Miracle-dz/UniFAR-FTUs huggingface dataset:
Recent Progress about Flight Delay under Complex Network

Flight delay is one of the most challenging threats to operation of air transportation network system. Complex network was introduced into research studies on flight delays due to its low complexity, high flexibility in model building, and accurate explanation about real world. We surveyed recent progress about flight delay which makes extensive use of complex network theory in this paper. We scanned analyses on static network and temporal evolution, together with identification about topologically important nodes/edges. And, we made a clarification about relations among robustness, vulnerability, and resilience in air transportation networks. Then, we investigated studies on causal relations, propagation modellings, and best spreaders identifications in flight delay. Ultimately, future improvements are summarized in fourfold. (1) Under Complex Network, flight operation relevant subsystems or sublayers are discarded by the majority of available network models. Hierarchical modelling approaches may be able to improve this and provide more capable network models for flight delay. (2) Traffic information is the key to narrow the gap between topology and functionality in current situations. Flight schedule and flight plan could be employed to detect flight delay causalities and model flight delay propagations more accurately. Real flight data may be utilized to validate and revise the detection and prediction models. (3) It is of great importance to explore how to predict flight delay propagations and identify best spreaders at a low cost of calculation complexity. This may be achieved by analyzing flight delay in frequency domain instead of time domain. (4) Summation of most critical nodes/edges may not be the most crucial group to network resilience or flight delay propagations. Effective algorithm for most influential sequence is to be developed.
Research on Risk Identification System Based on Random Forest Algorithm-High-Order Moment Model

With the continuous development of the stock market, designing a reasonable risk identification tool will help to solve the irrational problem of investors. This paper first selects the stocks with the most valuable investment value in the future through the random forest algorithm in the nine-factor model and then analyzes them by using the higher-order moment model to find that different investors’ preferences will make the weight of the portfolio change accordingly, which will eventually make the optimal return and risk set of the composition of the portfolio change. The risk identification system designed in this paper can provide an effective risk identification tool for investors and help them make rational judgments.
An Approach of Community Search with Minimum Spanning Tree Based on Node Embedding

Community search is a query-oriented variant of community detection problem, and the goal is to retrieve a single community from a given set of nodes. Most of the existing community search methods adopt handcrafted features, so there are some limitations in applications. Our idea is motivated by the recent advances of node embedding. Node embedding uses deep learning method to obtain feature representation of nodes directly from graph structure automatically and offers a new method to measure the distance between two nodes. In this paper, we propose a two-stage community search algorithm with a minimum spanning tree strategy based on node embedding. At the first stage, we propose a node embedding model NEBRW and map nodes to the points in a low-dimensional vector space. At the second stage, we propose a new definition of community from the distance viewpoint, transform the problem of community search to a variant of minimum spanning tree problem, and uncover the target community with an improved Prim algorithm. We test our algorithm on both synthetic and real-world network datasets. The experimental results show that our algorithm is more effective for community search than baselines.
A Hybrid Model for Drug-Drug Interaction Extraction from Structured Product Labeling Documents

Drug-Drug Interactions (DDI), which lead to adverse events, have been identiﬁed as the eighth leading cause of death in the United States (Goldstein et al., 2005). Structured Product Labeling (SPL) documents are a rich source of information for drug labels, but it is essential to convert them to discrete, coded information in order to enable automatic extraction of drug interactions. TAC 2019 DDI track deﬁned multiple Natural Language Processing (NLP) tasks, namely concept extraction of Pre-cipitant and SpeciﬁcInteraction (Task 1), relation extraction of interactions and their types (Task 2), concept normalization (Task 3) and generation of a global list of interactions per SPL document (Task 4). We participated in Tasks 1 and 2, where we used a combination of a novel tagging scheme, a ﬁne-tuned transformer-based language model, and a syntactic-parse based knowledge-injected pattern matching technique. We submitted three systems for both Tasks 1 and 2. We achieved an F1-score of 65.38, 64.62 and 65.18 for Task 1, and an F1-score of 49.03, 48.33 and 48.39 for Task 2. Our system ranked ﬁrst, with the highest F1-score for both Tasks 1 and 2 , thus demonstrating an effective adaptation of our hybrid system on the DDI extraction tasks.
Information Transfer and Multifractal Analysis of EEG in Mild Blast-Induced TBI

