Miracle-dz/UniFAR-FTUs huggingface dataset:
Experimental Three-Particle Quantum Nonlocality under Strict Locality Conditions

C. Erven1,6,∗ E. Meyer-Scott, K. Fisher, J. Lavoie, B. L. Higgins, Z. Yan, C. J. Pugh, J.-P. Bourgoin, R. Prevedel, L. K. Shalm, L. Richards, N. Gigov, R. Laflamme, G. Weihs, T. Jennewein, and K. J. Resch1† Institute for Quantum Computing and Department of Physics & Astronomy, University of Waterloo, Waterloo, ON, N2L 3G1, Canada Centre for Ultrahigh Bandwidth Devices for Optical Systems (CUDOS) & MQ Photonics Research Centre, Department of Physics & Astronomy, Macquarie University, Sydney, NSW 2109, Australia Research Institute of Molecular Pathology and Max F. Perutz Laboratories GmbH, Dr. Bohr-Gasse 7-9, 1030, Vienna, Austria National Institute of Standards and Technology, Boulder, CO 80305, USA Institut für Experimentalphysik, Universität Innsbruck, Technikerstrasse 25, 6020 Innsbruck, Austria and Centre for Quantum Photonics, H. H. Wills Physics Laboratory & Department of Electrical and Electronic Engineering, University of Bristol, Merchant Venturers Building, Woodland Road, Bristol, BS8 1UB, UK (Dated: June 8, 2021)
5G Experimentation: The Experience of the Athens 5GENESIS Facility

5G is the latest wireless standard designed to comply with stringent performance requirements for supporting diverse use cases spanning over different verticals, considered not feasible with previous cellular network technology. In this context, the European Commission has funded multiple research projects aiming at validating 5G Key Performance Indicators (KPIs) over large scale field trials on different use cases. The 5GENESIS project provides a flexible and open experimentation suite with network slicing, in order to support and facilitate validation of KPIs over 5G infrastructure. This paper discusses the experimentation framework of the Athens 5GENESIS Facility, presents results on throughput, Round-Trip-Time (RTT) and latency and summarizes the lessons learned from the activities that took place.
LZR: Identifying Unexpected Internet Services

Internet-wide scanning is a commonly used research technique that has helped uncover real-world attacks, ﬁnd cryptographic weaknesses, and understand both operator and mis-creant behavior. Studies that employ scanning have largely assumed that services are hosted on their IANA-assigned ports, overlooking the study of services on unusual ports. In this work, we investigate where Internet services are deployed in practice and evaluate the security posture of services on unexpected ports. We show protocol deployment is more diffuse than previously believed and that protocols run on many additional ports beyond their primary IANA-assigned port. For example, only 3% of HTTP and 6% of TLS services run on ports 80 and 443, respectively. Services on non-standard ports are more likely to be insecure, which results in studies dramatically underestimating the security posture of Internet hosts. Building on our observations, we introduce LZR (“Laser”), a system that identiﬁes 99% of identiﬁable unexpected services in ﬁve handshakes and dramatically reduces the time needed to perform application-layer scans on ports with few responsive expected services (e.g., 5500% speedup on 27017/MongoDB). We conclude with recommendations for future studies.
Deep Learning for COVID-19 Recognition

Pneumonia is a leading cause of death worldwide, and one of the most significant approaches to diagnose pneumonia is Chest X-ray (CXR) since it was used in clinical scenes. Convolutional neural networks (CNNs) have been widely used in computer vision community. Along with the development of CNNs, we want to make use of CNNs to recognize CXR of people who get pneumonia and make classification. It is important, especially during epidemic period. In this paper, we present a new type of residual learning framework, PEPX-Resnet, which makes use of a type of lightweight residual, and apply this network to CXR dataset. The result shows that PEPX-Resnet is easier to optimize and can have better results, especially for COVID-19 cases. PEPX-Resnet could reach higher accuracy, f1 score and some other evaluations for CXR dataset.
Face Biometrics as a Potential Predictor for COVID-19 Susceptibility

