Miracle-dz/UniFAR-FTUs huggingface dataset:
Application of neural networks to the analysis of the resistance of the human immunodeficiency virus to HIV reverse transcriptase inhibitors

AIDS and the opportunistic infections and some other complications, associated with this syndrome lead to more than one million of human deaths per year. Human Immunodeficiency Virus is a cause of AIDS. The drugs, targeted proteins of HIV, can only lead to decrease of HIV copies in the human organism but still do not eliminate HIV from an organism. The main cause of antiretroviral drug therapy failure is HIV resistance to main drug classes: inhibitors of HIV structural proteins, protease and reverse transcriptase. One of the approaches to the classification of HIV variants into the resistant and susceptible ones is the use of machine learning methods. The aim of this work is the classification of the HIV variants into susceptible and resistant based on the nucleotide sequence of the HIV protease using neural networks. In our work, we used two topologies of neural networks: multilayer perceptron and convolutional neural network. Neural Networks were built using Python Tensor Flow and Keras libraries, where optimization of the neural networks can be performed. The training and test sets include experimental data on the nucleotide sequence of HIV protease and their resistance. Sensitivity, specificity, balanced accuracy were used as the main parameters, reflected the quality of classification. Those parameters were calculated for the test set, collected in the later period comparing to the sequences of the data set.
Text Summarization of Product Titles

In this work, we investigate the problem of summarizing titles of e-commerce products. With the increase in popularity of voice shopping due to smart phones and (especially) in-home speech devices, it is necessary to shorten long text-based titles to more succinct titles that are appropriate for speech. We present two extractive summarization approaches using bi-directional long short-term memory encoder-decoder network with attention mechanism. The first a pproach t reats t he p roblem a s a m ulti-class n amed entity recognition problem while the second approach treats it as a binary class named entity recognition problem. As a comparison, we also evaluate two abstractive summarization approaches using the same neural network architecture. We compare the results with automated (ROUGE) and human evaluation. Our experiment demonstrates the effectiveness of both extractive summarization
Overview of the 2019 Open-Source IR Replicability Challenge (OSIRRC 2019)

The Open-Source IR Replicability Challenge (OSIRRC 2019), organized as a workshop at SIGIR 2019, aims to improve the replicability of ad hoc retrieval experiments in information retrieval by gathering a community of researchers to jointly develop a common Docker specification and build Docker images that encapsulate a diversity of systems and retrieval models. We articulate the goals of this workshop and describe the “jig” that encodes the Docker specification. In total, 13 teams from around the world submitted 17 images, most of which were designed to produce retrieval runs for the TREC 2004 Robust Track test collection. This exercise demonstrates the feasibility of orchestrating large, community-based replication experiments with Docker technology. We envision OSIRRC becoming an ongoing community-wide effort to ensure experimental replicability and sustained progress on standard test collections.
Production Ranking Systems: A Review

The problem of ranking is a multi-billion dollar problem. In this paper we present an overview of several production quality ranking systems. We show that due to conflicting goals of employing the most effective machine learning models and responding to users in real time, ranking systems have evolved into a system of systems, where each subsystem can be viewed as a component layer. We view these layers as being data processing, representation learning, candidate selection and online inference. Each layer employs different algorithms and tools, with every end-to-end ranking system spanning multiple architectures. Our goal is to familiarize the general audience with a working knowledge of ranking at scale, the tools and algorithms employed and the challenges introduced by adopting a layered approach.
Sequence Learning for Brain Computer Interfaces

Author(s): Elango, Venkatesh | Advisor(s): Gilja, Vikash | Abstract: A fundamental challenge in designing brain-computer interfaces (BCIs) is decoding behavior accurately from time-varying neural oscillations. Studies using BCIs to function as communication prosthesis have demonstrated the plausibility of using these systems for recording neural signals over the long term as well as the ability to decode user intention from these signals. In most scenarios, the decoder used in a BCI is trained specifically for a subject and also has to be trained for every session of use with limited training data. Given these dataset size constraints, the class of decoding algorithms typically explored have restricted complexity, often limited to linear models that process neural signals within a fixed duration. However, such constraints can limit the practicality and usability of BCIs.In this thesis, we investigate the utility of sequential models for decoding behavior from neural signals. To that end, we describe a robust, scalable approach for decoding sequences of neural signals using Long Short-Term Memory (LSTM) networks that work well even when training data is limited. The efficacy of our approach is demonstrated by decoding finger flexion from neural data collected from 4 subjects implanted with electrocorticographic (ECoG) electrode arrays. We also present an architecture for sequence transfer learning, which is able to learn a general representation of the sequential data across subjects, and show that it is able to achieve significant improvements over the state-of-the-models. We believe that these techniques using sequence learning and sequence transfer learning could be applied to the development of many neural systems and may help enable higher performance BCIs.
Manual Control with Time Delays in an Immersive Virtual Environment

