Miracle-dz/UniFAR-FTUs huggingface dataset:
A Novel Machine Learning-based Sentiment Analysis Method for Chinese Social Media Considering Chinese Slang Lexicon and Emoticons

Internet slang is an informal language used in everyday online communication which quickly becomes adopted or discarded by new generations. Similarly, pictograms (emoticons/emojis) have been widely used in social media as a mean for graphical expression of emotions. People can convey delicate nuances through textual information when supported with emoticons. Furthermore, we also noticed that when people use new words and pictograms, they tend to express a kind of humorous emotion which is difficult to clearly classify as positive or negative. Therefore, it is important to fully understand the influence of Internet slang and emoticons on social media. In this paper, we propose a machine learning method considering Internet slang and emoticons for sentiment analysis of Weibo, the most popular Chinese social media platform. In the first step, we collected 448 frequent Internet slang expressions as a slang lexicon, then we converted the 109 Weibo emoticons into textual features creating Chinese emoticon lexicon. To test the capability of recognizing humorous posts, we utilized both lexicons with several machine learning approaches, k-Nearest Neighbors, Decision Tree, Random Forest, Logistic Regression, Naı̈ve Bayes and Support Vector Machine for detecting humorous expressions on Chinese social media. Our experimental results show that the proposed method can significantly improve the performance for detecting expressions which are difficult to polarize into positivenegative categories.
Vasovagal tonus index in dog with myxomatous mitral valve disease

Brüler B.C., Giannico A.T. , Dittrich G. & Sousa M.G. 2017. Vasovagal tonus index in dog with myxomatous mitral valve disease. Pesquisa Veterinária Brasileira 37(10):1181-1186. Departamento de Medicina Veterinária, Universidade Federal do Paraná, Campus Agrárias, Juvevê, Rua dos Funcionários 1540, Curitiba, PR 80035-050, Brazil. E-mail: bbruler@gmail.com The vasovagal tonus index (VVTI) is a useful and assessable index, obtained from standard ECG recordings, that is used to estimate heart rate variability (HRV), and may provide valuable information regarding the likelihood of progression into congestive heart failure (CHF). In this paperwork, we investigated how the vasovagal tonus index (VVTI) behaves in dogs with naturally-occurring myxomatous mitral valve disease (MMVD) Electrocardiographic (ECG) recordings and echocardiographic data of 120 patients diagnosed with MMVD were reviewed. The VVTI was calculated from twenty consecutive RR intervals for each dog enrolled in the study. Lower VVTI values were found in MMVD patients in American College of Veterinary Internal Medicine (ACVIM) stage C compared with stages B1 and B2. Values were also lower in patients with severe cardiac remodeling. When a cut-off value of 6.66 is used, VVTI was able to discriminate MMVD patients in stage C from B1 and B2 dogs with a sensitivity of 70 per cent and a specificity of 77 per cent. MMVD dogs in which VVTI is lower than 6.66 are 30% more likely to develop congestive heart failure (CHF).
Unobtrusive and Automatic Classification of Multiple People’s Abnormal Respiratory Patterns in Real Time Using Deep Neural Network and Depth Camera

