Miracle-dz/UniFAR-FTUs huggingface dataset:
Scalable Video Coding for Backward-Compatible 360° Video Delivery Over Broadcast Networks

Recently, the coding and transmission of immersive 360° video has been intensely studied. The technologies provided by standards developing organizations mainly address requirements coming from over-the-top services. The terrestrial broadcast remains in many countries the mainstream medium to deliver high quality contents to a wide audience. To enable seamless introduction of immersive 360° video services over terrestrial broadcast, the deployed technologies shall fulfill requirements such as backward compatibility to legacy receivers and high bandwidth efficiency. While bandwidth efficiency is addressed by existing techniques, none of them enables legacy video services decoding. In this paper, a novel scalable coding scheme is proposed to enable immersive 360° video services introduction over broadcast networks. The experiments show that the proposed scalable coding scheme provides substantial coding gains of 14.99% compared to simulcast coding and introduces a limited coding overhead of 5.15% compared to 360° single-layer coding. A real-time decoding implementation is proposed, highlighting the relevance of the proposed design. Eventually, an end-to-end demonstrator illustrates how the proposed solution could be integrated in a real terrestrial broadcast environment.
ABSTRACT OF THE THESIS Polarization-Informed Non-Line-of-Sight Imaging on Diﬀuse Surfaces

Non-line-of-sight (NLOS) imaging has relevance in search & rescue, medical imaging, remote sensing, and robotics. Although NLOS methods are maturing, NLOS with normal cameras generally requires special occluders in the scene to remove light transport ambiguity. In this paper, it is shown that polarization reveals unique information about occluded environments, and computation in the polarization domain has sparsity beneﬁts that aid the inverse problem. This is demonstrated via non-line-of-sight imaging on rough, everyday surfaces such as oﬃce/home walls. If successful, it has the potential to enable direct and indirect occluded light source discrimination and passive shape recovery of hidden objects via shape from polarization.
BayesPostEst: An R Package to Generate Postestimation Quantities for Bayesian MCMC Estimation

BayesPostEst is an R (R Core Team, 2019) package with convenience functions to generate and present quantities of interest after estimating Bayesian regression models fit using MCMC via JAGS (Plummer, 2017), Stan (Stan Development Team, 2019), MCMCpack (Martin, Quinn, & Park, 2011), or other MCMC samplers. Quantities of interest include predicted probabilities and changes in probabilities in generalized linear models and analyses of model fit using ROC curves and precision-recall curves. The package also contains two functions to create publication-ready tables summarizing model results with an assessment of substantively meaningful effect sizes.
Neural saliency algorithm guide bi-directional visual perception style transfer

The artistic style transfer of images aims to synthesise novel images by combining the content of one image with the style of another, which is a long-standing research topic and already has been widely applied in real world. However, defining the aesthetic perception from the human visual system is a challenging problem. In this study, the authors propose a novel method for automatic visual perception style transfer. First, they render a novel saliency detection algorithm to automatically perceive the visual attention of an image. Then, different from conventional style transfer algorithm in which style transferring is applied uniformly across all image regions, the authors apply a saliency algorithm to guide the style transferring process, enabling different types of style transferring to occur in different regions. Extensive experiments show that the proposed saliency detection algorithm and the style transfer algorithm are superior in performance and efficiency.
Workflow Nets With Tables and Their Soundness

Workflow nets and their extensions with data flows, e.g., workflow nets with data and colored workflow nets, can well model the business processes of workflow systems, and their soundness guarantees that systems have neither deadlock nor livelock. However, some design requirements related to the changes of data values within multiple business cases are not reflected by these models, and thus, some errors of the modeled systems cannot be detected by them. In this article, we propose workflow nets with tables (WFT-nets) to model workflow systems that are closely related to the database (i.e., different users, records, and attributes). We define the firing rule of WFT-nets via data refinements in order to avoid infinite states caused by unbounded data. We use first-order linear temporal logic to represent the design requirements of records and attributes’ values and then define the soundness of WFT-nets. We construct an algorithm to verify soundness. A tool is developed, and a group of experiments are done to illustrate the effectiveness of our methods.
Wind Turbine Gearbox Fault Diagnosis using SAE-BP Transfer Neural Network

The gearbox is a key component in wind turbines, and the fault diagnosis of gearboxes in wind turbines is a significant process of reliability management. Therefore, a SAE-BP transfer neural network is proposed in this paper for fault diagnosis of gearboxes in wind turbines. The proposed method is conducted by two processes. Firstly, a source task data is served as the training process to pretrain the SAE-BP neural network. The final learned network structure is the transferable weights or parameters that contain the feature information. Then, the learned weights are transferred into a target task with different working and fault conditions as the initial weight of a neural network model. To extract more fault-sensitive features, fast Fourier transform (FFT) is introduced to transform the raw data into a frequency domain. Several comparison experiments are conducted to validate the proposed method, and the results show that the proposed method achieves higher classification accuracy.
Optimal Placement of UAV-Assisted Free-Space Optical Communication Systems With DF Relaying

