Miracle-dz/UniFAR-FTUs huggingface dataset:
My Privacy My Decision: Control of Photo Sharing on Online Social Networks

Photo sharing is an attractive feature which popularizes online social networks (OSNs). Unfortunately, it may leak users’ privacy if they are allowed to post, comment, and tag a photo freely. In this paper, we attempt to address this issue and study the scenario when a user shares a photo containing individuals other than himself/herself (termed co-photo for short). To prevent possible privacy leakage of a photo, we design a mechanism to enable each individual in a photo be aware of the posting activity and participate in the decision making on the photo posting. For this purpose, we need an efficient facial recognition (FR) system that can recognize everyone in the photo. However, more demanding privacy setting may limit the number of the photos publicly available to train the FR system. To deal with this dilemma, our mechanism attempts to utilize users’ private photos to design a personalized FR system specifically trained to differentiate possible photo co-owners without leaking their privacy. We also develop a distributed consensus-based method to reduce the computational complexity and protect the private training set. We show that our system is superior to other possible approaches in terms of recognition ratio and efficiency. Our mechanism is implemented as a proof of concept Android application on Facebook’s platform.
Changes in antidepressant use by young people and suicidal behavior after FDA warnings and media coverage: quasi-experimental study

Objective To investigate if the widely publicized warnings in 2003 from the US Food and Drug Administration about a possible increased risk of suicidality with antidepressant use in young people were associated with changes in antidepressant use, suicide attempts, and completed suicides among young people. Design Quasi-experimental study assessing changes in outcomes after the warnings, controlling for pre-existing trends. Setting Automated healthcare claims data (2000-10) derived from the virtual data warehouse of 11 health plans in the US Mental Health Research Network. Participants Study cohorts included adolescents (around 1.1 million), young adults (around 1.4 million), and adults (around 5 million). Main outcome measures Rates of antidepressant dispensings, psychotropic drug poisonings (a validated proxy for suicide attempts), and completed suicides. Results Trends in antidepressant use and poisonings changed abruptly after the warnings. In the second year after the warnings, relative changes in antidepressant use were −31.0% (95% confidence interval −33.0% to −29.0%) among adolescents, −24.3% (−25.4% to −23.2%) among young adults, and −14.5% (−16.0% to −12.9%) among adults. These reflected absolute reductions of 696, 1216, and 1621 dispensings per 100 000 people among adolescents, young adults, and adults, respectively. Simultaneously, there were significant, relative increases in psychotropic drug poisonings in adolescents (21.7%, 95% confidence interval 4.9% to 38.5%) and young adults (33.7%, 26.9% to 40.4%) but not among adults (5.2%, −6.5% to 16.9%). These reflected absolute increases of 2 and 4 poisonings per 100 000 people among adolescents and young adults, respectively (approximately 77 additional poisonings in our cohort of 2.5 million young people). Completed suicides did not change for any age group. Conclusions Safety warnings about antidepressants and widespread media coverage decreased antidepressant use, and there were simultaneous increases in suicide attempts among young people. It is essential to monitor and reduce possible unintended consequences of FDA warnings and media reporting.
HVC: A Hybrid Cloud Computing Framework in Vehicular Environments

As increasingly more applications are deployed in vehicles, how to provide the demanded computational capability becomes crucial. This paper proposes the hybrid vehicular cloud (HVC) framework for cloud computing on the road to increase the computational capability of vehicles by using resources from the centralized cloud, RSU, and neighboring vehicles. The proposed scheme can be used as a general framework for all types of applications. It is able to satisfy various types of job requirements, such as real time or host requirements, and it is adaptive to the dynamic vehicular environment. Specifically, this paper defines the procedures to support the autonomous organization of a vehicular cloud. An online scheduling algorithm is proposed to solve the job assignment problem with the objective of processing more jobs and reducing cellular network usage. Extensive simulations are conducted, and the results show the superiority of the proposed scheme over competing schemes in typical urban scenarios.
Implementation study of AODV for Microsoft Windows CE platform

The Ad hoc On-Demand Distance Vector (AODV) routing protocol is designed for use in mobile ad-hoc networks. There are a number of implementations of the Ad-hoc routing protocols available for the Linux platform, but not for any other platform. Windows CE . NET, the successor to Windows CE 3.0, combines an advanced real-time embedded operating system with the most powerful tools for rapidly creating the next generation of smart, connected, and small-footprint devices. This paper presents a design, implementation, and evaluation of AODV protocol for the Windows CE . NET operating system and describes the salient characteristics of the Windows CE platform for those unfamiliar with the system. It also focuses on the application level implementation of the algorithm and provides the framework for integrating the protocol in Windows CE . NET in form of an NDIS intermediate driver.
Stroke Investigative Research and Education Network

