OpenRaiser/Intern-Atlas huggingface dataset:
A New Framework for Retinex based Color Image Enhancement using Particle Swarm Optimization
Venue: arXiv cs.CV 2014
Year: 2014

A new approach for tuning the parameters of MultiScale Retinex (MSR) based color image enhancement algorithm using a popular optimization method, namely, Particle Swarm Optimization (PSO) is presented in this paper. The image enhancement using MSR scheme heavily depends on parameters such as Gaussian surround space constant, number of scales, gain and offset etc. Selection of these parameters, empirically and its application to MSR scheme to produce inevitable results are the major blemishes. The method presented here results in huge savings of computation time as well as improvement in the visual quality of an image, since the PSO exploited maximizes the MSR parameters. The objective of PSO is to validate the visual quality of the enhanced image iteratively using an effective objective criterion based on entropy and edge information of an image. The PSO method of parameter optimization of MSR scheme achieves a very good quality of reconstructed images, far better than that possible with the other existing methods. Finally, the quality of the enhanced color images obtained by the proposed method are evaluated using novel metric, namely, Wavelet Energy (WE). The experimental results presented show that color images enhanced using the proposed scheme are clearer, more vivid and efficient.
A space- and time-efficient Implementation of the Merkle Tree Traversal Algorithm
Venue: arXiv cs.CR 2014
Year: 2014

We present an algorithm for the Merkle tree traversal problem which combines the efficient space-time trade-off from the fractal Merkle tree [3] and the space efficiency from the improved log space-time Merkle trees traversal [8]. We give an exhaustive analysis of the space and time efficiency of our algorithm in function of the parameters H (the height of the Merkle tree) and h (h = H L where L is the number of levels in the Merkle tree). We also analyze the space impact when a continuous deterministic pseudo-random number generator (PRNG) is used to generate the leaves. We further program a low storage-space and a low time-overhead version of the algorithm in Java and measure its performance with respect to the two different implementations cited above. Our implementation uses the least space when a continuous PRNG is used for the leaf calculation.
Virtual Laboratories in Cloud Infrastructure of Educational Institutions
Venue: arXiv cs.DC 2014
Year: 2014

Modern educational institutions widely used virtual laboratories and cloud technologies. In practice must deal with security, processing speed and other tasks. The paper describes the experience of the construction of an experimental stand cloud computing and network management. Models and control principles set forth herein.
An Algorithm Based on Empirical Methods, for Text-to-Tuneful-Speech Synthesis of Sanskrit Verse
Venue: arXiv cs.CL 2014
Year: 2014

The rendering of Sanskrit poetry from text to speech is a problem that has not been solved before. One reason may be the complications in the language itself. We present unique algorithms based on extensive empirical analysis, to synthesize speech from a given text input of Sanskrit verses. Using a pre-recorded audio units database which is itself tremendously reduced in size compared to the colossal size that would otherwise be required, the algorithms work on producing the best possible, tunefully rendered chanting of the given verse. His would enable the visually impaired and those with reading disabilities to easily access the contents of Sanskrit verses otherwise available only in writing.
On the Termination of Linear and Affine Programs over the Integers
Venue: arXiv cs.DM 2014
Year: 2014

The termination problem for affine programs over the integers was left open in\cite{Braverman}. For more that a decade, it has been considered and cited as a challenging open problem. To the best of our knowledge, we present here the most complete response to this issue: we show that termination for affine programs over Z is decidable under an assumption holding for almost all affine programs, except for an extremely small class of zero Lesbegue measure. We use the notion of asymptotically non-terminating initial variable values} (ANT, for short) for linear loop programs over Z. Those values are directly associated to initial variable values for which the corresponding program does not terminate. We reduce the termination problem of linear affine programs over the integers to the emptiness check of a specific ANT set of initial variable values. For this class of linear or affine programs, we prove that the corresponding ANT set is a semi-linear space and we provide a powerful computational methods allowing the automatic generation of these $ANT$ sets. Moreover, we are able to address the conditional termination problem too. In other words, by taking ANT set complements, we obtain a precise under-approximation of the set of inputs for which the program does terminate.
Simulating Noisy Channel Interaction
Venue: arXiv cs.IT 2014
Year: 2014

