Miracle-dz/UniFAR-FTUs huggingface dataset:
Information About Sunscreen on YouTube and Considerations for Sun Safety Promotion: Content Analysis

Background: Sunscreen use is a popular sun protection method; however, application of sunscreen rarely meets the standards recommended for effectiveness. Access to information about how to effectively use sunscreen may play a role in proper sunscreen application. The internet is a common health information source; however, the quality of sunscreen-related content varies. Objective: The objective of this study was to examine information about sunscreen in YouTube videos by video source. Methods: In November 2017, the authors identified the 20 most popular YouTube videos (sorted by view count and relevance) for each of these 5 search terms: sunscreen cancer, sunscreen health, sunscreen information, sunscreen ingredients, and sunscreen natural. The inclusion criteria were English language and view count >1000 (N=111 unique videos). We double-coded videos for standard recommendations for sunscreen use (eg, apply 20 minutes before sun exposure), use of outdated terminology, and recommendation of complementary sun safety strategies. Results: The view counts ranged from 1100 to 671,142 (median 17,774, SD 109,651) and the average daily views ranged from 1 to 1448 (median 23, SD 234). End users (46/111, 41.4%) and health care providers (24/111, 21.6%) were the most common sources, and none of the most popular videos were produced by federal agencies or cancer-related nongovernmental organizations. Health care provider videos included marginally more recommendations than end user videos (mean 1.46, SD 1.96 vs mean 1.05, SD 1.20), but few (19/111, 17.1%) mentioned reapplication. The videos were generally positive toward sunscreen (82/111, 73.9%); however, some videos were negative (29/111, 26.1%), with warnings about the health risks of chemical sunscreens and their ingredients. Do-it-yourself sunscreen tutorials represented 19/111 (17.1%) of the sample. Conclusions: YouTube is a potential source for disseminating sun safety messages; however, the quality of its sunscreen content varies. Most of the videos in our study failed to include important sunscreen use recommendations. Clinicians should be prepared to address the information needs of patients by discussing effective, evidence-based sunscreen application and recommending a combined sun safety approach. (JMIR Dermatol 2020;3(1):e14411) doi: 10.2196/14411
An Economic Analysis of Software Development Process based on Cost Models

Software development process generally includes requirement analysis, design, implementation, and testing phases. The overall process is called Software Development Life Cycle (SDLC). Each of these steps was executed sequentially in earlier times, i.e. the output of a step is used as the input to the following step. This sequential execution is called waterfall process, and since the total duration of SDLC has been increasing, more dynamic models need to be employed in today’s software engineering methodologies. V-Shaped model, Spiral model, Incremental or iterative software development model, are some examples of these methodologies. On the other hand, due to the increase in the total number of projects for a company and due to the product variability, reusability aspect has entered into the domain. This aspect gained importance in the recent years, leading to the execution of framework-based models and software product line engineering process. In this study, the above methodologies are analyzed from an economic perspective with respect to their cost models. Reusability will require upfront investment, but the gain will be higher as the number of common software items increases, which are determined in commonality/variability analysis phase. Improvements in SDLC might also require organizational changes to adapt new methodologies. These considerations are discussed along with the cost model analysis, and a cost-evaluation criterion is provided in the paper.
On the normalization and visualization of author co-citation data: Salton's Cosine versus the Jaccard index

The debate about which similarity measure one should use for the normalization in the case of Author Co-citation Analysis (ACA) is further complicated when one distinguishes between the symmetrical co-citation--or, more generally, co-occurrence--matrix and the underlying asymmetrical citation--occurrence--matrix. In the Web environment, the approach of retrieving original citation data is often not feasible. In that case, one should use the Jaccard index, but preferentially after adding the number of total citations (occurrences) on the main diagonal. Unlike Salton's cosine and the Pearson correlation, the Jaccard index abstracts from the shape of the distributions and focuses only on the intersection and the sum of the two sets. Since the correlations in the co-occurrence matrix may partially be spurious, this property of the Jaccard index can be considered as an advantage in this case.
Moorhen Gallinula chloropus females lay eggs of different size and β-carotene content

