The science papers I've read

Today I read a paper titled “New Intelligent Transmission Concept for Hybrid Mobile Robot Speed Control”

The abstract is:
This paper presents a new concept of a mobile robot speed control by using two degree of freedom gear transmission.

The developed intelligent speed controller utilizes a gear box which comprises of epicyclic gear train with two inputs, one coupled with the engine shaft and another with the shaft of a variable speed dc motor.

The net output speed is a combination of the two input speeds and is governed by the transmission ratio of the planetary gear train.

This new approach eliminates the use of a torque converter which is otherwise an indispensable part of all available automatic transmissions, thereby reducing the power loss that occurs in the box during the fluid coupling.

By gradually varying the speed of the dc motor a stepless transmission has been achieved.

The other advantages of the developed controller are pulling over and reversing the vehicle, implemented by intelligent mixing of the dc motor and engine speeds.

This approach eliminates traditional braking system in entire vehicle design.

The use of two power sources, IC engine and battery driven DC motor, utilizes the modern idea of hybrid vehicles.

The new mobile robot speed controller is capable of driving the vehicle even in extreme case of IC engine failure, for example, due to gas depletion.

Today I read a paper titled “Spatiotemporal sensistivity and visual attention for efficient rendering of dynamic environments”

The abstract is:
We present a method to accelerate global illumination computation in dynamic environments by taking advantage of limitations of the human visual system.

A model of visual attention is used to locate regions of interest in a scene and to modulate spatiotemporal sensitivity.

The method is applied in the form of a spatiotemporal error tolerance map.

Perceptual acceleration combined with good sampling protocols provide a global illumination solution feasible for use in animation.

Results indicate an order of magnitude improvement in computational speed.

The method is adaptable and can also be used in image-based rendering, geometry level of detail selection, realistic image synthesis, video telephony and video compression.

Today I read a paper titled “Modular Traffic Sign Recognition applied to on-vehicle real-time visual detection of American and European speed limit signs”

The abstract is:
We present a new modular traffic signs recognition system, successfully applied to both American and European speed limit signs.

Our sign detection step is based only on shape-detection (rectangles or circles).

This enables it to work on grayscale images, contrary to most European competitors, which eases robustness to illumination conditions (notably night operation).

Speed sign candidates are classified (or rejected) by segmenting potential digits inside them (which is rather original and has several advantages), and then applying a neural digit recognition.

The global detection rate is ~90% for both (standard) U.S.

and E.U.

speed signs, with a misclassification rate <1%, and no validated false alarm in >150 minutes of video.

The system processes in real-time ~20 frames/s on a standard high-end laptop.

Today I read a paper titled “Image Authentication Based on Neural Networks”

The abstract is:
Neural network has been attracting more and more researchers since the past decades.

The properties, such as parameter sensitivity, random similarity, learning ability, etc., make it suitable for information protection, such as data encryption, data authentication, intrusion detection, etc.

In this paper, by investigating neural networks’ properties, the low-cost authentication method based on neural networks is proposed and used to authenticate images or videos.

The authentication method can detect whether the images or videos are modified maliciously.

Firstly, this chapter introduces neural networks’ properties, such as parameter sensitivity, random similarity, diffusion property, confusion property, one-way property, etc.

Secondly, the chapter gives an introduction to neural network based protection methods.

Thirdly, an image or video authentication scheme based on neural networks is presented, and its performances, including security, robustness and efficiency, are analyzed.

Finally, conclusions are drawn, and some open issues in this field are presented.

Today I read a paper titled “Strip Packing vs. Bin Packing”

The abstract is:
In this paper we establish a general algorithmic framework between bin packing and strip packing, with which we achieve the same asymptotic bounds by applying bin packing algorithms to strip packing.

More precisely we obtain the following results: (1) Any offline bin packing algorithm can be applied to strip packing maintaining the same asymptotic worst-case ratio.

Thus using FFD (MFFD) as a subroutine, we get a practical (simple and fast) algorithm for strip packing with an upper bound 11/9 (71/60).

A simple AFPTAS for strip packing immediately follows.

(2) A class of Harmonic-based algorithms for bin packing can be applied to online strip packing maintaining the same asymptotic competitive ratio.

It implies online strip packing admits an upper bound of 1.58889 on the asymptotic competitive ratio, which is very close to the lower bound 1.5401 and significantly improves the previously best bound of 1.6910 and affirmatively answers an open question posed by Csirik et.

al.

Today I read a paper titled “Competition for Popularity in Bipartite Networks”

The abstract is:
We present a dynamical model for rewiring and attachment in bipartite networks in which edges are added between nodes that belong to catalogs that can either be fixed in size or growing in size.

The model is motivated by an empirical study of data from the video rental service Netflix, which invites its users to give ratings to the videos available in its catalog.

