The science papers I've read

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.

Today I read a paper titled “Online Action Recognition based on Incremental Learning of Weighted Covariance Descriptors”

The abstract is:
Online action recognition aims to recognize actions from unsegmented streams of data in a continuous manner.

One of the challenges in online recognition is the accumulation of evidence for decision making.

This paper presents a fast and efficient online method to recognize actions from a stream of noisy skeleton data.

The method adopts a covariance descriptor calculated from skeleton data and is based on a novel method developed for incrementally learning the covariance descriptors, referred to as weighted covariance descriptors, so that past frames have less contributions to the descriptor and current frames and informative frames such as key frames contributes more towards the descriptor.

The online recognition is achieved using an efficient nearest neighbour search against a set of trained actions.

Experimental results on MSRC-12 Kinect Gesture dataset and our newly collocated online action recognition dataset have demonstrated the efficacy of the proposed method.

Today I read a paper titled “Generating Natural Questions About an Image”

The abstract is:
There has been an explosion of work in the vision & language community during the past few years from image captioning to video transcription, and answering questions about images.

These tasks have focused on literal descriptions of the image.

To move beyond the literal, we choose to explore how questions about an image are often directed at commonsense inference and the abstract events evoked by objects in the image.

In this paper, we introduce the novel task of Visual Question Generation (VQG), where the system is tasked with asking a natural and engaging question when shown an image.

We provide three datasets which cover a variety of images from object-centric to event-centric, with considerably more abstract training data than provided to state-of-the-art captioning systems thus far.

We train and test several generative and retrieval models to tackle the task of VQG.

Evaluation results show that while such models ask reasonable questions for a variety of images, there is still a wide gap with human performance which motivates further work on connecting images with commonsense knowledge and pragmatics.

Our proposed task offers a new challenge to the community which we hope furthers interest in exploring deeper connections between vision & language.

Today I read a paper titled “Building Machines That Learn and Think Like People”

The abstract is:
Recent progress in artificial intelligence (AI) has renewed interest in building systems that learn and think like people.

Many advances have come from using deep neural networks trained end-to-end in tasks such as object recognition, video games, and board games, achieving performance that equals or even beats humans in some respects.

Despite their biological inspiration and performance achievements, these systems differ from human intelligence in crucial ways.

We review progress in cognitive science suggesting that truly human-like learning and thinking machines will have to reach beyond current engineering trends in both what they learn, and how they learn it.

Specifically, we argue that these machines should (a) build causal models of the world that support explanation and understanding, rather than merely solving pattern recognition problems; (b) ground learning in intuitive theories of physics and psychology, to support and enrich the knowledge that is learned; and (c) harness compositionality and learning-to-learn to rapidly acquire and generalize knowledge to new tasks and situations.

We suggest concrete challenges and promising routes towards these goals that can combine the strengths of recent neural network advances with more structured cognitive models.

Today I read a paper titled “Combat Models for RTS Games”

The abstract is:
Game tree search algorithms, such as Monte Carlo Tree Search (MCTS), require access to a forward model (or “simulator”) of the game at hand.

However, in some games such forward model is not readily available.

This paper presents three forward models for two-player attrition games, which we call “combat models”, and show how they can be used to simulate combat in RTS games.

We also show how these combat models can be learned from replay data.

We use StarCraft as our application domain.

We report experiments comparing our combat models predicting a combat output and their impact when used for tactical decisions during a real game.

Today I read a paper titled “Memcomputing and Swarm Intelligence”

The abstract is:
We explore the relation between memcomputing, namely computing with and in memory, and swarm intelligence algorithms.

In particular, we show that one can design memristive networks to solve short-path optimization problems that can also be solved by ant-colony algorithms.

By employing appropriate memristive elements one can demonstrate an almost one-to-one correspondence between memcomputing and ant colony optimization approaches.

However, the memristive network has the capability of finding the solution in one deterministic step, compared to the stochastic multi-step ant colony optimization.