In birds, carotenoids deposited in the eggs can influence the progeny's prospects of survival. As a potentially limiting resource, carotenoids should be allocated optimally by the mother during egg production. The MoorhenGallinula chloropus produces large clutches. We tested if females differ in their allocation of macronutrients (proteins and lipids) and the key micronutrient, β-carotene. To this end, we measured intra- and inter-clutch variability in egg size and contents. All parameters showed large inter-clutch differences and low intra-clutch variability. The total amount of β-carotene decreased in smaller eggs, indicating that females did not compensate for smaller egg size with a higher content of this micronutrient.
Influence of barnyardgrass (Echinochloa crus-galli) time of emergence and density on corn (Zea mays)

Barnyardgrass is a serious weed problem in cornfields in Ontario. Field experiments were conducted at two locations in 1994 and 1995 to determine the influence of emergence time and barnyardgrass density on corn yield loss, leaf area at 50% silking, and barnyardgrass seed production. Selected barnyardgrass densities up to 200 plants m−1 were established within 12.5 cm on either side of the corn row. Barnyardgrass seeds were planted concurrently with corn and at the 3- to 5- or 1- to 2-leaf stage of corn growth in 1994 and 1995, respectively. Barnyardgrass density and seedling emergence relative to corn influenced the magnitude of corn yield loss. Maximum corn grain yield loss ranged from 26 to 35% for early emerging barnyardgrass, and less than 6% yield loss occurred from barnyardgrass seedlings emerging later than the 4-leaf stage of corn growth. Changes in corn leaf area index at 50% silking reflected the level of barnyardgrass competition in corn. Maximum leaf area reduction ranged from 21 to 23%. Barnyardgrass seed production varied with time of seedling emergence and density. Ten barnyardgrass plants emerging up to the 3-leaf stage of corn growth produced 14,400 to 34,600 seeds m−2 compared to only 1,200 to 2,800 seeds m−2 from plants emerging after the 4-leaf corn stage. The results of this study are essential in the development of an integrated weed management strategy for corn.
Feature Selection Using Rough Set Theory Algorithm for Breast Cancer Diagnosis

Feature selection is one of the pre-processing stages of classification carried out by selecting relevant features that affect the results of classification. The advantage of feature selection is that it increases the value of accuracy. Data mining in the medical world has excellent potential for knowing hidden patterns in medical data sets. However, medical data sets often have large dimensions and have irrelevant features that can decrease the performance of the algorithm. This study aims to analyse the performance of the rough set approach as an algorithm used for feature selection in breast cancer diagnosis cases. This study conducted a feature selection process on the Wisconsin Breast Cancer (Diagnostic) Data Set provided by the UCI machine learning repository. There are several steps taken in research to realize these goals, such as data pre-processing, feature selection, data randomization, classification and performance evaluation. The result shows that feature selection using the rough set of methods has proven to be effective in reducing a large number of features in the data set.
Caveats for the Use of Citation Indicators in Research and Journal Evaluations

Ageing of publications, percentage of self-citations, and impact vary from journal to journal within fields of science. The assumption that citation and publication practices are homogenous within specialties and fields of science is invalid. Furthermore, the delineation of fields and among specialties is fuzzy. Institutional units of analysis and persons may move between fields or span different specialties. The match between the citation index and institutional profiles varies among institutional units and nations. The respective matches may heavily affect the representation of the units. Non-ISI journals are increasingly cornered into "transdisciplinary" Mode-2 functions with the exception of specialist journals publishing in languages other than English. An "externally cited impact factor" can be calculated for these journals. The citation impact of non-ISI journals will be demonstrated using Science and Public Policy as the example.
PADL: Privacy-Aware and Asynchronous Deep Learning for IoT Applications

