Miracle-dz/UniFAR-FTUs huggingface dataset:
Federated Echo State Learning for Minimizing Breaks in Presence in Wireless Virtual Reality Networks

In this paper, the problem of enhancing the virtual reality (VR) experience for wireless users is investigated by minimizing the occurrence of breaks in presence (BIP) that can detach the users from their virtual world. To measure the BIP for wireless VR users, a novel model that jointly considers the VR application type, transmission delay, VR video quality, and users’ awareness of the virtual environment is proposed. In the developed model, base stations (BSs) transmit VR videos to the wireless VR users using directional transmission links so as to provide high data rates for the VR users, thus, reducing the number of BIP for each user. Since the body movements of a VR user may result in a blockage of its wireless link, the location and orientation of VR users must also be considered when minimizing BIP. The BIP minimization problem is formulated as an optimization problem which jointly considers the predictions of users’ locations, orientations, and their BS association. To predict the orientation and locations of VR users, a distributed learning algorithm based on the machine learning framework of deep echo state networks (ESNs) is proposed. The proposed algorithm uses federated learning to enable multiple BSs to locally train their deep ESNs using their collected data and cooperatively build a learning model to predict the entire users’ locations and orientations. Using these predictions, the user association policy that minimizes BIP is derived. Simulation results demonstrate that the developed algorithm reduces the users’ BIP by up to 16% and 26%, respectively, compared to centralized ESN and deep learning algorithms.
Matrix Factorization via Deep Learning

Matrix completion is one of the key problems in signal processing and machine learning. In recent years, deep-learning-based models have achieved state-of-the-art results in matrix completion. Nevertheless, they suffer from two drawbacks: (i) they can not be extended easily to rows or columns unseen during training; and (ii) their results are often degraded in case discrete predictions are required. This paper addresses these two drawbacks by presenting a deep matrix factorization model and a generic method to allow joint training of the factorization model and the discretization operator. Experiments on a real movie rating dataset show the efficacy of the proposed models.
A framework for constructing open data map visualizations

Open Government Data has been made available by public institutions in Brazil and the world, and can add value to various sectors of society. Open data is also linked to smart cities, and hence important in this context, as it is the first step towards public transparency. Despite the wide range of Open Government Data, interpreting such data sets is a non-trivial task, due to the massive amount of raw data. This stimulates the search for techniques and methodologies that allow the interpretation of implicit information and deduction of new knowledge. One of the approaches used for these tasks involves the use of data visualizations. In addition to data visualizations classically used in descriptive statistics for data analysis, such as line or bar charts, many web sites have been used map visualization techniques. This type of visualization is important, since visualization of georeferenced data combined with other types of information can aid its interpretation. However, for data visualization construction on maps, it is necessary that the objects to be visualized are georeferenced. There are standards for turning such data available, however they are diverse, which may make it difficult for a single tool to display views from different sources. This work aims to present a framework that defines data standards for constructing data visualizations on maps of various types. Based on this framework, a tool was implemented to facilitate the creation of different map views, both by developers of open data portals and by users who analyze such data.
Context-Driven and Real-Time Provisioning of Data-Centric IoT Services in the Cloud

The convergence of Internet of Things (IoT) and the Cloud has significantly facilitated the provision and management of services in large-scale applications, such as smart cities. With a huge number of IoT services accessible through clouds, it is very important to model and expose cloud-based IoT services in an efficient manner, promising easy and real-time delivery of cloud-based, data-centric IoT services. The existing work in this area has adopted a uniform and flat view to IoT services and their data, making it difficult to achieve the above goal. In this article, we propose a software framework, Context-driven And Real-time IoT (CARIoT) for real-time provisioning of cloud-based IoT services and their data, driven by their contextual properties. The main idea behind the proposed framework is to structure the description of data-centric IoT services and their real-time and historical data in a hierarchical form in accordance with the end-user application’s context model. CARIoT features design choices and software services to realize this service provisioning model and the supporting data structures for hierarchical IoT data access. Using this approach, end-user applications can access IoT services and subscribe to their real-time and historical data in an efficient manner at different contextual levels, e.g., from a municipal district to a street in smart city use cases. We leverage a popular cloud-based data storage platform, called Firebase, to implement the CARIoT framework and evaluate its efficiency. The evaluation results show that CARIoT’s hierarchical structure imposes no additional overhead with less data notification delay as compared to existing flat structures.
Type-After-Type: Practical and Complete Type-Safe Memory Reuse