This result paves the way for nanoscale hardware implementations of several swarm intelligence algorithms that are presently explored, from scheduling problems to robotics.

Today I read a paper titled “Watch-Bot: Unsupervised Learning for Reminding Humans of Forgotten Actions”

The abstract is:
We present a robotic system that watches a human using a Kinect v2 RGB-D sensor, detects what he forgot to do while performing an activity, and if necessary reminds the person using a laser pointer to point out the related object.

Our simple setup can be easily deployed on any assistive robot.

Our approach is based on a learning algorithm trained in a purely unsupervised setting, which does not require any human annotations.

This makes our approach scalable and applicable to variant scenarios.

Our model learns the action/object co-occurrence and action temporal relations in the activity, and uses the learned rich relationships to infer the forgotten action and the related object.

We show that our approach not only improves the unsupervised action segmentation and action cluster assignment performance, but also effectively detects the forgotten actions on a challenging human activity RGB-D video dataset.

In robotic experiments, we show that our robot is able to remind people of forgotten actions successfully.

Today I read a paper titled “3D Texture Coordinates on Polygon Mesh Sequences”

The abstract is:
A method for creating 3D texture coordinates for a sequence of polygon meshes with changing topology and vertex motion vectors.

Today I read a paper titled “Invariant EKF Design for Scan Matching-aided Localization”

The abstract is:
Localization in indoor environments is a technique which estimates the robot’s pose by fusing data from onboard motion sensors with readings of the environment, in our case obtained by scan matching point clouds captured by a low-cost Kinect depth camera.

We develop both an Invariant Extended Kalman Filter (IEKF)-based and a Multiplicative Extended Kalman Filter (MEKF)-based solution to this problem.

The two designs are successfully validated in experiments and demonstrate the advantage of the IEKF design.

Today I read a paper titled “Low-Cost Scene Modeling using a Density Function Improves Segmentation Performance”

The abstract is:
We propose a low cost and effective way to combine a free simulation software and free CAD models for modeling human-object interaction in order to improve human & object segmentation.

It is intended for research scenarios related to safe human-robot collaboration (SHRC) and interaction (SHRI) in the industrial domain.

The task of human and object modeling has been used for detecting activity, and for inferring and predicting actions, different from those works, we do human and object modeling in order to learn interactions in RGB-D data for improving segmentation.

For this purpose, we define a novel density function to model a three dimensional (3D) scene in a virtual environment (VREP).

This density function takes into account various possible configurations of human-object and object-object relationships and interactions governed by their affordances.

Using this function, we synthesize a large, realistic and highly varied synthetic RGB-D dataset that we use for training.

We train a random forest classifier, and the pixelwise predictions obtained is integrated as a unary term in a pairwise conditional random fields (CRF).

Our evaluation shows that modeling these interactions improves segmentation performance by ~7\% in mean average precision and recall over state-of-the-art methods that ignore these interactions in real-world data.

Our approach is computationally efficient, robust and can run real-time on consumer hardware.

Today I read a paper titled “A Dynamic Boundary Guarding Problem with Translating Targets”

The abstract is:
We introduce a problem in which a service vehicle seeks to guard a deadline (boundary) from dynamically arriving mobile targets.

The environment is a rectangle and the deadline is one of its edges.

Targets arrive continuously over time on the edge opposite the deadline, and move towards the deadline at a fixed speed.

The goal for the vehicle is to maximize the fraction of targets that are captured before reaching the deadline.

We consider two cases; when the service vehicle is faster than the targets, and; when the service vehicle is slower than the targets.

In the first case we develop a novel vehicle policy based on computing longest paths in a directed acyclic graph.

We give a lower bound on the capture fraction of the policy and show that the policy is optimal when the distance between the target arrival edge and deadline becomes very large.

We present numerical results which suggest near optimal performance away from this limiting regime.

