Archives for 2017

Today I read a paper titled “Preprint: Bringing immersive enjoyment to hyperbaric oxygen chamber users using virtual reality glasses”

The abstract is:
This is the preprint version of our paper on REHAB2015.

This paper proposed a novel immersive entertainment system for the users of hyperbaric oxygen therapy chamber.

The system is a hybrid of hardware and software, the scheme is described in this paper.

The hardware is combined by a HMD (i.e.

virtual reality glasses shell), a smartphone and a waterproof bag.

The software is able to transfer the stereoscopic images of the 3D game to the screen of the smartphone synchronously.

The comparison and selection of the hardware are discussed according to the practical running scene of the clinical hyperbaric oxygen treatment.

Finally, a preliminary guideline for designing this kind of system is raised accordingly.

Today I read a paper titled “Development of a wheelchair simulator for children with multiple disabilities”

The abstract is:
Virtual reality allows to create situations which can be experimented under the control of the user, without risks, in a very flexible way.

This allows to develop skills and to have confidence to work in real conditions with real equipment.

VR is then widely used as a training and learning tool.

More recently, VR has also showed its potential in rehabilitation and therapy fields because it provides users with the ability of repeat their actions several times and to progress at their own pace.

In this communication, we present our work in the development of a wheelchair simulator designed to allow children with multiple disabilities to familiarize themselves with the wheelchair.

Today I read a paper titled “When Augmented Reality Meets Big Data”

The abstract is:
With computing and sensing woven into the fabric of everyday life, we live in an era where we are awash in a flood of data from which we can gain rich insights.

Augmented reality (AR) is able to collect and help analyze the growing torrent of data about user engagement metrics within our personal mobile and wearable devices.

This enables us to blend information from our senses and the digitalized world in a myriad of ways that was not possible before.

AR and big data have a logical maturity that inevitably converge them.

The tread of harnessing AR and big data to breed new interesting applications is starting to have a tangible presence.

In this paper, we explore the potential to capture value from the marriage between AR and big data technologies, following with several challenges that must be addressed to fully realize this potential.

Today I read a paper titled “The Dark Side of Ethical Robots”

The abstract is:
Concerns over the risks associated with advances in Artificial Intelligence have prompted calls for greater efforts toward robust and beneficial AI, including machine ethics.

Recently, roboticists have responded by initiating the development of so-called ethical robots.

These robots would, ideally, evaluate the consequences of their actions and morally justify their choices.

This emerging field promises to develop extensively over the next years.

However, in this paper, we point out an inherent limitation of the emerging field of ethical robots.

We show that building ethical robots also necessarily facilitates the construction of unethical robots.

In three experiments, we show that it is remarkably easy to modify an ethical robot so that it behaves competitively, or even aggressively.

The reason for this is that the specific AI, required to make an ethical robot, can always be exploited to make unethical robots.

Hence, the development of ethical robots will not guarantee the responsible deployment of AI.

While advocating for ethical robots, we conclude that preventing the misuse of robots is beyond the scope of engineering, and requires instead governance frameworks underpinned by legislation.

Without this, the development of ethical robots will serve to increase the risks of robotic malpractice instead of diminishing it.

Today I read a paper titled “Spontaneous vs. Posed smiles – can we tell the difference?”

The abstract is:
Smile is an irrefutable expression that shows the physical state of the mind in both true and deceptive ways.

Generally, it shows happy state of the mind, however, `smiles’ can be deceptive, for example people can give a smile when they feel happy and sometimes they might also give a smile (in a different way) when they feel pity for others.

This work aims to distinguish spontaneous (felt) smile expressions from posed (deliberate) smiles by extracting and analyzing both global (macro) motion of the face and subtle (micro) changes in the facial expression features through both tracking a series of facial fiducial markers as well as using dense optical flow.

Specifically the eyes and lips features are captured and used for analysis.

