Miracle-dz/UniFAR-FTUs huggingface dataset:
TOOLBOX.ACADEMY: CODING & ARTIFICIAL INTELLIGENCE MADE EASY FOR KIDS, BIG DATA FOR EDUCATORS

Society has reached the point where there is considerable awareness about the need of teaching coding skills at the schools. Both, political leaders and educational systems start taking positions in this strategic new arena, but they lack from much of the expertise and necessary tools about how to teach this complex (but also intuitive to children and youngsters) field of knowledge. In this context, ToolboX Academy is a programming platform which subscribes a slightly new approach to attack the problem, in four differential aspects: 1) While most attention focuses on block-based approaches (Scratch and Code.org being the most popular initiatives), ToolboX embraces coding as it works for developers: text-based programming; 2) ToolboX embeds mainstream programming languages, which are widely used, are supported by big communities, are weakly typed, and closer to human natural language, like it is Javascript, for web applications, and GNU Octave, in engineering and scientific research; 3) it provides user-oriented problem-based learning, designed upon educational research results; and 4) it integrates curricular subjects in learning to code. In this paper, we review the design constraints taken into account to develop ToolboX Academy, and how it has been tested in the classroom. Some features are particularly stressed, like the use of open formats for content representation (definition of tasks or problems); the design of a simplified environment to represent most CS concepts, from basic loops to complex graph traversal strategies; and the use of learning analytics based on big data algorithms that can help educators and managers to learn from the interaction of their students with this environment.
The Anti-synchronization and Circuit Simulation of Hyperchaotic Systems with Uncertain Parameters

Abstract. In this paper, the hyperchaotic system in the same structure with unknown parameters are anti-synchronized using the active control technique. The designed controller ensures the full anti-synchronization between the drive and the response systems in state variables and the update rule of the unknown parameters ensures the identification of parameters. In addition, the drive and the response system as well as the identification circuit for unknown parameters are established and a series of circuit simulations are completed. The highly consistency between the numerical simulation and the circuit simulation has proved the validity of the scheme.
Which Supply Chain Strategies Can Guarantee Higher Manufacturer’s Operational Performance Applying Suitable Resources?

Due to the fact that scientists and practitioners alike have interested on the leveraging manufacturing companies' operational performance, this research examined which supply chain strategies promise manufacturers higher operational performance. Later on, we clarified whether suitable resources can play an important role in the mentioned causal relationshipsas a moderator and improve the impact of the strategies on operational performance. This study is a descriptive-exploratory research in which primary data was collected from 80 Malaysian manufacturing companies. Bivariate Correlation and Multiple Regression in SPSS was applied for analyzing data. Output showed that many suppliers, few suppliers, and keiretsu network strategies enable manufacturers to achieve satisfactory level of operational performance; but, vertical integration. More importantly, suitable resources can leverage the effect of just vertical integration strategy on operational performance.
Vehicular Visible Light Communication: a road-to-vehicle proof of concept

This paper proposes the use of Visible Light Communication (VLC) in Vehicular Communication Systems for vehicle safety applications. A vehicle lighting system that combines the functions of illumination, signaling, communications, and positioning is presented. Using traffic signals, applies the connected vehicle concept to traditional intersections. A generic model of cooperative transmissions for vehicular communications services is established, which share the common features among diverse vehicular communications scenarios. Three specific vehicular communications are analyzed. One is for Infrastructure-to-Vehicle (I2V) communications from the street lamps, located on roadside, to the vehicles; the other is for in line Vehicle-to-Vehicle (V2V) communications and the last for Vehicle-to-Infrastructure (V2I) communications from cars to the traffic lights, at the crossroad. For the V2V and V2I communication study, the emitter was developed based on the vehicle headlights, whereas for the study of I2V communication system, the emitter was built based on streetlights, whose primary purpose is to provide illumination, and are also used for data communication if modulated at fast rates. The VLC receivers extract the data from the modulated light beam coming from the LEDs emitters. The receivers consist in a double SiC pi’npin photodetector, with light filtering characteristics, located at the rooftop of the vehicle, for I2V communications; at the traffic lights, for V2I; and at the tails, for V2V reception. Clusters of emitters, in a square topology, are used in the I2V transmission. The encoded message contains ID code of each emitter concomitantly with a traffic message that is received, decoded and resent to another vehicle (V2V) or to traffic light, in the crossroad. An algorithm to decode the information at the receivers is established. A phasing traffic flow is presented as a proof of concept. The experimental results, confirmed that the proposed cooperative VLC architecture is suitable for the intended applications.
Reconfigurable Multi-Butterfly Parallel Radix-r FFT Processor

