Miracle-dz/UniFAR-FTUs huggingface dataset:
HSL: a Cyber Security Research Facility for Sensitive Data Experiments

In this paper, we detail the design of a cybersecurity facility to carry reproducible and long term research activities in a safe environment, including malware collection and analysis, network telescopes and honeypots, or hosting critical services, without worrying about side effects or loss of data. The facility, aka High Security Lab (HSL), is running since 2010, and is widely used by multiple research groups to carry sensitive data cybersecurity experiments. It includes an evolving infrastructure with tools and processes for building and running long-term and reproducible cyber security experiments. We report on our experience and lessons learned from the design, the setup and the evolution of this facility during 10 years while focusing on major cybersecurity experiments that have been conducted by researchers.
EFFECT OF HELICOBACTER PYLORI INFECTION ON NON VARICEAL BLEEDING IN PATIENTS WITH LIVER CIRRHOSIS

Background: Variceal bleeding in chronic liver disease has been studied extensively. However, 30-40% of upper digestive hemorrhages in cirrhotic patients are non variceal (NVUGIB). Clinical features and endoscopic findings of this population have rarely been reported. Objective: To determine the prevalence of H. pylori infection in cirrhotic patients with non variceal upper gastrointestinal bleeding. Patients and methods: This was carried out at Hepatology, Gastroenterology and Infectious Diseases Department of Al-Azhar University Hospitals, and conducted on 150 patients infected by H. pylori From May 2019 till November 2019. Pateints were selected and divided into three equal groups: Group A: Patients with recent active bleeding (maximum within two days) of non variceal origin diagnosed endoscopically. Group B Matched cirrhotic patients without history of upper gastrointestinal bleeding, and group C Non-cirrhotic patients without history of upper gastrointestinal bleeding. Upper GI Endoscopy was done for all patients of the studied groups. Multiple biopsies were taken and H.pylori infection was determined by Histopathology. Results: The mean age of participants was 54.3 years. Males were 79 (52.6%) while females were 71 (47.3%). 58 of 150 patients (38.6%) were positive for H. pylori. there was no significant relation between H. pylori positivity and cause of cirrhosis. there was significant relation between H. pylori positivity and Sex of the studied cases. And there was high significant difference between the studied groups as regard H. pylori distribution. Conclusion: There was significant association between H. pylori infection and non variceal upper gastrointestinal bleeding. Eradication therapy has a role in reducing the incidence of NVUGIB in cirrhotic patients.
Classi cation of GPCRs Using Family Speci c Motifs

The classi cation of G-Protein Coupled Receptor (GPCR) sequences is an important problem that arises from the need to close the gap between the large number of orphan receptors and the relatively small number of annotated receptors. Equally important is the characterization of GPCR Class A subfamilies and gaining insight into the ligand interaction since GPCR Class A encompasses a very large number of drug-targeted receptors. In this thesis, a method for Class A subfamily classi cation using sequence-derived motifs which characterizes the subfamilies by discovering receptor-ligand interaction sites is proposed. The motifs that best characterize a subfamily are selected by the proposed Distinguishing Power Evaluation (DPE) technique. The experiments performed on GPCR sequence databases show that the proposed method outperforms state-of-the-art classi cation techniques for GPCR Class A subfamily prediction. An important contribution of this thesis is to discover key receptor-ligand interaction sites which is very important for drug design.
Assessment of the socio-economic and environmental development of a region: A result-oriented approach

ARTICLE INFO Borys Burkynskyi, Valerii Horiachuk, Oleksandr Laiko, Nataliya Shlafman and Olena Krivtsova (2021). Assessment of the socio-economic and environmental development of a region: A result-oriented approach. Problems and Perspectives in Management, 19(2), 40-56. doi:10.21511/ppm.19(2). 2021.04 DOI http://dx.doi. org/10.21511/ppm.19(2). 2021.04 RELEASED ON Tuesday, 27 April 2021 RECEIVED ON Wednesday, 10 February 2021 ACCEPTED ON Wednesday, 21 April 2021
Losses, Back-Propagation, and Convolutional Neural Networks

