Miracle-dz/UniFAR-FTUs huggingface dataset:
Secrecy Outage and Diversity Analysis of Multiple Cooperating Source-Destination Pairs

We study the physical-layer security of multiple source-destination (SD) pairs communicating within a wireless network in the face of an eavesdropper attacking the SD pairs. In order to protect the wireless transmission against eavesdropping, we propose a cooperation framework relying on two stages. Specifically, an SD pair is selected to access the total allocated spectrum using an appropriately designed scheme at the beginning of the first stage. The other source nodes (SNs) simultaneously transmit their data to the SN of the above-mentioned SD pair relying on orthogonal resources during the first stage. Then, the SN of the chosen SD pair transmits the data packets containing its own messages and the other SNs’ messages to its dedicated destination node (DN) in the second stage. Finally, this dedicated DN will forward all the other DNs’ data to the application center via the core network. We conceive a specific SD pair selection scheme, termed as the transmit antenna selection aided source-destination pair selection (TAS-SDPS). We continue by deriving the secrecy outage probability (SOP) expressions of both the TAS-SDPS conceived, as well as of the conventional round-robin source-destination pair selection (RSDPS) and of the conventional non-cooperative (Non-coop) schemes for comparison. Furthermore, we carry out the secrecy diversity gain analysis in the high main-to-eavesdropper ratio (MER) region, showing that the TAS-SDPS scheme is capable of achieving the maximum attainable secrecy diversity order. Additionally, we show that increasing the number of transmitting pairs will reduce the SOP, whilst increasing the secrecy diversity order of the TAS-SDPS scheme. It is demonstrated that the SOP of the TAS-SDPS scheme is better than that of the RSDPS and of the conventional Non-coop schemes. We also demonstrate that the secrecy diversity gain of the proposed TAS-SDPS scheme is $M$ times that of the RSDPS scheme in the high-MER region, where $M$ is the number of the SD pairs.
Visual Information flow in Wilson-Cowan networks.

In this work we study the communication efficiency of a psychophysically-tuned cascade of Wilson-Cowan and Divisive Normalization layers that simulate the retina-V1 pathway. This is the first analysis of Wilson-Cowan networks in terms of multivariate total correlation. The parameters of the cortical model have been derived through the relation between the steady state of the Wilson-Cowan model and the Divisive Normalization model.Efficiency has been analyzed in two ways: First, we provide an analytical expression for the reduction of the total correlation among the responses of a V1-like population after the Wilson-Cowan interaction. Second, we empirically study the efficiency with visual stimuli and statistical tools that were not available before: (1) a recent, radiometrically calibrated, set of natural scenes, and (2) a recent technique to estimate the multivariate total correlation in bits from sets of visual responses which only involves univariate operations, thus giving better redundancy estimates.The theoretical and the empirical results show that although this cascade of layers was not optimized for statistical independence in any way, the redundancy between the responses gets substantially reduced along the pathway. Specifically, we show that (1) the efficiency of a Wilson-Cowan network is similar to its equivalent Divisive Normalization, (2) while initial layers (Von-Kries adaptation and Weber-like brightness) contribute to univariate equalization, the bigger contributions to the reduction in total correlation come from the nonlinear local contrast and the local oriented filters, and (3) psychophysically-tuned models are more efficient in the more populated regions of the luminance-contrast plane. These results are an alternative confirmation of the Efficient Coding Hypothesis for the Wilson-Cowan systems. And from an applied perspective, they suggest that neural field models could be an option in image coding to perform image compression.
Improved randomized algorithm for k-submodular function maximization

Submodularity is one of the most important properties in combinatorial optimization, and $k$-submodularity is a generalization of submodularity. Maximization of a $k$-submodular function requires an exponential number of value oracle queries, and approximation algorithms have been studied. For unconstrained $k$-submodular maximization, Iwata et al. gave randomized $k/(2k-1)$-approximation algorithm for monotone functions, and randomized $1/2$-approximation algorithm for nonmonotone functions. In this paper, we present improved randomized algorithms for nonmonotone functions. Our algorithm gives $\frac{k^2+1}{2k^2+1}$-approximation for $k\geq 3$. We also give a randomized $\frac{\sqrt{17}-3}{2}$-approximation algorithm for $k=3$. We use the same framework used in Iwata et al. and Ward and Živný with different probabilities.
Towards Pure End-to-End Learning for Recognizing Multiple Text Sequences from an Image

