Miracle-dz/UniFAR-FTUs huggingface dataset:
Enhanced confidence interpretations of GP based ensemble modeling results

In this paper we describe the integration of ensemble modeling into genetic programming based classification and discuss concepts how to use genetic programming specific features for achieving new confidence indicators that estimate the trustworthiness of predictions. These new concepts are tested on a real world dataset from the field of medical diagnosis for cancer prediction where the trustworthiness of modeling results is of highest importance.
Changing Students’ Approach to Learning Physics in Postsecondary Gateway Courses

This study investigated if and how a combined set of specially developed activities can help students change their approach to learning physics. These activities included (a) reflective-writing activities, (b) critique-writing activities, and (c) reflective write-pair-share activities combined with conceptual-conflict collaborative-group exercises. Each of these activities was previously successfully tested as a stand-alone activity. This investigation was conducted at two different institutions over a three-year period. At each institution the same instructor taught students in two sections. At the first, a university with a substantial graduate school, sections were relatively large (over 100 students each) covering a typical introductory calculus-based mechanics course. At the second, a community college, there were relatively small classes (32 students each) covering a typical algebra-based introductory course in mechanics, electricity, and magnetism. The courses at the two institutions used different textbooks and had different formats. Measured data included student interviews and writing products. We developed rubrics for evaluation of the impact of the writing products and interviews of students. The main results of this study were the changes in students’ approaches to learning physics, especially as revealed in the interviews. Students who experienced the full suite of activities (a) changed their understanding of physics from solving problems to creating a network of interrelated concepts, and they also (b) modified their approach to learning physics from repetitious review to consideration of the interconnections of the subject matter and (c) related their new learning to key concepts in an overall physics framework.
Evaluation of Parking Space Detection From Aerial Imagery Using Convolutional Neural Networks

In this thesis, the viability of using Convolutional Neural Networks to detect parking spaces using aerial imagery has been evaluated. Three state of the art networks have been tested - YOLOv3, RetinaNet, and Mask R-CNN. A dataset of urban parking lots and corresponding annotations was generated from scratch using a custom built GUI to annotate automatically generated images of parking lots from Open Street Map, from varying aerial imagery providers. This dataset was used to test and evaluate the different networks, and Mask R-CNN was used for a lengthy parameter tuning process, as it seemed to perform optimally of the three networks. The resulting model did not perform the best, believed to be because of the low amount of features represented in parking spaces. While results indicated that a somewhat complete solution is possible, it might not be feasible using a pure single CNN approach. (Less)
ConRS: A Requests Scheduling Framework for Increasing Concurrency Degree of Server Programs

Server programs always store a great deal of data and respond to plenty of requests in a short time. Most server programs are highly concurrent. Testing concurrent programs is difficult and costly because of their non-determinism. Researches have shown that increasing the degree of concurrency is more effective for testing process. This paper uses synchronization-pair and data race to quantify the degree of concurrency. The more synchronization-pairs and data races in a trace are, the higher concurrency degree is. A requests scheduling framework ConRS is proposed in this paper to reschedule requests in an existing test case and make the program "more concurrent". Compared with server stress testing tools, ConRS includes more categories of requests. Compared with coverage-guided bug detectors and data race detectors, ConRS are lower overhead and easier to understand. The experiments on MySQL database server show the effectiveness of ConRS. The synchronization-pairs and data races of test cases have been increased by at least 10% and 30% respectively after applying ConRS.
Edge Computing and Networking: A Survey on Infrastructures and Applications

As a concept to enhance and extend cloud-computing capabilities, edge computing aims to provide Internet-based services in the close proximity to users by placing IT infrastructures at the network edge in forms of tiny datacenters. Taking advantage of the close distance to end user and access networks, edge datacenters can provide low-latency and context-aware services and further improve users’ quality of experience. As the network edge is a geographically spread concept, the edge datacenters are usually highly distributed so that they can provide nearby storage and processing capabilities to most of the end users. Furthermore, edge datacenters also co-work with centralized cloud datacenters for service orchestration. Such decentralization and collaboration are expected to introduce significant transformations to both infrastructures and applications. To provide an overview of how edge can be integrated with cloud-computing and how edge computing can benefit applications, this paper studies the infrastructure and application issues of edge computing and networking in several sub-aspects, including related concepts, infrastructures, resource management and virtualization, performance, and applications.
Revisiting The 2018 Kalibening Earthquake Sequence In Central Java: Call for the Revision of Earthquake Hazard