We find that the distribution of the number of ratings given by users and that of the number of ratings received by videos both follow a power law with an exponential cutoff.

We also examine the activity patterns of Netflix users and find bursts of intense video-rating activity followed by long periods of inactivity.

We derive ordinary differential equations to model the acquisition of edges by the nodes over time and obtain the corresponding time-dependent degree distributions.

We then compare our results with the Netflix data and find good agreement.

We conclude with a discussion of how catalog models can be used to study systems in which agents are forced to choose, rate, or prioritize their interactions from a very large set of options.

Today I read a paper titled “Towards the characterization of individual users through Web analytics”

The abstract is:
We perform an analysis of the way individual users navigate in the Web.

We focus primarily in the temporal patterns of they return to a given page.

The return probability as a function of time as well as the distribution of time intervals between consecutive visits are measured and found to be independent of the level of activity of single users.

The results indicate a rich variety of individual behaviors and seem to preclude the possibility of defining a characteristic frequency for each user in his/her visits to a single site.

Today I read a paper titled “A Richer Understanding of the Complexity of Election Systems”

The abstract is:
We provide an overview of some recent progress on the complexity of election systems.

The issues studied include the complexity of the winner, manipulation, bribery, and control problems.

Today I read a paper titled “Stream Computing”

The abstract is:
Stream computing is the use of multiple autonomic and parallel modules together with integrative processors at a higher level of abstraction to embody “intelligent” processing.

The biological basis of this computing is sketched and the matter of learning is examined.

Today I read a paper titled “An Agent-based Simulation of the Effectiveness of Creative Leadership”

The abstract is:
This paper investigates the effectiveness of creative versus uncreative leadership using EVOC, an agent-based model of cultural evolution.

Each iteration, each agent in the artificial society invents a new action, or imitates a neighbor’s action.

Only the leader’s actions can be imitated by all other agents, referred to as followers.

Two measures of creativity were used: (1) invention-to-imitation ratio, iLeader, which measures how often an agent invents, and (2) rate of conceptual change, cLeader, which measures how creative an invention is.

High iLeader increased mean fitness of ideas, but only when creativity of followers was low.

High iLeader was associated with greater diversity of ideas in the early stage of idea generation only.

High cLeader increased mean fitness of ideas in the early stage of idea generation; in the later stage it decreased idea fitness.

Reasons for these findings and tentative implications for creative leadership in human society are discussed.

Today I read a paper titled “Learning from Scarce Experience”

The abstract is:
Searching the space of policies directly for the optimal policy has been one popular method for solving partially observable reinforcement learning problems.

Typically, with each change of the target policy, its value is estimated from the results of following that very policy.

This requires a large number of interactions with the environment as different polices are considered.

We present a family of algorithms based on likelihood ratio estimation that use data gathered when executing one policy (or collection of policies) to estimate the value of a different policy.

The algorithms combine estimation and optimization stages.

The former utilizes experience to build a non-parametric representation of an optimized function.

The latter performs optimization on this estimate.

We show positive empirical results and provide the sample complexity bound.

Today I read a paper titled “Off-line Optimization for Earley-style HPSG Processing”

The abstract is:
A novel approach to HPSG based natural language processing is described that uses an off-line compiler to automatically prime a declarative grammar for generation or parsing, and inputs the primed grammar to an advanced Earley-style processor.

This way we provide an elegant solution to the problems with empty heads and efficient bidirectional processing which is illustrated for the special case of HPSG generation.

Extensive testing with a large HPSG grammar revealed some important constraints on the form of the grammar..

Today I read a paper titled “New Algorithms for Regular Expression Matching”

The abstract is:
In this paper we revisit the classical regular expression matching problem, namely, given a regular expression R and a string Q, decide if Q matches one of the strings specified by R.

Today I read a paper titled “On Affinity Measures for Artificial Immune System Movie Recommenders”

The abstract is:
We combine Artificial Immune Systems ‘AIS’, technology with Collaborative Filtering ‘CF’ and use it to build a movie recommendation system.

We already know that Artificial Immune Systems work well as movie recommenders from previous work by Cayzer and Aickelin 3, 4, 5.

Here our aim is to investigate the effect of different affinity measure algorithms for the AIS.

Two different affinity measures, Kendalls Tau and Weighted Kappa, are used to calculate the correlation coefficients for the movie recommender.

We compare the results with those published previously and show that Weighted Kappa is more suitable than others for movie problems.

We also show that AIS are generally robust movie recommenders and that, as long as a suitable affinity measure is chosen, results are good.

Today I read a paper titled “Camera distortion self-calibration using the plumb-line constraint and minimal Hough entropy”

The abstract is:
In this paper we present a simple and robust method for self-correction of camera distortion using single images of scenes which contain straight lines.