In the second case, when the targets are slower than the vehicle, we propose a policy based on servicing fractions of the translational minimum Hamiltonian path.

In the limit of low target speed and high arrival rate, the capture fraction of this policy is within a small constant factor of the optimal.

Today I read a paper titled “Automatic Identification of Animal Breeds and Species Using Bioacoustics and Artificial Neural Networks”

The abstract is:
In this research endeavor, it was hypothesized that the sound produced by animals during their vocalizations can be used as identifiers of the animal breed or species even if they sound the same to unaided human ear.

To test this hypothesis, three artificial neural networks (ANNs) were developed using bioacoustics properties as inputs for the respective automatic identification of 13 bird species, eight dog breeds, and 11 frog species.

Recorded vocalizations of these animals were collected and processed using several known signal processing techniques to convert the respective sounds into computable bioacoustics values.

The converted values of the vocalizations, together with the breed or species identifications, were used to train the ANNs following a ten-fold cross validation technique.

Tests show that the respective ANNs can correctly identify 71.43\% of the birds, 94.44\% of the dogs, and 90.91\% of the frogs.

This result show that bioacoustics and ANN can be used to automatically determine animal breeds and species, which together could be a promising automated tool for animal identification, biodiversity determination, animal conservation, and other animal welfare efforts.

Today I read a paper titled “The eel-like robot”

The abstract is:
The aim of this project is to design, study and build an “eel-like robot” prototype able to swim in three dimensions.

The study is based on the analysis of eel swimming and results in the realization of a prototype with 12 vertebrae, a skin and a head with two fins.

To reach these objectives, a multidisciplinary group of teams and laboratories has been formed in the framework of two French projects.

Today I read a paper titled “Hand Segmentation for Hand-Object Interaction from Depth map”

The abstract is:
Hand-object interaction is important for many applications such as augmented reality, medical application, and human-robot interaction.

Hand segmentation is a necessary pre-process to estimate hand pose and to recognize hand gesture or object in interaction.

However, current hand segmentation method for hand-object interaction is based on color information which is not robust to objects with skin color, skin pigment difference, and light condition variations.

Therefore, we propose the first hand segmentation method for hand-object interaction using depth map.

This is challenging because of only small depth difference between hand and object during interaction.

The proposed method includes two-stage randomized decision forest (RDF) with validation process, bilateral filtering, decision adjustment, and post-processing.

We demonstrate the effectiveness of the proposed method by testing for five objects.

The proposed method achieves the average F1 score of 0.8826 using different model for each object and 0.8645 using a global model for entire objects.

Also, the method takes only about 10ms to process each frame.

We believe that this is the state-of-the-art hand segmentation algorithm using depth map for hand-object interaction.

Today I read a paper titled “Multi-command Chest Tactile Brain Computer Interface for Small Vehicle Robot Navigation”

The abstract is:
The presented study explores the extent to which tactile stimuli delivered to five chest positions of a healthy user can serve as a platform for a brain computer interface (BCI) that could be used in an interactive application such as robotic vehicle operation.

The five chest locations are used to evoke tactile brain potential responses, thus defining a tactile brain computer interface (tBCI).

Experimental results with five subjects performing online tBCI provide a validation of the chest location tBCI paradigm, while the feasibility of the concept is illuminated through information-transfer rates.

Additionally an offline classification improvement with a linear SVM classifier is presented through the case study.

Today I read a paper titled “A Framework for Analysing Driver Interactions with Semi-Autonomous Vehicles”

The abstract is:
Semi-autonomous vehicles are increasingly serving critical functions in various settings from mining to logistics to defence.

A key characteristic of such systems is the presence of the human (drivers) in the control loop.

To ensure safety, both the driver needs to be aware of the autonomous aspects of the vehicle and the automated features of the vehicle built to enable safer control.

In this paper we propose a framework to combine empirical models describing human behaviour with the environment and system models.

We then analyse, via model checking, interaction between the models for desired safety properties.

