Miracle-dz/UniFAR-FTUs huggingface dataset:
Estimation of Position and Intensity of Dynamic Light Sources Using Cast Shadows on Textured Real Surfaces

In this paper, we consider the problem of estimating the 3D position and intensity of multiple light sources without using any light probe or user interaction. The proposed approach is twofold and relies on RGB-D data acquired with a low cost 3D sensor. First, we separate albedo/texture and illumination using lightness ratios between pairs of points with the same reflectance property but subject to different lighting conditions. Our selection algorithm is robust in presence of challenging textured surfaces. Then, estimated illumination ratios are integrated, at each frame, within an iterative process to recover position and intensity of light sources responsible of cast shadows. Estimated lighting characteristics are finally used to achieve realistic Augmented Reality (AR).
Flex5G: Flexible Functional Split in 5G Networks

5G networks are expected to support various applications with diverse requirements in terms of latency, data rates, and traffic volume. Cloud–RAN (C–RAN) and densely deployed small cells are two of the tools at disposal of mobile network operators to cope with such challenges. In order to mitigate the fronthaul requirements imposed by the C–RAN architecture, several functional splits, each characterized by a different demarcation point between the centralized and the distributed units, have emerged. However, the selection of the appropriate centralization level (i.e., the functional split) still remains a challenging task, since a number of parameters have to be considered in order to make such a decision. In this paper, a virtual network embedding (VNE) algorithm is proposed to flexibly select the appropriate functional split for each small cell. The VNE is formulated as an integer linear programming (ILP) problem whose objective is to jointly minimize the inter-cell interference and the fronthaul bandwidth utilization by dynamically selecting the appropriate functional split. Specifically, dynamic and static ILP-based algorithms are proposed. Finally, dynamic and static VNE heuristics are proposed to address the scalability problem of the ILP-based algorithms in case of dense and ultra-dense mobile networks, respectively.
IEEE 802.15.4 Air-Ground UAV Communications in Smart Farming Scenarios

Smart farming is one of the most promising applications showing the benefits of using unmanned aerial vehicles (UAVs). Thus, precision agriculture in rural areas may largely benefit from low-cost and easy-to-deploy vehicles able to exchange data with ground sensors for monitoring and controlling automated cultivations. In this letter, we describe, both analytically and empirically, a real testbed implementing IEEE 802.15. 4-based communications between an UAV and fixed ground sensors. In our scenario, we found that aerial mobility limits the actual IEEE 802.15. 4 transmission range among the UAV and the ground nodes to approximately 1/3 of the nominal one. We also provide considerations to design the deployment of sensors in precision agriculture scenarios.
Impact of the Regional Network for AMI in the Management of STEMI on Care Processes, Outcomes and Health Inequities in the Veneto Region, Italy

Cardiovascular diseases are a leading cause of death in Europe. Outcomes in terms of mortality and health equity in the management of patients with ST-Elevation Myocardial Infarction (STEMI) are influenced by health care service organization. The main aim of the present study was to examine the impact of the new organizational model of the Veneto Region’s network for Acute Myocardial Infarction (AMI) to facilitate primary percutaneous coronary intervention (PCI) on STEMI, and its efficacy in reducing health inequities. A retrospective cohort study was conducted on HDRs in the Veneto Region for the period 2007–2016, analyzing 65,261 hospitalizations for AMI. The proportion of patients with STEMI treated with PCI within 24 h increased significantly for men and women, and was statistically much higher for patients over 75 years of age (APC, 75–84: 9.8; >85: 12.5) than for younger patients (APC, <45: 3.3; 45–64: 4.9), with no difference relating to citizenship. The reduction in in-hospital, STEMI-related mortality was only statistically significant for patients aged 75–84 (APC: −3.0 [−4.5;−1.6]), and for Italians (APC: −1.9 [−3.2;−0.6]). Multivariate analyses confirmed a reduction in the disparities between socio-demographic categories. Although the new network improved the care process and reduced health care disparities in all subgroups, these efforts did not result in the expected survival benefit in all patient subgroups.
Operational strategy of high rate anaerobic digester with mixed organic wastes: effect of co-digestion on biogas yield at full scale

