Miracle-dz/UniFAR-FTUs huggingface dataset:
Deep learning occurs through the use of neural networks, which are layered to recognize patterns and complex relationships in images, and run through lots of iterations of stochastic gradient descent and gradually refine the weights

Despite many successful efforts have been made in one-shot/few-shot learning tasks recently, learning from few data remains one of the toughest areas in machine learning. Regarding traditional machine learning methods, the more data gathered, the more accurate the intelligent system could perform. Hence for this kind of tasks, there must be different approaches to guarantee systems could learn even with only one sample per class but provide the most accurate results. Existing solutions are ranged from Bayesian approaches to meta-learning approaches. With the advances in Deep learning field, meta-learning methods with deep networks could be better such as recurrent networks with enhanced external memory (Neural Turing Machines NTM), metric learning with Matching Network framework, etc. In our research, we propose a metric learning method for few-shot learning tasks by taking advantage of NTMs and Matching Network to improve few-shot learning task’s learning accuracy on both Omniglot dataset and Mini-Imagenet dataset. In addition, a weighted prototype is also introduced to improve the overall performance of the proposed model, especially on the complicated benchmark datasets such as mini-ImageNet.
A new lidar inversion method using a surface reference target.
Application to the backcattering coefficient and lidar ratio retrievals
of a fog-oil plume at short-range

Abstract. In this paper, a new elastic lidar inversion equation is presented. It is based on the backscattering signal from a surface reference target (SRT) rather than that from a volumetric layer of reference (Rayleigh molecular scatterer) as usually done. The method presented can be used when the optical properties of such a layer are not available, e.g. in the case of airborne elastic lidar measurements or when the lidar-target line is horizontal. Also, a new algorithm is described to retrieve the lidar ratio and the backscattering coefficient of an aerosol plume without any a priori assumptions about the plume. In addition, our algorithm allows a determination of the instrumental constant. This algorithm is theoretically tested, viz. by means of simulated lidar profiles, and then using real measurements. Good agreement with available data in the literature has been found.
Association Analysis between the Mast Cell Dipeptidyl Peptidase I (DPPI)-T153I Polymorphism and Asthma: A Family-based Association Test

Background: Dipeptidyl Peptidase I (DPPI) is one of the essential components which initiate chain reaction for triggering the inflammatory response mediated allergic diseases such as asthma. Previous studies have reported that several mutations have been detected within the dipeptidyl peptidase I gene (CTSC) cause Papillon-Lefevre syndrome. It has been suggested that a mutation in this gene could give protection against the inflammatory response in allergic diseases. The aim of this study was to investigate wither the polymorphism (rs217086 (T153I)) in the gene for DPPI may be protective in asthma. Methods: To examine possible associations of DPP1 polymorphism with asthma susceptibility, we performed this study. 341 families with at least two siblings with asthma were genotyped for a CTSC polymorphism using high resolution melting curve analysis. Genetic association analysis was analysed using family based association tests (FBAT) and resultant called genotypes were sequenced. Results: DNA sequence data confirmed all variant genotypes. Analysis by FBAT showed no significant association between this polymorphism and asthma susceptibility or with any of asthma-related phenotypes tested. Conclusions: Our data proved that DPPI does not contribute substantially to susceptibility for asthma. Further studies examining both genotypes and environmental factors will be necessary to elucidate the role of DPPI in the development of allergic diseases.
Words and Topics: Content Representations for Book Recommendation

The paper describes an exploratory study on content-based book recommendation. We use a large dataset of book ratings along with book content. We experiment with several topic modeling variants and tf.idf representation. Predictions based on one of the topic modeling variants slightly outperform a simple baseline of averaged book scores. The obtained results suggest that content features can potentially improve hybrid book recommender systems.
Data Mining using Naive Bayes classifier: an application in short news

