Miracle-dz/UniFAR-FTUs huggingface dataset:
Real-time Classification, Geolocation and Interactive Visualization of COVID-19 Information Shared on Social Media to Better Understand Global Developments

As people communicate on social media during COVID-19, it can be an invaluable source of useful and up-to-date information. However, the large volume and noise-to-signal ratio of social media can make this impractical. We present a prototype dashboard for the real-time classification, geolocation and interactive visualization of COVID-19 tweets that addresses these issues. We also describe a novel L2 classification layer that outperforms linear layers on a dataset of respiratory virus tweets.
Sero-detection of Foot and Mouth Disease Virus Serotypes A and O in One-humped Camels (Camelus dromedarius) in the Middle of Iraq

Foot and mouth disease (FMD) is a severe trans-boundary animal disease caused by a foot and mouth disease virus (FMDV) that spreads through Asia and Arabic countries. The current study aimed to investigate the FMDV antibodies in Iraqi dromedary’s camels (Camelus dromedaries). A total of 520 serum samples were collected from clinically healthy camels (265 females and 255males) from different areas in the three Provinces of Iraq (Al-Najaf, Al-Muthanna, and Al-Qadisiyah) from February to July 2019 and divided into three groups based on the age of the camels. All sera samples were screened for antibodies against the non-structural protein (NSP) of FMDV using ELISA and further the NSP positive serum samples were screened for antibodies against structural proteins of FMDV serotype O A and C by liquid phase blocking ELISA (LPB-ELISA). The Result indicated that 10% of the sera were positive for NSP antibodies and FMDV serotype A found to be predominant. It was also observed that NSP positive was more in samples collected from female camels (11.30%) than male camels (8.60%), NSP FMDV antibodies were detected in camels of all ages. In summary, our study showed, for the first time in Iraq, that camels are more susceptible to the A and O FMD serotypes.
Experimental and bioinformatic analyses of amino acid metabolism in the hyperthermophilic archaeon Pyrococcus furiosus

Pyrococcus furiosus is a hyperthermophilic archaeon that grows well on complex media containing peptides and various carbohydrates. Its growth on defined media is less well characterized. In this study, the growth of P. furiosus was investigated on various defined media. P. furiosus grows well on a defined medium containing all 20 amino acids. It also shows consistent growth on a defined medium containing arginine, cysteine, glycine, lysine, proline, and serine as the only amino acids. P. furiosus grows inconsistently in defined media with cysteine as the only amino acid. The experimental results are compared with bioinformatics data to better understand the amino acid biosynthesis capabilities of this fascinating organism. INDEX WORDS: archaea, hyperthermophile, Thermococcales, Pyrococcus furiosus, amino acid biosynthesis, amino acid anabolism, metabolism, defined medium EXPERIMENTAL AND BIOINFORMATIC ANALYSES OF AMINO ACID METABOLISM IN THE HYPERTHERMOPHILIC ARCHAEON PYROCOCCUS FURIOSUS
Perceptual grouping of tone sequences by normally hearing and hearing‐impaired listeners

This study examined the perceptual grouping of rapid tone sequences for listeners with normal hearing and listeners with unilateral and bilateral cochlear hearing loss. The sequence ABA–ABA–was used, where A and B represent sinusoidal tones bursts (10-ms rise/fall, 80-ms steady state, 20-ms interval between tones) and – represents a silent interval of 120 ms. Tone A was fixed in frequency at 250, 500, 1000, or 2000 Hz. Tone B started with a frequency well above or below that of tone A, and its frequency was swept towards that of tone A so that the frequency separation between them decreased in an exponential manner. Listeners were required to indicate when they could no longer hear the tones A and B as two separate streams, but heard only a single stream with a “gallop” rhythm. This is called the fission boundary. For the normally hearing listeners, the separation between tones A and B at the fission boundary was roughly independent of the frequency of tone A when expressed as the difference in number of .
Fish species diversity and trophic level in the Jiulong Estuary