Respiratory pattern is a representation of human breathing activity, which can reflect people’s physical and psychological condition. Capturing the unexpected abnormal respiratory pattern unobtrusively of the patient or the potential patient has great significance. In the current work, we attempt to capitalize on depth camera and deep learning architecture to achieve the accurate and unobtrusive measurement of abnormal respiratory patterns, and the whole system can classify multiple people’s respiratory patterns in a real-time manner. The challenges in this task are threefold: 1) the real-time online system means that the Region of Interest (ROI) needs to be located and tracked automatically; 2) the amount of real-world data is not enough for training to obtain the robust deep neural network; and 3) the intraclass variation is large and the outer class variation is small. Consequently, human joints tracking is applied to determine the location of subjects shoulder and chest. Based on the characteristics of actual respiratory signals, a novel and efficient respiratory simulation model (RSM) is proposed to generate abundant and high-quality training data. Finally, we apply a gated recurrent unit (GRU) neural network with bidirectional and attentional mechanisms (BI-AT-GRU) to classify six clinically significant respiratory patterns (Eupnea, Tachypnea, Bradypnea, Biots, Cheyne–Stokes, and Central-Apnea). The performance of the obtained BI-AT-GRU is tested by the data that is actually measured by the depth camera. The experimental results demonstrate that the proposed model can classify six different respiratory patterns with the accuracy, precision, recall, and F1 of 94.5%, 94.4%, 95.1%, and 94.8%, respectively. In comparative experiments, the obtained BI-AT-GRU specific to respiratory pattern classification outperforms the existing state-of-the-art, viz., BI-AT-LSTM, GRU, long short-term memory (LSTM), and BI-AT-GRU. Moreover, other experimental results indicate that the proposed online measuring system, deep neural network, and the modeling ideas have the potential to be extended to the large-scale applications, such as public places, sleep scenario, and office environment. The demo videos of the proposed system are available at: https://doi.org/10.6084/m9.figshare. 11493666.v1.
Split-and-Merge: Detecting Unknown Botnets

On September 20th, 2016, Mirai struck the Internet in a massive surprise attack. This is just but one example of unknown botnets, not detected nor mitigated at the time they happened. Current anomaly detection techniques can only detect them after they have spread. However such attacks are generally preceded by several stages, including infection of hosts or fingerprinting of devices. Being able to capture this activity would allow their early detection. In this paper, we propose a strategy aimed at the early detection of unknown botnets, which acts by (i) splitting and merging distributed monitoring data and related metrics, (ii) monitoring at the port-level, with a simple yet efficient change-detection algorithm based on a modified Z-score measure. The analysis of port usage is first split over different parts of the network, and then merged to retain only similar anomalies. This ensures detection of large-scale attacks and drastically reduces false positives. We validate the approach on the MAWI data set, which provides daily traces of a transpacific backbone link. We demonstrate that the solution generates a very low number of false positives. We show how it detects some main attacks (including Mirai) arisen the last three years that traditional anomaly detectors have not seen. Details about noticed anomalies are provided to help the administrator qualifying them through specific features.
Red and Blue Voices: Effects of Political Ideology on Consumers’ Complaining and Disputing Behavior

Political ideology plays a pivotal role in shaping individuals’ attitudes, opinions, and behaviors. However, apart from a handful of studies, little is known about how consumers’ political ideology affects their marketplace behavior. The authors used three large consumer complaint databases from the Consumer Financial Protection Bureau, National Highway Traffic Safety Administration, and Federal Communication Committee in conjunction with a countylevel indicator of political ideology (the 2012 US presidential election results) to demonstrate that conservative consumers are not only less likely than liberal consumers to report complaints but also less likely to dispute complaint resolutions. A survey also sheds light on the relationship between political ideology and complaint/dispute behavior. Due to stronger motivations to engage in “system justification,” conservative (as opposed to liberal) consumers are less likely to complain or dispute. The present research offers a useful means of identifying those consumers most and least likely to complain and dispute, given that political ideology is more observable than most psychological factors and more stable than most situational factors. Furthermore, this research and its theoretical framework open opportunities for future research examining the influence of political ideology on other marketplace behaviors.
Activities of Enzymes for Sucrose-Starch Conversion in Developing Endosperm of Rice and Their Association with Grain Filling in Extra-Heavy Panicle Types