The aim is to analyse the design for safe vehicle-driver interaction.

We demonstrate the applicability of our approach using a case study involving semi-autonomous vehicles where the driver fatigue are factors critical to a safe journey.

Today I read a paper titled “Inverse Procedural Modeling of Facade Layouts”

The abstract is:
In this paper, we address the following research problem: How can we generate a meaningful split grammar that explains a given facade layout? To evaluate if a grammar is meaningful, we propose a cost function based on the description length and minimize this cost using an approximate dynamic programming framework.

Our evaluation indicates that our framework extracts meaningful split grammars that are competitive with those of expert users, while some users and all competing automatic solutions are less successful.

Today I read a paper titled “Image segmentation of cross-country scenes captured in IR spectrum”

The abstract is:
Computer vision has become a major source of information for autonomous navigation of robots of various types, self-driving cars, military robots and mars/lunar rovers are some examples.

Nevertheless, the majority of methods focus on analysing images captured in visible spectrum.

In this manuscript we elaborate on the problem of segmenting cross-country scenes captured in IR spectrum.

For this purpose we proposed employing salient features.

Salient features are robust to variations in scale, brightness and view angle.

We suggest the Speeded-Up Robust Features as a basis for our salient features for a number of reasons discussed in the paper.

We also provide a comparison of two SURF implementations.

The SURF features are extracted from images of different terrain types.

For every feature we estimate a terrain class membership function.

The membership values are obtained by means of either the multi-layer perceptron or nearest neighbours.

The features’ class membership values and their spatial positions are then applied to estimate class membership values for all pixels in the image.

To decrease the effect of segmentation blinking that is caused by rapid switching between different terrain types and to speed up segmentation, we are tracking camera position and predict features’ positions.

The comparison of the multi-layer perception and the nearest neighbour classifiers is presented in the paper.

The error rate of the terrain segmentation using the nearest neighbours obtained on the testing set is 16.6+-9.17%.

Today I read a paper titled “Starting engagement detection towards a companion robot using multimodal features”

The abstract is:
Recognition of intentions is a subconscious cognitive process vital to human communication.

This skill enables anticipation and increases the quality of interactions between humans.

Within the context of engagement, non-verbal signals are used to communicate the intention of starting the interaction with a partner.

In this paper, we investigated methods to detect these signals in order to allow a robot to know when it is about to be addressed.

Originality of our approach resides in taking inspiration from social and cognitive sciences to perform our perception task.

We investigate meaningful features, i.e.

human readable features, and elicit which of these are important for recognizing someone’s intention of starting an interaction.

Classically, spatial information like the human position and speed, the human-robot distance are used to detect the engagement.

Our approach integrates multimodal features gathered using a companion robot equipped with a Kinect.

The evaluation on our corpus collected in spontaneous conditions highlights its robustness and validates the use of such a technique in a real environment.

Experimental validation shows that multimodal features set gives better precision and recall than using only spatial and speed features.

We also demonstrate that 7 selected features are sufficient to provide a good starting engagement detection score.

In our last investigation, we show that among our full 99 features set, the space reduction is not a solved task.

This result opens new researches perspectives on multimodal engagement detection.

Today I read a paper titled “A new flocking model through body attitude coordination”

The abstract is:
We present a new model for multi-agent dynamics where each agent is described by its position and body attitude: agents travel at a constant speed in a given direction and their body can rotate around it adopting different configurations.

In this manner, the body attitude is described by three orthonormal axes giving an element in $SO(3)$ (rotation matrix).

Agents try to coordinate their body attitudes with the ones of their neighbours.

In the present paper, we give the Individual Based Model (particle model) for this dynamics and derive its corresponding kinetic and macroscopic equations.

Today I read a paper titled “Towards Building an RGBD-M Scanner”

The abstract is:
We present a portable device to capture both shape and reflectance of an indoor scene.

Consisting of a Kinect, an IR camera and several IR LEDs, our device allows the user to acquire data in a similar way as he/she scans with a single Kinect.

