Miracle-dz/UniFAR-FTUs huggingface dataset:
Towards Robot-based Manipulation, Characterization and Automation at the Nanoscale

Automated robot-based manipulation and characterization at the nanoscale constitutes a major challenge due to the large special and force uncertainties and the limited control options due to the lack of adequate sensor feedback information. This paper is a short introduction of this new research field of industrial robotics, highlighting the advantages and limits of this upcoming technology. It presents several examples from the current industry-driven projects in our Division, which deal with robot-based fabrication, measurement, and characterization inside the scanning electron microscope (SEM). When applying advanced fast image processing, the images delivered by the SEM enables proper control of sophisticated handling routines inside the SEM and allows for fully-automated robotic operation. Experimental results reveal a high repeatability of such processes.
Shape Self-Assembly for a Swarm of Agents

In this paper, we propose a distributed algorithm to demonstrate the self-assembly of an agent swarm to form a square shape. We define four primitive and collective behaviors including gradient communication, localization, cohesion, and separation for the agents to achieve the formation. With a reconstructed communication network by gradient values, each agent can select its corresponding local leader to transmit information. Any collective behavior is to be performed by the agents only through local interactions with their neighbors. Experiments with different scales of swarm by simulation validate the feasibility and performance of our proposed algorithm.
Intelligent Syndrome Differentiation of Traditional Chinese Medicine by ANN: A Case Study of Chronic Obstructive Pulmonary Disease

Traditional Chinese medicine (TCM) is effective in preventing and treating all manner of diseases, which has been incorporated into the latest global medical outline (Ver.2019) by World Health Organization (WHO). As one of the most important characteristics of TCM, syndrome differentiation (SD) provides curative effect assurance. SD is a high-dimensional complex function with symptoms/signs as input and syndrome type as output. Artificial neural network (ANN) provides an all-purpose data-driven solution to fit high-dimensional complex function, making ANN a promising approach for modeling intelligent SD (ISD) for TCM. In this paper, we chose chronic obstructive pulmonary disease (COPD) as an example for investigating ISD for TCM based on ANN. First, we built a full-group ANN model that combines ANN with full-group datasets composed of 18471 real clinical records. In addition, we built four extra models with ANN and four subgroup datasets. For comparison, we built another four models with four traditional machine-learning algorithms and the full-group datasets. We used accuracy and F1-scores to evaluate the models’ performance. With an accuracy of 86.45% and an F1 score of 82.93%, the full-group ANN model outperformed the four comparison models built from traditional machine-learning algorithms, and however, the four subgroup models achieved a better performance than the full-group ANN model. We concluded that the ANN can potentially provide a way for ISD for TCM, and our subgroup modeling suggests ideas for further optimizing the ISD.
How Much English Language Arts and Mathematics Instruction Do Students Receive? Investigating Variation in Instructional Time

The amount of instruction students receive has long been viewed as a foundational educational resource. This article presents an analysis of the time students spend in elementary English language arts (ELA) and mathematics instruction. In mathematics, the average student received about 140 hr of instruction, but students in the top sixth of classrooms in this distribution can expect to receive between 80 and 160 hr more instruction over the school year than students assigned to the bottom sixth of classrooms. We found similar magnitudes of variation in ELA. Although most of the variation was due to differences among classrooms, there was also substantial variation among schools. Some variation in instructional time is expected and probably favorable. However, we argue that the large variation demonstrated by these results represent substantial inequity in students’ opportunity to learn ELA and mathematics.
Improved Re-Entry Prediction Method Using the Last-Minute Motion of Re-Entering Objects

Japan Aerospace Exploration Agency (JAXA) has been carrying out the re-entry prediction of the debris for more than 30 years. Formerly, we had used only Two Line Elements (TLE) by U.S. Space Surveillance Network (SSN) for the re-entry prediction. After developing the Kamisaibara Space Guard Center (KSGC) which has a phased-array radar and is owned by Japan Space Forum, we have been performing the orbit determination and prediction by radar observation data in parallel. In this paper, focusing on re-entry objects in a low altitude with large fluctuations, we will report on and discuss about the orbit determination method using radar observation data of only a single arc and only two TLEs just before the re-entry. As a result, we confirmed an improvement in the prediction accuracy.
Prospective Trial of Daily and Extended Wear Disposable Contact Lenses