In an immersive virtual environment, inevitable time delays occur when a virtual hand responds to the hand movements of its operator. An experiment was conducted to examine the interactions among time delay, target distance, target width and movement times. Imposed hand-related time delays of 55 ms or more significantly increased the hand movement time. Data followed Fitts’ law with R greater than 0.75. Significant interactions were found between hand-related time delay and target width, but not between hand-related time delay and target distance. This suggests that, contrary to the common practice, the effects of target width and distance should be analyzed separately rather than in terms of a combined index-of-Difficulty.
DIVERSE DEPICTION OF PARTICLE SWARM OPTIMIZATION FOR DOCUMENT CLUSTERING

Document clustering algorithms play an important task towards the goal of organizing huge amounts of documents into a small number of significant clusters. Traditional clustering algorithms will search only a small sub-set of possible clustering and as a result, there is no guarantee that the solution found will be optimal. This paper presents different representation of particle in Particle Swarm Optimization (PSO) for document clustering. Experiments results are examined with document corpus. It demonstrates that the Discrete PSO algorithm statistically outperforms the Binary PSO and Simple PSO for document Clustering.
Prioritizing the health education needs based on community participation: AHP method

INTRODUCTION: The identification of health education needs through the community results in targeted services and health promotion. Prioritizing these needs, in addition to identifying the desired health system based on the needs of the community, is required for the optimal allocation of resources. Therefore, this study was conducted with the aim prioritizing the health educational needs of Sabzevar-Iran community. MATERIALS AND METHODS: This study was a two-step mix study. In the first stage, the qualitative method of the main themes of health educational needs of Sabzevar society was determined. Then, the questionnaire was prepared by a paired scale (36 questions) and quantitative method was used to prioritize the main themes with Analytic Hierarchy Process (AHP) software. The random sampling method was used in this study and data analysis was performed using Expert Choice software using AHP method. RESULTS: Social problems with 0.206, contagious diseases with 0.205, nutrition with 0.161, and public health with 0.132 were in the first to fourth grades of health educational needs in Sabzevar city and environmental health, health management, noncontagion diseases, elderly, and occupational health were in the fifth to ninth grades. The consistency index in this study was 0.08, although the adjustment index is <0.1 good and acceptable. CONCLUSION: Social problems are the first priority of Sabzevar society, which require appropriate training.
Effect of workshop training along with simulation and support of labor practitioners on their attitude and performance in conducting routine episiotomy

INTRODUCTION: Routine episiotomy is a common procedure to shorten the second stage of labor. The attitude of individuals is an important factor in episiotomy, which is often carried out without an indication. Therefore, this study aimed to determine the effect of education and support of labor practitioners on their attitude and performance in conducting routine episiotomy. MATERIALS AND METHODS: This was a quasi-experimental study. A sample of 66 midwives, physicians, and midwifery faculty members were selected through the convenience sampling method. The data collection instrument was the questionnaire of personnel's attitude toward episiotomy. This questionnaire was completed by the participants before and 3 months after the intervention. A 16-h workshop about the education of natural childbirth for 2 days was performed on the experimental group. Data analysis was performed using paired t-test and Pearson's correlation test. RESULTS: The findings showed that mean attitude of the participants toward episiotomy was 50.5 ± 1.58 before the intervention, which changed to 61.18 ± 2.5 after the intervention, demonstrating a significant difference between the attitude of the participants before and after the intervention using paired t-test (P < 0.001). In terms of the performance of the midwives, participating in the research, there was a significant decrease in the number of deliveries without episiotomy. CONCLUSIONS: According to the results of the study, education and support of midwifery personnel can change the attitude and performance of these individuals in conducting a routine episiotomy, thereby reducing the number of child deliveries without episiotomy.
Comparative transcriptomic and proteomic analyses of the green and white parts of chimeric leaves in Ananas comosus var. bracteatus