The design of reconfigurable multi-butterfly parallel radix-r FFT (Fast Fourier Transform) processors is proposed. FFT is widely used in signal processing, and the application needs real-time and high performance, while most of the traditional designs are limited to the power of two, which wastes the buffers and multipliers in big data. In response to the problem, we improve the parallel FFT algorithm with the design of reconfigurable control machine combined with buffer/multiplier, and the cost function with the input of radix/number/paddling number/time consuming is deduced. Constrained with the number of buffer and multipliers, the radix and number can be computed with the optimum cost function, and the resolution space of computing performance and hardware cost is presented. The proposed guarantees the real-time performance with better flexibility compared with the previous literature, and the comparison also suggests the effectiveness of the design.
Scientific Documents Classification Using Support Vector Machine Algorithm

One of the ways to publish scientific paper is to present it in a conference. Before being presented in the conference, the registered scientific paper has to be grouped into categories. Because there are so many scientific papers that have been registered, it is an urgent need to establish a system that can automatically identify the topic of scientific papers and classify them according to recognized topics. One method that can be used to organize documents according to their categories is classification. In this study, Support Vector Machine algorithm is used as a method to classify documents into 5 categories of computer science. System test is done by taking 150 documents as data sample and 50 documents as data testing. This study produced a classification system of scientific documents and showed the method used was able to classify documents with 90% accuracy results.
Forward error correcting code for high data rate LEO satellite optical downlinks

A simulation framework based on a physical-layer based abstraction to predict physical layer performances and to compare different forward error correcting (FEC) codes is presented. This framework is used to jointly design interleaving and FEC schemes for free space optical link. A sub-class of regular Low-Density Parity-Check codes is shown to be an interesting alternative to current space communication standard for optical links that require low error floor and high decoder throughput. End-to-end simulations show the feasibility of error free link from a LEO satellite to a high complexity ground station at 25Gbits/s and from a LEO satellite to low complexity optical ground station at 10 Gbits/s. The proposed protection scheme is composed of FG LDPC code and a bit interleaver to span the burst of errors.
Extremal properties and self-preserving behavior

In this reply to the commentaries of Lyon and Swenson, I clarify the arguments of my article, “On the nature and origins of cognition as a form of motivated activity. ” In particular, I focus on the nature of self-preserving behavior, considered not only as a basic and universal form of cognitive behavior but also as a key example of cognition as form of motivated activity. I clarify that my approach affirms the importance of self-preserving behavior but questions the widespread assumption that this and other varieties of motivated behavior can be explained as the product of homeostatic mechanisms. I suggest that extremal properties can not only do a better job of explaining homeostasis-like behaviors, but they can also be extended to other kinds of motivated behavior that are not homeostasis-like. Finally, I emphasize the explanatory promise of extremal properties with respect to fundamental questions about the emergence of order in living and nonliving systems.
Biomechanical study of the action of compression bandages on the lower leg

Compression bandages are commonly used for the treatment of chronic venous insufficiency. They apply a pressure onto the leg, called interface pressure, which is one of the key aspects of the treatment. The objective was to better understand the mechanisms impacting interface pressure applied by compression bandage on the lower leg. In collaboration with clinicians and a medical devices manufacturer, a biomechanical approach was proposed. This approach was composed of experimental pressure measurements and the numerical simulation of bandage application. Two preliminary studies, experimental and numerical, showed the limitations of the use of Laplace’s Law (current standard) for interface pressure computation. These studies also questioned the possible impact of bandage surface properties (bandage-to-bandage friction coefficient) on interface pressure. They also showed the need to consider soft tissues deformation induced by bandage application. Two characterization methods were designed for the identification of patient-specific soft tissue mechanical properties and the measurement of bandage-to-bandage friction coefficient. A new methodology for the prediction of interface pressure was developed thanks to the combination of the numerical simulation of bandage application and the leg geometry parametrization. The results were then confronted to experimental measurements. Finally, a clinical study was designed to investigate the pressure applied by superimposed compression bandages (very common in clinical practice for the treatment of venous ulcers).
TOWARDS AI-ASSISTED DISEASE DIAGNOSIS: LEARNING DEEP FEATURE REPRESENTATIONS FOR MEDICAL IMAGE ANALYSIS