Purpose The current study prospectively evaluates the response to daily and extended wear of disposable lenses with each patient acting as his/her own control. This paradigm allowed for elimination of individual variation as a confounding factor, as well as determining whether an intrinsic factor, other than lens wear, predisposed patients to complications. Methods One hundred thirty-four patients were randomly assigned to contralateral daily wear (DW) or extended wear (EW) with Acuvue lenses for 1 year. All lenses were disposed of on a weekly basis. Bilateral bacteriology of lids, conjunctivae, lenses, and fingers was carried out on those patients presenting with complications. Results The results after 1 year indicate a higher incidence of complications in the EW eye (peripheral infiltrative keratopathy, 12%; corneal striae, 3%). No significant clinical complications occurred in the DW eye. Microbiological cultures did not reveal any significant difference between those eyes with keratopathy and those of controls. The most common pathogen was Staphyloccocus epidermidis. No gram-negative organisms grew from any of the culture sites. Conclusions The results of this study indicate that extended wearing schedules, and not the lens entity, predispose the patient to a higher risk of keratopathy with disposable contact lenses. No microbiological association with the adverse events could be determined, and no intrinsic patient factor could be demonstrated that would predict the likelihood of complications.
Modeling Worker Performance Based on Intra-rater Reliability in Crowdsourcing : A Case Study of Speech Quality Assessment

Crowdsourcing has become a convenient instrument for addressing subjective user studies to a large amounts of users. Data from crowdsourcing can be corrupted due to users’ neglect, and different mechanisms has been proposed to address the users’ reliability and to ensure valid experiments’ results. Users that are consistent in their answers or present a high intra-rater reliability score, are desired for subjective studies. This work investigates the relationship between the intra-rater reliability and the user performance in the context of a speech quality assessment task. To this end, a crowdsourcing study has been conducted in which users were requested to rate speech stimuli with respect to their overall quality. Ratings were collected on a 5-point scale in accordance with the ITU-T Rec. P.808. The speech stimuli were taken from the database ITU-T Rec. P.501 Annex D, and the results are to be contrasted with ratings collected in a laboratory experiment. Furthermore, a model as a function of intra-rater reliability, root-mean-squared-deviation between the listeners ratings and age, has been built to predict the listener performance. Such a model is intended to provide a measure of how valid the crowdsourcing results are, when there is no laboratory results to compare to.
Monitoring of respiratory patterns and biosignals during speech from adults who stutter and do not stutter: A comparative analysis

Stuttering is a common speech disorder that can impact in the quality of life of adults who stutter (AWS). In order to manage this condition, it is necessary to measure and assess the stuttering severity before, during and after any therapeutic process. To evaluate it, monitoring biosignals -included the respiratory patterns - could be an option; however is not clear the difference between speech conditions. Therefore, we compare the respiratory patterns and biosignals during speech of adults who stutter (AWS) and who not stutter (AWNS) to describe the differences and patterns of blocks and fluent speech. Sixty-six participants (AWS=33, AWNS=33) were asked to perform a reading task. We record the respiratory patterns and biosignals (pulse, saturation and galvanic response) using standardized system. We assess the differences among three conditions: fluent speech from ANWS, blocks from AWS and fluent speech from AWS. A higher number of expiratory volume peaks and amplitudes were found during the blocks segments compared to the fluent speech segments. These different patterns could be used to differentiate speech conditions using a recognition algorithm to automate evaluations in a real-time environment for stuttering diagnosis or follow-up.
Proactive Robots With the Perception of Nonverbal Human Behavior: A Review

Intelligent robot companions contribute significantly in improving living standards in the modern society. Therefore, human-like decision making skills are sought after during the design of such robots. On the one hand, such features enable the robot to be easily handled by its non-expert human user. On the other hand, the robot will have the capability of dealing with humans without causing any disturbance by the robot’s behavior. Mimicing human emotional intelligence is one of the best and reasonable ways of laying the foundation for robotic emotional intelligence. As robots are widely deployed in social environments, perception of the situation or intentions of a user prior to an interaction is required to be proactive. Proactive robots are required to understand what is communicated by the human body language prior to approaching a human. Social constraints in an interaction could be demolished by this assessment in this regard. In this review, we incorporate various findings of human–robot interaction, social robotics and psychophysiology to assess intelligent systems which were capable of evaluating the emotional state of humans prior to an interaction. Second, we identify the cues and evaluation techniques that were utilized by such intelligent agents to simulate and evaluate the suitability of a proactive interaction. Available literature has been evaluated to distinguish limitations of existing methods and suggest possible improvements. These limitations, guiding principles to be adhered to and suggested improvements, are presented as an outcome of the review.
An Implementation of AutomationML for an Industry 4.0 Case Study: RoboCup@Work

This paper provides a system design of the mobile platform build by the RoboHub Eindhoven (a student organization) for the RoboCup@Work competition, as an industry 4.0 case study. The RoboCup@Work is an international competition that aims to foster research in industrial automation. The Robo-Hub Eindhoven is building a custom-made robot for competing in the RoboCup. To make the best possible robot a model-based breakdown in AutomationML of the Robot is made. This allows for better understanding of the competition and a system design view on the requirements for new robot design.
GP-ICP: Ground Plane ICP for Mobile Robots

