Miracle-dz/UniFAR-FTUs huggingface dataset:
Effects of initial stocking density on weaning and growth of juvenile yellowcheek carp (Elopichthys bambusa R.)

The present study aimed to investigate the effect of initial stocking density on the weaning and growth of juvenile yellowcheek carp(Elopichthys bambusa R.). Five groups were assigned in triplicate in the net cages(1.0m×1.0m×1.0m)with different initial stocking densities of 100ind/m3(D1),300ind/m3(D2),500ind/m3(D3),700ind/m3(D4)and 900ind/m3(D5). The experiment was carried out for 28days.The results showed that MR,CV decreased and then increased with the increasing of initial stocking density.CV in D1,D2,D5was significantly higher than that in D3,D4.CR increased with the increasing of initial rearing density.WSR with the increasing of initial rearing density firstly increased and then decreased.Relationship between WSR and stocking density based on quadratic regression analysis was:Y=-1.101 X2+13.590 X+44.093,correlation coefficient R2=0.931,the optimum initial stocking density was 617ind/m3.SGR decreased with the increasing initial stocking density.It was concluded that the optimal initial stocking density of net cage for the weaning of juvenile yellowcheek carp was 500-700ind/m3 based on WSR,weaning efficiency and manipulation strength.
LBRY: A Blockchain-Based Decentralized Digital Content Marketplace

Despite the critical need of publishing and consuming content online, a centralized content platform such as Amazon or YouTube may not always have their policy and practice aligned with the interest of their users and could be rent-seeking, censorious, and frequently exploitative, whereas a peer-to-peer solution such as BitTorrent may suffer from issues of content discovery, legitimacy, monetization, verifiable publisher identity, and poor user experience. In this paper, to improve significantly over both options, we propose a new approach called LBRY that enables a decentralized online content marketplace. In particular, it uses a blockchain to build a decentralized content platform controlled by the community, and allows its users to publish, host, find, access, download, and pay for content with ease. LBRY introduces a new naming scheme that gives users the full control of the names of their content, and uses a blockchain to not only support a digital currency (LBC) and transparent decentralized ledger, but also allow every user to access a synchronized name space and a global index of content metadata, thus supporting a new paradigm of digital content distribution. We detail how LBRY works in this paper, including how it designs its data structures for content, content metadata, and a novel content naming space; how it uses the blockchain to manage and synchronize the name space and implement an index for content metadata in order to support content sharing and purchase; and how it handles several issues in running LBRY.
Nitrogen Metabolism of Wheat Genotypes in Relation to Heat Stress Tolerance

In present study a set of wheat genotypes (40) have been evaluated for their potential to withstand heat stress. For this one laboratory experiment, two periodic field experiments (2011-12 and 2012 -13) with normal sowing (November 29) and late sowing (December 29) and a pot experiment (2013-14) were conducted. The cell membrane thermo stability supported lesser lipid peroxidation (MDA) and minimal changes in mitochondrial activity (TTC) of heat tolerant genotypes which ultimately increased the survival potential under laboratory conditions after heat stress (35 and 40±2°C). Delay in sowing date caused significant decrease in several physiological parameters viz., nitrogen use efficiency and grain growth and yield parameters. Nitrogen uptake efficiency (NupE), grain nitrogen utilization efficiency (GNutE), nitrogen remobilization efficiency (NRE) was significantly higher in timely sown and biomass nitrogen utilization efficiency (BNutE) was higher in late sown genotypes. Grain yield had positive correlation with NupE, GNutE, NRE and N partitioning in grain but negative correlation with BNutE, N partitioning in leaves and chaff indicating that N mobilization from leaves and chaff to the grain limited grain yield and GNutE. A significant increase in seed protein content and sedimentation value and the decrease in grain yield, biomass yield, plant height, harvest index, grain filling period, grain hardness and moisture content was recorded under the late sown conditions. Under the pot conditions relatively higher proline content, total amino acids, quaternary ammonium compounds and ascorbate content was recorded in heat stressed plants as compared to control. Yield contributing parameters (grain yield, grain weight plant -1 , number of grain plant -1 ) showed significant reduction under heat stress. The genotypes G3, G4, G6, G7, G9, G10, G31 and G32 had sustained yield contributing parameters under heat stress better as compared with others.
Searching, Examining, and Exploiting In-demand Technical (SEE IT) Skills using Web Data Mining