Fighting against the COVID-19 pandemic caused by the SARS-CoV-2 virus is one of the most critical challenges facing the global health system today. The possibility to identify the group of persons in the cohort of people under 50 years old, who are sensitive to the COVID-disease by non-invasive methods, is a very perspective approach for estimating the epidemiological state of the human population. The study aimed to identify the features of people's faces with COVID-19 that the most correlate with disease severity could serve as one of these approaches. For this aim, 525 photos of patients' faces with different outcomes of COVID-19 disease were analyzed using the Dlib face recognition convolutional neural network pre-trained for face recognition. Face descriptor vectors were obtained using the convolutional neural network. Facial features were found that predict a person's sensitivity to the SARS-CoV-2 virus (disease severity), and the contribution of each of the features to the risk of developing a severe form of COVID in a person was found. The accuracy of the binary classification of the individual severity of the COVID-19 course using the k-nearest neighbors algorithm on the test dataset was accuracy - 84%, AUC - 0.90.
Assessment of antinutritional factors of lentil based RTC foods as influenced by domestic processing

Pulses are imperative sources of protein, carbohydrates, dietary fibre and minerals inspired worldwide. Lentils are one of the most chief protein loaded pulses, botanically classified as of high nutritional quality. Lentil cook quickly compared to other pulses making them more desirable for human consumption. Antinutrional factors in lentils will bound the nutrients, thereby reducing the nutritional value. Domestic processing and cooking methods are recognized to diminish the antinutritional factors and progress the nutritive value of lentil. Hence, the present work was carried out to develop and reduce the antinutritional factors in lentil based traditional RTC foods such as Pongal, Bisibellabath and Kheer and Sambar from Canadian lentil varieties. The results obtained from this study that phytic acid content was considerably lowered in prepared traditional products. By applying heat, anti-nutritional factors such as trypsin inhibitors and tannin were almost removed. Demand to healthy RTC products is more nowadays because of buyers curiosity to new products and health concern. Hence, the results provide some understanding of the formulated lentil based RTC convenience foods had great potential to provide to population with highly nutritional, healthy foods.
10 Q&A

tients whose depression is severe enough to require hospitalization. The significant morbidity and mortality associated with major depression are in large part preventable. With appropriate treatment, response can be achieved in more than 70% of depressed individuals. However, the proportion of individuals who receive treatment for depression is quite small, and the proportion who receive appropriate treatment is smaller still. Data from the National Comorbidity Study indicate that only half of individuals with three or more mental disorders receive care in any setting over their lifetime, and 40% receive mental health specialty care (Figure 1). For those with one or two disorders, 40% receive care in any setting, and one quarter receive mental health specialty care. It is estimated that 5% to 10% of patients with depression are effectively treated over the course of their lifetime. Most patients with depression have their initial contact with the health care system in the primary care setting, ofBarriers to the Diagnosis of Depression in Primary Care
The Answer Question in Question Answering Systems

In today’s world, Question Answering (QA) Systems have become a viable answer to the problem of information explosion over the web, since they effectively compact the information we are looking for into a single answer. For that effect, a new QA system called Just.Ask was designed. While Just.Ask is already fully operable and giving correct answers to certain questions, there are still several problems that need to be addressed. One of them is the erroneous identification of potential answers and clustering of answers, which can lead to a wrong answer being returned by the system. This work deals with this problem, and is composed by the following tasks: a) create test corpora to properly evaluate the system and its answers module; b) present the baseline results of Just.Ask, with special attention to its answers module; c) perform a state of the art in relating answers and paraphrase identification in order to improve the extraction of answers of Just.Ask; d) analyze errors and detect error patterns over the answer extraction stage; e) present and implement a solution to the problems detected. This will include normalization/integration of the potential answers as well as implementing new clustering techniques; f) Evaluation of the system with the new features proposed.
Synthesis and Conductivity Studies of Poly(Methyl Methacrylate) (PMMA) by Co-Polymerization and Blending with Polyaniline (PANi)