As moderate devastating earthquake that impacted the economic loss was about $ 1.68 million US, the 2018 Kalibening earthquake quite shocked where the faulting mechanism still unconfirmed. There was no reference indicating the existence of the faulting zone exactly on the earthquake sequence. We take a benefit from seismic temporary network with the density ±2 km, which deployed while a macroseismic survey in purposing to evaluate the 2018 Kalibening earthquake sequence. Earthquake relocation using double difference combined with cross-correlation and determining earthquake moment tensor inversion solution using near source seismograms were applied. We suggested the deformation scheme of this earthquake with a thrust faulting with 307.5 / 28.8 / 118.5 (Strike / Dip / Rake) as a result from mainshock and supported by aftershock moment tensor solutions. This parameter consistent to aftershocks relocation results which formed a lineation trending NW-SE appropriate with Strike = 307.5. The cross-section exhibits aftershocks pattern which elongated deeper and formed a slope from SW to NE approximately fit to Dip = 28.8. The results from investigating the background seismicity in Banjarnegara Region using combined catalog (ISC-USGS-BMKG) compared to BJI (single station) showed the sparse and the lacking of InaTEWS seismic network configurations. Finally impacted to losses in earthquake cataloging, leads the low area coverages around Banjarnegara region. Together with our results, we called for the revision of earthquake hazard assessment for Central Java province, especially in Banjarnegara region.
Safe 3D Bipedal Walking through Linear MPC with 3D Capturability

We propose a linear MPC scheme for online computation of reactive walking motions, necessary for fast interactions such as physical collaboration with humans or collision avoidance in crowds. Unlike other existing schemes, it provides fully adaptable height, adaptable step placement and complete kinematic and dynamic feasibility guarantees, making it possible to walk perfectly safely on a piecewise horizontal ground such as stairs. A linear formulation is proposed, based on efficiently bounding the nonlinear term introduced by vertical motion, considering two linear constraints instead of one nonlinear constraint. Balance and Passive Safety guarantees are secured by enforcing a 3D capturability constraint. Based on a comparison between CoM and CoP trajectories involving exponentials instead of polynomials, this capturability constraint involves a CoM motion stopping along a segment of line, always maintaining complete kinematic and dynamic feasibility.
A real time hybrid controller for regulating bus operations and reducing stops at signals

We propose a hybrid controller which consists of holding and a Driver Advisory System (DAS). It combines the objectives of seeking the regularization of operation and the reduction of stop and go actions at signalized intersections. A simple headway based holding criterion is applied at stops to define the time needed to maintain even spaced headways between buses and additionally a speed recommendation is given to traverse during green indication at the downstream signalized intersection. The controller is tested using simulation for a bus line of the city of Luxembourg and compared to a benchmark scenario, the single application of bus holding, two variants of a Cooperative ITS-based speed advisory system and different levels of transit signal priority. Results show that there are additional benefits compared to traditional holding in terms of regularity while similar performance to strong transit signal priority is achieved in terms of time spent at traffic lights.
Enhancing eQTL analysis techniques with special attention to the transcript dependency structure

JOHN C. SCHWARZ: Enhancing eQTL Analysis Techniques with Special Attention to the Transcript Dependency Structure. (Under the direction of Dr. Wei Sun and Dr. Fred Wright.) Gene expression microarray analysis and genetic marker association studies are two common experimental methods in the genetic literature. A growing number of studies have begun combining these two experiments into a single study known as an expressed quantitative trait loci (eQTL) study. Analysis of eQTL data has been performed on several different organisms including yeast, maize, mouse, and human. We propose a set of methods to effectively analyze eQTL data by properly transforming and adjusting analysis models. Our method addresses multiple issues often left out of eQTL analysis that include population stratification and adjustment of racial and ethnic classifications, adjustment of multiple covariates, and the influence of extreme outlying observations. Additionally we propose a statistic that is able to provide significance for trans bands (i.e., genetic markers that harbor a large number of eQTL) without the computational intensity of permutation testing. Most methods that identify a significance threshold for trans band activity either use simple binning approaches or have complex statistical methods that may require many assumptions and restrictions. We use a parametric approach that uses known distributions and simple approximations to develop a significance threshold. The advantages of our methods are that they account for correlation structures in the gene expression data and correlation between genetic markers. Also by using a parametric approach we do not rely on permutation testing which can be computationally daunting for even modestly sized studies. In the second part we will focus in on multiple testing in genetic applications. We study the family-wise error control by quantifying the probability that our test statistic crosses a defined threshold. The existing methods that employ this technique leave room for adjustments and modifications that allow for use in a variety of situations. We also explore the idea
Gestural interaction with 3D objects shown on public displays: an elicitation study

