Archives for 2018

Today I read a paper titled “Usability Engineering of Games: A Comparative Analysis of Measuring Excitement Using Sensors, Direct Observations and Self-Reported Data”

The abstract is:
Usability engineering and usability testing are concepts that continue to evolve.

Interesting research studies and new ideas come up every now and then.

This paper tests the hypothesis of using an EDA based physiological measurements as a usability testing tool by considering three measures which are observers opinions, self reported data and EDA based physiological sensor data.

These data were analyzed comparatively and statistically.

It concludes by discussing the findings that has been obtained from those subjective and objective measures, which partially supports the hypothesis.

Today I read a paper titled “Detecting and avoiding frontal obstacles from monocular camera for micro unmanned aerial vehicles”

The abstract is:
In literature, several approaches are trying to make the UAVs fly autonomously i.e., by extracting perspective cues such as straight lines.

However, it is only available in well-defined human made environments, in addition to many other cues which require enough texture information.

Our main target is to detect and avoid frontal obstacles from a monocular camera using a quad rotor Ar.Drone 2 by exploiting optical flow as a motion parallax, the drone is permitted to fly at a speed of 1 m/s and an altitude ranging from 1 to 4 meters above the ground level.

In general, detecting and avoiding frontal obstacle is a quite challenging problem because optical flow has some limitation which should be taken into account i.e.

lighting conditions and aperture problem.

Today I read a paper titled “Unscented Bayesian Optimization for Safe Robot Grasping”

The abstract is:
We address the robot grasp optimization problem of unknown objects considering uncertainty in the input space.

Grasping unknown objects can be achieved by using a trial and error exploration strategy.

Bayesian optimization is a sample efficient optimization algorithm that is especially suitable for this setups as it actively reduces the number of trials for learning about the function to optimize.

In fact, this active object exploration is the same strategy that infants do to learn optimal grasps.

One problem that arises while learning grasping policies is that some configurations of grasp parameters may be very sensitive to error in the relative pose between the object and robot end-effector.

We call these configurations unsafe because small errors during grasp execution may turn good grasps into bad grasps.

Therefore, to reduce the risk of grasp failure, grasps should be planned in safe areas.

We propose a new algorithm, Unscented Bayesian optimization that is able to perform sample efficient optimization while taking into consideration input noise to find safe optima.

The contribution of Unscented Bayesian optimization is twofold as if provides a new decision process that drives exploration to safe regions and a new selection procedure that chooses the optimal in terms of its safety without extra analysis or computational cost.

Both contributions are rooted on the strong theory behind the unscented transformation, a popular nonlinear approximation method.

We show its advantages with respect to the classical Bayesian optimization both in synthetic problems and in realistic robot grasp simulations.

The results highlights that our method achieves optimal and robust grasping policies after few trials while the selected grasps remain in safe regions.

Today I read a paper titled “Some Experimental Issues in Financial Fraud Detection: An Investigation”

The abstract is:
Financial fraud detection is an important problem with a number of design aspects to consider.

Issues such as algorithm selection and performance analysis will affect the perceived ability of proposed solutions, so for auditors and re-searchers to be able to sufficiently detect financial fraud it is necessary that these issues be thoroughly explored.

In this paper we will revisit the key performance metrics used for financial fraud detection with a focus on credit card fraud, critiquing the prevailing ideas and offering our own understandings.

There are many different performance metrics that have been employed in prior financial fraud detection research.

We will analyse several of the popular metrics and compare their effectiveness at measuring the ability of detection mechanisms.

We further investigated the performance of a range of computational intelligence techniques when applied to this problem domain, and explored the efficacy of several binary classification methods.

Today I read a paper titled “Data Driven Robust Image Guided Depth Map Restoration”

The abstract is:
Depth maps captured by modern depth cameras such as Kinect and Time-of-Flight (ToF) are usually contaminated by missing data, noises and suffer from being of low resolution.

In this paper, we present a robust method for high-quality restoration of a degraded depth map with the guidance of the corresponding color image.

We solve the problem in an energy optimization framework that consists of a novel robust data term and smoothness term.

To accommodate not only the noise but also the inconsistency between depth discontinuities and the color edges, we model both the data term and smoothness term with a robust exponential error norm function.

We propose to use Iteratively Re-weighted Least Squares (IRLS) methods for efficiently solving the resulting highly non-convex optimization problem.

More importantly, we further develop a data-driven adaptive parameter selection scheme to properly determine the parameter in the model.

