Miracle-dz/UniFAR-FTUs huggingface dataset:
InfoSurgeon: Cross-Media Fine-grained Information Consistency Checking for Fake News Detection

To defend against machine-generated fake news, an effective mechanism is urgently needed. We contribute a novel benchmark for fake news detection at the knowledge element level, as well as a solution for this task which incorporates cross-media consistency checking to detect the fine-grained knowledge elements making news articles misinformative. Due to training data scarcity, we also formulate a novel data synthesis method by manipulating knowledge elements within the knowledge graph to generate noisy training data with specific, hard to detect, known inconsistencies. Our detection approach outperforms the state-of-the-art (up to 16.8% accuracy gain), and more critically, yields fine-grained explanations.
Evaluating Evaluation Measures for Ordinal Classification and Ordinal Quantification

Ordinal Classification (OC) is an important classification task where the classes are ordinal. For example, an OC task for sentiment analysis could have the following classes: highly positive, positive, neutral, negative, highly negative. Clearly, evaluation measures for an OC task should penalise misclassifications by considering the ordinal nature of the classes. Ordinal Quantification (OQ) is a related task where the gold data is a distribution over ordinal classes, and the system is required to estimate this distribution. Evaluation measures for an OQ task should also take the ordinal nature of the classes into account. However, for both OC and OQ, there are only a small number of known evaluation measures that meet this basic requirement. In the present study, we utilise data from the SemEval and NTCIR communities to clarify the properties of nine evaluation measures in the context of OC tasks, and six measures in the context of OQ tasks.
HamiltonDinggg at SemEval-2021 Task 5: Investigating Toxic Span Detection using RoBERTa Pre-training

This paper presents our system submission to task 5: Toxic Spans Detection of the SemEval-2021 competition. The competition aims at detecting the spans that make a toxic span toxic. In this paper, we demonstrate our system for detecting toxic spans, which includes expanding the toxic training set with Local Interpretable Model-Agnostic Explanations (LIME), fine-tuning RoBERTa model for detection, and error analysis. We found that feeding the model with an expanded training set using Reddit comments of polarized-toxicity and labeling with LIME on top of logistic regression classification could help RoBERTa more accurately learn to recognize toxic spans. We achieved a span-level F1 score of 0.6715 on the testing phase. Our quantitative and qualitative results show that the predictions from our system could be a good supplement to the gold training set’s annotations.
Adapting Unsupervised Syntactic Parsing Methodology for Discourse Dependency Parsing

One of the main bottlenecks in developing discourse dependency parsers is the lack of annotated training data. A potential solution is to utilize abundant unlabeled data by using unsupervised techniques, but there is so far little research in unsupervised discourse dependency parsing. Fortunately, unsupervised syntactic dependency parsing has been studied by decades, which could potentially be adapted for discourse parsing. In this paper, we propose a simple yet effective method to adapt unsupervised syntactic dependency parsing methodology for unsupervised discourse dependency parsing. We apply the method to adapt two state-of-the-art unsupervised syntactic dependency parsing methods. Experimental results demonstrate that our adaptation is effective. Moreover, we extend the adapted methods to the semi-supervised and supervised setting and surprisingly, we find that they outperform previous methods specially designed for supervised discourse parsing. Further analysis shows our adaptations result in superiority not only in parsing accuracy but also in time and space efficiency.
Employing Argumentation Knowledge Graphs for Neural Argument Generation

Generating high-quality arguments, while being challenging, may benefit a wide range of downstream applications, such as writing assistants and argument search engines. Motivated by the effectiveness of utilizing knowledge graphs for supporting general text generation tasks, this paper investigates the usage of argumentation-related knowledge graphs to control the generation of arguments. In particular, we construct and populate three knowledge graphs, employing several compositions of them to encode various knowledge into texts of debate portals and relevant paragraphs from Wikipedia. Then, the texts with the encoded knowledge are used to fine-tune a pre-trained text generation model, GPT-2. We evaluate the newly created arguments manually and automatically, based on several dimensions important in argumentative contexts, including argumentativeness and plausibility. The results demonstrate the positive impact of encoding the graphs’ knowledge into debate portal texts for generating arguments with superior quality than those generated without knowledge.
A Hybrid Approach for Heart Disease Prediction Using Artificial Neural Network and K-means