Background Ananas comosus var. bracteatus has high ornamental value due to its chimeric leaves. However, the chimeric trait is very unstable in red pineapple plants, and transcriptional variation between the two types of cells (white/green cells) and the molecular mechanism responsible for their albino phenotype remain poorly understood. Methods Comparative transcriptomic and proteomic analyses of the white parts (Whs) and green parts (Grs) of chimeric leaves were performed. Results In total, 1,685 differentially expressed genes (DEGs) (712 upregulated and 973 downregulated) and 1,813 differentially abundant proteins (DAPs) (1,018 with low abundance and 795 with high abundance) were identified. Based on Gene Ontology (Go) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses, the DEGs were mostly involved in carbon fixation in photosynthetic organisms, porphyrin and chlorophyll metabolism and oxidative phosphorylation, while proteomic analysis revealed that DAPs were mostly related to ribosomes, photosynthesis, photosynthesis antennas, and porphyrin and chlorophyll metabolism. Combined analysis showed increased mRNA levels but low abundance of nine proteins level in Whs /Grs related to photosynthetic pigment and photosynthesis. Transcriptional changes, posttranscriptional regulation and translational alterations of key enzymes involved in chlorophyll biosynthesis and photosynthesis may play important roles in the albino parts of chimeric leaves.
An Efficient Algorithm of Thinning Scanned Pencil Drawings

In computer aided 2D cartoon producing systems, the vectorization of scanned pencil drawings can greatly enhance the efficiency of inking and painting, retrieve lines and closures contained in the drawings and therefore lay the basis for implementation of more advanced functions such as autocoloring, inbetweening and so on. Thinning of scanned pencil drawings is the first step of the vectorization process. Having investigated and compared a lot of thinning algorithms, an improved noniterative thinning algorithm based on the idea of line following is presented. The improved algorithm is very efficient, which, requiring only one pass of image scanning and two passes of contour tracing, meets the high efficiency standard in computer aided 2D cartoon producing systems. Time complexity of the algorithm is analyzed in detail, and the method to reduce two kinds of image noises and to delete redundant branches after thinning are given.
A Survey Paper on Data Mining With Big Data

Big Data is a new term used to identify the datasets that due to their large size and complexity. Big Data are now rapidly expanding in all science and engineering domains, including physical, biological and biomedical sciences. Big Data mining is the capability of extracting useful information from these large datasets or streams of data, that due to its volume, variability, and velocity, it was not possible before to do it. The Big Data challenge is becoming one of the most exciting opportunities for the next years. This study paper includes the information about what is big data, Data mining, Data mining with big data, Challenging issues and its related work. Today is the era of Google. The thing which is unknown for us, we Google it. And in fractions of seconds we get the number of links as a result. This would be the better example for the processing of Big Data. This Big Data is not any different thing than out regular term data. Just big is a keyword used with the data to identify the collected datasets due to their large size and complexity? We cannot manage them with our current methodologies or data mining software tools. Another example, the first strike of Anna Hajare triggered number of tweets within 2 hours. Among all these tweets, the special comments that generated the most discussions actually revealed the public interests. Such online discussions provide a new means to sense the public interests and generate feedback in real-time, and are mostly appealing compared to generic media, such as radio or TV broadcasting. This example demonstrates the rise of Big Data applications. The data collection has grown tremendously and is beyond the ability of commonly used software tools to capture, manage, and process within a tolerable time.
To analyze and calculate unilateral magnetic force of an induction motor

Several calculating formulas of unilateral ma gnetic force of an inducti on motor are simply deduced. Based on abroad and practical design experience, a ki nd of reasonable calculating method is proposed. By analyzing and comparing the c alculating results of the formulas, the validity of the method presented is show ed.
Naive Bayes based criminal text classification of unbalanced classes

According to the feature of case text,this paper explores the special text preprocessing method and compares two effective feature selection methods.An improved model based on multi-variate Bernoulli model is proposed,due to the unbalanced distribution of criminal case categories.The experiment indicates that the improved Naive Bayes method performs better in the case text classification.
An E-cient Algorithm for Combining Linear Lumped Networks with the FDTD Method

In this study, an e-cient approach is presented for processing two terminal networks consisting of arbitrary linear lumped elements into a single grid of FDTD (flnite difierence time domain) method. The relation of the node voltage to current of the networks can be expressed as a rational function (transfer function) in frequency domain. Field-updating equations and the rational function can be combined together into a new rational function since a single FDTD grid can be regarded as a Norton (or Thevenine) equivalent circuit. The proposed method is based on control theory and leads to an explicit e-cient updating algorithm. A six-order circuit terminated with a transmission line is simulated to demonstrate the accuracy and e-ciency of the proposed method. The simulation time of the proposed methods is about 30% less than a similar conflguration using the equivalent current source (ECSM) method. Simulation results agree well with data from the ECSM and Agilent's commercial software ADS.
Face processing in human-computer Interaction using curvelet analysis

Vision interaction is one of important aspects of human-computer interaction,and the facial feature extraction is crucial to vision interaction.This paper applies the curvelet transform to the face processing to extract facial feature more effectively.It overcomes the weakness of the wavelet transform which is unable to efficiently extract curve features of face images.An optimized method based on Adaboost and curvelet transform is proposed for face detection.A new approach combining SVM and curvelet transform is designed for facial expression recognition.Experiments on face detection,face recognition and facial expression recognition are carried out.The results reveal that curvelet transform has distinct advantages in facial feature extraction,and lays a good foundation for the further work of the natural human-computer interaction.
Study on Quality Grading of Tea by Digital Image Processing Techniques