It aims to automatically classify all smiles into either `spontaneous’ or `posed’ categories, by using support vector machines (SVM).

Experimental results on large database show promising results as compared to other relevant methods.

Today I read a paper titled “Deep convolutional neural networks for predominant instrument recognition in polyphonic music”

The abstract is:
Identifying musical instruments in polyphonic music recordings is a challenging but important problem in the field of music information retrieval.

It enables music search by instrument, helps recognize musical genres, or can make music transcription easier and more accurate.

In this paper, we present a convolutional neural network framework for predominant instrument recognition in real-world polyphonic music.

We train our network from fixed-length music excerpts with a single-labeled predominant instrument and estimate an arbitrary number of predominant instruments from an audio signal with a variable length.

To obtain the audio-excerpt-wise result, we aggregate multiple outputs from sliding windows over the test audio.

In doing so, we investigated two different aggregation methods: one takes the average for each instrument and the other takes the instrument-wise sum followed by normalization.

In addition, we conducted extensive experiments on several important factors that affect the performance, including analysis window size, identification threshold, and activation functions for neural networks to find the optimal set of parameters.

Using a dataset of 10k audio excerpts from 11 instruments for evaluation, we found that convolutional neural networks are more robust than conventional methods that exploit spectral features and source separation with support vector machines.

Experimental results showed that the proposed convolutional network architecture obtained an F1 measure of 0.602 for micro and 0.503 for macro, respectively, achieving 19.6% and 16.4% in performance improvement compared with other state-of-the-art algorithms.

Today I read a paper titled “On the Complexity of Connection Games”

The abstract is:
In this paper, we study three connection games among the most widely played: Havannah, Twixt, and Slither.

We show that determining the outcome of an arbitrary input position is PSPACE-complete in all three cases.

Our reductions are based on the popular graph problem Generalized Geography and on Hex itself.

We also consider the complexity of generalizations of Hex parameterized by the length of the solution and establish that while Short Generalized Hex is W[1]-hard, Short Hex is FPT.

Finally, we prove that the ultra-weak solution to the empty starting position in hex cannot be fully adapted to any of these three games.

Today I read a paper titled “Content Based Image Indexing and Retrieval”

The abstract is:
In this paper, we present the efficient content based image retrieval systems which employ the color, texture and shape information of images to facilitate the retrieval process.

For efficient feature extraction, we extract the color, texture and shape feature of images automatically using edge detection which is widely used in signal processing and image compression.

For facilitated the speedy retrieval we are implements the antipole-tree algorithm for indexing the images.

Today I read a paper titled “Computoser – rule-based, probability-driven algorithmic music composition”

The abstract is:
This paper presents the Computoser hybrid probability/rule based algorithm for music composition (this http URL) and provides a reference implementation.

It addresses the issues of unpleasantness and lack of variation exhibited by many existing approaches by combining the two methods (basing the parameters of the rules on data obtained from preliminary analysis).

A sample of 500+ musical pieces was analyzed to derive probabilities for musical characteristics and events (e.g.

scale, tempo, intervals).

The algorithm was constructed to produce musical pieces using the derived probabilities combined with a large set of composition rules, which were obtained and structured after studying established composition practices.

Generated pieces were published on the Computoser website where evaluation was performed by listeners.

The feedback was positive (58.4% approval), asserting the merits of the undertaken approach.

The paper compares this hybrid approach to other approaches to algorithmic composition and presents a survey of the pleasantness of the resulting music.

Today I read a paper titled “Notes on Pervasive Virtuality”

The abstract is:
This paper summarizes current notes about a new mixed-reality paradigm that we named as “pervasive virtuality”.

This paradigm has emerged recently in industry and academia through different initiatives.

In this paper we intend to explore this new area by proposing a set of features that we identified as important or helpful to realize pervasive virtuality in games and entertainment applications.