Since the most common distortion can be modelled as radial distortion, we illustrate the method using the Harris radial distortion model, but the method is applicable to any distortion model.

The method is based on transforming the edgels of the distorted image to a 1-D angular Hough space, and optimizing the distortion correction parameters which minimize the entropy of the corresponding normalized histogram.

Properly corrected imagery will have fewer curved lines, and therefore less spread in Hough space.

Since the method does not rely on any image structure beyond the existence of edgels sharing some common orientations and does not use edge fitting, it is applicable to a wide variety of image types.

For instance, it can be applied equally well to images of texture with weak but dominant orientations, or images with strong vanishing points.

Finally, the method is performed on both synthetic and real data revealing that it is particularly robust to noise.

Today I read a paper titled “Personalizing Image Search Results on Flickr”

The abstract is:
The social media site Flickr allows users to upload their photos, annotate them with tags, submit them to groups, and also to form social networks by adding other users as contacts.

Flickr offers multiple ways of browsing or searching it.

One option is tag search, which returns all images tagged with a specific keyword.

If the keyword is ambiguous, e.g., “beetle” could mean an insect or a car, tag search results will include many images that are not relevant to the sense the user had in mind when executing the query.

We claim that users express their photography interests through the metadata they add in the form of contacts and image annotations.

We show how to exploit this metadata to personalize search results for the user, thereby improving search performance.

First, we show that we can significantly improve search precision by filtering tag search results by user’s contacts or a larger social network that includes those contact’s contacts.

Secondly, we describe a probabilistic model that takes advantage of tag information to discover latent topics contained in the search results.

The users’ interests can similarly be described by the tags they used for annotating their images.

The latent topics found by the model are then used to personalize search results by finding images on topics that are of interest to the user.

Today I read a paper titled “The Semiotic Machine”

The abstract is:
A semiotic model of the user interface in human-computer interaction.

Today I read a paper titled “Player co-modelling in a strategy board game: discovering how to play fast”

The abstract is:
In this paper we experiment with a 2-player strategy board game where playing models are evolved using reinforcement learning and neural networks.

The models are evolved to speed up automatic game development based on human involvement at varying levels of sophistication and density when compared to fully autonomous playing.

The experimental results suggest a clear and measurable association between the ability to win games and the ability to do that fast, while at the same time demonstrating that there is a minimum level of human involvement beyond which no learning really occurs.

Today I read a paper titled “A Library-Based Synthesis Methodology for Reversible Logic”

The abstract is:
In this paper, a library-based synthesis methodology for reversible circuits is proposed where a reversible specification is considered as a permutation comprising a set of cycles.

To this end, a pre-synthesis optimization step is introduced to construct a reversible specification from an irreversible function.

In addition, a cycle-based representation model is presented to be used as an intermediate format in the proposed synthesis methodology.

The selected intermediate format serves as a focal point for all potential representation models.

In order to synthesize a given function, a library containing seven building blocks is used where each building block is a cycle of length less than 6.

To synthesize large cycles, we also propose a decomposition algorithm which produces all possible minimal and inequivalent factorizations for a given cycle of length greater than 5.

All decompositions contain the maximum number of disjoint cycles.

The generated decompositions are used in conjunction with a novel cycle assignment algorithm which is proposed based on the graph matching problem to select the best possible cycle pairs.

Then, each pair is synthesized by using the available components of the library.

The decomposition algorithm together with the cycle assignment method are considered as a binding method which selects a building block from the library for each cycle.

Finally, a post-synthesis optimization step is introduced to optimize the synthesis results in terms of different costs.

Today I read a paper titled “Performance Bounds for Lambda Policy Iteration and Application to the Game of Tetris”

The abstract is:
We consider the discrete-time infinite-horizon optimal control problem formalized by Markov Decision Processes.

We revisit the work of Bertsekas and Ioffe, that introduced $\lambda$ Policy Iteration, a family of algorithms parameterized by $\lambda$ that generalizes the standard algorithms Value Iteration and Policy Iteration, and has some deep connections with the Temporal Differences algorithm TD($\lambda$) described by Sutton and Barto.

We deepen the original theory developped by the authors by providing convergence rate bounds which generalize standard bounds for Value Iteration described for instance by Puterman.

Then, the main contribution of this paper is to develop the theory of this algorithm when it is used in an approximate form and show that this is sound.

Doing so, we extend and unify the separate analyses developped by Munos for Approximate Value Iteration and Approximate Policy Iteration.

Eventually, we revisit the use of this algorithm in the training of a Tetris playing controller as originally done by Bertsekas and Ioffe.

We provide an original performance bound that can be applied to such an undiscounted control problem.

Our empirical results are different from those of Bertsekas and Ioffe (which were originally qualified as “paradoxical” and “intriguing”), and much more conform to what one would expect from a learning experiment.

We discuss the possible reason for such a difference.