In this letter, by characterizing source-to-relay and relay-to-destination channel models of the considered unmanned aerial vehicle (UAV)-assisted free-space optical (FSO) relay link, we derive the optimal 3D coordinates of UAV relay as well as optimal optical beam pattern in order to minimize the outage probability. Moreover, we study the impact of physical parameters (such as strength of orientation fluctuations of UAV, height and position of obstacles) on the optimal location of UAV relay.
Feature Weights Menggunakan Particle Swarm Optimization Untuk Sentiment Analysis Penilaian Kepuasan Pelanggan Makanan Kuliner

Mendapatkan rekomendasi makanan kuliner dengan rasa enak dan pelayanan restoran yang terbaik merupakan hal yang paling diharapkan oleh setiap para penikmat kuliner, dan hal ini masih sangat terbatas. Sentiment analysis diterapkan sebagai solusi dengan menggunakan algoritma Support Vector Machine (SVM) karena dapat mengatasi permasalahan yang ada dan merupakan model terbaik saat ini untuk sentiment analysis. Feature weight dengan menggunakan Particle Swarm Optimization (PSO) diusulkan untuk dapat meningkatkan tingkat akurasi klasifikasi. Hasil eksperimen dengan model SVM+PSO dihasilkan sebuah peningkatan akurasi yang lebih baik, hal ini terlihat dari evaluasi yang dilakukan. Berdasarkan hasil yang didapatkan makan model SVM+PSO dapat meningkatkan tingkat akurasi klasifikasi sentiment analysis pada penilaian kepuasan pelanggan terhadap makanan kuliner.
Opportunistic Topological Interference Management

The topological interference management (TIM) problem studies the degrees of freedom (DoF) of partially-connected interference networks with no channel state information (CSI) at the transmitters except the network topology (i.e., partial connectivity). In this paper, we consider a variant of the TIM problem with uncertainty in network topology, where the channel state with partial connectivity is only known to belong to one of <inline-formula> <tex-math notation="LaTeX">$M$ </tex-math></inline-formula> states at the transmitters. In particular, the transmitter has access to all network topological information over <inline-formula> <tex-math notation="LaTeX">$M$ </tex-math></inline-formula> states, but is unaware of which state it falls in exactly for communication. The receiver at any state is aware of the exact state it falls in besides the network topologies of all states, and wish to recover as much highly-prioritized information at current state as possible. We formulate it as the opportunistic TIM problem with network uncertainty modeled by <inline-formula> <tex-math notation="LaTeX">$M$ </tex-math></inline-formula> state-varying network topologies. To adapt to network topology uncertainty and different message decoding priority, joint encoding and opportunistic decoding are enabled at the transmitters and receivers respectively. Specifically, being aware of all possible network topologies, each transmitter sends a signal jointly encoded from all messages desired over <inline-formula> <tex-math notation="LaTeX">$M$ </tex-math></inline-formula> states, say <inline-formula> <tex-math notation="LaTeX">$M$ </tex-math></inline-formula> distinct messages, and at a certain State <inline-formula> <tex-math notation="LaTeX">$m$ </tex-math></inline-formula>, Receiver <inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> wishes to opportunistically decode the first <inline-formula> <tex-math notation="LaTeX">$\pi _{k}(m) \in \{1,2,\cdots,M\}$ </tex-math></inline-formula> higher-priority messages. Under this opportunistic TIM setting, we construct a multi-state conflict graph to capture the mutual conflict of messages over <inline-formula> <tex-math notation="LaTeX">$M$ </tex-math></inline-formula> states, and characterize the optimal DoF region of two classes of network topologies via polyhedral combinatorics. A remarkable fact is that, under an additional mild monotonous condition, the optimality conditions of orthogonal access and one-to-one interference alignment still apply to TIM with uncertainty in network topology.
Face Dynamic Modeling Based on Deep Learning and Feature Extraction

Facial modeling is a key step to model visual effects in special movie effects and computer games. In this paper, a method based on the combination of deep learning and feature extraction is proposed for the modeling of 3D face model. Firstly, the face region is located for the captured face image. And then, the facial feature points are extracted by the landmark algorithm and the Convolutional Neural Network (CNN) is used to classify the facial expressions. Next, a special expression 3D face model is created by the deformation of the standard 3D face model based on the facial expressions classification result. Finally, the 3D face model and the extracted facial feature points are combined to perform personalized adjustment of the 3D model to complete a 3D facial expression animation system. The experimental results show that the proposed method can effectively perform the dynamic 3D face modeling which has high reality.
CONSISTENCY IN REMOTE SENSING-BASED URBAN MAPPING FOR GROWTH AND MOBILITY ANALYSIS