—There has always been a mismatch between academic education and industry demands in various industry ﬁelds. Especially, in the computer related jobs such as software developers or engineers, employers and job seekers are experiencing a large curriculum or skill gap compared to other industry ﬁelds due to its fast-changing nature. In order to minimize the gap, identifying the current job skill trends and offering/learning relevant skills are critically important for both educators and students in the fast-paced job market. However, due to the lack of relative information about the trends of in demand technical skills in certain regions or around the nation, it is difﬁcult for educators to keep up with these changes and determine what needs to be conveyed to students so they can be ready for the job on day one. In this paper, in order to address these challenges, we focused on the analysis of in demand technical skills and their trends in software developer and engineer jobs around the nation. We collected and analyzed over 120,000 software developer or engineer job postings and summarized demanding and required skills from different regions throughout the nation.
Detecting Spammers from Hot Events on Microblog Platforms: An Experimental Study

—With the development of Web 2.0, social media such as Sina Weibo, Douban, and Zhihu have become an important platform for the dissemination and fermentation of hot events. At the same time, many spammers are hidden in the network, and they are driven by the interests to participate in the process of event dissemination, disseminating information with a propensity, and guiding public opinion through speculation, malicious comments, malicious attacks, etc. It interferes with the network order and the decision making based on social networks, and even affects social stability. Therefore, it is important for government and enterprises to accurately detect spammers from hot events on microblog platforms and further make sure whether a hot event is natural developing or driven by spammers. In this paper, we focus on the hot event list on Sina Weibo and collect relevant microblogs and involved users of each hot event. Then, we employ typical machine learning methods to conduct an experimental study on detecting spammers. Specially, we develop a new set of features based on three aspects, including user profile, user behavior, and user relationships, to reflect various factors affecting the detection of spammers. Finally, we conduct experiments on a real data set from the Sina Weibo, and compare three machine learning models including the Naive Bayes, the J48 Decision Tree, and the Logistic Regression model, concerning various metrics like precision, recall, F-measure and AUC. The results show that the Logistic Regression model achieves the best average F-measure in detecting both spammers and non-spammers.
Sentiment Analysis of Online Reviews with a Hierarchical Attention Network

—Sentiment analysis of online reviews has been an important task in online shopping and electronic commerce to understand customers’ opinions and behavior. Generally, customers can express their opinions by posting comments and uploading images, therefore online reviews can be regarded as the combination of a textual document and a few related images. Images may not contain obvious sentimental information, but the visual aspects of images can augment the sentiment information of textual comments. Thus, it is a better way to consider both textual comments and visual images in sentiment analysis of online reviews. Following this idea, in this paper, we propose a hierarchical attention network that combines visual aspect attention, sentence attention, and self-attention to provide effective sentiment analysis of online reviews. With this mechanism, we can model the interactions among words within one sentence as well as the interactions between texts and images. We conduct experiments on a dataset about online restaurant reviews from Yelp.com and compare our model with five existing models. The results suggest the superiority of our proposal.
An Evaluation of Recommendation Algorithms for Tourist Attractions

—Tourism is an important activity for the economy of several places in the world. In this context, the use of information systems might bring some beneﬁts for tourists, for example, by helping people to select tourist attractions, such as restaurants, beaches, and museums. In this paper, we perform two evaluations of 22 recommendation algorithms provided by existing recommendation system libraries, aiming to identify efﬁcient algorithms in terms of prediction accuracy. In the ﬁrst evaluation, we compare the algorithms by measuring different metrics, such as RMSE , MAE , precision , recall , and F1 Score . In the second evaluation, we compare the recommendation algorithms based on the answers of 172 people that participated in our survey by evaluating different kinds of tourist attractions. The results of our study show that some recommendation algorithms remain on the top list with regards to efﬁciency on both studies, such as the SVD++ , Baseline Only , and KNN Z Score with Pearson Baseline Similarity . Others are efﬁcient in the ﬁrst evaluation, or for some metrics, but not in the second study, for example, or the other way around. The results of our study are useful for people that are creating solutions in the tourism domain.
Exploratory karyological and genome size studies in Chilean Sophora species