Scene geometry is reconstructed by KinectFusion.

To estimate reflectance from incomplete and noisy observations, 3D vertices of the same material are identified by our material segmentation propagation algorithm.

Then BRDF observations at these vertices are merged into a more complete and accurate BRDF for the material.

Effectiveness of our device is demonstrated by quality results on real-world scenes.

Today I read a paper titled “Quantification of Human Movement for Assessment in Automated Exercise Coaching”

The abstract is:
Quantification of human movement is a challenge in many areas, ranging from physical therapy to robotics.

We quantify of human movement for the purpose of providing automated exercise coaching in the home.

We developed a model-based assessment and inference process that combines biomechanical constraints with movement assessment based on the Microsoft Kinect camera.

To illustrate the approach, we quantify the performance of a simple squatting exercise using two model-based metrics that are related to strength and endurance, and provide an estimate of the strength and energy-expenditure of each exercise session.

We look at data for 5 subjects, and show that for some subjects the metrics indicate a trend consistent with improved exercise performance.

Today I read a paper titled “Can background baroque music help to improve the memorability of graphical passwords?”

The abstract is:
Graphical passwords have been proposed as an alternative to alphanumeric passwords with their advantages in usability and security.

However, they still tend to follow predictable patterns that are easier for attackers to exploit, probably due to users’ memory limitations.

Various literatures show that baroque music has positive effects on human learning and memorizing.

To alleviate users’ memory burden, we investigate the novel idea of introducing baroque music to graphical password schemes (specifically DAS, PassPoints and Story) and conduct a laboratory study to see whether it is helpful.

In a ten minutes short-term recall, we found that participants in all conditions had high recall success rates that were not statistically different from each other.

After one week, the music group coped PassPoints passwords significantly better than the group without music.

But there was no statistical difference between two groups in recalling DAS passwords or Story passwords.

Further more, we found that the music group tended to set significantly more complicated PassPoints passwords but less complicated DAS passwords.

Today I read a paper titled “Motion Planning Strategies for Autonomously Mapping 3D Structures”

The abstract is:
This paper presents a system capable of autonomously mapping the visible part of a bounded three-dimensional structure using a mobile ground robot equipped with a depth sensor.

We describe motion planning strategies to determine appropriate successive viewpoints and attempt to fill holes automatically in a point cloud produced by the sensing and perception layer.

We develop a local motion planner using potential fields to maintain a desired distance from the structure.

The emphasis is on accurately reconstructing a 3D model of a structure of moderate size rather than mapping large open environments, with applications for example in architecture, construction and inspection.

The proposed algorithms do not require any initialization in the form of a mesh model or a bounding box.

We compare via simulations the performance of our policies to the classic frontier based exploration algorithm.

We illustrate the efficacy of our approach for different structure sizes, levels of localization accuracy and range of the depth sensor.

Today I read a paper titled “Change Blindness in 3D Virtual Reality”

The abstract is:
In the present change blindness study subjects explored stereoscopic three dimensional (3D) environments through a virtual reality (VR) headset.

A novel method that tracked the subjects’ head movements was used for inducing changes in the scene whenever the changing object was out of the field of view.

The effect of change location (foreground or background in 3D depth) on change blindness was investigated.

Two experiments were conducted, one in the lab (n = 50) and the other online (n = 25).

Up to 25% of the changes were undetected and the mean overall search time was 27 seconds in the lab study.

Results indicated significantly lower change detection success and more change cycles if the changes occurred in the background, with no differences in overall search times.

The results confirm findings from previous studies and extend them to 3D environments.

The study also demonstrates the feasibility of online VR experiments.

Today I read a paper titled “A Light Transport Model for Mitigating Multipath Interference in TOF Sensors”

The abstract is:
Continuous-wave Time-of-flight (TOF) range imaging has become a commercially viable technology with many applications in computer vision and graphics.

