Miracle-dz/UniFAR-FTUs huggingface dataset:
Advanced Wireless Sensors Used to Monitor the Impact of Environment Design on Human Physiology

This article describes modern wireless sensor devices and their application in the measurements of the human physiology. We used our own advanced ECG Holter device and EEG helmet to record the heart and brain activity impacted by different environments, materials, colors or body positions during work. In this paper, we want to show the interactions between humans and architecture design, which modify human work performance and well-being. This paper is a conclusion of the 3 different pilot studies, where different scopes of human-space interaction were explored. In the experiments, we aimed mostly at wood materials and their beneficial effects on the nervous system. The research in its actual state is primarily focused on optimizing the methods of the ECG data analysis from our Holter device and the EEG data from helmet. Based on these data, we will improve the methodology of the experiments for the next enhanced research with aspiration to automate data analysi
From stixels to asteroids: Towards a collision warning system using stereo vision

This paper explores the use of stixels in a probabilistic stereo vision-based collision-warning system that can be part of an ADAS for intelligent vehicles. In most current systems, collision warnings are based on radar or on monocular vision using pattern recognition (and ultra-sound for park assist). Since detecting collisions is such a core functionality of intelligent vehicles, redundancy is key. Therefore, we explore the use of stereo vision for reliable collision prediction. Our algorithm consists of a Bayesian histogram filter that provides the probability of collision for multiple interception regions and angles towards the vehicle. This could additionally be fused with other sources of information in larger systems. Our algorithm builds upon the disparity Stixel World that has been developed for efficient automotive vision applications. Combined with image flow and uncertainty modeling, our system samples and propagates asteroids, which are dynamic particles that can be utilized for collision prediction. At best, our independent system detects all 31 simulated collisions (2 false warnings), while this setting generates 12 false warnings on the real-world data.
Darwinian code optimisation

Programming is laborious. A long-standing goal is to reduce this cost through automation. Genetic Improvement (GI) is a new direction for achieving this goal. It applies search to the task of program improvement. The research conducted in this thesis applies GI to program optimisation and to enable program optimisation. In particular, it focuses on automatic code optimisation for complex managed runtimes, such as Java and Ethereum Virtual Machines. We introduce the term Darwinian Data Structures (DDS) for the data structures of a program that share a common interface and enjoy multiple implementations. We call them Darwinian since we can subject their implementations to the survival of the fittest. We introduce ARTEMIS, a novel cloud-based multi-objective multi-language optimisation framework that automatically finds optimal, tuned data structures and rewrites the source code of applications accordingly to use them. ARTEMIS achieves substantial performance improvements for 44 diverse programs. ARTEMIS achieves 4.8%, 10.1%, 5.1% median improvement for runtime, memory and CPU usage. Even though GI has been applied succesfully to improve properties of programs running in different runtimes, GI has not been applied in Blockchains, such as Ethereum. The code immutability of programs running on top of Ethereum limits the application of GI. The first step of applying GI in Ethereum is to overcome the code immutability limitations. Thus, to enable optimisation, we present PROTEUS, a state of the art framework that automatically extends the functionality of smart contracts written in Solidity and makes them upgradeable. Thus, allowing developers to introduce alternative optimised versions of code (e.g., code that consumes less gas), found by GI, in newer versions.
PTCCR: A Path Transmission Costs-Based Multi-Lane Connectivity Routing Protocol for Urban Internet of Vehicles

In Internet of Vehicles (IoV), high vehicular mobility causes frequent changes in the density of vehicles, discontinuity in inter-vehicle communication, variation of network topology and constraints for routing protocols. Besides, with vehicular positions and street-level digital maps available, the intersection-based geographic routing becomes indispensable considering its ability for avoid forwarding packets through segments with low network density and high scale of network disconnections. In this paper, considering the benefits of intersection-based routing and challenges of high dynamic IoV, a Path Transmission Costs-based Multi-lane Connectivity Routing protocol (PTCCR) is proposed with the help from intersection nodes and/or neighboring nodes. First, we investigate the multi-lane connectivity based on vehicular speed under free-flow state for various types of road sections. Second, A Path Transmission costs (PTC) measurement mechanism is proposed considering the impact of the sequence of selected sections or intersections on the routing performance. After that, the PTC of sent packets is quantitatively analyzed and used as the path selection metric. Finally, the path with the largest multi-lane connectivity and lowest PTC is selected as the optimal path taking the transmission direction, neighbor’s location and destination position into account. Numerical results show that our proposed PTCCR outperforms two state-of-art routings, i.e., the real-time intersection-based segment aware routing protocol (RTISAR) and Reliable Traffic Aware Routing protocol (RTAR), in terms of packet delivery ratio, average end-to-end delay and communication overhead.
Extending resources for avoiding overloads of mixed-criticality tasks in cyber-physical systems