Abstract. This extended abstract presents initial results from a study that focus on the accuracy of satellite-based maps of Accra’s urban expansion as well as their comparability and ability to provide a basis for assessing urban spatial growth dynamics. Within the study we have analysed five different satellite-derived maps of urban development in Accra and compared the results and underlying methods. It is discussed how these maps can be operationalized and, combined with census data and digital road maps, used for calculating how flood events that disable parts of the transport infrastructure impact the overall mobility of the Accra population.
Pricing the cloud: a QoS-based auction approach

ABSTRACT Cloud computing is an evolutionary technology that offers on-demand resources and elastic services through the Internet. Most providers adopt fixed-price mechanisms (e.g. pay-as-you-go). However, a few providers have recently employed auction-like approaches to price cloud services. Meanwhile, cloud consumers pay more attention to Quality of Service (QoS) such as availability, which measures how well a service is performed. This paper proposes a novel auction approach that can efficiently allocate resources according to customers’ QoS preferences. The QoS-based pricing can generate more revenue than a fixed-price strategy. This research lies at the intersection of cloud computing, economics, and information systems.
Real-time 360 Body Scanning System for Virtual Reality Research Applications

Here, we present a low-cost solution to perform the online and realistic representation of users using an array of depth cameras. The system is composed of a cluster of 10 Microsoft Kinect 2 cameras, each one associated to a compact NUC PC to stream live depth & color images to a master PC which reconstructs live the point cloud of the scene and can in particular show the body of users standing in the capture area. A custom geometric calibration procedure allows accurate reconstruction of the different 3D data streams. Despite the inherent limitations of depth cameras, in particular sensor noise, the system provides a convincing representation of the user’s body, is not limited by changes in clothing (also during immersion), can capture complex poses and even interactions between two persons or with physical objects. The advantage of using depth cameras over conventional cameras is that little processing is required for dynamic reconstruction of unknown shapes, thus allowing true interactive applications. The resulting live 3D model can be inserted in any virtual environment (e.g. Unity 3D software integration plugin), and can be subject to all usual 3D manipulation and transformations.
How knowledge sharing leads to innovative work behaviour

PurposeStudies have examined the influence of knowledge-sharing factors on attitudes and intentions to share knowledge; thus, there is a need to add to the limited research to examine individuals’ actual knowledge-sharing behaviour (KSB). Drawing upon the social cognitive theory (SCT) and transformational leadership, this study aims to develop a new research model which modifies the standard SCT model and augments it with other theories to examine academics’ KSBs.Design/methodology/approachQuestionnaire surveys based on literature and pilot study were conducted with 785 academic staff from four Vietnamese public universities. This study applied structural equation modelling to test the proposed research model and hypotheses.FindingsThe findings show that environmental factors (subjective norms, trust) and personal factors (knowledge self-efficacy, enjoyment in helping others) had positive impacts on KSB; KSB had a strongly positive effect on innovative behaviour; and transformational leadership positively moderated the effects of subjective norms, trust and knowledge self-efficacy on KSB. Interestingly, psychological ownership of knowledge was found to have insignificant associations with KSB.Practical implicationsThe study findings can be used by university leaders, academic staff and researchers in other similar contexts.Originality/valueUntil now, to the best of the researchers’ knowledge, no studies have applied SCT as a primary lens, in which transformational leadership positioned in a focal behaviour also affected KSB, to investigate research on KSB in organisations, especially in institutions of higher education.
An Automatic Texture Generation Algorithm for 3D Shapes Based on cGAN

Texturing 3D shapes is of great importance in computer graphics with applications ranging from game design to augmented reality. However, the processes of texture generation are usually tedious, time-consuming and labor-intensive. In this paper, we propose an automatic texture generation algorithm for 3D shapes based on conditional Generative Adversarial Networks (cGAN). The core of our algorithm includes sampling the model outline and building a cGAN in order to generate model textures automatically. In particular, we propose a novel edge detection method using 3D model information which can accurately find the outline of the model to improve the quality of the generated texture. Due to the adaptability of the algorithm, our approach is suitable for texture generation for most 3D models. Experimental results show the efficiency of our algorithm which can easily generate high quality model textures.
Pompe disease: A case report

