Miracle-dz/UniFAR-FTUs huggingface dataset:
Efficient allocation of elastic interfederation encrypted streaming sessions

Audio and video (A/V) collaboration platforms often use Internet cloud technologies to ensure elasticity. They generally operate on a best‐effort basis, without quality or delivery guarantees. However, such guarantees are a premise of business‐focused platforms, which often rely on static/dedicated infrastructure and hardware‐based components. This article presents results obtained in the final stage of the Elastic Media Distribution (EMD) project, which targets the migration of a business‐focused hardware‐based A/V collaboration tool towards a more elastic, reliable, and secure cloud‐based model. The use case under investigation is an educational scenario: teachers and students located at distributed sites collaborate under different data encryption policies. An existing model of collaboration streaming is extended to accommodate encryption‐enabled streaming components, and new resource allocation heuristics are proposed to deploy these components under stringent service level agreement (SLA) constraints. An extended version of our evaluation framework, based on the CloudSim simulator, manages encryption‐enabled components. A resource usage dataset was obtained by prototyping selected streaming components and evaluating their performance on the Virtual Wall large‐scale test bed. This dataset is fed into the extended simulation framework. Simulation results show longer than expected delays when loading streaming components, an issue that jeopardises the user experience that can be alleviated by the algorithms proposed in this article. Results show that the proposed algorithms enable policy‐based secured communications under bandwidth and virtual machine (VM) cost increases of 48% and 23%, respectively, if compared with a nonencrypted previous solution, and with set‐up times remaining under the required 2‐second deadline.
High-field 11B NMR of alkali borates. Aqueous polyborate equilibria

Spectres RMN de 11 B a 127 et 160 MHz de solutions aqueuses de KB 5 O 8 •4H 2 O, K 2 B 5 O 8 (OH)•2H 2 O, K 2 B 4 O 7 •4H 2 O et NaB 5 O 8 •5H 2 O. Separation des signaux et discussion des especes formees en fonction de la concentration
Amelioration Effects of Aphanizomenon Flos-aqua (AFA) on Hazardous Effects of Mixture (Sodium Benzoate, Mono Sodium glutamate and Chlorophyllin) Intake in Male Albino Rats

Background: Food additives are products added to the basic foodstuffs with an aim of improving its aspect, flavor, taste, color, texture, food value and conservation. Aim of the work: This study aimed to determine the effects of AFA against the hazardous effects of sodium benzoate, chlorophyllin and monosodium glutamate. Materials and methods: This study was done on thirty male albino rats with an average body weight 120-140 g. The animals were divided into three groups (10 rats/each) as follows: Group 1: control, Group 2: rats treated with food additives mixture (sodium benzoate, chlorophyllin, and monosodium glutamate) and Group 3: rats treated with mixture, in addition to AFA (blue green algae). Blood samples were collected, and the separated sera were used for estimation of some biochemical parameters (liver enzymes, kidney function tests, glucose, HOMA-IR, protein profile and lipid profile) and hormonal levels [testosterone, triiodothyronine (T3) and thyroxine (T4)]. Results: The study showed an increase in levels of fasting glucose, insulin, HOMA-IR, activities of AST and ALT, urea, creatinine, total cholesterol (TC), triglycerides (TG), LDL-C, VLDL, ratios of TC/HDL-C and LDL-C/HDLC (risk factors) in mixture group. This was accompanied by a significant decrease in protein profile (total protein, albumin and globulin), HDL-and testosterone hormone levels as compared to the control rats. On the other hand, these results turned back nearly to the normal values after receiving the AFA (blue green algae). Conclusion: the present study clearly revealed the therapeutic capability of AFA to fight the grievous effects of food additives mixture on major physiological parameters.
Maize Leaf Disease Image Classification Using Bag of Features

Image classification is an image grouping based on similarities features. The features extraction stage is a crucial factor for classifying an image. In the conventional image classification, the features commonly used are morphology, color, and texture with various derivative features. The type and number of appropriate features will affect the classification results. In this study, image classification by using the Bag of Features (BOF) method which can generate features automatically. It consists of 4 stages: feature point location by using grid method, feature extraction by using Speed Up Robust Feature (SURF), clustering word-visual vocabularies by using k-means, and classification by using Support Vector Machine (SVM). The classification data are maize leaf images from the PlantVillage-Dataset. The data consists of 3 types of images: RGB, grayscale and segmentation images. Each type includes four classes: healthy, Cercospora, common rust, and northern leaf blight. There are 50 images for each class. We used two scenarios of testing for each type of data: training and validation, 70% and 80% images for training, and the rest for validation. Experimental results showed that the validation accuracies of RGB, grayscale, and segmentation images were 82%, 77%, and 85%.
Multimodal Emotion and Sentiment Modeling From Unstructured Big Data: Challenges, Architecture, & Techniques

