Miracle-dz/UniFAR-FTUs huggingface dataset:
Evaluation of Effectiveness of a Community-Based Intervention for Control of Dengue Virus Vector, Ouagadougou, Burkina Faso

We evaluated the effectiveness of a community-based intervention for dengue vector control in Ouagadougou, the capital city of Burkina Faso. Households in the intervention (n = 287) and control (n = 289) neighborhoods were randomly sampled and the outcomes collected before the intervention (October 2015) and after the intervention (October 2016). The intervention reduced residents’ exposure to dengue vector bites (vector saliva biomarker difference −0.08 [95% CI −0.11 to −0.04]). The pupae index declined in the intervention neighborhood (from 162.14 to 99.03) and increased in the control neighborhood (from 218.72 to 255.67). Residents in the intervention neighborhood were less likely to associate dengue with malaria (risk ratio 0.70 [95% CI 0.58–0.84]) and had increased knowledge about dengue symptoms (risk ratio 1.44 [95% CI 1.22–1.69]). Our study showed that well-planned, evidence/community-based interventions that control exposure to dengue vectors are feasible and effective in urban settings in Africa that have limited resources.
Facial Based Attendance Monitoring System Using Discriminative Robust Local Binary Pattern

In this research we have proposed Students attendance marking system using face recognition for high efficient signal transfer system applications. Face detection is an important role in many applications. Face detection overlapping is one of the important works in face detecting applications. In this paper DRLBP algorithm is used to identify overlapped faces detection. Initially the face is detected from database images using DRLBP feature extraction. This feature extraction result is compared to our test images. Finally classified method is used where we can classify and get the attendance validation result.
Robust Recognition of Speech with Background Music in Acoustically Under-Resourced Scenarios

This paper addresses the task of Automatic Speech Recognition (ASR) with music in the background. We consider two different situations: 1) scenarios with very small amount of labeled training utterances (duration 1 hour) and 2) scenarios with large amount of labeled training utterances (duration 132 hours). In these situations, we aim to achieve robust recognition. To this end we investigate the following techniques: a) multi-condition training of the acoustic model, b) denoising autoencoders for feature enhancement and c) joint training of both above mentioned techniques. We demonstrate that the considered methods can be successfully trained with the small amount of labeled acoustic data. We present substantially improved performance compared to acoustic models trained on clean speech. Further, we show a significant increase of accuracy in the under-resourced scenario, when utilizing additional amount of non-labeled data. Here, the non-labeled dataset is used to improve the accuracy of the feature enhancement via autoencoders. Subsequently, the autoencoders are jointly fine-tuned along with the acoustic model using the small amount of labeled utterances.
Online Multiple View Tracking: Targets Association Across Cameras

Most multiple object tracking algorithms relying on a single view have failed to follow the trajectories of targets when they have been completely hidden by obstacles. In this paper, we introduce a novel method of collaborative tracking in a synchronized over-lapping cameras network. We propose an efﬁcient target association method between cameras based on the tracking results of each target on each view. Our framework naturally handles obstacle occlusions and mutual target occlusions. We implemented our multiple object tracking algorithm by Decision Making algo-rithm [30] on each view. The tracking outcomes on each camera are collected and associated into targets. The feedback from the central association helps the individual cameras in tracking hidden targets, even in the case of complete occlusion. We use the standard MOT metric to validate our method. The experimental results on each view show that the multiple view tracking system outperforms the single view ones. The source code will be available publicly.
Comparison of signal predistortion schemes based on the contraction mapping for satellite communications with channel identification

The expected increase of data rates in future satellite communications (DVB-S2X) will require higher-order modulations and sharper roll-off, which makes the transmission more sensitive to non-linear interference due to on-board amplifiers. One solution is the predistortion of the signal that is carried out at the transmitter either on the data sequence (data-based) or on the modulated signal (signal-based). In this paper, we focus on single carrier communication and consider two signal-based predistortion techniques relying on the contraction mapping theorem, optimized from an error defined either from the data (data-optimized) or from the modulated signal (signal-optimized). We compare both schemes according to different figures of merit (normalized minimum square error, adjacent channel interference, total degradation and robustness towards channel identification). We show that, under realistic satellite transmission conditions, including satellite channel model and channel identification, the data-optimized signal predistortion scheme outperforms the signal-optimized scheme in terms of total degradation, whereas the signal-optimized scheme minimizes the adjacent channel interference due to spectral regrowth.
Extreme Learning Machine (ELM)-Based Classification of Benign and Malignant Cells in Breast Cancer

