Miracle-dz/UniFAR-FTUs huggingface dataset:
Application of Training Dictionary and Sparse Representation in Diesel Property Detection Using Near-infrared Spectroscopy

It is an important method for predicting the diesel quality parameter by combination of near-infrared spectroscopy and chemometrics. In order to improve the accuracy of prediction model for diesel property, this paper puts forth a modeling method based on training dictionary and its sparse representation, and this method can test the cetane number, boiling point and total aromatics fast. The first stage of the method searches the particular redundant dictionary for diesel oil calibration samples, and the redundant dictionary can be trained by using K-means singular value decomposition algorithm (K-SVD). After finding of this redundant dictionary, it uses the orthogonal matching pursuit algorithm (OMP) to search the sparse representation coefficients of diesel spectroscopy under the training dictionary. This coefficients of the training dictionary, which is similar with the sparse representation coefficients of wavelet dictionary and fourier dictionary, is scale expansion of spec-tral characteristics that can effectively use to establish the pre-test partial least squares prediction (PLS) model of cetane number, boiling point and total aromatics for diesel. The spectra of diesel samples with different quality parameters from same factory are collected and the quality parameters of diesel samples are measured by ASTM certification method. The sparse representation coefficients of diesel spectroscopy under fourier dictionary and wavelet dictionary are figured in the experiments, inter-compared the performance of prediction model for diesel property under sparse representation coefficients of training dictionary, fourier dictionary and wavelet dictionary. Furthermore, the PLS model correlation coefficient (R) and standard deviation of prediction set (SEP) under sparse representation coefficients in training dictionary achieve 0.99 and 0.05 separately, it has optimal performance and also has considerable improvement to compare with the prediction performance of fourier dictionary and wavelet dictionary, which validated the efficiency of the method in predicting the diesel quality parameter using near-infrared spectroscopy and the proposed method can substantially improve the accuracy and stability of near-infrared spectroscopy prediction method.
The Disinformation Battle: Linguistics and Artiﬁcial Intelligence Join to Beat it La Batalla de la Desinformaci´on: la Ling¨u´ıstica y la Inteligencia Artiﬁcial se Unen para Vencerla

: At present, the era of digitalisation has led to the era of disinformation by witnessing a peak of the spreading of fake news that are disseminated in order to get proﬁt. Like everything, fake news has an Achilles’ heel and it could be language. The linguistic structure as well as the expression of emotions through language could be key in detecting deception. The modelling of a language of deception typical of fake news and its later automation through machine learning would allow to take a further step towards the ﬁght against disinformation.
A Deep Reinforcement Learning Approach for Robust Control in Robotic Applications

—A methodology for developing robust control systems using Deep Reinforcement Learning (DRL) is proposed in this work. To this end, a numerical simulation of the cart-pole swing-up problem is used as a case study. The agent is trained with the Deep Deterministic Policy Gradient (DDPG). Then, its sensibility is evaluated by modifying the parameters of the physical system. Subsequently, the presence of dry friction between the cart and the horizontal plane is considered for testing the robustness of the agent after employing the post-training method proposed. The numerical results found demonstrates the broad potential of the methodology considered in this paper.
Why Ethiopia Should Develop Domestic Tourism and Who are Responsible for this?

Since domestic tourism development in Ethiopia is a missed opportunity, the main objective of this study focused on identifying and explaining reasons of domestic tourism development in the nation and its key actors. To achieve these objectives, qualitative approach with explanatory design and expert purposive sampling techniques were employed for focus group discussion and interview data gathering instruments. The qualitative data was collected from twenty private and governmental tourism experts and twenty eight domestic tourists in the form of interview and six focus group discussions with nine discussants each were analyzed. The interpretation of qualitative data was a content descriptive approach. The main result revealed that domestic tourism development in the nation has a benefit of promoting ethnic and religious tolerance, contributes to the quality of life through educational travel, retain the hard currency and brings development to rural areas. The key stakeholders of domestic tourism development in Ethiopia also include the Ministry of culture and tourism, tourism Ethiopia, Ethiopian airlines, event organizers, tourism and hospitality related associations, religious leaders, media, schools, tour operators and travel agents, universities & research institutions, consultants and hotels. Finally, based on the results of the study involvement of religious and cultural institutions and their leaders, strengthen the national tourism media forum, establishment of national domestic tourism stakeholder platform and supporting and strengthening the establishment of tourism related associations were some of the recommended points to the sustainable development of domestic tourism in the nation.
An Integrated Disease Recommendation Framework for Multi-Label Unstructured Data Sources

