and congruency. We collect publicly available data from 3.36 million users to test the influence of these constructs on team performance. We also conduct focus groups with novice and elite players to understand how players' team design practices vary with expertise. We find that player proficiency increases team performance more than team congruency. These findings have implications for players, designers, and theorists about how to recommend team designs that jointly prioritize individuals' expertise and teams' compatibility.
A Fast Recommendation Algorithm for Social Tagging Systems : A Delicious Case
Venue: arXiv cs.IR 2015
Year: 2015

The tripartite graph is one of the commonest topological structures in social tagging systems such as Delicious, which has three types of nodes (i.e., users, URLs and tags). Traditional recommender systems developed based on collaborative filtering for the social tagging systems bring very high demands on CPU time cost. In this paper, to overcome this drawback, we propose a novel approach that extracts non-overlapping user clusters and corresponding overlapping item clusters simultaneously through coarse clustering to accelerate the user-based collaborative filtering and develop a fast recommendation algorithm for the social tagging systems. The experimental results show that the proposed approach is able to dramatically reduce the processing time cost greater than $90\%$ and relatively enhance the accuracy in comparison with the ordinary user-based collaborative filtering algorithm.
Maximium Priority Matchings
Venue: arXiv cs.DS 2015
Year: 2015

Let $G=(V,E)$ be an undirected graph with $n$ vertices and $m$ edges, in which each vertex $u$ is assigned an integer priority in $[1,n]$, with 1 being the "highest" priority. Let $M$ be a matching of $G$. We define the priority score of $M$ to be an $n$-ary integer in which the $i$-th most-significant digit is the number of vertices with priority $i$ that are incident to an edge in $M$. We describe a variation of the augmenting path method (Edmonds' algorithm) that finds a matching with maximum priority score in $O(mn)$ time.
Optimal Selective Attention in Reactive Agents
Venue: arXiv cs.LG 2015
Year: 2015

In POMDPs, information about the hidden state, delivered through observations, is both valuable to the agent, allowing it to base its actions on better informed internal states, and a "curse", exploding the size and diversity of the internal state space. One attempt to deal with this is to focus on reactive policies, that only base their actions on the most recent observation. However, even reactive policies can be demanding on resources, and agents need to pay selective attention to only some of the information available to them in observations. In this report we present the minimum-information principle for selective attention in reactive agents. We further motivate this approach by reducing the general problem of optimal control in POMDPs, to reactive control with complex observations. Lastly, we explore a newly discovered phenomenon of this optimization process - period doubling bifurcations. This necessitates periodic policies, and raises many more questions regarding stability, periodicity and chaos in optimal control.
Key Agreement over an Interference Channel with Noiseless Feedback: Achievable Region & Distributed Allocation
Venue: arXiv cs.IT 2015
Year: 2015

Secret key establishment leveraging the physical layer as a source of common randomness has been investigated in a range of settings. We investigate the problem of establishing, in an information-theoretic sense, a secret key between a user and a base-station (BS) (more generally, part of a wireless infrastructure), but for two such user-BS pairs attempting the key establishment simultaneously. The challenge in this novel setting lies in that a user can eavesdrop another BS-user communications. It is thus paramount to ensure the two keys are established with no leakage to the other user, in spite the interference across neighboring cells. We model the system with BS-user communication through an interference channel and user-BS communication through a public channel. We find the region including achievable secret key rates for the general case that the interference channel (IC) is discrete and memoryless. Our results are examined for a Gaussian IC. In this setup, we investigate the performance of different transmission schemes for power allocation. The chosen transmission scheme by each BS essentially affects the secret key rate of the other BS-user. Assuming base stations are trustworthy but that they seek to maximize the corresponding secret key rate, a game-theoretic setting arises to analyze the interaction between the base stations.We model our key agreement scenario in normal form for different power allocation schemes to understand performance without cooperation. Numerical simulations illustrate the inefficiency of the Nash equilibrium outcome and motivate further research on cooperative or coordinated schemes.
Discrete power law with exponential cutoff and Lotka's Law
Venue: arXiv cs.DL 2015
Year: 2015

The first bibliometric law appeared in Alfred J. Lotka's 1926 examination of author productivity in chemistry and physics. The result is that the productivity distribution is thought to be described by a power law. In this paper, Lotka's original data on author productivity in chemistry is reconsidered by comparing the fit of the data to both a discrete power law and a discrete power law with exponential cutoff.
Dynamic Computation of Runge Kutta Fourth Order Algorithm for First and Second Order Ordinary Differential Equation Using Java
Venue: arXiv cs.MS 2015
Year: 2015