Background Breast cancer is one of the most common cancer types in the world and is a serious threat to health. This type of cancer is complex; it is a hereditary disease and does not result from a single cause. The diagnosis of cancer starts with a biopsy. Various methods are used to detect and recognize cancer cells, from microscopic images and mammography to ultrasonography and magnetic resonance images (MRI). Material/Methods Detection and characterization of benign and malignant cells by image-processing-based segmentation for breast cancer diagnosis is important for early diagnosis. In the present study, Extreme Learning Machine (ELM) classification was performed for 9 features based on image segmentation in the Breast Cancer Wisconsin (Diagnostic) data set in the UC Irvine Machine Learning Repository database. Results The results obtained with the developed method were compared with the results of other machine learning methods (Naive Bayes, Support Vector Machine, and Artificial Neural Network) and it showed the highest performance, with a result of 98.99%. Conclusions It was found that both accuracy and speed were good. We present a method that can be applied in cell morphology detection and classification in automated systems that classify by computer-aided mammogram image features.
Predicting Clinical Visits Using Recurrent Neural Networks and Demographic Information

Early detection of diseases is a tricky problem for doctors due to the concealment of the disease itself and the patient's negligence. Electronic health records (EHRs) contain a wealth of patient information that can be used to assist in the early detection of diseases. The EHRs have the characteristics of high dimensionality and timing. The traditional non-sequential method using EHRs is not accurate for the prediction of disease and the calculation efficiency is relatively low. We use previous medical records of patients based on recurrent neural networks (RNNs) to present a prediction model named “RNN-INFO” for multiple diseases. The model can predict the probability and diagnosis of clinical visits simultaneously in a time window. The model reduces the dimension of the data through the hidden layer of the neural network and extracts the hidden representation between the medical records. In addition, in order to further improve the performance of the model, we introduce demographic information about patients. After data preprocessing, the final dataset of experiment we used contains more than 28 thousand patients and 289 thousand clinical visits. Our experiments are finished with the final dataset. The experimental results show that our model has better performance than non-temporal models on both probability prediction and diagnostic prediction. “RNN - INFO” gets better performance than RNN model without demographic information on probability prediction. It shows that the addition of demographic information can indeed be improved performance of probability prediction.
A Unified Conflict Prevention Framework for Feature-Based 3D Collaborative Designing Environment

Feature-based collaborative designing environment has provided designers with a platform where products are developed in a parallel way. Conflicts among concurrent operations can never be ignored. Other than detecting and resolving conflicts after they occurred and caused inconsistencies, in this paper, a unified conflict prevention framework considering do, undo, redo operations is presented. By defining the reading and writing actions imposed on a part model, 6 operation transactions are identified. Potential conflicts are detected by monitoring the states of concurrent operation transactions and estimating possible topological entity change. A partial and total operation transaction rollback based confliction prevention method is proposed to preserve the design intentions of multiple users and maintain the data consistency of system. The proposed methods are tested in a prototype system with several case studies.
On the Analysis of Training Data for Wavenet-Based Speech Synthesis

In this paper, we analyze how much, how consistent and how accurate data WaveNet-based speech synthesis method needs to be able to generate speech of good quality. We do this by adding artificial noise to the description of our training data and observing how well WaveNet trains and produces speech. More specifically, we add noise to both phonetic segmentation and annotation accuracy, and we also reduce the size of training data by using a fewer number of sentences during training of a WaveNet model. We conducted MUSHRA listening tests and used objective measures to track speech quality within the conducted experiments. We show that WaveNet retains high quality even after adding a small amount of noise (up to 10%) to phonetic segmentation and annotation. A small degradation of speech quality was observed for our WaveNet configuration when only 3 hours of training data were used.
mGlu1 tonically regulates levels of calcium‐permeable AMPA receptors in cultured nucleus accumbens neurons through retinoic acid signaling and protein translation

In several brain regions, ongoing metabotropic glutamate receptor 1 (mGlu1) transmission has been shown to tonically suppress synaptic levels of Ca2+‐permeable AMPA receptors (CP‐AMPARs) while pharmacological activation of mGlu1 removes CP‐AMPARs from these synapses. Consistent with this, we previously showed in nucleus accumbens (NAc) medium spiny neurons (MSNs) that reduced mGlu1 tone enables and mGlu1 positive allosteric modulation reverses the elevation of CP‐AMPAR levels in the NAc that underlies enhanced cocaine craving in the “incubation of craving” rat model of addiction. To better understand mGlu1/CP‐AMPAR interactions, we used a NAc/prefrontal cortex co‐culture system in which NAc MSNs express high CP‐AMPAR levels, providing an in vitro model for NAc MSNs after the incubation of cocaine craving. The non‐specific group I orthosteric agonist dihydroxyphenylglycine (10 min) decreased cell surface GluA1 but not GluA2, indicating CP‐AMPAR internalization. This was prevented by mGlu1 (LY367385) or mGlu5 (MTEP) blockade. However, a selective role for mGlu1 emerged in studies of long‐term antagonist treatment. Thus, LY367385 (24 hr) increased surface GluA1 without affecting GluA2, whereas MTEP (24 hr) had no effect. In hippocampal neurons, scaling up of CP‐AMPARs can occur through a mechanism requiring retinoic acid (RA) signaling and new GluA1 synthesis. Consistent with this, the LY367385‐induced increase in surface GluA1 was blocked by anisomycin (translation inhibitor) or 4‐(diethylamino)‐benzaldehyde (RA synthesis inhibitor). Thus, mGlu1 transmission tonically suppresses cell surface CP‐AMPAR levels, and decreasing mGlu1 tone increases surface CP‐AMPARs via RA signaling and protein translation. These results identify a novel mechanism for homeostatic plasticity in NAc MSNs.
Domain Independent Key Term Extraction from Spoken Content Based on Context and Term Location Information in the Utterances