Artificial Intelligence (AI) has impacted our lives in many meaningful ways. For our research, we focus on improving disease diagnosis systems by analyzing medical images using AI, specifically deep learning technologies. The recent advances in deep learning technologies are leading to enhanced performance for medical image analysis and computer-aided disease diagnosis. In this dissertation, we explore a major research area in medical image analysis Image classification. Image classification is the process to assign an image a label from a fixed set of categories. For our research, we focus on the problem of Alzheimer’s Disease (AD) diagnosis from 3D structural Magnetic Resonance Imaging (sMRI) and Positron Emission Tomography (PET) brain scans. Alzheimer’s Disease is a severe neurological disorder. In this dissertation, we address challenges related to Alzheimer’s Disease diagnosis and propose several models for improved diagnosis. We focus on analyzing the 3D Structural MRI (sMRI) and Positron Emission Tomography (PET) brain scans to identify the current stage of Alzheimer’s Disease: Normal Controls (CN), Mild Cognitive Impairment (MCI), and Alzheimer’s Disease (AD). This dissertation demonstrates ways to improve the performance of a Convolutional Neural Network (CNN) for Alzheimer’s Disease diagnosis. Besides, we present approaches to solve the class-imbalance problem and improving classification performance with limited training data for medical image analysis. To understand the decision of the CNN, we present methods to visualize the behavior of a CNN model for disease diagnosis. As a case study, we analyzed brain PET scans of AD and CN patients to see how CNN discriminates among data samples of different classes. Additionally, this dissertation proposes a novel approach to generate synthetic medical images using Generative Adversarial Networks (GANs). Working with the limited dataset and small amount of annotated samples makes it difficult to develop a robust automated disease diagnosis model. Our proposed model can solve such issue and generate brain MRI and PET images for three different stages of Alzheimer’s Disease Normal Control (CN), Mild Cognitive Impairment (MCI), and Alzheimer’s Disease (AD). Our proposed approach can be generalized to create synthetic data for other medical image analysis problems and help to develop better disease diagnosis model. INDEX WORDS: Medical Imaging, Deep Learning, Alzheimer’s Disease, MRI, PET, Brain Imaging, Synthetic Data Generation, Visualization, Convolutional Neural Network, Generative Adversarial Networks, Computeraided-diagnosis TOWARDS AI-ASSISTED DISEASE DIAGNOSIS: LEARNING DEEP FEATURE REPRESENTATIONS FOR MEDICAL IMAGE ANALYSIS
Drawing Disruptions: Representing Automated Distortions of Multi-Perspectival Form

SIFT descriptors exist within the lineage of flattened architectural representation but pose new disciplinary ambiguities for architecture given their subscription to particular algorithmic subroutines that unflatten images for the purpose of producing a singular image of the environment. A selection of contemporary architectural projects is used to identify different modes of flatness in architectural representation that SIFTs both subscribe to and complicate given the disruptions they induce within the objects they image. Particular attention is given to the algorithmic components within SIFTs that track, agitate, and exploit differences between multiple images of individual objects. A small architectural project is subjected to SIFTs in an effort to experiment with their otherwise hidden aesthetics of disruption.
Leveraging Communicating UAVs for Emergency Vehicle Guidance in Urban Areas

The response time to emergency situations in urban areas is considered as a crucial key in limiting material damage or even saving human lives. Thanks to their “bird’s eye view” and their flexible mobility, Unmanned Aerial Vehicles (UAVs) can be a promising candidate for several vital applications. Under these perspectives, we investigate the use of communicating UAVs to detect any incident on the road, provide rescue teams with their exact locations, and plot the fastest path to intervene, while considering the constraints of the roads. To efficiently inform the rescue services, a robust routing scheme is introduced to ensure a high level of communication stability based on an efficient backbone, while considering both the high mobility and the restricted energy capacity of UAVs. This allows both predicting any routing path breakage prior to its occurrence, and carrying out a balanced energy consumption among UAVs. To ensure a rapid intervention by rescue teams, UAVs communicate in an ad hoc fashion with existing vehicles on the ground to estimate the fluidity of the roads. Our system is implemented and evaluated through a series of experiments. The reported results show that each part of the system reliably succeeds in achieving its planned objective.
Inferring Swarm Models Using a Single Monitoring Robot

Infiltrating a swarm of artificial or living agents using a single monitoring robot could allow for the assessment of their swarm rules and parameters without the need for any external infrastructure. The inferred swarm model could then be used to control these swarms, for example to guide them to safe areas. In this study we introduce a scheme for autonomous artificial agents to extract knowledge about the interactions within a swarm of interest. By infiltrating the swarm of interest with a monitoring robot and constantly measuring the distance between the infiltrator and its nearest neighbour, the repulsion radius of the swarm agents can be estimated. Though this method works for a range of tested parameters, it is still limited to a specific model of interaction.
Provisioning Reproducible Computational Science

In this thought piece we are interested in the problem of “computational reproducibility,” or of replicating the computational steps carried out in producing scientific findings [1]. This definition stands in contraposition to an independent replication of a research process (including writing a new software implementation), or a de novo re-collection of data processed with the same computational tools given the same commands. Starting from the exact same input data, can another researcher run the same commands and produce the same output? This question raises three immediate concerns in our minds:
Mapping results for a set of cGAL effectors and drivers