The zero-one loss was introduced in an earlier note as a desirable loss function for classification. Unfortunately, this loss cannot be used for training, as Section 1 will show, so that Section also introduces appropriate proxies for it. Section 2 then shows how to apply the chain rule for differentiation to compute the gradient of the empirical training risk with respect to the parameters of a neural network. Section 3 describes Convolutional Neural Networks (CNNs), a restricted form of deep network with a vastly reduced number of parameters. Finally, Section 4 sketches the state of the art in deep neural networks for image recognition.
F LORAL LONGEVITY , NECTAR PRODUCTION , ANTHER DEHISCENCE , AND STIGMA RECEPTIVITY IN H ASKAP ( L ONICERA CAERULEA )

Haskap (Lonicera caerulea L.) is a temperate fruiting shrub grown commercially in northern regions of Europe, Asia, and North America. Haskap is self-incompatible and requires insect pollinators in order to set fruit; however, very little is currently known about its floral biology or pollinator specializations, particularly in North American cultivars. Here, we examine floral longevity, nectar dynamics, the timing of anther dehiscence, and stigma receptivity in flowers of greenhouse-grown ‘Tundra’, a Haskap cultivar developed and grown in Saskatchewan, Canada. Anthesis lasted 83.3 ± 25.9 hours (mean ± SD) in un-pollinated flowers; pollination caused early senescence within 34.3 ± 15.2 hours after pollination. Nectar was present from the onset of anthesis, and nectar volume peaked at 9-16 hours after opening. Nectar volume was maintained throughout anthesis and was not resorbed prior to abscission of the corolla from the ovary, and nectar removed during anthesis was replenished to the original volume. The stigma showed a reaction to hydrogen peroxide while still in the bud stage, suggesting it is receptive even before the flower opens. Early stigma receptivity, nectar production, and anther dehiscence maximize opportunities to be successfully pollinated, along with high floral longevity and pollination-triggered senescence. These results suggest that Haskap flowers utilize a generalist, rather than a specialized, pollination strategy. Observations that some flowers open in the evening or were already open in the morning suggest that nocturnal pollinators such as moths may be important, in addition to known diurnal pollinators.
HuGE: An Entropy-driven Approach to Efficient and Scalable Graph Embeddings

Graph embedding is becoming widely adopted as an efficient way to learn graph representations required to solve graph analytics problems. However, most existing graph embedding methods, owing to computation-efficiency challenges for large-scale graphs, generally employ a one-size-fits-all strategy to extract information, resulting in a large amount of redundant or inaccurate representations. In this work, we propose HuGE, an efficient and scalable graph embedding method enabled by an entropy-driven mechanism. Specifically, HuGE leverages hybrid-property heuristic random walk to capture node features, which considers both node degree and the number of common neighbors in each walking step. More importantly, to guarantee information effectiveness of sampling, HuGE adopts two heuristic methods to decide the random walk length and the number of walks per node, respectively. Extensive experiments on real-world graphs demonstrate that HuGE achieves both efficiency and performance advantages over recent popular graph embedding approaches. For link prediction and multi-label classification, our approach not only offers >10% average gains, but also exhibits 22×–126× speedup compared with existing sampling-based methods.
Do work ability and well-being matter for return to work? Cut-off points for the Work Ability Index and Life Satisfaction Questionnaire among women with long-term musculoskeletal pain

Background To determine whether work ability and well-being predict return to work (RTW) among women with long-term neck/shoulder and/or back pain at 1-year follow-up, and to assess the ability of the Work Ability Index (WAI) and Life Satisfaction Questionnaire (LiSat-11) to discriminate between those who did RTW and those who did not RTW (NRTW). Methods A survey was sent to 600 women receiving sick leave (SL) benefits from the Swedish Social Insurance Agency. In total, 208 women responded at baseline, and 141 at a 1-year follow-up. To identify whether work ability and well-being predicted RTW, multiple logistic regression analyses were performed with and without adjustment for type of work and pain intensity. To assess the discriminative ability of the WAI and LiSat-11 for women who did RTW and those who did NRTW, receiver operating characteristic curves were fitted. Results Work ability predicted RTW, and the results remained significant after adjusting for type of work and pain intensity (OR 1.12, 95% CI: 1.04–1.22). Well-being was not significant. The WAI at baseline adequately discriminated between RTW and NRTW after one year (AUC 0.78, 95% CI: 0.70–0.86), but the LiSat-11 did not. Conclusions Our results indicate that work ability is an important factor for RTW among women on SL for long-term neck/shoulder and/or back pain. The findings also indicate that the WAI, but not the LiSat-11, is able to discriminate between RTW and NRTW in the population under study. The WAI may be used to identify women at risk of NRTW.
KLASIFIKASI KANKER SERVIKS MENGGUNAKAN MODEL CONVOLUTIONAL NEURAL NETWORK ALEXNET