However, the depth images obtained from TOF cameras contain scene dependent errors due to multipath interference (MPI).

Specifically, MPI occurs when multiple optical reflections return to a single spatial location on the imaging sensor.

Many prior approaches to rectifying MPI rely on sparsity in optical reflections, which is an extreme simplification.

In this paper, we correct MPI by combining the standard measurements from a TOF camera with information from direct and global light transport.

We report results on both simulated experiments and physical experiments (using the Kinect sensor).

Our results, evaluated against ground truth, demonstrate a quantitative improvement in depth accuracy.

Today I read a paper titled “High-dimensional Black-box Optimization via Divide and Approximate Conquer”

The abstract is:
Divide and Conquer (DC) is conceptually well suited to high-dimensional optimization by decomposing a problem into multiple small-scale sub-problems.

However, appealing performance can be seldom observed when the sub-problems are interdependent.

This paper suggests that the major difficulty of tackling interdependent sub-problems lies in the precise evaluation of a partial solution (to a sub-problem), which can be overwhelmingly costly and thus makes sub-problems non-trivial to conquer.

Thus, we propose an approximation approach, named Divide and Approximate Conquer (DAC), which reduces the cost of partial solution evaluation from exponential time to polynomial time.

Meanwhile, the convergence to the global optimum (of the original problem) is still guaranteed.

The effectiveness of DAC is demonstrated empirically on two sets of non-separable high-dimensional problems.

Today I read a paper titled “The role of RGB-D benchmark datasets: an overview”

The abstract is:
The advent of the Microsoft Kinect three years ago stimulated not only the computer vision community for new algorithms and setups to tackle well-known problems in the community but also sparked the launch of several new benchmark datasets to which future algorithms can be compared 019 to.

This review of the literature and industry developments concludes that the current RGB-D benchmark datasets can be useful to determine the accuracy of a variety of applications of a single or multiple RGB-D sensors.

Today I read a paper titled “How to avoid ethically relevant Machine Consciousness”

The abstract is:
This paper discusses the root cause of systems perceiving the self experience and how to exploit adaptive and learning features without introducing ethically problematic system properties.

Today I read a paper titled “Is swarm intelligence able to create mazes?”

The abstract is:
In this paper, the idea of applying Computational Intelligence in the process of creation board games, in particular mazes, is presented.

For two different algorithms the proposed idea has been examined.

The results of the experiments are shown and discussed to present advantages and disadvantages.

Today I read a paper titled “Quantum machine learning with glow for episodic tasks and decision games”

The abstract is:
We consider a general class of models, where a reinforcement learning (RL) agent learns from cyclic interactions with an external environment via classical signals.

Perceptual inputs are encoded as quantum states, which are subsequently transformed by a quantum channel representing the agent’s memory, while the outcomes of measurements performed at the channel’s output determine the agent’s actions.

The learning takes place via stepwise modifications of the channel properties.

They are described by an update rule that is inspired by the projective simulation (PS) model and equipped with a glow mechanism that allows for a backpropagation of policy changes, analogous to the eligibility traces in RL and edge glow in PS.

In this way, the model combines features of PS with the ability for generalization, offered by its physical embodiment as a quantum system.

We apply the agent to various setups of an invasion game and a grid world, which serve as elementary model tasks allowing a direct comparison with a basic classical PS agent.

Today I read a paper titled “A Simultaneous-Movement Mobile Multiplayer Game Design based on Adaptive Background Partitioning Technique”

The abstract is:
Implementations of mobile games have become prevalent industrial technology due to the ubiquitous nature of mobile devices.

However, simultaneous-movement multiplayer games, games that a player competes simultaneously with other players, are usually affected by such parameters as latency, type of game architecture and type of communication technology.

This paper makes a review of the above parameters, considering the pros and cons of the various techniques used in addressing each parameter.

It then goes ahead to propose an enhanced mechanism for dealing with packet delays based on partitioning the game background into grids.

