Miracle-dz/UniFAR-FTUs huggingface dataset:
Fusing Stylistic Features with Deep-learning Methods for Profiling Fake News Spreader

False or unveriﬁed information spreads just like accurate information on social media platforms, thus possibly going viral and inﬂuencing the public opinion and its decisions. Fake news represents one of the most popular forms of false and unveriﬁed information, and should be identiﬁed as soon as possible for minimizing their dramatic effects. In order to face this challenge, in this paper we describe our system developed for participating in the Author Proﬁling task: “ Proﬁling Fake News Spreaders on Twitter ” proposed at the PAN 2020 Forum. Our proposal learns two representations for each tweet in an account’s proﬁle. The ﬁrst one is based on CNN and LSTM nets analyzing the tweets at word level, the second one is learned by using the same architecture without sharing the weights, but at this time, the tweets are analyzed at character-level. These representations are used for modeling the accounts’ proﬁles. Also is conceived for the whole account’s proﬁle a general representation based on stylistic features, which contains information about the writing style. Finally, the proﬁles’ representations are given as inputs to our classiﬁcation model which is based on LSTM neural nets with attention mechanism. Experimental results show that our model achieves encourage results.
bigIR at CheckThat! 2020: Multilingual BERT for Ranking Arabic Tweets by Check-worthiness

This paper describes the third-year participation of our bi-gIR group at Qatar University in CheckThat! lab at CLEF. This year we participated only in Arabic Task 1 that focuses on detecting check-worthy tweets on a given topic. We submitted four runs using both traditional classiﬁcation models and a pre-trained language model: multilingual BERT (mBERT). Oﬃcial results showed that our run using mBERT was the best among all our submitted runs. Furthermore, bigIR team was ranked third among all eight teams participated in the lab, with our best run ranked 6th among 28 runs.
Teaching primary school children about computer viruses: preliminary results of an intervention study

Children increasingly use computing devices. However, it is unclear whether they have basic knowledge of security-related issues such as computer viruses and, in case they do not, what they can learn about them. In this study, we first examined preconceptions of computer viruses among Czech and German primary school students using a newly developed test. Second, the experimental group (but not the control group) received an intervention to learn about computer viruses, antiviruses, and software updates by means of a 45-min lesson combining a cartoon series on viruses, frontal instruction, and discussion. Both groups again completed the test. A preliminary analysis indicated that Czech and German children already knew key points concerning computer viruses. However, overall, their knowledge was patchy, and children also had various misconceptions. The experimental group improved from pre- to posttest (d = 1.06), while this was not the case for the control group. These initial results indicated that knowledge of computer viruses should and can be taught to primary school children.
Assembly of Polarity, Emotion and User Statistics for Detection of Fake Profiles

The explosive growth of fake news on social networks has aroused great interest from researchers in different disciplines. To achieve efﬁcient and effective detection of fake news requires scientiﬁc contributions from various disciplines, such as computational linguistics, artiﬁcial intelligence, and sociology. Here we illustrate how polarity, emotion, and user statistics can be used to detect fake proﬁles on Twitter’s social network. This paper presents a novel strategy for the characterization of the Twitter proﬁle based on the generation of an assembly of polarity, emotion, and user statistics characteristics that serve as input to a set of classiﬁers. The results are part of our participation in the PAN 2020 in the CLEF in the task of Proﬁling Fake News Spreaders on Twitter.
NCU-IISR: Using a Pre-trained Language Model and Logistic Regression Model for BioASQ Task 8b Phase B

Recent successes in pre-trained language models, such as BERT, RoB-ERTa, and XLNet, have yielded state-of-the-art results in the natural language processing field. BioASQ is a question answering (QA) benchmark with a public and competitive leaderboard that spurs advancement in large-scale pre-trained language models for biomedical QA. In this paper, we introduce our system for the BioASQ Task 8b Phase B. We employed a pre-trained biomedical language model, BioBERT, to generate “exact” answers for the questions, and a logistic regression model with our sentence embedding to construct the top-n sentences/snippets as a prediction for “ideal” answers. On the final test batch, our best configuration achieved the highest ROUGE-2 and ROUGE-SU4 F1 scores among all participants in the 8th BioASQ QA task (Task 8b, Phase B).
Specimen Copy: Not for sale

