Reviewerly/exHarmony huggingface dataset:
Pupil Center Detection Based on the UNet for the User Interaction in VR and AR Environments

Finding the location of a pupil center is important for the human-computer interaction especially for the user interface in AR/VR devices. In this paper, we propose an indirect use of the convolutional neural network (CNN) for the task, which first segments the pupil region by a CNN, and then finds the center of mass of the region. For this, we create a dataset by labeling the pupil area on 111,581 images from 29 IR video sequences. We also label the pupil region of widely used datasets to test and validate our method on a variety of inputs. Experiments show that the proposed method provides better accuracies than the conventional ones, showing robustness to the noise.
BICOS—An Algorithm for Fast Real-Time Correspondence Search for Statistical Pattern Projection-Based Active Stereo Sensors

Pattern projection-based 3D measurement systems are widely used for contactless, non-destructive optical 3D shape measurements. In addition, many robot-operated automation tasks require real-time reconstruction of accurate 3D data. In previous works, we have demonstrated 3D scanning based on statistical pattern projection-aided stereo matching between two cameras. One major advantage of this technology is that the actually projected patterns do not have to be known a priori in the reconstruction software. This allows much simpler projector designs and enables high-speed projection. However, to find corresponding pixels between cameras, it is necessary to search the best match amongst all pixels within the geometrically possible image area (that is, within a range on the corresponding epipolar line). The well-established method for this search is to compare each candidate pixel by temporal normalized cross correlation of the brightness value sequences of both pixels. This is computationally expensive and interdicts fast real-time applications on inexpensive computer hardware. We show two variants of our algorithm “Binary Correspondence Search” (BICOS), which solve this task in significantly reduced calculation time. In practice, our algorithm is much faster than traditional, purely cross-correlation-based search while maintaining a similar level of accuracy.
Vehicle target detection in complex scenes based on YOLOv3 algorithm

In view of the low accuracy of traditional vehicle target detection methods in complex scenes, combined with the current hot development of deep learning, this paper applies the YOLOv3 algorithm framework to achieve vehicle target detection. By using PASCAL VOC2007 and VOC2012 data sets, images containing vehicle targets were screened out to constitute the VOC car data set, and the target detection problem was converted into a binary classification problem. Then loading the pre-trained YOLOv3 model weight, and training the vehicle target detection model weight based on YOLOv3 algorithm, which is used to detect the test samples. Experimental results show that this method has advantages over the traditional target detection algorithms in recognition accuracy and detection speed.
MAPE-SAC: A Framework to Dynamically Manage Security Assurance Cases

Assuring security compliance in self-adaptive systems is challenging, notably as both functional and security conditions may change at run time, where adaptation of functional behavior may violate security requirements or vice versa. In traditional systems, certification is performed at design time on the mechanisms that will be deployed to guarantee the effectiveness of organizationally chosen and instantiated security controls defined by standards bodies (e.g., NIST SP800-53). In contrast, adaptive systems benefit by run-time adaptations for which dynamic certification could be difficult. Confidence in an information system's compliance with security constraints can be expressed using security assurance cases (SACs). Specifically, NIST security controls follow a repeated structure that make them amenable to their specification in terms of SACs. The collection of SACs for the related security controls form a network that can be used to assess the level of the system's compliance through certification-based evidence. Once the system is deployed, environmental and functional uncertainties may require more complex adaptations that include the coordination of functional and security adaptations. This paper introduces the MAPE-SAC control loop and its interaction with the MAPE-K control loop to dynamically manage run-time adaptations in response to changes in functional and security conditions. We illustrate the use of both control loops and their interaction using an example of an autonomous rover responding to a potential security incident.
VAS360: QoE-Driven Viewport Adaptive Streaming for 360 Video

Since 360 video requires extremely high bitrate, viewport adaptive streaming is an effective way to transmit 360 video, mainly considering the accurate prefetching of the viewport content and playback fluency. In this paper, we propose VAS360, a 360 video streaming system. In viewport adaptation, we prefetch tiles in advance based on viewport prediction. The innovative tile scheduler downloads compensation tiles based on prediction errors in different directions, which can tolerate inaccuracy in viewport prediction. In QoE-driven rate adaptation, we use the weighted sum of QoE metrics as a utility, maximizing the utility under the constraint of total bitrate. At the same time, our buffer-based rate control can significantly reduce the stall duration. Finally, through extensive evaluation of the experiment, VAS360 does improve the user experience. Compared to non-viewport adaptive schemes, VAS360 increases the viewport quality by 23%-45%.
Silicon photonics integration technologies for future computing systems

