Miracle-dz/UniFAR-FTUs huggingface dataset:
Generative Adversarial Learning for Spectrum Sensing

A novel approach of training data augmentation and domain adaptation is presented to support machine learning applications for cognitive radio. Machine learning provides effective tools to automate cognitive radio functionalities by reliably extracting and learning intrinsic spectrum dynamics. However, there are two important challenges to overcome, in order to fully utilize the machine learning benefits with cognitive radios. First, machine learning requires significant amount of truthed data to capture complex channel and emitter characteristics, and train the underlying algorithm (e.g., a classifier). Second, the training data that has been identified for one spectrum environment cannot be used for another one (e.g., after channel and emitter conditions change). To address these challenges, a generative adversarial network (GAN) with deep learning structures is used to 1)~generate additional synthetic training data to improve classifier accuracy, and 2) adapt training data to spectrum dynamics. This approach is applied to spectrum sensing by assuming only limited training data without knowledge of spectrum statistics. Machine learning classifiers are trained to detect signals using either limited, augmented or adapted training data. Results show that training data augmentation increases the classifier accuracy significantly and this increase is sustained with domain adaptation while spectrum conditions change.
Supply Chain Coordination Mechanism Logistics Cost Optimization Analysis

This paper deals with a two-stage supply chain including one supplier and one purchaser. The two coordination mechanisms: traditional supply chain and vendor-managed inventory, are analyzed on their logistics cost optimization capabilities under the centralized and decentralized decision-making conditions. The conclusions are that the logistics costs are same under the centralized decision-making; vendor-managed inventory coordination mechanism is better than traditional supply chain coordination mechanism under the decentralized decision-making.
An Absorptive Balanced-to-Balanced Power Divider

In this paper, an absorptive balanced-to-balanced power divider is proposed. The new constraint rules at the central frequency are provided for the six-port mixed-mode S-parameters with only four elements being nonzero. Two equivalent three-port networks are deduced for the odd- and even-mode analysis. A microstrip prototype is developed to validate our design. The theoretical, simulated, and measured results all show good mixed-mode performance.
Making Core Memory: Design Inquiry into Gendered Legacies of Engineering and Craftwork

This paper describes the Making Core Memory project, a design inquiry into the invisible work that went into assem-bling core memory, an early form of computer information storage initially woven by hand. Drawing on feminist tradi-tions of situated knowing, we designed an electronic quilt and a series of participatory workshops that materialize the work of the core memory weavers. With this case we not only broaden dominant stories of design, but we also reflect on the entanglement of predominantly male, high status labor with the ostensibly low-status work of women's hands. By integrating design and archival research as a means of cultural analysis, we further expand conversations on design research methods within human-computer inter-action (HCI), using design to reveal legacies of practice elided by contemporary technology cultures. In doing so, this paper highlights for HCI scholars that worlds of hand-work and computing, or weaving and space travel, are not as separate as we might imagine them to be.
Cooking Up An Open Source EMR For Developing Countries: OpenMRS - A Recipe For Successful Collaboration

Millions of people are continue to die each year from HIV/AIDS. The majority of infected persons (>95%) live in the developing world. A worthy response to this pandemic will require coordinated, scalable, and flexible information systems. We describe the OpenMRS system, an open source, collaborative effort that can serve as a foundation for EMR development in developing countries. We report our progress to date, lessons learned, and future directions.
INSECTICIDE RESISTANCE IN MOSQUITOES : A PRAGMATIC REVIEW

Descriptions of the World Health Organization standard methods of assessing susce ptibility or resistance in larval and in adult mosquitoes are presented, and the evaluation of their results are discussed. Other susceptibility test methods are also mentioned, including those based on esterase zymograms. Recent work on the biochemical mechanisms of resistance and cross-resistance are reviewed, along with possible countermeasures for the problem of mosquito resistance, now known in ll3 species of culicines and anophelines.
Bad Ai: Investigating the Effect of Half-Toning Techniques on Unwanted Face Detection Systems