Menurut Kementerian Kesehatan Republik Indonesia, kanker serviks memiliki kasus sebesar 23,4 per 100.000 penduduk pada tahun 2018. Tingkat prevalensi angka kejadian yang cukup tinggi tersebut tentunya menyulitkan baik pihak pemerintah dan masyarakat dalam upaya pencegahan dan pengendalian kanker. Pada penelitian ini, kami menggunakan model convolutional neural network dengan arsitektur AlexNet untuk mengklasifikasikan gambar dari sel kanker serviks. Model ini terdiri atas 3 lapisan konvolusi, 3 lapisan ReLu, 2 lapisan pooling, dan 3 lapisan fully-connected yang akan mengklasifikasi gambar input serviks menjadi 3 tipe kanker melalui fungsi aktivasi softmax. Dataset yang digunakan merupakan dataset standar yaitu dataset Intel & MobileODT Cervical Cancer Screening. Pengujian model AlexNet dilakukan terhadap parameter iterasi (epoch), subset data training untuk mengupdate bobot AlexNet (minibatch), laju pembelajaran (learning rate), dan resolusi gambar input yang digunakan sebagai patch pembelajaran AlexNet. Dari hasil eksperimen didapati bahwasanya akurasi sangat dipengaruhi oleh jumlah iterasi dari proses pembelajaran model, dimana hasil akurasi terbaik diberikan pada epoch 300. Selain itu, waktu komputasi yang dibutuhkan berbanding lurus dengan jumlah iterasi. Menariknya, parameter lain dari arsitektur model AlexNet seperti minibatch, learning rate, dan resolusi gambar input tidak memberikan pengaruh yang signifikan atau bahkan justru membuat network gagal meningkatkan performanya. Hal ini menunjukkan model AlexNet masih memerlukan modifikasi baik dari segi arsitektur maupun optimisasi terhadap hyperparameter selain dari yang telah diuji pada penelitian ini.
Colored grains: Chemistry, health benefits and processing

Grains are important part of our diet and among them colored grains are getting more research interest among scientist due to their enhanced nutritional, antioxidant and health promoting properties. According to different studies anthocyanins like cyanidin, malvidin, delphinidin, petunidin, peonidin and pelargonidin glycosides were identified and quantified in different colored grains like rice, maize, barley and wheat mainly by chromatographic and spectroscopic methods. Antioxidant activity of these pigmented grains cereals are measured using DPPH, FRAP, ORAC and ABTS assays and it was found to be related to the total anthocyanin content. Currently researches are being conducted on baked and extruded products from colored cereals like bread, biscuit, muffins, pasta, noodles etc. Application of colored grains are limited in to food colorants and in small scale as functional food products due to the thermal stability related issue of the bioactive compounds in colored grains and this area should be taken more in to consideration to stabilize these compounds and to introduce these pigmented grain based products to consumers in large scale after proper market analysis.
Response to Mario Hoppema ’ s comments