We show that $T$ rounds of interaction over the binary symmetric channel $BSC_{1/2-ε}$ with feedback can be simulated with $O(ε^2 T)$ rounds of interaction over a noiseless channel. We also introduce a more general "energy cost" model of interaction over a noisy channel. We show energy cost to be equivalent to external information complexity, which implies that our simulation results are unlikely to carry over to energy complexity. Our main technical innovation is a self-reduction from simulating a noisy channel to simulating a slightly-less-noisy channel, which may have other applications in the area of interactive compression.
Tackling the "Gremlins Paradox" : Autonomous Hot Swap Healing Protocol for Chronic Performance Problem Aversion in On-board Mission Critical Systems for Lethal Autonomous Weapons
Venue: arXiv cs.CR 2014
Year: 2014

This paper gives insights into the issue of Chronic Performance Problem Aversion in On-board Mission Critical Systems for Lethal Autonomous Weapons
An In Depth Study into Using EMI Signatures for Appliance Identification
Venue: arXiv cs.SY 2014
Year: 2014

Energy conservation is a key factor towards long term energy sustainability. Real-time end user energy feedback, using disaggregated electric load composition, can play a pivotal role in motivating consumers towards energy conservation. Recent works have explored using high frequency conducted electromagnetic interference (EMI) on power lines as a single point sensing parameter for monitoring common home appliances. However, key questions regarding the reliability and feasibility of using EMI signatures for non-intrusive load monitoring over multiple appliances across different sensing paradigms remain unanswered. This work presents some of the key challenges towards using EMI as a unique and time invariant feature for load disaggregation. In-depth empirical evaluations of a large number of appliances in different sensing configurations are carried out, in both laboratory and real world settings. Insights into the effects of external parameters such as line impedance, background noise and appliance coupling on the EMI behavior of an appliance are realized through simulations and measurements. A generic approach for simulating the EMI behavior of an appliance that can then be used to do a detailed analysis of real world phenomenology is presented. The simulation approach is validated with EMI data from a router. Our EMI dataset - High Frequency EMI Dataset (HFED) is also released.
Scalable and Efficient Self-Join Processing technique in RDF data
Venue: arXiv cs.DB 2014
Year: 2014

Efficient management of RDF data plays an important role in successfully understanding and fast querying data. Although the current approaches of indexing in RDF Triples such as property tables and vertically partitioned solved many issues; however, they still suffer from the performance in the complex self-join queries and insert data in the same table. As an improvement in this paper, we propose an alternative solution to facilitate flexibility and efficiency in that queries and try to reach to the optimal solution to decrease the self-joins as much as possible, this solution based on the idea of "Recursive Mapping of Twin Tables". Our main goal of Recursive Mapping of Twin Tables (RMTT) approach is divided the main RDF Triple into two tables which have the same structure of RDF Triple and insert the RDF data recursively. Our experimental results compared the performance of join queries in vertically partitioned approach and the RMTT approach using very large RDF data, like DBLP and DBpedia datasets. Our experimental results with a number of complex submitted queries shows that our approach is highly scalable compared with RDF-3X approach and RMTT reduces the number of self-joins especially in complex queries 3-4 times than RDF-3X approach
Decentralised Multi-Agent Reinforcement Learning for Dynamic and Uncertain Environments
Venue: arXiv cs.MA 2014
Year: 2014