Abstract Rice cultivars with numerous spikelets per panicle (extra-heavy panicle types) frequently fail to exhibit their high yield potential due to low grain filling. Existing genetic variation in grain filling, however, opens possibilities for genetic improvement for this trait. We studied the correlation between grain filling and the activities of enzymes for sucrose-starch conversion in developing endosperm. The activity of sucrose synthase (EC 2.4.1.13, SuSy) and ADPglucose pyrophosphorylase (EC 2.7.7.27, AGPase), were measured in three extra-heavy panicle types and a standard cultivar grown at two locations under different environmental conditions. The proportions of grains with definite specific gravities and the rate of grain filling were adopted as the parameters related to grain filling. AGPase activity, but not that of SuSy, was consistently correlated to high proportions of high-density grains (specific gravity > 1.20) and high rates of grain growth in spikelets, particularly in those on secondary branches in which low grain filling is the rule. Such correlation was also detected in spikelets on primary branches which generally show better grain filling, but only early stages. Therefore, a high activity of AGPase might contribute to the reduction of the sucrose concentration by accelerating sucrose metabolism at the developing seed, a sink terminus of the phloem. Thus the sink-directed phloem transport of sucrose would be promoted, resulting in improved grain filling of extra-heavy panicle types. SuSy would play some roles in such a cultivar difference in grain filling, but depending on environments.
Effect of Enteral Administration of Insulin on Feeding Tolerance of Preterm Infants

DOI: 10.21276/sjams.2018. 6.5. 69 Abstract: The incidence of prematurity has risen drastically over the last twenty-five years. Premature deliveries are the leading cause of neonatal morbidity and mortality in the United States. One of the primary setbacks to establishing full enteral feeds in preterm infants is feeding intolerance. Certain bioactive substances and live cells in milk appear to influence neonatal gut maturation and growth through their transfer of developmental information to the newborn. The aim of the present study was to evaluate the effect of enteral administration of insulin in preterm infants born less than 32 weeks gestational age and weighing less than 1500 grams to determine whether enteral insulin supplement to the basic preterm formula enhances feeding tolerance or not. Also, to detect weight gain in infants after receiving enteral insulin compared to those not receiving enteral insulin. 50 of the preterm infants fulfilling the forementioned criteria received 1 U/kg regular human insulin every six hours and other 50 of the preterm infant fulfilling the mentioned criteria were fed preterm milk formula without receiving regular human insulin and criteria of feeding intolerance will be monitored the Predictive Analytics Software (PASW Statistics version 18). , we found that feeding intolerance attacks are less frequent with preterm babies who received enteral insulin. Feeding intolerance is frequently encountered in the neonatal intensive care unit and the use of enteral insulin is a promising therapy that enhance GI development and reduce feed intolerance in preterm infants.
Utility of Driving Pressure and Mechanical Power to Guide Protective Ventilator Settings in Two Cohorts of Adult and Pediatric Patients With Acute Respiratory Distress Syndrome: A Computational Investigation

Supplemental Digital Content is available in the text. Objectives: Mechanical power and driving pressure have been proposed as indicators, and possibly drivers, of ventilator-induced lung injury. We tested the utility of these different measures as targets to derive maximally protective ventilator settings. Design: A high-fidelity computational simulator was matched to individual patient data and used to identify strategies that minimize driving pressure, mechanical power, and a modified mechanical power that removes the direct linear, positive dependence between mechanical power and positive end-expiratory pressure. Setting: Interdisciplinary Collaboration in Systems Medicine Research Network. Subjects: Data were collected from a prospective observational cohort of pediatric acute respiratory distress syndrome from the Children’s Hospital of Philadelphia (n = 77) and from the low tidal volume arm of the Acute Respiratory Distress Syndrome Network tidal volume trial (n = 100). Interventions: Global optimization algorithms evaluated more than 26.7 million changes to ventilator settings (approximately 150,000 per patient) to identify strategies that minimize driving pressure, mechanical power, or modified mechanical power. Measurements and Main Results: Large average reductions in driving pressure (pediatric: 23%, adult: 23%), mechanical power (pediatric: 44%, adult: 66%), and modified mechanical power (pediatric: 61%, adult: 67%) were achievable in both cohorts when oxygenation and ventilation were allowed to vary within prespecified ranges. Reductions in driving pressure (pediatric: 12%, adult: 2%), mechanical power (pediatric: 24%, adult: 46%), and modified mechanical power (pediatric: 44%, adult: 46%) were achievable even when no deterioration in gas exchange was allowed. Minimization of mechanical power and modified mechanical power was achieved by increasing tidal volume and decreasing respiratory rate. In the pediatric cohort, minimum driving pressure was achieved by reducing tidal volume and increasing respiratory rate and positive end-expiratory pressure. The Acute Respiratory Distress Syndrome Network dataset had limited scope for further reducing tidal volume, but driving pressure was still significantly reduced by increasing positive end-expiratory pressure. Conclusions: Our analysis identified different strategies that minimized driving pressure or mechanical power consistently across pediatric and adult datasets. Minimizing standard and alternative formulations of mechanical power led to significant increases in tidal volume. Targeting driving pressure for minimization resulted in ventilator settings that also reduced mechanical power and modified mechanical power, but not vice versa.
Social Media Response Analysis System for Air India