The flow (flux) of climate critical gases, such as carbon dioxide (CO2), between the ocean 25 and the atmosphere is a fundamental component of our climate and an important driver of the biogeochemical systems within the oceans. Therefore, the accurate calculation of these air-sea gas fluxes is critical if we are to monitor the oceans and assess the impact that these gases are having on Earth’s climate and ecosystems. FluxEngine is an open source software toolbox that allows users to easily perform calculations of air-sea gas fluxes from model, in-situ and Earth observation data. The 30 original development and verification of the toolbox was described in a previous publication. The toolbox has now been considerably updated to allow its use as a Python library, to enable simplified installation, verification of its installation, to enable the handling of multiple sparingly soluble gases and greatly expanded functionality for supporting in situ dataset analyses. This new functionality for supporting in situ analyses includes user-defined grids, time periods and projections, the ability to re35 analyse in situ CO2 data to a common temperature dataset and the ability to easily calculate gas fluxes using in situ data from drifting buoys, fixed moorings and research cruises. Here we describe these new capabilities and then demonstrate their application through illustrative case studies. The first case study demonstrates the workflow for accurately calculating CO2 fluxes using in situ data from four research cruises from the Surface Ocean CO2 Atlas (SOCAT) database. The second case study calculates air-sea 40 CO2 fluxes using in situ data from a fixed monitoring station in the Baltic Sea. The third case study focuses on nitrous oxide (N2O) and through a user defined gas transfer parameterisation identifies that Revision 5/11/2019 10:49 Deleted: development of Revision 5/11/2019 10:49 Deleted: health of the Revision 5/11/2019 10:49 Deleted: changes to the 45 Revision 5/11/2019 10:49 Deleted: and the toolbox is already being used by scientists across multiple disciplines. Revision 5/11/2019 10:49 Formatted: Font:Italic
Using an ARIMA Model to Forecast GDP Until 2031 for Portugal and Germany

The world economy has been struck by Covid-19 the same way people are struck by a lightning, fast and without warning, leaving nations out to dry on little to no reserves on their crucial supply side. Consequently, over the past year, economies shrunk, production drastically diminished, and governments were forced to take measures. In an unprecedented moment of contemporary history, many economists wondered what the consequences of such an event would be for macroeconomic variables over time and when would the economy be able to bounce back. In the monetary union, the challenge goes even further: how can the union adjust to these changes with common policies, and what measures should be taken to enhance the chances of a fast recovery? In this paper, we use a model to predict the values of real GDP growth and debt to GDP ratio for the next decade for two disparate EU countries, Germany and Portugal. This will help us better understand the evolution of macroeconomic variables to then make the connection with the efforts that can be taken by the European Union to provide a fast and safe recovery back to normality.
The Effects of Restrictive Measures on Cross-Border Investment: Evidence from OECD and Emerging Countries

This paper investigates the effects of global and sectoral restrictive measures on crossborder FDI among 49 advanced and emerging countries. We use a gravity model with panel data from 2010 to 2019 and the FDI Restrictiveness Index of the OECD that quantifies the level of restriction in FDI. Our results suggest that global restrictive measures do not significantly affect cross-border FDI in OECD countries, while restrictions in the service sector have negative and significant effects on FDI. Moreover, the overall restrictive measures and those in the service sector negatively impact inward FDI among OECD and big emerging countries. In addition, restrictions in the services sector impede inward FDI in African countries. Interestingly, restrictions in the secondary sector boost FDI between advanced and African countries, with larger effects for inward investments in African countries. The analysis of disaggregated sectoral restrictive measures shows that restrictions in business and other financial services are negatively associated with intra-OECD FDI, while restrictions in the banking sector have a significant positive impact on FDI. We also find that restrictions in the manufacturing sector have restrictive impacts on inwrad FDI in big emerging countries, and those in the mining, quarrying, and oil extraction sector hinder inward FDI in African countries. Reforms to liberalize sectoral restrictions by country have positive effects on FDI, but deregulation of the services sector has beneficial effects on inward FDI in all countries.
Transferable Sparse Adversarial Attack

Deep neural networks have shown their vulnerability to adversarial attacks. In this paper, we focus on sparse adversarial attack based on the $\ell_0$ norm constraint, which can succeed by only modifying a few pixels of an image. Despite a high attack success rate, prior sparse attack methods achieve a low transferability under the black-box protocol due to overfitting the target model. Therefore, we introduce a generator architecture to alleviate the overfitting issue and thus efficiently craft transferable sparse adversarial examples. Specifically, the generator decouples the sparse perturbation into amplitude and position components. We carefully design a random quantization operator to optimize these two components jointly in an end-to-end way. The experiment shows that our method has improved the transferability by a large margin under a similar sparsity setting compared with state-of-the-art methods. Moreover, our method achieves superior inference speed, 700$\times$ faster than other optimization-based methods. The code is available at https://github.com/shaguopohuaizhe/TSAA.
Valley-related multiple Hall effect in monolayer 
VSi2P4