ABSTRACT The genome of Sophora toromiro (Phil.) Skottsb. (Papilionaceae) is characterised by means of chromosome counts of accessions called Viña del Mar Botanical Garden (JBV), Göteborg (Got) and Titze. Karyotypes were obtained and genome size quantified using flow cytometry and compared to the closely related species Sophora macrocarpa J.E. Sm. and Sophora cassioides (Phil.) Sparre. No differences were detected in chromosome number of Sophora at intra-specific or inter-specific levels with 2n = 2x = 18. Analyses on the symmetry coefficients CVcl and Mca shows affinity between the analysed material of the Titze line. The graph clearly indicates differences between S. toromiro lines if compared with S. cassioides and S. macrocarpa. Genome size range from 788 Mbp for S. cassioides, 795 Mbp for S. macrocarpa and a range of 796–808 Mbp in S. toromiro. This is the first report of chromosome number and genome size for S. toromiro, S. macrocarpa and S. cassioides. This is of particular importance in S. toromiro, as it is has been considered extinct in its natural habitat for the past 50 years.
Trends in Software Reverse Engineering

The polymorphic domain of software reverse engineering varies since 90s due to multiple reasons. Some of the primary reasons include the acceptance of new programming languages, underlying technique of reverse engineering and the desired output notation of the reverse engineering that varies with evolution of software. The purpose of this paper is to provide a trend-based taxonomy of reverse engineering that can classify the differences and similarities in the reverse engineering throughout the years
Uso da propolis no controle in vitro da bactéria gram-positiva Staphylococcus aureus causadora de mastite em vacas leiteiras

As bacterias Gram-positivas Streptococcus agalactiae e Staphylococcus aureus sao as principais causadoras da mastite em vacas leiteiras. Estima-se que as perdas anuais causadas por essa doenca chegam a 35 bilhoes de dolares. Considerando a seguranca de alimentos, o presente trabalho objetivou determinar o potencial da propolis no combate a bacteria Staphylococcus aureus, causadora da mastite bovina, visto que, os tratamentos convencionais a base de antibioticos, se administrados de maneira incorreta e se nao forem respeitados os periodos de carencia, podem deixar residuos no leite. Para tanto, cepas da bacteria foram isoladas do leite de vacas portadoras, identificadas atraves de testes de CMT (California Mastit Test) e mantidas em agar nutriente a 37°C. A atividade antimicrobiana da propolis foi determinada atraves do desenvolvimento colonial da bacteria em meio de cultura com a referida substância nas concentracoes de 2, 4, 8 e 16 mL/L de agua destilada. O diâmetro medio da colonia bacteriana submetida a concentracao de 2 mL/L de extrato etanolico de propolis (EEP) variou de 2,19 a 3,11cm, enquanto que na concentracao de 4 mL/L o crescimento observado foi de 1,44 a 1,81. Para as concentracoes de 8 e 16 mL/L os crescimentos foram de 0,69 a 0,90 cm e 0,41 a 0,55 cm, respectivamente. A propolis possui acao inibitoria in vitro sobre a bacteria estudada causadora da mastite, sendo uma alternativa para estudos in vivo do tratamento em substituicao aos bactericidas convencionais.
An Ensemble Approach to Detect Code Comment Inconsistencies using Topic Modeling