In the information age, a plethora of content is available on a wide range of subjects, requiring an organization capable of making that content more accessible and engaging. An interesting application of classification tasks was identified in the Index project, developed by the Amsterdam-based company The Next Web. To solve this classification task, the Naive Bayes (NB) technique was applied to classify short news in four topics. To evaluate the results produced by such a classifier, a series of tests using cross-validation were carried out. It was possible to conclude that the NB classifier had satisfactory performance, achieving about 70% of accuracy in the best cases. In this paper, we intend to present the context of the Index project and discuss the results obtained with the NB classifiers. Despite the good results, the project is still in progress, as it is necessary to test variations as classification techniques and text representation approaches.
Vascular Cognitive Disorder: A Diagnostic and Pharmacological Treatment Updating

Cognitive impairment underpins some of the clinical spectrum of the cerebrovascular disease (CVD), as well as contributes to the patients impaired social and behavioral functioning, and the higher mortality. When cognitive function is affected by CVD, we name it as vascular cognitive impairment (VCI). The cognitive impairment may be mild, or may be severe enough to warrant a diagnosis of dementia. Pure vascular dementia is not common. Because of that the concept of mixed dementia has been included in the clinical diagnosis of VCI. Despite a general emphasis in the international literature on the primary and secondary prevention of CVD to avoid vascular dementia or their combination with Alzheimer's disease, the controversy concerning their diagnostic criteria and optimal treatment is still open. Given its growing burden, the prevention and treatment of CVD and the spectrum of VCI are critical priorities for clinical care and research. We performed a selective review about the current status of vascular dementia, mild cognitive impairment due to CVD, and mixe d dementia, with special emphasis on available evidence of pharmacological strategies for treatment and prevention from controlled clinical trials.
Testing the Robustness of Linear Programming* Using a Diet Problem on a Multi-Shop System

Time, raw materials and labour are some of the finite resources in the world. Due to this, Linear Programming* (LP) is adopted by key decision-markers as an innovative tool to wisely consume these resources. This paper test the strength of linear programming models and presents an optimal solution to a diet problem on a multi-shop system formulated as linear, integer linear and mixed-integer linear programming models. All three models gave different least optimal values, that is, in linear programming, the optimal cost was GHS15.26 with decision variables being continuous (R) and discrete (Z). The cost increased to GHS17.50 when the models were formulated as mixed-integer linear programming with decision variables also being continuous (R) and discrete (Z) and lastly GHS17.70 for integer linear programming with discrete (Z) decision variables. The difference in optimal cost for the same problem under different search spaces sufficiently establish that, in programming, the search space undoubtedly affect the optimal value. Applications to most problems like the diet and scheduling problems periodically require both discrete and continuous decision variables. This makes integer and mixed-integer linear programming models also an effective way of solving most problems. Therefore, Linear Programming* is applicable to numerous problems due to its ability to provide different required solutions. *Corresponding author: E-mail: adjei175@gmail.com; Appiah et al.; JAMCS, 34(5): 1-12, 2019; Article no.JAMCS. 53208
Effect of Corroded and non Corroded Pipe Materials on Biofilm Formation in Water Distribution Systems

The effect of pipe materials on biofilm formation in drinking water distribution system was studied using simulated drinking water distribution system. Biofilm was grown on different pipe materials under laminar flow rate (Reynolds number: 2000). The parameters analyzed were the numbers of cultivable bacteria using chromogenic media. The impact of different pipe materials were assessed after the biofilms reached steady-state. The total numbers of Salmonella Typhimuruim biofim was mostly higher than Listeria monocytogenes biofim cells. The steady-state of L. monocytogenes and S. Typhimuruim biofilm gorwon on PVC, PP and PE pipe materials was achieved in 70 days. While, steady-state biofilm formed in R material was at 60 days of L. monocytogenes and 80 d ays of S. Typhimuruim. In addition to, steady-state biofilm formed in I pipe material was achieved in 60 days of L. monocytogenes with total numbers of biofilm cells (10 CFU/cm ) and 8 2 70 days of S. Typhimuruim with total numbers of biofilm cells (10 CFU/cm ). In case of biofilm formed in Cu, 9 2 L. monocytogenes and S. Typhimuruim biofilm have no steady-state. These results concluded that, the plasticbased materials are the most appropriate to be used for carrying the drinking water. While, the iron pipes are not recommended for carrying water in DWDS.
The Utilization of Palm Oil Mill Effluent For Renewable Energy

