Miracle-dz/UniFAR-FTUs huggingface dataset:
Efficient and Secure Mobile Cloud Networking

MCC (Mobile Cloud Computing) est un candidat tres fort pour le NGN (Next Generation Network) qui permet aux utilisateurs mobiles d’avoir une mobilite etendue, une continuite de service et des performances superieures. Les utilisateurs peuvent s’attendre a executer leurs travaux plus rapidement, avec une faible consommation de batterie et a des prix abordables ; mais ce n’est pas toujours le cas. Diverses applications mobiles ont ete developpees pour tirer parti de cette nouvelle technologie, mais chacune de ces applications possede ses propres exigences. Plusieurs MCA (Mobile Cloud Architectures) ont ete proposees, mais aucune n'a ete adaptee pour toutes les applications mobiles, ce qui a mene a une faible satisfaction du client. De plus, l'absence d'un modele d'affaires (business model) valide pour motiver les investisseurs a empeche son deploiement a l'echelle de production. Cette these propose une nouvelle architecture de MCA (Mobile Cloud Architecture) qui positionne l'operateur de telephonie mobile au cœur de cette technologie avec un modele d'affaires de recettes. Cette architecture, nommee OCMCA (Operator Centric Mobile Cloud Architecture), relie l'utilisateur d’un cote et le fournisseur de services Cloud (CSP) de l'autre cote, et heberge un cloud dans son reseau. La connexion OCMCA / utilisateur peut utiliser les canaux multiplex menant a un service beaucoup moins cher pour les utilisateurs, mais avec plus de revenus, et de reduire les embouteillages et les taux de rejet pour l'operateur. La connexion OCMCA / CSP est basee sur la federation, ainsi un utilisateur qui a ete enregistre avec n’importe quel CSP, peut demander que son environnement soit decharge de cloud heberge par l'operateur de telephonie mobile afin de recevoir tous les services et les avantages de OCMCA.Les contributions de cette these sont multiples. Premierement, nous proposons OCMCA et nous prouvons qu'il a un rendement superieur a toutes les autres MCA (Mobile Cloud Architectures). Le modele d'affaires (business model) de cette architecture se concentre sur la liberte de l'abonnement de l'utilisateur, l'utilisateur peut ainsi etre abonne a un fournisseur de cloud et etre toujours en mesure de se connecter via cette architecture a son environnement a l'aide du dechargement et de la federation.
Growth and demography of Pinaleno high elevation forests

The project goal is to understand how multiple disturbance events including fire, insect outbreaks, and climate variability interact in space and time, and how they combine to influence forest species composition, spatial structure, and tree population dynamics in high elevation forests of the Pinaleno Mountains. Information from each of these components is needed in order to understand the events and processes that resulted in contemporary forest conditions, and to guide management plans toward restoration of a sustainable healthy ecosystem during a period of potentially rapid climate change. The objectives of this agreement are to understand the demographic nature of contemporary tree populations, to understand the frequency and spatial extent of historic disturbances that resulted in contemporary forest conditions, and to develop detailed spatial and temporal histories of fire and bark beetle outbreaks in high elevation ecosystems of the Pinaleno Mountains.
Information theoretic clustering via divergence maximization among clusters

Information-theoretic clustering is one of the most promising and principled approaches to ﬁnding clusters with minimal apriori assumptions. The key criterion therein is to maximize the mutual information between the data points and their cluster labels. Such an approach, however, does not explicitly promote any type of inter-cluster behavior. We instead propose to maximize the Kullback–Leibler divergence between the underlying data distributions associated to clusters (referred to as cluster distributions ). We show it to entail the mutual information criterion along with maximizing cross entropy between the cluster distributions. For practical efﬁciency, we propose to empirically estimate the objective of KL-D between clusters in its dual form leveraging deep neural nets as a dual function approximator. Remarkably, our theoretical analysis establishes that estimating the divergence measure in its dual form simpliﬁes the problem of clustering to one of optimally ﬁnding k − 1 cut points for k clusters in the 1-D dual functional space . Overall, our approach enables linear-time clustering algorithms with theoretical guarantees of near-optimality, owing to the submodularity of the objective. We show the empirical superiority of our approach w.r. t. current state-of-the-art methods on the challenging task of clustering noisy time-series as observed in domains such as neuroscience, healthcare, ﬁnancial markets, spatio-temporal environmental dynamics, etc.
Analysis of The Influence of Customer Loyalty to GoFood Products in Indonesia

