Miracle-dz/UniFAR-FTUs huggingface dataset:
A Credibility Analysis System for Assessing Information on Twitter

Information credibility on Twitter has been a topic of interest among researchers in the fields of both computer and social sciences, primarily because of the recent growth of this platform as a tool for information dissemination. Twitter has made it increasingly possible to offer near-real-time transfer of information in a very cost-effective manner. It is now being used as a source of news among a wide array of users around the globe. The beauty of this platform is that it delivers timely content in a tailored manner that makes it possible for users to obtain news regarding their topics of interest. Consequently, the development of techniques that can verify information obtained from Twitter has become a challenging and necessary task. In this paper, we propose a new credibility analysis system for assessing information credibility on Twitter to prevent the proliferation of fake or malicious information. The proposed system consists of four integrated components: a reputation-based component, a credibility classifier engine, a user experience component, and a feature-ranking algorithm. The components operate together in an algorithmic form to analyze and assess the credibility of Twitter tweets and users. We tested the performance of our system on two different datasets from 489,330 unique Twitter accounts. We applied 10-fold cross-validation over four machine learning algorithms. The results reveal that a significant balance between recall and precision was achieved for the tested dataset.
A novel role in skeletal segment regeneration of extracellular vesicles released from periodontal-ligament stem cells

Purpose The combination of oral derived stem cells and 3-D scaffolds is considered advantageous in bone repair. In particular, collagen membranes possess ideal biological properties and can support infiltration and proliferation of osteoblasts, promoting bone regeneration. Our study aimed to develop a new biocompatible osteogenic construct composed of a commercially available collagen membrane (Evolution [Evo]), human periodontal-ligament stem cells (hPDLSCs) enriched with extracellular vesicles (EVs), or polyethylenimine (PEI)-engineered EVs (PEI-EVs). Methods Osteogenic ability and expression of osteogenic genes were evaluated in vitro in hPDLSCs cultured with or without Evo, with Evo and EVs, or PEI-EVs. In addition, the bone-regeneration capacity of Evo, Evo enriched with hPDLSCs, Evo enriched with hPDLSCs and EVs/PEI-EVs was investigated in rats subjected to calvarial defects. Results Our results showed that Evo enriched with EVs and PEI-EVs showed high biocompatibility and osteogenic properties in vitro and in vivo. In addition, quantitative reverse-transcription polymerase chain reaction demonstrated the upregulation of osteogenic genes, such as TGFB1, MMP8, TUFT1, TFIP11, BMP2, and BMP4, in the presence of PEI-EVs. Upregulation of BMP2/4 was confirmed for Evo enriched with PEI-EVs and hPDLSCs both in vitro by Western blot and in vivo by immunofluorescence. Conclusion Our results indicated that Evo enriched with hPDLSCs and PEI-EVs is able to promote a bone-regeneration process for the treatment of calvarium and ossification defects caused by accidental or surgery trauma. In particular, PEI-EVs had a significant role in activation of the osteogenic process.
Sexuality and childbearing as it is experienced by women living with HIV in Sweden: a lifeworld phenomenological study

ABSTRACT The effectiveness of antiretroviral treatment has reduced sexual HIV transmission and mother-to-child-transmission. To optimally support women living with HIV, health care providers need deepened knowledge about HIV, sexuality and childbearing. The aim of this study was to describe the phenomenon sexuality and childbearing as experienced by women living with HIV in Sweden. Data were collected by phenomenon-oriented interviews with 18 HIV-positive women. A reflective lifeworld analysis based on phenomenological philosophy was conducted, describing the meaning structure of the phenomenon. The essence of the phenomenon is that perceptions about HIV and its contagiousness profoundly influence sexual habits and considerations in relation to pregnancy and childbearing. These perceptions are formed in combination with knowledge and interpretations about HIV by the women themselves and by their environments. The essence is further described by its constituents: Risk of transmission imposes demands on responsibility; The contagiousness of HIV limits sexuality and childbearing; Knowledge about HIV transmission provides confident choices and decisions; and To re-create sexuality and childbearing. Although HIV has a low risk of transmission if being well treated, our study shows that HIV-positive women feel more or less contagious, which influences sexuality and decision-making in relation to become pregnant and give birth.
Approximate Leave-One-Out for Fast Parameter Tuning in High Dimensions