We show that the proposed approach can preserve fine details and sharp depth discontinuities even for a large upsampling factor ($8\times$ for example).

Experimental results on both simulated and real datasets demonstrate that the proposed method outperforms recent state-of-the-art methods in coping with the heavy noise, preserving sharp depth discontinuities and suppressing the texture copy artifacts.

Today I read a paper titled “Watch-n-Patch: Unsupervised Learning of Actions and Relations”

The abstract is:
There is a large variation in the activities that humans perform in their everyday lives.

We consider modeling these composite human activities which comprises multiple basic level actions in a completely unsupervised setting.

Our model learns high-level co-occurrence and temporal relations between the actions.

We consider the video as a sequence of short-term action clips, which contains human-words and object-words.

An activity is about a set of action-topics and object-topics indicating which actions are present and which objects are interacting with.

We then propose a new probabilistic model relating the words and the topics.

It allows us to model long-range action relations that commonly exist in the composite activities, which is challenging in previous works.

We apply our model to the unsupervised action segmentation and clustering, and to a novel application that detects forgotten actions, which we call action patching.

For evaluation, we contribute a new challenging RGB-D activity video dataset recorded by the new Kinect v2, which contains several human daily activities as compositions of multiple actions interacting with different objects.

Moreover, we develop a robotic system that watches people and reminds people by applying our action patching algorithm.

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

Today I read a paper titled “Assessing 3D scan quality through paired-comparisons psychophysics test”

The abstract is:
Consumer 3D scanners and depth cameras are increasingly being used to generate content and avatars for Virtual Reality (VR) environments and avoid the inconveniences of hand modeling; however, it is sometimes difficult to evaluate quantitatively the mesh quality at which 3D scans should be exported, and whether the object perception might be affected by its shading.

We propose using a paired-comparisons test based on psychophysics of perception to do that evaluation.

As psychophysics is not subject to opinion, skill level, mental state, or economic situation it can be considered a quantitative way to measure how people perceive the mesh quality.

In particular, we propose using the psychophysical measure for the comparison of four different levels of mesh quality (1K, 5K, 10K and 20K triangles).

We present two studies within subjects: in one we investigate the quality perception variations of seeing an object in a regular screen monitor against an stereoscopic Head Mounted Display (HMD); while in the second experiment we aim at detecting the effects of shading into quality perception.

At each iteration of the pair-test comparisons participants pick the mesh that they think had higher quality; by the end of the experiment we compile a preference matrix.

The matrix evidences the correlation between real quality and assessed quality.

Regarding the shading mode, we find an interaction with quality and shading when the model has high definition.

Furthermore, we assess the subjective realism of the most/least preferred scans using an Immersive Augmented Reality (IAR) video-see-through setup.

Results show higher levels of realism were perceived through the HMD than when using a monitor, although the quality was similarly perceived in both systems.

Today I read a paper titled “Contextual Media Retrieval Using Natural Language Queries”

The abstract is:
The widespread integration of cameras in hand-held and head-worn devices as well as the ability to share content online enables a large and diverse visual capture of the world that millions of users build up collectively every day.

We envision these images as well as associated meta information, such as GPS coordinates and timestamps, to form a collective visual memory that can be queried while automatically taking the ever-changing context of mobile users into account.

As a first step towards this vision, in this work we present Xplore-M-Ego: a novel media retrieval system that allows users to query a dynamic database of images and videos using spatio-temporal natural language queries.

We evaluate our system using a new dataset of real user queries as well as through a usability study.

One key finding is that there is a considerable amount of inter-user variability, for example in the resolution of spatial relations in natural language utterances.

We show that our retrieval system can cope with this variability using personalisation through an online learning-based retrieval formulation.

Today I read a paper titled “An Improved Tracking using IMU and Vision Fusion for Mobile Augmented Reality Applications”

The abstract is:
Mobile Augmented Reality (MAR) is becoming an important cyber-physical system application given the ubiquitous availability of mobile phones.

With the need to operate in unprepared environments, accurate and robust registration and tracking has become an important research problem to solve.

In fact, when MAR is used for tele-interactive applications involving large distances, say from an accident site to insurance office, tracking at both the ends is desirable and further it is essential to appropriately fuse inertial and vision sensors data.

In this paper, we present results and discuss some insights gained in marker-less tracking during the development of a prototype pertaining to an example use case related to breakdown or damage assessment of a vehicle.