Currently, automatic face detection systems are widely used in various social media. The improvement of the automated intelligent face recognition can easily result in the invasion of the user 's privacy if the predefined functions for their so- called artificial intelligent systems are designed with no respect to the user privacy, this means that users do not get the right to prevent these types of automatic face detection. The problem does not stop here, and recently some organization attempted to use automated face recognition system as a digital personal judgment tools. For instance, judging if a person is criminal only based on the attributes of his/her face. In the present technology, spreading the personal information such as individual digital images are fast and inevitable. Thus, in this paper, some of the techniques to evade automatic face recognition is investigated. The focused of this article is to highlight the advantages of different Half-Toning algorithms concerning avoiding unwanted automated face detection/recognition. In the experimental phase, the result of varying filtering and modification algorithms compared to the proposed filtering technique.
Modified mixed nanomicelles with collagen peptides enhanced oral absorption of Cucurbitacin B: preparation and evaluation

Abstract Polymer nanoparticles modified with collagen peptides (CPs) are an attractive strategy for the oral delivery of active ingredients from Chinese medicine. Thus, in the present study, collagen cationic CPs were simply separated using ion-exchange resin from bovine CPs, to modify mixed nanomicelles (MMs) on the surface to improve the oral bioavailability of Cucurbitacin B (CuB). The physicochemical property of micelles was characterized, which confirmed the successful modification of the nanomicelles. CPs-modified nanomicelles in vitro were found to significantly increase cellular uptake and transportation. Compared to unmodified micelles, the quantity of CPs-modified micelles internalized by Caco-2 cells were 3.74 times greater and the cumulative transportation flux (AP-BL) was 2.81 times greater. The membrane transportation process of CuB-MMs-CPs was found to be associated with energy consumption and clathrin- and caveolin-mediated endocytosis. In vivo studies performed on rats indicated that in comparison to CuB and CuB-MMs, the relative bioavailability of CuB-MMs-CPs increased by 3.43 times and 2.14 times, respectively. In addition, the tumor inhibition caused by CuB-MMs-CPs was increased significantly. Therefore, the nanomicelles co-modified with isolated CPs could act as attractive carriers for oral delivery of CuB.
A 5‐year randomized clinical trial comparing minimally with moderately rough implants in patients with severe periodontitis

AIM To compare the clinical and microbiological performance of minimally and moderately rough implants in patients with a history of severe periodontitis. MATERIAL AND METHODS Forty-eight minimally (Turned surface [Tur]) and moderately (TiUnite surface [TiU]) rough implants were placed in eighteen patients according to a split-mouth protocol. Marginal bone loss, probing pocket depth (PPD), clinical attachment level (CAL), and bleeding on probing (BoP) were recorded, and microbial samples were analysed by means of quantitative PCR. RESULTS The amount of bone loss over the 5-year period tended to be lower along Tur when compared with that of TiU surfaces (1.0 versus 1.7 mm, p = .06). Although the clinical outcomes tended to be better for Tur surfaces, there were no significant differences between both surfaces in mean PPD (Tur: 3.1 versus TiU: 4.2 mm, p = .09) or CAL (Tur: 0.5 versus TiU: 1.7 mm, p = .06). More bone loss and deeper pockets were recorded for partially than for fully edentulous patients. The cumulative survival rate at 5-year follow-up was 95.8% for Tur, and 100% for TiU surface implants. No significant differences were found between the surfaces in counts for key pathogens. CONCLUSION In patients with a history of severe periodontitis minimally rough implants showed more favourable clinical parameters after 5 years of loading, when compared with moderately rough implants.
Dynamics of paediatric urogenital schistosome infection, morbidity and treatment: a longitudinal study among preschool children in Zimbabwe