The demand of energy in the world today has increased exponentially, therefore, more efforts have been focused on looking for alternative renewable sources, such as biodiesel, which involves fuel produced from oil of plant, or animal fat. The objective of the research, therefore, was to utilize the palm oil mill effluent (POME) as a raw material in the production of biodiesel, by applying varying solvent, extraction time and the effluent to solvent ratio. Furthermore, the best output of oil rendement of (81.07%), in comparison with the total sample taken, was obtained using n-hexane, at an extraction time of 3 days, and ratio of 1:1. Furthermore, the output possessed a high acid number. Hence, it is considered in the process of esterification and transesterification, required in the production of biodiesel. The HCl catalyst ratio of 1.25% (%v) was used in the esterification process, and the lowest acid number of 2.08 mg-KOH/gr was obtained, followed by the transesterification process, using 1.5% (%w) of NaOH catalyst of the oil weight. Hence, the characteristics of biodiesel produced were in accordance with SNI 04-7182-2006. Keywords: POME, Biodiesel, Esterification and Transesterification, Extraction, n-Hexane
Proteomic approaches to control lactational parameters in dairy cows 1

14 The use of a proteomic approach to investigate changes in the milk proteome is 15 growing and has parralleled the increasing technological developments in proteomics 16 moving from early investigation using a gel based two dimensional separation 17 approach to more quantitative method of current focus applying chromatography and 18 mass spectrometry. Proteomic approaches to investigate lactational performance 19 have made substantial findings especially in the alterations in lactation during 20 mastitis. An experimental model of Streptococcus uberis infection of the mammary 21 gland has been used as a means to determine change not only in the milk proteome, 22 but also in the peptidome and in the metabolome caused by the infection. 23 Examination of the peptidome, that is the peptides of less than 25kDa in molecular 24 weight, demonstrated an increase in small peptides most of which were casein 25 degradation products but also included small bioactive peptides such as mammary 26 associated serum amyloid A3 (MSAA3). The peptidome has also been shown to 27 differ depending on the causative bacteria of naturally occuring mastitis. The use of a 28 non-gel based relative quantitative proteomic methodology has revealed major 29 changes in the protein component of milk in mastitis. The S. uberis infection lead to 30 increases in the concentrations of proteins such as cathelicidins, haptoglobin, 31 MSAA3 and decreases milk content of proteins such as xanthine oxidase, 32 butyrophilin and -1,4-galactosyltransferase. Analysis of all protein change data 33 identified the acute phase, coagulation and complement pathways as well as proteins 34 related to bile acid metabolism as being most modified. Examination of the small 35 molecular weight organic molecules of milk using a metabolomic approach identified 36 an increase in the content in milk during mastitis of bile acids such as 37 taurochenodeoxycholic acid. Notable changes were also found in metabolites 38 responding to infection of the mammary gland. Carbohydrate and nucleic acid 39 metabolites were reduced while lipid and nitrogen containing metabolites were 40 increased. The latter included increases in amino acids along with di and tri peptides, 41 likely to be the result of casein degradation. The use of proteomics and other omic 42 technology is in its infancy in investigation of lactational parameters, but can already 43 provide additional insight into the changes involved in disease and will have further 44 value in physiological and nutritional investigation of lactation. 45 46
Introjective and anaclitic psychopathology in self-narratives: Idiographic assessment with Hermans’ Self-Confrontation Method