Loyalty is an effort to establish long-term relationships with customers. This study examines the effects of e-service quality and sales promotion on customer loyalty and satisfaction, considering satisfaction as a mediating variable in the context of GoFood products. The main objective of this research is to analyze the causal relationship between the independent variables and the dependent variable with the presence of intervening variables. The primary data source for this study is GoFood application users. Data was collected from a total of 327 customers across Indonesia who are GoFood users. The majority of GoFood customers in the sample are women (61%), have undergraduate education (56%), are students (50%), and have an income ranging from five hundred thousand rupiahs to one million one hundred thousand rupiahs (25%). The results obtained from this study are as follows: First, e-service quality contributes to customer satisfaction. Second, sales promotion contributes to customer satisfaction. Third, customer satisfaction contributes to customer loyalty. Fourth, e-service quality contributes to customer loyalty. Fifth, sales promotion contributes to customer loyalty. Sixth, sales promotion influences customer loyalty through the intermediary variable of satisfaction. Seventh, e-service quality influences customer loyalty through the intermediary variable of satisfaction.
Fatiguing Joint Angle Does Not Influence Torque and Neuromuscular Responses Following Sustained, Isometric Forearm Flexion Tasks Anchored to Perceptual Intensity in Men

This study examined the effects of joint angle (JA) on maximal voluntary isometric contraction (MVIC) and neuromuscular responses following fatiguing tasks anchored to RPE. Nine men (mean ± SD: age = 20.7 ± 1.2 yrs) performed forearm flexion MVICs at elbow JAs of 75° and 125° before and after sustained, isometric forearm flexion tasks to failure at fatiguing joint angles (FJA) of 75° and 125° anchored to RPE = 8. The amplitude and frequency of the electromyographic and mechanomyographic signals were recorded. Neuromuscular efficiency was calculated by dividing normalized torque by normalized electromyographic amplitude. A dependent t-test was used to assess the mean difference for time to task failure (TTF) between FJA. Repeated measure ANOVAs were used to assess mean differences for pre-test to post-test MVIC and neuromuscular responses. There was no significant difference between FJA for TTF (p = 0.223). The MVIC (collapsed across FJA and MVIC JA) decreased from pre-test to post-test (51.1 ± 5.0 vs. 45.3 ± 5.6 Nm, p < 0.001). Normalized neuromuscular parameters remained unchanged (p > 0.05). The FJA resulted in similar torque and neuromuscular responses, and the decreases in MVIC were not tracked by changes in the neuromuscular parameters. Thus, the neuromuscular parameters were not sensitive to fatigue, and pre-test to post-test measures may be compared between different FJA.
Profiles of parents’ emotion socialization within a multinational sample of parents