Multi-Agent Reinforcement Learning (MARL) is a widely used technique for optimization in decentralised control problems. However, most applications of MARL are in static environments, and are not suitable when agent behaviour and environment conditions are dynamic and uncertain. Addressing uncertainty in such environments remains a challenging problem for MARL-based systems. The dynamic nature of the environment causes previous knowledge of how agents interact to become outdated. Advanced knowledge of potential changes through prediction significantly supports agents converging to near-optimal control solutions. In this paper we propose P-MARL, a decentralised MARL algorithm enhanced by a prediction mechanism that provides accurate information regarding up-coming changes in the environment. This prediction is achieved by employing an Artificial Neural Network combined with a Self-Organising Map that detects and matches changes in the environment. The proposed algorithm is validated in a realistic smart-grid scenario, and provides a 92% Pareto efficient solution to an electric vehicle charging problem.
Improving files availability for BitTorrent using a diffusion model
Venue: arXiv cs.NI 2014
Year: 2014

The BitTorrent mechanism effectively spreads file fragments by copying the rarest fragments first. We propose to apply a mathematical model for the diffusion of fragments on a P2P in order to take into account both the effects of peer distances and the changing availability of peers while time goes on. Moreover, we manage to provide a forecast on the availability of a torrent thanks to a neural network that models the behaviour of peers on the P2P system. The combination of the mathematical model and the neural network provides a solution for choosing file fragments that need to be copied first, in order to ensure their continuous availability, counteracting possible disconnections by some peers.
Ultimate boundedness of droop controlled Microgrids with secondary loops
Venue: arXiv cs.SY 2014
Year: 2014

In this paper we study theoretical properties of inverter-based microgrids controlled via primary and secondary loops. Stability of these microgrids has been the subject of a number of recent studies. Conventional approaches based on standard hierarchical control rely on time-scale separation between primary and secondary control loops to show local stability of equilibria. In this paper we show that (i) frequency regulation can be ensured without assuming time-scale separation and, (ii) ultimate boundedness of the trajectories starting inside a region of the state space can be guaranteed under a condition on the inverters power injection errors. The trajectory ultimate bound can be computed by simple iterations of a nonlinear mapping and provides a certificate of the overall performance of the controlled microgrid.
A Mixtures-of-Experts Framework for Multi-Label Classification
Venue: arXiv cs.LG 2014
Year: 2014

We develop a novel probabilistic approach for multi-label classification that is based on the mixtures-of-experts architecture combined with recently introduced conditional tree-structured Bayesian networks. Our approach captures different input-output relations from multi-label data using the efficient tree-structured classifiers, while the mixtures-of-experts architecture aims to compensate for the tree-structured restrictions and build a more accurate model. We develop and present algorithms for learning the model from data and for performing multi-label predictions on future data instances. Experiments on multiple benchmark datasets demonstrate that our approach achieves highly competitive results and outperforms the existing state-of-the-art multi-label classification methods.
ICE: Enabling Non-Experts to Build Models Interactively for Large-Scale Lopsided Problems
Venue: arXiv cs.AI 2014
Year: 2014

Quick interaction between a human teacher and a learning machine presents numerous benefits and challenges when working with web-scale data. The human teacher guides the machine towards accomplishing the task of interest. The learning machine leverages big data to find examples that maximize the training value of its interaction with the teacher. When the teacher is restricted to labeling examples selected by the machine, this problem is an instance of active learning. When the teacher can provide additional information to the machine (e.g., suggestions on what examples or predictive features should be used) as the learning task progresses, then the problem becomes one of interactive learning. To accommodate the two-way communication channel needed for efficient interactive learning, the teacher and the machine need an environment that supports an interaction language. The machine can access, process, and summarize more examples than the teacher can see in a lifetime. Based on the machine's output, the teacher can revise the definition of the task or make it more precise. Both the teacher and the machine continuously learn and benefit from the interaction. We have built a platform to (1) produce valuable and deployable models and (2) support research on both the machine learning and user interface challenges of the interactive learning problem. The platform relies on a dedicated, low-latency, distributed, in-memory architecture that allows us to construct web-scale learning machines with quick interaction speed. The purpose of this paper is to describe this architecture and demonstrate how it supports our research efforts. Preliminary results are presented as illustrations of the architecture but are not the primary focus of the paper.
Stochastic Testing Simulator for Integrated Circuits and MEMS: Hierarchical and Sparse Techniques
Venue: arXiv cs.CE 2014
Year: 2014

