justin

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.