Introduction Seminal emotion socialization theories classify parents according to two patterns of parent emotion socialization processes: ‘emotion coaching’ (i.e., parents validate and teach children about emotions) versus ‘emotion dismissing’ parenting (i.e., parents minimize and dismiss their children’s emotions). However, empirical evidence supporting this binary distinction of parents remains limited. Our objective was to investigate whether parents can be differentiated by distinct patterns in their (1) beliefs about children’s emotions, (2) emotion regulation, and (3) emotion-related parenting practices. Method Participants were parents of children aged 4–10 years from the Child and Parent Emotion Study (N = 869) (https://bmjopen.bmj.com/content/10/10/e038124). Parents completed self-reported measures of emotion socialization processes via an online survey, which took 20–30 min to complete. Data included in the current study were collected May–August 2019. We conducted a latent profile analysis of parents’ emotion socialization (13 indicators). To assess reliability of the profiles, we examined stability of the profiles across (1) parents of children in early versus middle childhood, and (2) fathers versus mothers, via measurement invariance testing. Further, to assess for construct validity of the profiles, we examined concurrent associations between six criterion constructs and parents’ emotion socialization profiles. Results A three-profile model emerged characterizing parents by: (1) emotion coaching; (2) emotion dismissing; (3) emotion disengaged. There was strong support for construct validity and reliability. Discussion Our study provides empirical support for distinct differentiated classifications of emotion coaching and emotion dismissing parenting, aligned with emotion socialization theories. We further extend on extant theory and suggest a third ‘emotion disengaged’ classification, describing parents with moderate levels of emotion dismissing parenting and low levels of emotion coaching parenting. It should be noted that the profiles were derived with self-report data, therefore, data may have been biased by contextual factors. Furthermore, the study sample consisted of Western families from affluent backgrounds. The field should focus efforts on conducting person-centered studies with more diverse samples in future.
ASSOCIATION OF BODY MASS INDEX WITH POWER, STRENGTH AND FLEXIBILITY OF ELITE KABADDI PLAYERS

BACKGROUND OF STUDY: Kabaddi is a well known game usually played in every corners of India as well as in some other countries. We studied the correlation between selected fitness parameters (power, strength. flexibility) and body mass index of elite kabaddi players. METHODOLOGY: Total 47 Kabaddi players aged 18-25 were selected for the study based on inclusion and exclusion criteria from Sports Authority of India Stadia, Delhi NCR, India. Every individual’s height, weight, BMI, power, strength and flexibility were assessed. Data analysis was done by using Pearson’s correlation coefficient test. RESULTS: There was a varying level of correlation seen in normal and high BMI groups. Normal BMI group shows positive correlation with lower limb power, no correlation with hand grip strength and low back and hamstring flexibility, whereas, high BMI group shows negative correlation with power, low back and hamstring flexibility and positive correlation with hand grip strength. CONCLUSION: We concluded that lower limb power positively correlates with normal BMI. Lower back and hamstring flexibility and lower limb power negatively correlates whereas grip strength positively correlates with high BMI.
Two-stage Kernel Bayesian Optimization in High Dimensions

Bayesian optimization is a popular method for optimizing expensive black-box functions. Yet it of-tentimes struggles in high dimensions, where the computation could be prohibitively heavy. While a complex kernel with many length scales is prone to overﬁtting and expensive to train, a simple coarse kernel with too few length scales cannot effectively capture the variations of the high dimensional function in different directions. To alleviate this problem, we introduce CobBO: a Bayesian optimization algorithm with two-stage kernels and a coordinate backoff stopping rule. It adaptively selects a promising low dimensional subspace and projects past measurements into it using a computational efﬁcient coarse kernel. Within the subspace, the computational cost of conducting Bayesian optimization with a more ﬂexible and accurate kernel becomes affordable and thus a sequence of consecutive observations in the same subspace are collected until a stopping rule is met. Extensive evaluations a show that CobBO ﬁnds solutions comparable to or better than other state-of-the-art meth-ods for dimensions ranging from tens to hundreds, while reducing both the trial complexity and computational costs.
Splash due to rim transverse instability

In this theoretical study the dynamics of a rim bounding a free liquid sheet is considered. This rim is formed by capillary forces. When rim is unstable it creates cusps, jets, and secondary droplets. In this study the linear stability analysis is performed for small transverse disturbances of the rim centreline. The influence of the film thickness and rim acceleration has been investigated. The results of the study are important for the understanding of splash produced by drop impact and spray wall interaction. On the base of the theoretical analysis a length scale is proposed for the size of the secondary drops generated by spray impact. This assumed scaling is confirmed by the experimental data.
Boosting Graph Contrastive Learning via Graph Contrastive Saliency