The novelty of this paper is in bringing together different components and modules with appropriate enhancements towards a complete working system.

Today I read a paper titled “Perceiving Mass in Mixed Reality through Pseudo-Haptic Rendering of Newton’s Third Law”

The abstract is:
In mixed reality, real objects can be used to interact with virtual objects.

However, unlike in the real world, real objects do not encounter any opposite reaction force when pushing against virtual objects.

The lack of reaction force during manipulation prevents users from perceiving the mass of virtual objects.

Although this could be addressed by equipping real objects with force-feedback devices, such a solution remains complex and impractical.In this work, we present a technique to produce an illusion of mass without any active force-feedback mechanism.

This is achieved by simulating the effects of this reaction force in a purely visual way.

A first study demonstrates that our technique indeed allows users to differentiate light virtual objects from heavy virtual objects.

In addition, it shows that the illusion is immediately effective, with no prior training.

In a second study, we measure the lowest mass difference (JND) that can be perceived with this technique.

The effectiveness and ease of implementation of our solution provides an opportunity to enhance mixed reality interaction at no additional cost.

Today I read a paper titled “On using the Microsoft Kinect$^{\rm TM}$ sensors in the analysis of human motion”

The abstract is:
The present paper aims at providing the theoretical background required for investigating the use of the Microsoft Kinect$^{\rm TM}$ (`Kinect’, for short) sensors (original and upgraded) in the analysis of human motion.

Our methodology is developed in such a way that its application be easily adaptable to comparative studies of other systems used in capturing human-motion data.

Our future plans include the application of this methodology to two situations: first, in a comparative study of the performance of the two Kinect sensors; second, in pursuing their validation on the basis of comparisons with a marker-based system (MBS).

One important feature in our approach is the transformation of the MBS output into Kinect-output format, thus enabling the analysis of the measurements, obtained from different systems, with the same software application, i.e., the one we use in the analysis of Kinect-captured data; one example of such a transformation, for one popular marker-placement scheme (`Plug-in Gait’), is detailed.

We propose that the similarity of the output, obtained from the different systems, be assessed on the basis of the comparison of a number of waveforms, representing the variation within the gait cycle of quantities which are commonly used in the modelling of the human motion.

The data acquisition may involve commercially-available treadmills and a number of velocity settings: for instance, walking-motion data may be acquired at $5$ km/h, running-motion data at $8$ and $11$ km/h.

We recommend that particular attention be called to systematic effects associated with the subject’s knee and lower leg, as well as to the ability of the Kinect sensors in reliably capturing the details in the asymmetry of the motion for the left and right parts of the human body.

The previous versions of the study have been withdrawn due to the use of a non-representative database.

Today I read a paper titled “Neural Language Correction with Character-Based Attention”

The abstract is:
Natural language correction has the potential to help language learners improve their writing skills.

While approaches with separate classifiers for different error types have high precision, they do not flexibly handle errors such as redundancy or non-idiomatic phrasing.

On the other hand, word and phrase-based machine translation methods are not designed to cope with orthographic errors, and have recently been outpaced by neural models.

Motivated by these issues, we present a neural network-based approach to language correction.

The core component of our method is an encoder-decoder recurrent neural network with an attention mechanism.

By operating at the character level, the network avoids the problem of out-of-vocabulary words.

We illustrate the flexibility of our approach on dataset of noisy, user-generated text collected from an English learner forum.

When combined with a language model, our method achieves a state-of-the-art $F_{0.5}$-score on the CoNLL 2014 Shared Task.

We further demonstrate that training the network on additional data with synthesized errors can improve performance.

Today I read a paper titled “Second Life Physics: Virtual, real, or surreal?”

The abstract is:
Science teaching detached itself from reality and became restricted to the classrooms and textbooks with their overreliance on standardized and repetitive exercises, while students keep their own alternative conceptions.

Papert, displeased with this inefficient learning process, championed physics microworlds, where students could experience a variety of laws of motion, from Aristotle to Newton and Einstein or even new laws invented by the students themselves.

While often mistakenly seen as a game, Second Life (SL), the online 3-D virtual world hosted by Linden Lab, imposes essentially no rules on the residents beyond reasonable restrictions on improper behavior and the physical rules that guarantee its similitude to the real world.

As a consequence, SL qualifies itself as an environment for personal discovery and exploration as proposed by constructivist theories.