Today I read a paper titled “Wavelet Based Iterative Learning Control with Fuzzy PD Feedback for Position Tracking of A Pneumatic Servo System”

The abstract is:
In this paper, a wavelet-based iterative learning control (WILC) scheme with Fuzzy PD feedback is presented for a pneumatic control system with nonsmooth nonlinearities and uncertain parameters.

The wavelet transform is employed to extract the learnable dynamics from measured output signal before it can be used to update the control profile.

The wavelet transform is adopted to decompose the original signal into many low-resolution signals that contain the learnable and unlearnable parts.

The desired control profile is then compared with the learnable part of the transformed signal.

Thus, the effects from unlearnable dynamics on the controlled system can be attenuated by a Fuzzy PD feedback controller.

As for the rules of Fuzzy PD controller in the feedback loop, a genetic algorithm (GA) is employed to search for the inference rules of optimization.

A proportional-valve controlled pneumatic cylinder actuator system is used as the control target for simulation.

Simulation results have shown a much-improved positiontracking performance.

Today I read a paper titled “Laser Actuated Presentation System”

The abstract is:
We present here a pattern sensitive PowerPoint presentation scheme.

The presentation is actuated by simple patterns drawn on the presentation screen by a laser pointer.

A specific pattern corresponds to a particular command required to operate the presentation.

Laser spot on the screen is captured by a RGB webcam with a red filter mounted, and its location is identified at the blue layer of each captured frame by estimating the mean position of the pixels whose intensity is above a given threshold value.

Measured Reliability, Accuracy and Latency of our system are 90%, 10 pixels (in the worst case) and 38 ms respectively.

Today I read a paper titled “Fitness landscape of the cellular automata majority problem: View from the Olympus”

The abstract is:
In this paper we study cellular automata (CAs) that perform the computational Majority task.

This task is a good example of what the phenomenon of emergence in complex systems is.

We take an interest in the reasons that make this particular fitness landscape a difficult one.

The first goal is to study the landscape as such, and thus it is ideally independent from the actual heuristics used to search the space.

However, a second goal is to understand the features a good search technique for this particular problem space should possess.

We statistically quantify in various ways the degree of difficulty of searching this landscape.

Due to neutrality, investigations based on sampling techniques on the whole landscape are difficult to conduct.

So, we go exploring the landscape from the top.

Although it has been proved that no CA can perform the task perfectly, several efficient CAs for this task have been found.

Exploiting similarities between these CAs and symmetries in the landscape, we define the Olympus landscape which is regarded as the ”heavenly home” of the best local optima known (blok).

Then we measure several properties of this subspace.

Although it is easier to find relevant CAs in this subspace than in the overall landscape, there are structural reasons that prevent a searcher from finding overfitted CAs in the Olympus.

Finally, we study dynamics and performance of genetic algorithms on the Olympus in order to confirm our analysis and to find efficient CAs for the Majority problem with low computational cost.

Today I read a paper titled “Algorithms for Image Analysis and Combination of Pattern Classifiers with Application to Medical Diagnosis”

The abstract is:
Medical Informatics and the application of modern signal processing in the assistance of the diagnostic process in medical imaging is one of the more recent and active research areas today.

This thesis addresses a variety of issues related to the general problem of medical image analysis, specifically in mammography, and presents a series of algorithms and design approaches for all the intermediate levels of a modern system for computer-aided diagnosis (CAD).

The diagnostic problem is analyzed with a systematic approach, first defining the imaging characteristics and features that are relevant to probable pathology in mammo-grams.

Next, these features are quantified and fused into new, integrated radio-logical systems that exhibit embedded digital signal processing, in order to improve the final result and minimize the radiological dose for the patient.

In a higher level, special algorithms are designed for detecting and encoding these clinically interest-ing imaging features, in order to be used as input to advanced pattern classifiers and machine learning models.

Finally, these approaches are extended in multi-classifier models under the scope of Game Theory and optimum collective deci-sion, in order to produce efficient solutions for combining classifiers with minimum computational costs for advanced diagnostic systems.

The material covered in this thesis is related to a total of 18 published papers, 6 in scientific journals and 12 in international conferences.

Today I read a paper titled “Finding Cliques of a Graph using Prime Numbers”

The abstract is:
This paper proposes a new algorithm for solving maximal cliques for simple undirected graphs using the theory of prime numbers.

A novel approach using prime numbers is used to find cliques and ends with a discussion of the algorithm.

Today I read a paper titled “Region-based active contour with noise and shape priors”

The abstract is:
In this paper, we propose to combine formally noise and shape priors in region-based active contours.

On the one hand, we use the general framework of exponential family as a prior model for noise.

On the other hand, translation and scale invariant Legendre moments are considered to incorporate the shape prior (e.g.

fidelity to a reference shape).