Mild, blast-induced traumatic brain injury (mbTBI) is a common combat brain injury characterized by typically normal neuroimaging findings, with unpredictable future cognitive recovery. Traditional methods of electroencephalography (EEG) analysis (e.g., spectral analysis) have not been successful in detecting the degree of cognitive and functional impairment in mbTBI. We therefore collected resting state EEG (5 minutes, 64 leads) from twelve patients with a history of mbTBI, along with repeat neuropsychological testing (D-KEFS Tower test) to compare two new methods for analyzing EEG (multifractal detrended fluctuation analysis (MF-DFA) and information transfer modeling (ITM)) with spectral analysis. For MF-DFA, we extracted relevant parameters from the resultant multifractal spectrum from all leads and compared with traditional power by frequency band for spectral analysis. For ITM, because the number of parameters from each lead far exceeded the number of subjects, we utilized a reduced set of 10 leads which were compared with spectral analysis. We utilized separate 30 second EEG segments for training and testing statistical models based upon regression tree analysis. ITM and MF-DFA models both generally had improved accuracy at correlating with relevant measures of cognitive performance as compared to spectral analytic models ITM and MF-DFA both merit additional research as analytic tools for EEG and cognition in TBI.
Optimal Length of Heart Rate Variability Data and Forecasting Time for Ventricular Fibrillation Prediction Using Machine Learning

Ventricular fibrillation (VF) is a cardiovascular disease that is one of the major causes of mortality worldwide, according to the World Health Organization. Heart rate variability (HRV) is a biomarker that is used for detecting and predicting life-threatening arrhythmias. Predicting the occurrence of VF in advance is important for saving patients from sudden death. We extracted features from seven HRV data lengths to predict the onset of VF before nine different forecast times and observed the prediction accuracies. By using only five features, an artificial neural network classifier was trained and validated based on 10-fold cross-validation. Maximum prediction accuracies of 88.18% and 88.64% were observed at HRV data lengths of 10 and 20 s, respectively, at a forecast time of 0 s. The worst prediction accuracy was recorded at an HRV data length of 70 s and a forecast time of 80 s. Our results showed that features extracted from HRV signals near the VF onset could yield relatively high VF prediction accuracies.
Culture under Complex Perspective: A Classification for Traditional Chinese Cultural Elements Based on NLP and Complex Networks

The cultural element is the minimum unit of a cultural system. The systematic categorizing, organizing, and retrieval of the traditional Chinese cultural elements are essential prerequisites for the realization of effective extracting and rational utilization, as well as the prerequisite for exploiting the contemporary value of the traditional Chinese culture. To build an objective, integrated, and reliable classification method and a system of traditional Chinese cultural elements, this study takes the text of Taiping Imperial Encyclopedia in Northern Song Dynasty as the primary data source. The unsupervised word segmentation methods are used to detect Out-of-Vocabulary (OOV), and then the segmentation results by the THULAC tool with and without custom dictionary are compared. The TF-IDF algorithm is applied to extract the keywords of cultural elements and the Ochiia coefficient is introduced to create complex networks of traditional Chinese cultural elements. After analyzing the topological characteristics of the network, the community detection algorithm is used to identify the topics of cultural elements. Finally, a “Means-Ends” two-dimensional orthogonal classification system is established to categorize the topics. The results showed that the degree distribution in the complex network of Chinese traditional cultural elements is a scale-free network with γ = 2.28. The network shows a structure of community and hierarchy features. The top 12 communities have taken up to 91.77% of the scale of the networks. Those 12 topics of the traditional Chinese cultural elements are circularly distributed in the orthogonal system of cultural elements’ categorization.
Diffbot's Fine-Grained Entity Typing System for TAC KBP 2019