With the increasing number of services and industries including nuclear, chemical, aerospace, and automotive sectors in cyber-physical systems (CPSs), systems are being severely overloaded. CPSs comprises mixed-critical tasks which are of either safety-critical (high) or non-safety critical (low). In traditional task scheduling, most of the existing scheduling algorithms provide poor performance for high-criticality tasks when the system experiences overload and do not show explicit separation among different criticality tasks to take advantage of using cloud resources. Here, we propose a framework to schedule the mixed-criticality tasks by analyzing their deadlines and execution times which leverage the performance of parallel processing through OpenMP. The proposed framework introduces a machine learning-based prediction for a task offloading in the cloud. Moreover, it illustrates to execute a selected number of low-criticality tasks in the cloud while the high-criticality tasks are run on the local processors during the system overload. As a result, the high-criticality tasks meet all their deadlines and the system achieves a significant improvement in the overall execution time and better throughput. In addition, the experimental results employing OpenMP show the effectiveness of using the partitioned scheduling over the global scheduling method upon multiprocessor systems to achieve the tasks isolation.
A Common Compiler Framework for Big Data Languages: Motivation, Opportunities, and Benefits.

We are in the era of Big Data and cluster computing. Data sizes have been growing at an exponential rate. At the same time, growth in computing power has been stagnating due to physical limits in processor technology. The only cost effective way to keep up with the growing data trend has been to harness multiple commodity computers in a shared-nothing configuration. Google, needing to manage extremely large amounts of web data, developed the MapReduce [16] platform. The MapReduce system provides a simple way for developers to express a data-oriented computation, by implementing two single-threaded functions (map and reduce), that is then automatically parallelized to run on large clusters of commodity machines. Yahoo! soon created the Hadoop [5] platform, based on the MapReduce specification, and made it available as open source software. Hadoop has since become the de facto MapReduce implementation outside of Google. Initially MapReduce (Hadoop) greatly empowered engineers to run parallel jobs on large clusters to crunch virtually unlimited amounts of data while writing what appeared to be simple functions. However, over time many developers found themselves writing similar, yet different, functions to implement new jobs. Having to write MapReduce functions in an imperative language like Java proved to be time consuming and the proliferation of functions created a maintenance problem, prompting developers to explore the possibility of creating declarative high-level languages to express computation. Sawzall [30] was created inside Google for processing large corpora of text in parallel using MapReduce. Yahoo! developed the Pig [29] system along with its Pig Latin [27] language to express data processing in a declarative language resembling the relational algebra [15]. Facebook created Hive [2], an implementation of a SQL-like language. IBM developed the Jaql [23] language for processing large amounts of JSON data. Pig, Hive, and Jaql all compile queries in their respective languages into MapReduce jobs to run on the Hadoop platform. Microsoft proposed SCOPE [13], a system to compile a sequence of SQL-like statements to run in parallel on their own Dryad [21] data-parallel platform. The Dremel [25] system was created by Google for expressing analytical queries interactively in a subset of SQL using a custom column-based runtime platform. At the University of California, Irvine, we developed the AQL language for processing large amounts of semi-structured data, as part of the ASTERIX platform [9, 12, 7]. VXQuery [6] is a project under incubation at the Apache Software Foundation that aims to run XQuery [4] queries over large corpora of XML documents using a cluster of shared-nothing computers. Declarative languages targeting various data-parallel platforms have seen dramatic growth in popularity in the past few years. In 2010 Facebook told us that upwards of 95% of their Hadoop jobs were automatically
Detailed Modeling and Novel Scheduling of Plug-in Electric Vehicle Energy Storage Systems for Energy Management of Multi-microgrids Considering the Probability of Fault Occurrence

Article history: Received: 11 June 2019 Accepted: 26 Aug 2019 Published: 1 Sept , 2019 As an effective means of displacing fossil fuel consumption and reducing greenhouse gas emissions, plug-in electric vehicles (PEVs) and plug-in hybrid electric vehicles (PHEVs) have attracted more and more attentions. From the power grid perspective, PHEVs and PEVs equipped with batteries can also be used as energy storage facilities, due to the fact that, these vehicles are parked most of the time. Since, the temperature has a strong influence on the battery life-time and also the inherent characteristics of PHEV/PEV energy storage systems limit their use as appropriate resources for energy tuning, this paper, at first, presents a detailed model for energy storage systems of PEVs considering the cooling system and set temperature, and then, it proposes a reliable energy management method for scheduling of PEVs in the multimicrogrid (MMG) systems for both faulted and normal operations using parametric multi-objective function. The simulation results indicate that, considering proper energy management of energy storage systems of PEVs has significant influence on energy scheduling of MMG systems. For this investigation, all data analysis and simulations were done and implemented in MATLAB/Simulink environment.
Prioritized Multi-Objective Robot Control (Priorisierte Roboter-Regelung für mehrere simultane Aufgaben)