The proposed design is implemented and tested using Bluetooth and Wi-Fi communication technologies.

The efficiency and effectiveness of the design are also analyzed.

Today I read a paper titled “Massively Parallel Ray Tracing Algorithm Using GPU”

The abstract is:
Ray tracing is a technique for generating an image by tracing the path of light through pixels in an image plane and simulating the effects of high-quality global illumination at a heavy computational cost.

Because of the high computation complexity, it can’t reach the requirement of real-time rendering.

The emergence of many-core architectures, makes it possible to reduce significantly the running time of ray tracing algorithm by employing the powerful ability of floating point computation.

In this paper, a new GPU implementation and optimization of the ray tracing to accelerate the rendering process is presented.

Today I read a paper titled “Verifiable Source Code Documentation in Controlled Natural Language”

The abstract is:
Writing documentation about software internals is rarely considered a rewarding activity.

It is highly time-consuming and the resulting documentation is fragile when the software is continuously evolving in a multi-developer setting.

Unfortunately, traditional programming environments poorly support the writing and maintenance of documentation.

Consequences are severe as the lack of documentation on software structure negatively impacts the overall quality of the software product.

We show that using a controlled natural language with a reasoner and a query engine is a viable technique for verifying the consistency and accuracy of documentation and source code.

Using ACE, a state-of-the-art controlled natural language, we present positive results on the comprehensibility and the general feasibility of creating and verifying documentation.

As a case study, we used automatic documentation verification to identify and fix severe flaws in the architecture of a non-trivial piece of software.

Moreover, a user experiment shows that our language is faster and easier to learn and understand than other formal languages for software documentation.

Today I read a paper titled “Kinects and Human Kinetics: A New Approach for Studying Crowd Behavior”

The abstract is:
Modeling crowd behavior relies on accurate data of pedestrian movements at a high level of detail.

Imaging sensors such as cameras provide a good basis for capturing such detailed pedestrian motion data.

However, currently available computer vision technologies, when applied to conventional video footage, still cannot automatically unveil accurate motions of groups of people or crowds from the image sequences.

We present a novel data collection approach for studying crowd behavior which uses the increasingly popular low-cost sensor Microsoft Kinect.

The Kinect captures both standard camera data and a three-dimensional depth map.

Our human detection and tracking algorithm is based on agglomerative clustering of depth data captured from an elevated view – in contrast to the lateral view used for gesture recognition in Kinect gaming applications.

Our approach transforms local Kinect 3D data to a common world coordinate system in order to stitch together human trajectories from multiple Kinects, which allows for a scalable and flexible capturing area.

At a testbed with real-world pedestrian traffic we demonstrate that our approach can provide accurate trajectories from three Kinects with a Pedestrian Detection Rate of up to 94% and a Multiple Object Tracking Precision of 4 cm.

Using a comprehensive dataset of 2240 captured human trajectories we calibrate three variations of the Social Force model.

The results of our model validations indicate their particular ability to reproduce the observed crowd behavior in microscopic simulations.

Today I read a paper titled “Neurally-Guided Procedural Models: Learning to Guide Procedural Models with Deep Neural Networks”

The abstract is:
We present a deep learning approach for speeding up constrained procedural modeling.

Probabilistic inference algorithms such as Sequential Monte Carlo (SMC) provide powerful tools for constraining procedural models, but they require many samples to produce desirable results.

In this paper, we show how to create procedural models which learn how to satisfy constraints.

We augment procedural models with neural networks: these networks control how the model makes random choices based on what output it has generated thus far.

We call such a model a neurally-guided procedural model.

As a pre-computation, we train these models on constraint-satisfying example outputs generated via SMC.

They are then used as efficient importance samplers for SMC, generating high-quality results with very few samples.

We evaluate our method on L-system-like models with image-based constraints.

Given a desired quality threshold, neurally-guided models can generate satisfactory results up to 10x faster than unguided models.