Description: Recently, the GAL4-UAS system (cGAL) has been adapted for use in C. elegans for control of gene expression across 15°C 25°C (Wang et al., 2017). In order to create a desired gene expression pattern, one crosses a transgenic strain containing a driver construct with another strain containing an effector gene. Here we mapped several cGAL driver and effector integrations. We first crossed each of the cGAL driver and effector strains with N2 males, picked the heterozygous male progeny, crossed them with hermaphrodites of the mapping strain (DA438), picked L4 hermaphrodites with the corresponding transgenic marker of the driver or effector strain and scored the progeny in the next generation. The DA438 strain contains six recessive mutations, each of which locates on one of the six chromosomes and produces visible phenotypes (Bli on chromosome I, Rol on II, Vab on III, Unc on IV, Dpy on V, and Lon on X (Avery, 1993). F2 progeny with each of the six phenotypes were selected and examined for the presence or absence of the dominant marker associated with the transgene. In the cases where the dominant transgene marker is unlinked to the recessive phenotypic marker, about three quarters of the F2 progeny will have the dominant marker. If the two markers are linked, very few or no animals are expected to have the dominant transgenic marker. The following tables summarize the mapping results for each cGAL strain, stating the ratios of the F2 mutant progeny with and without the dominant transgenic marker.
Series Elastic Behavior of Biarticular Muscle-Tendon Structure in a Robotic Leg

We investigate the role of lower leg muscle-tendon structures in providing serial elastic behavior to the hip actuator. We present a leg design with physical elastic elements in leg angle and virtual leg axis direction, and its impact onto energy efficient legged locomotion. By testing and comparing two robotic lower leg spring configurations, we can provide potential explanations of the functionality of similar animal leg morphologies with lower leg muscle-tendon network structures. We investigate the effects of leg angle compliance during locomotion. In a proof of concept, we show that a leg with a gastrocnemius inspired elasticity possesses elastic components that deflect in leg angle directions. The leg design with elastic elements in leg angle direction can store hip actuator energy in the series elastic element. We then show the leg's advantages in mechanical design in a vertical drop experiment. In the drop experiments the biarticular leg requires 46% less power. During drop loading, the leg adapts its posture and stores the energy in its springs. The increased energy storing capacity in leg angle direction reduces energy requirements and cost of transport by 31% during dynamic hopping to a cost of transport of 1.2 at 0.9 kg body weight. The biarticular robot leg design has major advantages, especially compared to more traditional robot designs. Despite its high degree of under-actuation, it is easy to converge into and maintain dynamic hopping locomotion. The presented control is based on a simple-to-implement, feed-forward pattern generator. The biarticular legs lightweight design can be rapidly assembled and is largely made from elements created by rapid prototyping. At the same time it is robust, and passively withstands drops from 200% body height. The biarticular leg shows, to the best of the authors' knowledge, the lowest achieved relative cost of transport documented for all dynamically hopping and running robots of 64% of a comparable natural runner's COT.
Example Based Caricature Synthesis

The likeness of a synthesized caricature and the original face image is an essential and often overlooked part of caricature production. In this paper, we present an example based caricature synthesis technique, consisting of shape exaggeration, relationship exaggeration, and optimization for likeness. Rather than relying on a large training set of caricature face pairs, our shape exaggeration step is based on only one or a small number of examples of facial elements (e.g., eyes and nose). The relationship exaggeration step introduces two definitions which facilitate global facial feature synthesis. The first is the T-shape rule, which describes the relative relationship between the facial elements in an intuitive manner. The second is the so-called proportions, which characterizes the facial features in a proportion form. Finally, we introduce a similarity metric as the likeness metric based on the Modified Hausdorff Distance (MHD) which allows us to optimize the configuration of facial elements, maximizing the likeness while satisfying a number of constraints. The effectiveness of our algorithm is demonstrated with experimental results.
Unraveling E-WOM Patterns Using Text Mining and Sentiment Analysis

Electronic word-of-mouth (e-WOM) is a very important way for firms to measure the pulse of its online reputation. Today, consumers use e-WOM as a way to interact with companies and share not only their satisfaction with the experience, but also their discontent. E-WOM is even a good way for companies to co-create better experiences that meet consumer needs. However, not many companies are using such unstructured information as a valuable resource to help in decision making: first, because e-WOM is mainly textual information that needs special data treatment and second, because it is spread in many different platforms and occurs in near-real-time, which makes it hard to handle. The current chapter revises the main methodologies used successfully to unravel hidden patterns in e-WOM in order to help decision makers to use such information to better align their companies with the consumer’s needs.