: Usage of strong historical data in electronic health record (EHR), it is an Integrated Disease Recommendation Framework for Multi-label Unstructured Data Sources, a generalized model that covers the conditions that have been observed and medication uses. An Integrated Disease Recommendation Framework is a period model that uses Recurrent Neural Network (RNN) was developed and stamped at longitudinal time to EHR data from 260K patients over 8 years. The patient history to make multi - label forecasts The Framework assesses the patient history yet another label for each diagnosis or prescription type. Based on an assessment of the entire lab test collection, the system will conduct diagnostic differentials with up to 79% recall, substantially above several baselines. They also demonstrate great generalizability of Framework by acclimating the resulting models from one organization to another without losing significant accuracy.
A COMPACT BROADBAND CIRCULARLY-POLARIZED PATCH ANTENNA WITH WIDE AXIAL-RATIO BEAMWIDTH FOR UNIVERSAL UHF RFID APPLICATIONS

Abstract—A compact broadband circularly-polarized (CP) patch antenna with a wide 3-dB axial-ratio (AR) beamwidth is proposed for universal ultra-high-frequency (UHF) radio frequency identification (RFID) applications. The proposed antenna consists of four triangular radiation patches and a compact feed network. Each of the radiation patches is grounded by shorting pins for 3-dB AR beamwidth enhancement and patch miniaturization. The feed network having a miniaturized hybrid coupler and two trans-directional couplers is proposed for good circular polarization. Measured results show that the −10-dB impedance bandwidth of the antenna is 18.4% (820–986 MHz); the 2.5-dB AR bandwidth is 32.8% (700–975 MHz); and the gain is 5.12 dBic. The measured 3-dB AR beamwidths for the planes of phi = 0◦ and phi = 45◦ are 177◦ and 190◦, respectively. The overall antenna size is 0. 408λ0 × 0. 408λ0 × 0. 053λ0 at 900 MHz.
An Automated Brain Tumour Boundary Detection using Region based Segmentation Technique along with SVM Classifier

We presents a fully automated method for an automated brain-tumour boundary detection using region based segmentation technique along with SVM Classifier of Magnetic Resonance Imaging (MRI). The procedure is based on artificial intelligence technique and classification of each super-pixel in MRI. A number of novel image features extraction approaches including intensity-based, texture based, fractal analysis and curvatures are calculated from each super-pixel within the entire brain area in MRI to ensure a robust classification. Brain tumor is the malignant types of cancer and their classification in earlier stage is biggest issue. While curable with early classification is useful, only extremely trained specialists are capable of accurately recognizing the cancer from skin MRI data. As expertise is in limited contribute, an automated systems capable of classifying cancer could save human lives, and also help to reduce unnecessary MRI, and reduce extra costs. On the way to achieve this goal, we proposed a Brain Tumour Detection and Classification System (BTDCS) that combines recent developments in machine learning with Support Vector Machine (SVM) structure, creating hybrid algorithm of threshold based segmentation with Maximally Stable External Regions (MSER) that are capable of segmenting accurate super-pixel region from MRI, as well as analyzing the detected area and surrounding tissue for malignant. Using threshold based segmentation technique, the foreground and background component is separated into two regions. To improve the segmentation results, MSER is used with the novel concept of region detection and feature extraction mechanism. The proposed system is evaluated using the largest publicly accessible standard BRATS 2015 dataset of MRI, containing benign and malignant images. When the evaluation parameters of proposed work is compared with a few other state-of-art methods, the proposed means attains the best performance of 98.2% concerning classification accuracy using only the MSER approach and SVM as classifier. The ultimate aim of this research is to devise an automated experimental approach that can segment the tumor boundary in a fast and efficient manner.
Molecular Dynamics Simulations of Biomolecular Systems

During the last decade the main goal of molecular biology became to understand biological function in terms of structure, interactions and processes at the microscopic level. Some experimental techniques such as X-ray crystallography, NMR spectroscopy, timeresolved fluorescence spectroscopy are routinely used to provide an atomic picture of the structure and mobility of biomolecules like proteins, peptides, nucleic acids and their f ragments and components. More flexible molecules are less accessible to structure determination by these methods. The most of the spectroscopic techniques yield the information about dynamics limited only to the groups of atoms, f ragments of the molecules, but not for all biomolecule. Energetic information may be obtained experimentally for whole systems by differential scanning microcalorimetry, but cannot be measured at the atomic level. Due to the limitations of experimental techniques, the characterisation of a biomolecular system at the atomic level is incomplete. This incomplete molecular picture makes difficult to understand the link between the biological function of the molecular system and its molecular structure flexibility, conformational dynamics and interactions. A new alternative way to study biomolecular systems at the atomic level is the simulation on a computer. Despite the extremely fast progress of the computing power available such calculations have to be based on a compromise between the complexity of the description of the molecular system, the number of atoms included in the system and relative numbers of computational cycles required for reliable description of the processes investigated. We briefly present the physical basis and strategies of computer approaches utilized for the calculations of molecular properties. Then we present two examples of the different complexity to show the general methodology and procedures used for the simulations of biomolecular systems. One of them concerns the predictions of the enzymatic properties of genetically engineered protein, second one describes the analysis of different properties of ribonucleic and deoxyribonucleic acids on the level of its constituents.
Supporting Information for “Simulation-Selection-Extrapolation: Estimation in High Dimensional Errors–in–Variables Models”

