OpenRaiser/Intern-Atlas huggingface dataset:
Multiple model software for airflow and thermal building simulation. A case study under tropical humid climate, in Réunion Island
Venue: arXiv cs.CE 2012
Year: 2012

The first purpose of our work has been to allow -as far as heat transfer modes, airflow calculation and meteorological data reconstitution are concerned- the integration of diverse interchangeable physical models in a single software tool for professional use, CODYRUN. The designer's objectives, precision requested and calculation time consideration, lead us to design a structure accepting selective use of models, taking into account multizone description and airflow patterns. With a building case study in Reunion Island, we first analyse the sensibility of the thermal model to diffuse radiation reconstitution on tilted surfaces. Then, a realistic balance between precision required and calculation time leads us to select detailed models for the zone of main interest, but to choose simplified models for the other zones.
Multiple model approach and experimental validation of a residential air-to-air heat pump
Venue: arXiv cs.CE 2012
Year: 2012

The beginning of this work is the achievement of a design tool, which is a multiple model software called " CODYRUN ", suitable for professionnals and usable by researchers. The original aspect of this software is that the designer has at his disposal a wide panel of choices between different heat transfer models More precisely, it consists in a multizone software integrating both natural ventilation and moisture tranfers . This software is developed on PC micro computer and gets advantage of the Microsoft WINDOWS front-end. Most of time, HVAC systems and specially domestic air conditioners, are taken into account in a very simplified way, or in a elaborated one. On one side,they are just supposed to supply the demand of cooling loads with an ideal control loop (no delay between the sollicitations and the time response of the system), The available outputs are initially the hourly cooling and heating consumptions without integrating the real caracteristics of the HVAC system This paper is also following the same multiple model approach than for the building modelling by defining different modelling levels for the air conditionning systems, from a very simplified one to a detailled one. An experimental validation is achieved in order to compare the sensitivity of each defined model and to point out the interaction between the thermal behaviour of the envelop and the electrical system consumption. For validation purposes, we will describe the data acquisition system. and the used real size test cell located in the University of Reunion island, Indian Ocean.
Cumulative Distance Enumerators of Random Codes and their Thresholds
Venue: arXiv cs.IT 2012
Year: 2012

Cumulative weight enumerators of random linear codes are introduced, their asymptotic properties are studied, and very sharp thresholds are exhibited; as a consequence, it is shown that the asymptotic Gilbert-Varshamov bound is a very sharp threshold point for the density of the linear codes whose relative distance is greater than a given positive number. For arbitrary random codes, similar settings and results are exhibited; in particular, the very sharp threshold point for the density of the codes whose relative distance is greater than a given positive number is located at half the asymptotic Gilbert-Varshamov bound.
Strategy-Proof Prediction Markets
Venue: arXiv cs.GT 2012
Year: 2012

Prediction markets aggregate agents' beliefs regarding a future event, where each agent is paid based on the accuracy of its reported belief when compared to the realized outcome. Agents may strategically manipulate the market (e.g., delay reporting, make false reports) aiming for higher expected payments, and hence the accuracy of the market's aggregated information will be in question. In this study, we present a general belief model that captures how agents influence each other beliefs, and show that there are three necessary and sufficient conditions for agents to behave truthfully in scoring rule based markets (SRMs). Given that these conditions are restrictive and difficult to satisfy in real-life, we present novel strategy-proof SRMs where agents are truthful while dismissing all these conditions. Although achieving such a strong form of truthfulness increases the worst-case loss in the new markets, we show that this is the minimum loss required to dismiss these conditions.
Self-embeddings of Hamming Steiner triple systems of small order and APN permutations
Venue: arXiv cs.IT 2012
Year: 2012