In this report, we give a detailed description of Diffbot’s system for the TAC-KBP 2019 Fine-Grained Entity Typing track. Fine-grained entity typing is a task to extract mentions of pre-deﬁned types and assign ﬁne-grained types to those mentions. Instead of modeling the task as a multi-label classiﬁcation problem
CARACTERIZAÇÃO QUÍMICO BROMATOLÓGICA DA BIOMASSA FÚNGICA DO RESÍDUO AGROINDUSTRIAL DO MARACUJÁ

Objetivou-se identificar a composicao quimico bromatologica do coproduto proveniente do processamento do maracuja fermentado atraves do uso do fungo Aspergillus niger para uso na alimentacao de ruminantes. Foi realizada a fermentacao em estado solido do coproduto in natura de maracuja com a inoculacao do A. niger. A biomassa apos fermentacao foi submetida ao processo de pre-secagem com posterior trituracao em moinho e passagem em peneira, seguindo para analise do teor de materia seca, materia orgânica, proteina bruta, extrato etereo, fibra em detergente neutro, fibra em detergente acido, materia mineral e lignina. O teor de materia seca, materia orgânica, proteina bruta, extrato etereo, fibra em detergente acido, fibra em detergente neutro, lignina e materia mineral foi de 62,71%; 95,35%; 11,80%; 4,05%; 54,33%; 38,82%; 12,21%; 4,65%; respectivamente. A composicao quimico bromatologica da biomassa fungica do coproduto do maracuja, fermentada com Aspergillus niger apresenta consideravel qualidade nutritiva, alem disso, o teor proteico apresentado atende as exigencias minimas dos ruminantes e apresenta potencial para uso na alimentacao de ruminantes.
Quantitative evaluation of antibacterial activities of nanoparticles (ZnO, TiO2, ZnO/TiO2, SnO2, CuO, ZrO2, and AgNO3) incorporated into polyvinyl butyral nanofibers

Novel hybrid polyvinyl butyral nanofibers have been developed for antimicrobial applications. The nanofiber mats were obtained from a needleless rod electrospinning system. The novel inorganic antibacterial agents were incorporated into the nanofibers, and their antibacterial activity was compared. The obtained nanoparticle/nanofiber hybrid mats have a good surface morphology. The results indicated that the CuO, ZnO, ZnO/TiO2, and AgNO3 nanoparticle-incorporated nanofiber layers have excellent antibacterial activity against to Escherichia coli compared with TiO2, SnO2, and ZrO2 ones. Copyright © 2016 John Wiley & Sons, Ltd.
A green chemistry approach for synthesizing thermostable antimicrobial peptide-coated gold nanoparticles immobilized in an alginate biohydrogel

Developing rapid, green, and cost effective approaches for synthesizing metal nanoparticles with biochemical and biomedical applications is currently a top priority. Here, we report a rapid synthesis of gold nanoparticles (AuNPs) in an alginate polymer using thermostable antimicrobial peptides. At a high temperature, these antimicrobial peptides acted as reducing agents and converted Au3+ to Au0 in 15 min while retaining their antimicrobial activity. The alginate polymer acted as a stabilizing agent for this reaction. The resulting Au–peptide–alginate biohydrogel was characterized by UV-visible (UV-Vis) spectroscopy, X-ray diffraction (XRD), field emission scanning electron microscopy (FE-SEM), energy dispersive X-ray spectroscopy (EDS), X-ray photoelectron spectroscopy (XPS), and transmission electron microscopy (TEM). TEM analysis showed AuNPs of less than 25 nm in diameter, with peptides present on their surfaces. The Au–peptide–alginate biohydrogel showed effective catalytic activity in reducing 4-nitrophenol and hexacyanoferrate(III) in the presence of sodium borohydride with durable reusability. The Au–peptide–alginate biohydrogel was also demonstrated to have antimicrobial activity against pathogenic bacteria. The method presented here for the synthesis of a metal–polymer conjugate is eco-friendly and robust. Large-scale production of the Au–alginate polymer would be possible in a very short time using this method, which makes it potentially useful for biomedical and industrial applications.
ANIMAL GROWTH STIMULATION BY ENZYMATIC PROTEIN HYDROLYSATE OF CHICKEN RESIDUES