The community structure and species diversity of fishes in the Jiulong Estuary were evaluated based on two trawling surveys conducted in May and November,2009.Totally 35 fish species were identified,belonging to two classes,12 orders and 22 families.According to their habitats,three species are pelagic fish,seven near-demersal fish and 25 demersal;and 10 species are estuarine and 25 marine.Based on their temperature adaptions,22 species are warm water fish,and the rest 13 are temperate warm water fish.According to their feeding habits,seven species are lower carnivorous fish and 28 middle carnivorous fish.Arius sinensis,Coilia mystus and Odontamblyopus rubicundus were dominant species in the samples of May,and A.sinensis,C.mystus and Johnius belengerii were dominant species in the samples of November.The weight densities were 471.53 and 143.97kg. km 2,and the individual densities were 40811 and 3804ind.km 2 in May and November,respectively.The Shannon-Wiener index,Pielou index,Margalef index and Trophic index of the fishes in the Jiulong Estuary were 1.20,0.39,3.11 and 3.55,respectively.Compare to the survey data conducted in the same periods in 1985,the fish community structure and species diversity changed largely,and the trophic index deceased significantly in the Jiulong Estuary.Overfishing,environmental pollution and habitat loss are suggested to be the main factors contributing to the decline of fish diversity in the Jiulong Estuary.
Cross-Situational Learning with Reservoir Computing for Language Acquisition Modelling

Understanding the mechanisms enabling children to learn rapidly word-to-meaning mapping through cross-situational learning in uncertain conditions is still a matter of debate. In particular, many models simply look at the word level, and not at the full sentence comprehension level. We present a model of language acquisition, applying cross-situational learning on Recurrent Neural Networks with the Reservoir Computing paradigm. Using the co-occurrences between words and visual perceptions, the model learns to ground a complex sentence, describing a scene involving different objects, into a perceptual representation space. The model processes sentences describing scenes it perceives simultaneously via a simulated vision module: sentences are inputs and simulated vision are target outputs of the RNN. Evaluations of the model show its capacity to extract the semantics of virtually hundred of thousands possible combinations of sentences (based on a context-free grammar); remarkably the model generalises only after a few hundred of partially described scenes via cross-situational learning. Furthermore, it handles polysemous and synonymous words, and deals with complex sentences where word order is crucial for understanding. Finally, further improvements of the model are discussed in order to reach proper reinforced and self-supervised learning schemes, with the goal to enable robots to acquire and ground language by them-selves (with no oracle supervision).
EXTERNAL GEOPHYSICAL VALIDATION AND CALIBRATION OF AN ORBITING GRAVITY GRADIOMETER

In most satellite based Earth observation programs ground truth verification of a sensor is normally considered to be a part of the activities. In the case of GOCE, cf. [3], a similar task can be expected. The level 2 product of this mission is a spherical harmonic coefficient set of the Earth’s gravity field. The solution will be obtained by combining the observation equations from precise orbit determination (POD) and satellite gravity gradiometry (SGG). In this paper the problem of in flight performance monitoring of the GOCE measurements is considered using a collinear track technique in analogy to a data reduction method employed in satellite altimetry. We discuss the results of a new algorithm designed for a sun-synchronous frozen repeat orbit and treat two extreme scenarios. For the worst case scenario our conclusion is that it is possible to monitor the stability of the instrument within the measurement band to around 10 E/sqrt(Hz) whereas our best case scenario shows that a factor 10 improvement is feasible. The temporal gravity effect caused by atmosphere and tides is considered separately; its magnitude, relative accuracy, spectral characteristics and relevance are discussed.
International Journal of Engineering Technology and Scientific Innovation