— Cardiovascular diseases is one of the reasons behind death of people universally, more people pass away from cardiovascular diseases than from any other cause annually. To stay away from heart disease or find symptoms early, adults over 54 must have a complete cardiovascular checkup. Many experts will be developing intelligent decision support systems related to medical to get better ability of the doctors in detection of heart disease. In heart disease diagnosis and treatment, single data mining techniques are providing the reasonable accuracy. But to boost the accuracy level we can use hybrid data mining techniques. The Heart Disease Prediction proposed system guides through an intelligent decision support system. In our proposed model a predictive analysis is carried out on UCI Heart Disease Data Set using K-means and ANN data mining techniques. Medical data is combination of fuzzy and crisp values. This data is classified according to their properties. This classification is implemented by developing a model using Artificial Neural Network and K-means algorithm. The focus of our model is to classify the data according to cardiovascular diseases better so as to have more reliable diagnosis.
AI-based Frame work for IoT based Sensor Networks and 5G Integrated Spectrum Selection and Spectrum Access

: The convulsion of multi-entry multi-output and ultra-dense networks was commonly regarded as the main facilitators to promote the creation and establishment of 5G systems. The exponential growth of wireless devices involves the use of Internet of Things (IoT), the potential that wireless communications will interconnect diversified things. Artificial Intelligence (AI) plays an important role in the 5G network to allow wireless connectivity to IoT users. Although current end-to-end learning and adaptive model need continuous monitoring and complex adjustments due to wireless signal classificators are unable to achieve global optimisation. More intrusion and more. In this paper an optimised range of spectrum and access to spectrums through a greedy AI-based architecture is presented to satisfy the upcoming requirements for 5Gs and beyond. A fractional Greedy approach is implemented and the Langrange Hyperplane approach is used to incorporate AI-based strategies to pick spectrum and assign spectrum for IoT-capable sensor systems. This system is referred to as the Langrange and Fractional Knapsack Hyperspectrum Access (FK-LHSA). The FKMSS model is developed in conjunction with an energy consumption model to optimise the channel. Or throughput of spectrum. Next, the spectrum access model of the LagrangeHyperflugzeug (LH) is designed to reduce speed delay and increase the accuracy of spectrum access. The results of the simulation show that the proposed FKM and LH models will minimise the access time and increase the accuracy of throughput and spectrum.
Wastewater treatment processes and mechanisms of organic matter, phosphorus, and nitrogen removal in a multi-soil-layering system.

The treatment processes inside a Multi-Soil-Layering system (MSL) were investigated by using a laboratoryscale system, which was set up in a D10×W50×H23-73cm acrylic box enclosing “soil mixture blocks” alternating with permeable zeolite layers. Six MSL systems consisted of 1~6 layers of soil mixture layers were constructed. For the study of the treatment processes inside the system, wastewater, with mean concentrations (mg/L) of BOD: 28.1, COD: 65.7, T-N: 9.8, T-P: 1.0, was introduced into the system at a loading rate of 1000 L/m 2 /day. In the both of BOD and COD, the concentrations in soil mixture layers (SML) were lower than those in permeable layers between SML (PLb). As the flow rate in SML decreased and the rate in PLb increased, the concentrations in the PLb increased in each system. Phosphorus concentration was lower in the SML than in the PLb, probably because P was adsorbed mainly by soil and mixed iron particles. Therefore, phosphorus removal efficiency was strongly influenced by the flow rate in SML. The ammonia adsorption and nitrification were almost completed up to 3rd layer in this study. However, the removal of nitrogen did not so much proceed below 4th layer because of low denitrification efficiency.
Psychological Factors Predicting Organic Food Consumption in Social Commerce