- Since the globalisation of World Wide Web and internet, Opinion mining and Sentiment analysis have proved to be an valuable and significant field of study. Sentiment analysis or opinion mining is the computational study of people’s opinions, sentiments, attitudes, and emotions expressed in written language. It is one of the most popular active research areas in natural language processing and text mining in recent years. Its popularity is mainly due to two reasons. First, it has a wide range of applications because opinions are central to almost all human activities and are key influencers of our behaviors. Whenever we need to make a decision, we want to hear others’ opinions. Second, it presents many challenging research problems, which have never been tried before the year 2000. Opinion is termed as the process of extraction of those lines or phrase in a vast amount of data which express an opinion. We propose the sentiment analysis of the opinions by performing opinion extraction, summarization, and tracking the response of customers for Airline industry. In this paper, we propose to use an algorithm to retrieve the collaborated opinion of the passengers. The opinion which is generated would be represented as very high, moderate, low and very low in terms of their like or dislike of the service provided. The paper presents a case study where people provide their feedback about the service given by Airline industry and the recommended algorithm for sentiment analysis is applied for extraction of opinion and further representation of it in a graphical format.
Acoustic and Linguistic Properties of Turkish Whistle Language

This paper presents the acoustic and linguistic properties of Turkish Whistle Language. Whistle Language is a natural communication method usually used for far-distance interaction in some regions in the world. In a whistled speech, auditory features of spoken languages are transposed. Therefore, whistle languages carry some properties of vocal speech with its own vocabulary, grammar, phonology and prosodic features. There are a few places in the world using this whistled communication style and Kuskoy region in Turkey is one of them. Although there are some researches on Turkish Whistle Language, unfortunately there have been a limited number of scientific publications in the literature. On the other hand, the research results present very stunning results such as people can still continue articulating some words while whistling and there is a high understandability rate while communicating. Therefore, it is described as an incomplete form of Turkish Language. The research results also indicate that Turkish Whistle Language is a non-tonal language transposing formants and therefore it can be used to evaluate the formant changes in the transform of a language. These research results indicate many other valuable properties of Turkish whistle language. But, unfortunately there has not been a collective study combining all these properties. Besides, there are still unclear and conflicted points in the literature as can be implied in this paper. This study aims to bring together the research results to underline the distinct features of Turkish Whistle Language and to motivate researchers to go forward on this subject. The paper is mainly focused on acoustic and phonetic properties of Turkish Whistle Language. Historical or cultural based details are out of the scope of this study.
Robust Joint Representation of Intrinsic Mean and Kernel Function of Lie Group for Remote Sensing Scene Classification

Remote sensing scene classification is used to label specific semantic categories for images. The current methods have achieved competitive performances, but they are only for Euclidean space samples. As a result, their representations are not robust for non-Euclidean space samples, which affects the classification accuracy. In this letter, we introduce the Lie group manifold into the traditional feature representation method and propose a novel intrinsic mean representation method within the Lie group. At the same time, the kernel function based on the sample of the Lie group is designed to further improve the robustness and accuracy of classification. In addition, our method achieves satisfactory performance on two public and challenging remote sensing data sets of UC Merced and NWPU-RESISC45.
Haptics and its Effect on Consumers' Intentions Using Neuroscientific Methods: Literature Review