ABSTRACT The present research work is aimed at providing an economically feasible solution for the farmers to exploit the mixed organic wastes (MOW) as resources for the generation of biogas based electrical power and utilize the same for irrigation purpose to reduce the dependence on electricity board. A full scale biomethanation plant has been installed based on anaerobic gas lift reactor (AGR) technology to analyse, understand the operational parameters of anaerobic digestion and assess the performance of a high rate biomethanation plant by co-digesting the MOW such as poultry litter (PL), cattle manure (CM) and napier grass (NG) at ambient temperature. The biomethanation plant was incorporated with inline pre and post-processing unit assembly. The plant was fed with 1000 kg of MOW per day having 250 kg of total solids, about 178–200 kg of volatile solids and operated continuously for 52 weeks under ambient temperature. Electrical power generated (84.5–104 kWh/day) from biogas (65–80 m3/day) containing methane (40–48 m3/day) was used for operating the water pumps for agricultural purpose and the digestate (115–130 kg/day) was exploited as organic manure for growing crops in the same field. Napier grass was grown in the same land and other feedstocks were procured from the nearby area at the cost of $10–$15 per ton. Around 6 acres of land was being cultivated using the biogas based power generated from the MOW that was being used for growing vegetables and maize. GRAPHICAL ABSTRACT
Using the Partial Directed Coherence to Assess Functional Connectivity in Electroencephalography Data for Brain–Computer Interfaces

In this paper, we propose a statistical selection procedure by which various mental tasks can be characterized by specific brain functional connectivity. Different connectivity patterns are identified by the partial directed coherence (PDC) which is a frequency-domain metric that provides information about directionality in the interaction between signals recorded at different sensors. The basis of our selection is a statistical analysis of the directed connectivities revealed by their repeated appearance and larger PDC magnitudes in sets of electroencephalography (EEG) sensors treated as networks. Hence, our proposed method identifies significant differences between directed connectivities on EEG-sensor networks that are specific to the mental tasks involved. A combinatory analysis of different possible networks allows us to find those that characterize and discriminate the tasks and, as proof-of-concept, we analyze the connectivities of movement imageries (MIs) used in the operation of a brain–computer interface. The directed interconnections revealed by our proposed method are in agreement with brain functional connectivities already reported for MIs, and good classification rates are achieved when such interconnections are used as features in a Mahalanobis-distance-based classifier.
Algorithmic Music Composition Comparison

This paper presents the application of Machine Learning (ML) algorithm as an algorithmic music composer, compared to a rule-based algorithm. The ML model is based on LSTMs which takes in previous notes and predicts the next set of notes based on a midi format. For the rule-based method, we apply chord progression rules and binary rhythm pattern theory. We used both algorithms to generate music in two different genres, namely rock, and jazz. To evaluate the effectiveness of the algorithms, fifteen raters are asked to identify the genre of the generated songs. The results showed 77.33% of the rulebased algorithms Jazz songs were correctly identified, compared to the 62.67% generated by the LSTM. For the rock genre, only 49.33% percent of rule-based algorithms songs and 44% Machine Learning algorithms songs were correctly identified. In terms of music satisfaction, the rule-based algorithm on average obtains higher scores in both genres, 2.17 for Jazz and 2.42 for Rock while Machine Learning algorithm receives 1.83 for Jazz songs and 1.57 for Rock.
On the quality of an expressive audiovisual corpus: a case study of acted speech

In the context of developing an expressive audiovisual speech synthesis system, the quality of the audiovisual corpus from which the 3D visual data will be extracted is important. In this paper, we present a perceptive case study on the quality of the expressiveness of a set of emotions acted by a semi-professional actor. We have analyzed the production of this actor pronouncing a set of sentences with acted emotions, during a human emotion-recognition task. We have observed different modalities: audio, real video, 3D-extracted data, as unimodal presentations and bimodal presentations (with audio). The results of this study show the necessity of such perceptive evaluation prior to further exploitation of the data for the synthesis system. The comparison of the modalities shows clearly what the emotions are, that need to be improved during production and how audio and visual components have a strong mutual influence on emotional perception.
Gendered violence and overdose prevention sites: a rapid ethnographic study during an overdose epidemic in Vancouver, Canada

BACKGROUND AND AIMS North America's overdose epidemic is increasingly driven by fentanyl and fentanyl-adulterated drugs. Supervised consumption sites, including low-threshold models (termed overdose prevention sites; OPS), are now being debated in the United States and implemented in Canada. Despite evidence that gendered and racialized violence shape access to harm reduction among women who use drugs (WWUD), this has not been examined in relation to OPS and amid the overdose epidemic. This study explores how overlapping epidemics of overdose and gendered and racialized violence in Vancouver's Downtown Eastside, one of North America's overdose epicenters, impacts how marginalized WWUD experience OPS. DESIGN Qualitative analysis using rapid ethnographic fieldwork. Data collection included 185 hours of naturalistic observation and in-depth interviews; data were analyzed thematically using NVivo. SETTING Vancouver, Canada. PARTICIPANTS Thirty-five WWUD recruited from three OPS. MEASUREMENTS Participants' experiences of OPS and the public health emergency. FINDINGS The rapid onset and severity of intoxication associated with the use of fentanyl-adulterated drugs in less regulated drug use settings not only amplified WWUD's vulnerability to overdose death but also violence. Participants characterized OPS as safer spaces to consume drugs in contrast to less regulated settings, and accommodation of assisted injections and injecting partnerships was critical to increasing OPS access among WWUD. Peer-administered injections disrupted gendered power relations to allow women increased control over their drug use; however, participants indicated that OPS were also gendered and racialized spaces that jeopardized some women's access. CONCLUSION Although women who use drugs in Vancouver, Canada appear to feel that overdose prevention sites address forms of everyday violence made worse by the overdose epidemic, these sites remain 'masculine spaces' that can jeopardize women's access.
Coordinated Control of DFIG Based Wind Farms and SGs for Improving Transient Stability