This paper proposes a domain independent approach for extracting key terms from spoken content based on context and term location information, or the sentence structures. Once it is trained with data of enough different domains, it is able to extract key terms in other unseen domains. This is obviously very attractive because of the unlimited number of domains over the Internet. Its performance degrades only very slightly with recognition errors, so very useful for spoken content. The basic idea here is that the sentence structures or context and term location information are in general domain independent, and remain essentially unchanged with recognition errors. For example, the fact that the key term for the sentence “The subject of this article is primarily about neural networks” is “neural networks” can be extended to any other unseen term other than “neural networks” in any other unseen domain, and this is more or less preserved under recognition errors. In the experiments a model trained with data for five different domains can extract key terms from data in the sixth unseen domain with very good performance.
Compressive Regularized Discriminant Analysis of High-Dimensional Data with Applications to Microarray Studies

We propose a modification of linear discriminant analysis, referred to as compressive regularized discriminant analysis (CRDA), for analysis of high-dimensional datasets. CRDA is especially designed for feature elimination purpose and can be used as gene selection method in microarray studies. CRDA lends ideas from $\ell_{q,1}$ norm minimization algorithms in the multiple measurement vectors (MMV) model and utilizes joint-sparsity promoting hard thresholding for feature elimination. A regularization of the sample covariance matrix is also needed as we consider the challenging scenario where the number of features (variables) is comparable or exceeding the sample size of the training dataset. A simulation study and four examples of real life microarray datasets evaluate the performances of CRDA based classifiers. Overall, the proposed method gives fewer misclassification errors than its competitors, while at the same time achieving accurate feature selection.
Measuring Developers' Contribution in Source Code using Quality Metrics

People involved in software development seek better ways of developing quality software. As software development is a collaborative activity dependent on technology and performed by group of people, the quality can be directly linked to the degree of collaboration and commitment of the development team. This research aims to identify and analyze software quality metrics that can measure software development team members' participation regarding their contribution in the project source code. Analyzing such information can contribute with software engineering project managers to conduct and better organize project development teams. To achieve this goal, we defined a set of quality metrics, applied them to open source code and analyzed them in other to evaluate members' contribution. Results show that it is possible to determine whether the team member contribution increase, decrease or had no influence on source code quality.
Responsiveness of the Zambian mine tax regimes to attributes of ‘good tax’ criteria

Formulation of the fiscal regime for the extractive industry requires meeting the competing needs for both investors and government. Such desires are tackled when government policymakers put in place fiscal regimes that respond to the attributes of the ‘good tax’ criteria dealing with, among others; stability, equity, progressiveness, transparency and clarity, risk sharing, economic efficiency, neutrality and non-regressiveness. The objective of this study was to assess the responsiveness of the Zambian mine fiscal regimes to the attributes of ‘good tax’ criteria. The study design was descriptive with data collection employing both questionnaires and semi-structured interviews meant to gather respondents’ opinions using Likert items addressing the attributes of ‘good tax’ criteria. Non-probability purposive sampling was conducted with 82 questionnaire respondents and 13 interviewees from the mining industry. Questionnaire survey findings indicated that 63% of the respondents disagreed to the Zambian mine tax systems being responsive to the attributes of ‘good tax’ criteria. Responses from interviewees supported this outcome and signified that the taxation regimes fail to embrace the economic perspective dealing with stability, neutrality, transparency, risk sharing and progressivity. Interviewees felt that weak institutional capacities, policy inconsistencies, information asymmetry, and lack of stabilisation agreements in the fiscal regimes affected the responsiveness of the Zambian mine taxation systems to attributes of ‘good tax’ criteria.
Enhancing data privacy with semantic trajectories: A raster‐based framework for GPS stop/move management