Frozen chicken process normally generates chicken residues as a by-product. The chicken residues contain high proteins of approximately 73%. To add a higher value to the residues, they could be hydrolysed by a protease to become peptides with functionality. The aim of this study was to produce protein hydrolysates from chicken residues to be applied in the animal feed industry as a feed ingredient for animal growth stimulatory purpose. The residues were hydrolysed by five commercial proteases: Alcalase, Neutrase, papain, pepsin and trypsin. During the hydrolysis, papain, pepsin and trypsin provided high degrees of hydrolysis which was found the highest at the end of hydrolysis, while Alcalase and Neutrase digested the residues poorly. However, cysteamine, a chemical responsible for animal growth promotion, was found at a maximum of 31 mg/ml in Alcalase hydrolysate at 6 h and Neutrase hydrolysate at 10 h. For animal growth experiments, chicken feeds supplemented with 6 h-Alcalase hydrolysate at cysteamine concentrations of 90 and 180 mg/kg diet gave significantly higher chicken growth than a controlled diet which was no hydrolysate added. Thus, chicken residue hydrolysate could have a potential use as a growth stimulant in animal feeds.
'This Is Not What It’s Supposed to Be Like’: Avoiding Unwelcome Identifications Associated With Public Breastfeeding

From a discursive perspective, identities fall to be understood not as inherent properties of individuals but rather as matters that are negotiated within and emerge from social interactions with others. Adopting this perspective, we examine how mothers who breastfeed their infants in public negotiate issues of identity. The activity of public breastfeeding presents problems for identity in that it is often seen by others, and sometimes by breastfeeding mothers themselves, as socially inappropriate in that by engaging in public breastfeeding women are partly exposing their bodies. The aim of this study was to investigate how mothers who breastfeed their infants in public seek to address identity problems that can arise from engaging in this activity. We examine discussions from a focus group conducted with five members of a drop-in support group for breastfeeding mothers. Discourse analysis of group discussions shows that group members provide descriptions of difficulties that they have experienced when breastfeeding in public, and partly exposing their bodies to co-present others. These descriptions, however, rely on detail that allows the participants or other group members to undermine them and to ward off the potentially negative identities with which they are associated. The descriptions, then, are designed to attend to social concerns surrounding public breastfeeding and thereby to allow participants to construct identities that are not associated with problems.
Contents of α-O-4 and β-O-4 Bonds in Native Lignin and Isolated Lignin Preparations

An analytical calculation method for the estimation of the contents of alkyl aryl ether bonds (α-O-4 and β-O-4) in lignin was developed. In the framework of the method, Alkyl–O–Aryl type bonds are described as coupled phenolic hydroxyls (OHphen). The method is based on the balance equation including the free and coupled OHphen contents in dissolved and residual lignins, on the one hand, and their respective contents in native lignin, on the other. The free OHphen content is calculated on the basis of the OHphen contents of dissolved and residual lignin, determined by the aminolysis method in the course of kraft cooking of softwood. The calculation results for soluble lignin preparations are in good agreement with the 13C NMR (nuclear magnetic resonance) spectral data for the solutions. The content of Alkyl–O–Aryl bonds in native softwood (pine, spruce) lignin was estimated at 79/100 PPU (phenylpropane unit). In isolated lignin preparations, the contents of these bonds decrease in the sequence: Freudenberg lignin (71/100 PPU)> Bjorkman lignin (61/100 PPU)> Pepper lignin (44/100 PPU). Dissolved alkaline lignin still contains small amounts of Alkyl–O–Aryl bonds (36/100 PPU in soda lignin and an average of 23/100 PPU in soda-AQ lignin, kraft lignin, and kraft-AQ lignin). Residual lignin which represents the fraction of native lignin with inter-unit bonds resistant to kraft pulping contains 66/100 PPU of such bonds. A relatively high content of Alkyl–O–Aryl bonds (61/100 PPU) is preserved in technical hydrolysis lignins.
THE IMPACT OF IMPRESSION MANAGEMENT BEHAVIOR ON ORGANIZATIONAL POLITICS AMONG MALE AND FEMALE EMPLOYEES IN ORGANIC AND MECHANISTIC ORGANIZATIONAL SYSTEMS OF PAKISTAN TELECOMMUNICATION SECTOR