This paper proposes a coordinated control of doubly fed induction generator (DFIG)-based wind farms’ (WFs) post-fault active power and synchronous generators’ (SGs) tripping with the aim of improving transient stability of both the first swing and multi swings. To achieve it, the impact mechanism of the WFs’ post-fault active power and SGs’ tripping on the stability margin of each swing is first presented by using extended equal area criterion (EEAC). Based on this, a principle of the coordinated control is put forward. The WFs’ control period is designed as six stages, and the value of post-fault active power in each stage is suggested to improve the stability of the first five swings and maintain the post-fault steady state. To decrease the tripping amount of the SGs, the SGs are tripped only when WFs’ control effect is not sufficient to avoid the instability. Then, by utilizing an “online pre-decision and real-time matching” scheme, an engineering application method for the control principle is proposed, where the control procedures and detailed parameters calculation are demonstrated. The numerical simulations show that the coordinated control has a better damping effect and a less control cost (less amount of tripped SGs) than the traditional DFIG control or traditional SG tripping.
A High-Performance and Cost-Effective Hardware Merge Sorter without Feedback Datapath

We propose a high-performance and cost-effective hardware merge sorter (HMS) without any feedback datapaths in order to develop the fastest hardware sorting accelerator. The operating frequencies of existing HMSs are severely limited by the presence of feedback datapaths. We show the idea of eliminating the feedback datapaths, and propose a concrete architecture adopting the idea and some implementation optimizations. The evaluation results show that our HMS achieves 1.59x throughput improvement with less hardware resources compared to the state-of-the-art HMS.
Subchannel Allocation and Hybrid Precoding in Millimeter-Wave OFDMA Systems

Constrained by the number of transmitted data streams, this paper proposes sub-carrier allocation (SA) and hybrid precoding (HP) designs for sum-rate maximization in mm-wave OFDMA systems. The optimization is first formulated as a computation sparsity-constrained HP design problem, which is non-convex and challenging to solve. Two two-stage solution approaches are proposed. In the first approach, a fully digital precoder (FDP) is optimized considering the computation sparsity constraint in the first stage. In the second approach, the sparsity constraint is only imposed in the second stage. To find the FDP, we employ the minimization of the weighted mean-squared error and the $\ell 1$ -reweighted methods to tackle the non-convex objective function and sparsity constraints, respectively. In the second stage of each approach, we exploit an alternating weighted mean-squared error minimization algorithm to reconstruct HP’s based on the FDP found in the first stage. Two novel analog precoding designs, namely semi-definite-relaxation-based and projected-gradient-descent-based, are then proposed to optimize the analog part of the obtained HP’s. We also study the impacts of various system parameters on the system sum-rate and provide resource provisioning insights for HP systems. Numerical results show the superior performances of the proposed designs over joint SA and HP benchmark algorithms.
Very Wide-Band and Compact VO2 Based Switches

This paper presents the design, fabrication and high frequency characterization of two terminals RF switches based on Vanadium dioxide (VO2) material. This material presents a thermally induced metal-insulator transition showing a resistivity variation up to five orders of magnitude as the material change between the insulator state and the metallic state. We present the design of 2-terminal RF switch and the integration of this structure into series-shunt switch structure to increase the isolation on the OFF state. The proposed device has less than 2 dB loss and more than 30 dB isolation up to 70 GHz.
A Deep Learning Methodology for Automatic Assessment of Portrait Image Aesthetic Quality