Consider the following class of learning schemes: $$\hat{\boldsymbol{\beta}} := \arg\min_{\boldsymbol{\beta}}\;\sum_{j=1}^n \ell(\boldsymbol{x}_j^\top\boldsymbol{\beta}; y_j) + \lambda R(\boldsymbol{\beta}),\qquad\qquad (1) $$ where $\boldsymbol{x}_i \in \mathbb{R}^p$ and $y_i \in \mathbb{R}$ denote the $i^{\text{th}}$ feature and response variable respectively. Let $\ell$ and $R$ be the loss function and regularizer, $\boldsymbol{\beta}$ denote the unknown weights, and $\lambda$ be a regularization parameter. Finding the optimal choice of $\lambda$ is a challenging problem in high-dimensional regimes where both $n$ and $p$ are large. We propose two frameworks to obtain a computationally efficient approximation ALO of the leave-one-out cross validation (LOOCV) risk for nonsmooth losses and regularizers. Our two frameworks are based on the primal and dual formulations of (1). We prove the equivalence of the two approaches under smoothness conditions. This equivalence enables us to justify the accuracy of both methods under such conditions. We use our approaches to obtain a risk estimate for several standard problems, including generalized LASSO, nuclear norm regularization, and support vector machines. We empirically demonstrate the effectiveness of our results for non-differentiable cases.
A Formal Analysis of the Neuchatel e-Voting Protocol

Remote electronic voting is used in several countries for legally binding elections. Unlike academic voting protocols, these systems are not always documented and their security is rarely analysed rigorously. In this paper, we study a voting system that has been used for electing political representatives and in citizen-driven referenda in the Swiss canton of Neuchâtel. We design a detailed model of the protocol in ProVerif for both privacy and verifiability properties. Our analysis mostly confirms the security of the underlying protocol: we show that the Neuchâtel protocol guarantees ballot privacy, even against a corrupted server; it also ensures cast-as-intended and recorded-as-cast verifiability, even if the voter's device is compromised. To our knowledge, this is the first time a full-fledged automatic symbolic analysis of an e-voting system used for politicallybinding elections has been realized.
High treatment adherence, satisfaction, motivation, and health-related quality of life with fingolimod in patients with relapsing-remitting multiple sclerosis – results from a 24-month, multicenter, open-label Danish study

Purpose Treatment adherence is a prerequisite for treatment success and therefore an important consideration to assure that therapeutic goals are achieved both from a patient point of view and for optimal health care resource utilization. Published data on treatment adherence with fingolimod (Gilenya®) are limited. Therefore, this study investigated treatment adherence in patients with relapsing-remitting multiple sclerosis (RRMS) treated with fingolimod in Denmark. Patients and methods This was a 24-month, multicenter, open-label study, investigating treatment adherence, satisfaction, motivation, and health-related quality of life (QoL) in RRMS patients treated with fingolimod. In addition, the effect of a motivational interview support program on these measures was evaluated. Treatment adherence was assessed by pill count. Treatment satisfaction, motivation, and QoL were assessed by patient-reported outcomes (PROs). Results A total of 195 patients were enrolled in the study. A very high treatment adherence was observed during the entire study with no statistically significant difference between study visits before (99%) and after (97%) the motivational interview. In accordance, a high level of treatment satisfaction was found in the Treatment Satisfaction Questionnaire for Medication 9, which was scored high throughout the study with the highest scores seen for the convenience domain (ranging from 94.51 to 95.78). Furthermore, additional PROs demonstrated a high health-related QoL, a self-determined form of motivation for taking medication, and a patient perception of an autonomy supportive approach provided by the health care provider, at all study visits. Conclusion High levels of treatment adherence, satisfaction, motivation, and QoL were observed in Danish RRMS patients treated with fingolimod. As these positive measures were observed at all study visits and throughout the study, no effect of the motivational interview support program was found.
Deep Semantic Segmentation in an AUV for Online Posidonia Oceanica Meadows Identification

Recent studies have shown evidence of a significant decline of the Posidonia oceanica (P.O.) meadows on a global scale. The monitoring and mapping of these meadows are fundamental tools for measuring their status. We present an approach based on a deep neural network to automatically perform a high-precision semantic segmentation of the P.O. meadows in sea-floor images, offering several improvements over the state-of-the-art techniques. Our network demonstrates outstanding performance over diverse test sets, reaching a precision of 96.57% and an accuracy of 96.81%, surpassing the reliability of labeling the images manually. Moreover, the network is implemented in an autonomous underwater vehicle, performing an online P.O. segmentation, which will be used to generate real-time semantic coverage maps.
Edit metric decoding: Return of the side effect machines

Side Effect Machines (SEMs) are an extension of finite state machines which place a counter on each node that is incremented when that node is visited. Previous studies examined a genetic algorithm to discover node connections in SEMs for edit metric decoding for biological applications, namely to handle sequencing errors. Edit metric codes, while useful for decoding such biologically created errors, have a structure which significantly differentiates them from other codes based on Hamming distance. Further, the inclusion of biologically- motivated restrictions on allowed words makes development of decoders a bespoke process based on the exact code used. This study examines the use of evolutionary programming for the creation of such decoders, thus allowing for the number of states to be evolved directly, not witnessed in previous approaches which used genetic algorithms. Both direct and fuzzy decoding are used, obtaining correct decoding rates of up to 95% in some SEMs.
Detection of false data injection attacks in smart grids using Recurrent Neural Networks