The combination of the two prior terms in the active contour functional yields the final evolution equation whose evolution speed is rigorously derived using shape derivative tools.

Experimental results on both synthetic images and real life cardiac echography data clearly demonstrate the robustness to initialization and noise, flexibility and large potential applicability of our segmentation algorithm.

Today I read a paper titled “Generalized Kernel-based Visual Tracking”

The abstract is:
In this work we generalize the plain MS trackers and attempt to overcome standard mean shift trackers’ two limitations.

It is well known that modeling and maintaining a representation of a target object is an important component of a successful visual tracker.

However, little work has been done on building a robust template model for kernel-based MS tracking.

In contrast to building a template from a single frame, we train a robust object representation model from a large amount of data.

Tracking is viewed as a binary classification problem, and a discriminative classification rule is learned to distinguish between the object and background.

We adopt a support vector machine (SVM) for training.

The tracker is then implemented by maximizing the classification score.

An iterative optimization scheme very similar to MS is derived for this purpose.

Today I read a paper titled “Variations of the Turing Test in the Age of Internet and Virtual Reality”

The abstract is:
Inspired by Hofstadter’s Coffee-House Conversation (1982) and by the science fiction short story SAM by Schattschneider (1988), we propose and discuss criteria for non-mechanical intelligence.

Firstly, we emphasize the practical need for such tests in view of massively multiuser online role-playing games (MMORPGs) and virtual reality systems like Second Life.

Secondly, we demonstrate Second Life as a useful framework for implementing (some iterations of) that test.

Today I read a paper titled “Perfect Hashing for Data Management Applications”

The abstract is:
Perfect hash functions can potentially be used to compress data in connection with a variety of data management tasks.

Though there has been considerable work on how to construct good perfect hash functions, there is a gap between theory and practice among all previous methods on minimal perfect hashing.

On one side, there are good theoretical results without experimentally proven practicality for large key sets.

On the other side, there are the theoretically analyzed time and space usage algorithms that assume that truly random hash functions are available for free, which is an unrealistic assumption.

In this paper we attempt to bridge this gap between theory and practice, using a number of techniques from the literature to obtain a novel scheme that is theoretically well-understood and at the same time achieves an order-of-magnitude increase in performance compared to previous “practical” methods.

This improvement comes from a combination of a novel, theoretically optimal perfect hashing scheme that greatly simplifies previous methods, and the fact that our algorithm is designed to make good use of the memory hierarchy.

We demonstrate the scalability of our algorithm by considering a set of over one billion URLs from the World Wide Web of average length 64, for which we construct a minimal perfect hash function on a commodity PC in a little more than 1 hour.

Our scheme produces minimal perfect hash functions using slightly more than 3 bits per key.

For perfect hash functions in the range $\{0,…,2n-1\}$ the space usage drops to just over 2 bits per key (i.e., one bit more than optimal for representing the key).

This is significantly below of what has been achieved previously for very large values of $n$.

Today I read a paper titled “Integration of navigation and action selection functionalities in a computational model of cortico-basal ganglia-thalamo-cortical loops”

The abstract is:
This article describes a biomimetic control architecture affording an animat both action selection and navigation functionalities.

It satisfies the survival constraint of an artificial metabolism and supports several complementary navigation strategies.

It builds upon an action selection model based on the basal ganglia of the vertebrate brain, using two interconnected cortico-basal ganglia-thalamo-cortical loops: a ventral one concerned with appetitive actions and a dorsal one dedicated to consummatory actions.

The performances of the resulting model are evaluated in simulation.

The experiments assess the prolonged survival permitted by the use of high level navigation strategies and the complementarity of navigation strategies in dynamic environments.

The correctness of the behavioral choices in situations of antagonistic or synergetic internal states are also tested.

Finally, the modelling choices are discussed with regard to their biomimetic plausibility, while the experimental results are estimated in terms of animat adaptivity.

Today I read a paper titled “Face Detection Using Adaboosted SVM-Based Component Classifier”

The abstract is:
Recently, Adaboost has been widely used to improve the accuracy of any given learning algorithm.

In this paper we focus on designing an algorithm to employ combination of Adaboost with Support Vector Machine as weak component classifiers to be used in Face Detection Task.

To obtain a set of effective SVM-weaklearner Classifier, this algorithm adaptively adjusts the kernel parameter in SVM instead of using a fixed one.

Proposed combination outperforms in generalization in comparison with SVM on imbalanced classification problem.

The proposed here method is compared, in terms of classification accuracy, to other commonly used Adaboost methods, such as Decision Trees and Neural Networks, on CMU+MIT face database.

Results indicate that the performance of the proposed method is overall superior to previous Adaboost approaches.

Today I read a paper titled “Teaching Physics Using Virtual Reality”

The abstract is:
We present an investigation of game-like simulations for physics teaching.