In both Sections 1 and 2 of the main paper, it was mentioned that SIMEX does not perform well in high-dimensional errors-in-variables models without suitably modifying the procedure. Specifically, standard SIMEX inflates the number of estimated nonzero components considerably, even when combined with a procedure such as the lasso. Here, a simulated example is presented to illustrate. For the example, data pairs (Wi, Yi) were generated according to the linear model Yi = X > i θ+εi with additive measurement error Wi = Xi +Ui. Both the true covariates Xi and the measurement error components Ui were generated to be i.i. d. p-variate normal. Specifically, Xi ∼ Np(0,Σ), with Σ having entries Σij = ρ |i−j| with ρ = 0.25, and Ui ∼ Np(0,Σu) with Σu = σ2 uIp×p with σ2 u = 0.45. The error components εi were simulated to be i.i. d. univariate normal, εi ∼ N(0, σ2 ε) with σε = 0.128. The sample sizes was fixed at n = 300, and the number of covariates was p = 500. The parameter vector was taken to be θ = {1, 1, 1, 1, 1, 0, . , 0} with s = 5 nonzero coefficients and p− s = 495 zero coefficients. For the simulation step of SIMEX, a grid of M = 13 equally spaced λ-values ranging from 0.2 to 2 were used. For each value of λ, a total of B = 100 sets of pseudo-data were generated. In applying the lasso, the tuning parameter was chosen based on the one-standard-error rule based on 10-fold cross-validation. The lasso was implemented using the glmnet package in R. For the extrapolation step, a quadratic function was used. The analysis of the simulated data shows that SIMEX applied to the lasso results in 174 nonzero parameter estimates. Of the 169 false positives, 156 are fairly small (less than 0.001 in absolute value), with 13 false positives being larger (greater than 0.001 in absolute value). Comparatively, a naive application of the lasso (not correcting for measurement error) gives only 5 non-zero parameter estimates. Implementing SIMEX, even when using a method such as the lasso that enforces sparsity, can result in an inflated number of variables in the model.
Contactless Lead-Through Robot Interface

—This paper presents a novel contactless lead-through method to jog and program a generic robot in intuitive way. Lead-through robot teaching (kinesthetic teaching) proved its effectiveness especially in the industrial sector, enabling insiders to hand-guide the robot through motion paths in different ways, although in many cases it results expensive or not very intuitive. The proposed approach enables the user to jog both manipulators and mobile bases in intuitive and contactless way by using the same device (e.g. a smartphone) composed of an IMU and a RGB camera. Sensor fusion algorithms are used to estimate the device’s pose which is sent to the robot controller as a cartesian reference, enabling the user to jog the robot in different conﬁguration. In addition, the device user interface (UI) provides several jog modalities and teaching interface. Finally, we present the implementation on a common smartphone and the experiments performed on a mobile robots composed of a mobile platform and a dual arm robot.
KOWO SOLOMON AKPOVIRORO, KADIRI ISMAILA BOLARINWA, ZEKERI ABU IMPACT OF EMPLOYEES DIVERSITY ON ORGANIZATIONAL PERFORMANCE OF SELECTED MULTINATIONAL FOOD PRODUCING INDUSTRY IN SOUTH-WEST NIGERIA