In order to avoid the errors brought by human grading,the paper discussed the tea quality grading by the image processing technique,including image segmentation,labeling and HSI color model of.Results showed that the technique successfully segmented the tea images and distinguished the tea grades by using the H and S component histograms in the HSI color model.
A Layered SLAM based Approach for Unknown Environment Hierarchical Map Building

A layered simultaneous localization and mapping(SLAM) approach for building spatial hierarchical maps is proposed in situations that obstacles,environmental sensors and robot poses are all unknown in large-scale environments. Firstly,a two-layered environmental model is established,which is composed of a submap layer and a topological node layer. Then the perception information of the environmental sensors is employed to assist the online creation and update of submaps local coordinate framework.While building the topological structure,the coupling summation equation is utilized to compute the relative positions between nodes.Based on the loop-closure detection result,the weighted scan matching algorithm and the relaxation algorithm are introduced to optimize the topological structure,which ensures global consistency of the map. Experimental results validate the feasibility and effectiveness of the approach.
Derivation of Four Morphologically Affiliated Species of Macrobrachium (Caridea: Palaemonidae) with Divergent Reproductive Characteristics in Northeastern Asia

Abstract We investigated four morphologically affiliated species of Macrobrachium in northeastern Asia (M. nipponense and M. formosense, which spawn many small eggs, and M. shokitai and M. asperulum, which spawn a few large eggs). Multi-locus allozyme analysis supported the hypothesis that M. shokitai, endemic to the southernmost island (Iriomote Island) of the Ryukyus, Japan, differentiated recently (approximately 1.0 Myr ago) from nearby M. asperulum populations in Taiwan. M. nipponense and M. formosense are inferred to have differentiated later (0.48 Myr ago). The group of M. shokitai and M. asperulum was presumed to have separated from the group of M. nipponense and M. formosense in several million years, evolving their characteristic reproductive traits. Some populations of M. shokitai and M. asperulum in upland streams show extremely low genetic variability.
ULTRASTRUCTURAL FEATURES OF THE FUNNEL OF GAMMARUS OCEANICUS (AMPHIPODA)

Abstract The funnel of Gammarus oceanicus extends as a fold of the foregut wall into the midgut lumen. It comprises an anterior segment, to which both foregut epithelium and cuticle contribute, and a posterior segment that lacks the epithelium. The latter segment consists of two layers of foregut cuticle that are in close contact and extend distally beyond the point where the foregut epithelium turns back on itself; contact between the two cuticular layers develops as the epithelium retracts from between them, in postmolt. Light microscopy does not consistently distinguish between the funnel's posterior segment and the peritrophic membrane found within mid- and hindguts; however, their ultrastructural organizations are distinctly different, and an origin of peritrophic membrane from the funnel of some amphipods, as suggested in the literature, is not the case for Gammarus oceanicus. Scattered within the posterior segment cuticle are clusters of short, highly curved, electron-lucent rods. Such features have not been described from other crustacean cuticles. However, rather than being novel structures, they are interpreted as artifacts produced by sectioning of the abundant chitin macrofibrils of the cuticle.
An Empirical Analysis on Reducing Open Source Software Development Tasks using Stack Overflow

Objectives: The cross repository analysis between Open Source Software (OSS) and Community Question Answering (CQA) site is presented in order to speed the development process of OSS. Methods/Analysis: The OSS development is becoming popular nowadays due to fact that the source codes, the developer specifications and bug lists are made available online to the public. Anyone can contribute to the development of software by referring these files. Similarly, Stack Overflow is an interactive CQA site that caters programming related questions with their answers online and turned into repositories of software engineering knowledge. In order to track the correlation of such sites with software development tasks, we employ the two repositories to find the semantic similarity between bugs and Question and Answer (Q&A) posts posted on OSS projects and Stack Overflow respectively. The semantic similarity is analyzed by integrating the contents of the repositories based on text mining approach. The relationship between a bug and Q&A post is established through the semantic similarity and metadata features. Findings: The statistics of our analysis is presented for five OSS projects in terms of number of bugs and average bug fix time. The statistical result shows that the bug fix time can be reduced by posting the bugs into Stack Overflow. Application/Improvement: The presented approach can be utilized to find the similar Q&A posts for reported OSS bug and helps developers of OSS projects to resolve the bugs quickly by leveraging programming skills of users' in the form of Q&A posts.