Temporal memory errors, such as use-after-free bugs, are increasingly popular among attackers and their exploitation is hard to stop efficiently using current techniques. We present a new design, called Type-After-Type, which builds on abstractions in production allocators to provide complete temporal type safety for C/C++ programs---ensuring that memory reuse is always type safe---and efficiently hinder temporal memory attacks. Type-After-Type uses static analysis to determine the types of all heap and stack allocations, and replaces regular allocations with typed allocations that never reuse memory previously used by other types. On the heap, Type-After-Type splits available memory into separate pools for each type. For the stack, Type-After-Type efficiently implements type-safe memory reuse for the first time, pushing variables on separate stacks according to their types, unless they are provably safe (e.g., their address is not taken), in which case they are zero-initialized and kept on a special stack. In our evaluation, we show that Type-After-Type stops a variety of real-world temporal memory attacks and on SPEC CPU2006 incurs a performance overhead of 4.3% and a memory overhead of 17.4% (geomean).
Simulation Analysis of Ultrasonic Guided Waves on Gas Flow Measurement

Clamp-on gas flowmeters based on ultrasonic guided waves are easy to installed, with a wide range of application, without affecting the pipe structure. In this paper, the gas flow measurement based on ultrasonic guided waves is studied. And the finite element simulation method is used to conduct guided wave excitation and gas flow measurement experiments on gas-containing pipelines. The excitation simulation of guided wave modes is performed at different frequencies on different material pipelines. The results show that the selection of the excitation signal frequency and guided wave modes needs to consider with the dispersion curve and the vibration displacement distribution of the guided waves on the pipe wall together. Finally, the flow simulation experiment of the gas-containing pipeline is performed. The ratio of the time difference between the simulation result and the theoretical value is constant, which further explains the necessity of the compensation coefficient in the flow measurement formula.
Chaplaincy, Spiritual Care and Moral Injury: Considerations Regarding Screening and Treatment

Moral injury is a complex trauma related syndrome involving a correlation of biological, psychological, social, and spiritual symptoms that can have substantial impact upon health and well-being. This paper argues for a holistic bio-psycho-social-spiritual approach to moral injury, by including chaplaincy in the screening and treatment of moral injury among actively serving military members and retired veterans. As part of the moral injury treatment process, and in alignment with the World Health Organization's Spiritual Intervention Codings, a new technique is proposed, “Pastoral Narrative Disclosure” (PND), as a guide for chaplains and others trained in spiritual care to assist those suffering from moral injury.
Deep Inception Generative Network for Cognitive Image Inpainting

Recent advances in deep learning have shown exciting promise in filling large holes and lead to another orientation for image inpainting. However, existing learning-based methods often create artifacts and fallacious textures because of insufficient cognition understanding. Previous generative networks are limited with single receptive type and give up pooling in consideration of detail sharpness. Human cognition is constant regardless of the target attribute. As multiple receptive fields improve the ability of abstract image characterization and pooling can keep feature invariant, specifically, deep inception learning is adopted to promote high-level feature representation and enhance model learning capacity for local patches. Moreover, approaches for generating diverse mask images are introduced and a random mask dataset is created. We benchmark our methods on ImageNet, Places2 dataset, and CelebA-HQ. Experiments for regular, irregular, and custom regions completion are all performed and free-style image inpainting is also presented. Quantitative comparisons with previous state-of-the-art methods show that ours obtain much more natural image completions.
Hunting out graphic images from real images using recurrent neural network and extended principal color components

With recent graphics technology creates surprisingly realistic contents, most of such artificial creatures help immersive virtual experience. On the other hand, still human can recognize whether an observed visual information is real or graphic model. In this work, we propose a deep learning based graphic and real image classification method to hunt out a graphic image from real images. In order to employ a deep learning approach, we have built graphic-real image data set consists of around 25K images. Quantitative classification and qualitative graphic image hunting results are presented that helps interesting applications such as fake image detection or image realism enhancement.
Temporal sparse feature auto‐combination deep network for video action recognition

In order to deal with action recognition for large‐scale video data, we present a spatio‐temporal auto‐combination deep network, which is able to extract deep features from short video segments by making full use of temporal contextual correlation of corresponding pixels among successive video frames. Based on conventional sparse encoding, we further consider the representative features in adjacent nodes of the hidden layers according to activation states similarities. A sparse auto‐combination strategy is applied to multiple input maps in each convolution stage. An information constraint of the representative features of hidden layer nodes is imposed to handle the adaptive sparse encoding of the topology. As a result, the learned features can represent the spatio‐temporal transition relationships better and the number of hidden nodes can be restricted to a certain range. We conduct a series of experiments on two public data sets. The experimental results show that our approach is more effective and robust in video action recognition compared with traditional methods.
Solving the Missing Data Problem in Urban Traffic Estimation with Principal Component Analysis