As a promising data-driven technology, deep learning has been widely employed in a variety of Internet-of-Things (IoT) applications. Examples include automated navigation, telemedicine, and smart home. To protect the data privacy of deep-learning-based IoT applications, a few privacy-preserving approaches have also been exploited, designed, and implemented in various scenarios. However, state-of-the-art works are still defective in accuracy, efficiency, and functionality. In this article, we propose the privacy-aware and asynchronous deep-learning-assisted IoT applications (PADL), a privacy-aware and asynchronous deep learning framework that enables multiple data collecting sites to collaboratively train deep neural networks (DNNs), while keeping the confidentiality of private data to each other. Specifically, we first design a layerwise importance propagation (LIP) algorithm to quantify the importance of the model’s weights held by each site. Then, we present the customized perturbation mechanism, a precise combination of the LIP algorithm and differential privacy mechanism, which helps to make optimal tradeoffs between the availability and privacy of local models. Furthermore, to fully use the computing resources of all sites, for the first time, we propose an advanced asynchronous optimization (AAO) protocol to perform global updates without waiting. Theoretical analysis shows that the PADL is robust to extreme collusion even with only one reliable site while supporting lock-free optimization. Finally, extensive experiments conducted on real-world data sets using TensorFlow library show that the PADL outperforms the existing systems in terms of efficiency and prediction accuracy.
Financial Defaulter Detection on Online Credit Payment via Multi-view Attributed Heterogeneous Information Network

Default user detection plays one of the backbones in credit risk forecasting and management. It aims at, given a set of corresponding features, e.g., patterns extracted from trading behaviors, predicting the polarity indicating whether a user will fail to make required payments in the future. Recent efforts attempted to incorporate attributed heterogeneous information network (AHIN) for extracting complex interactive features of users and achieved remarkable success on discovering specific default users such as fraud, cash-out users, etc. In this paper, we consider default users, a more general concept in credit risk, and propose a multi-view attributed heterogeneous information network based approach coined MAHINDER to remedy the special challenges. First, multiple views of user behaviors are adopted to learn personal profile due to the endogenous aspect of financial default. Second, local behavioral patterns are specifically modeled since financial default is adversarial and accumulated. With the real datasets contained 1.38 million users on Alibaba platform, we investigate the effectiveness of MAHINDER, and the experimental results exhibit the proposed approach is able to improve AUC over 2.8% and Recall@Precision=0.1 over 13.1% compared with the state-of-the-art methods. Meanwhile, MAHINDER has as good interpretability as tree-based methods like GBDT, which buoys the deployment in online platforms.
Multi-Label Active Learning Algorithms for Image Classification

Image classification is a key task in image understanding, and multi-label image classification has become a popular topic in recent years. However, the success of multi-label image classification is closely related to the way of constructing a training set. As active learning aims to construct an effective training set through iteratively selecting the most informative examples to query labels from annotators, it was introduced into multi-label image classification. Accordingly, multi-label active learning is becoming an important research direction. In this work, we first review existing multi-label active learning algorithms for image classification. These algorithms can be categorized into two top groups from two aspects respectively: sampling and annotation. The most important component of multi-label active learning is to design an effective sampling strategy that actively selects the examples with the highest informativeness from an unlabeled data pool, according to various information measures. Thus, different informativeness measures are emphasized in this survey. Furthermore, this work also makes a deep investigation on existing challenging issues and future promises in multi-label active learning with a focus on four core aspects: example dimension, label dimension, annotation, and application extension.
Differentially Private Location Privacy in Practice