Poly(methyl methacrylate) (PMMA) is a lightweight insulating polymer that possesses good mechanical stability. On the other hand, polyaniline (PANi) is one of the most favorable conducting materials to be used, as it is easily synthesized, cost-effective, and has good conductivity. However, most organic solvents have restricted potential applications due to poor mechanical properties and dispersibility. Compared to PANi, PMMA has more outstanding physical and chemical properties, such as good dimensional stability and better molecular interactions between the monomers. To date, many research studies have focused on incorporating PANi into PMMA. In this review, the properties and suitability of PANi as a conducting material are briefly reviewed. The major parts of this paper reviewed different approaches to incorporating PANi into PMMA, as well as evaluating the modifications to improve its conductivity. Finally, the polymerization condition to prepare PMMA/PANi copolymer to improve its conductivity is also discussed.
Bimetric polytropes

We present a method for solving the constraint equations in the Hassan–Rosen bimetric theory to determine the initial data for the gravitational collapse of spherically symmetric dust. The setup leads to equations similar to those for a polytropic fluid in general relativity, here called a generalized Lane–Emden equation. Using a numerical code which solves the evolution equations in the standard 3+1 form, we also obtain a short term development of the initial data for these bimetric polytropes. The evolution highlights some important features of the bimetric theory such as the interwoven and oscillating null cones representing the essential nonbidiagonality in the dynamics of the two metrics. The simulations are in the strong-field regime and show that, at least at an early stage, the collapse of a dust cloud is similar to that in general relativity, and with no instabilities, albeit with small oscillations in the metric fields.
RMHP_A_293144 331..344

Department of Health Management, School of Public Health, Fujian Medical University, Fuzhou 350122, People’s Republic of China Background: Limited diffusion of health technology has greatly halted the improvement of resource integration and healthcare outcomes. The importance of understanding the dynamics of health technology diffusion is increasingly highlighted. However, the dynamic mechanism of health technology diffusion in the context of the integrated care system (ICS) remained largely unknown. Purpose: To develop and validate the scale on Dynamics of Health Technology Diffusion in Integrated Care System (DHTDICS) for providing an instrument to investigate the health technology diffusion in the ICS in China, by taking the Des-gamma-Carboxy Prothrombin (DCP) test as an example. Methods: Based on previous classical theories such as the theory of planned behavior (TPB), technology acceptance model (TAM), and technology-organization-environment framework (TOE), the scale with 34 items was initially developed. It was tested in a crosssectional questionnaire survey including 246 participants from February to August 2019 in China. Cronbach’s alpha, corrected item-total correlation, and factor loadings were used to assess reliability. Exploratory factor analysis and confirmatory factor analysis were applied to evaluate the validity by assessing factor structures and correlations. Results: Reliability analysis revealed excellent internal consistency. Acceptable validity was confirmed through tests of convergent validity and discriminant validity. Regarding the domains that DHTDICS contributes, the results highlighted 4 domains: personal beliefs (including dimensions of attitudes, subjective norms and perceived behavioral control), technical drivers (including dimensions of ease of use and price rationality), organizational readiness (including dimensions of organizational culture, technology absorptive willingness and technology sharing willingness), and external environment (dimension of industry competition pressure). Conclusion: The findings confirmed the reliability and validity of the scale on DHTDICS. The scale will be not only a scientific tool in determining the dynamics of health technology diffusion in the ICS, but also a helpful reference for developing future interventions to promote health technology diffusion.
Predicting Higher Education MOOCs Engagement-Level Odds; A Stochastic Approach (0098)

Massive open online courses (MOOCs) are said to be different from previous Open Education Resources (OER) is a way that they almost always provide some kind of assessment, certification or accreditation. With millions of users enrolled in MOOCs, low students retention and certification rate can be considered as a potential threat to the overall phenomenon. [1] Research shows that a clear purpose of participation in MOOCs is to attain some form of certificate, professional diploma or accredited credit hour. Presently, this can be accomplished with or without accessing a substantial amount of course contents available. To maximize the utilization of MOOCs, it is important to understand the association between accessed course contents and certification or retention rate. This study used Markov chain to predict the retention and certification likelihood based on the fraction of course material learners accessed. Since the MOOCs under analysis were higher education MOOCs offered by two prestigious universities; MIT and Harvard, we expect this study to eventually contribute towards useful integration of MOOCs in formal academia.
Bountychain: Toward Decentralizing a Bug Bounty Program with Blockchain and IPFS