Stroke is the leading cause of neurological hospital admissions in sub-Saharan Africa (SSA) and the second leading cause of death globally. The Stroke Investigative Research and Education Network seeks to comprehensively characterize the genomic, sociocultural, economic, and behavioral risk factors for stroke and to build effective teams for research to address and decrease the burden of stroke and other noncommunicable diseases in SSA. One of the first steps to address this goal is to effectively engage the communities that suffer the high burden of disease. The purpose of this article is to describe plans to elucidate information about knowledge, attitudes, beliefs, and practices about stroke and genomics from patients, caregivers, and local leaders, to recruit participation in research activities and dissemination of ongoing results, as well as to facilitate research uptake and impact within the broader communities of scientists, health professionals, policy makers, and others. We describe the (a) study sites and their communities; (b) plans for community advisory boards, focus groups, and surveys; (c) methods for data management in REDCap database; (d) analyses of qualitative data; (e) evaluation of community and public engagement across multiple sites and research teams in SSA and the United States; (f) use of RE-AIM for presentation of evaluation data; and (g) community indicators of success. This is the first of its kind public outreach engagement initiative to evaluate stroke and genomics in SSA, and has implications as a model for assessment in other high–stroke risk populations.
A dynamic‐adversarial mining approach to the security of machine learning

Operating in a dynamic real‐world environment requires a forward thinking and adversarial aware design for classifiers beyond fitting the model to the training data. In such scenarios, it is necessary to make classifiers such that they are: (a) harder to evade, (b) easier to detect changes in the data distribution over time, and (c) be able to retrain and recover from model degradation. While most works in the security of machine learning have concentrated on the evasion resistance problem (a), there is little work in the areas of reacting to attacks (b) and (c). Additionally, while streaming data research concentrates on the ability to react to changes to the data distribution, they often take an adversarial agnostic view of the security problem. This makes them vulnerable to adversarial activity, which is aimed toward evading the concept drift detection mechanism itself. In this paper, we analyze the security of machine learning from a dynamic and adversarial aware perspective. The existing techniques of restrictive one‐class classifier models, complex learning‐based ensemble models, and randomization‐based ensemble models are shown to be myopic as they approach security as a static task. These methodologies are ill suited for a dynamic environment, as they leak excessive information to an adversary who can subsequently launch attacks which are indistinguishable from the benign data. Based on empirical vulnerability analysis against a sophisticated adversary, a novel feature importance hiding approach for classifier design is proposed. The proposed design ensures that future attacks on classifiers can be detected and recovered from. The proposed work provides motivation, by serving as a blueprint, for future work in the area of dynamic‐adversarial mining, which combines lessons learned from streaming data mining, adversarial learning, and cybersecurity.
Hydrochlorothiazide and acute urinary acidification: The “voltage hypothesis” of ENaC‐dependent H+ secretion refuted

The “voltage hypothesis” of H+ secretion states that urinary acidification following increased Na+ delivery to the collecting duct (CD) is ENaC dependent leading to transepithelial voltage‐dependent increase in H+ secretion. We recently showed that furosemide acidifies the urine independently of ENaC activity. If the voltage hypothesis holds, hydrochlorothiazide (HCT) must acidify the urine. We here tested the acute effect of HCT on urine pH under normal and high ENaC expression.
Multilevel Small-Area Estimation of Colorectal Cancer Screening in the United States

Background: The U.S. Preventive Services Task Force recommends routine screening for colorectal cancer for adults ages 50 to 75 years. We generated small-area estimates for being current with colorectal cancer screening to examine sociogeographic differences among states and counties. To our knowledge, nationwide county-level estimates for colorectal cancer screening are rarely presented. Methods: We used county data from the 2014 Behavioral Risk Factor Surveillance System (BRFSS; n = 251,360 adults), linked it to the American Community Survey poverty data, and fitted multilevel logistic regression models. We post-stratified the data with the U.S. Census population data to run Monte Carlo simulations. We generated county-level screening prevalence estimates nationally and by race/ethnicity, mapped the estimates, and aggregated them into state and national estimates. We evaluated internal consistency of our modeled state-specific estimates with BRFSS direct state estimates using Spearman correlation coefficients. Results: Correlation coefficients were ≥0.95, indicating high internal consistency. We observed substantial variations in current colorectal cancer screening estimates among the states and counties within states. State mean estimates ranged from 58.92% in Wyoming to 75.03% in Massachusetts. County mean estimates ranged from 40.11% in Alaska to 79.76% in Florida. Larger county variations were observed in various race/ethnicity groups. Conclusions: State estimates mask county variations. However, both state and county estimates indicate that the country is far behind the “80% by 2018” target. Impact: County-modeled estimates help identify variation in colorectal cancer screening prevalence in the United States and guide education and enhanced screening efforts in areas of need, including areas without BRFSS direct-estimates. Cancer Epidemiol Biomarkers Prev; 27(3); 245–53. ©2018 AACR.
Web site mining: a new way to spot competitors, customers and suppliers in the world wide web