Here we address a challenging problem: recognizing multiple text sequences from an image by pure end-to-end learning. It is twofold: 1) Multiple text sequences recognition. Each image may contain multiple text sequences of different content, location and orientation, and we try to recognize all the text sequences contained in the image. 2) Pure end-to-end (PEE) learning.We solve the problem in a pure end-to-end learning way where each training image is labeled by only text transcripts of all contained sequences, without any geometric annotations. Most existing works recognize multiple text sequences from an image in a non-end-to-end (NEE) or quasi-end-to-end (QEE) way, in which each image is trained with both text transcripts and text locations.Only recently, a PEE method was proposed to recognize text sequences from an image where the text sequence was split to several lines in the image. However, it cannot be directly applied to recognizing multiple text sequences from an image. So in this paper, we propose a pure end-to-end learning method to recognize multiple text sequences from an image. Our method directly learns multiple sequences of probability distribution conditioned on each input image, and outputs multiple text transcripts with a well-designed decoding this http URL evaluate the proposed method, we constructed several datasets mainly based on an existing public dataset andtwo real application scenarios. Experimental results show that the proposed method can effectively recognize multiple text sequences from images, and outperforms CTC-based and attention-based baseline methods.
Experiences of treating phantom limb pain using immersive virtual reality

Phantom limb pain (PLP) is a phenomenon that affects millions of amputees worldwide. Its causes are poorly understood, and traditional forms of pain relief are largely ineffective. For over a decade virtual reality (VR) has shown tantalising possibilities of treating or managing this debilitating condition. Until recently however, the cost, complexity and fragility of VR hardware made exploring this unorthodox approach at any meaningful scale challenging; patients have had to travel to the location of specialist equipment to participate in studies, and missed appointments, dropouts or broken hardware have hampered data-gathering. Improvements in 'consumer grade' VR headsets now makes larger trials of this visual approach to pain management viable. We describe a trial of a VR system for PLP reduction utilising lightweight, standalone and low-cost VR hardware suitable for independent home use.
Exploring a Design Space of Graphical Adaptive Menus

Graphical Adaptive Menus are Graphical User Interface menus whose predicted items of immediate use can be automatically rendered in a prediction window. Rendering this prediction window is a key question for adaptivity to enable the end-user to efficiently differentiate predicted items from normal ones and to consequently select appropriate items. Adaptivity for graphical menus has been investigated more for normal screens, such as desktops, than for small screens, such as smartphones, where real estate imposes severe rendering constraints. To address this question, this article defines and explores a design space where graphical adaptive menus are structured based on Bertin’s eight visual variables (i.e., position, size, shape, value, color, orientation, texture, and motion) and their combination by comparing their rendering for small screens with respect to normal screens. Based on this design space, previously introduced graphical adaptive menus are revisited in terms of four stability properties (i.e., spatial, physical, format, and temporal), and new menu designs are introduced and discussed for both normal and small screens. The resulting set of graphical adaptive menu has been subject to a preference analysis from which a particular design emerged: the cloud menu, where predicted items are arranged in an adaptive tag cloud. We investigate empirically the effect of the cloud menu on the item selection time and the error rate with respect to a static menu and an adaptive linear menu. This article then suggests a set of usability guidelines for designers and practitioners to design graphical adaptive menus in general and cloud menus in particular.
Perfluoro-1,3-dioxolane and perfluoro-oxetane: promising gases for electrical insulation

Perfluorinated cyclic ethers represent an interesting class of molecules to use as dielectric gases for electrical insulation. Measurements of the two compounds 4,4,5,5-tetrachloro-2,2-difluoro-1,3-dioxolane (, CAS 87075-01-2) and octafluoro-1,4-dioxane (, CAS 32981-22-9) in a pulsed Townsend experiment are presented, their ionization and attachment rate coefficients are obtained and their electron attachment cross sections are estimated. Both gases have a high critical electric field strength of about and times the one of SF, respectively, which is presumably due to very high electron attachment cross sections in the range from 2–4 eV. In particular, for the values appear to be very close to the S-wave scattering limit around 3 eV. Based on these results, we recommend the two analogous compounds perfluoro-1,3-dioxolane (, CAS 21297-65-4) and perfluoro-oxetane (, CAS 425-82-1) for further research for the use as insulation and refrigerant gases, since they are chemically comparable, presumably environmentally friendly, and have high vapor pressures.
Recommendation System in Business Intelligence Solutions for Grocery shops: Challenges and Perspective

With the surge in digital platforms and extension of e-commerce, the field of recommendation has been a topic of interest not only for the data scientist but deemed important by the business experts to enhance the user-centric services. A large number of retail & service-oriented companies such as Amazon, Netflix, Goodreads, and Spotify etc. use Business Intelligence (BI) and recommendation systems to provide users with various choices of products based on their interest. Evidently, such a customized user-experience not only provide them with a better service, but also enables the companies to understand customer behavior and enhance their business. The aim of this paper is to introduce a recommendation system in the business intelligence platform to a new-system where no user's previous interaction information is available. We present an exploratory study of implementing recommendation system in the project SmartEmma, a grocery shop application in Aachen, funded by EFRE.NRW, European Union and WIRTSCHAFT.NRW.
A Hybrid Material Point Method for Frictional Contact with Diverse Materials