Integrated photonics technology offers great potential for applications in neuromorphic systems. We discuss two examples; integrated photonic non-volatile optical weights and a photonic non-volatile memory based analog accelerator for the inference and training of deep neural networks.
Efficient Adaptive Resource Provisioning for Cloud Applications using Reinforcement Learning

An appealing feature of cloud computing is elasticity, that allows shrinking or expanding the resources allocated to an application in order to adjust to workload variations. The resource provisioning algorithm must also adhere to the performance requirements specified in the Service Level Agreement between the cloud provider and the client who runs the application. While the use of Reinforcement learning algorithms such as Q-learning has been proposed already to address this problem, those suffer from slow convergence and scalability issues. In this paper, we explore methods for overcoming such challenges and ensuring effective resource utilization. Preliminary experiments on CloudSim platform demonstrate the superiority of some of these methods over static, threshold-based and other reinforcement learning based allocation schemes.
Smart data at play: improving accessibility in the urban transport system

Human mobility is one of the most important concerns in smart city initiatives and is especially relevant when combined with accessibility issues. This paper describes work in the context of the Access@City Research Project, which seeks to improve the accessibility in the public transport system by using available information (open data, semantic-aware knowledge) provided by transport organisations. However, these organisations provide partial data, and a lot of information is still available only on their websites, or simply does not exist. This absence can be tackled using a playful approach – the use of gaming apps to obtain and update accessibility information. In this paper, we describe the use of a hybrid reality game (HRG) to enrich information regarding the accessibility of subway stations. In turn, the player improves her score, which is included as part of the game. The correlation between these observations is able to provide, in a relatively short time, an accurate description of the accessibility of these stations. In summary, this playful approach makes it possible to recover a set of accessibility data that ultimately provide these smart capabilities, which are the core of this ‘accessible city’ endeavour.
Temperature extraction for Brillouin optical fiber sensing system based on extreme learning machine

The use of extreme learning machine (ELM) network to extract temperature distribution from the measured Brillouin gain spectra (BGSs) along the sensing fiber obtained by Brillouin optical fiber sensors is proposed and demonstrated experimentally. Compared with conventional curve fitting method (CFM), ELM network trained by a set of ideal BGSs can extract temperature information directly from the measured BGSs obtained by Brillouin optical time domain reflectometer (BOTDR) system without the need of determining Brillouin frequency shift (BFS) and converting BFS to temperature. The BGSs linewidth is taken into account to construct the ideal BGSs by using Pseudo-Voigt curve for ELM training. The performance of ELM is analyzed in detail and compared with that of widely-used Lorentzian CFM, and the experiment results show that ELM can provide higher accuracy even at large frequency scanning step and faster processing speed. Therefore, the proposed ELM approach is feasible and effective for temperature extraction in Brillouin optical fiber sensing system.
A TEFL Virtual Reality System for High-Presence Distance Learning

VR technologies can be effective for distance learning. However, currently available virtual reality systems for teaching English as a foreign language (TEFL) are limited: they are often a supplementary experience within a traditional classroom or do not offer a high level of student-teacher interaction. This research considers an HMD-based platform with two linked subsystems to address these issues. The first is a series of high-presence learning scenarios with roles played by the student(s) and teacher through avatars. The second is an interactive web portal within the VR environment that allows for learning, testing, and feedback. In order to assess the potential of this proposed system, student learning outcomes and ease of use are evaluated through preliminary testing, and this research also posits a more comprehensive system for managing longer-term courses.
Augmented Learning for Sports Using Wearable Head-worn and Wrist-worn Devices

Novices can learn sports in a variety of ways ranging from guidance from an instructor to watching video tutorials.In each case, subsequent and repeated self-directed practice sessions are an essential step.However, during such self-directed practice, constant guidance and feedback is absent.As a result, the novices do not know if they are making mistake or if there are any areas for improvement.In this position paper, we propose using wearable devices to augment such self-directed practice sessions by providing augmented guidance and feedback.In particular, a head-worn display can provide real-time guidance whilst wrist-worn devices can provide real-time tracking and monitoring of various states.We envision this approach being applied to various sports, and in particular this is suitable for sports that utilize precise hand motion such as snooker, billiards, golf, archery, cricket, tennis and table tennis.
Learning Action Representations for Self-supervised Visual Exploration

Learning to efficiently navigate an environment using only an on-board camera is a difficult task for an agent when the final goal is far from the initial state and extrinsic rewards are sparse. To address this problem, we present a self-supervised prediction network to train the agent with intrinsic rewards that relate to achieving the desired final goal. The network learns to predict its future camera view (the future state) from a current state-action pair through an Action Representation Module that decodes input actions as higher dimensional representations. To increase the representational power of the network during exploration we fuse the responses from the Action Representation Module in the transition network, which predicts the future state. Moreover, to enhance the discrimination capability between predictions from different input actions we introduce joint regression and triplet ranking loss functions. We show that, despite the sparse extrinsic rewards, by learning action representations we achieve a faster training convergence than state-of-the-art methods with only a small increase in the number of the model parameters.
A readiness assessment tool for GDPR compliance certification