Anthocyanins accumulation and gene expression in berries of Vitis vinifera L. cv. Tannat trained in Lyre system was investigated. Expression of genes involved in anthocyanins biosynthetic pathway as chalcone synthase (CHS), fl avonoid 3-hydroxylase (F3H), dihydrofl avonol 4reductase (DFR), and UDP-glucose fl avonoid-3-Oglucosyl transferase (UFGT) was examined. On the other hand, the infl uence of plant architecture, Lyre and vertical shoot positioned (VSP) trellis systems, on anthocyanins accumulation and gene expression was also analyzed. Final contents of total anthocyanins were not affected by trellis systems but varied in two years with different water defi cit imposition period. However, the individual profi le of the genes and anthocyanidins modifi ed according to the moment of water defi cit imposition (veraison or harvest) and by the trellis systems (Lyre or VSP). Analysis of gene expression in Lyre along the berry development period showed that low leaf water potentials after veraison cause an earlier and greater induction compared with expression in a year with low leaf water potential at harvest. At harvest, the hydric defi cit induced an increase in the expression of CHS, F3H and DFR genes and a higher total anthocyanins content. The study revealed that plant architecture affect the expression of anthocyanins related gene in berries possibly by modifying the canopy microclimate.
Poly (Lactic Acid) Nano-structures for Cartilage Regeneration and Joint Repair: Strategies and Ideas

Cartilage repair and regeneration remain mainly obstinate owing to a very low regenerative potential of this tissue. The field of Cartilage Tissue Engineering, which is aimed to repair, regenerate, and improve injured or diseased cartilage functionality, has induced penetrating attention and grips inordinate possible for successful Cartilage-Therapy. Improvements in bio-material investigation and High-tech enhanced constructing of scaffolds have improved the productivity of Tissue-Engineering. Nano fibrous scaffolds mimic the native extracellular matrix of cartilage, offering a provision for cartilage tissue engineering through advancing Cell Viability, Cell Adhesion, Cell Propagation, Osteo-genic Isolation, Vascularization, Host Integration, and Load Bearing. Fabrication of these scaffolds with Poly Lactic Acid (PLA) Bio Polymer has permitted modified cartilage repair. PLA is effortlessly processable, degrades and degenerates to natural-metabolites. Consequently, the prospective of using PLA Nano fibers for cartilage repair is a thoughtful objective for researchers in new examinations. In this review, we deliberate the various methodologies applied in constructing PLA Nano-fibrous scaffolds for Cartilage Tissue Engineering. Also a general report of the present state and development in cartilage repair and regeneration by Poly Lactic Acid Nano-fibers will be delivered.
I STITUTO DI A NALISI DEI S ISTEMI ED I NFORMATICA

In this work we propose a new decomposition algorithm for singly linearly constrained problems subject to simple bounds. In particular we focus on the training of Support Vector Machines. Our method, by exploiting a new space transformation technique combined with an intense caching strategy, aims to speed up the training task for large dimensional dataset. The practical implementation of the algorithm and some numerical experiments in comparison with two state-of-the-art training algorithms, are presented.
Comparison of DL- β-amino-n-butyric acid, salicylic acid and abscisic acid in induction of resistance in tomato infected by Meloidogyne incognita.