When automatically extracting information from the world wide web, most established methods focus on spotting single HTML-documents. However, the problem of spotting complete web sites is not handled adequately yet, in spite of its importance for various applications. Therefore, this paper discusses the classification of complete web sites. First, we point out the main differences to page classification by discussing a very intuitive approach and its weaknesses. This approach treats a web site as one large HTML-document and applies the well-known methods for page classification. Next, we show how accuracy can be improved by employing a preprocessing step which assigns an occurring web page to its most likely topic. The determined topics now represent the information the web site contains and can be used to classify it more accurately. We accomplish this by following two directions. First, we apply well established classification algorithms to a feature space of occurring topics. The second direction treats a site as a tree of occurring topics and uses a Markov tree model for further classification. To improve the efficiency of this approach, we additionally introduce a powerful pruning method reducing the number of considered web pages. Our experiments show the superiority of the Markov tree approach regarding classification accuracy. In particular, we demonstrate that the use of our pruning method not only reduces the processing time, but also improves the classification accuracy.
GUAJE, A Java environment for generating understandable and accurate models

The term Soft Computing is usually used to refer to a family of several preexisting techniques (Fuzzy Logic, Neuro-computing, Probabilistic Reasoning, Evolutionary Computation, etc.) able to work in a cooperative way, taking profit from the main advantages of each individual technique, in order to solve lots of complex real-world problems for which other classical techniques are not quite well suited. In the specialized literature there are many Soft Computing tools, most of them freely available as open source software. This work gives an overview on existing tools for system modeling. Moreover, it introduces a new environment for building interpretable and accurate systems by means of combining several preexisting tools.
Self-aggregated nanoparticles based on amphiphilic poly(lactic acid)-grafted-chitosan copolymer for ocular delivery of amphotericin B

Background The purpose of this study was to develop a self-aggregated nanoparticulate vehicle using an amphiphilic poly(lactic acid)-grafted-chitosan (PLA-g-CS) copolymer and to evaluate its potential for ocular delivery of amphotericin B. Methods A PLA-g-CS copolymer was synthesized via a “protection-graft-deprotection” procedure and its structure was confirmed by Fourier transform infrared spectroscopy, 1H nuclear magnetic resonance, and X-ray diffraction spectra. Amphotericin B-loaded nanoparticles based on PLA-g-CS (AmB/PLA-g-CS) were prepared by the dialysis method and characterized for particle size, zeta potential, and encapsulation efficiency. Studies of these AmB/PLA-g-CS nanoparticles, including their mucoadhesive strength, drug release properties, antifungal activity, ocular irritation, ocular pharmacokinetics, and corneal penetration were performed in vitro and in vivo. Results Fourier transform infrared spectroscopy, 1H nuclear magnetic resonance, and X-ray diffraction spectra showed that the PLA chains were successfully grafted onto chitosan molecules and that crystallization of chitosan was suppressed. The self-aggregated PLA-g-CS nanoparticles had a core-shell structure with an average particle size of approximately 200 nm and zeta potentials higher than 30 mV. Amphotericin B was incorporated into the hydrophobic core of the nanoparticles with high encapsulation efficiency. Sustained drug release from the nanoparticles was observed in vitro. The ocular irritation study showed no sign of irritation after instillation of the PLA-g-CS nanoparticles into rabbit eyes. The minimal inhibitory concentration of the AmB/PLA-g-CS nanoparticles showed antifungal activity similar to that of free amphotericin B against Candida albicans. The in vivo ocular pharmacokinetic study suggested that the PLA-g-CS nanoparticles have the advantage of prolonging residence time at the ocular surface. The corneal penetration study showed that the PLA-g-CS nanoparticles could penetrate into the cornea. Conclusion Our results suggest that this nanoparticulate vehicle based on a PLA-g-CS copolymer might be a promising system for effective ocular delivery of amphotericin B.
Artefacts and biases affecting the evaluation of scoring functions on decoy sets for protein structure prediction

Motivation: Decoy datasets, consisting of a solved protein structure and numerous alternative native-like structures, are in common use for the evaluation of scoring functions in protein structure prediction. Several pitfalls with the use of these datasets have been identified in the literature, as well as useful guidelines for generating more effective decoy datasets. We contribute to this ongoing discussion an empirical assessment of several decoy datasets commonly used in experimental studies. Results: We find that artefacts and sampling issues in the large majority of these data make it trivial to discriminate the native structure. This underlines that evaluation based on the rank/z-score of the native is a weak test of scoring function performance. Moreover, sampling biases present in the way decoy sets are generated or used can strongly affect other types of evaluation measures such as the correlation between score and root mean squared deviation (RMSD) to the native. We demonstrate how, depending on type of bias and evaluation context, sampling biases may lead to both over- or under-estimation of the quality of scoring terms, functions or methods. Availability: Links to the software and data used in this study are available at http://dbkgroup.org/handl/decoy_sets. Contact: simon.lovell@manchester.ac. uk Supplementary information: Supplementary data are available at Bioinformatics online.
Essential Dietary Bioactive Lipids in Neuroinflammatory Diseases.