The classification, up to isomorphism, of all self-embedding monomial power permutations of Hamming Steiner triple systems of order n=2^m-1 for small m, m < 23, is given. As far as we know, for m in {5,7,11,13,17,19}, all given self-embeddings in closed surfaces are new. Moreover, they are cyclic for all m and nonorientable at least for all m < 21. For any non prime m, the nonexistence of such self-embeddings in a closed surface is proven.
Image Steganography Method Based on Brightness Adjustment
Venue: arXiv cs.CR 2012
Year: 2012

Steganography is an information hiding technique in which secret data are secured by covering them into a computer carrier file without damaging the file or changing its size. The difference between steganography and cryptography is that steganography is a stealthy method of communication that only the communicating parties are aware of; while, cryptography is an overt method of communication that anyone is aware of, despite its payload is scribbled. Typically, an irrecoverable steganography algorithm is the algorithm that makes it hard for malicious third parties to discover how it works and how to recover the secret data out of the carrier file. One popular way to achieve irrecoverability is to digitally process the carrier file after hiding the secret data into it. However, such process is irreversible as it would destroy the concealed data. This paper proposes a new image steganography method for textual data, as well as for any form of digital data, based on adjusting the brightness of the carrier image after covering the secret data into it. The algorithm used is parameterized as it can be configured using three different parameters defined by the communicating parties. They include the amount of brightness to apply on the carrier image after the completion of the covering process, the color channels whose brightness should be adjusted, and the bytes that should carry in the secret data. The novelty of the proposed method is that it embeds bits of the secret data into the three LSBs of the bytes that compose the carrier image in such a way that does not destroy the secret data when restoring back the original brightness of the carrier image. The simulation conducted proved that the proposed algorithm is valid and correct.
Weak Visibility Queries of Line Segments in Simple Polygons
Venue: arXiv cs.CG 2012
Year: 2012

Given a simple polygon P in the plane, we present new algorithms and data structures for computing the weak visibility polygon from any query line segment in P. We build a data structure in O(n) time and O(n) space that can compute the visibility polygon for any query line segment s in O(k log n) time, where k is the size of the visibility polygon of s and n is the number of vertices of P. Alternatively, we build a data structure in O(n^3) time and O(n^3) space that can compute the visibility polygon for any query line segment in O(k + log n) time.
Large Scale Strongly Supervised Ensemble Metric Learning, with Applications to Face Verification and Retrieval
Venue: arXiv cs.CV 2012
Year: 2012

Learning Mahanalobis distance metrics in a high- dimensional feature space is very difficult especially when structural sparsity and low rank are enforced to improve com- putational efficiency in testing phase. This paper addresses both aspects by an ensemble metric learning approach that consists of sparse block diagonal metric ensembling and join- t metric learning as two consecutive steps. The former step pursues a highly sparse block diagonal metric by selecting effective feature groups while the latter one further exploits correlations between selected feature groups to obtain an accurate and low rank metric. Our algorithm considers all pairwise or triplet constraints generated from training samples with explicit class labels, and possesses good scala- bility with respect to increasing feature dimensionality and growing data volumes. Its applications to face verification and retrieval outperform existing state-of-the-art methods in accuracy while retaining high efficiency.
Human-Recognizable Robotic Gestures
Venue: arXiv cs.RO 2012
Year: 2012

For robots to be accommodated in human spaces and in humans daily activities, robots should be able to understand messages from the human conversation partner. In the same light, humans must also understand the messages that are being communicated by robots, including the non-verbal ones. We conducted a web-based video study wherein participants gave interpretations on the iconic gestures and emblems that were produced by an anthropomorphic robot. Out of the 15 gestures presented, we found 6 robotic gestures that can be accurately recognized by the human observer. These were nodding, clapping, hugging, expressing anger, walking, and flying. We reviewed these gestures for their meaning from literatures in human and animal behavior. We conclude by discussing the possible implications of these gestures for the design of social robots that are aimed to have engaging interactions with humans.
LNOS - Live Network Operating System
Venue: arXiv cs.NI 2012
Year: 2012