With the wide adoption of handheld devices (e.g. smartphones, tablets) a large number of location-based services (also called LBSs) have flourished providing mobile users with real-time and contextual information on the move. Accounting for the amount of location information they are given by users, these services are able to track users wherever they go and to learn sensitive information about them (e.g. their points of interest including home, work, religious or political places regularly visited). A number of solutions have been proposed in the past few years to protect users location information while still allowing them to enjoy geo-located services. Among the most robust solutions are those that apply the popular notion of differential privacy to location privacy (e.g. Geo-Indistinguishability), promising strong theoretical privacy guarantees with a bounded accuracy loss. While these theoretical guarantees are attracting, it might be difficult for end users or practitioners to assess their effectiveness in the wild. In this paper, we carry on a practical study using real mobility traces coming from two different datasets, to assess the ability of Geo-Indistinguishability to protect users' points of interest (POIs). We show that a curious LBS collecting obfuscated location information sent by mobile users is still able to infer most of the users POIs with a reasonable both geographic and semantic precision. This precision depends on the degree of obfuscation applied by Geo-Indistinguishability. Nevertheless, the latter also has an impact on the overhead incurred on mobile devices resulting in a privacy versus overhead trade-off. Finally, we show in our study that POIs constitute a quasi-identifier for mobile users and that obfuscating them using Geo-Indistinguishability is not sufficient as an attacker is able to re-identify at least 63% of them despite a high degree of obfuscation.
Power System Oscillation Mode Prediction Based on the Lasso Method

This paper utilizes modern statistical and machine learning methodology to predict the oscillation mode of interest in complex power engineering systems. The damping ratio of the electromechanical oscillation mode is formulated as a function of the power of the generators and loads as well as bus voltage magnitudes in the entire power system. The celebrated Lasso algorithm is implemented to solve this high-dimension modeling problem. By the nature of the $L_{1}$ design, the Lasso algorithm can automatically render a sparse solution, and by eliminating redundant features, it provides desirable prediction power. The resultant model processes a simple structure, and it is easily interpretable. The precision of our sparse modeling framework is demonstrated in the context of an IEEE 50-Generator 145-Bus power network and an online learning framework for the power system oscillation mode prediction is also provided.
Performance analysis of Various Embedding Models Based on Hyper Parameters

본 논문은 다양한 워드 임베딩 모델(word embedding model)들과 하이퍼 파라미터(hyper parameter)들 을 조합하였을 때 특정 영역에 어떠한 성능을 보여주는지에 대한 연구이다. 3 가지의 워드 임베딩 모델인 Word2Vec, FastText, Glove의 차원(dimension)과 윈도우 사이즈(window size), 최소 횟수(min count)를 각기 달리하여 총 36개의 임베딩 벡터(embedding vector)를 만들었다. 각 임베딩 벡터를 Fast and Accurate Dependency Parser 모델에 적용하여 각 모들의 성능을 측정하였다. 모든 모델에서 차원이 높 을수록 성능이 개선되었으며, FastText가 대부분의 경우에서 높은 성능을 내는 것을 알 수 있었다.
Enabling supply chain analytics for enterprise information systems: a topic modelling literature review and future research agenda

ABSTRACT This paper provides a literature review of the research within the framework of 1) analytics,2) supply chain management, and 3) enterprise information systems, and relate the findings to competitive enablers. The findings are used to construct a future research agenda. The methodology is a systematic two-stage approach, based on a Smart Literature review framework using topic modelling. The research agenda proposes future research within the themes of 1) context, 2) cross-functional analytics, 3) cross-planning level analytics, 4) implementation and assimilation of analytics in EIS, 5) analytics and big data for SCM, 6) managerial aspects of analytics, and 7) data and system heterogeneity.
Social networks and new technologies

espanolLas redes sociales y nuevas tecnologias de lacomunicacion influyen cada vez mas en distintosaspectos de nuestras vidas, y moldean laforma en la que nos relacionamos, aprendemosy expresamos. Necesitamos contextualizarlos fenomenos mediaticos en funcion delentorno en el que se desempenan, ya que suaplicacion da resultados diferentes en lugaresdiversos, por ser culturalmente sensibles. EnglishSocial networks and new communication technologiesincreasingly influence different aspects ofour lives. They shape the way we interact, learnand express ourselves. There is a need to contextualizethe media phenomena according to theenvironment in which they work, since their applicationgives different results in different places becausethey are culturally sensitive.
Design and Analysis of Secure Physically Unclonable Function Compositions