We present a new hybrid Lagrangian Material Point Method for simulating elastic objects like hair, rubber, and soft tissues that utilizes a Lagrangian mesh for internal force computation and an Eulerian mesh for self collision as well as coupling with external materials. While recent Material Point Method (MPM) techniques allow for natural simulation of hyperelastic materials represented with Lagrangian meshes, they utilize an updated Lagrangian discretization where the Eulerian grid degrees of freedom are used to take variations of the potential energy. This often coarsens the degrees of freedom of the Lagrangian mesh and can lead to artifacts. We develop a hybrid approach that retains Lagrangian degrees of freedom while still allowing for natural coupling with other materials simulated with traditional MPM, e.g. sand, snow, etc. Furthermore, while recent MPM advances allow for resolution of frictional contact with codimensional simulation of hyperelasticity, they do not generalize to the case of volumetric materials. We show that our hybrid approach resolves these issues. We demonstrate the efficacy of our technique with examples that involve elastic soft tissues coupled with kinematic skeletons, extreme deformation, and coupling with multiple elastoplastic materials. Our approach also naturally allows for two-way rigid body coupling.
Source form an automated crowdsourced object generator

Source Form is a stand-alone device capable of collecting crowdsourced images of a user-defined object, stitching together available visual data (e.g., photos tagged with search term) through photogrammetry, creating watertight models from the resulting point cloud and 3D printing a physical form. This device works completely independent of subjective user input resulting in two possible outcomes: 1. Produce iterative versions of a specific object (e.g., the Statue of Liberty) increasing in detail and accuracy over time as the collective dataset (e.g., uploaded images of the statue) grows. 2. Produce democratized versions of common objects (e.g., an apple) by aggregating a spectrum of tagged image results. This project demonstrates that an increase in readily available image data closes the gap between physical and digital perceptions of form through time. For example, when Source Form is asked to print the Statue of Liberty today and then print again 6 months from now, the later result will be more accurate and detailed than the previous version. As people continue to take pictures of the monument and upload them to social media, blogs and photo sharing sites, the database of images grows in quantity and quality. Because Source Form gathers a new dataset with each print, the resulting forms will always be evolving. The collection of prints the machine produces over time are cataloged and displayed in linear groupings, providing viewers an opportunity to see growth and change in physical space. In addition to rendering change over time, a snapshot of a more common object's web perception could be created. For example, when an image search for "apple" is performed, the results are a spectrum of condition and species from rotting crab apples to gleaming Granny-Smith's. Source Form aggregates all of these images into one model and outputs the collective web presence of an "apple". Characteristics of the model are guided by the frequency and order in response to the image web search. The resulting democratized forms are emblematic of the web's collective and popular perceptions.
Muscle-based facial retargeting with anatomical constraints

We present a physically based facial retargeting algorithm that is suitable for use in high-end production. Given an actor's facial performance, we first run a targeted muscle simulation on the actor in order to determine the actor blendshape muscles that best match the performance. The deformation of the actor blendshape muscles are then transferred onto the creature to obtain the corresponding creature blendshape muscles. Finally, the resulting creature blendshape muscles are used to drive a creature muscle simulation which yields the retargeted performance. In order to ensure the anatomically correct placement of the muscles, cranium, and jaw, we introduce novel anatomically motivated constraints during the transfer process. Compared to previous approaches, these constraints not only improve the expressiveness of the retargeted creature performance to the actor performance but also eliminate spurious visual artifacts.
Repairnator patches programs automatically

Repairnator is a bot. It constantly monitors software bugs discovered during continuous integration of open-source software and tries to fix them automatically. If it succeeds in synthesizing a valid patch, Repairnator proposes the patch to the human developers, disguised under a fake human identity. To date, Repairnator has been able to produce patches that were accepted by the human developers and permanently merged into the code base. This is a milestone for human-competitiveness in software engineering research on automatic program repair.
The Use of WeChat Official Accounts in Macau: From the Customers' Perspectives