Within a hybrid framework of attachment and depression theory, we investigated whether introjective and anaclitic vulnerabilities can be detected in a person’s self-narrative as elicited with the Self-Confrontation Method (SCM). One hundred participants held a thorough self-investigation with one of five trainees, in which every person formulated ‘valuations’ (i.e., short sentences about personally relevant concerns) and, subsequently, rated to what degree certain feelings were evoked by every separate text. We extracted several measures from these idiographic data, including scales representing the prominence of certain themes according to the SCM typology (e.g., ‘unfulfilled longing and loss’, ‘anger and opposition’, and ‘powerlessness and isolation’). By analyzing the correlational patterns of the aggregated SCM-based measures and the questionnaire-based measures about attachment orientation and depressive personality vulnerability, we uncovered meaningful relationships. The results of a canonical correlational analysis indicated that an intensified sense of ‘powerlessness and isolation’ is a sign of a general psychopathological vulnerability (related to depression), ‘anger and opposition’ is associated with introjective features (distrust in others and need for control), and ‘unfulfilled longing and loss’ is associated with anaclitic features (pleasing and dependency). In an exploratory qualitative study, we used a cluster-based classification into attachment groups for the exhaustive screening of the content of negative valuations of (a selection of) insecurely attached persons (n = 15). This hermeneutic approach disclosed characteristic themes for each of the preoccupied, dismissive-avoidant and fearful-avoidant attachment styles which are discussed in great detail.
Design and Application of an Image Classification Algorithm Based on Semantic Discrimination

Received: 10 March 2019 Accepted: 7 July 2019 Semantic gap is a common problem for most distance metric learning (DML) algorithms. Because of this problem, the semantic information may be inconsistent with the image features, which negatively affects the image classification accuracy. To solve the problem, this paper puts forward a new supervised DML method called semantic discriminative metric learning (SDML). The SDML maximizes the geometric mean of the normalized dispersion, making dispersions between different classes as identical as possible. Moreover, the Log function was combined with the geometric mean to further balance the dispersion between classes, and the maximum-margin criterion (MMC) was introduced to further enhance the discrimination ability of the distance metric. Finally, two constraints were applied to optimize the distance metric matrix. The effectiveness of the SDML algorithm was fully proved through experiments on actual datasets. The experimental results show that our algorithm outperformed many other typical DML methods in classification accuracy. This research provides an effectively way to measure the similarity between image samples and classify high-dimensional images.
Improved Fuzzy Logic Control Strategy of Induction Machine based on Direct Torque Control

his paper presents an improved direct torque controlled (DTC) inducion motor drive based on fuzzy logic strategy. The goal of this study is to reduce ripples for both electromagnetic torque and stator flux caused by hysteresis comparators and sector changes in conventional DTC. In the proposed control scheme two fuzzy logic controllers are designed to replace the hysteresis comparators and generate the right module and angle of reference voltage vector, then a PWM strategy is used to deliver the inverter switching states. Simulation results shows the compared performances of fuzzy logic and classical DTC control.
Effective Observation of Modified Liuwei Dihuang Decoction Combined with Bicalutamide on Treating Advanced Prostatic Cancer

Objective:Observing the curative effect of Modified Liuwei Dihuang Decoction combining with Bicalutamide on treating advanced prostatic cancer.Methods:Randomly dividing 84 outpatients and inpatients who was suffering from advanced prostatic cancer into two groups as 42 cases in the treatment group(oral administration of Modified Liuwei Dihuang Decoction combining with Bicalutamide) and the other 42 cases in the controlled group(oral administration of Bicalutamide simply). Results:The follow up observation indicated that remission rate of PSA was 52% for the treatment group while 40% for the control group for which there was statistical difference of comparison(P0.01). The median survival time of patients from the treatment group was 35.2 months while that of the control group was 18.9 months for which there was statistical difference of comparison(P0.01). The median TTPP(till the time to progression of PSA) of the treatment group was 10.2 months while that of the control group was 6.3 months for which there was statistical difference of comparison(P0.01). Conclusion:The combined medication of Modified Liuwei Dihuang Decoction combining with Bicalutamide on treating advanced prostatic cancer can prolong survival time(OS) and the time to progression of PSA(TTPP) as well as improving the remission rate of PSA.
TÜRK FiZYOTERAPi VE REHABiLiTASYON DERGiSi