Public displays have the potential to reach a broad group of stakeholders and stimulate learning, particularly when they are interactive. Therefore, we investigated how people interact with 3D objects shown on public displays in the context of an urban planning scenario. We report on an elicitation study, in which participants were asked to perform seven tasks in an urban planning scenario using spontaneously produced hand gestures (with their hands) and phone gestures (with a smartphone). Our contributions are as follows: (i) We identify two sets of user-defined gestures for how people interact with 3D objects shown on public displays; (ii) we assess their consistency and user acceptance; and (iii) we give insights into interface design for people interacting with 3D objects shown on public displays. These contributions can help interaction designers and developers create systems that facilitate public interaction with 3D objects shown on public displays (e.g. urban planning material).
Sonar attentive underwater navigation in structured environment

One of the fundamental requirements of a persistently Autonomous Underwater Vehicle (AUV) is a robust navigation system. The success of most complex robotic tasks depends on the accuracy of a vehicle’s navigation system. In a basic form, an AUV estimates its position using an on-board navigation sensors through Dead-Reckoning (DR). However DR navigation systems tends to drift in the long run due to accumulated measurement errors. One way of mitigating this problem require the use of Simultaneous Localization and Mapping (SLAM) by concurrently mapping external environment features. The performance of a SLAM navigation system depends on the availability of enough good features in the environment. On the contrary, a typical underwater structured environment (harbour, pier or oil field) has a limited amount of sonar features in a limited locations, hence exploitation of good features is a key for effective underwater SLAM. This thesis develops a novel attentive sonar line feature based SLAM framework that improves the performance of a SLAM navigation by steering a multibeam sonar sensor, which is mounted on a pan and tilt unit, towards feature-rich regions of the environment. A sonar salience map is generated at each vehicle pose to identify highly informative and stable regions of the environment. Results from a simulated test and real AUV experiment show an attentive SLAM performs better than a passive counterpart by repeatedly visiting good sonar landmarks.
A New Method to Detect Bursty Events from Micro-blog Posts Based on Bursty Topic Words and Agglomerative Hierarchical Clustering Algorithm

: [ Objective ] This paper proposes a new method to detect real time bursty events accurately and efficiently from massive micro-blog posts. It provides decision-making information to public opinion emergency management. [ Methods ] First, we introduced the reference time window mechanism, and then designed an algorithm to process the data of word frequency, document frequency, Hashtags, and word frequency growth rates. Second, used this dynamic threshold based algorithm to extract bursty words. Third, transformed micro-blog texts to feature vector of the bursty words. Finally, we detected the bursty events using agglomerative hierarchical clustering algorithm. [ Results ] The bursty events detection method reached 80% of accuracy rate compared with real world cases. Thus, the proposed method was feasible and effective. [ Limitations ] We could not describe the detected emergencies automatically due to the limits of data and size of the current study. More research is needed to analyze users’ emotion and semantic relationships among the bursty events. [ Conclusions ] Our study fills the knowledge gaps left by previous research, and improves the efficiency of retrieving bursty events from micro-blog posts.
Robotic Process Automation

RPA, or Robotic Process Automation, is a software that mimics the steps a human takes to complete rules-based, repetitive tasks. The robot carries out work with speed and precision, utilizing the same applications your employees use every day. In traditional automation, all the actions are primarily based on the programming/scripting, APIs or other ways of integration methods to the backend systems or internal applications. In distinction, RPA automates software that can migrate the work from the human to the computer which can stop paying humans to do work ripe for automation, faster front and back office transaction processing, near “Instant On” integration at the lowest cost, optimization of User Interface to drive long call/transaction times down, accelerate digital transformation objectives, eliminate errors thereby improving productivity by making workers smarter.
Measurement uncertainty evaluation in FaroArm-machine using the bootstrap method