The exponential growth of multimodal content in today’s competitive business environment leads to a huge volume of unstructured data. Unstructured big data has no particular format or structure and can be in any form, such as text, audio, images, and video. In this paper, we address the challenges of emotion and sentiment modeling due to unstructured big data with different modalities. We first include an up-to-date review on emotion and sentiment modeling including the state-of-the-art techniques. We then propose a new architecture of multimodal emotion and sentiment modeling for big data. The proposed architecture consists of five essential modules: data collection module, multimodal data aggregation module, multimodal data feature extraction module, fusion and decision module, and application module. Novel feature extraction techniques called the divide-and-conquer principal component analysis (Div-ConPCA) and the divide-and-conquer linear discriminant analysis (Div-ConLDA) are proposed for the multimodal data feature extraction module in the architecture. The experiments on a multicore machine architecture are performed to validate the performance of the proposed techniques.
UJI AKTIVITAS ANTIBAKTERI HASIL FRAKSINASI DARI EKSTRAK METANOL DAUN KATUK (Sauropus androgynus) TERHADAP BEBERAPA BAKTERI PATOGEN

Majority of traditional medicine uses herbal medicine to support health. Besides being able to cure the disease, traditional medicine also has side effects low or even non-existent. One of the plants used as traditional medicine is cinnamon leaf ( Sauropus androgynous ). Leaves widely used as a laxative breast milk for breastfeeding mothers. In addition, cinnamon leaf can be used as a drug for ulcerative lesions. This study aims to investigate the antibacterial activity of the methanol extract of leaves of cinnamon fraction ( Sauropus androgynous ). Characterization of compound akitif based thin-layer chromatography penampak various spots, including FeCl3, Dragendorf, Lieberman Buchard, ethanolic KOH, and Aluminum Chloride. Crude katuk leaves macerated using methanol for 3 days. The results showed that the methanol extract of cinnamon leaf has activity against the bacteria Salmonella and Staphylococcus aureus thyposa. Katuk leaf extract was fractionated by Vacuum Liquid Chromatography method. In this fractionation process, the eluent used was ethyl acetate and methanol (5:1). Results of fractionation of methanol extract in the form of a combined fraction of the number 6 keselurahan 14 fractions. Based on the TLC-profile Bioautografi fraction is the fraction of active 4 inhibits the growth of bacteria Salmonella and Staphylococcus aureus thyposa. From the results of the identification of the chemical components showed that compounds that inhibit the growth of bacteria on the 4th fraction are flavonoids.
Estimation and hypothesis testing with additive kernel machines for high-dimensional data

JENNIFER J. CLARK: Estimation and Hypothesis testing with Additive Kernel Machines for High-Dimensional Data (Under the direction of Dr. Mike Wu) Advances in high throughput biotechnology have culminated in the development of large scale, population based studies for identifying genomic features (e.g. genes, SNPs, CpGs, etc.) associated with complex diseases and traits. Understanding an individual’s genetic disposition for particular traits and diseases can provide information toward the development of individualized risk profiles and treatment regimes and simultaneously provides clues as to the biological mechanisms underlying complex traits. However, the high-dimensionality of the feature space, the limited availability of samples, and our incomplete understanding of how features biologically influence various diseases impose a grand challenge for statisticians. To mitigate some of these challenges, we propose several new methods. First, we develop the additive least square kernel machine (ALSKM) approach for nonparametrically modeling and testing the cumulative effect of a group of features (such as multiple biologically related CpGs) while nonparametrically adjusting for complex, nonlinear covariates. Our proposed methods model both the genomic features and the complex covariates using the kernel machine framework. Second, building on the ALSKM, we develop a novel approach for testing for interactions between two different groups of (biologically related) features. Specifically, we develop a multi-marker test which can test for epistasis, or gene-gene interactions, between two different groups of genomic features. Finally, we again use on the machinery developed under Topics 1 and 2 to develop an approach for testing the association between rare variants and a phenotype in the presence of common variants while accommodating
DATA ANALYTICS AND PREDICTIVE ANALYTICS IN THE ERA OF BIG DATA

