Miracle-dz/UniFAR-FTUs huggingface dataset:
Suppléments péréquatifs : problèmes et évaluation dans le contexte vaudois

On reproche souvent aux systemes perequatifs en vigueur de faire dependre de beaucoup les transferts financiers - appeles communement subventions - du montant de la depense engagee et de la situation financiere de la collectivite recipiendaire. Les collectivites en situation financiere difficile peuvent ainsi beneficier d'un taux de subventionnement plus eleve. Cette part additionnelle de transferts constitue ce que l'on appelle un supplement perequatif. Bien que ce type de dispositif soit considere de maniere positive, il peut amener un certain nombre de problemes. L'objectif de cet article est de mettre en evidence ces problemes, et de proposer une facon d'y remedier. Dans cette optique, le point est fait sur la terminologie et la maniere dont sont organises les transferts financiers. Une analyse theorique des effets des systemes existants par rapport aux objectifs d'efficience, d'efficacite et d'equite est egalement menee. L'exemple des transferts financiers entre le canton de Vaud et ses communes permet ensuite d'apprehender concretement cette dimension des supplements perequatifs. Finalement, une solution aux effets pervers provoques par les systemes en vigueur est proposee, et l'exemple vaudois permet de replacer la question des supplements perequatifs dans la perspective de l'ensemble du dispositif de perequation financiere.
Predictor: an app cold start recommender system based on user similarity and app periodicity

The Cold Start Recommender System (RS) for App usage prediction on mobile phones is important for improving new user experience on mobile operating systems. At present, the existing Cold Start RS computes the probability of App launching mainly by mining the potential information of new users and similar users (i.e., collaborative filtering algorithm CF). However, they all fail to take into account App usage periodicity. Therefore, we designed Predictor, a RS that provides App Cold Start prediction for new users on mobile devices. It dynamically combines both App preferences of similar users (user-based CF) and App usage periodicity (item-based CF) through the conditional combination. Compared to other traditional methods, Predictor proposes more appropriate App launching recommendations, and matches the launching expectations of most users.
Solution for Licensing of Software Modules in Automotive Industry

Electronic control units, used in automotive industry, often have many different functionalities and manufacturers need to control the distribution and prices of different variants of the same product. In this paper, analyses of different models for license security and encryption methods are carried out and several potent solutions for licensing of software modules are designed. Ultimately the most beneficial module for automotive industry is proposed. With defined module and with subsequent testing, a program-based library is created ready to be put into the intended devices as licensing controller for software modules.
Generating VR Live Videos with Tripod Panoramic Rig

Recent breakthrough in consumer-level virtual reality (VR) devices brings an increasing demand of VR live content. As converting real life content into VR need complex computations, current techniques can not synthesize 360° 3D VR content with high performance, not to mention real time. We propose an end-to-end system that records a scene using a tripod panoramic rig and broadcasts 360° stereo panorama videos in real time. The system performs a panorama stitching technique which pre-compute 3 stitching seam candidates for dynamic seam switching in the live broadcasting. This technique achieves high frame rates (>30fps) with minimum foreground cutoff and temporal jittering artifacts. Stereo vision quality is also better preserved by a proposed weighting-based image alignment scheme. We demonstrate the effectiveness of our approach on a variety of videos delivering live events. And our system has been successfully used in broadcasting live shows to mobile phone users on a professional live broadcasting platform with about 390 million user visits per month.
Outdoor People Detection in Low Resolution Thermal Images

Presence detection is a main functionality to make our living spaces smarter and is implemented through several kinds of sensors and smart devices. Recent advancements in embedded systems market and technology enable the design of sophisticated solutions in a low-cost and scalable fashion. However, applications of presence detection, such as surveillance or occupancy detection, home automation or smart lighting are built for indoor scenarios. Therefore, many systems weaken their performance when applied outdoor, where ambient conditions have higher variability. In this work, we describe our exploratory study on people detection in outdoor scenarios by use of an 8×8 pixels resolution thermal sensor. We tested different techniques to extract the presence of a person crossing the detection area. We observed that signal to noise ratio depends on the difference between background and human body temperature. To address this, we collected a dataset spanning a wide range of background conditions and different user clothing and we used it to tune and evaluate the proposed detection techniques. As a possible solution, we propose to adapt the threshold with temperature, providing a regression curve to select it and demonstrate benefits against the use of a fixed threshold with all explored techniques.
Terahertz Image Improvement for an Environmentally Friendly Sandwich Structure

In this paper, THz-TDS was used for an environmentally friendly laminate based on agglomerated cork. Fast Fourier transform (FFT) was applied on the raw data to retrieve the phase and amplitude information. Then, a new amplitude polynomial fitting (APF) algorithm was proposed to improve the image performance. Finally, a comparative analysis was given to conclude the advantages of different applied methods.
Salient object detection via multi-scale neural network