In this paper, we propose a robust point cloud registration method for ground vehicles. Given the vast developments in the field of autonomous vehicles, the use of point cloud data has increased. The simultaneous localization and mapping (SLAM) algorithm is typically used to generate sophisticated point cloud maps. In the SLAM algorithm, the quality of the map depends on the performance of loop closure algorithms. The iterative closest point (ICP) algorithm is widely used for loop closure of the point cloud. However, the ICP algorithm might not work well for ground vehicles because it was originally developed for 3D reconstruction in computer vision field. Therefore, this paper proposes a method to find robust matching correspondences in the ICP algorithm on ground vehicle conditions. The performance of the proposed method is compared with other conventional methods by using KITTI open datasets. The source code is publicly released on the Github website.
Air Pollution Forecasting Using a Deep Learning Model Based on 1D Convnets and Bidirectional GRU

Air pollution forecasting can provide reliable information about the future pollution situation, which is useful for an efficient operation of air pollution control and helps to plan for prevention. Dynamics of air pollution are usually reflected by various factors, such as the temperature, humidity, wind direction, wind speed, snowfall, rainfall, and so on, which increase the difficulty in understanding the change of air pollutant concentration. In this paper, a short-term forecasting model based on deep learning is proposed for PM2.5 (particulate matter with an aerodynamic diameter less than or equal to $2.5~\mu \text{m}$ ) concentration, and the convolutional-based bidirectional gated recurrent unit (CBGRU) method is presented, which combines 1D convnets (convolutional neural networks) and bidirectional GRU (gated recurrent unit) neural networks. The case is carried out by using the Beijing PM2.5 data set in UCI Machine Learning Repository. Comparing the prediction results with the traditional ones, it is proved that the error of the CBGRU model is lower and the prediction performance is better.
Underwater Image Recovery Using Structured Light

Structured lighting techniques have increasingly been employed in underwater imaging. Except for the limitation in underwater illumination, the desire of underwater image acquisition comes from the applicability of removal of the back scattered light, the critical problem of underwater imaging, using structured lighting techniques. This paper presents an approach for underwater image recovery using structured light and CCD camera. By integrating the scanned frame images, we generate an integration image which can be formulated as the convolution of the surface albedo and the illumination function. Thus, underwater image acquisition is addressed as an optimization problem of image recovery and resolved by deconvolution, rather than the traditional geometric manipulation of frame tiling. The significance of the proposed method is that the forward scattering effect in the recovered image is fully eliminated by the integration operation and collection of the forward scattered light enhances the total imaging energy. By means of the structured lighting technique, an algorithm of using virtual aperture to limit the field of view is proposed to eliminate the back scattered light in frame images. Concerned with applicability of using the broad lighting pattern in structured light systems, the coded structured light with binary pseudo-random sequence is introduced, by which the high-frequency details in image recovery can be preserved through deconvolution. The results of underwater experiments are given.
Extended PSO Based Collaborative Searching for Robotic Swarms With Practical Constraints

Applying swarm intelligence to actual swarm robotic systems is a challenging task especially with adequately consideration of corresponding practical constraints. Under the restrictions of the field-of-view limited relative positioning, local sensing and communication, kinematic limitations as well as anti-collision issues, this paper presents a constrained particle swarm optimization (PSO) based collaborative searching method for robotic swarms. Besides, the proposed method follows the concept of evolution speed and a modified aggregation degree to determine the adaptive weights in the robotic PSO model. The modified aggregation degree is associated with the number of members in one’s field-of-view. Unlike the traditional position update method, the proposed method updates the forward speed and angular velocity of the robot using the non-holonomic model to realize the motion control of each robot. The simulation results show that the proposed method has the potential for the practical implementation of collaborative searching tasks for robotic swarms in different types of environments.
Experiment with Lexicon Based Techniques on Domain-Specific Malay Document Sentiment Analysis

The nature of Sentiment Analysis (SA) mostly is generated by human beings. They expressed their emotion in writing or expressing their feeling via social media or blog. The Advancement of Internet and the increasing number of users in social media are the factors on why the sentiment analysis gaining its popularity in Malay languages. This research aims to implement the Sentiment Analysis on Malay language documents and propose a lexicon-based technique for Malay based sentiment analysis on specific domain such as Song, Politic and Product to find the best SA classifier on the Domain-Specific Malay Document Sentiment Analysis. Analysis of the evaluation result is based on the comparison of expert evaluation, Lexicon-based evaluation’s result and Naïve Bayes SA classification’s result, which is Naïve Bayes represent Machine Learning approach in this study. The result shows Lexicon-based Classification has outperformed Naïve Bayes SA classification in overall 3 topics which are Song, Politic and Product in average of 70% compared to 50% average for Naïve Bayes. For the future works, the researcher would like to improve in the particular area such as Sentiment Analysis based on the Malay dialect, increase the data in the dictionary and applying phrase level for better and optimum results.
Feeding Convolutional Neural Network by hand-crafted features based on Enhanced Neighbor-Center Different Image for color texture classification

