Miracle-dz/UniFAR-FTUs huggingface dataset:
Evolutionary data sampling for user movement classification

Smartphones are nowadays used for recognizing people's daily activities and habits, by collecting and analysing user activity information in real-time. In order to demonstrate this methodology, we have developed GPSTracker1 a prototype application for Android phones, which collects position, speed, altitude and time information and performs real-time classification of user's movement. The GPSTracker application also uses geo-location information abouts Points Of Interest (POIs) such as bus or metro routes, parks and stadiums in order to improve the set of features used for the classification of a type of movement. In this work, we use evolutionary algorithms, in order to reduce the number of samples required for training our classifier, without loosing in classification accuracy. The resulting model, a) is able to provide personalized solutions, tuned to each individual users movement abilities, b) better adapts to unbalanced training data, due to the generation of training samples from the existing ones, c) performs an initial sampling of the training data, which reduces requirements for computational resources and improves the classification performance.
Visualization of News Distribution in Blog Space

This paper proposes a visualization method of news distribution in blog space. Recently, blog is becoming one of the important information resources on the Web, from which subjective data and trend information could be obtained. On the other hand, online news site is another information resource, which reports latest events in the world. This paper focuses on the combination of both resources, and proposes a method for visualizing how news is mentioned and distributed in blog space. The target objects to be visualized are news articles, blog entries, and blog sites. The paper defines news to news relationship, news to entry relationship, entry to entry relationship, and entry to site relationship, based on which blog space is visualized on 2D space by interactive information visualization system. This paper shows with a small data set that news distribution in blog space can be visualized from multiple viewpoints
Towards Measuring Membership Privacy

Machine learning models are increasingly made available to the masses through public query interfaces. Recent academic work has demonstrated that malicious users who can query such models are able to infer sensitive information about records within the training data. Differential privacy can thwart such attacks, but not all models can be readily trained to achieve this guarantee or to achieve it with acceptable utility loss. As a result, if a model is trained without differential privacy guarantee, little is known or can be said about the privacy risk of releasing it. In this work, we investigate and analyze membership attacks to understand why and how they succeed. Based on this understanding, we propose Differential Training Privacy (DTP), an empirical metric to estimate the privacy risk of publishing a classier when methods such as differential privacy cannot be applied. DTP is a measure of a classier with respect to its training dataset, and we show that calculating DTP is efficient in many practical cases. We empirically validate DTP using state-of-the-art machine learning models such as neural networks trained on real-world datasets. Our results show that DTP is highly predictive of the success of membership attacks and therefore reducing DTP also reduces the privacy risk. We advocate for DTP to be used as part of the decision-making process when considering publishing a classifier. To this end, we also suggest adopting the DTP-1 hypothesis: if a classifier has a DTP value above 1, it should not be published.
Introduction: how can we improve asthma management?

ABSTRACT Asthma remains a poorly controlled disease both in Europe and the USA despite the availability of effective asthma treatments. Patient non-compliance, incorrect use of inhaler devices, insufficient treatment of peripheral airway inflammation as well as limitations of the asthma management guidelines themselves may all contribute to this poor control. Asthma control may be improved by improving the consultation process during the visit at the doctor. The ideal consultation would involve critical listening to the patient, accurate assessment of asthma symptoms as indicators for asthma control and prescribing the appropriate medication and dose for the individual patient according to the degree of severity of asthma. In addition, correctness of inhalation technique as performed by the patient should be regularly checked; patients should be educated and trained how to manage their personal condition and should be offered convenient follow-up options. Choosing the right inhaler for the patient may improve patient compliance. Inhaler choice should be based on an evidence-based rationale rather than on an empirical basis. The preference of the patient should also be taken into consideration, as it is the patient who has to use the inhaler daily over a long period of time. The ideal inhaler should demonstrate sufficient drug delivery to the lower airways as well as good drug distribution to both the central and peripheral airways. It should ensure consistency of the emitted dose, be easy to teach and use, be small in size and convenient to handle. It should also be multi-dose, require a low inspiratory airflow for activation, provide feedback to the patient on correct inhalation technique, be re-usable/refillable, have an appealing design, and have a reliable dose counter.
Analytical Multimodal Network Approach for 2-D Arrays of Planar Patches/Apertures Embedded in a Layered Medium

A fully analytical multimodal equivalent circuit is presented for the modeling of the scattering of an obliquely incident plane wave by a two-dimensional (2-D) periodic array of metallic patches (or apertures in a metallic screen) embedded in a layered medium. The topology of the equivalent network is rigorously derived in the analysis and all the network parameters are given in closed form. In contrast with the previously reported explicit circuit models, the proposed approach accounts for dynamical effects over a very wide frequency range, which enables the application of the model to a great variety of situations. The key advantages of the reported multimodal network representation are its analytical nature, its extremely low-computational cost and that the physical phenomena involved in the scattering can be easily understood in terms of transmission line and lumped circuital reasonings.
Adrenomedullin in experimental congestive heart failure: cardiorenal activation.