This chapter outlines the key principles of machine learning and predictive analytics. It explains the new fundamentals of big data and the evolving technology. The chapter follows by the practical advice on how organizations can establish a new culture in order to truly transform their business in the new era. The wave of data frenzy did not happen overnight. Rather, it is a crescendo of events happening since the early 1980s where the fields of business intelligence and predictive analytics were known as 'data mining', a preexisting discipline with another closely related term known as knowledge discovery in databases (KDD), which is the aim of performing data mining. Analytics has a spectrum of methodologies, techniques, and approaches from descriptive, diagnostic, predictive and prescriptive analytics. Most data mining projects today follow the cross industry standard process for data mining (CRISP‐DM) which was conceived in 1996.
A 0.6 V 1.63 fJ/c.-s. Detective Open-Loop Dynamic System Buffer for SAR ADC in Zero-Capacitor TDDI System

In this paper, a low-power detective open-loop dynamic (DeOLD) system buffer codesigned with a SAR ADC that is pertinent for zero-capacitor touch with a display driver integration (TDDI) system is proposed. The proposed DeOLD system buffer not only blocks the supply coupling interference off the ADC but also compensates the lost charge back to the supply by actively detecting the ADC input signal. Its third-harmonic distortion is suppressed to under 70 dB. The dynamic charge sharing technique reduces the power of the charge compensators by 40% compared with the well-known pump squeezing technique. A supply reset (RST) circuit makes up the charge loss and avoids the usage of an unpractical charge compensator to save additional chip area and energy. The system buffer is open loop, which is fast and energy-efficient; furthermore, it is codesigned with a 400-kS/s 10-b SAR ADC. The system buffer provides all voltage sources for the ADC, including the reference, digital, and analog voltages. It consumes 334 nW under a 0.6-V supply. Its active area is only 0.0098 mm2 in 40-nm CMOS technology. It achieves an signal-to-noise and distortion ratio (SNDR) of 55.96 dB, resulting in the Walden figure-of-merit (FoMW) of 1.63 fJ/c. -s.
Problems and techniques for cross language information retrieval

Cross Language Information Retrieval concerns the problem of finding foreign language documents after using your own native language to write your information request. It supposes that documents are indexed in their original language, and that you do not have the possibility to translate all the documents into your native language but must work with these indexes. We present here a short overview of the particular problems caused by this situation and some techniques that have been proposed to attack it
Ranking of Public Bus Alternatives using Hybrid Multi-Criteria Decision Making Approach under Fuzzy Environment: A Case Study of Kathmandu

User satisfaction is an intrinsic aspect of any service and since public transport is a service based supply, it has to be continuously assessed for user satisfaction. This paper aims at depicting the results of the hybrid approach based on user satisfaction and multi criteria decision making, applied for the ranking of the public bus alternatives in Kathmandu. The implemented approach expounded vividly in the methodology is abode of three stages, explicitly, collection of data pertaining to user satisfaction under each established criteria, determination of relative weights of criteria used to evaluate the alternatives on the basis of judgements from the experts and finally the integration of user satisfaction with expert opinion to rank the alternatives. Fuzzy environment endows a comfortable opportunity to the users and the experts to provide pertinent ratings in qualitative terms like high, very high or low rather than ratings in discrete numbers.
Gas production from different classes of methane hydrate deposits by the depressurization method

There are four primary classes of natural gas hydrate deposits in natural world. The differences between them are in the distribution of the methane hydrate, free gas, and free water layers. A reactor which the height was 120 mm and the inner diameter was 103 mm was used in hydrate formation and dissociation, and 17 thermocouples measured the distribution of temperature during the mining process. Different experimental means were applied to simulate three classes of methane hydrate deposits, in which water and glass beads filled in different orders. The hydrate samples were dissociated by depressurization, and the results showed no need to test a backpressure greater than 94% of the equilibrium pressure of methane hydrate. Class 1 sample's methane hydrate decomposition rate was slower than that of class 3 sample at the beginning of depressurization when backpressure was 2.3 and 2.6 MPa, but then, the opposite happened. The average dissociation rate decreased nearly linearly with the increase in backpressure for the class 1 samples.
Research of FAQ based on the semantic similarities of HowNet

Frequently Asked Question (FAQ) is the main method to provide online help. A candidate question set was built up according to the user's query by search engine. Semantic similarities of sentences, based on HowNet, were computed between the user's query and the candidate questions. The answer, which is the most similar to the query, was returned to the user. Experiments show that the method has done better in the matching between questions and answers.
Implementation and Assessment of Non-Traditional Instructional Practices in a College Biology Course