The modern manufacturing systems and technologies produce products that are more accurate day by day. This can be reached mainly by improvement the manufacturing process with at the same time restricting more and more the quality specifications and reducing the uncertainty in part. The main objective an industry becomes to lower the part’s variability, since the less variability – the better is product. One of the part of this task is measuring the object’s uncertainty. The main purpose of this study is to understand the application of bootstrap method for uncertainty evaluation. Bootstrap method is a collection of sample re-use techniques designed to estimate standard errors and confidence intervals. In the case study a surface of an automobile engine block – (Top view side) is measured by Coordinate Measuring Machine (CMM) and analyzed for uncertainty using Geometric Least Squares in complex with bootstrap method. The designed experiment is composed by three similar measurements (the same features in unique reference system), but with different points (5, 10, 20) concentration at each level. Then each cloud of points was independently analyzed by means of non-linear Least Squares, after estimated results have been reported. A MatLAB software tool used to generate new samples using bootstrap function. The results of the designed experiment are summarized and show that the bootstrap method provides the possibility to evaluate the uncertainty without repeating the Coordinate Measuring Machine (CMM) measurements many times, i.e. potentially can reduce the measuring time.
Intuitive Instruction of Industrial Robots : A Knowledge-Based Approach

With more advanced manufacturing technologies, small and medium sized enterprises can compete with low-wage labor by providing customized and high quality products. For small production series, robotic systems can provide a cost-effective solution. However, for robots to be able to perform on par with human workers in manufacturing industries, they must become flexible and autonomous in their task execution and swift and easy to instruct. This will enable small businesses with short production series or highly customized products to use robot coworkers without consulting expert robot programmers. The objective of this thesis is to explore programming solutions that can reduce the programming effort of sensor-controlled robot tasks. The robot motions are expressed using constraints, and multiple of simple constrained motions can be combined into a robot skill. The skill can be stored in a knowledge base together with a semantic description, which enables reuse and reasoning. The main contributions of the thesis are 1) development of ontologies for knowledge about robot devices and skills, 2) a user interface that provides simple programming of dual-arm skills for non-experts and experts, 3) a programming interface for task descriptions in unstructured natural language in a user-specified vocabulary and 4) an implementation where low-level code is generated from the high-level descriptions. The resulting system greatly reduces the number of parameters exposed to the user, is simple to use for non-experts and reduces the programming time for experts by 80%. The representation is described on a semantic level, which means that the same skill can be used on different robot platforms. The research is presented in seven papers, the first describing the knowledge representation and the second the knowledge-based architecture that enables skill sharing between robots. The third paper presents the translation from high-level instructions to low-level code for force-controlled motions. The two following papers evaluate the simplified programming prototype for non-expert and expert users. The last two present how program statements are extracted from unstructured natural language descriptions.
Modular time-of-flight image sensor for light detection and ranging: A digital approach to LIDAR

Constant increase in data processing efficiency has enabled, among many other things, the intensive use of depth mapping technologies. Consumer applications, such as gaming, augmented and virtual realities (AR/VR), and other human-machine interfaces, are typically based on intensive image processing, either by triangulation and/or structured light, which has limitations on speed, resolution, range, and robustness to background noise. On the other hand, TOF depth sensing has been investigated in the academic and industrial engineering communities for several years, as an alternative to solve such restrictions, and few products are emerging. Direct time-of-flight (dTOF), specifically, requires more elaborate detectors and data processing, but it has the potential of reaching much longer distances at higher speed and accuracy, with the advantage of being robust to high background noise, making it suitable for space, automotive and consumer applications. One known drawback of dTOF, however, is data volume. For instance, automotive applications require over 100m range, only few centimeters accuracy, and multiple measurements for a reasonable precision, which produce data rates that can reach tens or even hundreds of Gbps, in large sensors, thus setting processing constraints to even very efficient GPUs, as well as chip readout capability. It is essential to provide as much on-chip processing as possible, in order to reduce data throughput, thus reducing power consumption and speeding up processing time. Some architectures have been proposed attempting to solve this problem, but the required memory renders them only feasible for an SiPM, single-pixel approach. Another known issue with light detection and ranging (LiDAR) is regarding the interference of multiple systems on each other. A software-based approach has been implemented, but requiring intensive post-processing resources. In this thesis, a novel approach for on-chip processing is proposed. With the use of cutting-edge 3D-stacking technologies, more flexibility and computational power can be spent on the chip, while not compromising fill factor. A novel proposal for dealing with external interferes is introduced, as well as novel phase/frequency locking solution at the sensor level, as a reference for timing measurements.