Adrenomedullin (ADM) is a new member of a family of vasodilating and natriuretic peptides that plays an important role in cardiorenal regulation. This study was designed to establish the plasma, urinary, cardiac, and renal tissue concentrations and immunohistochemical localizations of ADM in normal dogs and dogs with experimental congestive heart failure (CHF) produced by rapid ventricular pacing. Plasma ADM concentration was 5.6 ± 0.4 pg/ml in normal dogs and significantly increased to 14.5 ± 2.5 pg/ml in CHF dogs ( P < 0.05). Ventricular and renal tissue ADM were significantly increased in CHF dogs compared with normals. Immunohistochemical examination revealed positive ADM immunostaining within the myocytes, and ventricular ADM immunoreactivity was significantly more intense in CHF dogs than in normals. ADM immunoreactivity was also observed in the glomerulus, distal tubules, and medullary collecting duct cells in the kidney, and the intensities of ADM immunoreactivity in these sites were increased in CHF dogs compared with normals. In addition, ventricular ADM was a powerful marker for left ventricular mass, and circulating ADM correlated positively with left ventricular end-diastolic pressure and inversely with cardiac output and ejection fraction. Despite an increase in renal tissue ADM, urinary ADM did not increase in CHF dogs. The current study demonstrates that plasma concentration of ADM is increased in experimental CHF and that ventricular and renal ADM is activated in the progression of CHF. Tissue and circulating ADM also are markers for the alterations in myocardial structure and function. This study supports a potential role for ADM in the neurohumoral activation in experimental CHF.
Supplementation with beta-carotene in vivo and in vitro does not inhibit low density lipoprotein oxidation.

The inhibition of low density lipoprotein (LDL) oxidation has been postulated as one mechanism by which antioxidants may prevent the development of atherosclerosis. Available data on the ability of beta-carotene to inhibit LDL oxidation are conflicting. We examined the role of in vivo and in vitro supplementation with beta-carotene on metal ion-dependent (cupric ions, Cu2+) and metal ion-independent (2,2'-azobis[2-amidinopropane]dihydrochloride, AAPH) oxidation of LDL as measured by the formation of conjugated dienes (absorbance at 234 nm). Sixteen subjects were supplemented with 50-100 mg of beta-carotene on alternate days for 3 weeks following a week-long loading dose of 100 mg/day. Plasma beta-carotene levels rose 5.5-fold, while LDL beta-carotene levels rose 8.5-fold. Oxidation of LDL by Cu2+ or AAPH was not significantly delayed after in vivo supplementation with beta-carotene compared with baseline. For AAPH, the lag phase (in minutes) was 75 +/- 8 at baseline and 83 +/- 14 after supplementation (P = 0.07). For Cu2+, the lag phase was 172 +/- 41 at baseline and decreased to 130 +/- 24 after supplementation (P < 0.01). Similarly, no protective effect against Cu(2+)-induced oxidation was observed when beta-carotene was added to LDL in vitro. Supplementation of plasma with beta-carotene in vitro prior to LDL isolation also did not enhance LDL's resistance to Cu(2+)- or AAPH-induced oxidation, despite a 5-fold increase in LDL beta-carotene levels over vehicle control. (ABSTRACT TRUNCATED AT 250 WORDS)
Living in the Vicinity of Wind Turbines — A Grounded Theory Study

Little is known of wind turbines' impact on people living in their vicinity. The aim of this study was to gain a deeper understanding of how people perceive and are affected by wind turbines in their living environment. In-depth interviews with 15 informants, strategically chosen to form a heterogeneous group, were analyzed using the constant comparative method of grounded theory. The informants were to different extents affected by the swishing noise, flickering light, and constant movement of the turbines' rotor blades. Some informants perceived the exposures as outside their territory while others perceived them as intrusion into privacy; a divergence partly determined by the informants' personal values about the living environment. The feeling of intrusion was associated with feeling a lack of control, subjected to injustice, a lack of influence, and not being believed. Informants used various coping strategies, such as rebuilding their houses or complaining, but mainly tried to ignore exposures from the wind turbines. The findings can help us to better understand the severe reactions wind turbines sometimes evoke and contribute to the knowledge base used when planning for new wind farms.
On the reliability gain of neighborhood coalitions: A data-driven study