This paper examines consumer perceptions of the purchase of organic food in social commerce. We extend the Theory of Planned Behavior to perceived information usefulness and the perceived consequences of s-commerce use. We empirically test our hypothesized conceptual model among 261 consumers in market conditions with limited access to organic food products via conventional grocery stores. The results show that the perceived usefulness of organic food information in s-commerce has a highly significant effect both on the consumer attitude to using s-commerce and on the subjective norm. Perceived consequences influence the subjective norm and perceived behavioral control, but not on attitude. Attitude and the subjective norm significantly predict the consumer’s intention to use s-commerce for organic food purchases; perceived behavioral control does not. Our results highlight the importance of information in social commerce as a driver of purchasing, especially in markets offering little product information and availability in conventional
Clonal hematopoiesis and myeloid malignancies: clonal dynamics and clinical implications

Purpose of review Clinical and experimental studies have uncovered relevant clinical implications of clonal hematopoiesis. However, the true magnitude of this process, clonal dynamics over time and mechanisms of progression into overt malignancy remain to be largely elucidated. In this article, the consequences of clonal hematopoiesis, its significance in the context of cytopenia, and its implications in the clinical management of patients with myeloid malignancies are reviewed and discussed. Recent findings Clonal hematopoiesis has been associated with higher risk of hematologic cancers, as well as of death from cardiovascular causes. Clonal hematopoiesis has been proven clinically relevant in the context of disorders characterized by peripheral blood cytopenia, including aplastic anemia, cytopenia of undetermined significance, as well as unexplained anemia of the elderly. Summary The available evidence has been proving the utility of somatic mutational analysis in patients with unexplained cytopenia, as well as in those receiving a diagnosis of myeloid neoplasm, enabling more accurate diagnosis, risk assessment, effective therapeutic strategies and residual disease monitoring. The access to a minimally invasive assessment is paving the way for screening programs of clonal hematopoiesis in individuals with absent or mild hematologic phenotype, as well as for therapeutic targeting of preleukemia cells.
Non-Emergency Utilization of EMS: Contributing Factors and Strategies to Promote Effective Care with Appropriate Resources

Non-emergency use of EMS resources can impose a substantial burden on emergency response systems and the communities they serve by reducing the response capacity for time-sensitive and life-threatening situations and through subsequent diversion of funding that could be used to more effectively address health disparities. National EMS response data suggest that a large proportion of EMS patient encounters are for non-emergent reasons. Despite being a nation-wide issue, inadequate research has been conducted to understand why non-emergency and frequent use occurs and what strategies may be effective in mitigating avoidable use. Community paramedicine (CP), an emerging specialty in EMS that focuses on linking underserved populations with available resources to close gaps in care, may hold the key to reducing inappropriate use while simultaneously improving community health. The purpose of this comprehensive review is to examine existing emergency response date, research, and reports from an array of sources to provide insight into effective strategies for reducing non-emergency use while promoting health with appropriate resources.
Investor Attention and Early Announcements in Mergers and Acquisitions

By focusing on mergers and acquisitions (M&As) with the public announcement dates before the definitive agreements, we find that acquiring firms tend to have higher abnormal trading volume and abnormal Google search volume, which indicates higher investors’ attention. The early announced takeovers combined with excessive investor attention are associated with higher short-term abnormal returns, which are reserved in the long-term. The high investor attention on early announced deals are more likely to reduce the deal competition probability. Collectively, our findings support the price pressure hypothesis that increased investor attention causes temporary price run-ups at the early announcement date to frighten the competing bidders.
Mechanistic Study of HPMC-Prolonged Supersaturation of Hydrocortisone