Background Recent research has shown that in schistosome-endemic areas preschool-aged children (PSAC), that is, ≤5 years, are at risk of infection. However, there exists a knowledge gap on the dynamics of infection and morbidity in this age group. In this study, we determined the incidence and dynamics of the first urogenital schistosome infections, morbidity and treatment in PSAC. Methods Children (6 months to 5 years) were recruited and followed up for 12 months. Baseline demographics, anthropometric and parasitology data were collected from 1502 children. Urinary morbidity was assessed by haematuria and growth-related morbidity was assessed using standard WHO anthropometric indices. Children negative for Schistosoma haematobium infection were followed up quarterly to determine infection and morbidity incidence. Results At baseline, the prevalence of S haematobium infection and microhaematuria was 8.5% and 8.6%, respectively. Based on different anthropometric indices, 2.2%–8.2% of children were malnourished, 10.1% underweight and 18.0% stunted. The fraction of morbidity attributable to schistosome infection was 92% for microhaematuria, 38% for stunting and malnutrition at 9%–34%, depending on indices used. S haematobium-positive children were at greater odds of presenting with microhaematuria (adjusted OR (AOR)=25.6; 95% CI 14.5 to 45.1) and stunting (AOR=1.7; 95% CI 1.1 to 2.7). Annual incidence of S haematobium infection and microhaematuria was 17.4% and 20.4%, respectively. Microhaematuria occurred within 3 months of first infection and resolved in a significant number of children, 12 weeks post-praziquantel treatment, from 42.3% to 10.3%; P<0.001. Conclusion We demonstrated for the first time the incidence of schistosome infection in PSAC, along with microhaematuria, which appears within 3 months of first infection and resolves after praziquantel treatment. A proportion of stunting and malnutrition is attributable to S haematobium infection. The study adds scientific evidence to the calls for inclusion of PSAC in schistosome control programmes.
Prediction of Glucose Tolerance without an Oral Glucose Tolerance Test

Introduction Impaired glucose tolerance (IGT) is diagnosed by a standardized oral glucose tolerance test (OGTT). However, the OGTT is laborious, and when not performed, glucose tolerance cannot be determined from fasting samples retrospectively. We tested if glucose tolerance status is reasonably predictable from a combination of demographic, anthropometric, and laboratory data assessed at one time point in a fasting state. Methods Given a set of 22 variables selected upon clinical feasibility such as sex, age, height, weight, waist circumference, blood pressure, fasting glucose, HbA1c, hemoglobin, mean corpuscular volume, serum potassium, fasting levels of insulin, C-peptide, triglyceride, non-esterified fatty acids (NEFA), proinsulin, prolactin, cholesterol, low-density lipoprotein, HDL, uric acid, liver transaminases, and ferritin, we used supervised machine learning to estimate glucose tolerance status in 2,337 participants of the TUEF study who were recruited before 2012. We tested the performance of 10 different machine learning classifiers on data from 929 participants in the test set who were recruited after 2012. In addition, reproducibility of IGT was analyzed in 78 participants who had 2 repeated OGTTs within 1 year. Results The most accurate prediction of IGT was reached with the recursive partitioning method (accuracy = 0.78). For all classifiers, mean accuracy was 0.73 ± 0.04. The most important model variable was fasting glucose in all models. Using mean variable importance across all models, fasting glucose was followed by NEFA, triglycerides, HbA1c, and C-peptide. The accuracy of predicting IGT from a previous OGTT was 0.77. Conclusion Machine learning methods yield moderate accuracy in predicting glucose tolerance from a wide set of clinical and laboratory variables. A substitution of OGTT does not currently seem to be feasible. An important constraint could be the limited reproducibility of glucose tolerance status during a subsequent OGTT.
An automatic temperature control for induction cooktops to reduce energy consumption