Salient object detection, a fundamental of many computer vision tasks, aims to find the most attractive objects in a given image. In this paper, we propose an end-to-end multi-scale neural network for salient object detection. Firstly, we propose heterogeneous dilated block to effectively increases the receptive field of the network, while alleviating the gridding effect problem caused by dilated convolution. Secondly, we replace the traditional interpolation up-sampling layer with a fully learnable up-sampling module to solve the blurry artifacts and improve the accuracy. Finally, we calculate the loss at three different scales, enabling the network to learn better through back-propagation. The proposed method is validated on MSRA and ECSSD datasets, and shown to outperform the state-of-the-art methods.
Radar Signal Intra-Pulse Modulation Recognition Based on Convolutional Neural Network

In this paper, to solve the problem of the low recognition rate of the existing approaches at low signal-to-noise ratio (SNR), an intra-pulse modulation recognition approach for radar signal is proposed. The approach identifies the modulation of radar signals using the techniques of time-frequency analysis, image processing, and convolutional neural network (CNN). Through Cohen class time-frequency distribution (CTFD), the time-frequency images (TFIs) of received signals are extracted. In order to obtain the high-quality TFIs of received signals, we introduce a new kernel function for the CTFD, which has stronger anti-noise ability than Choi–Williams time-frequency distribution. A series of image processing techniques, including 2-D Wiener filtering, bilinear interpolation, and Otsu method, are applied to remove the background noise of the TFI and obtain a fixed-size binary image that contains only morphological features of the TFI. We design a CNN classifier to identify the processed TFIs. The proposed approach can identify up to 12 kinds of modulation signals, including frequency modulation, phase modulation, and composite modulation. Simulation results show that, for 12 kinds of modulation signals, the proposed approach achieves an overall probability of successful recognition of 96.1% when SNR is −6 dB.
Dimensions of Body-Awareness and Depressed Mood and Anxiety

Interoception, the multidimensional ability to sense the physiological condition of the body, is a key mechanism in emotional processing. However, the relationships between interoceptive dimensions and depressed mood and anxiety have not been widely studied. The aim of this secondary analysis, correlational and cross-sectional study, was to determine the relationships among interoceptive accuracy, interoceptive sensibility, depressed mood, and anxiety in adults with hypertension. The sample consisted of 76 adults, predominately African American women. Correlational analysis showed that most participants had low levels of interoceptive accuracy and relatively high levels of interoceptive sensibility. Interoceptive sensibility was negatively associated with depressed mood and anxiety. Interoceptive accuracy and interoceptive sensibility were not associated between each other. Further examination of the dimensions of interoception is needed to better understand the mechanisms by which it is associated with emotions that are known to have an influence on health behaviors and quality of life.
Multivariable Finite-Time Control of 5 DOF Upper-Limb Exoskeleton Based on Linear Extended Observer

An output feedback finite-time control strategy for five degree of freedoms (DOF) upper-limb exoskeleton is investigated in this paper. First, a multivariable second-order sliding mode control is proposed to improve the response speed and stability of the exoskeleton system with parameter uncertainties and external disturbances. Furthermore, for the sake of high control precision with limited information, a finite-time linear extended state observer is constructed to estimate both the unmeasured states and the lumped perturbations. Finally, by utilizing the 5-DOF upper-limb model, simulation results are presented to demonstrate the effectiveness of the control strategy.
Mobile Robot Neuro-Fuzzy Navigation Based VSLAM Features Learning

The presented approach was focused around building intelligence for mobile robot navigation. That was achieved by creating navigation intelligence capabilities while the robot is in motion. The adopted learning paradigm was a five layers Neuro-Fuzzy (NF) learning architecture, due to ability to create and inference for enhanced navigation. To meet such visual data gathering, the mobile robot platform have fully computer-interfaced stereo vision, and reliable 3D perception. Mobile robot intelligence (NF), hence learns navigation (SLAM) maps visual features, as it travels within spaces. Blinding intelligence with visual maps has resulted in better navigation capabilities.
Robot Gripper Movement Accuracy Estimation by Using UWB Radar

This article deals with a non-destructive sensing method for estimation of the robot gripper which performs the single repetitive motions. This method comprises use of an UltraWideband (UWB) Radar, that can perform remote surveillance of a given process. This solution can be applied in some particular cases when conventional methods (e.g., induction or ultrasonic sensors) are not able to detect or estimate anomalies in the long-term behavior of at the first glance device in the suitable conditions. We suppose the case of mechanical damages or situations, e.g., in the meaning of using some malicious practices- attacking software. In the context of accuracy estimation, we demonstrate remote sensing technique by using the UWB radars for control and tracking of the movements in the technological process included robot gripper as a major object of interest.
PASCAV Gripper: a Pneumatically Actuated Soft Cubical Vacuum Gripper