Subject matter: Diversity has, nonetheless, been a powerful influence on terminology and practice worldwide. Employee diversity encompasses a wide variety of differences, including age, gender, culture, work experience, parental status, educational background, geographic location, and much more. Goal: Examine the effect of gender diversity on sales Patronage of multinationals Food Industry and also to determine the effect of age diversity on multinationals operational cost. Tasks: The optimum outcome of this study is to benefit the Multinational Food industry in Nigeria by getting along with the top management and workers from different backgrounds that would find the information in this research study useful in accessing the value of workforce diversity in their organization. Methods: Two research questions were posed for the study and two hypotheses formulated in line with the objectives. The paper adopted descriptive research design. Simple random sampling was employed. The total population of the study consists of 9,376 employees of Multinational Food Companies in South-West Nigeria. The study made used of macorr sample size determination. The research questionnaire was administered to three hundred and seventy-five (375) employees which was the sample size representing the chosen study population of the 5 selected multinational food industry in South-West Nigeria. The study made use of statistical tools such as Hierarchical regression analysis in testing hypotheses, Cross tabulations, Inter-item correlation test and ANOVA which helped in the interpretation of results. Results: The study found out that there is a positive relationship between gender diversity and sales patronage such that sales patronage is affected by gender diversity. The findings of the study further revealed a positive relationship between age diversity and operation cost such that operation cost is affected by age diversity in the organization. Conclusions: The research concludes that there exists a relationship between employee diversity and organizational performance and organizations should ensure that diversity existent in their employees is aimed at improving organizational performance. The research recommends that multinational food industry should not only ensure employee diversity in their businesses but they should also make sure that diversity is used to improve organizational performance. Further studies should focus on other sector and not limited to food and beverage and conglomerate sectors of the Nigeria economy.
YOUR INNOVATIVE RESEARCH TaqMan Advanced miRNA Assays TaqMan Advanced miRNA Assays reliably detect miRNAs from small volumes of nipple aspiration fluid

medium to measure miRNAs implicated in disease [1]. Breast tissue and fluid derived from the ductal system provide a rich source of cells and biomarkers that have the potential to aid in the assessment of short-term risk of breast cancer development, and to assess responses to interventional prevention efforts [1,3]. Release of miRNAs into blood, milk, and ductal fluids is selective and these circulating miRNAs may correlate with malignancy [4,5]. Studying miRNA expression patterns in NAF and ductal lavage has been a challenge due to the small sample volumes (on average 10−20 μL) and the invasiveness of the procedure, respectively. In this document we demonstrate:
Indonesian Trade: Understanding the Duration and the Determinants of Its Hazard Rate

Over the last two decades (1990-2011), Indonesian trade showed an increasing growth rate. The value of Indonesian exports and imports increased tenfold with other ASEAN countries and sixfold with non-ASEAN countries. During this period, Indonesian trade was dominated by trade with non-ASEAN countries (>79%), while intra-ASEAN accounted for almost 21%. In order to have trades occur in the first place, it is important to have not only a flourishing trade flows but also sustainable ones (Hess and Persson, 2011). Therefore, it is important to understand the process by which trades are sustained and trade flows grow in volume. Regarding the important role of trade duration, it’s crucial to investigate those of Indonesian trade with its partners. Therefore, this paper tries to explore the duration of Indonesian trade and to identify the factors that affect its hazard rate. This paper offers a new insight to accumulated studies of Indonesian trade which rarely discuss the impact of trade duration. This study uses discrete timeof-duration analysis which is suitable for trading data and repeated events, and is the first to apply the concept of duration to Indonesian trade. In our aim to explore the duration of Indonesian trade, we start by performing a thorough descriptive analysis. We found that most of Indonesian trade flows are short-lived; the median duration is merely 2 years for all spells, independent of whether one considers imports or exports. However, some trade flows last longer. These consist of essential imports such as wheat and export goods in which Indonesia has a strong comparative advantage (e.g. palm oil, rubber). Moreover, in many instances there is evidence of trade being frequently interrupted (‘stopped and re-started’). In our aim to identify the factors that affect the hazard rate, we performed a separate regression analysis for import and export. We estimate the baseline specification using discrete-time probit, logit, and cloglog models which include random effects for every partner-product-trade flow combination. We found factors that have impacts on hazard rate of stopping import (export) are distance, partner countries, initial trade value, market size, supplier size (only for import), the growth of credit ratio, exchange rate and product type. Moreover, we found that trading with developed countries as well as trading differentiated product has a lower hazard rate of stopping trade.
Gesture recognition through inertial and visual data

—This paper presents a system composed of Pepper robot that supports an RGB-D camera and an inertial device called SensHand. The fusion of multi-modal data aims to improve the recognition of gestures. Twenty users performed ﬁve activities of daily livings (i.e. Having Lunch, Personal Hygiene, Working, House Cleaning, Relax). Each activity was composed of at least two ”basic” gestures for a total of 10 gestures. The acquisition of visual data was performed laterally and frontally to mimic real conditions. Acquired data were analysed off-line considering different combinations of sensors to evaluate how the sensor fusion approach improves the recognition abilities. The results show a signiﬁcant improvement of the accuracy when fusing camera and sensors data, i.e. 0.81 for the whole system conﬁguration when the robot is in a frontal position from the user and 0.75 when the robot is in a lateral position.