Miracle-dz/UniFAR-FTUs huggingface dataset:
Research in and through design: an interaction design research approach

This paper presents and discusses an approach to interaction design research entitled research in and through design. This denotes the study of the design process through the active involvement of the researcher in experimental design activities. The approach is exemplified by a case in which interaction design researchers engaged in the development of an interactive facade in order to generate insights into how to plan and carry out design for this type of interactive systems. This is followed by a discussion of the criteria by which the process and outcomes of research in and through design can be evaluated.
Adaptive Monte Carlo Localization for Mobile Robots

Local and global localization are of utmost importance in mobile robotics and play a crucial role in a robot going from remote-controlled to completely autonomous. A probabilistic localization algorithm known as the Adaptive Monte Carlo Localization (AMCL) uses a set of weighted particles to approximate the position and orientation of a robot. In this experiment, this algorithm is used to localize a simulated mobile robot in the Gazebo environment. After deploying the AMCL and tuning its parameters to a specific mobile robot, the particles quickly converge on the pose. These parameters were then tested on a similar robot in the same environment and a similar convergence resulted. These results show that the AMCL is a viable localization algorithim and performs well in these conditions.
BIG DATA ANALYTICS AND PRECISION ANIMAL AGRICULTURE SYMPOSIUM: Machine learning and data mining advance predictive big data analysis in precision animal agriculture

Abstract Precision animal agriculture is poised to rise to prominence in the livestock enterprise in the domains of management, production, welfare, sustainability, health surveillance, and environmental footprint. Considerable progress has been made in the use of tools to routinely monitor and collect information from animals and farms in a less laborious manner than before. These efforts have enabled the animal sciences to embark on information technology-driven discoveries to improve animal agriculture. However, the growing amount and complexity of data generated by fully automated, high-throughput data recording or phenotyping platforms, including digital images, sensor and sound data, unmanned systems, and information obtained from real-time noninvasive computer vision, pose challenges to the successful implementation of precision animal agriculture. The emerging fields of machine learning and data mining are expected to be instrumental in helping meet the daunting challenges facing global agriculture. Yet, their impact and potential in “big data” analysis have not been adequately appreciated in the animal science community, where this recognition has remained only fragmentary. To address such knowledge gaps, this article outlines a framework for machine learning and data mining and offers a glimpse into how they can be applied to solve pressing problems in animal sciences.
A TVC based Clock Fault Detection Circuit for Modern Clock Distribution Network

Modern clock distribution design continues to face challenges in spite of significant advances in the last decade. Clock fault has become a problem in clock distribution network (CDN) in recent years as clock frequency is more than 1GHz now. In this paper, we propose a time-to-voltage converter (TVC) based on-line clock fault detect circuit to enhance flexibility and reliability in design of CDN, which only consists of twelve transistors and two minor capacitors. The proposed circuit transforms the clock fault to deviation of voltage (TVC) but without constant current source and analog-to-digital converter (ADC). Simulation-based results show that the proposed circuit can enhance the reliability of clock distribution network and detect clock jitter fault on-chip as high as 1GHz.
3D Object Recognition Based on Local and Global Features Using Point Cloud Library

3D object recognition from point clouds is considered as a field of research that is growing fast. Based on the types of features used to represent an object, 3D object recognition approaches can be classified into two broad categories—local and global feature-based techniques. Local feature-based techniques are more robust to clutter and partial occlusions that are frequently present in a real-world scene. Whereas, global feature-based techniques are suitable for model retrieval and 3D shape classification especially with the weak geometric structure. Most systems for 3D object recognition use either local or global feature-based techniques. This is because of the difficulty of integrating a set of local features with a single global feature vector in an appropriate manner. In this paper, a 3D object recognition system based on local and global features of the objects using Point Cloud Library (PCL) is proposed. The proposed system uses a hybrid technique based on Viewpoint Feature Histogram (VFH) method and Fast Point Feature Histogram (FPFH) method. VFH method is used as a global descriptor to recognize the object. Whereas, FPFH method is used as a local descriptor to estimate the position of the object in the real-world scene. The performance of the proposed system is evaluated by calculating the accuracy of the recognition process. The experimental results reveal that this system performs well on the tested objects as compared to some state of the art techniques.
Distributed Energy Storage: Role In Building-Based PV Systems