Pompe disease is inherited in an autosomal recessive manner, and is usually observed in the children of asymptomatic carriers. Pompe disease, known as Glycogen Storage Disorder type II, is caused by pathogenic mutations in the gene encoding lysosomal acid alpha-glucosidase ( GAA ). There are three types of Pompe disease: classical infantile form, non-classical infantile form and late-onset Pompe disease. Age of onset and severity of the disease determine the type of Pompe disease. We aimed to identify a mutation in GAA gene in parents who were first cousins and their baby girl was passed away due to the Pompe disease. The baby girl had reduced acid alpha-glucosidase activity, but genetic analysis had not been performed. Mutation analysis of parents was performed using high-throughput DNA sequencing method. Heterozygous mutation of c.896 T>C in exon 5 was found in parents, and prenatal diagnosis was performed for their next pregnancy. In conclusion, c.896 T>C substitution in GAA gene may lead to the severe type of Pompe disease. Using a relatively fast and reliable molecular genetic analysis method to confirm the early diagnosis of the Pompe disease is important for the management of the disease. Key words: Pompe disease, GSD2, GAA deficiency, high-throughput DNA sequencing
Causal Inference in Generalizable Environments: Systematic Representative Design

ABSTRACT Causal inference and generalizability both matter. Historically, systematic designs emphasize causal inference, while representative designs focus on generalizability. Here, we suggest a transformative synthesis – Systematic Representative Design (SRD) – concurrently enhancing both causal inference and “built-in” generalizability by leveraging today’s intelligent agent, virtual environments, and other technologies. In SRD, a “default control group” (DCG) can be created in a virtual environment by representatively sampling from real-world situations. Experimental groups can be built with systematic manipulations onto the DCG base. Applying systematic design features (e.g., random assignment to DCG versus experimental groups) in SRD affords valid causal inferences. After explicating the proposed SRD synthesis, we delineate how the approach concurrently advances generalizability and robustness, cause-effect inference and precision science, a computationally-enabled cumulative psychological science supporting both “bigger theory” and concrete implementations grappling with tough questions (e.g., what is context? ) and affording rapidly-scalable interventions for real-world problems.
A Survey on LoRaWAN Architecture, Protocol and Technologies

Internet of Things (IoT) expansion led the market to find alternative communication technologies since existing protocols are insufficient in terms of coverage, energy consumption to fit IoT needs. Low Power Wide Area Networks (LPWAN) emerged as an alternative cost-effective communication technology for the IoT market. LoRaWAN is an open LPWAN standard developed by LoRa Alliance and has key features i.e., low energy consumption, long-range communication, builtin security, GPS-free positioning. In this paper, we will introduce LoRaWAN technology, the state of art studies in the literature and provide open opportunities.
Face Recognition System for Students’ Attendance Register

Face recognition is very important topic because of its applications. The purpose of this work is developing a system that can recognize a person and register postgraduate students' attendance at faculty of specific education, Damietta University, Egypt. The proposed system consists of five stages: image acquisition, face detection, pre-processing, features extraction and classification. Image acquisition to capture real-time images. Face detection to detect face region from the image. Pre-processing stage involve the effective way of suppressing the unwanted distortion of image. Feature Extraction is a method of capturing visual content of images such as extraction of color, texture. In this work, Gray Level Co-occurrence Matrix is used for calculating texture features of the image. Four features namely, Angular Second Moment, Correlation, Inverse Difference Moment and Contrast are computed. Four classifiers were used: KNN (Nearest Neighbor), Naïve Bayes, Decision Tree and Discriminant Analysis. The accuracy of KNN is better than other classifiers therefore, KNN is used. The performance of the proposed system is evaluated by using dataset of postgraduate students' faces.
Optimization Techniques for Prognostics of On-Board Electromechanical Servomechanisms Affected by Progressive Faults

In relatively recent years, electromechanical actuators have gradually replaced systems based on hydraulic power for flight control applications. Electromechanical servosystems are typically operated by electrical machines that transfer rotational power to the controlled elements (e.g. the aerodynamic control surfaces) by means of gearings and mechanical transmission. Compared to electrohydraulic systems, electromechanical actuators offer several advantages, such as reduced weight, simplified maintenance and complete elimination of contaminant, flammable or polluting hydraulic fluids. On-board actuators are often safety critical; then, the practice of monitoring and analyzing the system response through electrical acquisitions, with the aim of estimating fault evolution, has gradually become an essential task of the system engineering. For this purpose, a new discipline, called Prognostics, has been developed in recent years. Its aim is to study methodologies and algorithms capable of identifying such failures and foresee the moment when a particular component loses functionality and is no longer able to meet the desired performance. In this paper, authors have introduced the use of optimization techniques in prognostic methods (e.g. model-based parametric estimation algorithms) and have proposed a new model-based fault detection and identification method, based on Genetic Algorithms optimization approach, able to perform an early identification of the aforesaid progressive failures, investigating its ability to identify timely symptoms alerting that a component is degrading.