With the widespread deployment of intelligent transportation systems (ITS), there is an urgent need for observing real-time traffic conditions and trends more reliably. However, due to sensor and communication errors, the probe vehicle data commonly used in traffic estimation often face a serious missing data problem. To solve this problem, this paper proposes an algorithm based on principal component analysis (PCA). First, the missing data problem is described with a real probe car data. Then, an algorithm based on PCA is proposed to estimate the missing elements in the traffic condition matrix. Finally, some experiments were conducted to evaluate the performance of the proposed algorithm with a real probe dataset of over 10,000 vehicles in Beijing, China. Results of these experiments indicate that the proposed algorithm outperforms other competing algorithms significantly, including the KNN (K-Nearest Neighbor) algorithm and the baseline algorithm.
Towards Continuous Domain adaptation for Healthcare

Deep learning algorithms have demonstrated tremendous success on challenging medical imaging problems. However, post-deployment, these algorithms are susceptible to data distribution variations owing to \emph{limited data issues} and \emph{diversity} in medical images. In this paper, we propose \emph{ContextNets}, a generic memory-augmented neural network framework for semantic segmentation to achieve continuous domain adaptation without the necessity of retraining. Unlike existing methods which require access to entire source and target domain images, our algorithm can adapt to a target domain with a few similar images. We condition the inference on any new input with features computed on its support set of images (and masks, if available) through contextual embeddings to achieve site-specific adaptation. We demonstrate state-of-the-art domain adaptation performance on the X-ray lung segmentation problem from three independent cohorts that differ in disease type, gender, contrast and intensity variations.
Optimization of checkpointing/recovery strategy in cloud computing with adaptive storage management

Cloud Computing is a type of distributed system that is usually based on the services offered to the user based on SLA contract. In this case, the implementation of a fault‐tolerant system that ensures the reliability and the services continuity becomes a major requirement. In this paper, we propose a fault tolerance strategy based on checkpointing and replication. Our approach uses a smart checkpoint infrastructure for cloud computing tasks. The checkpoints are stored in alternative already paid VMs. This allows resuming a task execution faster and cheaper after a node crash. Since checkpoints are distributed and replicated, our approach increases also the system reliability. The experimental results show the effectiveness of the proposed strategy in term of energy consumption, SLA (System Level Aggregation) violation, and reliability.
Oral Health Impact Profile and Associated Variables in Southern Brazilian Drug Users

Background: This study investigated the association between sociodemographic, behavioral, oral health, variables of drug consumption, and the short version of the Oral Health Impact Profile (OHIP-14), in Southern Brazilian drug users. Methods: In this cross-sectional study, 202 drug users aged 18 or over admitted for treatment at the Institute for Research and Treatment of Alcoholism (Instituto de Pesquisa e Tratamento do Alcoolismo -IPTA) from the municipality of Campo Largo, PR, Brazil, from 2012 to 2014, were involved. They answered a questionnaire and were examined by a calibrated researcher. Data were collected and bivariate (Chi-square test) and multivariate (logistic regression and Wald’s test) analyses were performed. Results: There was statistical difference (P<0.05) in bivariate analysis and the worst impacts were reported by non-whites, those who reported feeling metallic taste in mouth and tooth mobility, which use cigarettes for over 15 yr and consume more than 3 g of crack/day, with DMFT >10, and number of teeth in mouth ≤27. In the multivariate analysis, statistical difference remained, except for ethnicity (P=0.207). Conclusion: Self-perception of oral health was associated with the variables explored, which suggests the need for strategies focused on this population.
Integrating Experiment and Theory to Understand TCR-pMHC Dynamics

The conformational dynamism of proteins is well established. Rather than having a single structure, proteins are more accurately described as a conformational ensemble that exists across a rugged energy landscape, where different conformational sub-states interconvert. The interaction between αβ T cell receptors (TCR) and cognate peptide-MHC (pMHC) is no exception, and is a dynamic process that involves substantial conformational change. This review focuses on technological advances that have begun to establish the role of conformational dynamics and dynamic allostery in TCR recognition of the pMHC and the early stages of signaling. We discuss how the marriage of molecular dynamics (MD) simulations with experimental techniques provides us with new ways to dissect and interpret the process of TCR ligation. Notably, application of simulation techniques lags behind other fields, but is predicted to make substantial contributions. Finally, we highlight integrated approaches that are being used to shed light on some of the key outstanding questions in the early events leading to TCR signaling.
Evolution of cooperation on an epithelium.