Recent research has shown that the benefits of large-scale photovoltaic (PV) penetration could be diminished by adverse impacts on the grid resulting from the non-dispatchable nature of PV. Because many central generation technologies cannot easily reduce generation PV generation could exceed demand and therefore be unusable. Shoulder months, when PV generation is significant, but space conditioning loads are low, generally present the greatest likelihood of excess generation. For example, Figure 1 shows that in Texas in March PV penetration capable of providing 11% of annual energy demand could result in a reduction in load on traditional central generation of over 50% for a few hours on a sunny day. To allow for this level of load variation more generation and T&D assets will need to be on standby, increasing overhead cost. Furthermore, increasing flexibility to allow for non-dispatchable generation will increase costs and lower the efficiency of the central generation mix (i.e. higher percentage of generally more expensive and less efficient peaking plants vs. baseload plants). Finally, local voltage instability can result from sudden changes in generation output of large PV installations (for example, when cloud cover changes suddenly).
Three-dimension blocks of neurons Control of collagen fibers THREE-DIMENSINAL NEURON CULTURE METHOD CONTROLLING THE DIRECTION OF NEURITE ELONGATION AND THE POSITION OF SOMA

We have developed a 3D neuronal circuit culturing technique by controlling the position of nerve cell body and the direction of neurite elongation using Collagen fiber orientation techniques and poly-dimethylsiloxane (PDMS) micro-processing. Using these methods, we have constructed the 3D neuronal network model extending the neurite into one-way direction with multiple layers, and confirmed the function of it by electrical measurement using multi-electrode array system. These techniques can potentially be used for regenerative medicine and development of drug screening model, as well as research in basic neural biology.
Facial expression synthesis by radial basis function network and image warping

This paper presents a novel approach in synthesizing various facial expressions by image warping, using spatial displacement of facial landmark point generated by radial basis function (RBF) networks. We trained two RBF networks from test images to obtain these spatial displacement. One RBF network can be used to generate different degrees of expressions, while the other can be used to obtain mixed facial expressions. We discuss the method used and demonstrate the results.
Introduction : A Framework for Thinking about Participation in Post-Secondary Education

On reconnaît de plus en plus que l'accès aux études postsecondaires (EPS) est tributaire d'un ensemble complexe de facteurs reliés entre eux et qui vont bien au-delà d'éléments de nature financière comme les frais de scolarité ou l'aide financière offerte aux étudiants. Sont également en jeu la préparation des jeunes et leur attitude face aux EPS, leurs aspirations et d'autres facteurs fortement associés au milieu familial et qui s'enracinent tôt dans la vie des individus. Ce texte d'introduction propose un cadre qui tient compte de toutes ces influences et aborde les questions de l'analyse et de l'interprétation des résultats d'études empiriques, des politiques publiques et des recherches qui seront nécessaires. Chacun des articles présentés dans cet ouvrage est réinterprété dans cette optique.
GEEST LEARNING COHERENT MATHEMATICS THROUGH SEQUENCES OF MICROTASKS : MAKING A DIFFERENCE FOR SECONDARY LEARNERS

We report on the work of 3 schools which set out to make a difference for their previously low-attaining students. Through naturalistic enquiry over 3 years, we built a picture of their practices. Against a national trend, students reported positive attitudes to mathematics and, in 2 out of 3 schools, showed improvement in attainment, so we probe more deeply into the teaching. The lessons broadly conformed to an approach combining inclusive participation with complex and coherent development of mathematical ideas. To understand how the teachers orchestrated this, we developed lesson analysis techniques that focus on how thinking, repertoire and understanding are scaffolded by teachers through sequences of microtasks.
NEGATING THE EFFECT OF TRAFFIC WITH IN-ROW SUBSOILING

Extensive cone index measurements were used to evaluate the soil condition resulting from five years of a cotton ( Gossypium hirsutum L.) wheat ( Triticum aestivum L.) double cropping experiment. Four cotton tillage systems including a conservation tillage practice of in-row subsoiling and planting into wheat residue stubble and two traffic systems were analyzed. The USDA-ARS Wide-Frame Tractive Vehicle (WFTV) was used to control traffic in the experimental plots. Contour graphs of cone index were used to determine differences in tillage and traffic systems. Traffic was found to reconsolidate soil that was initially completely disrupted to a 0.51-m depth into a soil condition similar to one that had never received a subsoiling treatment. Traffic was also found to decrease the total soil volume estimated for root growth using a 2 MPa limiting cone index value, hut not the maximum rooting depth beneath the row, when an annual in-row subsoiling practice was used.
A Method for Malware Analysis by Virtual Machine Introspection Technique