The haptic properties of a product have mostly been underestimated with most studies focusing on visual aspects of objects. Nonetheless, in the last years, it has been found that tactile stimuli are in some cases even more important than the visual ones. However, the traditional paper-based surveys cannot fully and objectively examine their effects on consumers. Therefore, neuroscientific methods, which overcome these obstacles, are becoming more used but there is still only a small number of studies focusing on the effect of haptics in marketing. Using the keywords haptics, tactile input, EEG, fMRI and tactile, seven relevant studies have been found and used in this literature review, out of which four have used EEG and three fMRI. Thus, the main objective of this paper is to review the research that has been already conducted and to identify the areas in which further research should be made and the neuroscientific methods which could be used.
PARK 2 enhancement is able to compensate mitophagy alterations found in sporadic Alzheimer ’ s disease

Mitochondrial anomalies have been previously reported in patients′ brain and peripheral tissue, suggesting their relevance in sporadic Alzheimer’s disease (AD). The present work evaluates mitochondrial function and recycling in human fibroblasts and brain biopsies. Functional studies using patients′ skin fibroblasts showed slower mitochondrial membrane potential recovery after amitochondrial insult togetherwith alterations in lysosomes and autophagy, accompanied by an increase of oxidized and ubiquitinated proteins. Impairment in mitophagy has been proven in these cells due to diminished PARK2 and insufficient vesicle induction, accumulating depolarized mitochondria and PINK1. Augmented Δ1 PINK1 fragment levels suggest an inhibitory effect over PARK2 translocation to the mitochondria, causing the accumulation of activated PINK1. Moreover, the overexpression of PARK2 diminished ubiquitinated proteins accumulation, improves its targeting to mitochondria and potentiates autophagic vesicle synthesis. This allows the reversion of mitophagy failure reflected in the recovery of membrane potential and the decrease of PINK1 and mitochondria accumulation. Sporadic AD fibroblasts exhibited alterations similar to what it could be found in patients’ hippocampal samples at early stages of the disease, where there was an accumulation of PINK1 and Δ1 PINK1 together with abnormally increased mitochondrial content. Our findings indicate that mitophagy alterations can be considered a new hallmark of sporadic AD and validate the use of fibroblasts for modelling this pathology.
Efficient Identification of Corn Cultivation Area with Multitemporal Synthetic Aperture Radar and Optical Images in the Google Earth Engine Cloud Platform

The distribution of corn cultivation areas is crucial for ensuring food security, eradicating hunger, adjusting crop structures, and managing water resources. The emergence of high-resolution images, such as Sentinel-1 and Sentinel-2, enables the identification of corn at the field scale, and these images can be applied on a large scale with the support of cloud computing technology. Hebei Province is the major production area of corn in China, and faces serious groundwater overexploitation due to irrigation. Corn was mapped using multitemporal synthetic aperture radar (SAR) and optical images in the Google Earth Engine (GEE) cloud platform. A total of 1712 scenes of Sentinel-2 data and 206 scenes of Sentinel-1 data acquired from June to October 2017 were processed to composite image metrics as input to a random forest (RF) classifier. To avoid speckle noise in the classification results, the pixel-based classification result was integrated with the object segmentation boundary completed in eCognition software to generate an object-based corn map according to crop intensity. The results indicated that the approach using multitemporal SAR and optical images in the GEE cloud platform is reliable for corn mapping. The corn map had a high F1-Score of 90.08% and overall accuracy of 89.89% according to the test dataset, which was not involved in model training. The corn area estimated from optical and SAR images was well correlated with the census data, with an R2 = 0.91 and a root mean square error (RMSE) of 470.90 km2. The results of the corn map are expected to provide detailed information for optimizing crop structure and water management, which are critical issues in this region.