SIGNIFICANCE Under physiological conditions, neurons and glia are in a healthy, redox-balanced environment; when injury perturbs this equilibrium, a neuroinflammatory state is established by activated microglia that triggers pro-inflammatory responses and alters the oxidant/antioxidant balance, thus leading to neuronal loss and neurodegeneration. In neurodegenerative diseases (such as Alzheimer's disease, Parkinson's disease, amyothrophic lateral sclerosis, and multiple sclerosis), the brain is in a constitutively self-sustaining cycle of inflammation and oxidative stress that prompts and amplifies brain damage. Recent Advances: Recently, an increasing amount of scientific data highlight the ability of specific nutrients to cross the blood-brain barrier, and to modulate inflammatory and oxidative pathways. Therefore, nutritional approaches may contribute to restore the lost equilibrium among factors accounting for neurodegeneration. CRITICAL ISSUES Herein, we critically examine how essential lipids (including fatty acids, liposoluble vitamins and phytosterols) might contribute to accelerate or prevent the onset and progression of such pathologies. In particular, we highlight that experimental and clinical findings, although promising, are still inadequate to draw definitive conclusions. FUTURE DIRECTIONS More research is warranted in order to establish the real impact of lipid intake on brain health, especially when redox balance and inflammatory responses have been already compromised. In the future, it would be hoped to gain a detailed knowledge of chemical modifications and dynamic properties of such nutrients, before planning to exploit them as potential therapeutics. Antioxid. Redox Signal. 29, 37-60.
Sensitivity analysis of skull fracture

Results from multiple high profile experiments on the parameters influencing the impacts that cause skull fractures to the frontal, temporal, and parietal bones were gathered and analyzed. The location of the impact as a binary function of frontal or lateral strike, the velocity, the striking area of the impactor, and the force needed to cause skull fracture in each experiment were subjected to statistical analysis using the JMP statistical software pack. A novel neural network model predicting skull fracture threshold was developed with a high statistical correlation (R 2 =0.978) and presented in this text. Despite variation within individual studies, the equation herein proposes a 3 kN greater resistance to fracture for the frontal bone when compared to the temporoparietal bones. Additionally, impacts with low velocities (<4.1 m/s) were more prone to cause fracture in the lateral regions of the skull when compared to similar velocity frontal impacts. Conversely, higher velocity impacts (>4.1 m/s) showed a greater frontal sensitivity.
Monocular indoor localization techniques for smartphones

Abstract In the last decade huge research work has been put to the indoor visual localization of personal smartphones. Considering the available sensor capabilities monocular odometry provides promising solution, even reecting requirements of augmented reality applications. This paper is aimed to give an overview of state-of-the-art results regarding monocular visual localization. For this purpose essential basics of computer vision are presented and the most promising solutions are reviewed.
Incidence of Pulmonary Arterial Hypertension in Patients with Psoriasis: A Retrospective Cohort Study.

CONTEXT Inflammatory signaling may play an important role in the pathogenesis of pulmonary arterial hypertension (PAH). OBJECTIVE To assess the incidence of PAH in patients with mild and severe psoriasis compared with their respective controls. DESIGN From January 2004 to November 2012, we performed a retrospective cohort study of patients with psoriasis in the Kaiser Permanente Southern California Health Plan. Patients with an International Classification of Diseases, Ninth Revision Clinical Modification diagnostic code for psoriasis (696.1) or psoriatic arthritis (696.0) without a prior diagnosis of primary PAH (416.0) or secondary PAH (416.8) were eligible for inclusion. Patients who had never received a diagnosis of psoriasis were frequency-matched by age, sex, and race to form the control cohorts. MAIN OUTCOME MEASURES Incidence of PAH in patients with psoriasis compared with matched controls. RESULTS There were 10,115 patients with mild psoriasis, 3821 with severe psoriasis, and 69,360 matched controls. On multivariable analysis, there was a significantly increased risk of PAH developing in the severe psoriasis cohort vs their controls (hazard ratio = 1.46, 95% confidence interval = 1.09-1.94). CONCLUSION The systemic inflammatory process underlying psoriasis may be a cause for an increased risk of PAH, but there are numerous secondary causes of PAH, some of which were not accounted for in our study. Further prospective, randomized controlled trials are necessary to establish psoriasis as a risk factor for PAH.