WeChat was first developed as an instant messaging application for mobile phones in 2011. It is most often likened to WhatsApp, Facebook Messenger, Viber, Line, etc. for their abilities to send text, image, audio messages, or even to make live audio and video chats. More features were later incorporated into the app, making it became a comprehensive platform now. Besides being an instant messager, WeChat is now a social networking app, a mobile payment app, a multipurpose platform for other mini programs, games, and more. Since launch in 2012, WeChat Official Accounts has become an interactive way for content sharing and corporate marketing interaction. As of the end of 2017, there were over 20 million official accounts. WeChat Official Accounts help micro, small and medium-sized enterprises to improve operating efficiency and trim operational costs. Although WeChat Official Account has gained a big success in China, it is not sure whether this success can be extended beyond territories. Most research about WeChat focused on the China market. Limited studies about the overseas markets of WeChat [12] mainly focused on the demographic reports of WeChat users. This research attempted to study users' acceptance of WeChat Official Accounts in a market outside Mainland China.
Deep reality: an underwater VR experience to promote relaxation by unconscious HR, EDA, and brain activity biofeedback

DeepReality uses biometric information for reflection and relaxation. We monitor realtime brain activity, heart rate, and electrodermal activity to decrease heart rate and respiration by subtle, almost imperceptible light flickering, sound pulsations, and slow movements of underwater 3D creatures that reflect the internal state of the viewer.
LEDs as sensors

Imagine an LED that turns itself on and off in response to light levels, or one that you can blow out like a candle. These are circuits you can build with just an Arduino, a resistor, an LED and a little code. In this workshop, we examine some surprising properties of LEDs to create systems that sense light, temperature and wind.
Physics-based character animation with cascadeur

In this workshop we will create a realistic acrobatic 3D fighting animation using the animation software Cascadeur. We will learn the key features of physics-based character animation and will immediately apply the learned knowledge by creating an action sequence. Cascadeur is a new toolset that enables animators to create physical correct character animations for video games and movies - without the use of motion capture.
CAPTCHA: Impact on User Experience of Users with Learning Disabilities

CAPTCHA is one of the most common solutions to check if the user trying to enter a Website is a real person or an automated piece of software. This challenge-response test, implemented in many Internet Websites, emphasizes the gaps between accessibility and security on the Internet, as it poses an obstacle for the learning-impaired in the reading and comprehension of what is presented in the test. Various types of CAPTCHA tests have been developed in order to address accessibility and security issues. The objective of this study is to investigate how the differences between various CAPTCHA tests affect user experience among populations with and without learning disabilities. A questionnaire accompanied by experiencing five different tests was administered to 212 users, 60 of them with learning disabilities. Response rates for each test and levels of success were collected automatically. Findings suggest that users with learning disabilities have more difficulties in solving the tests, especially those with distorted texts, have more negative attitudes towards the CAPTCHA tests, but the response time has no statistical difference from users without learning disabilities. These insights can help to develop and implement solutions suitable for many users and especially for population with learning disabilities.
Spare Parts Demand Forecasting in Energy Industry: A Stacked Generalization-Based Approach

This paper deals with spare parts demand forecasting problem in energy industry. Forecasting spare parts demand has its own challenges because in general spare parts demand is characterized by high variation in its demand size and in its inter-demand interval. In this paper, a forecasting approach to deal with spare parts demand is proposed. The proposed approach utilized stacked generalization technique to combine traditional time series forecasting method and machine learning method into a single ensemble. To test its performance, a case study in a natural gas liquefaction company is provided in this paper. In the case study, the proposed approach is utilized to forecast the monthly demand of spare parts used for maintenance operations. To compare its performance, several traditional time series forecasting methods (including Moving Average, Single Exponential Smoothing, Croston's method, Syntetos-Boylan Approximation, and Teunter-Syntetos-Babai) and several machine learning methods (including Linear Regression, Elastic Net, Neural Network, Support Vector Machine, and Random Forests) are also used in the case study. As results, the proposed approach performed better than other methods in terms of forecast error minimization.
Simulation and Visualization of Ductile Fracture with the Material Point Method

We present novel techniques for simulating and visualizing ductile fracture with the Material Point Method (MPM). We utilize traditional particle-based MPM [Stomakhin et al. 2013; Sulsky et al. 1994] as well as the Lagrangian energy formulation of [Jiang et al. 2015] that utilizes a tetrahedron mesh, rather than particle-based estimation of the deformation gradient and potential energy. We model failure and fracture via elastoplasticity with damage. Material is elastic until its deformation exceeds a Rankine or von Mises yield condition, at which point we use a softening model that shrinks the yield surface until a damage threshold is reached. Once damaged, the material Lamé coefficients are modified to represent failed material. We design visualization techniques for rendering the boundary of the material and its intersections with evolving crack surfaces. Our approach uses a simple and efficient element splitting strategy for tetrahedron meshes to represent crack surfaces that utilizes an extrapolation technique based on the MPM simulation. For traditional particle-based MPM we use an initial Delaunay tetrahedralization to connect randomly initialized MPM particles. Our visualization technique is a post-process and can be run after the MPM simulation for efficiency. We demonstrate our method with a number of challenging simulations of ductile failure with considerable and persistent self-contact.