Purpose: The objective of current study is to construct validity and reliability of Vestibular Disorders Activities of Daily Life in Turkish language. And also it was aimed to determine effects of symptoms on activities of daily life in patients with vestibular disorders. Methods: Subjects with peripheral vestibular disorders and chronic vertigo complaints were included in the study. Subjects with neurologic, systemic and physical problems and also subjects with acute Meniere disease were excluded from the study. Turkish version of scale was applied to 233 subjects over 18 ages. However, statistical analyses were done with fully completed 175scales. Results: Internal consistency reliability was found to significant for total Cronbach’ alpha=0.96; and for subscales of functional=0.92, ambulation=0.93 and instrumental=0.89. Test-retest correlation coefficient was 0.93 for total score (for functional, ambulation and instrumental was 0.85, 0.70 and 0.95). There were no significant difference between applications (p>0.05). Bartlett test (χ2=4640.05; df=300) p=0.00 results were significant and KMO result (0.90) was above the critic value. Factor analysis yielded one factor explaining 57.32 % of variance. The corrected item-total correlation coefficients ranged from 0.64 to 0.86. Significant correlation (r=0.518) was found between Turkish VADL and Turkish DHI (p<0.05). Discussion: Turkish VADL reliability scores were similar to original VADL scores. It was shown that Turkish VADL is a reliable scale in evaluation of adult patients with vertigo and dizziness.
Chatbots for Enterprises: Outlook

Chatbots are going to be the main tool for automated conversations with customers. Still, there is no consistent methodology for choosing a suitable chatbot platform for a particular business. This paper proposes a new method for chatbot platform evaluation. To describe the current state of chatbot platforms, two high-level approaches to chatbot platform design are discussed and compared. WYSIWYG platforms aim to simplicity but may lack some advanced features. All-purpose chatbot platforms require extensive technical skills and are more expensive but give their users more freedom in chatbot design. We provide an evaluation of six major chatbot solutions. The proposed method for the chatbot selection is demonstrated on two sample businesses – a large bank and a small taxi service.
Glacier shrinkage in the Alps continues unabated as revealed by a new glacier inventory from Sentinel-2

Abstract. The ongoing glacier shrinkage in the Alps requires frequent updates of glacier outlines to provide an accurate database for monitoring, modelling purposes (e.g. determination of run-off, mass balance, or future glacier extent), and other applications. With the launch of the first Sentinel-2 (S2) satellite in 2015, it became possible to create a consistent, Alpine-wide glacier inventory with an unprecedented spatial resolution of 10 m. The first S2 images from August 2015 already provided excellent mapping conditions for most glacierized regions in the Alps and were used as a base for the compilation of a new Alpine-wide glacier inventory in a collaborative team effort. In all countries, glacier outlines from the latest national inventories have been used as a guide to compile an update consistent with the respective previous interpretation. The automated mapping of clean glacier ice was straightforward using the band ratio method, but the numerous debris-covered glaciers required intense manual editing. Cloud cover over many glaciers in Italy required also including S2 scenes from 2016. The outline uncertainty was determined with digitizing of 14 glaciers several times by all participants. Topographic information for all glaciers was obtained from the ALOS AW3D30 digital elevation model (DEM). Overall, we derived a total glacier area of 1806±60 km2 when considering 4395 glaciers >0.01 km2. This is 14 % (−1.2 % a−1) less than the 2100 km2 derived from Landsat in 2003 and indicates an unabated continuation of glacier shrinkage in the Alps since the mid-1980s. It is a lower-bound estimate, as due to the higher spatial resolution of S2 many small glaciers were additionally mapped or increased in size compared to 2003. Median elevations peak around 3000 m a.s. l. , with a high variability that depends on location and aspect. The uncertainty assessment revealed locally strong differences in interpretation of debris-covered glaciers, resulting in limitations for change assessment when using glacier extents digitized by different analysts. The inventory is available at https://doi.org/10.1594/PANGAEA.909133 (Paul et al., 2019).