Induction cooktops are very used as an efficient alternative to traditional cooking systems such as gas hobs. Even if the energy efficiency of induction cooktops is twice as much as the traditional gas burners, the differences in terms of energy price can limit the diffusion and marketing of induction cooktops. Recent eco-normative are regulating the energy consumption of this kind of household appliances sold in different countries. This paper proposes an automatic temperature control to reduce the energy consumption for induction cooktops. The water boiling test was used as case study to simulate and validate the proposed approach.
Error Detector Placement for Soft Computing Applications

The scaling of Silicon devices has exacerbated the unreliability of modern computer systems, and power constraints have necessitated the involvement of software in hardware error detection. At the same time, emerging workloads in the form of soft computing applications (e.g., multimedia applications) can tolerate most hardware errors as long as the erroneous outputs do not deviate significantly from error-free outcomes. We term outcomes that deviate significantly from the error-free outcomes as Egregious Data Corruptions (EDCs). In this study, we propose a technique to place detectors for selectively detecting EDC-causing errors in an application. We performed an initial study to formulate heuristics that identify EDC-causing data. Based on these heuristics, we developed an algorithm that identifies program locations for placing high coverage detectors for EDCs using static analysis. Our technique achieves an average EDC coverage of 82%, under performance overheads of 10%, while detecting 10% of the Non-EDC and benign faults. We also evaluate the error resilience of these applications under the 14 compiler optimizations.
A Checkpoint of Research on Parallel I/O for High-Performance Computing

We present a comprehensive survey on parallel I/O in the high-performance computing (HPC) context. This is an important field for HPC because of the historic gap between processing power and storage latency, which causes application performance to be impaired when accessing or generating large amounts of data. As the available processing power and amount of data increase, I/O remains a central issue for the scientific community. In this survey article, we focus on a traditional I/O stack, with a POSIX parallel file system. We present background concepts everyone could benefit from. Moreover, through the comprehensive study of publications from the most important conferences and journals in a 5-year time window, we discuss the state of the art in I/O optimization approaches, access pattern extraction techniques, and performance modeling, in addition to general aspects of parallel I/O research. With this approach, we aim at identifying the general characteristics of the field and the main current and future research topics.
Training VAEs Under Structured Residuals

Variational auto-encoders (VAEs) are a popular and powerful deep generative model. Previous works on VAEs have assumed a factorised likelihood model, whereby the output uncertainty of each pixel is assumed to be independent. This approximation is clearly limited as demonstrated by observing a residual image from a VAE reconstruction, which often possess a high level of structure. This paper demonstrates a novel scheme to incorporate a structured Gaussian likelihood prediction network within the VAE that allows the residual correlations to be modelled. Our novel architecture, with minimal increase in complexity, incorporates the covariance matrix prediction within the VAE. We also propose a new mechanism for allowing structured uncertainty on color images. Furthermore, we provide a scheme for effectively training this model, and include some suggestions for improving performance in terms of efficiency or modelling longer range correlations. The advantage of our approach is illustrated on the CelebA face data and the LSUN outdoor churches dataset, with substantial improvements in terms of samples over traditional VAE and better reconstructions.
The use of iterative reconstruction in ultra low dose computed tomography for bodypacker screening

Poster: "ECR 2011 / C-2068 / The use of iterative reconstruction in ultra low dose computed tomography for bodypacker screening" by: "K. H. Nieboer1, N. Buls2, J. de Mey2, G. Van Gompel2; 1Schaarbeek/BE, 2Brussels/BE"
Forecasting the Stock Price Volatilities by Integratingthe Support Vector Regression and the Krill Herd Algorithm