هديكچ يسررب روظنم هب فلتخم حوطسريثأت ديـسا كـيريتوبونيمآاتب لماـش كيمتـسيس تـمواقم ءاـقلا رد رثؤـم ييايميـش بـيكرت هس ) BABA ( ، لاس ي كيليس ديسا ) SA ( كيزيسبآ و ديسا ) ABA ( هجوگ رد تمواقم ءاقلا رد ،يـهرگ هـشير دوتاـمن هب هدولآ يگنرف Meloidogyne incognita ، اك حرط بلاق رد ليروتكاف تروص هب يشيامزآ يفداصت ًلام هدش لرتنك طيارش رد راركت هس رد ) 16 يامد اب ييانشور تعاس °C 30 تشه و يامد اب يكيرات تعاس °C 25 ( تفريذپ ماجنا . هجوگ ناهايگ روظنم نيدب مقر يگنرف Moneymaker ،رفـص حوطس اب ،يگرب راهچ هلحرم رد 25 / 0 ، 5 / 0 و 1 يليم زا رلاوم SA و BABA لولحم تروص ود هب يشاپ ،رفص حوطس و كاخ ندناسيخ و يگرب 50 ، 100 و 200 رلاوـموركيم زا ABA لولحم تروص هب دنديدرگ راميت يگرب يشاپ . تشذگ زا سپ 24 اب ناهايگ ،تعاس 3 / 1 هيام كاخ مرگ رد مود نس ورلا دندـش ينز . 30 هيام زا دعب زور صخاش ،ينز تفرگ رارق يسررب دروم دوتامن و هايگ يشيور ياه . جياتن ءاقلا فلتخم حوطس هك داد ناشن رـگ دروـم ياـه صخاش رب ينادنچ ريثأت هدافتسا ينعم شهاك ثعاب اما ،دنتشادن هايگ يدشر ياه صخاش راد يريثكت ياه هـسيك ،لاـگ دادـعت لماـش دوتامن راميت ناهايگ رد لثمديلوت روتكاف و ،هشير نورد مخت لك دادعت ،مخت هسيك ره نورد رد مخت دادعت ،مخت ناـهايگ هـب تبـسن رگءاقلا اب هدش دنديدرگ دهاش . تظلغ اب هدش راميت ناهايگ رد لثمديلوت روتكاف 5 / 0 يليم زا رلاوم SA و BABA لولحم تروص هب هـب تبـسن يـگرب يـشاپ دودح رد دهاش ناهايگ 60 % لولحم ناهايگ رد و تظلغ اب هدش يگرب يشاپ 100 يليم زا رلاوم ABA د ،دهاش ناهايگ هب تبسن دودـح ر 17 % درك اديپ شهاك . يم ناشن جياتن نيا يم هك دهد زا ناوت ياهرگءاقلا SA ، BABA و ABA هشير دوتامن لرتنك تهج ،يـهرگ M. incognita رد ، هجوگ درك هدافتسا يگنرف .
Comparison of monotonicity challenges encountered by the inverse scattering series and the Marchenko demultiple method for elastic waves

The reflection response of strongly scattering media often contains complicated interferences between primaries and (internal) multiples, which can lead to imaging artifacts unless handled correctly. Internal multiples can be kinematically predicted, for example by the Jakubowicz method or by the inverse scattering series (ISS), as long as monotonicity, that is, “correct” temporal event ordering, is obeyed. Alternatively, the (conventional) Marchenko method removes all overburdenrelated wavefield interactions by formulating an inverse problem that can be solved if the Green’s and the so-called focusing functions are separable in the time domain, except for an overlap that must be predicted. For acoustic waves, the assumptions of the aforementioned methods are often satisfied within the recording regimes used for seismic imaging. However, elastic media support wave propagation via coupled modes that travel with distinct velocities. Compared to the acoustic case, not only does the multiple issue become significantly more severe, but also violation of monotonicity becomes much more likely. By quantifying the assumptions of the conventional Marchenko method and the ISS, unexpected similarities as well as differences between the requirements of the two methods come to light. Our analysis demonstrates that the conventional Marchenko method relies on a weaker form of monotonicity. However, this advantage must be compensated by providing more prior information, which in the elastic case is an outstanding challenge. Rewriting, or remixing, the conventional Marchenko scheme removes the need for prior information but leads to a stricter monotonicity condition, which is now almost as strict as for the ISS. Finally, we introduce two strategies on how the remixed Marchenko solutions can be used for imperfect, but achievable, demultiple purposes.
Full Reference Image Quality Assessment Method based on Wavelet Features and Edge Intensity

: In this paper a new method for Full Reference Image Quality Assessment is presented. In current work the edge of the image is analyzed and the best scenario about the color is found. In first step the RGB, HSI and YCbCr color aretested. For each color the wavelet transform is applied and the edge of the images are extracted. With edge intensity specification the image quality assessment is tested. As result shown in this paper the PSNR, SROCC and Pearson is calculated.
Evaluation of the Acceptability of Instant Fiber Powder from Thai Vegetables Supplemented with Probiotic Bacteria