The General Data Protection Regulation (GDPR) is new legislation that governs users’ private data and applies across all member states of the European Union (EU), particularly to all organisations processing the data of EU users, wherever the organisation is geographically based. It came into force on 25 May 2018.
Survey of Artificial Intelligence for Card Games and Its Application to the Swiss Game Jass

In the last decades we have witnessed the success of applications of Artificial Intelligence to playing games. In this work we address the challenging field of games with hidden information and card games in particular. Jass is a very popular card game in Switzerland and is closely connected with Swiss culture. To the best of our knowledge, performances of Artificial Intelligence agents in the game of Jass do not outperform top players yet. Our contribution to the community is two-fold. First, we provide an overview of the current state-of-the-art of Artificial Intelligence methods for card games in general. Second, we discuss their application to the use-case of the Swiss card game Jass. This paper aims to be an entry point for both seasoned researchers and new practitioners who want to join in the Jass challenge.
Toward Lateral Aerial Grasping &amp; Manipulation Using Scalable Suction

This paper is an initial step toward the realization of an aerial robot that can perform lateral physical work, such as drilling a hole or fastening a screw in a wall. Aerial robots are capable of high maneuverability and can provide access to locations that would be difficult or impossible for ground-based robots to reach. However, to fully utilize this mobility, systems would ideally be able to perform functional work in those locations, requiring the ability to exert lateral forces. To substantially improve a hovering vehicle's ability to stably deliver large lateral forces, we propose the use of a versatile suction-based gripper that can establish pulling contact on featureless surfaces. Such contact enables access to environmental forces that can be used to further stabilize the vehicle and also increase the lateral force delivered to the surface through a possible secondary mechanism. This paper introduces the concept, describes the design of a new self-sealing suction cup based on a previous design, details the design of a gripper using those cups, and describes the arm and flight vehicle. It then evaluates the cup and gripper performance in several ways, culminating in physical grasping demonstrations using the arm and gripper, including one in the presence of simulated flight noise based on data from preliminary indoor flight experiments.
From Interlanguage grammar to target grammar in L2 processing of definiteness as uniqueness

This study investigated the processing of English articles by second language (L2) speakers whose first language (L1) is Korean. Previous studies in L2 English article use had some issues unresolved such as using offline tasks, conflating definiteness with real-world knowledge, and operationalizing definiteness and relevant constructs in ways that participants can be primed or get metalinguistic cues. To revisit such issues, the construct ‘definiteness’ was operationalized as unique identifiability, a self-paced reading task was used to collect data, and regression models were employed to analyse logarithm residuals of raw reading time data, which can detect subtle differences that are otherwise buried. The results show that L1 speakers show sensitivity to the use of definite and indefinite articles in response to given contexts and that both advanced and intermediate L2 speakers first resort to their non-target-like Interlanguage grammar, but the advanced group later revises their initial interpretation and eventually shows the effect of target grammar. The L2 behavior is discussed in terms of its theoretical implications.
EarthquakeGen: Earthquake generator using generative adversarial networks

Earthquake event detection in seismic time series data is an important and challenging problem. The current state-of-the-art machine-learning based detection methods mostly belong to supervised learning category and the detection accuracy highly relies on the quality and quantity of the labeled data. How- ever, acquisition of high-quality training set can be technical challenging and expensive in that it requires intensive training on domain knowledge. Therefore, expanding the dataset with artificially generated labeled data can be extremely useful. In this paper, we develop an earthquake generator, EarthquakeGen, by using generative adversarial networks (GAN). GAN is a recently raised generative model based on neural net- work. By training in a min max game process, GAN is able to produce samples looks similar but actually different with the real ones. To verify the performance of our EarthquakeGen, we apply it to seismic field data acquired at Oklahoma, where induced earthquake events have been reported. Through our numerical results, we show that our EarthquakeGen can yield high-quality artificial earthquakes. More importantly, we show that the earthquake detection accuracy can be significantly improved by using augmented training sets combining both artificial and real samples. Presentation Date: Wednesday, September 18, 2019 Session Start Time: 10:35 AM Presentation Time: 10:35 AM Location: Poster Station 13 Presentation Type: Poster
From Commands to Goal-Based Dialogs: A Roadmap to Achieve Natural Language Interaction in RoboCup@Home