Cooperation is prevalent in nature, not only in the context of social interactions within the animal kingdom, but also on the cellular level. In cancer for example, tumour cells can cooperate by producing growth factors. The evolution of cooperation has traditionally been studied for well-mixed populations under the framework of evolutionary game theory, and more recently for structured populations using evolutionary graph theory. The population structures arising due to cellular arrangement in tissues however are dynamic and thus cannot be accurately represented by either of these frameworks. In this work we compare the conditions for cooperative success in an epithelium modelled using evolutionary graph theory, to those in a mechanical model of an epithelium =- the Voronoi tessellation model. Crucially, in this latter model cells are able to move, and birth and death are not spatially coupled. We calculate fixation probabilities in the Voronoi tessellation model through simulation and an approximate analytic technique and show that this leads to stronger promotion of cooperation in comparison with the evolutionary graph theory model.
BaDS: Blockchain-Based Architecture for Data Sharing with ABS and CP-ABE in IoT

Internet of Things (IoT) and cloud computing are increasingly integrated, in the sense that data collected from IoT devices (generally with limited computational and storage resources) are being sent to the cloud for processing, etc., in order to inform decision making and facilitate other operational and business activities. However, the cloud may not be a fully trusted entity, like leaking user data or compromising user privacy. Thus, we propose a privacy-preserving and user-controlled data sharing architecture with fine-grained access control, based on the blockchain model and attribute-based cryptosystem. Also, the consensus algorithm in our system is the Byzantine fault tolerance mechanism, rather than Proof of Work.
Self-supervised Learning of Dense Shape Correspondence

We introduce the first completely unsupervised correspondence learning approach for deformable 3D shapes. Key to our model is the understanding that natural deformations (such as changes in pose) approximately preserve the metric structure of the surface, yielding a natural criterion to drive the learning process toward distortion-minimizing predictions. On this basis, we overcome the need for annotated data and replace it by a purely geometric criterion. The resulting learning model is class-agnostic, and is able to leverage any type of deformable geometric data for the training phase. In contrast to existing supervised approaches which specialize on the class seen at training time, we demonstrate stronger generalization as well as applicability to a variety of challenging settings. We showcase our method on a wide selection of correspondence benchmarks, where we outperform other methods in terms of accuracy, generalization, and efficiency.
Do parliaments underrepresent women’s policy preferences? Exploring gender equality in policy congruence in 21 European democracies

ABSTRACT Although there are considerably more men than women in most parliaments around the world, we know little about whether male-dominated legislatures neglect women’s policy preferences. Our article addresses this gap by analysing the congruence of policy preferences between women, men and their elected representatives. We endeavour to answer two questions. Are women’s policy preferences underrepresented in modern democracies? If so, which factors explain the size of the gender gaps in policy preference congruence? Comparing 21 European countries, we show that women’s preferences actually tend to be more accurately represented in parliaments than those of men. Moreover, our analyses reveal that this unanticipated finding is not driven by the share of female office-holders, but rather by levels of women’s turnout, which leads us to conclude that who votes is more important than who represents for policy preference congruence.
Cell-specific targeting by engineered M13 bacteriophage expressing VEGFR2 nanobody

Objective(s): Filamentous bacteriophage M13 was genetically engineered to specifically target mammalian cells for gene delivery purpose. Materials and Methods: A vascular endothelial growth factor receptor 2 (VEGFR2)-specific nanobody was genetically fused to the capsid gene III of M13 bacteriophage (pHEN4/3VGR19). A mammalian expression construct containing Cop-green fluorescent protein (Cop-GFP), as a reporter gene, was amplified by PCR and then sub-cloned in the pHEN4/3VGR19 phagemid. The resulting construct was transfected into 293KDR cell. The recombinant phage was extracted and confirmed and then transduced into VEGFR2 expressing cell (293KDR). Results: Seventy-two hr after transfection, green fluorescence was detected in 30% of the cells. About 1% of the cells which transduced by recombinant phages were able to express GFP. Conclusion: It is hoped that the results from this study will help to find potential vectors to improve the efficiency of gene delivery. Taken together, we conclude that this newly-introduced vector can be used in cancer researches.