Writing news headlines requires ensuring that not only the essence of a news story is accurately captured but that the headline attracts its audiences. With the rapid circulation of information on the Internet, headlines designed to elicit emotions have replaced conventional headlines to attract “clicks. ” In this study, a crawler was used to obtain approximately 4,000 news headlines published within a given month from a popular news website in Taiwan. Word segmentation was subsequently conducted through Jieba, and high-frequency headline words were placed on a list. Ten headlines from five umbrella themes (i.e., headlines news, entertainment news, world news, sports news, and lifestyle supplements) were then selected, and 90 readers from different age groups were invited to label the headlines according to the sentiment evoked. The quantitative results suggested that headlines that elicited happiness and surprise captured the attention of the majority of the participants. Interviews conducted through the lens of grounded theory suggested that most participants reported overall limited attention to the content of headlines, because they tend to be hyperbolic or misleading. Moreover, the participants prioritized themes of interest over their emotional responses when selecting headlines. They also remarked that emotionand curiosity-inducing news titles, written primarily to attract clicks, involve commercial intentions because numerous ads are embedded in the news articles, whose visibility depends on their click-through rates.
Congruences modulo p between ρ-twisted Hasse-Weil L-values

Suppose E1 and E2 are semistable elliptic curves over Q with good reduction at p, whose associated weight two newforms f1 and f2 have congruent Fourier coefficients modulo p. Let RS(E? , ρ) denote the algebraic p-adic L-value attached to each elliptic curve E? , twisted by an irreducible Artin representation, ρ, factoring through the Kummer extension Q ( μp∞ ,∆ 1/p∞ ) . If E1 and E2 have good ordinary reduction at p, we prove that RS(E1, ρ) ≡ RS(E2, ρ) mod p, under an integrality hypothesis for the modular symbols defined over the field cut out by Ker(ρ). Under this hypothesis, we establish that E1 and E2 have the same analytic λ-invariant at ρ. Alternatively, if E1 and E2 have good supersingular reduction at p, we show that ∣∣RS(E1, ρ)−RS(E2, ρ)∣∣p < pp. These congruences generalise some earlier work of Vatsal [Vat99], Shekhar-Sujatha [SS15] and Choi-Kim [CK16], to the false Tate curve setting.
Advances in Clinical Research of Imaging Technology for Parkinson’s Disease

Parkinson currently lacks effective indicators for early diagnosis. The cause of the disease is mainly related to the degeneration of dopaminergic neurons in the dense part of the substantia nigra. By studying the changes in the substantia nigra, early Parkinson’s patients can be diagnosed by MRI, Ultrasound and Imaging techniques such as positron emission tomography. They have demonstrated the substantia nigra degeneration of Parkinson’s patients through many studies. This article reviews the research status of imaging techniques in the diagnosis of Parkinson’s disease, and hopes to provide more reliable basis to detect Parkinson’s disease.
Volatile Organic Compound Based Markers for the Aroma Trait of Rice Grain

A study was conducted to determine the volatile organic compounds (VOCs) associated with rice grain aroma in 37 commonly grown lines within Uganda, as well as elites. The aim of the study was to identify potential volatile biochemical markers, if any, for the rice grain aroma trait. Certified rice seeds were obtained from the Uganda National Crops Resources Research Institute germplasm collection. The seeds were sown into experimental plots, under field conditions and the mature paddy harvested. Polished rice grains were heated to 80 oC and the liberated VOCs subjected to untargeted metabolite analysis using gas chromatography-time-of-flight mass spectrometry. In total, nine functional groups were present; hydrocarbons, alcohols, ketones, aldehydes, N-containing compounds, S-containing compounds, esters, oxygen heterocycles and carboxylic acids. More specifically, 148 VOCs were identified across the 37 rice lines, of which 48 (32.4%) including 2-acetyl-1-pyrroline (2-AP) appeared to elucidate the difference between non-aromatic and aromatic rice. Furthermore, 41 (27.7%) VOCs were found to be significantly correlated with 2-AP abundance, the principle rice aroma compound. Amongst the 41 VOCs, only ten compounds were found to contribute highly towards variation in 2-AP abundance, indicative of their possible modulation roles in regard to rice aroma. Within the ten influential volatiles, three aroma active compounds; toluene, 1-hexanol, 2-ethyl and heptane, 2,2,4,6,6-pentamethyl- were established as the most reliable biochemical surrogates to the rice aroma trait. Thus, the aforementioned compounds may be used in rice breeding programme for enhancing development of the grain aroma trait.
Feature and Nuclear Norm Minimization for Matrix Completion