False Data Injection (FDI) attacks create serious security challenges to the operation of power systems, especially when they are carefully constructed to bypass conventional state estimation bad data detection techniques implemented in the power system control room. This paper investigates the utilization of Recurrent Neural Networks (RNN) as a machine learning technique to detect these FDI attacks. The proposed detection algorithm is validated throughout simulations of FDI in power flow data over the span of five years using IEEE-30 Bus system. The simulation results confirm that the proposed RNN-based algorithm achieves high accuracy in detecting anomalies in the data, by observing the temporal variation in the successive data sequence.
Continuous-Scale Kinetic Fluid Simulation

Kinetic approaches, i.e., methods based on the lattice Boltzmann equations, have long been recognized as an appealing alternative for solving incompressible Navier-Stokes equations in computational fluid dynamics. However, such approaches have not been widely adopted in graphics mainly due to the underlying inaccuracy, instability and inflexibility. In this paper, we try to tackle these problems in order to make kinetic approaches practical for graphical applications. To achieve more accurate and stable simulations, we propose to employ the non-orthogonal central-moment-relaxation model, where we develop a novel adaptive relaxation method to retain both stability and accuracy in turbulent flows. To achieve flexibility, we propose a novel continuous-scale formulation that enables samples at arbitrary resolutions to easily communicate with each other in a more continuous sense and with loose geometrical constraints, which allows efficient and adaptive sample construction to better match the physical scale. Such a capability directly leads to an automatic sample construction which generates static and dynamic scales at initialization and during simulation, respectively. This effectively makes our method suitable for simulating turbulent flows with arbitrary geometrical boundaries. Our simulation results with applications to smoke animations show the benefits of our method, with comparisons for justification and verification.
Passive os fingerprinting methods in the jungle of wireless networks

Operating system fingerprinting methods are well- known in the domain of static networks and managed environments. Yet few studies tackled this challenge in real networks, where users can bring and connect any device. We evaluate the performance of three OS fingerprinting methods on a large dataset collected from university wireless network. Our results show that method based on HTTP User-agents is the most accurate but can identify only low portion of the traffic. TCP/IP parameters method proved to be the opposite with high coverage but low accuracy. We also implemented a new method based on detection of communication to OS-specific domains. Its performance is comparable to the two established ones. Next, we discuss the impacts of traffic encryption and embracing new protocols such as IPv6 or HTTP/2.0 on OS fingerprinting. Our findings suggest that OS identification based on specific domain detection is viable and corresponds to the current directions of network traffic evolution, while methods based on TCP/IP parameters and User-agents will become ineffective in the future.
Socioeconomic Status and Vulnerability to HIV Infection in Uganda: Evidence from Multilevel Modelling of AIDS Indicator Survey Data

Background There is controversy on the association between socioeconomic status (SES) and HIV infection. Some evidence claims higher SES is negatively associated with HIV infection while others report the reverse. Objectives To examine the association between SES and HIV infection in Uganda and to examine whether the SES-HIV relationship varies by gender, rural-urban place of residence, and time (2004-2005 and 2011) in Uganda. Methods Multilevel analysis was applied to 39,766 individual cases obtained in 887 clusters of Uganda HIV/AIDS Indicators Survey conducted in 2004-2005 and 2011. Results Household wealth is associated with increased vulnerability in the general population and in rural areas. Compared with no educational attainment, secondary or higher education is associated with reduced vulnerability to the risk of HIV infection by 37% in the general population. However, this effect was stronger in urban than rural areas. Besides individual-level factors, unobserved community factors too play an important role and account for 9% of unexplained variance after individual-level factors are considered. Conclusion Household wealth increases vulnerability but education reduces it. The social environment influences vulnerability to HIV infection independent of individual-level factors. HIV/AIDS awareness targeting sexual practices of wealthy individuals and those with primary-level educational attainment together with improving educational attainment and addressing contextual factors influencing vulnerability to HIV infection are necessary strategies to reduce HIV infections in Uganda.
Continuous Patient Monitoring With a Patient Centric Agent: A Block Architecture