Texture analysis has many important applications, including material recognition, face recognition, object detection, image segmentation. Local feature descriptors were the principle approach for texture analysis in the past. Recently, Convolutional Neural Network (CNN) has provided more promising results for texture recognition and other related computer vision tasks. Standard CNN takes labeled RGB images as input. However, other encoded images were used as extra input to CNN, which have been shown to improve the performance. We propose to feed CNN with the new encoded image. The experimental results on four benchmark color texture database show the efficiency of our proposed approach. The source code of our algorithm and all simulations used for this paper are publicly available at: https://sites.google. com/view/vinhsiam/codes
Dense-Residual Network With Adversarial Learning for Skin Lesion Segmentation

Skin lesion segmentation in dermoscopic images is a challenging task in the domain of medical images analysis because of the irregular and blurring edges of the lesion and the presence of various artifacts. Inspired by the successful applications of the generative adversarial network (GAN), we propose a new neural network for the segmentation of skin lesion. Different from the traditional adversarial network, the segmentation network uses encoder–decoder with the dense-residual block which enables the network to be trained more efficiently. It can also establish a direct relationship between adjacent pixels to improve segmentation accuracy. A multi-scale objective loss function is used to utilize deep supervision in multiple layers of the critic network. End point error and Jaccard distance are combined as the content loss function. It can solve the problem of boundary ambiguity and solve the lesion-background imbalance in pixel-level classification for skin lesion segmentation. We finally use a joint loss function including a multi-scale objective loss function, end point error, and Jaccard distance content loss function. The experiment results show that our algorithm is superior to other state-of-the-art algorithms on the ISBI2017 and PH2 datasets.
In Vivo or in Vitro? Influence of the Study Design on Crowdsourced Video QoE

Evaluating the QoE of video streaming and its influence factors has become paramount for streaming providers, as they want to maintain high satisfaction for their customers. In this context, crowdsourced user studies became a valuable tool to evaluate different factors which can affect the perceived user experience on a large scale.In general, we observed that most of these crowdsourcing studies either use an in vivo or an in vitro design. In vivo design means that the study participant has to rate the QoE of a video that is embedded in an application similar to a real streaming service, e.g., YouTube or Netflix. In vitro design refers to a setting, in which the video stream is separated from a specific service and thus, the video plays on a plain background. Although these designs vary widely, the results are often compared and generalized.Therefore, in this work, we investigate the influence of these two study design alternatives on the perceived QoE. In crowdsourced user studies, participants rate the video streaming with respect to different stalling patterns (no stalling, different positions) and study designs (in vivo or in vitro). Contrary to our expectations, the results indicate that there is statistically no significant influence of the study design on the perceived video QoE and acceptance. In addition, we found that the in vivo design does not reduce the test takers’ attentiveness.
Influence of UI Complexity and Positioning on User Experience During VR Exergames

In past years, as virtual reality (VR) technology is extensively developing, more and more people are using it in different fields. One of the fast developing fields in VR are exergames, a combination of physical exercise and game. With a goal to engage people in physical activity, VR exergames should look and feel good for users. Therefore, user interface (UI) in VR is important and has to be built in a way to enhance user experience. In this paper, ergometer is used together with VR rowing environment as VR exergame for a study aiming to explore possibilities of UI in VR. Accordingly, different metrics commonly used to quantify rowing action (e.g, speed and distance) were visualized. The visualizations that were created had different positioning (closer or further away from the player) and different level of complexity (more or less metrics shown as only numbers or in a gamified design). Participants (N = 27) during experiment for this study rowed four times in different conditions depending were metrics of the game were shown: 1) as a cockpit at the front of the rowing e.g, with gamified visualization of metrics; 2) as a coach boat that follows the player with gamified visualization of metrics on a screen; 3) as a cockpit at the front of the rowing e.g, with digital visualization of metrics; 4) as a coach boat that follows the player with digital visualization of metrics on a screen. Results show that level of UI complexity has a significant influence on readability, while positioning of UI elements significantly influences users’ perception of support from system. Furthermore, participants preferred the opposite level of complexity depending on the position where metrics were shown.