Matrix completion, whose goal is to recover a matrix from a few entries observed, is a fundamental model behind many applications. Our study shows that, in many applications, the to-be-complete matrix can be represented as the sum of a low-rank matrix and a sparse matrix associating with side information matrices. The low-rank matrix depicts the global patterns while the sparse matrix characterizes the local patterns, which are often described by the side information. Accordingly, to achieve high-quality matrix completion, we propose a Feature and Nuclear Norm Minimization (FNNM) model. The rationale of FNNM is to employ transductive completion to generalize the global pattern and inductive completion to recover the local pattern. Alternative minimization algorithm based on fixed-point iteration is developed to numerically solve the FNNM model. FNNM has demonstrated promising results on a variety of applications, including movie recommendation, drug-target interaction prediction, and multi-label learning, consistently outperforming the state-of-the-art matrix completion algorithms.
Revealing the Demonstration of Blockchain and Implementing Scope in COVID-19 Outbreak

Blockchain is gaining attention since its development due to its features like immutability, transparency, decentralization It can also be used as decentralized storage Blockchain offers a wide range of digital services both in financial and non-financial sectors In this paper, we discuss the basic concepts and terminologies associated with blockchain and discuss the application areas both in financial and non-financial sectors such as Digital voting, Digital identity, Education, etc and discuss the existing framework in these areas, also the progress and challenges of global acquisition The main highlight of this paper is COVID-19 and we discussed existing solutions to fight the COVID-19 pandemic by using such technology This survey paper will be helpful for a data scientist as well as researchers to explore in the area COVID-19
CRITICAL DISCOURSE ANALYSIS OF POSITIVESELF AND NEGATIVE-OTHER REPRESENTATIONS THROUGH ENGLISH LANGUAGE TEXTBOOKS IN PUNJAB, PAKISTAN

The present research is a critical discourse analysis of the contents related to positive-self representation and negative-other representation projected through Punjab Textbook Board’s (PTB) School English textbooks which are taught in government and non-elite English medium schools at the secondary level (Grades 9 & 10). The present research aims at finding out the ideologies of positive-self representation and negative-other representation in the discourses of PTB School English textbooks. It employs Van Dijk’s (2005) analytical framework of critical discourse analysis to explore the ideologies of positive-self representation and negative-other representation in the discourses of School English textbooks. The findings show that PTB School English Textbooks are replete with the ideologies of positive-self representation and negative-other representation. The said textbooks are more religious, nationalistic, anti-Israel, anti-Hindus, and anti-Christians. The findings of the study can be useful in a number of ways. They can be useful in developing new curricula and teaching materials including textbooks with less nationalistic, religious and cultural biases. They can be helpful for policy makers and textbook authors to realize the politics of language textbooks to establish a symmetrical educational system.
Distilling Essence of a Question: A Hierarchical Architecture for Question Quality in Community Question Answering Sites

Community question answering (CQA) sites have grown to be useful platforms where users search for highly specific information to resolve a problem. However, the significant increase in the number of user-generated content with high variance in quality on these sites not only presents challenges for user navigation but also outgrow the community’s peer reviewing capacity. This necessitates ways to automatically assess the quality of new questions so as to maintain quality of content served to its users. While existing methods commonly employ social network indicators as features, our model aims to avoid social influence biases arising from these indicators by predicting the quality from semantic evaluation of the question text. Formulation of the proposed model is non-trivial as it requires the extraction of meaningful features from the noisy question text at different granularities while filtering redundant information. In this work, a neural architecture is proposed to address this problem by aggregating the textual features extracted at word- and sentence-level in a hierarchical manner. In addition, a unique attention mechanism that focuses on sentence segments for interpreting a question is developed. This new mechanism employs the global topical information from common problem contexts. The proposed approach is verified on the Stack Overflow question dataset and is shown to outperform existing neural models.
A review of open-source machine learning algorithms for twitter text sentiment analysis and image classification