several studies have confirmed the potential of Thai vegetables as good sources of dietary fiber. This research evaluated the survival of the Lactobacillus acidophilus FBRLB07 and acceptability in dietary fiber crucial to dietary fiber production for application in the food industry. Thai vegetables (basil seed, malva nut, rice bran, garlic, eggplant) were washed with water, dried, ground to fine powder (particle size of 1.0 mm) and mixed into dietary fiber formulas in a ratio of 1:2:2 (w/w). The study examined three different dietary fiber formulae; Formula 1: mucilage of basil seed, powdered mucilage of malva nut, and powdered rice bran. Formula 2: mucilage of basil seed, powdered mucilage of malva nut, powdered garlic and Formula 3: mucilage of basil seed, powdered mucilage of malva nut, powdered Thai pea eggplant. A probiotic bacterium (L. acidophilus FBRL-B07; 109 CFU/ml) was added to each of the dietary fiber formulas. Evaluation of the dietary fiber formulas focused on their functional properties. Survival of the L. acidophilus FBRL-B07 was monitored during 1 month of storage at 4°C, 25°C and 37°C. Results indicated that storage temperatures of 4°C promoted survival of the L. acidophilus FBRL-B07 in each of the dietary fiber formulas. The highest (p<0.01) approximately 9 10 log CFU/g. Acceptability results, where 0.1% dietary fiber formulas were added to soft drinks, dietary fiber formulas 1 and 3 yielded a preference score of (p>0.01) which was not significant when compared with the control formula. Research findings supported the hypothesis that the production of instant fiber powder from Thai vegetables shows a great potential for application in the food industry, in soft drinks and dairy products such as ice cream. Keywords-dietary fiber; Thai vegetables; rice bran; probiotic; Lacbacillus acidophilus; prebiotic
Combined MEK and EGFR inhibition demonstrates synergistic activity in EGFR-dependent NSCLC

Epidermal growth factor receptor (EGFR) inhibitors are highly effective in treating non-small cell lung cancers (NSCLC) expressing activated EGFR, particularly those harboring EGFR mutations. However, most patients who benefit from EGFR inhibitors achieve only partial responses or stable disease, facilitating the emergence of resistance. Thus, progression-free survival advantages in responding patients are modest. Combination therapy, preferably using agents with synergistic activity, could both improve responses and reduce acquired resistance rates. We hypothesized that combining MEK inhibitors with EGFR inhibitors could result in such a benefit. The MAPK pathway lies downstream of EGFR and transduces both proliferative and survival signals in a variety of cancer types. Inhibitors of this pathway are currently in clinical trials, but little evidence exists supporting the use of these agents as monotherapy in EGFR-dependent NSCLC. In this study, we find EGFR-dependent NSCLC cell lines are moderately sensitive to loss of ERK1/2 activity, either by small molecule inhibition or by siRNA knockdown. The consequence of inhibition is dependent upon the trophic content of the culture media, primarily anti-proliferative in serum-rich conditions and pro-apoptotic in serum-poor conditions. However, when MEK inhibition was combined with EGFR inhibitors, cytotoxic synergy was observed for all EGFR-dependent cell lines tested in serum-containing media. Enhanced cytotoxicity is demonstrated in cell lines with and without EGFR mutations, including those harboring the T790M escape mutation. These findings support future clinical studies that combine EGFR- and MEK1/2-targeted agents to investigate whether improved outcomes can be achieved in clinically screened EGFR-dependent NSCLC.
User Independent Static Hand Gesture Recognition System using Boundary Information

Hand gesture can be object of analysis in many scientific fields due to some of its intrinsic characteristics. Specifically in pattern recognition, it can be studied as a problem that presents a large number of classes and also high variation among the patterns within a class. To deal with such characteristics, we propose to use MLP neural network. The features, based on the boundary information, are presented as histograms to attenuate the effect of non-linear boundary deformations. The performance was evaluated using a 26 postures database, captured from different users. The method presented better performance when compared with DP Matching. In addition, the number of classes in the system can also be extended without significant growth in the processing time
Purification and Characterization of the Glucose Oxidase from Penicillium notatum

This study aims to purification and characterization of the glucose oxidase enzyme from Penicillium notatum, the enzyme was purified by ammonium sulfate precipitation (60%), dialysis and gel filtration chromatography using sephadex G-200, A trial for the purification of glucose oxidase using gel filtration technique resulted in one type of glucose oxidase with specific activity of (62.382 U/mg) with (7.385 folds) purification. the purified glucose oxidase had a maximum activity at pH = 5.5, 45 °C, glucose oxidase was stable with pH values ranging between (5 – 6) and the enzyme was maintained the activity when it incubated into (25 -35) °C for 15 minutes, analyses of the glucose oxidase for molecular weight was carried out by PAGE and SDS-PAGE electrophoresis, which revealed 78 KDa, also molecular weight of the glucose oxidase was achieved by gel filtration technique and was found 87 KDa this means that enzyme consisting of only one subunit, the Km and Vmax value of glucoamylase (B) were (19.6 mM, 7.5 mM/min ) respectively using different concentration of glucose.
SWIM TOOL FOR STUDYING HUMAN PAPILLARY THYROID CARCINOMA