Process variations are a major concern in today's chip design since they can significantly degrade chip performance. To predict such degradation, existing circuit and MEMS simulators rely on Monte Carlo algorithms, which are typically too slow. Therefore, novel fast stochastic simulators are highly desired. This paper first reviews our recently developed stochastic testing simulator that can achieve speedup factors of hundreds to thousands over Monte Carlo. Then, we develop a fast hierarchical stochastic spectral simulator to simulate a complex circuit or system consisting of several blocks. We further present a fast simulation approach based on anchored ANOVA (analysis of variance) for some design problems with many process variations. This approach can reduce the simulation cost and can identify which variation sources have strong impacts on the circuit's performance. The simulation results of some circuit and MEMS examples are reported to show the effectiveness of our simulator
Efficient Uncertainty Quantification for the Periodic Steady State of Forced and Autonomous Circuits
Venue: arXiv cs.CE 2014
Year: 2014

This brief paper proposes an uncertainty quantification method for the periodic steady-state (PSS) analysis with both Gaussian and non-Gaussian variations. Our stochastic testing formulation for the PSS problem provides superior efficiency over both Monte Carlo methods and existing spectral methods. The numerical implementation of a stochastic shooting Newton solver is presented for both forced and autonomous circuits. Simulation results on some analog/RF circuits are reported to show the effectiveness of our proposed algorithms.
Software Cognitive Complexity Measure Based on Scope of Variables
Venue: arXiv cs.SE 2014
Year: 2014

In this paper, we define a Mathematical model of program structure. Mathematical model of program structure defined here provides unified mathematical treatment of program structure, which reveals that a program is a large and finite set of embedded binary relations between current statement and previous ones. Then, a program is considered as a composed listing and a logical combination of multiple statements according to the certain composing rules. We also define the Scope Information Complexity Number (SICN) and present the cognitive complexity based on functional decomposition of software, including theoretical validation through nine Weyuker's properties.
Statistical inference with probabilistic graphical models
Venue: arXiv cs.LG 2014
Year: 2014

These are notes from the lecture of Devavrat Shah given at the autumn school "Statistical Physics, Optimization, Inference, and Message-Passing Algorithms", that took place in Les Houches, France from Monday September 30th, 2013, till Friday October 11th, 2013. The school was organized by Florent Krzakala from UPMC & ENS Paris, Federico Ricci-Tersenghi from La Sapienza Roma, Lenka Zdeborova from CEA Saclay & CNRS, and Riccardo Zecchina from Politecnico Torino. This lecture of Devavrat Shah (MIT) covers the basics of inference and learning. It explains how inference problems are represented within structures known as graphical models. The theoretical basis of the belief propagation algorithm is then explained and derived. This lecture sets the stage for generalizations and applications of message passing algorithms.
Identifying sparse and dense sub-graphs in large graphs with a fast algorithm
Venue: arXiv cs.DS 2014
Year: 2014

Identifying the nodes of small sub-graphs with no a priori information is a hard problem. In this work, we want to find each node of a sparse sub-graph embedded in both dynamic and static background graphs, of larger average degree. We show that exploiting the summability over several background realizations of the Estrada-Benzi communicability and the Krylov approximation of the matrix exponential, it is possible to recover the sub-graph with a fast algorithm with computational complexity O(N n). Relaxing the problem to complete sub-graphs, the same performance is obtained with a single background. The worst case complexity for the single background is O(n log(n)).
Decidability Problems for Actor Systems
Venue: arXiv cs.PL 2014
Year: 2014