Operating Systems exists since existence of computers, and have been evolving continuously from time to time. In this paper we have reviewed a relatively new or unexplored topic of Live OS. From networking perspective, Live OS is used for establishing Clusters, Firewalls and as Network security assessment tool etc. Our proposed concept is that a Live OS can be established or configured for an organizations specific network requirements with respect to their servers. An important server failure due to hardware or software could take time for remedy of the problem, so for that situation a preconfigured server in the form of Live OS on CD/DVD/USB can be used as an immediate solution. In a network of ten nodes, we stopped the server machine and with necessary adjustments, Live OS replaced the server in less than five minutes. Live OS in a network environment is a quick replacement of the services that are failed due to server failure (hardware or software). It is a cost effective solution for low budget networks. The life of Live OS starts when we boot it from CD/DVD/USB and remains in action for that session. As soon as the machine is rebooted, any work done for that session is gone, (in case we do not store any information on permanent storage media). Live CD/DVD/USB is normally used on systems where we do not have Operating Systems installed. A Live OS can also be used on systems where we already have an installed OS. On the basis of functionality a Live OS can be used for many purposes and has some typical advantages that are not available on other operating systems. Vendors are releasing different distributions of Live OS and is becoming their sole identity in a particular domain like Networks, Security, Education or Entertainment etc. There can be many aspects of Live OS, but Linux based Live OS and their use in the field of networks is the main focus of this paper.
Efficient Decomposition of Bimatrix Games
Venue: arXiv cs.GT 2012
Year: 2012

Exploiting the algebraic structure of the set of bimatrix games, a divide-and-conquer algorithm for finding Nash equilibria is proposed. The algorithm is fixed-parameter tractable with the size of the largest irreducible component of a game as parameter. An implementation of the algorithm is shown to yield a significant performance increase on inputs with small parameters.
The Weight Distribution of a Class of Cyclic Codes Related to Hermitian Forms Graphs
Venue: arXiv cs.IT 2012
Year: 2012

The determination of weight distribution of cyclic codes involves evaluation of Gauss sums and exponential sums. Despite of some cases where a neat expression is available, the computation is generally rather complicated. In this note, we determine the weight distribution of a class of reducible cyclic codes whose dual codes may have arbitrarily many zeros. This goal is achieved by building an unexpected connection between the corresponding exponential sums and the spectrums of Hermitian forms graphs.
Energy in computing systems with speed scaling: optimization and mechanisms design
Venue: arXiv cs.GT 2012
Year: 2012

We study a simple scheduling game for the speed scaling model. Players want their job to complete early, which however generates a big energy consumption. We address the game from the mechanism design side, and by charging the energy usage to the players we seek for a good compromize between quality of service and energy usage.
Trajectory tracking control of kites with system delay
Venue: arXiv cs.SY 2012
Year: 2012

A previously published algorithm for trajectory tracking control of tethered wings, i.e. kites, is updated in light of recent experimental evidence. The algorithm is, furthermore, analyzed in the framework of delay differential equations. It is shown how the presence of system delay influences the stability of the control system, and a methodology is derived for gain selection using the Lambert W function. The validity of the methodology is demonstrated with simulation results. The analysis sheds light on previously poorly understood stability problems.
Discovering Basic Emotion Sets via Semantic Clustering on a Twitter Corpus
Venue: arXiv cs.AI 2012
Year: 2012