—In modern era, the size of software is increasing, as a result a large number of software developers are assigned into software projects. To have a better understanding about source codes these developers are highly dependent on code comments. However, comments and source codes are often inconsistent in a software project because keeping comments up-to-date is often neglected. Since these comments are written in natural language and consist of context related topics from source codes, manual inspection is needed to ensure the quality of the comment associated with the corresponding code. Existing approaches consider entire texts as feature, which fail to capture dominant topics to build the bridge between comments and its corresponding code. In this paper, an effective approach has been proposed to automatically extract dominant topics as well as to identify the consistency between a code snippet and its corresponding comment. This approach is evaluated with a benchmark dataset containing 2.8K Java code-comment pairs, which showed that proposed approach has achieved better performance with respect to the several evaluation metrics than the existing state-of-the-art Support Vector Machine on vector space model.
A Combined Model for Extractive and Abstractive summarization based on Transformer model

—Summary generates by summarizing automatically main information from the critical sentences of the article. The traditional method of generating text summarization uses extractive or abstractive algorithm model built based on neural attention sequence to sequence framework. This kind of model has performance bug and weak parallel computing capability when getting summary, which causes the summary doesn’t ﬁt the meaning of the original and has no smooth sentences. Therefore, we put up with a joint summary generation model based on improving transformer. This model can put attention on sentence and provide sequence information for periodical transformer model by recurrent neural network. On the other hand, in the generation stage, Transformer model is used to learn the long distance dependence between words, and the summary statement is more consistent with the original meaning by adding pointer mechanism and consistency loss function. Experiments were carried out on three datasets and a manual evaluation was added to verify that the model has good summary signiﬁcance.
A Detect-and-Modify Region-based Classifier to Defend Evasion Attacks

Deep Neural Networks (DNNs) are powerful models that have achieved impressive results on image classiﬁcations. However, they are vulnerable to be attacked by adversarial examples, which are crafted to cause prediction errors in DNNs. In order to make the networks more robust and reliable, in this paper, we present an improved region-based classiﬁcation to mitigate the evasion attack, which is well known to attack DNNs via generating adversarial examples. Speciﬁcally, in our framework, an image is considered as a matrix of Markov Chains and we detect possible adversarial examples according to the Image Transition Probabilities (ITPs) in Markov Chains. Furthermore, we modify the original ITPs of the detected adversarial examples by using the saliency map of ITPs, and we employ our improved region-based classiﬁcation on these updated adversarial examples to get a better output prediction. Finally, our experiments illustrate that our approach reduces the test errors imposed by adversarial examples on MNIST datasets and CIFAR-10 datasets.
Conditional Normalizing Flow-based Generative Model for Zero-Shot Recognition

—Most existing studies on zero-shot recognition （ ZSR ） are typically about learning a shared embedding space to allow comparison of class prototypes by using nearest-neighbor methods, which suffer from hubness and bias problem. Recent studies attempted to directly synthesize samples of unseen classes by using generative model and have not encountered the aforementioned problems. However, their performance is limited by the inherent problems of VAE and GAN, such as reconstruction loss, mode collapse and unstable training procedure. In this paper, we explore and exploit a novel architecture of the generative model for ZSR, referred to as conditional normalizing flow-based generative model (CNFG). The proposed model consists of a cascade of affine couple transformations and can capture the low-distribution modes of real data density by virtue of its stable and exact log-likelihood maximum training procedure. Extensive experiments and result comparisons of 5 benchmarks have indicated that the normalizing flow-based model is superior to other generative models for ZSR in generalized settings.
The Worldwide Crisis in Fisheries

The world's marine fisheries are in trouble, as a direct result of overfishing and the overcapacity of fishing fleets. Despite intensive management efforts, the problems still persist in many areas, resulting in many fisheries being neither sustainable nor profitable. Using bio-economic models of commercial fisheries, this book demonstrates that new management methods, based on individual or community catch quotas, are required to resolve the overfishing problem. Uncertainty about marine systems may be another factor contributing to overfishing. Methods of decision analysis and Bayesian inference are used to discuss risk management and the precautionary principle, arguing that extensive marine reserves may be the best way to protect fisheries, alongside a controlled catch quota system. This book will be of interest to environmental scientists, economists and fisheries managers, providing novel insights into many well-known but poorly understood aspects of fisheries management.
Education Policy: Globalisation, Citizenship & Democracy [Book Review]

Publishers details for: Education Policy: Globalisation, Citizenship & Democracy, by M. Olssen, J. Codd and A. O'Neill, London, Thousand Oaks, California, and New Delhi: Sage Publications (2004), ISBN 0761974709 (pbk) 327 pp. Includes references.
Combining ability estimates in virus resistant and susceptible lines of chilli (Capsicum annuum L.).