This paper presents the design and testing of a pneumatically actuated silicone rubber gripper - PASCAV. The gripper is based on a Soft Cubic Module (SCM), which has been designed for a variety of applications in a single-block or multi-block arrangements. For PASCAV, an internal chamber in SCM is pneumatically operated to generate a suction effect, which makes the gripper able to grasp objects. The surfaces of the chamber's soft walls enwrap and stick to the target surface, generating sufficient grip to grasp and uphold the object. The preliminary findings demonstrate its ability to grip a range of objects with flat and, uneven or curved surfaces. The approach is successful for objects that fit the size of the actuating vacuum chamber and the designed payload with respect to the size of the cube, hence making it scalable depending upon the application. We describe the development of the gripper and evaluation of its structure and behavior presenting experimental results. The actuation behavior has also been validated based on the finite element analysis (FEA). The results endorse the simple and cost-effective functional design of PASCAV gripper.
Unit Commitment with AC Power Flow Constraints for a Hybrid Transmission Grid

A unit commitment formulation satisfying AC optimal power flow constraints models the resource optimization problem accurately, but is challenging to solve for traditional transmission systems. Integration of generation based on renewable energy sources is often limited by transmission congestion issues in existing grids. For such grids, AC to HVDC conversion schemes are viable and attractive options for capacity expansion. In this respect, we utilize the structural properties of a hybrid AC/HVDC grid architecture with a specific topology to enable an exact mixed integer conic relaxation of the aforementioned problem. The simulation results for the PJM 5-bus system show that the relaxation is exact for the hybrid architecture. Further, it alleviates the network congestion leading to a substantial reduction in generation cost at normal and increased load conditions compared to the reference AC grid. Moreover, the hybrid architecture improves the utilization of the grid to accommodate more demand and generation.
Analyzing Traces from a Google Data Center

Traces collected from an operational Google data center over 29 days represent a very rich and useful source of information for understanding the main features of a data center. In this paper, we characterize the strong heterogeneity of jobs and the medium heterogeneity of machine configurations. We analyze the off-periods of machines. We study the distribution of jobs per category, per scheduling class, per priority and per number of tasks. The distribution of job execution durations shows a high disparity, as does the job waiting time before being scheduled. The resource requests in terms of CPU and memory are also analyzed. The distribution of these parameter values is very useful to develop accurate models and algorithms for resource allocation in data centers.
Ethnic minority position and migrant status as risk factors for psychotic symptoms in the general population: a meta-analysis

Abstract Psychotic symptoms (PS) are experienced by a substantial proportion of the general population. When not reaching a threshold of clinical relevance, these symptoms are defined as psychotic experiences (PEs) and may exist on a continuum with psychotic disorders. Unfavorable socio-environmental conditions, such as ethnic minority position (EMP) and migrant status (MS), may increase the risk of developing PS and PEs. We conducted an electronic systematic review and a meta-analysis assessing the role of EMP and MS for the development and persistence of PS in the general population. Sub-group analyses were performed investigating the influence of ethnic groups, host countries, age, types of PS, and scales. Twenty-four studies met our inclusion criteria. EMP was a relevant risk factor for reporting PS [odds ratio (OR) 1.44, 95% confidence interval (CI) 1.22–1.70) and PEs (OR 1.36, 95% CI 1.16–1.60). The greatest risk was observed in people from the Maghreb and the Middle East ethnic groups in Europe (OR 3.30, 95% CI 2.09–5.21), in Hispanic in the USA (OR 1.98, 95% CI 1.43–2.73), and in the Black populations (OR 1.85, 95% CI 1.39–2.47). We found a significant association between MS and delusional symptoms (OR 1.47, 95% CI 1.33–1.62). We found no association between EMP and persistence of PEs. EMP was associated with increased risk of reporting PS and PEs, and the risk was higher in ethnic groups facing deprivation and discrimination. We found an association between MS and delusional symptoms. These results raise questions about the precise role of socio-environmental factors along the psychosis continuum.
Blind Image Denoising Using Low Rank Matrix Minimization

This paper presents a simple approach for blind image denoising on Low Rank Matrix Minimization (LRMM). The LRMM has been shown to achieve a great success on Additive White Gaussian Noise (AWGN) image denoising. However, the LRMM requires information about noise level before performing the denoising task. This paper modifies the LRMM approach into blind denoising environment in which the noise level is directly estimated from a noisy image. Thus, the denoising process can be effectively performed. As documented in experimental section, the proposed method yields a promising result on blind image denoising.
Machine learning-based approaches to analyse and improve the diagnosis of endothelial dysfunction