One of the major challenges of the future power systems is managing the substantial unreliability and unpredictability induced by the large share of renewables. As one of the promising solutions, energy cooperatives have recently drawn attention. Unlike most of the existing proposed strategies (e.g., storage), there is not much known yet about the practical importance of energy cooperatives. Taking a data-driven approach, we quantify the power variability reduction (which we also call the reliability gain) for three neighborhood level types of cooperatives: energy generation, energy demand, and energy prosumer. We show how the reliability is scaled by the size of coalition and the time scale for these three energy cooperatives. We gain several interesting insights which are vital for designing and planning neighborhood level energy cooperatives. For example, we find that energy demand cooperatives have the largest reliability gain among all. Using the central limit theorem asymptotic results, we show that the demand energy variations up to 15 minutes of different homes are almost independent of each other. We also find that small coalitions of sizes 10 and 20, respectively, for energy demand and energy prosumer cooperatives are sufficient to capture most of the reliability gain of the grand coalition. For the special case of energy generation cooperatives, our results differ from an existing empirical study (in the context of geographical diversity) at a different location, suggesting that designing an efficient energy generation cooperative is jurisdiction-dependent.
Exposure-response relationship between gastrointestinal problems among waste collectors and bioaerosol exposure.

OBJECTIVES A high frequency of gastrointestinal problems has previously been reported for waste collectors. The present study relates the gastrointestinal problems to measurements of the bioaerosols that waste collectors are exposed to during work. METHODS A job-exposure matrix was constructed from a combination of questionnaire data and field measurements. The questionnaire data were collected from 1747 male waste collectors and a comparison group of 1111 male municipal workers. Moreover a total of 189 full-shift personal samples was collected. The samples were used for characterizing the bioaerosol exposure described by viable fungi, total count of fungal spores, microorganisms, and endotoxins. RESULTS In a multivariate analysis, high exposure to endotoxins was associated with nausea [prevalence proportion ratio (PPR) 1.60], and the risk of reporting nausea decreased with decreasing exposure so that workers with low exposure had the fewest reports (PPR 1.39) in the comparison with the unexposed group. High exposure to endotoxins was also associated with reports of diarrhea (PPR 5.60), and the risk of reporting diarrhea decreased with decreasing exposure so that the workers with low exposure had the fewest reports (PPR 3.02). The same pattern existed for exposure to fungi, for which high exposure resulted in the most reports (PPR = 4.59), and for diarrhea, for which low exposure resulted in the fewest reports (PPR = 3.15). CONCLUSIONS An exposure-response relationship was found between nausea and endotoxin exposure and between diarrhea and exposure to both endotoxins and viable fungi.
RNA Interference in Moths: Mechanisms, Applications, and Progress

The vast majority of lepidopterans, about 90%, are moths. Some moths, particularly their caterpillars, are major agricultural and forestry pests in many parts of the world. However, some other members of moths, such as the silkworm Bombyx mori, are famous for their economic value. Fire et al. in 1998 initially found that exogenous double-stranded RNA (dsRNA) can silence the homolog endogenous mRNA in organisms, which is called RNA interference (RNAi). Soon after, the RNAi technique proved to be very promising not only in gene function determination but also in pest control. However, later studies demonstrate that performing RNAi in moths is not as straightforward as shown in other insect taxa. Nevertheless, since 2007, especially after 2010, an increasing number of reports have been published that describe successful RNAi experiments in different moth species either on gene function analysis or on pest management exploration. So far, more than 100 peer-reviewed papers have reported successful RNAi experiments in moths, covering 10 families and 25 species. By using classic and novel dsRNA delivery methods, these studies effectively silence the expression of various target genes and determine their function in larval development, reproduction, immunology, resistance against chemicals, and other biological processes. In addition, a number of laboratory and field trials have demonstrated that RNAi is also a potential strategy for moth pest management. In this review, therefore, we summarize and discuss the mechanisms and applications of the RNAi technique in moths by focusing on recent progresses.
Discovering Risk of Disease with a Learning Classifier System

A learning classifier system, EpiCS, was used to de rive a continuous measure of disease risk in a series of 250 individuals. Using the area under the receiver-ope rating characteristic curve, this measure was compared wit h the risk estimate derived for the same individuals by logistic regression. Over 20 training-testing trial s, risk estimates derived by EpiCS were consistently more accurate (mean area=0.97, SD=0.01) than that derive d by logistic regression (mean area=0.89, SD=0.02). The areas for the trials with minimum and maximum classification performance on testing were signific antly greater (p=0.019 and p<0.001, respectively) than th e area for the logistic regression curve. This inves tigation demonstrated the ability of a learning classifier s ystem to produce output that is clinically meaningful in diagnostic classification.
Mobile banking and economic development: linking adoption, impact, and use