Today I read a paper titled “Estimation of Human Body Shape and Posture Under Clothing”

The abstract is:
Estimating the body shape and posture of a dressed human subject in motion represented as a sequence of (possibly incomplete) 3D meshes is important for virtual change rooms and security.

To solve this problem, statistical shape spaces encoding human body shape and posture variations are commonly used to constrain the search space for the shape estimate.

In this work, we propose a novel method that uses a posture-invariant shape space to model body shape variation combined with a skeleton-based deformation to model posture variation.

Our method can estimate the body shape and posture of both static scans and motion sequences of dressed human body scans.

In case of motion sequences, our method takes advantage of motion cues to solve for a single body shape estimate along with a sequence of posture estimates.

We apply our approach to both static scans and motion sequences and demonstrate that using our method, higher fitting accuracy is achieved than when using a variant of the popular SCAPE model as statistical model.

Today I read a paper titled “Global Localization Based on 3D Planar Surface Segments”

The abstract is:
Global localization of a mobile robot using planar surface segments extracted from depth images is considered.

The robot’s environment is represented by a topological map consisting of local models, each representing a particular location modeled by a set of planar surface segments.

The discussed localization approach segments a depth image acquired by a 3D camera into planar surface segments which are then matched to model surface segments.

The robot pose is estimated by the Extended Kalman Filter using surface segment pairs as measurements.

The reliability and accuracy of the considered approach are experimentally evaluated using a mobile robot equipped by a Microsoft Kinect sensor.

Today I read a paper titled “Development of Interactive Instructional Model Using Augmented Reality based on Edutainment to Enhance Emotional Quotient”

The abstract is:
The research aims to develop an interactive instructional model using augmented reality based on edutainment to enhance emotional quotient and evaluate the model.

Two phases of the research will be carried out: a development and an evaluation of the model.

Samples are experts in the field of IT, child psychology, and 7th grade curriculum management.

Ten experts are selected by purposive sampling method.

The obtained data are analyzed using mean and standard deviation.

The research result demonstrates the following findings: 1) The results of this research show that Model consists of 3 elements: IIAR, EduLA, and EQ.

EQ is a means to assess EQ based on Time Series Experimental Design using 2 kinds of tools; i.e.

EQ Assessment by programs in tablet computers, and EQ Assessment by behavioral observation.

2) The ten experts have evaluated the model and commented that the developed model showed high suitability.

Today I read a paper titled “Volumetric Reconstruction Applied to Perceptual Studies of Size and Weight”

The abstract is:
We explore the application of volumetric reconstruction from structured-light sensors in cognitive neuroscience, specifically in the quantification of the size-weight illusion, whereby humans tend to systematically perceive smaller objects as heavier.

We investigate the performance of two commercial structured-light scanning systems in comparison to one we developed specifically for this application.

Our method has two main distinct features: First, it only samples a sparse series of viewpoints, unlike other systems such as the Kinect Fusion.

Second, instead of building a distance field for the purpose of points-to-surface conversion directly, we pursue a first-order approach: the distance function is recovered from its gradient by a screened Poisson reconstruction, which is very resilient to noise and yet preserves high-frequency signal components.

Our experiments show that the quality of metric reconstruction from structured light sensors is subject to systematic biases, and highlights the factors that influence it.

Our main performance index rates estimates of volume (a proxy of size), for which we review a well-known formula applicable to incomplete meshes.

Our code and data will be made publicly available upon completion of the anonymous review process.

Today I read a paper titled “Phoenix: A Self-Optimizing Chess Engine”

The abstract is:
Since the advent of computers, many tasks which required humans to spend a lot of time and energy have been trivialized by the computers’ ability to perform repetitive tasks extremely quickly.

However there are still many areas in which humans excel in comparison with the machines.

One such area is chess.

Even with great advances in the speed and computational power of modern machines, Grandmasters often beat the best chess programs in the world with relative ease.

This may be due to the fact that a game of chess cannot be won by pure calculation.