On the one hand, speech is a key aspect to people’s communication. On the other, it is widely acknowledged that language proficiency is related to intelligence. Therefore, intelligent robots should be able to understand, at least, people’s orders within their application domain. These insights are not new in RoboCup@Home, but we lack of a long-term plan to evaluate this approach. In this paper we conduct a brief review of the achievements on automated speech recognition and natural language understanding in RoboCup@Home. Furthermore, we discuss main challenges to tackle in spoken human-robot interaction within the scope of this competition. Finally, we contribute by presenting a pipelined road map to engender research in the area of natural language understanding applied to domestic service robotics.
Minimized Makespan Based Improved Cat Swarm Optimization for Efficient Task Scheduling in Cloud Datacenter

Inefficient scheduling of tasks on cloud datacenter resources can result in underutilization leading to poor revenue generation. To show efficient tasks scheduling on cloud datacenter, the makespan time needs to be minimized. In this paper, we introduced a conventional Cat Swarm Optimization (CSO) task scheduling technique as an ideal solution. Although the CSO is promising in terms of convergence speed, certain improvements are required to make it efficient for cloud task scheduling since it suffers entrapment at the local search. To overcome this, we incorporated a Linear Descending Inertia Weight (LDIW) equation at the local search of the CSO technique. This led to better convergence speed and possibly ensured efficient tasks mapping on virtual resources that minimizes the makespan time. The proposed CSO-LDIW technique is implemented on CloudSim simulator tool with five (5) heterogeneous Virtual Machines (VMs) under consideration to show its performance. The results of the simulation indicate that a comparison with that of the Particle Swarm Optimization-Linear Descending Inertia Weight (PSO-LDIW) and the CSO shows that our proposed CSO-LDIW can schedule task effectively on cloud resource with a promising makespan time.
Efficient Large-Scale Deep Learning Framework for Heterogeneous Multi-GPU Cluster

This paper presents a novel "Distributed Deep Learning Framework" for heterogeneous multi-GPU cluster that can effectively improve overall resource utilization without sacrificing training accuracy. Specifically, we employ a hybrid aggregation approach of parameter-server and All-reduce schemes in order to address potential performance degradation problems in running deep learning applications on a heterogeneous computing system. In addition, we designed and implemented an asynchronous large mini-batch training mechanism to maintain training accuracy for asynchronous data-paralleled deep learning processing with enhanced collective communication capability based on MPI. We have successfully implemented our proposed framework on Tensorflow, and performed extensive experiments in both of homogeneous and heterogeneous computing systems. Evaluation results show that our proposed framework can improve computing performance by decreasing I/O bottlenecks, and effectively increase the resource utilization in the heterogeneous multi-GPU cluster.
Adaptive Graph Regularization Discriminant Nonnegative Matrix Factorization for Data Representation

Nonnegative matrix factorization, as a classical part-based representation method, has been widely used in pattern recognition, data mining and other fields. However, the traditional nonnegative matrix factorization directly factoring decomposes the original data, and the original data often contains a lot of redundancy and noise, which seriously affect the subsequent processing of the data. In this work, we propose an adaptive graph regularization discriminant nonnegative matrix factorization (AGDNMF) for image clustering. The AGDNMF algorithm makes full use of local structure information and a small amount of label information. In AGDNMF, the local structure information can be more accurate and the label information can prevent the points with the same label from being merged into one point. These two items are combined into the objective function of NMF. In addition, we provide the update rules for the corresponding optimization functions and prove its convergence. A large number of experiments on different data sets show that the proposed algorithm has good clustering performance.
Your Eyes Reveal Your Secrets: An Eye Movement Based Password Inference on Smartphone

The widespread use of smartphones has brought great convenience to our daily lives, while at the same time we have been increasingly exposed to security threats. Keystroke security is essential to user privacy protection. In this paper, we present GazeRevealer, a novel side-channel based keystroke inference framework to infer sensitive inputs on smartphone from video recordings of victim's eye patterns captured from smartphone front camera. We observe that eye movements typically follow the keystrokes typing on the number-only soft keyboard during password input. By exploiting eye movement patterns, we are able to infer the passwords being entered. We propose a novel algorithm to extract sensitive eye images from video streams, and classify these images with Support Vector Classification. We also propose a novel classification enhancement algorithm to further improve classification accuracy. Compared with prior keystroke detection approaches, GazeRevealer does not require any external auxiliary devices, and it only relies on smartphone front camera. We evaluate the performance of GazeRevealer on several smartphones under different real-life usage scenarios. The results show that GazeRevealer achieves an inference rate of 77.89 percent for single key number and an inference rate of 84.38 percent for 6-digit password in the ideal case.