The derivatives which have the property of the high leverage have become popular tools for investing in the era with a low interest rate. Among these derivatives, the options are considered a simpler way for investing since the Black-Scholes (B-S) pricing model can be used to estimate their reasonable prices. However, the parameter "volatility ± in the B-S model cannot be known in advance and needs be guessed based on the historical trading information regarding the options or the underlying assets. Hence, the problems of forecasting future volatilities had become an interesting and attractive research topic for both researchers and practioners. Among the previous researches, the artificial intelligent techniques had been extensively used and acquired satisfactory results. Therefore, the support vector regression (SVR) technique and krill herd (KH) optimization algorithm are utilized to develop an integrated approach for forecasting the volatilities more accurately in this study. The proposed approach is demonstrated by a case study aiming at forecasting the volatilities of the Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) to verify its feasibility and effectiveness. According to the experimental results, the proposed integrated forecasting methodology can produce better forecasting performance, based on the RMSE, R2 or MAPE, than the forecasting models which are built solely based on the SVR. Therefore, it can conclude that the proposed integrated approach can really indeed improve the forecasting, and can be considered an effective and useful assistant tool for an investor to obtain more accurate estimation for the volatility thus helping his/her decision making.
How to Systematically Distribute Candidate Models and Robust Controllers in Multiple-Model Adaptive Control: A Coverage Control Approach

Distributing nominal models in multiple-models applications constitutes a long standing problem. The set of models needs to be distributed in such a way that their corresponding controllers can stabilize all possible system configurations in a large uncertainty set. This paper presents a systematic solution by phrasing the distribution as coverage control problem, in which each model covers a subset of the uncertainty. The subsets are derived based on a combination of the <inline-formula><tex-math notation="LaTeX">$\nu$</tex-math></inline-formula>-gap metric, which serves as a distance measure, and the generalized stability margin. Characterizing coverage in terms of the <inline-formula><tex-math notation="LaTeX">$\nu$</tex-math></inline-formula>-gap also motivates the use of <inline-formula><tex-math notation="LaTeX">$\mathcal {H}_{\infty }$</tex-math></inline-formula> controller synthesis to design a set of controllers. The proposed algorithms are initialized with suboptimal model configurations. Two update laws optimize the model parameters and minimize the coverage function. The first algorithm performs a gradient descent on the coverage function and the second algorithm performs pairwise optimizations. Due to computational complexity, a discretized implementation is derived, which reduces the optimization to an efficient graph search. The proposed algorithms are evaluated in numeric benchmark examples.
Why do psychiatric patients stop antipsychotic medication? A systematic review of reasons for nonadherence to medication in patients with serious mental illness

Background Antipsychotic medication reduces the severity of serious mental illness (SMI) and improves patient outcomes only when medicines were taken as prescribed. Nonadherence to the treatment of SMI increases the risk of relapse and hospitalization and reduces the quality of life. It is necessary to understand the factors influencing nonadherence to medication in order to identify appropriate interventions. This systematic review assessed the published evidence on modifiable reasons for nonadherence to antipsychotic medication in patients with SMI. Methods Articles published between January 1, 2005, and September 10, 2015, were searched on MEDLINE through PubMed. Abstracts were independently screened by 2 randomly assigned authors for inclusion, and disagreement was resolved by another author. Selected full-text articles were divided among all authors for review. Results A qualitative analysis of data from 36 articles identified 11 categories of reasons for nonadherence. Poor insight was identified as a reason for nonadherence in 55.6% (20/36) of studies, followed by substance abuse (36.1%, 13/36), a negative attitude toward medication (30.5%, 11/36), medication side effects (27.8%, 10/36), and cognitive impairments (13.4%, 7/36). A key reason directly associated with intentional nonadherence was a negative attitude toward medication, a mediator of effects of insight and therapeutic alliance. Substance abuse was the only reason consistently associated with unintentional nonadherence, regardless of type and stage of SMI. Discussion Although adherence research is inherently biased because of numerous methodological limitations and specific reasons under investigation, reasons for nonadherence consistently identified as significant across studies likely reflect valid existing associations with important clinical implications. Conclusion This systematic review suggests that a negative attitude toward medication and substance abuse are consistent reasons for nonadherence to antipsychotic medication among people with SMI. Adherence enhancement approaches that specifically target these reasons may improve adherence in a high-risk group. However, it is also important to identify drivers of poor adherence specific to each patient in selecting and implementing intervention strategies.