Supersaturation formulation is an effective way to improve the oral bioavailability of poorly water-soluble drugs, and water-soluble polymers are commonly used to prolong the supersaturation. However, the mechanism for such supersaturation prolonging effect is not clearly understood. Using the hydrocortisone/hydroxypropyl methylcellulose (HPMC) pair as a model system, we found that HPMC prolongs hydrocortisone supersaturation by inducing the formation of a metastable crystal polymorph of higher solubility, hence lowering the actual supersaturation level in the solution. A possible mechanism is proposed to explain the selective polymorph formation in the presence of polymers.
Neuroprotective effects of Coriandrum sativum and its constituent, linalool: A review

Objective: Coriander (Coriandrum sativum L.) is an annual herb belonging to the Apiaceae (Umbellifera) family that is used as food additives traditionally. This plant is called “Geshniz” in Persian and is native to Mediterranean regions but it is currently cultivated in several countries. All parts of coriander are edible and have been traditionally used to treat different disorders, including digestive problems, flatulence, diarrhea, colic and other gastrointestinal diseases. Materials and Methods: The databases PubMed, Web of Science, Google Scholar and Scopus were considered. The search terms were “Coriandrum sativum” or “linalool” and “anti-anxiety”, “sedative”, “antioxidant effect”, “anticonvulsant” and “neuroprotective effect”. Results: Antioxidant, diuretic, cholesterol lowering, anxiolytic, sedative-hypnotic and anticonvulsant activities were reported for the seeds and leaves of the plant. Furthermore, linalool as the main component of coriander has different neuropharmacological effects, including anti-anxiety, sedative, anticonvulsant and anti-Alzheimer’s disease activities. Conclusion: Various neuropharmacological effects of C. sativum and its component which have antioxidant and anti-inflammatory effects, have been summarized in the current review article.
Influence of Carbonization Temperature on Physicochemical Properties of Biochar derived from Slow Pyrolysis of Durian Wood (Durio zibethinus) Sawdust

The objective of this study was to explore the influence of pyrolysis temperature on the physicochemical properties of biochar synthesized from durian wood (Durio zibethinus) sawdust. Surface morphological features, including the porosity and BET surface area of biochars, provide appropriate dimensions for growing clusters of microorganisms with excellent water retention capacity in soil. Oxygen-containing surface functional groups play a vital role in improving soil fertility by increasing its cation and anion exchange capacities with reduced leaching of nutrients from the soil surface. Biochar was produced via slow pyrolysis of woody biomass (WS) using a fixed bed reactor under an oxygen-free atmosphere at different pyrolysis temperatures (350, 450, and 550 °C). The biochars obtained were characterized using ultimate and proximate analyses, Brunauer-Emmett-Teller (BET) surface area, field-emission scanning electron microscopy (FE-SEM), Fourier transform infrared spectroscopy (FTIR), and X-ray diffraction (XRD). The yield of biochar decreased from 66.46 to 24.56%, whereas the BET surface area increased sharply from 2.567 to 220.989 m2/g, when the pyrolysis temperature was increased from 350 to 550 °C. The results highlighted the effect of pyrolysis temperature on the structure of the biochar, which could be advantageous for agricultural industries.
1D VS MODELS BY SINGLE-STATION NOISE DATA INVERSION AND JOINT INTERPRETATION WITH INDEPENDENT DATA