We introduce a nominal actor-based language and study its expressive power. We have identified the presence/absence of fields as a crucial feature: the dynamic creation of names in combination with fields gives rise to Turing completeness. On the other hand, restricting to stateless actors gives rise to systems for which properties such as termination are decidable. This decidability result still holds for actors with states when the number of actors is bounded and the state is read-only.
Comparative Study of Geometric and Image Based Modelling and Rendering Techniques
Venue: arXiv cs.GR 2014
Year: 2014

This is a comparative study of the traditional 3D computer graphics technique of geometric modelling and image-based rendering techniques that were surveyed and implemented.We have discussed the classifications and representative methods of both the techniques. The study has shown that there is a strong continuum between both the techniques and a hybrid of the two is most suitable for further implementations.This hybridisation study is underway to create models of real life situations and provide disaster management training.
Plug-and-play fault diagnosis and control-reconfiguration for a class of nonlinear large-scale constrained systems
Venue: arXiv cs.SY 2014
Year: 2014

This paper deals with a novel Plug-and-Play (PnP) architecture for the control and monitoring of Large-Scale Systems (LSSs). The proposed approach integrates a distributed Model Predictive Control (MPC) strategy with a distributed Fault Detection (FD) architecture and methodology in a PnP framework. The basic concept is to use the FD scheme as an autonomous decision support system: once a fault is detected, the faulty subsystem can be unplugged to avoid the propagation of the fault in the interconnected LSS. Analogously, once the issue has been solved, the disconnected subsystem can be re-plugged-in. PnP design of local controllers and detectors allow these operations to be performed safely, i.e. without spoiling stability and constraint satisfaction for the whole LSS. The PnP distributed MPC is derived for a class of nonlinear LSS and an integrated PnP distributed FD architecture is proposed. Simulation results show the effectiveness and the potential of the general methodology.
Generalized Virtual Networking: an enabler for Service Centric Networking and Network Function Virtualization
Venue: arXiv cs.NI 2014
Year: 2014

In this paper we introduce the Generalized Virtual Networking (GVN) concept. GVN provides a framework to influence the routing of packets based on service level information that is carried in the packets. It is based on a protocol header inserted between the Network and Transport layers, therefore it can be seen as a layer 3.5 solution. Technically, GVN is proposed as a new transport layer protocol in the TCP/IP protocol suite. An IP router that is not GVN capable will simply process the IP destination address as usual. Similar concepts have been proposed in other works, and referred to as Service Oriented Networking, Service Centric Networking, Application Delivery Networking, but they are now generalized in the proposed GVN framework. In this respect, the GVN header is a generic container that can be adapted to serve the needs of arbitrary service level routing solutions. The GVN header can be managed by GVN capable end-hosts and applications or can be pushed/popped at the edge of a GVN capable network (like a VLAN tag). In this position paper, we show that Generalized Virtual Networking is a powerful enabler for SCN (Service Centric Networking) and NFV (Network Function Virtualization) and how it couples with the SDN (Software Defined Networking) paradigm.
Promoting Inclusive Design Practice at the Global Game Jam: A Pilot Evaluation
Venue: arXiv cs.CY 2014
Year: 2014

Games are a popular form of entertainment. However, many computer games present unnecessary barriers to players with sensory, motor and cognitive impairments. In order to overcome such pitfalls, an awareness of their impact and a willingness to apply inclusive design practice is often necessary. The Global Game Jam offers a potential avenue to promote inclusive design practices to students of game development. As such, this paper evaluates the impact of an initiative to promote inclusive design practices during the 2014 Global Game Jam. An attitude questionnaire was distributed to both participants and non-participants at one event venue. The results indicate that, having enrolled in the initiative, students' attitudes improved. Furthermore, all attendees reported they were likely to pursue further learning opportunities and consider accessibility issues in their future games. This suggests that the Global Game Jam, and other similar events, present an attractive avenue to promote inclusive design practice within the context of digital game development. However, further analysis of submitted games, additional qualitative inquiry and a large-scale trial are needed to determine impact on practice and to form recommendations for future events.