2D materials with valley-related multiple Hall effect are both fundamentally intriguing and practically appealing to explore novel phenomena and applications, but have been largely overlooked up to date. Here, using first-principles calculations, we present that valley related multiple Hall effect can exist in singlelayer VSi2P4. We identify single-layer VSi2P4 as a ferromagnetic semiconductor with out-of-plane magnetization and valley physics. Arising from the joint effect of inversion symmetry breaking and time reversal symmetry breaking, the exotic spontaneous valley polarization occurs in single-layer VSi2P4, thus facilitating the observation of anomalous valley Hall effect. Moreover, under external strain, band inversion can occur at only one of the valleys of single-layer VSi2P4, enabling the long-sought valley-polarized quantum anomalous Hall effect, and meanwhile the anomalous valley Hall effect is well preserved. Our work not only enriches the research on valley-related multiple Hall effect, but also opens a new avenue for exploring valley-polarized quantum anomalous Hall effect.
3D U-NetR: Low Dose Computed Tomography Reconstruction via Deep Learning and 3 Dimensional Convolutions

In this paper, we introduced a novel deep learning-based reconstruction technique for low-dose CT imaging using 3 dimensional convolutions to include the sagittal information unlike the existing 2 dimensional networks which exploits correlation only in transverse plane. In the proposed reconstruction technique, sparse and noisy sinograms are back-projected to the image domain with FBP operation, then the denoising process is applied with a U-Net like 3-dimensional network called 3D U-NetR. The proposed network is trained with synthetic and real chest CT images, and 2D U-Net is also trained with the same dataset to show the importance of the third dimension in terms of recovering the fine details. The proposed network shows better quantitative performance on SSIM and PSNR, especially in the real chest CT data. More importantly, 3D U-NetR captures medically critical visual details that cannot be visualized by a 2D network on the reconstruction of real CT images with 1/10 of the normal dose.
Generating Interesting Song-to-Song Segues With Dave

We introduce a novel domain-independent algorithm for generating interesting item-to-item textual connections, or segues. Pivotal to our contribution is the introduction of a scoring function for segues, based on their ‘interestingness’. We provide an implementation of our algorithm in the music domain. We refer to our implementation as Dave. Dave is able to generate 1553 different types of segues, that can be broadly categorized as either informative or funny. We evaluate Dave by comparing it against a curated source of song-to-song segues, called The Chain. In the case of informative segues, we find that Dave can produce segues of the same quality, if not better, than those to be found in The Chain. And, we report positive correlation between the values produced by our scoring function and human perceptions of segue quality. The results highlight the validity of our method, and open future directions in the application of segues to recommender systems research.
Sketch and Refine: Towards Faithful and Informative Table-to-Text Generation

Table-to-text generation refers to generating a descriptive text from a key-value table. Traditional autoregressive methods, though can generate text with high fluency, suffer from low coverage and poor faithfulness problems. To mitigate these problems, we propose a novel Skeleton-based two-stage method that combines both Autoregressive and Non-Autoregressive generations (SANA). Our approach includes: (1) skeleton generation with an autoregressive pointer network to select key tokens from the source table; (2) edit-based non-autoregressive generation model to produce texts via iterative insertion and deletion operations. By integrating hard constraints from the skeleton, the non-autoregressive model improves the generation's coverage over the source table and thus enhances its faithfulness. We conduct automatic and human evaluations on both WikiPerson and WikiBio datasets. Experimental results demonstrate that our method outperforms the previous state-of-the-art methods in both automatic and human evaluation, especially on coverage and faithfulness. In particular, we achieve PARENT-T recall of 99.47 in WikiPerson, improving over the existing best results by more than 10 points.