Humans are capable of pursuing multiple objectives simultaneously in everyday life, thereby flexibly prioritizing motion tasks and constraints among others. A major goal of current robotics research is to equip highly redundant robots with similar adaptive behavior. The aim of this thesis is to endow such robots with the abilities to optimize priorities for possibly conflicting objectives, to smoothly rearrange such priorities and to decouple the objectives related to motion generation from the objective of simultaneous interaction with the environment through forces. A commonly adopted approach is to tune priorities by hand once which then remain constant over time. In contrast to such constant prioritization, this thesis promotes a different view: A framework for automated learning soft priorities is introduced. Employing state-of-the-art optimization, different sets of priorities are evaluated offline and improved until the prioritized superposition of all underlying controllers satisfies a desired high-level goal. As an alternative approach to soft prioritization with scalar weights, the well-known Stack-of-Tasks prioritization scheme relies on projectors and enforces strict priorities. The second part of this thesis performs a thorough formal analysis of projectors and proposes to smoothly shape between idempotent matrix operators. A novel prioritization scheme is presented with the help of this method, which generalizes previous approaches implementing either strict or soft priorities. It enables to insert new or remove outdated objectives if necessary and allows to rearrange priorities continuously online without inertia coupling while offering the ability to control the interference between objectives. Finally, underactuated robots subject to contact constraints are studied. A projection operator is derived which enables to decouple contact wrench control from constraint-consistent motion generation for robots with passive or virtual joints. This is essential for controlling legged robots which are represented by a floating-base. Moreover, this thesis shows that the same principle holds for underactuated grasping scenarios. The approach is validated with a large panel of experiments on various robot platforms both in simulation and the real-world. This thesis provides numerous tools that endows existing and future robot systems with ever-increasing degrees of freedom to create complex motion aiming to interact with their environment effectively.
Two-User Network-Coded Cooperation With NOMA and Advanced Successive Interference Cancellation

In this work, we evaluate the outage probability of a two-user network-coded cooperative (NCC) uplink system subjected to non-orthogonal multiple access (NOMA). We consider that the destination performs a recently proposed advanced successive interference cancellation (Adv-SIC) in order to decode the individual messages from the superimposed received signal, which requires only statistical channel state information (CSI). Our results indicate, through mathematical analysis and numerical simulations, that the benefits of Adv-SIC are boosted by NCC, improving the performance of both users, differently from a non-cooperative (NonCooP) NOMA system, where only the user with the worst average channel condition is benefited from Adv-SIC.
Designing and Implementing an Organoleptic Test Application for Food Products Using Android Based Decision Tree Algorithm

The development of food and beverage products increases over the years. The high level of food consumption led to the presence of new food and beverage brands produced in Salatiga. In accordance with this fact, Organoleptic Tests or in other words evaluation of food products could be conducted well if the obtained dataset had a complete, rapid, and accurate information. The required information could be clarified into the dataset that was used to calculate the testing. Manually calculating the process was a problem that usually appeared. As a result, losing or having a broken report on the organoleptic test often occurred. Mileage of the research setting became another problem that appeared when the research conducted out of the town, Salatiga. A combination of R&D method and application of algorithm was implemented in this research by using the performed stages. These stages started from the identification of problems to the testing of the systems. During the process of organoleptic testing, Weka 3.8. 2 was used as a tool to test the C4.5 algorithm and an implementation of the applied algorithm on the android. The results of this study revealed that the average classification accuracy of the J48 algorithm in Weka could achieve above 90% accuracy and average score of hedonic scaling system was in “agree” category. It also showed that the algorithm and the testing of the systems which implemented on android has a credible performance in classifying data.
A Survey of User Profiling: State-of-the-Art, Challenges, and Solutions

Advancements in information and communication technology, and online web users have given attention to the virtual representation of each user, which is crucial for effective service personalization. Meeting users need and preferences is an ongoing challenge in service personalization. This issue can be addressed through the building of a comprehensive user profile. A user profile is the summary of the user’s interests, characteristics, behaviours, and preferences, while user profiling is the system of collecting, organizing and inferring the user profile information. Many reviews on user profiling have been conducted but none focused on the effective profile modeling process. Hence, this article aims to provide a review of the recent state-of-the-art approach to user profiling. These include methods, description, characteristics, and taxonomy of the user profile. The study of the existing user profiling modeling in the aspect of data acquisition, feature extraction, profiling techniques, and profiling approaches (with the identification of their strengths and weaknesses) and the performance measures are also provided. In addition, the research challenges were also discussed with a focus on privacy, datasets, cold start issues, trust issues, and computational complexity. Moreover, the article identified an open research direction that serves as solutions to the identified challenges and motivation for further researchers in advancing user profiling. The findings showed that an effective modeling process enhances the construction of accurate user profile for service personalization.
Distance construction and clustering of football player performance data