Differential equations arise in mathematics, physics,medicine, pharmacology, communications, image processing and animation, etc. An Ordinary Differential Equation (ODE) is a differential equation if it involves derivatives with respect to only one independent variable which can be studied from different perspectives, such as: analytical methods, graphical methods and numerical methods. This research paper therefore revises the standard Runge - Kutta fourth order algorithm by using compiler techniques to dynamically evaluate the inputs and implement the algorithm for both first and second order derivatives of the ODE. We have been able to develop and implement the software that can be used to evaluate inputs and compute solutions (approximately and analytically) for the ODE function at a more efficient rate than the traditional method.
Combining Fuzzy Cognitive Maps and Discrete Random Variables
Venue: arXiv cs.AI 2015
Year: 2015

In this paper we propose an extension to the Fuzzy Cognitive Maps (FCMs) that aims at aggregating a number of reasoning tasks into a one parallel run. The described approach consists in replacing real-valued activation levels of concepts (and further influence weights) by random variables. Such extension, followed by the implemented software tool, allows for determining ranges reached by concept activation levels, sensitivity analysis as well as statistical analysis of multiple reasoning results. We replace multiplication and addition operators appearing in the FCM state equation by appropriate convolutions applicable for discrete random variables. To make the model computationally feasible, it is further augmented with aggregation operations for discrete random variables. We discuss four implemented aggregators, as well as we report results of preliminary tests.
Apollonius Solutions in Rd
Venue: arXiv cs.CG 2015
Year: 2015

Voronoi and related diagrams have technological applications, for example, in motion planning and surface reconstruction, and also find significant use in materials science, molecular biology, and crystallography. Apollonius diagrams arguably provide the most natural division of space for many materials and technology problems, but compared to Voronoi and power diagrams, their use has been limited, presumably by the complexity of their calculation. In this work, we report explicit equations for the vertices of the Apollonius diagram in a d-dimensional Euclidean space. We show that there are special lines that contain vertices of more than one type of diagram and this property can be exploited to develop simple vertex expressions for the Apollonius diagram. Finding the Apollonius vertices is not significantly more difficult or expensive than computing those of the power diagram and have application beyond their use in calculating the diagram. The expressions reported here lend themselves to the use of standard vector and matrix libraries and the stability and precision their use implies. They can also be used in algorithms with multi-precision numeric types and those adhering to the exact algorithms paradigm. The results have been coded in C++ for the 2-d and 3-d cases and an example of their use in characterizing the shape of a void in a molecular crystal is given.
The Edge Group Coloring Problem with Applications to Multicast Switching
Venue: arXiv cs.DS 2015
Year: 2015

This paper introduces a natural generalization of the classical edge coloring problem in graphs that provides a useful abstraction for two well-known problems in multicast switching. We show that the problem is NP-hard and evaluate the performance of several approximation algorithms, both analytically and experimentally. We find that for random $χ$-colorable graphs, the number of colors used by the best algorithms falls within a small constant factor of $χ$, where the constant factor is mainly a function of the ratio of the number of outputs to inputs. When this ratio is less than 10, the best algorithms produces solutions that use fewer than $2χ$ colors. In addition, one of the algorithms studied finds high quality approximate solutions for any graph with high probability, where the probability of a low quality solution is a function only of the random choices made by the algorithm.
Cross-Cultural Differences in Students' Intention to Use RSS Feeds between Lebanon and the United Kingdom: A Multi-Group Invariance Analysis Based on the Technology Acceptance Model
Venue: arXiv cs.CY 2015
Year: 2015

Really Simple Syndication (RSS) offers a means for university students to receive timely updates from virtual learning environments. However, despite its utility, only 21% of students surveyed at a Lebanese university claim to have ever used the technology. To investigate whether a cultural influence is affecting intention to use RSS, the survey was extended to the British context to conduct a cross-cultural comparison. Using the Technology Adoption Model (TAM) as a research framework, 437 students responded to a questionnaire containing four constructs: intention to use (INT); attitude towards benefit (ATT); perceived usefulness (PU); and perceived ease of use (PEOU). Principle components analysis (PCA) and structural equation modelling (SEM) were used to explore the psychometric qualities of the scale. The results show that adoption was significantly higher, but also modest, in the British context at 36%. Configural and metric invariance were fully supported, while scalar and factorial invariance were partially supported. Analysis reveals that, as a potential consequence of culture, there are significant differences between PU and PEOU across the two contexts studied, potentially as a consequence of culture. It is recommended that faculty demonstrate to students how RSS can be used effectively in order to increase awareness and emphasise usefulness.