Sentiment analysis (SA) plays an important role in inferring sentiment or emotion from text and visual contents, such as images and videos to determine the overall contextual polarity of a document. Today, image recognition and classification are rapidly growing fields in the area of machine learning (ML). This paper presents a review of open-source machine learning algorithms, built using neural network-based frameworks such as TensorFlow and Keras, to serve as a benchmark for bespoke SA algorithms. This research also advocates open-source scikit-learn models for text tweets and image classification.Two prominent, publicly available and manually annotated benchmark text and image datasets were used to enable and assist in the correlation of this work with existing, present and future avant-garde and innovative methods. Quantitative results across four statistical criteria, including precision, recall, F1-score and accuracy compare favourably to the often complicated and tailored state-of-the-art methodologies developed. For SA, empirical results suggest deep-learning model frameworks to outperform scikit-learn algorithms. All experiments were conducted on computer hardware comprising 64GB of RAM and a NVIDEA GeForce RTX 2080 Ti GPU.
Model and Hybrid Algorithm of Collaborative Distribution System with Multiple Drones and a Truck

This paper studies a coordinated system for multidrone single-truck distribution, where a truck delivers goods to a group of customers along a closed ground path with the help of a number of drones. For each delivery, the truck departs from the distribution centre with drones and all goods needed and returns back to the centre after fulfilling the delivery tasks. That is, the truck assigns these delivery tasks to several of its drones, each of which is responsible for sending goods to a different subgroup of customers in the empty air space. This study provides a new mixed-integer programming model of the routing problem with this distribution system based on urban road network. Meanwhile, a hybrid genetic algorithm and a hybrid particle swarm algorithm are designed. Experimental results show that the performance of the hybrid algorithms is better than that of the corresponding basic algorithms.
Analysis of exclusive kT jet algorithms in electron-positron annihilation

We study the factorization of the dijet cross section in e+e− annihilation using the generalized exclusive jet algorithm which includes the cone-type, the JADE, the kT , the anti-kT and the Cambridge/Aachen jet algorithms as special cases. In order to probe the characteristics of the jet algorithms in a unified way, we consider the generalized kT jet algorithm with an arbitrary weight of the energies, in which various types of the kT -type algorithms are included for specific values of the parameter. We show that the jet algorithm respects the factorization property for the parameter α < 2. The factorized jet function and the soft function are well defined and infrared safe for all the jet algorithms except the kT algorithm. The kT algorithm (α = 2) breaks the factorization since the jet and the soft functions are infrared divergent and are not defined for α = 2, though the dijet cross section is infrared finite. In the jet algorithms which enable factorization, we give a phenomenological analysis using the resummed and the fixed-order results. ∗ E-mail:chay@korea.ac. kr † E-mail:chul@seoultech.ac. kr ‡ E-mail:vorfeed@korea.ac. kr 1 ar X iv :1 50 8. 04 25 4v 2 [ he pph ] 7 O ct 2 01 5
Contest with Incomplete Information: When to Turn Up the Heat, and How?

I investigate the optimal design of contests when contestants have both private information and convex effort costs. The designer has a fixed prize budget and her objective is to maximize the expected total effort. I first demonstrate that it is always optimal for the designer to employ a static, grand all-pay-contest with as many as possible participants. In addition, I identify a sufficient and necessary condition for the winnertakes-all prize structure to be optimal. When this condition fails, the designer may prefer to award multiple prizes of descending sizes. I also provide a characterization of the optimal prize structure for this case. Lastly, I illustrate how the optimal prize structure evolves as contest size grows: the prize structure first becomes more unequal until the optimal level of competition intensity is obtained, and then becomes less unequal to maintain the optimal intensity.