Tracking facilities on smartphones generate enormous amounts of GPS trajectories, which provide new opportunities to study movement patterns and improve transportation planning. Converting GPS trajectories into semantically meaningful trips is attracting increasing research effort with respect to the development of algorithms, frameworks, and software tools. There are, however, few works focused on designing new semantic enrichment functionalities taking privacy into account. This article presents a raster‐based framework which not only detects significant stop locations, segments GPS records into stop/move structures, and brings semantic insights to trips, but also provides possibilities to anonymize users’ movements and sensitive stay/move locations into raster cells/regions so that a multi‐level data sharing structure is achieved for a variety of data sharing purposes.
The use of colposcopic punch biopsy in the management of abnormal cervical cytology: a 5–year retrospective audit

Abstract The aim of this study was to determine the accuracy of colposcopic punch biopsy to detect cervical epithelial neoplasia (CIN) II + in patients with abnormal cervical cytology and the major colposcopic findings in patients who underwent a loop electrosurgical excision procedure (LEEP), subsequently. A total of 231 patients with abnormal cervical cytology who underwent a colposcopy guided cervical biopsy and subsequent LEEP were analysed. The mean age was 33.4 ± 8.7 years. CIN II + rate on LEEP pathology was significantly higher in patients with high-grade cytology, compared to those with a low-grade cytology (92 vs. 55%, p < .0001). CIN II + was found in 80, 98 and 100% of colpocopic biopsies of patients with LSIL, HSIL and AGC, respectively. The overall concordance rate between a colposcopic biopsy and LEEP was 41% with a kappa coefficient. The overall underestimation of CIN II + was 10.5%. On a patient-based analysis, the sensitivity, specificity, PPV and NPV of colposcopic biopsy were 89.4, 47.1, 79.5 and 66%, respectively. More than two cervical biopsies had 100% sensitivity for CIN II + on LEEP pathology. The specificity and PPV decreased with increasing number of cervical biopsies. A see-and-treat strategy may be considered for high-grade cytologies. Patients with a low-grade cytology should be managed with more than two colposcopic biopsies. Impact statement What is already known on this subject? The improper management of women with CIN can increase the risk for cervical cancer. The ability of the colposcopic punch biopsy to detect the severity of a cervical abnormality has been called into question as false negative rates of up to 50% being reported. What do the results of this study add? The overall underestimation of CIN II + was found to be 10.5%. More than two cervical biopsies had 100% sensitivity for CIN II + on LEEP pathology. What are the implications of these findings for clinical practice and/or further research? The see-and-treat strategy is an appropriate choice for patients with a high-grade cytology. Patients with a low-grade cytology should be managed with more than two colposcopic biopsies.
AIA: Artificial intelligence for art

There are limits to state-of-the-art AI that separate it from human-like intelligence. Today’s AI algorithms are limited in how much previous knowledge they are able to keep through each new training phase and how much they can reuse. There is domain called AGI where will be possible to find solutions for this problems. Artificial general intelligence (AGI) describes research that aims to create machines capable of general intelligent action. "General" means that one AI program realises number of different tasks and the same code can be used in many applications.
Architectural aspects of digital twins in IIoT systems

Industrial Internet of Things (IIoT) systems enable the connectivity of numerous devices, which are heterogeneous in terms of their interfaces and supported protocols, into one system to derive more intelligent actions from data. Digital Twins are a key enabler for IIoT systems, which allow the acquisition, access, and exchange of far greater variety of data than today and previously unseen interoperability out of the box. This paper elaborates on the definitions of digital twins, their role in IIoT systems and the necessary architectural decisions that software/system architects have to make to design digital twins.
Natural Product Chemistry of Gorgonian Corals of Genus Junceella–Part III

The structures, names, bioactivities, and references of 82 natural products, including 48 new metabolites, purified from the gorgonian corals belonging to the genus Junceella are described in this review. All compounds mentioned in this review were obtained from Junceella fragilis, Junceella gemmacea, Junceella juncea, and Junceella sp. , collected from tropical Indo-Pacific Ocean. Some of these compounds exhibited potential biomedical activities.
Temporal anonymity in the AMS scenario without a TTP

Smart meters provide fine-grained electricity consumption reporting to electricity providers. This constitutes an invasive factor into the privacy of the consumers, which has raised many privacy concerns. Although billing requires attributable consumption reporting, consumption reporting for operational monitoring and control measures can be non-attributable. However, the privacy-preserving AMS schemes in the literature tend to address these two categories disjointly --- possibly due to their somewhat contradictory characteristics. In this paper, we propose an efficient two-party privacy-preserving cryptographic scheme that addresses operational control measures and billing jointly. It is computationally efficient as it is based on symmetric cryptographic primitives. No online trusted third party (TTP) is required.