Generally, a traditional methodology to assess the aesthetics (appreciating beauty) of a photograph involves a number of professional photographers rating the photo based on given criteria and providing ensemble feedback minimize bias. Such a traditional photo assessment method, however, is not applicable to massive users, especially in real-time. To mitigate such an issue, recent studies have devoted on developing algorithms to automatically provide feedback to photo takers. Most of such algorithms train variants of neural networks using ground-truth photos assessed by professional photographers. Regardless, most existing photo assessment algorithms provide the aesthetic score as a single number. From our observation, users typically use multiple criteria to justify the beautifulness of a photo, and hence a single rating score may not be informative. In this paper, we propose a novel Fine-tuned Inception with Fully Connected and Regression Layers model which gives five attribute scores: vivid colour, colour harmony, lighting, balance of elements, and depth of field. T his s olution i ncorporates t he p re-trained inception model which is the state-of-the-art model for processing images. Our proposed algorithm enhances the existing state-of-the-art by fine-tuning the parameters, introducing fully connected layers, and attaching the regression layers to compute the numeric score for each focus attribute. The experimental results show that our model helps to decrease the mean absolute error (MAE) to 0.211, benchmarking on the aesthetics and attributes datasets provided in the previous studies.
Eeg-Based Video Identification Using Graph Signal Modeling and Graph Convolutional Neural Network

This paper proposes a novel graph signal-based deep learning method for electroencephalography (EEG) and its application to EEG-based video identification. We present new methods to effectively represent EEG data as signals on graphs, and learn them using graph convolutional neural networks. Experimental results for video identification using EEG responses obtained while watching videos show the effectiveness of the proposed approach in comparison to existing methods. Effective schemes for graph signal representation of EEG are also discussed.
Challenges for Scheduling Scientific Workflows on Cloud Functions

Serverless computing, also known as Function-as-a-Service (FaaS) or Cloud Functions, is a new method of running distributed applications by executing functions on the infrastructure of cloud providers. Although it frees the developers from managing servers, there are still decisions to be made regarding selection of function configurations based on the desired performance and cost. The billing model of this approach considers time of execution, measured in 100ms units, as well as the size of the memory allocated per function. In this paper, we look into the problem of scheduling scientific workflows, which are applications consisting of multiple tasks connected into a dependency graph. We discuss challenges related to workflow scheduling and propose the Serverless Deadline-Budget Workflow Scheduling (SDBWS) algorithm adapted to serverless platforms. We present preliminary experiments with a small-scale Montage workflow run on the AWS Lambda infrastructure.
The World of Malware: An Overview

Malware, short for malicious software is a program code that is hostile and often used to corrupt or misuse a system. Introducing malware into a computer network environment has different effects depending on the design intent of the malware and the network layout. Malware detection and prevention systems are bypassed by malicious files in computer systems as malware become more complex and large in numbers. This paper presents an overview of the world of malware with the intent of providing the underlying information for the intended study into developing malware detection approaches.
MDPbiome: microbiome engineering through prescriptive perturbations

Motivation Recent microbiome dynamics studies highlight the current inability to predict the effects of external perturbations on complex microbial populations. To do so would be particularly advantageous in fields such as medicine, bioremediation or industrial scenarios. Results MDPbiome statistically models longitudinal metagenomics samples undergoing perturbations as a Markov Decision Process (MDP). Given a starting microbial composition, our MDPbiome system suggests the sequence of external perturbation(s) that will engineer that microbiome to a goal state, for example, a healthier or more performant composition. It also estimates intermediate microbiome states along the path, thus making it possible to avoid particularly undesirable/unhealthy states. We demonstrate MDPbiome performance over three real and distinct datasets, proving its flexibility, and the reliability and universality of its output ‘optimal perturbation policy’. For example, an MDP created using a vaginal microbiome time series, with a goal of recovering from bacterial vaginosis, suggested avoidance of perturbations such as lubricants or sex toys; while another MDP provided a quantitative explanation for why salmonella vaccine accelerates gut microbiome maturation in chicks. This novel analytical approach has clear applications in medicine, where it could suggest low‐impact clinical interventions that will lead to achievement or maintenance of a healthy microbial population, or alternately, the sequence of interventions necessary to avoid strongly negative microbiome states. Availability and implementation Code (https://github.com/beatrizgj/MDPbiome) and result files (https://tomdelarosa.shinyapps.io/MDPbiome/) are available online. Supplementary information Supplementary data are available at Bioinformatics online.
A Bibliographic and Coauthorship Analysis of IEEE T-ITS Literature Between 2014 and 2016

We present a bibliographic and coauthorship-based collaboration analysis of papers published in the IEEE Transactions on Intelligent Transportation Systems (T-ITS) between 2014 and 2016 from the aspects of productivity, topics, citations, usage, and coauthorship networks. The most productive authors, institutions, countries/regions, and the most cited papers, most popular papers, as well as the most frequent topics and their trends are identified and analyzed. Social network methods are employed for revealing collaboration patterns among contributors through author- and institution-level coauthorship. The results show that China is playing a critical role in ITS research during this period but the interinstitution collaborations are less prevalent than it used to be. Overall data have indicated that IEEE T-ITS has made tremendous progress and contributed significantly to the accelerated growth of ITS fields over the last three years.