Around the globe, various initiatives use the mobile phone to provide financial services to those without access to traditional banks. Relatively little scholarly research explores the use of these m-banking/m-payments systems. This paper calls attention to this gap in the research literature, emphasizing the need for research focusing on the context(s) of m-banking/m-payments use. Presenting illustrative data from exploratory work with small enterprises in urban India, it argues that contextual research is a critical input to effective ‘adoption’ or ‘impact’ research. Further, it suggests that the challenges of linking studies of use to those of adoption and impact reflect established dynamics within the Information and Communication Technologies and Development (ICTD) research community. The paper identifies three cross-cutting themes from the broader literature (amplification vs. change, simultaneous causality, and a multi-dimensional definition of trust), each of which can offer increased theoretical clarity to future research on m-banking/m-payments systems.
Competency of different cell models to predict human hepatotoxic drugs

Introduction: The liver is the most important target for drug-induced toxicity. This vulnerability results from functional liver features and its role in the metabolic elimination of most drugs. Drug-induced liver injury is a significant leading cause of acute, chronic liver disease and an important safety issue when developing new drugs. Areas covered: This review describes the advantages and limitations of hepatic cell-based models for early safety risk assessment during drug development. These models include hepatocytes cultured as monolayer, collagen-sandwich; emerging complex 3D configuration; liver-derived cell lines; stem cell-derived hepatocytes. Expert opinion: In vitro toxicity assays performed in hepatocytes or hepatoma cell lines can potentially provide rapid and cost–effective early feedback to identify toxic candidates for compound prioritization. However, their capacity to predict hepatotoxicity depends critically on cells’ functional performance. In an attempt to improve and prolong functional properties of cultured cells, different strategies to recreate the in vivo hepatocyte environment have been explored. 3D cultures, co-cultures of hepatocytes with other cell types and microfluidic devices seem highly promising for toxicological studies. Moreover, hepatocytes derived from human pluripotent stem cells are emerging cell-based systems that may provide a stable source of hepatocytes to reliably screen metabolism and toxicity of candidate compounds.
Comparing determinants of website satisfaction and loyalty across the e-government and e-business domains

Do the same website qualities determine end-user satisfaction - and through satisfaction, consumer loyalty - across the e-government and e-business domains, or do these different groups of consumers seek different things? This question is an important one, as considerable pressure is currently being placed on e-government to improve by better emulating the private sector, while at the same time almost no research has examined the similarities of these two domains from the consumer perspective. Using a sample of survey responses from end-users of e-business and e-government websites, data from the American Customer Satisfaction Index (ACSI), this study employs structural equation modeling to compare determinants and outcomes of satisfaction across these domains. Results from the models show that while some similarities do exist, for e-business users satisfaction is predominantly a function of the "personalizability" of the website, while satisfaction with e-government is determined more equally by the various predictors. We discuss the implications of these and other findings to the proposed private sector-driven transformation of e-government.
Engaging Children With Autism in Shared Book Reading

Margot is a mother to three young children and is particularly invested in helping her children become strong readers. She makes an effort to read with her children daily, because she has been told by teachers and other professionals that reading with children is especially important in helping them develop reading skills. Sam, the youngest of her children, is a reluctant reader. He was diagnosed with autism last year when he was three years old, and he engages in challenging behaviors that make it difficult to read with him. Sam’s siblings show a natural affinity toward books and are excited to read with Margot, but when she includes Sam in book readings, he loses interest quickly and engages in behaviors that are distracting to his siblings, such as poking them or yawning loudly. When Margot asks if he would like to read a book just with her, he usually ignores her and continues playing with his trains (his favorite activity). Forceful attempts to make him read books result in screaming tantrums. Because of his challenging behaviors, Margot has stopped trying to get Sam to read with her or his siblings, but she worries that he will have difficulties reading in the future. The presence of a developmental disability has been associated with failures in learning to read (Landgren, Kjellman, & Gillberg, 2003). Given that children with disabilities are at higher risk for reading difficulties, it is especially important that they receive repeated opportunities to develop emergent literacy skills—particularly oral language, phonological awareness, and concepts about print—that can support later reading achievement (National Early Literacy Panel, 2008). A common early literacy practice valued by early childhood professionals and parents is reading aloud with children. Reading books with children gained popularity during the 1980s in large part, because early childhood policy makers publicized that reading aloud was an excellent, developmentally appropriate instructional practice (Bredekamp, 1986). The frequency of book reading at early ages has been related to children’s oral language ability and reading comprehension skills in elementary school (Wells, 1985). Reading aloud to children emerged as a key facet of family literacy programs and the central focus of several public library outreach efforts (e.g., the Carnegie Library’s Beginning to Read program, Reading is Fundamental, “