I present a new idea to map football players information by using multidimensional scaling, and to cluster football players. The actual goal is to define a proper distance measure between players. The data was assembled from whoscored.com. Variables are of mixed type, containing nominal, ordinal, count and continuous information. In the data pre-processing stage, four different steps are followed through for continuous and count variables: 1) representation (i.e., considerations regarding how the relevant information is most appropriately represented, e.g., relative to minutes played), 2) transformation (football knowledge as well as the skewness of the distribution of some count variables indicates that transformation should be used to decrease the effective distance between higher values compared to the distances between lower values), 3) standardisation (in order to make within-variable variations comparable), and 4) variable weighting including variable selection. In a final phase, all the different types of distance measures are combined by using the principle of the Gower dissimilarity (Gower, 1971). As the second part of this thesis, the aim was to choose a suitable clustering technique and to estimate the best number of clusters for the dissimilarity measurement obtained from football players data set. For this aim, different clustering quality indexes have been introduced, and as first proposed by Hennig (2017), a new concept to calibrate the clustering quality indexes has been presented. In this respect, Hennig (2017) proposed two random clustering algorithms, which generates random clustering points from which standardised clustering quality index values can be calculated and aggregated in an appropriate way. In this thesis, two new additional random clustering algorithms have been proposed and the aggregation of clustering quality indexes has been examined with different types of simulated and real data sets. At the end, this new concept has been applied on the dissimilarity measurement of football players.
Online Damage Monitoring of SiCf-SiCm Composite Materials Using Acoustic Emission and Deep Learning

SiCf-SiCm composites are being actively developed as fuel cladding for improving accident tolerance of light water reactor fuel. Online monitoring of the degradation process in SiCf-SiCm composites is of great importance to ensure the safety of the nuclear reactor system. The degradation monitoring task can be mapped as a classification problem: given the Acoustic Emission(AE) events at a given timeslot, the model is expected to predict which one of the following three stages the material is in: elastic, matrix-driven and fiber-driven cracking. In this paper, degradation tests on SiCf-SiCm composite tubes were conducted using a bladder-based internal pressure technique with AE monitoring. We then trained a deep learning based end-to-end convolutional neural network (CNN) model for online monitoring of the damage progression process of SiCf-SiCm composite tubes using the AE data as the raw input. As a comparison, we also applied Random Forest (RF) with expert-crafted audio event features to the damage stage prediction problem. Experimental results show that both RF and CNN models yield good results but on average our end-to-end CNN models outperform the RF models due to its high-level feature extraction capability. The CNN model with single events can reach an average prediction accuracy of 84.4% compared to 74% of the RF models. Combining multiple audio samples typically improves the accuracy of the models with RF accuracy reaching 82.8% and CNN accuracy reaching 86.6%.
A COMPARISON OF VEHICLE HANDLING FIDELITY BETWEEN THE GAZEBO AND ANVEL SIMULATORS

This paper provides a comparison of the Gazebo and ANVEL simulators and analyzes the aspects of vehicle modeling fidelity that are critical to the design of unmanned ground vehicle (UGV) control and estimation algorithms. The robotic simulators Gazebo [1], from the Open Source Robotics Foundation (OSRF), and Autonomous Navigation Virtual Environment Laboratory (ANVEL) [2], from Quantum Signal, are two popular new tools that are being used extensively in academic, commercial, and military development of perception, navigation, and control algorithms for UGVs. Despite the similarities between Gazebo and ANVEL there has been no direct comparison between the two simulators with respect to their validity as vehicle dynamics simulators. Citation: R. Brothers, D. Bevly, “A Comparison of Vehicle Handling Fidelity Between the Gazebo and ANVEL Simulators”, In Proceedings of the Ground Vehicle Systems Engineering and Technology Symposium (GVSETS), NDIA, Novi, MI, Aug. 13-15, 2019.
Experience, education, and nurses' ability to make clinical judgments.

: Del Bueno makes us think anew about some old assumptions concerning the relationship of experience, education, and clinical judgment. In spite of our prejudices in one direction or another, del Bueno's study shows more within-group differences than between-group differences. Additionally, the author shows the potential for use of video simulation in making judgments about clinical decision making.