IMPLEMENTATION AND ASSESSMENT OF NON-TRADITIONAL INSTRUCTIONAL PRACTICES IN A COLLEGE BIOLOGY COURSE by Stephen M. Rybczynski There continues to be interest in reforming educational practices to better prepare students to succeed. For example, cooperative learning, scientific inquiry, and explicit and reflective (ER) discussions are advocated to improve student attitude and achievement; however, research suggests this is not always the case. The efficacy of reform efforts should be assessed to make informed decisions regarding best instructional practices. In this dissertation, we used qualitative and quantitative methods to assess the effects of two non-traditional instructional practices in an introductory undergraduate biology course; out-of-class collaborative group study in a large-lecture course and the revision of a lab course from expository to inquiry instruction, with and without ER discussions. In chapter 1, we related out-of-class study group (SG) usage to performance on content exams, explored patterns of SG useage, and qualitatively described student perceptions of SGs. No relationship was found between gains in content knowledge and SG use. Students that participated in SGs did, however, believe they were beneficial and four patterns of SG use were identified. Thematic analysis revealed pre-conceptions and in-class experiences influence student decisions to utilize SGs. Students require guidance in the successful use of SGs, and instructors can help by making students aware of potential group composition problems, helping organize groups that are compatible, and providing materials to focus study efforts. In chapters 2 and 3, we assessed how different instructional models affected three aspects of student attitude towards biology lab: confidence, perception of usefulness, and motivation to overcome challenges (effectence motivation; EM). We quantified the effects of inquiry versus expository instruction, both with and without ER, on sub-aspects of attitude, and used qualitative methods to identify factors influencing attitude. There were no differences among treatment combinations in relative change in attitude (RCA) for confidence, usefulness, or EM; however, significant differences in RCA for confidence and EM were found among lab sections. Factors other than instructional model, especially course content, the teaching assistant, assessment, and students’ personal characteristics played a larger role in determining attitude and should be considered when implementing curriculum reforms. IMPLEMENTATION AND ASSESSMENT OF NON-TRADITIONAL INSTRUCTIONAL PRACTICES IN A COLLEGE BIOLOGY COURSE
An investigation of biometric-based user predictability in the online game League of Legends

Computer games have been consolidated as a favourite activity for years now. Although such games were created to promote competition and promote self-improvement, there are some recurrent issues. One that has received the least amount of attention so far is the problem of "account sharing" which is when a player shares his/her account with more experienced players in order to progress in the game. The companies running those games tend to punish this behaviour, but this specific case is hard to identify. Since, the popularity of machine learning techniques have never been higher, the aim of this study is to better understand how biometric data from online games behaves, to understand how the choice of character impacts a player and how different algorithms perform when we vary how frequently a sample is collected. The experiments showed through the use of statistic tests how consistent a player can be even when he/she changes characters or roles, what are the impacts of more training samples, how the tested machine learning algorithms results are affected by how often we collect our samples, and how dimensionality reduction techniques, such as Principal Component Analysis affect our data, all providing more information about how this state of art game database works.
Influence of Biochars on Yield and Nitrogen and Phosphorus Use Efficiency of Pisum sativum under Groundwater and Wastewater Irrigation in Arid Climate

ABSTRACT Present study evaluated the influence of slow-pyrolyzed wood-derived and cow manure-derived biochars on growth performance (i.e., yield production, nitrogen use efficiency (NUE), and phosphorus use efficiency (PUE)) of Pisum sativum under groundwater and municipal wastewater irrigation. Biochars were applied at 5, 10, and 15 t ha−1 rates for 2 years. The amendment of biochars did not enhance yield during first year under both irrigation types; rather, small-particle-sized (<0.65 mm) wood-derived biochar reduced significantly yield at higher application rates under groundwater irrigation. During second year, amendment of biochars significantly increased the yield especially at higher application rates under groundwater irrigation while no influence of biochars was observed for wastewater irrigation treatment. The yield was significantly lower under wastewater irrigation for all treatments as compared to first year. The NUE and PUE of pods and stover were not different during first year and second year under both irrigation treatments except for the small-particle-sized wood-derived biochar applied at 10 and 15 t ha−1, which tended to reduce significantly the NUE and PUE of pods and stover under groundwater irrigation during first year of cropping. The NUE and PUE were significantly lower during following year under wastewater irrigation. Our results suggest that amendment of biochars had positive influence on the growth of P. sativum under groundwater irrigation during second year of cropping while these amendments had no influence under wastewater irrigation.