We report on the effectiveness of the interactive simulation “Real Time Relativity” for learning special relativity.

We argue that the simulation not only enhances traditional learning, but also enables new types of learning that challenge the traditional curriculum.

The lessons drawn from this work are being applied to the development of a simulation for enhancing the learning of quantum mechanics.

Today I read a paper titled “Robust Global Localization Using Clustered Particle Filtering”

The abstract is:
Global mobile robot localization is the problem of determining a robot’s pose in an environment, using sensor data, when the starting position is unknown.

A family of probabilistic algorithms known as Monte Carlo Localization (MCL) is currently among the most popular methods for solving this problem.

MCL algorithms represent a robot’s belief by a set of weighted samples, which approximate the posterior probability of where the robot is located by using a Bayesian formulation of the localization problem.

This article presents an extension to the MCL algorithm, which addresses its problems when localizing in highly symmetrical environments; a situation where MCL is often unable to correctly track equally probable poses for the robot.

The problem arises from the fact that sample sets in MCL often become impoverished, when samples are generated according to their posterior likelihood.

Our approach incorporates the idea of clusters of samples and modifies the proposal distribution considering the probability mass of those clusters.

Experimental results are presented that show that this new extension to the MCL algorithm successfully localizes in symmetric environments where ordinary MCL often fails.

Today I read a paper titled “Asynchronous Remote Medical Consultation for Ghana”

The abstract is:
Computer-mediated communication systems can be used to bridge the gap between doctors in underserved regions with local shortages of medical expertise and medical specialists worldwide.

To this end, we describe the design of a prototype remote consultation system intended to provide the social, institutional and infrastructural context for sustained, self-organizing growth of a globally-distributed Ghanaian medical community.

The design is grounded in an iterative design process that included two rounds of extended design fieldwork throughout Ghana and draws on three key design principles (social networks as a framework on which to build incentives within a self-organizing network; optional and incremental integration with existing referral mechanisms; and a weakly-connected, distributed architecture that allows for a highly interactive, responsive system despite failures in connectivity).

We discuss initial experiences from an ongoing trial deployment in southern Ghana.

Today I read a paper titled “Modeling Epidemic Spread in Synthetic Populations – Virtual Plagues in Massively Multiplayer Online Games”

The abstract is:
A virtual plague is a process in which a behavior-affecting property spreads among characters in a Massively Multiplayer Online Game (MMOG).

The MMOG individuals constitute a synthetic population, and the game can be seen as a form of interactive executable model for studying disease spread, albeit of a very special kind.

To a game developer maintaining an MMOG, recognizing, monitoring, and ultimately controlling a virtual plague is important, regardless of how it was initiated.

The prospect of using tools, methods and theory from the field of epidemiology to do this seems natural and appealing.

We will address the feasibility of such a prospect, first by considering some basic measures used in epidemiology, then by pointing out the differences between real world epidemics and virtual plagues.

We also suggest directions for MMOG developer control through epidemiological modeling.

Our aim is understanding the properties of virtual plagues, rather than trying to eliminate them or mitigate their effects, as would be in the case of real infectious disease.

Today I read a paper titled “Efficient Binary and Run Length Morphology and its Application to Document Image Processing”

The abstract is:
This paper describes the implementation and evaluation of an open source library for mathematical morphology based on packed binary and run-length compressed images for document imaging applications.

Abstractions and patterns useful in the implementation of the interval operations are described.

A number of benchmarks and comparisons to bit-blit based implementations on standard document images are provided.

Today I read a paper titled “Olfactory search at high Reynolds number”

The abstract is:
Locating the source of odor in a turbulent environment – a common behavior for living organisms – is non-trivial because of the random nature of mixing.

Here we analyze the statistical physics aspects of the problem and propose an efficient strategy for olfactory search which can work in turbulent plumes.

The algorithm combines the maximum likelihood inference of the source position with an active search.

Our approach provides the theoretical basis for the design of olfactory robots and the quantitative tools for the analysis of the observed olfactory search behavior of living creatures (e.g.

odor modulated optomotor anemotaxis of moth) .

Today I read a paper titled “Recognition of expression variant faces using masked log-Gabor features and Principal Component Analysis”

The abstract is:
In this article we propose a method for the recognition of faces with different facial expressions.

For recognition we extract feature vectors by using log-Gabor filters of multiple orientations and scales.

Using sliding window algorithm and variances -based masking these features are extracted at image regions that are less affected by the changes of facial expressions.

Extracted features are passed to the Principal Component Analysis (PCA) -based recognition method.

The results of face recognition experiments using expression variant faces showed that the proposed method could achieve higher recognition accuracy than many other methods.

For development and testing we used facial images from the AR and FERET databases.