The purpose of this research is to focus on the impact of impression management on level of organizational politics. The level of organizational politics and impression management changes with employees’ gender and the organizational system. A sample of 300 employees of Telecommunication Sector of Pakistan (150 each from both organic and mechanistic organizations) was taken; primary data through questionnaire of 29 items was scrutinized to know impact of impression management on level of organizational politics. By using regression and correlation analysis, it was found out that in mechanistic system, employees both male and female are more inclined towards impression management to manage their political behavior then in organic organizations. Results of our research can further help employers and managers to reduce negative aspect of organizational politics.
Self-Organizing Data Layouts for Databricks Delta

Storing large amounts of data has become increasingly accessible over the years and Big Data systems grew popular for easy storage and processing. However, compared to warehousing, in Big Data, information is often diverse and stored without a precise goal. This leads to more varied and complex query workloads, in order to extract meaningful information. To make analytical queries on such large amounts of data possible, it is important to avoid operating on big volumes. Data skipping is one technique that uses file metadata, like minimum and maximum values, in order to prune the data that needs to be read. However, efficient data skipping relies on underlying data being clustered. Due to the varied nature of Big Data, upfront storage optimization is difficult. With this project we automate the data layout optimization, based on patterns extracted from the workload. We implement our solution for Databricks Delta, which uses an in-house version of Apache Spark. We first explore a physical design mechanism that improves data skipping, based on workload analysis. Then, we implement an automatic optimizer that periodically reorganizes the data according to the characteristics of the workload. The main challenges lie in extracting the relevant features from the workload and implementing this type of optimizer in a distributed Big Data system where monitoring and data access capabilities are restricted by design. Lastly, we build an evaluation framework that outputs representative results over real data and workloads. We consider the optimizer successful if it correctly detects when data re-organizing is beneficial and improves query processing to a degree that outweighs the associated costs. We obtained up to 3.2x speedup compared with no data optimization.
MelBERT: Metaphor Detection via Contextualized Late Interaction using Metaphorical Identification Theories

Automated metaphor detection is a challenging task to identify the metaphorical expression of words in a sentence. To tackle this problem, we adopt pre-trained contextualized models, e.g., BERT and RoBERTa. To this end, we propose a novel metaphor detection model, namely metaphor-aware late interaction over BERT (MelBERT). Our model not only leverages contextualized word representation but also benefits from linguistic metaphor identification theories to detect whether the target word is metaphorical. Our empirical results demonstrate that MelBERT outperforms several strong baselines on four benchmark datasets, i.e., VUA-18, VUA-20, MOH-X, and TroFi.
The Role of Context Definition in Choice Experiments: a Methodological Proposal Based on Customized Scenarios

One of the most critical points for the validity of Discrete Choice Experiments lies in their capability to render the experiment as close to actual market conditions as possible. In particular, when dealing with products characterized by a large number of attributes, the construction of the experiment poses the issue of how to express the choice question providing sufficient information. Our study verifies the role of scenario definition in choice experiments and proposes a methodology to build customized scenarios by eliciting responses from interviewees on the main choice criteria, which makes it possible to render the conditions of the experiment more realistic. This methodology is applied to the case study of wine and is introduced by a systematic review of the Discrete Choice Experiments conducted on wine. The findings show that customized scenarios result in different preference estimates compared to the conventional approach. In particular, we found a significant decline in the importance of the price attribute, which could be attributed to a better definition of the product being evaluated. Moreover, the methodology is capable of gathering information on the decision-making process that would otherwise remain unobserved and that can be used for a better segmentation analysis.