A plethora of words are used to describe the spectrum of human emotions, but how many emotions are there really, and how do they interact? Over the past few decades, several theories of emotion have been proposed, each based around the existence of a set of 'basic emotions', and each supported by an extensive variety of research including studies in facial expression, ethology, neurology and physiology. Here we present research based on a theory that people transmit their understanding of emotions through the language they use surrounding emotion keywords. Using a labelled corpus of over 21,000 tweets, six of the basic emotion sets proposed in existing literature were analysed using Latent Semantic Clustering (LSC), evaluating the distinctiveness of the semantic meaning attached to the emotional label. We hypothesise that the more distinct the language is used to express a certain emotion, then the more distinct the perception (including proprioception) of that emotion is, and thus more 'basic'. This allows us to select the dimensions best representing the entire spectrum of emotion. We find that Ekman's set, arguably the most frequently used for classifying emotions, is in fact the most semantically distinct overall. Next, taking all analysed (that is, previously proposed) emotion terms into account, we determine the optimal semantically irreducible basic emotion set using an iterative LSC algorithm. Our newly-derived set (Accepting, Ashamed, Contempt, Interested, Joyful, Pleased, Sleepy, Stressed) generates a 6.1% increase in distinctiveness over Ekman's set (Angry, Disgusted, Joyful, Sad, Scared). We also demonstrate how using LSC data can help visualise emotions. We introduce the concept of an Emotion Profile and briefly analyse compound emotions both visually and mathematically.
Autonomously Learning to Visually Detect Where Manipulation Will Succeed
Venue: arXiv cs.RO 2012
Year: 2012

Visual features can help predict if a manipulation behavior will succeed at a given location. For example, the success of a behavior that flips light switches depends on the location of the switch. Within this paper, we present methods that enable a mobile manipulator to autonomously learn a function that takes an RGB image and a registered 3D point cloud as input and returns a 3D location at which a manipulation behavior is likely to succeed. Given a pair of manipulation behaviors that can change the state of the world between two sets (e.g., light switch up and light switch down), classifiers that detect when each behavior has been successful, and an initial hint as to where one of the behaviors will be successful, the robot autonomously trains a pair of support vector machine (SVM) classifiers by trying out the behaviors at locations in the world and observing the results. When an image feature vector associated with a 3D location is provided as input to one of the SVMs, the SVM predicts if the associated manipulation behavior will be successful at the 3D location. To evaluate our approach, we performed experiments with a PR2 robot from Willow Garage in a simulated home using behaviors that flip a light switch, push a rocker-type light switch, and operate a drawer. By using active learning, the robot efficiently learned SVMs that enabled it to consistently succeed at these tasks. After training, the robot also continued to learn in order to adapt in the event of failure.
Private Broadcasting over Independent Parallel Channels
Venue: arXiv cs.IT 2012
Year: 2012

We study private broadcasting of two messages to two groups of receivers over independent parallel channels. One group consists of an arbitrary number of receivers interested in a common message, whereas the other group has only one receiver. Each message must be kept confidential from the receiver(s) in the other group. Each of the sub-channels is degraded, but the order of receivers on each channel can be different. While corner points of the capacity region were characterized in earlier works, we establish the capacity region and show the optimality of a superposition strategy. For the case of parallel Gaussian channels, we show that a Gaussian input distribution is optimal. We also discuss an extension of our setup to broadcasting over a block-fading channel and demonstrate significant performance gains using the proposed scheme over a baseline time-sharing scheme.
Continuation Methods for Mixing Heterogenous Sources
Venue: arXiv cs.LG 2012
Year: 2012

A number of modern learning tasks involve estimation from heterogeneous information sources. This includes classification with labeled and unlabeled data as well as other problems with analogous structure such as competitive (game theoretic) problems. The associated estimation problems can be typically reduced to solving a set of fixed point equations (consistency conditions). We introduce a general method for combining a preferred information source with another in this setting by evolving continuous paths of fixed points at intermediate allocations. We explicitly identify critical points along the unique paths to either increase the stability of estimation or to ensure a significant departure from the initial source. The homotopy continuation approach is guaranteed to terminate at the second source, and involves no combinatorial effort. We illustrate the power of these ideas both in classification tasks with labeled and unlabeled data, as well as in the context of a competitive (min-max) formulation of DNA sequence motif discovery.
The Thing That We Tried Didn't Work Very Well : Deictic Representation in Reinforcement Learning
Venue: arXiv cs.LG 2012
Year: 2012