Using facial photographs of more than one thousand persons from the FERET database the proposed method achieved 96.6-98.9% first one recognition rate and 0.2-0.6% Equal Error Rate (EER).

Today I read a paper titled “How to Beat the Adaptive Multi-Armed Bandit”

The abstract is:
The multi-armed bandit is a concise model for the problem of iterated decision-making under uncertainty.

In each round, a gambler must pull one of $K$ arms of a slot machine, without any foreknowledge of their payouts, except that they are uniformly bounded.

A standard objective is to minimize the gambler’s regret, defined as the gambler’s total payout minus the largest payout which would have been achieved by any fixed arm, in hindsight.

Note that the gambler is only told the payout for the arm actually chosen, not for the unchosen arms.

Almost all previous work on this problem assumed the payouts to be non-adaptive, in the sense that the distribution of the payout of arm $j$ in round $i$ is completely independent of the choices made by the gambler on rounds $1, \dots, i-1$.

In the more general model of adaptive payouts, the payouts in round $i$ may depend arbitrarily on the history of past choices made by the algorithm.

We present a new algorithm for this problem, and prove nearly optimal guarantees for the regret against both non-adaptive and adaptive adversaries.

After $T$ rounds, our algorithm has regret $O(\sqrt{T})$ with high probability (the tail probability decays exponentially).

This dependence on $T$ is best possible, and matches that of the full-information version of the problem, in which the gambler is told the payouts for all $K$ arms after each round.

Previously, even for non-adaptive payouts, the best high-probability bounds known were $O(T^{2/3})$, due to Auer, Cesa-Bianchi, Freund and Schapire.

The expected regret of their algorithm is $O(T^{1/2}) for non-adaptive payouts, but as we show, $\Omega(T^{2/3})$ for adaptive payouts.

Today I read a paper titled “Optimal Tracking Controller Design for a Small Scale Helicopter”

The abstract is:
A model helicopter is more difficult to control than its full scale counterparts.

This is due to its greater sensitivity to control inputs and disturbances as well as higher bandwidth of dynamics.

This works is focused on designing practical tracking controller for a small scale helicopter following predefined trajectories.

A tracking controller based on optimal control theory is synthesized as part of the development of an autonomous helicopter.

Some issues in regards to control constraints are addressed.

The weighting between state tracking performance and control power expenditure is analyzed.

Overall performance of the control design is evaluated based on its time domain histories of trajectories as well as control inputs.

Today I read a paper titled “Comparing Typical Opening Move Choices Made by Humans and Chess Engines”

The abstract is:
The opening book is an important component of a chess engine, and thus computer chess programmers have been developing automated methods to improve the quality of their books.

For chess, which has a very rich opening theory, large databases of high-quality games can be used as the basis of an opening book, from which statistics relating to move choices from given positions can be collected.

In order to find out whether the opening books used by modern chess engines in machine versus machine competitions are “comparable” to those used by chess players in human versus human competitions, we carried out analysis on 26 test positions using statistics from two opening books one compiled from humans’ games and the other from machines’ games.

Our analysis using several nonparametric measures, shows that, overall, there is a strong association between humans’ and machines’ choices of opening moves when using a book to guide their choices.

Today I read a paper titled “Training Gamble leads to Corporate Grumble?”

The abstract is:
Fifteen years of research studies have concluded unanimously that spreadsheet errors are both common and non-trivial.

Now we must seek ways to reduce spreadsheet errors.

Several approaches have been suggested, some of which are promising and others, while appealing because they are easy to do, are not likely to be effective.

To date, only one technique, cell-by-cell code inspection, has been demonstrated to be effective.

We need to conduct further research to determine the degree to which other techniques can reduce spreadsheet errors.

Today I read a paper titled “Bayesian Approach to Neuro-Rough Models”

The abstract is:
This paper proposes a neuro-rough model based on multi-layered perceptron and rough set.

The neuro-rough model is then tested on modelling the risk of HIV from demographic data.

The model is formulated using Bayesian framework and trained using Monte Carlo method and Metropolis criterion.

When the model was tested to estimate the risk of HIV infection given the demographic data it was found to give the accuracy of 62%.

The proposed model is able to combine the accuracy of the Bayesian MLP model and the transparency of Bayesian rough set model.

Today I read a paper titled “Multilevel Thresholding for Image Segmentation through a Fast Statistical Recursive Algorithm”

The abstract is:
A novel algorithm is proposed for segmenting an image into multiple levels using its mean and variance.

Starting from the extreme pixel values at both ends of the histogram plot, the algorithm is applied recursively on sub-ranges computed from the previous step, so as to find a threshold level and a new sub-range for the next step, until no significant improvement in image quality can be achieved.

The method makes use of the fact that a number of distributions tend towards Dirac delta function, peaking at the mean, in the limiting condition of vanishing variance.