Malicious code has become one of the biggest threats in the field of computer security. Traditional malware monitoring tools are installed in the physical host, they trust in the integrity of the host, however, they are vulnerable to being infected by malware and delivering erroneous results about monitoring. In this paper, a method based on Virtual Machine Introspection technique is proposed to obtain the memory image of a Virtual Machine, from outside, with the help of the VirtualBox API, also analyze its internal content such as running processes, threads, network connections, and open files with the use of the Volatility Framework to interpret the low-level bytes into high-level information and finally, report this information in a monitoring register. This approach has been tested with the execution of 3 samples of malware inside a 32-bit Microsoft Windows XP SP3 Virtual Machine and the results obtained support the main hypothesis that if the Virtual Machine Introspection technique is applied to a Virtual Machine then it is possible to obtain the activities of a process and according to its behavior, identify malware.
Towards Argumentation-based Classification

Classification is the problem of categorizing new observations by using a classifier learnt from already categorized examples. In general, the area of machine learning [Mitchell, 1997] has brought forth a series of different approaches to deal with this problem, from decision trees to support vector machines and others. Recently, approaches to statistical relational learning [De Raedt et al., 2016] even take the perspective of knowledge representation and reasoning into account by developing models on more formal logical and statistical grounds. In this position paper, we envisage to significantly generalize this reasoning aspect of machine learning towards the use of computational models of argumentation [Baroni et al., 2011], a popular approach to commonsense reasoning, for reasoning within machine learning. More concretely, we consider the following two-step classification approach. In the first step, rule learning algorithms are used to extract frequent patterns and rules from a given data set. The output of this step comprises a huge number of rules (given fairly low confidence and support parameters) and these cannot directly be used for the purpose of classification as they are usually inconsistent with one another. Therefore, in the second step, we interpret these rules as the input for approaches to structured argumentation, such as ASPIC [Modgil and Prakken, 2014] or DeLP [Garcia and Simari, 2004]. Using the argumentative inference procedures of these approaches and given a new observation, the classification of the new observation is determined by constructing arguments on top of these rules for the different classes and determining their justification status. The use of argumentation techniques allows to obtain classifiers, which are by design able to explain their decisions, and therefore addresses the recent need for Explainable AI: classifications are accompanied by a dialectical analysis showing why arguments for the conclusion are preferred to counterarguments. Argumentation techniques in machine learning also allows the easy integration of additional expert knowledge in form of arguments. While there are some previous works considering the combination of machine learning and computational argumentation techniques—see e. g. [Možina et al., 2008; Riveret and Governatori, 2016]—, the proposed two-step process offers a novel perspective on this combination, which is likely to bring new insights on the general relationship between machine learning and knowledge representation and reasoning. Preliminary experiments already suggest that our framework can yield performance comparable to state-of-the-art, while being explainable.
Frequency Band Sharing between NRZ and Split Spectrum Signals- Analysis and Simulation Results

Srini Raghavan has over 25 years of experience in design, simulation and analysis of satellite communication systems, spread spectrum systems, and signal processing. Currently, he is supporting several communication systems and Global Positioning System (GPS)-related activities at The Aerospace Corporation, where he is a Senior Engineering Specialist. He has a B.S. and M.Tech. from India, and an M.S. and Ph.D. in Electrical Engineering from the University of MissouriRolla, and is a Senior Member of IEEE and a member of ION, Eta Kappa Nu and Sigma Xi. Jack K. Holmes is a Distinguished Engineer at The Aerospace Corporation in the Communication Systems Subdivision. He has over 40 year’s of experience in analysis, simulation, and design of communication and spread spectrum systems. He is author of the book entitled “Coherent Spread Spectrum Systems” and has published approximately 45 papers in the area of communications, synchronization, and GPS-related subjects. He has been heavily involved in the military side of the GPS Modernization effort since February 1997. He is currently the GPS Code Acquisition design risk assessment subteam leader for the GPS Modernization risk assessment team. Jack received his B.S., M.S., and Ph.D. degrees at UCLA in Electrical Engineering, and is a senior member of the IEEE and a member of ION, Tau Beta Pi, and Sigma Xi.
A Multi-platform Sensor Coordinated Earth Observing Missions Scheduling Method for Hazard Monitoring

The earth observing mission scheduling problem is an important real-world problem that impacts the opportunity of dealing with emergencies and the collection of hazard monitoring research data. The period of traditional data acquisition cycle for earth science research is often too long to receive some important observation data in time. Besides, with rapid developing of sensor web techniques, the envisioned future earth scientists and emergency workers would like to investigate the natural phenomenon by using large numbers of sensors based on different platforms, such as satellite, balloon, aircraft and ground-based. How to schedule these sensors that are frequency agile and capable of multi-scene observations for completing a hazard monitoring research mission is a difficult task. In this paper, we focus on solving two problems above. The active observation model based on hazard monitoring domain knowledge is proposed for reducing responsive time of abnormal phenomenon. And then information gain model is introduced for evaluating observation schedule. On this basis, multi-platform sensor optimization scheduling model is constructed. Simulation and analysis show that the proposed model can solve the problem effectively. Moreover, the normal requests are also taken into account during these events for maximizing the value of various sensors.
Multiscale Predictive Modeling of Wave Velocity and Its Distribution Across a Rocky Intertidal Landscape