Graph augmentation plays a crucial role in achieving good generalization for contrastive graph self-supervised learning. However, mainstream Graph Contrastive Learning (GCL) often favors random graph augmentations, by relying on random node dropout or edge perturbation on graphs. Random augmentations may inevitably lead to semantic information corruption during the training, and force the network to mistakenly focus on semantically irrelevant environmental background structures. To address these limitations and to improve generalization, we propose a novel self-supervised learning framework for GCL, which can adaptively screen the semantic-related sub-structure in graphs by capitalizing on the proposed gradient-based Graph Contrastive Saliency (GCS). The goal is to identify the most semantically discriminative structures of a graph via contrastive learning, such that we can generate semantically meaningful augmentations by leveraging on saliency. Empirical evidence on 16 benchmark datasets demonstrates the exclusive merits of the GCS-based framework. We also provide rigorous theoretical justification for GCS’s robustness properties. Code is available at https: //github.com/weicy15/GCS .
GANMasker: A Two-Stage Generative Adversarial Network for High-Quality Face Mask Removal

Deep-learning-based image inpainting methods have made remarkable advancements, particularly in object removal tasks. The removal of face masks has gained significant attention, especially in the wake of the COVID-19 pandemic, and while numerous methods have successfully addressed the removal of small objects, removing large and complex masks from faces remains demanding. This paper presents a novel two-stage network for unmasking faces considering the intricate facial features typically concealed by masks, such as noses, mouths, and chins. Additionally, the scarcity of paired datasets comprising masked and unmasked face images poses an additional challenge. In the first stage of our proposed model, we employ an autoencoder-based network for binary segmentation of the face mask. Subsequently, in the second stage, we introduce a generative adversarial network (GAN)-based network enhanced with attention and Masked–Unmasked Region Fusion (MURF) mechanisms to focus on the masked region. Our network generates realistic and accurate unmasked faces that resemble the original faces. We train our model on paired unmasked and masked face images sourced from CelebA, a large public dataset, and evaluate its performance on multi-scale masked faces. The experimental results illustrate that the proposed method surpasses the current state-of-the-art techniques in both qualitative and quantitative metrics. It achieves a Peak Signal-to-Noise Ratio (PSNR) improvement of 4.18 dB over the second-best method, with the PSNR reaching 30.96. Additionally, it exhibits a 1% increase in the Structural Similarity Index Measure (SSIM), achieving a value of 0.95.
Children diagnosed with congenital cardiac malformations at the national university departments of pediatric cardiology: positive predictive values of data in the Danish national Patient registry

the positive predictive of congenital cardiac malformation diagnoses registered in the National Patient Registry (NPR), whether the NPR can serve as a valid tool for epidemiologic studies of congenital cardiac malformations. Materials and methods: The study population comprised every individual born from 2000 to 2008 who was registered in the NPR with a congenital cardiac malformation diagnosis and treated at one of the two national departments of pediatric cardiology. Positive predictive values were established comparing NPR information with the clinical record of each individual. Results: A total of 2952 patients with a total of 3536 diagnoses were eligible for validation. Review of their clinical records unveiled no patient without cardiac malformation. In 98% (98%–99%) of the cases, the NPR diagnosis could be found as the discharge diagnosis in the patient’s clinical record, and in 90% (89%–91%) of the cases the NPR diagnosis was considered a true reflection of the patient’s actual malformation. Conclusions: Our study verifies that the present study population retrieved from the NPR is a valid tool for epidemiological research within the topic of congenital cardiac malformations, given that the research question is not dependent on a fully established sensitivity of the NPR. Precautions should be made regarding cardiac malformations characterized by low prevalence or poor predictive values, and the reported validity should not be extrapolated beyond the study period. Clinical peer-reviewed, access focusing on disease and drug epidemiology, identification of risk factors and screening procedures to develop optimal preventative initiatives and programs. Specific topics include: diagnosis, prognosis, treatment, screening, prevention, risk factor modification, systematic reviews, risk & safety of medical interventions, epidemiology & bio- statical methods, evaluation of guidelines, translational medicine, health policies & economic evaluations. The manuscript management system is includes very fair peer-review system, is all easy
Maintenance optimization of a two‐component series system considering masked causes of failure