The procedure naturally provides for variable size segmentation with bigger blocks near the extreme pixel values and finer divisions around the mean or other chosen value for better visualization.

Experiments on a variety of images show that the new algorithm effectively segments the image in computationally very less time.

Today I read a paper titled “Towards a human eye behavior model by applying Data Mining Techniques on Gaze Information from IEC”

The abstract is:
In this paper, we firstly present what is Interactive Evolutionary Computation (IEC) and rapidly how we have combined this artificial intelligence technique with an eye-tracker for visual optimization.

Next, in order to correctly parameterize our application, we present results from applying data mining techniques on gaze information coming from experiments conducted on about 80 human individuals.

Today I read a paper titled “Embedding Data within Knowledge Spaces”

The abstract is:
The promise of e-Science will only be realized when data is discoverable, accessible, and comprehensible within distributed teams, across disciplines, and over the long-term–without reliance on out-of-band (non-digital) means.

We have developed the open-source Tupelo semantic content management framework and are employing it to manage a wide range of e-Science entities (including data, documents, workflows, people, and projects) and a broad range of metadata (including provenance, social networks, geospatial relationships, temporal relations, and domain descriptions).

Tupelo couples the use of global identifiers and resource description framework (RDF) statements with an aggregatable content repository model to provide a unified space for securely managing distributed heterogeneous content and relationships.

Today I read a paper titled “Methods for Analytical Understanding of Agent-Based Modeling of Complex Systems”

The abstract is:
Von Neuman’s work on universal machines and the hardware development have allowed the simulation of dynamical systems through a large set of interacting agents.

This is a bottom-up approach which tries to derive global properties of a complex system through local interaction rules and agent behaviour.

Traditionally, such systems are modeled and simulated through top-down methods based on differential equations.

Agent-Based Modeling has the advantage of simplicity and low computational cost.

However, unlike differential equations, there is no standard way to express agent behaviour.

Besides, it is not clear how to analytically predict the results obtained by the simulation.

In this paper we survey some of these methods.

For expressing agent behaviour formal methods, like Stochastic Process Algebras have been used.

Such approach is useful if the global properties of interest can be expressed as a function of stochastic time series.

However, if space variables must be considered, we shall change the focus.

In this case, multiscale techniques, based on Chapman-Enskog expansion, was used to establish the connection between the microscopic dynamics and the macroscopic observables.

Also, we use data mining techniques,like Principal Component Analysis (PCA), to study agent systems like Cellular Automata.

With the help of these tools we will discuss a simple society model, a Lattice Gas Automaton for fluid modeling, and knowledge discovery in CA databases.

Besides, we show the capabilities of the NetLogo, a software for agent simulation of complex system and show our experience about.

Today I read a paper titled “Breaking Out of the Cell: On The Benefits of a New Spreadsheet User-Interaction Paradigm”

The abstract is:
Contemporary spreadsheets are plagued by a profusion of errors, auditing difficulties, lack of uniform development methodologies, and barriers to easy comprehension of the underlying business models they represent.

This paper presents a case that most of these difficulties stem from the fact that the standard spreadsheet user-interaction paradigm – the ‘cell-matrix’ approach – is appropriate for spreadsheet data presentation but has significant drawbacks with respect to spreadsheet creation, maintenance and comprehension when workbooks pass a minimal threshold of complexity.

An alternative paradigm for the automated generation of spreadsheets directly from plain-language business model descriptions is presented along with its potential benefits.

Sunsight Modeller (TM), a working software system implementing the suggested paradigm, is briefly described.

Today I read a paper titled “Rapid Development of Morphological Descriptions for Full Language Processing Systems”

The abstract is:
I describe a compiler and development environment for feature-augmented two-level morphology rules integrated into a full NLP system.

The compiler is optimized for a class of languages including many or most European ones, and for rapid development and debugging of descriptions of new languages.

The key design decision is to compose morphophonological and morphosyntactic information, but not the lexicon, when compiling the description.

This results in typical compilation times of about a minute, and has allowed a reasonably full, feature-based description of French inflectional morphology to be developed in about a month by a linguist new to the system..

Today I read a paper titled “Finding overlapping communities in networks by label propagation”

The abstract is:
We propose an algorithm for finding overlapping community structure in very large networks.

The algorithm is based on the label propagation technique of Raghavan, Albert, and Kumara, but is able to detect communities that overlap.

Like the original algorithm, vertices have labels that propagate between neighbouring vertices so that members of a community reach a consensus on their community membership.

Our main contribution is to extend the label and propagation step to include information about more than one community: each vertex can now belong to up to v communities, where v is the parameter of the algorithm.

Our algorithm can also handle weighted and bipartite networks.

Tests on an independently designed set of benchmarks, and on real networks, show the algorithm to be highly effective in recovering overlapping communities.

It is also very fast and can process very large and dense networks in a short time.