Bug Bounty Programs (BBPs) play an important role in providing and maintaining security in software applications. These programs allow testers to discover and resolve bugs before the general public is aware of them, preventing incidents of widespread abuse. However, they have shown problems such as organizations providing accountability of reporting bugs and nonrecognition of testers. In this paper, we discuss Bountychain, a decentralized application using Ethereum-based Smart Contracts (SCs) and the Interplanetary File System (IPFS), a distributed file storage system. Blockchain and SCs provide a safe, secure and transparent platform for a BBP. Testers can submit bug reports and organizations can accept or reject the defect via the SCs. Transactions on the blockchain serve as a persistent and transparent record of software bugs, while IPFS serves as a long-term storage system for
Analysis of Dynamic Characteristics of the Multistage Planetary Gear Transmission System with Friction Force

Multistage planetary gear transmission system has been widely utilized in engineering practice due to the salient characteristics, such as high bearing load and large speed ratio. This paper addresses a two-stage planetary gearbox and establishes a system coupling torsional dynamical model which considers the time-varying mesh stiffness, friction forces, and interstage coupling factors. Meanwhile, the friction and lubrication states are classified to comprehensively analyze the calculation of friction coefficients under different conditions. Considering the time-varying influence of friction on the tooth surface under the condition of fluid lubrication, the vibration response under parametric excitation is solved by a numerical method. A multistage planetary transmission test bench is built in the back-to-back form so as to test the vibration of the two-stage planetary gearbox. It shows that the simulation results of the dynamical model are consistent with the test data. Consideration of the calculation of friction on the tooth surface and the friction coefficients is helpful for the establishment of the more accurate dynamical model and lays the foundation for the structural design, fault diagnosis, and dynamic optimization of the multistage planetary gear transmission system.
Study of IoT Architecture and Application Invariant Functionalities

IoT is a conjunction of many technologies and it has spanned across diverse and multidisciplinary application domains. Each domain has its own set of application requirements thereby inhibiting the use of conventional programming models. As a result there is a strong need for intermediate software abstraction layer to hide various technological details underlying IoT. In this paper, we propose two high level concepts of IoT, first is the IoT architecture for systematic study of common characteristics and second is the IoT application invariant functionalities and programming patterns. We think that our work exposes a step forward to review on the overall IoT architecture and provides a lightweight approach when designing software abstraction layer with minimalist programming functionalities that are IoT application invariant.
Molecular dynamics simulation of polmitoyl–coa hydrolase interactions with fmn, clofibrate, 2,4- dichlorophenoxy acetic acid ligands