There is more to the goodness of a chess position than some numerical value which apparently can be seen only by the human brain.

Here an effort has been made to improve current chess engines by letting themselves evolve over a period of time.

Firstly, the problem of learning is reduced into an optimization problem by defining Position Evaluation in terms of Positional Value Tables (PVTs).

Next, the PVTs are optimized using Multi-Niche Crowding which successfully identifies the optima in a multimodal function, thereby arriving at distinctly different solutions which are close to the global optimum.

Today I read a paper titled “Evaluation of Pose Tracking Accuracy in the First and Second Generations of Microsoft Kinect”

The abstract is:
Microsoft Kinect camera and its skeletal tracking capabilities have been embraced by many researchers and commercial developers in various applications of real-time human movement analysis.

In this paper, we evaluate the accuracy of the human kinematic motion data in the first and second generation of the Kinect system, and compare the results with an optical motion capture system.

We collected motion data in 12 exercises for 10 different subjects and from three different viewpoints.

We report on the accuracy of the joint localization and bone length estimation of Kinect skeletons in comparison to the motion capture.

We also analyze the distribution of the joint localization offsets by fitting a mixture of Gaussian and uniform distribution models to determine the outliers in the Kinect motion data.

Our analysis shows that overall Kinect 2 has more robust and more accurate tracking of human pose as compared to Kinect 1.

Today I read a paper titled “Text to 3D Scene Generation with Rich Lexical Grounding”

The abstract is:
The ability to map descriptions of scenes to 3D geometric representations has many applications in areas such as art, education, and robotics.

However, prior work on the text to 3D scene generation task has used manually specified object categories and language that identifies them.

We introduce a dataset of 3D scenes annotated with natural language descriptions and learn from this data how to ground textual descriptions to physical objects.

Our method successfully grounds a variety of lexical terms to concrete referents, and we show quantitatively that our method improves 3D scene generation over previous work using purely rule-based methods.

We evaluate the fidelity and plausibility of 3D scenes generated with our grounding approach through human judgments.

To ease evaluation on this task, we also introduce an automated metric that strongly correlates with human judgments.

Today I read a paper titled “Online Searching with an Autonomous Robot”

The abstract is:
We discuss online strategies for visibility-based searching for an object hidden behind a corner, using Kurt3D, a real autonomous mobile robot.

This task is closely related to a number of well-studied problems.

Our robot uses a three-dimensional laser scanner in a stop, scan, plan, go fashion for building a virtual three-dimensional environment.

Besides planning trajectories and avoiding obstacles, Kurt3D is capable of identifying objects like a chair.

We derive a practically useful and asymptotically optimal strategy that guarantees a competitive ratio of 2, which differs remarkably from the well-studied scenario without the need of stopping for surveying the environment.

Our strategy is used by Kurt3D, documented in a separate video.

Today I read a paper titled “Indicators of Good Student Performance in Moodle Activity Data”

The abstract is:
In this paper we conduct an analysis of Moodle activity data focused on identifying early predictors of good student performance.

The analysis shows that three relevant hypotheses are largely supported by the data.

These hypotheses are: early submission is a good sign, a high level of activity is predictive of good results and evening activity is even better than daytime activity.

We highlight some pathological examples where high levels of activity correlates with bad results.

Today I read a paper titled “A programme to determine the exact interior of any connected digital picture”

The abstract is:
Region filling is one of the most important and fundamental operations in computer graphics and image processing.

Many filling algorithms and their implementations are based on the Euclidean geometry, which are then translated into computational models moving carelessly from the continuous to the finite discrete space of the computer.

The consequences of this approach is that most implementations fail when tested for challenging degenerate and nearly degenerate regions.

We present a correct integer-only procedure that works for all connected digital pictures.

It finds all possible interior points, which are then displayed and stored in a locating matrix.

Namely, we present a filling and locating procedure that can be used in computer graphics and image processing applications.