We explore the applicability of single-station HVSR curve inversion to determine the local 1D Vs structure, following Hobiger et al. (2012), who showed that if the shallow 1D velocity profile is known, the uniqueness of the ellipticity inversion is much better constrained. Since shallow information can be directly derived from smallscale active or passive tests, or indirectly assessed from other (geophysical, geotechnical or geological) information, we evaluate this approach in practice. Single-station ambient noise measurements were performed at 73 calibration sites obtained from the database of Stewart et al. (2014), which contains VS-depth information for a large number of sites in Greece. HVSR curves were calculated and their reliability as proxies for Rayleigh wave ellipticity was checked as suggested by Hobiger et al. (2009). HVSR inversions were performed by constraining the average shear-wave velocity of the first 5 m (VS5) from the database. An automated inversion procedure was developed, allowing the method application by non-experts. The obtained 1D Vs models were compared with the database, focusing on inferred seismic bedrock depth and VS30 results.VS30 estimates from VS5 proxies following Stewart et al. (2014) were also examined for comparison. The results suggest that the inversion of HVSR curves using VS5 constraints can lead to improved VS30 estimates for stiffer formations (EC8 soil class B), providing also improved VS30 assessments for several C soil class sites. These observations suggest that HVSR inversions can be considered as a supplementary tool for efficient, large-scale/low-cost VS30 estimations, employing a simple datacollection and processing strategy.
EXAMINING FACTORS AFFECTING CUSTOMERS’ INTENTION TO USE E-BANKING IN VIETNAM

Received: 16/4/2021 The paper aims to focus on e-banking in Vietnam a country with a low percentage of intention to adopt e-banking and account for the slow uptake. A representative sample of 235 customers in Hanoi was employed to examine this issue empirically. To estimate the study model, the author conducted the multiple regression analysis using SPSS and AMOS software packages. The results highlight that the intention to use e-banking was positively affected by Performance Expectancy, Brand Image, Law Factor and Subjective Norms but negative relationships were found between the intention to use ebanking and Perceived Risk and Perceived Switching Cost. Based on the results, recommendations are drawn for banks, involving focusing on factors of bank services’ features, and external factors such as subjective norms. The paper also sheds light on the full functionality of bank’s e-banking systems, emphasizing the need to ensure individual and media interactions, which can escalate the intention to use ebanking in Vietnam. Revised: 11/5/2021
Determining the Knowledge Level of Parents about Their Children’s Rational Drug Use

The main responsibility for the rational use of drugs (RUD) in children belongs to the parents. The aim of this study was to determine the knowledge and awareness level of parents about RUD in children. The study included literate volunteer parents with no learning disability. The rational drug use scale (RDUS), developed by Demirtas, was modified to assess parents' knowledge about RUD in children (higher score indicates higher knowledge level). Questionnaires for which all questions were not answered were excluded from the study. Two-group differences (gender, employment status, health insurance) were compared using the Mann-Whitney U test. Kruskal Wallis test was used to compare the differences among three or more groups (age: ≤30, 30-40 and >40 years; education level: elementary, high school, associate degree and bachelor degree or higher; income: <2000 TL, 2000-5000TL and >5000 TL; number of children: 1, 2, 3 and ≥4). The study included 410 participants (mean age: 35±8.2; female: 58.3%). The mean scores obtained from the scale was 21.31 ± 4.1. Among females, the knowledge of RUD varied across education level (X2=32.333, p<0.001), income class (X2=16.096, p<0.001) and the number of children (X2=7.868, p=0.049). RDUS score was higher in employed females compared to non-employed females (U=2337.5, p<0.001). In male participants, the knowledge of RUD varied across education level (X2=32.130, p<0.001), income class (X2=16.893, p<0.001) and the number of children (X2=8.307, p=0.040). It was concluded that parents displayed incorrect attitudes and behavours regarding the use of drugs for in children.
Effervescent formulation based on variation of nanocapsules matrix type and roselle (Hibiscus sabdariffa) nanocapsules percentage

Purbowati, I.S. M. , Maksum, A. and Anggriawan, R. Food Processing Technology Laboratory, Department of Agricultural Technology, Faculty of Agriculture, Universitas Jenderal Soedirman, Dr. Suparno Street, Karangwangkal, Purwokerto 53123, Indonesia. Food Business Technology, School of Applied Science, Technology, Engineering and Mathematics (STEM), Prasetiya Mulya University, Tangerang 15339, Indonesia Head of Research, Community Service and Industry Relation Section, STEM, Prasetiya Mulya University, Tangerang 15339, Indonesia