CoASH Palmitate O H CoA Palmitoyl + → + − 2 The enzyme has 35KDa of molecular weight and uses a magnesium ion and pH 6.5 for optimum action. The enzyme is predominantly found in cytosol, mitochondria and peroxisome. The enzyme belongs to the family of hydrolases which acting on thioester bonds. Other names of this enzyme are long chain fatty acid hydrolase, palmitoyl CoA thioesterase and palmitoyl CoA deacylase [1-2]. palmitoyl-CoA hydrolase has an important role in lipid metabolism, i.e. having role in fatty acid esterification or de esterification, controlling cellular beta oxidation, fatty acid traffic through membrane, signal transduction and even in gene regulation. Defects in palmitoyl-CoA hydrolase leads to decrease in keton body production, hypoglycemia and dysfunction of fatty acid dependent tissues. Enzyme inhibition by inhibitors, e.g. ibuprofen, valproate and 2, 4dichlorophenoxyacetate increases mitochondrial membrane permeability leading to uncoupling of oxidative phosphorylation. In physiological condition, fasting conditions significantly decreases the mitochondrial activity of palmitoylCoA hydrolase and hence causes elevated levels of intra mitochondrial long-chain acyl-CoA accumulation [3]. It have been shown that administration of the antilipemic drugs such as clofibrate causes significant reduction in body weight via increasing cellular beta-oxidation and mitochondrial uncoupling. The plasma concentrations of cholesterol and triglyceride decreased in treated animals with antilipemic drugs [4]. The inhibition of palmitoyl CoA hydrolase by antilipemic drugs are reversible and the drugs lowered the palmitoyl CoAbinding capacity of microsomes [5]. Molecular dynamic (MD) is a powerful technique used to simulate protein-protein, protein-nucleic acid and protein-ligand complexes. Basically, MD simulations gives a valuable and reliable information about enzyme-inhibitor interactions. Based on this fact we used MD method to simulate our enzymeinhibitor systems in hope of understanding the nature of interactions and the mechanisms by which inhibitors inactivate palmitoyl-CoA hydrolase activity [6-9].
A GENERAL OVERVIEW ON THE REGION-TIME BEHAVIORS OF THE RECENT EARTHQUAKE ACTIVITY IN THE MARMARA REGION OF TURKEY

The principal aim of this study is to make a general assessment of the regional and temporal behaviors of the seismicity in the Marmara region of Turkey. For this purpose, the well-known spatio-temporal tools such as Mc-value, b-value, Dc-value, Z-value, recurrence period and annual probability were evaluated. The data catalog is homogeneous for local magnitude, ML, and includes 18,662 events with 0.4≤ML≤5.7 between 2000 and 2020. Earthquake magnitudes change between 1.5 and 3.0 and hence, Mc-value was taken as 2.7. b-value was calculated as 1.140.07 and the earthquakes with small magnitude can be effective on this relatively large value. Dc-value was estimated as 1.570.03 with a scale invariance from 5.03 to 87.64 km. This moderate value means that distance between the epicenters approaches the diameter of cluster and, earthquake activity is more clustered in larger areas or at smaller scales. Time-magnitude estimations of annual probability and recurrence period indicate that the Marmara region has an intermediate/long terms hazard for the occurrence of strong/large earthquakes. Anomaly regions with low b-value and large Z-value observed at the beginning of 2019 can be considered as the most likely regions for strong/large earthquake occurrences in the next. As a remarkable fact, a correlation and combined interpretation of these seismotectonic parameters may supply a preliminary and essential perspective in the seismic hazard for strong/large earthquake occurrences in the Marmara region of Turkey in the intermediate/long terms.
Student Success: Strivers, Striders and Thrivers. (0181)

Part 1: Abstract This paper, based on a small-scale study in a UK university, argues that bespoke learning development (LD) support, which is designed and delivered by Faculty-based academics, can better support students who are striving for academic success, it can give them the confidence and ability to stride through their studies and can enable them to thrive both personally and academically as they transition through and beyond university. Tinto’s (1993) integration model offered a framework within which to gain understandings of how institutional commitment to Learning Development support can harness and cultivate individual attributes, enhancing retention, success and the student experience. The students articulated experiences of LD support that had led them to develop self-efficacy and autonomy. The study has implications for institutional policy and practice, as it has found that bespoke 1:1 LD support has positive impacts on retention and success, which are of increasing importance in HE.
Research on Small Sample Bird Recognition Based on B-CNN

: Bird recognition is a typical fine-grained image classification task. Bilinear-CNN (B-CNN) is a widely used fine-grained image recognition framework. Based on the Object Detection API in the TensorFlow framework, this paper combines the B-CNN algorithm, uses data set fusion to achieve data amplification, and then performs secondary training to solve the problem of small sample training difficulties. Finally, the high-risk airport is realized by B-CNN. Automatic recognition of bird images. The experimental results show that the image neural network detection model based on B-CNN can fully utilize the flexibility of TensorFlow and B-CNN, and effectively improve the stability, speed effectiveness and accuracy of image recognition. The average detection accuracy reaches 85.8. %.