Multiscale Predictive Modeling of Wave Velocity and its Distribution Across a Rocky Intertidal Landscape by Daniel Orr Master of Science in Applied Marine and Watershed Science California State University Monterey Bay, 2015 Much importance has been placed on understanding and quantifying how diversity, community structure and mechanistic processes change over different scales. There is a need in ecology for multiscale approaches quantitatively linking scales to characterize, quantify and better understand how ecological patterns and processes functioning at different scales interact. Many of the structural mechanisms established in rocky intertidal systems are driven or influenced by larger scale oceanographic processes that scale down to sub-meter scale effects. Here I develop and integrate two models that link these two scales. Velocity was measured in a swath along shore using an array of dynamometers. Intertidal and subtidal landscapes were mapped using field survey techniques. Digital elevation models were created using GIS, from which landscape characteristics such as slope, curvature and aspect were quantified at a fine scale. A large scale model (p-value < 0.001, R2 = 0.803) uses buoy data, local bathymetry and landscape characteristics to predict the mean maximum velocity experienced on rocky intertidal shorelines. A fine scale model (p-value < 0.001, R2 = 0.633) uses the mean maximum velocity and fine scale landscape characteristics to predict the fine scale distribution of flow speeds across rocky intertidal landscapes. Their integration (p-value < 0.001, R2 = 0.6849) links large scale oceanographic conditions to the fine scale distribution of wave velocity across rocky intertidal landscapes. The multiscale models presented here address the need in ecology for multiscale modeling approaches to quantify and link multiscale patterns to better understand how processes and mechanisms interact at different scales.
Tumaco: Collaboration of Mobile Devices in Hypermedia-based In-Vehicle Infotainment Systems

In-Vehicle Infotainment systems nowadays are largely limited to one-to-one relationships with the drivers’ mobile phone. In this paper, we propose Tumaco, a SOA-based IVI system, which enables the seamless integration of all devices of all passengers. Collaborative services can be executed within a cross-platform ecosystem through the use of semantic hypermedia. In addition, passengers may customize their own user experience in order to reduce mental workload. Our prototypical implementation showed the benefits of opening the user experience to all passengers on a car. Finally, we present a framework that enables developers to provide their own services within our ecosystem.
NON-ELECTRIC MEASUREMENTS-BASED ON-LINE DIAGNOSIS METHOD FOR THE FAULT OF TRANSFORMER WINDINGS

Transformers are the critical equipments in the power system. The online diagnosis of transformers is one of the most important measures to prevent the accidents. This paper proposes an online diagnosis method to detect the winding’s deformation based on the vibration severity method. To validate the effectiveness of this method, we design a set of on-line signal acquisition device and a series of destructive experiments. Experimental results show that the proposed method is more sensitive than short circuit reactance method when detecting the winding fault caused by the shock of short circuit currents.
Using Computer-Assisted Language Learning To Improve Students' English Language Achievement in Universal Basic Education

This study sought to determine if Computer-assisted language learning (CALL) would improve students' achievement in English grammar more than Conventional English Language Instruction (CELI). Guided by four r esearch questions, this study was a quasi-experimental involving four intact classes of junior secondary III students. Two of the classes were randomly assigned to the experimental group while the other two were randomly assigned to the control group. Grammar proficiency tests were used to collect data. The study, which lasted for 8 weeks, utilized the computer for the experimental group and printed texts for the control group. Data was analysed using mean and standard deviation scores. Findings revealed that CALL had an overall positive effect on students' achievement in English language more than CELI. Based on these findings, some recommendations made include that for improved English language instruction in the UBE, schools that have computers should encourage English language teachers to use CALL in their classroom; government at all levels should strive to provide schools with high quality teaching materials such as computers and teachers should be trained to use computer- assisted language learning in schools.
A LEO SATELLITE NAVIGATION ALGORITHM BASED ON GPS AND MAGNETOMETER DATA

ABSTRACT The Global Positioning System (GPS) has become a standard method for low cost onboard satellite orbit determination. The use of a GPS receiver as an attitude and rate sensor has also been developed in the recent past. Additionally, focus has been given to attitude and orbit estimation using the magnetometer, a low cost, reliable sensor. Combining measurements from both GPS and a magnetometer can provide a robust navigation system that takes advantage of the estimation qualities of both measurements. Ultimately a low cost, accurate navigation system can result, potentially eliminating the need for more costly sensors, including gyroscopes.