Combining ability analysis of 8 x 8 diallel cross (including reciprocals) was carried out in chilli, involving cucumber mosaic virus (CMV) resistant and susceptible lines. The variances for general combining ability (gca) and specific combining ability (sca) were highly significant for all the characters, suggesting the importance of both additive and non-additive gene action. The sca variance played an important role in the genetic control of days to 50% flowering, days to 50% ripening , fruit width, plant height, plant spread, number of fruits per plant, green fruit yield per plant and per cent CMV incidence. On the contrary additive gene action was observed for fruit length. The genotype VR-27 was judged to be the best general combiner for fruit yield per plant and Perennial, Punjab Lal, Punjab Gucchedar and Pant C-1 proved to be good general combiners for per cent CMV incidence. The crosses Punjab Gucchedar x Pant C-1 and Tiwari x EG-174 had greater sca effect for fruit yield. These two crosses may directly be used for commercial cultivation after further testing over a range of environments. Fifteen of the twenty eight crosses had significant reciprocal effect for green fruit yield per plant. Hence, the influence of maternal effect on the economic traits is evident in the present material.
Blazing the Trail in Poverty Alleviation among University Students in Nigeria: the Federal University of Technology Yola

This study reports on the innovative ? study and work? programme for students at the Federal University of Technology, Yola. In particular, the use of student part-time workers in the Library is discussed. The study reviews the widespread and severe poverty in Nigeria and among students in particular.This study reveals that the ? study and work scheme? to alleviate poverty is beneficial to both students and university departments. The scheme extolls the dignity of labour in addition to alleviating the suffering of indigent students. The advantages of the scheme to the Library are discussed and it is recommended that the ? study and work? scheme should be incorporated into the country-wide Poverty Alleviation Progamme of the Obasanjo administration.
Research and Development of Instant Messaging Worms

With the abroad application of instant messaging and explosive growth of users in current years, IM worms have higher outbreak frequency, wider coverage and deeper hurt, which have been a serious threat to network security. In this paper, the concept and research situation of IM (instant messaging)worms, function component, execution mechanism and comparison with other worms are presented, then the network topology and propagation models are discussed, and finally the critical techniques of IM worm prevention are given. Some major problems and research trends in this area are also addressed.
Effects of Astragalus ultramicro powder on the small intestinal villus structure,intestinal contents activities of digestive enzymes and cecum microflora of three yellow broilers

225 three-yellow cocks were randomly divided into 5 groups with 3 replicates after hatching,each replicate included 15 animals. Group Ⅰ was fed with basal diet as blank control group. Group Ⅱ,Ⅲ,Ⅳ were fed basal diet supplemented with 0. 50%,0.75%,1. 00% Astragalus ultramicro powder respectively,group Ⅴ was fed basal diet supplemented with 1. 00% Astragalus ordinary powder. The experiment lasted for 70 days,on the 28 th day and 70 th day,small intestinal villus structure; duodenum contents digestive enzymes activities and cecum microflora in broilers were measured,which would provide the basis for the application of Astragalus superfine powder in three yellow chicken. The results showed that: compared with blank control group and ordinary powder group,basal diet supplemented with low and medium dose of Astragalus superfine powder increased the ratio of villus length to crypt depth,improved the activities of amylase,lipase and trypsin in duodenum,decreased the number of Escherichia coli in the cecum,increased the number of Lactobacillus in the cecum. The research results show that dietary supplemented with Astragalus ultramicro powder could improve small intestinal villus structure,the intestinal digestive enzymes activities and the composition of caecum microflora,the best additive amount of Astragalus superfine powder is 0. 75%.