Endothelial Dysfunction is achieving increasing importance, because it is strictly related to cardiovascular risks and it provides important prognostic data in addition to the classical ones. This paper introduces a machine learning approach for predicting Endothelial Dysfunction. The approach was applied and tested on a newly collected dataset, “Endothelial Dysfunction Dataset (EDD)” and several machine learning algorithms are compared. This method comprises features related to the anthropometric or pathological characteristics of the analysed subjects. The experiments yield high accuracy, demonstrating the effectiveness and suitability of the proposed approach.
A Comparison of NoSQL and SQL Databases Over the Hadoop and Spark Cloud Platforms Using Machine Learning Algorithms

Machine learning (ML) algorithms have been widely applied to data analytics and prediction. This paper extends our previous work, which proposed how to automatically convert data from the traditional SQL database to the new NoSQL database, to support the entity-relationship (ER) model. Based on our experimental results over two different cloud platforms, the NoSQL database can always provide better performance than the SQL database while executing ML algorithms.
Visually-Induced Motion Sickness Reduction via Static and Dynamic Rest Frames

Visually-induced motion sickness (VIMS), also known as cyber-sickness, is a major challenge for wide-spread Virtual Reality (VR) adoption. VIMS can be reduced in different ways, for example by using high-quality tracking systems and reducing the user's field of view. However, there are no universal solutions for all situations, and a wide variety of techniques are needed in order for developers to choose the most appropriate options depending on their needs. One way to reduce VIMS is through the use of rest frames-portions of the virtual environment that remain fixed in relation to the real world and do not move as the user virtually moves. We report the results of two multi-day within-subjects studies with 44 subjects who used virtual travel to navigate the environment. In the first study, we investigated the influence of static rest frames with fixed opacity on user comfort. For the second study, we present an enhanced version of rest frames that we call dynamic rest frames, where the opacity of the rest frame changes in response to visually perceived motion as users virtually traversed the virtual environment. Results show that a virtual environment with a static or dynamic rest frame allowed users to travel through more waypoints before stopping due to discomfort compared to a virtual environment without a rest frame. Further, a virtual environment with a static rest frame was also found to result in more real-time reported comfort than when there was no rest frame.
Effect of Electrical Stimulation Haptic Feedback on Perceptions of Softness-Hardness and Stickiness While Touching a Virtual Object

With the advantages of small size and light weight, electrical stimulation devices have been investigated for providing haptic feedback in relation to virtual objects. Electrical stimulation devices can directly activate sensory receptors to produce a reaction force or touch sensations. In the current study, we tested a new method of electrically inducing force sensation in the fingertip, presenting haptic feedback designed to alter perceptions of softness, hardness and stickiness. We developed a 3D virtual reality system combined with finger-motion capture and electrical stimulation devices. We conducted two experiments to evaluate our electrical stimulation method and analyzed the effects of electrical stimulation on perception. The first experiment confirmed that participants could distinguish between the directions of the illusory force sensation, reporting whether the stimulation flexed their index finger forward or extended it backward. The second experiment examined the effects of the electric current itself on the intensity of their perception of the softness, hardness and stickiness of a virtual object.
The Experience of Pleasure: A Perspective Between Neuroscience and Psychoanalysis

Pleasure is more than a mere sensory event, but rather it can be conceptualized as a complex, multiform experience involving memory, motivation, homeostasis, and, sometimes, negative affects. According to Freud, affect is a perceptual modality that registers the internal drive state of the subject rather than the objective experience of the external world, and the quality of this perceptual modality is calibrated in degrees of pleasure and displeasure. Within this conceptual framework, the aim of drive is always pleasure, and objects become significant in so far as they provide a way of discharging drives pressure. Subsequent conceptual psychoanalytic developments have partially rejected such metapsychological theorizations, postulating that other intrinsic motivations that are independent from libido can be observed in humans. Intrinsic motivation broadly refers to a set of psychological concepts including the inherent propensity to pursue one’s choices, to seek out novelty and challenges, to satisfy curiosity and competence, and to extend one’s capacities and control over events. What these concepts have in common is an inner endorsement of one’s action, which is the sense that action is self-generated and is one’s own. The notions of pleasure, drives, and affects are all of utmost importance for a neuropsychoanalytic understanding of mental functioning, due to their capability to explain desire, thought, and behavior from the perspective of human subjective experience. The purpose of this paper is thus to discuss psychoanalytic conceptual developments that have addressed pleasure, drives, and affects, in the light of recent findings coming from neurosciences. In particular, we will explore for insights from Panksepp’s theory of primary-process emotional feelings, including the notion of “wanting” and “liking” as dissociable components of reward. In the last part of the paper, we will indicate possible theoretical implications for a neuropsychoanalytic understanding of libido-independent intrinsic motivations and their relationship with the self, including neuroscientific observations on self-related processes, agency, body-ownerships, and attachment.