Thyroid cancer is the most frequent endocrine malignancy, and accounts for 1% of all tumours. Papillary thyroid carcinoma (PTC) and follicular thyroid carcinoma are the most frequent. PTC has a strong genetic component, since it displays the highest relative risks in first degree relatives. PTC has a social as well as economic impact that motivates investigation of novel methods and tools that can exploit the wealth of whole-genome expression data made available in recent years to improve cancer clinical practice. Such “big data” provide at the same time an opportunity and a challenge since devising algorithms and tools effectively harvesting the knowledge buried inside them is a major computational endeavor. Here, we study the PTC dataset from TCGA by running a recently developed software called SWIM, in order to extract a small pool of genes, called switch genes, crucially for the transition from physiological to pathological phenotype of the disease understudy. In particular, SWIM unveiled 131 switch genes out of 1718 differential expressed genes whose up-regulation was found strongly associated with p53 signaling pathway. Among the switch genes, we selected some promising candidate to be disease genes for thyroid carcinoma.
An IDS For Attacks Classification Using Recurrent Neural Network For Classification

— In modern life, networks play a significant role, and cybersecurity has been a core field of study. A cyber-security-important intrusion detection system (IDS) monitors the network’s state of software & hardware. Despite decades of growth, there are still challenges for current IDSs to boost detector accuracy, reduce false alarm rates, and detect unknown attacks. Many researchers have been working to create IDSs that force machine learning (ML) methods to solve the above problems. The critical variations between normal data or abnormal data can be found through machine learning procedures with high precision. Deep learning (DL) is an area that has remarkable performance and has become a center of research. In this research, we have used an efficient Neural Network method as the taxonomy of the IDS attacks. The results are remarkable with Recurrent Neural Network (RNN) on the NSL-KDD dataset using python as the simulation tool.
Performance of Ultra-High Performance Fiber Reinforced Concrete at High Temperatures

DOI:10.17605/OSF.IO/KUGRZ Page 1 Abstract— Ultra-high performance fiber reinforced concrete (UHPFRC) represents a new class of concretes. UHPFRC is typically very dense, has ultra-compressive strength in excess of 150 MPa and a low water to binder ratio. High Packing density results in very low permeability and superior durability over high performance concretes. The research on this composite materials with respect to fire is limited. In this investigation, the results of an experimental study carried out at high temperatures on UHPFRCs are presented. Also, the effects of elevated temperatures of 200, 300, 400, 600, and 800°C for one hour on the main mechanical properties of ultra-high performance concrete were studied. Also, the effect of fibers type on spalling behavior of ultra-high performance concrete were studied. For this purpose, the testing was conducted on four ultra-high performance concrete mixes (UHPC): standard UHPC, steel fiber UHPC, polypropylene UHPC and hybrid steel fibers and polypropylene fibers. Compressive strength and tensile strength of UHPC are obtained after exposure to elevated temperatures. The results indicated that, the strength, heat resistance and spalling resistance of the UHPFRC have increased with the use of hybrid steel and polypropylene fibers. The results have shown the effectiveness of adding polypropylene fibers for UHPC in reducing the spalling risk.
DESIGN OF SOME TOPICAL DELIVERY SYSTEMS WITH LIDOCAINE BASED ON COLLAGEN FOR WOUND TREATMENT

For the acute or chronic wounds, the associated pain is an important aspect that has to be considered in the management of their therapy. Among the local anesthetics, the lidocaine proved its efficiency for topical application, in different aetiology pains. Collagen, a well tolerated biomaterial and biodegradable, represents a favorable matrix for the drugs release. In this study some collagen hydrogels different cross-linked with lidocaine were developed, according to a 32 factorial design. The lidocaine in vitro release from the hydrogels was determined using a modified Franz diffusion cell. For establishing the mechanism of the lidocaine release from hydrogel different kinetic models were used. The formulations were also characterized from their rheological properties. Rheological measurements were performed using a rotational viscometer. The flow behaviour was quantified through different rheological models. The designed topical systems presented a thinning through shearing behaviour. Quantitative relations were built between the rheological parameters with biopharmaceutical implications and the lidocaine release kinetic characteristics.
Topological Defects in a Spin 1 BEC

Topological defects and states have been observed in a wide array of systems including superfluid helium films, solid state systems and ultracold atomic experiments. This paper will review the theory of topological defects in ordered media, specifically in ultracold atomic spin 1 systems. We will find a nematic phase in an anti-ferromagnetic spin 1 BEC, similar to the one in liquid crystals. Lastly, we will discuss experimental results in Rb/Na BEC experiments, where the ground state has total spin 1 and topological defects have been directly observed.