The Internet of Things (IoT) has facilitated services without human intervention for a wide range of applications, including continuous remote patient monitoring (RPM). However, the complexity of RPM architectures, the size of data sets generated and limited power capacity of devices make RPM challenging. In this paper, we propose a tier-based End to End architecture for continuous patient monitoring that has a patient centric agent (PCA) as its center piece. The PCA manages a blockchain component to preserve privacy when data streaming from body area sensors needs to be stored securely. The PCA based architecture includes a lightweight communication protocol to enforce security of data through different segments of a continuous, real time patient monitoring architecture. The architecture includes the insertion of data into a personal blockchain to facilitate data sharing amongst healthcare professionals and integration into electronic health records while ensuring privacy is maintained. The blockchain is customized for RPM with modifications that include having the PCA select a Miner to reduce computational effort, enabling the PCA to manage multiple blockchains for the same patient, and the modification of each block with a prefix tree to minimize energy consumption and incorporate secure transaction payments. Simulation results demonstrate that security and privacy can be enhanced in RPM with the PCA based End to End architecture.
Fruit Quantity and Ripeness Estimation Using a Robotic Vision System

Accurate localization of crop remains highly challenging in unstructured environments, such as farms. Many developed systems still rely on the use of hand selected features for crop identification and often neglect the estimation of crop quantity and ripeness, which is a key to assigning labor during farming processes. To alleviate these limitations, we present a robotic vision system that can accurately estimate the quantity and ripeness of sweet pepper (Capsicum annuum L), a key horticultural crop. This system consists of three parts: detection, ripeness estimation, and tracking. Efficient detection is achieved using the FasterRCNN (FRCNN) framework. Ripeness is then estimated in the same framework by learning a parallel layer which we experimentally show results in superior performance than treating ripeness as extra classes in the traditional FRCNN framework. Evaluation of these two techniques outlines the improved performance of the parallel layer, where we achieve an $F_1$ score of 77.3 for the parallel technique yet only 72.5 for the best scoring (red) of the multiclass implementation. To track the crop, we present a vision only tracking via detection approach, which uses the FRCNN with parallel layers as input. Being a vision only solution, this approach is cheap to implement as it only requires a camera and in experiments, using field data, we show that our proposed system can accurately estimate the number of sweet pepper present, to within 4.1% of the visual ground truth.
From Rank Estimation to Rank Approximation: Rank Residual Constraint for Image Denoising

In this paper, we propose a novel approach for the rank minimization problem, termed rank residual constraint (RRC). Different from existing low-rank based approaches, such as the well-known weighted nuclear norm minimization (WNNM) and nuclear norm minimization (NNM), which aim to estimate the underlying lowrank matrix directly from the corrupted observation, we progressively approximate or approach the underlying low-rank matrix via minimizing the rank residual. By integrating the image nonlocal self-similarity (NSS) prior with the proposed RRC model, we develop an iterative algorithm for image denoising. To this end, we first present a recursive based nonlocal means method to obtain a good reference of the original image patch groups, and then the rank residual of the image patch groups between this reference and the noisy image is minimized to achieve a better estimate of the desired image. In this manner, both the reference and the estimated image in each iteration are improved gradually and jointly. Based on the groupbased sparse representation model, we further provide a theoretical analysis on the feasibility of the proposed RRC model. Experimental results demonstrate that the proposed RRC model outperforms many state-of-the-art denoising methods in both the objective and perceptual qualities.
Density-aware power allocation in mobile networks using edge computing

While the main concern in mobile networks was increasing network capacity and expanding coverage in the past, green operation have recently become a major concern. To conserve energy in mobile networks, we employ mobile edge computing to access real-time data and offload processing tasks to the edges for reducing complexity and latency. An edge cloud has a larger view of the network than a base station. Therefore, it can evaluate the probability of coverage by considering the density of base stations to enhance the overall system throughput while preventing coverage holes. This may enhance user experience and decrease response time in the network. We propose a density-adaptive power control algorithm that reduces energy consumption in an LTE network and improves the mean throughput. We show how power allocation and interference may affect system performance. The simulation results show that the proposed algorithm can reduce the energy wastage up to 67% in comparison with the basic model (without any power allocation model) and 28% in comparison with a recent work while the mean throughput can be improved by 22%.
Deep Learning-Based Sphere Decoding

In this paper, a deep learning (DL)-based sphere decoding algorithm is proposed, where the radius of the decoding hypersphere is learned by a deep neural network (DNN). The performance achieved by the proposed algorithm is very close to the optimal maximum likelihood decoding (MLD) over a wide range of signal-to-noise ratios (SNRs), while the computational complexity, compared to existing sphere decoding variants, is significantly reduced. This improvement is attributed to the DNN’s ability of intelligently learning the radius of the hypersphere used in decoding. The expected complexity of the proposed DL-based algorithm is analytically derived and compared with existing ones. It is shown that the number of lattice points inside the decoding hypersphere drastically reduces in the DL-based algorithm in both the average and worst-case senses. The effectiveness of the proposed algorithm is shown through the simulation for high-dimensional multiple-input multiple-output (MIMO) systems, using high-order modulations.