Silicon Physically Unclonable Function (PUF) circuits exploit device-level CMOS manufacturing process induced variations to define a random and IC-specific physical challenge-response mapping. Since most of the primitive delay PUF designs such as Arbiter PUF (APUF) are proven to be insecure against modeling attacks, compositions of primitive PUFs are used to achieve robustness against modeling attacks. However, the reliability of the resultant PUF is usually significantly less compared to primitive PUF designs. The primary goal of this thesis is the design and security analyses of secure PUF compositions with higher reliability assurance. Our proposed Composite PUF design framework can be used to design a PUF using a set of primitive PUFs with enhanced performance and security metrics. One of the major problems with most of the existing PUF compositions is that reliability of the resulting PUF reduces with increasing number of primitive PUFs used in compositions. As a solution to this problem, we propose a multiplexer based composition of APUFs, called MPUF. Our proposed MPUF variant, called rMPUF, can achieve higher reliability and security against reliability based modeling attack compared to any practical instance of XOR APUF. To the best of our knowledge, rMPUF is the only APUF composition that is robust against most powerful reliability based modeling attack. Since APUF designs are often used in composition due to its lightweight property, we propose an architectural analysis technique to select good APUF variants for composition. Our analysis reveals that the classic APUF architecture is superior to its two FPGA variants, namely Programmable delay line based APUF (PAPUF) and Double APUF (DAPUF). Thus, in ASIC, APUF should be used in composition instead of architecturally weak PAPUF and DAPUF. For security analysis, we develop an unpredictability test based on the propagation property of PUF. We show that APUF, XOR APUF and Lightweight Secure PUF (LSPUF) cannot pass this test. As a result of the failure in this test, it is revealed that adversary can generate related challenge-response pairs (CRPs). These related CRPs should not be used in security applications. In addition, we develop various cryptanalysis techniques that can be used by an adversary to reduce the security level of PUF design. We also demonstrate how these cryptanalysis techniques are used along with traditional machine learning techniques to achieve an enhanced modeling attack. Our proposed cryptanalysis assisted modeling attack on LSPUF shows that multibit output LSPUF instances are not secure. Finally, we provide fault-tolerant implementations of various delay PUFs implemented on FPGA against laser fault attacks. Our fault tolerant solutions can detect runtime faults, and prevent information leakage through faulty output. To recover the faulty PUFs on FPGA, we implement PUFs using configurable LUT and random-sliding features.
Novel Drug Delivery Systems in Management of Cardiovascular Diseases

Cardiovascular (CV) diseases like atherosclerosis, angina pectoris, acute myocardial infarction, are a major cause of mortality in the whole world owing to the present day hectic lifestyle. In this review we will concentrate on the various novel drug delivery systems (DDS) like transdermal DDS, micelles, liposomes, nanoparticles (NPs), dendrimers, drug eluting stents (DES) and drug eluting balloons (DEB) that are being used over the years and in present day for the treatment of these diseases.
A Systematic Literature Review of Data Classification Techniques

The data mining and their different applications are becomes more popular now in these days a number of large and small scale applications are developed with the help of data mining techniques i.e. predictors, regulators, weather forecasting systems and business intelligence. Many of classification algorithms are available to analyze data. Classification is used to classify each item in a data set into one of a predefined set of classes or groups. Classification is the chore of identifying a model or function. There are two kinds of model are available for namely supervised and unsupervised. The performance and accuracy of the supervised data mining techniques are higher as compared to unsupervised techniques therefore in sensitive applications the supervised techniques are used for prediction and classification. In this presented work the supervised learning based data mining techniques for classification and prediction are analyzed.