The physical laws are implemented through the well-known physics engine Havok, whose design aims to provide game-players a consistent, realistic environment.

The Havok User Guide (2008) explicitly encourages developers to use several tricks to cheat the simulator in order to make games funnier or easier to play.

As it is shown in this study, SL physics is unexpectedly neither the Newtonian idealized physics nor a real world physics virtualization, intentionally diverging from reality in such a way that it could be called hyper-real.

As a matter of fact, if some of its features make objects behave more realistically than real ones, certain quantities like energy have a totally different meaning in SL as compared to physics.

Far from considering it as a problem, however, the author argues that its hyper-reality may be a golden teaching opportunity, allowing surreal physics simulations and epistemologically rich classroom discussions around the what is a physical law? issue, in accordance with Papert’s never-implemented proposal.

Today I read a paper titled “3D ShapeNets: A Deep Representation for Volumetric Shapes”

The abstract is:
3D shape is a crucial but heavily underutilized cue in today’s computer vision systems, mostly due to the lack of a good generic shape representation.

With the recent availability of inexpensive 2.5D depth sensors (e.g.

Microsoft Kinect), it is becoming increasingly important to have a powerful 3D shape representation in the loop.

Apart from category recognition, recovering full 3D shapes from view-based 2.5D depth maps is also a critical part of visual understanding.

To this end, we propose to represent a geometric 3D shape as a probability distribution of binary variables on a 3D voxel grid, using a Convolutional Deep Belief Network.

Our model, 3D ShapeNets, learns the distribution of complex 3D shapes across different object categories and arbitrary poses from raw CAD data, and discovers hierarchical compositional part representations automatically.

It naturally supports joint object recognition and shape completion from 2.5D depth maps, and it enables active object recognition through view planning.

To train our 3D deep learning model, we construct ModelNet — a large-scale 3D CAD model dataset.

Extensive experiments show that our 3D deep representation enables significant performance improvement over the-state-of-the-arts in a variety of tasks.

Today I read a paper titled “Violation of classical inequalities by photon frequency-filtering”

The abstract is:
The violation of the Cauchy-Schwarz and Bell inequalities ranks among the major evidences of the genuinely quantum nature of an emitter.

We show that by dispensing from the usual approximation of mode correlations and studying directly correlations between the physical reality -the photons- these violations can be optimized.

This is achieved by extending the concept of photon correlations to all frequencies in all the possible windows of detections, with no prejudice to the supposed origin of the photons.

We identify the regions of quantum emission as rooted in collective de-excitation involving virtual states instead of, as previously assumed, cascaded transitions between real states.

Today I read a paper titled “Curve Networks for Surface Reconstruction”

The abstract is:
Man-made objects usually exhibit descriptive curved features (i.e., curve networks).

The curve network of an object conveys its high-level geometric and topological structure.

We present a framework for extracting feature curve networks from unstructured point cloud data.

Our framework first generates a set of initial curved segments fitting highly curved regions.

We then optimize these curved segments to respect both data fitting and structural regularities.

Finally, the optimized curved segments are extended and connected into curve networks using a clustering method.

To facilitate effectiveness in case of severe missing data and to resolve ambiguities, we develop a user interface for completing the curve networks.

Experiments on various imperfect point cloud data validate the effectiveness of our curve network extraction framework.

We demonstrate the usefulness of the extracted curve networks for surface reconstruction from incomplete point clouds.

Today I read a paper titled “Managing Overstaying Electric Vehicles in Park-and-Charge Facilities”

The abstract is:
With the increase in adoption of Electric Vehicles (EVs), proper utilization of the charging infrastructure is an emerging challenge for service providers.

Overstaying of an EV after a charging event is a key contributor to low utilization.

Since overstaying is easily detectable by monitoring the power drawn from the charger, managing this problem primarily involves designing an appropriate penalty during the overstaying period.

Higher penalties do discourage overstaying; however, due to uncertainty in parking duration, less people would find such penalties acceptable, leading to decreased utilization (and revenue).

To analyze this central trade-off, we develop a novel framework that integrates models for realistic user behavior into queueing dynamics to locate the optimal penalty from the points of view of utilization and revenue, for different values of the external charging demand.

Next, when the model parameters are unknown, we show how an online learning algorithm, such as UCB, can be adapted to learn the optimal penalty.

Our experimental validation, based on charging data from London, shows that an appropriate penalty can increase both utilization and revenue while significantly reducing overstaying events.