Most reinforcement learning methods operate on propositional representations of the world state. Such representations are often intractably large and generalize poorly. Using a deictic representation is believed to be a viable alternative: they promise generalization while allowing the use of existing reinforcement-learning methods. Yet, there are few experiments on learning with deictic representations reported in the literature. In this paper we explore the effectiveness of two forms of deictic representation and a naïve propositional representation in a simple blocks-world domain. We find, empirically, that the deictic representations actually worsen learning performance. We conclude with a discussion of possible causes of these results and strategies for more effective learning in domains with objects.
Statistical Decisions Using Likelihood Information Without Prior Probabilities
Venue: arXiv cs.AI 2012
Year: 2012

This paper presents a decision-theoretic approach to statistical inference that satisfies the likelihood principle (LP) without using prior information. Unlike the Bayesian approach, which also satisfies LP, we do not assume knowledge of the prior distribution of the unknown parameter. With respect to information that can be obtained from an experiment, our solution is more efficient than Wald's minimax solution.However, with respect to information assumed to be known before the experiment, our solution demands less input than the Bayesian solution.
Reduction of Maximum Entropy Models to Hidden Markov Models
Venue: arXiv cs.AI 2012
Year: 2012

We show that maximum entropy (maxent) models can be modeled with certain kinds of HMMs, allowing us to construct maxent models with hidden variables, hidden state sequences, or other characteristics. The models can be trained using the forward-backward algorithm. While the results are primarily of theoretical interest, unifying apparently unrelated concepts, we also give experimental results for a maxent model with a hidden variable on a word disambiguation task; the model outperforms standard techniques.
Unconstrained Influence Diagrams
Venue: arXiv cs.AI 2012
Year: 2012

We extend the language of influence diagrams to cope with decision scenarios where the order of decisions and observations is not determined. As the ordering of decisions is dependent on the evidence, a step-strategy of such a scenario is a sequence of dependent choices of the next action. A strategy is a step-strategy together with selection functions for decision actions. The structure of a step-strategy can be represented as a DAG with nodes labeled with action variables. We introduce the concept of GS-DAG: a DAG incorporating an optimal step-strategy for any instantiation. We give a method for constructing GS-DAGs, and we show how to use a GS-DAG for determining an optimal strategy. Finally we discuss how analysis of relevant past can be used to reduce the size of the GS-DAG.
Optimal Time Bounds for Approximate Clustering
Venue: arXiv cs.DS 2012
Year: 2012

Clustering is a fundamental problem in unsupervised learning, and has been studied widely both as a problem of learning mixture models and as an optimization problem. In this paper, we study clustering with respect the emph{k-median} objective function, a natural formulation of clustering in which we attempt to minimize the average distance to cluster centers. One of the main contributions of this paper is a simple but powerful sampling technique that we call emph{successive sampling} that could be of independent interest. We show that our sampling procedure can rapidly identify a small set of points (of size just O(klog{n/k})) that summarize the input points for the purpose of clustering. Using successive sampling, we develop an algorithm for the k-median problem that runs in O(nk) time for a wide range of values of k and is guaranteed, with high probability, to return a solution with cost at most a constant factor times optimal. We also establish a lower bound of Omega(nk) on any randomized constant-factor approximation algorithm for the k-median problem that succeeds with even a negligible (say 1/100) probability. Thus we establish a tight time bound of Theta(nk) for the k-median problem for a wide range of values of k. The best previous upper bound for the problem was O(nk), where the O-notation hides polylogarithmic factors in n and k. The best previous lower bound of O(nk) applied only to deterministic k-median algorithms. While we focus our presentation on the k-median objective, all our upper bounds are valid for the k-means objective as well. In this context our algorithm compares favorably to the widely used k-means heuristic, which requires O(nk) time for just one iteration and provides no useful approximation guarantees.