Maintenance planning of two‐component systems has been extensively studied in recent decades. In the literature, most studies assume that the failure cause of a two‐component system is self‐announcing. In some real applications, the failure cause is masked, and a diagnosis with professional equipment is needed to reveal the failed component. This study investigates a preventive replacement policy of a two‐component series system considering masked causes of failure. When an unexpected failure occurs, we can carry out a diagnosis to reveal the failed component and replace it subsequently, or we can directly replace the whole system without diagnosis. Meanwhile, when we carry out a preventive replacement on a component, the other component can be replaced opportunistically. We formulate the problem as a semi‐Markov decision process, and prove the existence of the stationary optimal policy. The optimal preventive replacement age thresholds for each component and the corresponding optimal maintenance actions upon each failure are jointly obtained to minimize the long‐term average maintenance cost per time unit. A comprehensive numerical study is provided to illustrate the effectiveness of our proposed model.
Assessment of the durability and environmental impact of seawater-activated portlandite-calcined clay binder

This study investigated the performance of seawater-cured calcined clay-portlandite binder as a potential alternative to ordinary portland cement (OPC) for marine concrete applications. The samples for the investigation were prepared by mixing calcined kaolinite and bentonite clays in different ratios with portlandite. Seawater served as both the mixing and curing agent while acting as an activator due to its chloride and sulfate ion contents. The process involved three sequential steps: evaluating the changes in the mechanical performances, assessing the microstructural features, and estimating the environmental impacts. The results showed that the bound and total chloride content was significantly higher in the calcined clay mixes than in the OPC. The higher kaolinite enhanced the mechanical properties, and the strength-providing phases were Friedel’s salt, C-A-S-H, ettringite, and zeolites. It was concluded that this novel binder has the potential to reduce global warming by 85–90% more than OPC.
Bibliography of proportional representation

in Tasmania

Proportional representation was introduced in Tasmania by the Electoral Act of 1896, in which a form of the Hare system was used for two of the electorates of the House of Assembly. The causes which led to the adoption of the Hare system were thus stated by the late Mr. Justice Andrew Inglis Clark in the following paragraphs, contributed by him to the report by Messrs. J. G. Davies and R. M. Johnston on the elections for the Senate and House of Representatives in Tasmania in 1901. The Clark-Hare system of voting was introduced into the electoral law of Tasmania in consequence of the frequent failure of the ordinary system of voting to secure a proportionate representation of the preponderating opinions of the electors on political questions either in single or plural electorates.
False memory in between-language conditions: a brief review on the effect of encoding and retrieving in different languages

False memories have been extensively investigated over the past few decades using the Deese/Roediger-McDermott (DRM) paradigm. In this paradigm, participants study lists of words associatively related to a non-presented critical lure. During a memory test, these critical lures are falsely recalled or recognized. Most studies have focused on false memories that arise when both encoding and retrieval are conducted in the same language (i.e., within-language conditions), which is typically the participant’s native or first language (L1). However, much less is known about false memories when critical lures appear in the memory test in a different language than the studied lists (i.e., between-language conditions), being one of them the participant’s second language (L2). The main objective of this exhaustive review was to provide an overview of the current state of research on false recognition using the DRM paradigm in between-language conditions, where languages are switched between encoding and retrieval (i.e., L1L2 versus L2L1). The results revealed a language dominance effect in between-language false memories. In other words, false recognition rates were dependent on the study language, with a trend toward higher false recognition when words were enconded in the L1 (L1L2) compared to when words were encoded in the L2 (L2L1). This review enhances our understanding of how studying words in a first or second language affects false memory in the DRM paradigm, emphasizing the significance of investigating false memory in second language speakers and the necessity for further research in the field.