Miracle-dz/UniFAR-FTUs huggingface dataset:
K0: An investigation into using private tokens on smart contract platforms

Smart contract blockchain platforms enable new kinds of decentralised applications used by mutually distrusting parties. However on most such platforms all transactional data is public, which renders them unusable for use cases requiring privacy. “Privacy coin” cryptocurrencies like Zerocash solve this problem for the money transfer use case by enabling private payments on a decentralised (special-purpose) blockchain. We describe an adaptation of this scheme for (general-purpose) smart contract blockchain platforms and propose a slight modiﬁcation to the protocol to allow the use of private payments as triggers for actions in secondary smart contracts independent of the payment system itself.
Nonlinear radial envelope evolution equations and energetic particle transport in tokamak plasmas

This work provides a general description of the self-consistent energetic particle phase space transport in burning plasmas, based on nonlinear gyrokinetic theory. The self consistency is ensured by the evolution equations of the Alfvénic fluctuations by means of nonlinear radial envelope evolution equations, while energetic particle fluxes in the phase space are explicitly constructed from long-lived phase space zonal structures, which are undamped by collisionless processes. As a result, this work provides a viable route to computing fluctuation induced energetic particle transport on long time scales in realistic tokamak plasmas.
Comparison of Mass Transfer Efficiency and Energy Consumption in Static Mixers

The aim of this study was to compare the pressure drop (Δp) generated by a static mixer with sieve plates in two-phase downflow (water as a continuous phase), and the mass transfer efficiency (kLa, a) with the performance of other static mixers (Sulzer, Kenics, Karman, etc.). The relationships for Δp, kLa and interfacial area (a) calculation depending on liquid and gas phase velocities and geometry of the plates (sieves) in this static mixer are presented. kLa was found to be strictly proportional to the power consumption (P/V) and its values were quite close to those obtained in Sulzer & Kenics mixers with an 8-element mesh. Enhancement factors for oxygen absorption in the sodium sulphite solution and for ozone absorption in Lake Ülemiste water were calculated and the plausible values of the interfacial area (a) were estimated.
“Dealumination” and Aluminum Intercalation of Vermiculite

Dealumination of vermiculite was carried out using (NH4)2SiF6 solutions. The dealuminated products were studied by high-resolution solid state 29Si and 27Al nuclear magnetic resonance. A decrease in the cation-exchange capacity (CEC) resulted from the partial removal of Al from the tetrahedral layer, which decreased the framework negative charge, and from the partial replacement of Mg by Al in the octahedral layer, which increased its positive charge contribution. The lowest CEC was obtained by swelling the structure with butyl-ammonium prior to the reaction with (NH4)2SiF6. Thus, CECs in the range observed for beidellite were measured; however, the lowest (Al/Si)IV ratio was still more than twice as high as in beidellite. In addition, the dealumination reaction yielded noncrystalline silica as a by-product.In contact with a solution of Al hydroxypolymer (Al13), the dealuminated vermiculite showed no 18-Å reflection characteristic of Al13-intercalated smectite; instead it showed an ill-defined interstratification. For some samples, however, a significant increase in the specific surface area (as much as 230 m2/g) was observed, suggesting that an intercalation of Al moieties did occur. The 27Al resonance spectra of the intercalated structure showed at least two components in octahedral coordination. On thermal activation, a resonance line attributable to pentacoordinated Al was observed.
Waiting-time distribution and market efficiency: evidence from statistical arbitrage

Statistical arbitrage strategies are market neutral trading strategies, which exploit market inefficiencies by taking long and short positions in a spread in order to generate a positive excess returns. This study analyses the relationship between the variation of waiting-time distribution of the traded spread and market efficiency. A statistical arbitrage optimisation procedure is obtained for both the Markovian and non-Markovian forms of Continuous-time random walk. A detrended spread between the two classes of ordinary shares of Royal Dutch Shell Plc and a spread between the two listings of Australia and New Zealand Banking Group Limited are used to observe market inefficient states. We find that the waitingtime and the survival probabilities have a significant impact on the price dynamics and indicate market inefficiency. keywords: Market inefficiency; Waiting-time distribution; Continuous-time random walk; Markovian process; non-Markovian process
Impact of Alternative Learning System to the Out-of-school-youths, Kasambahay, Indigenous People and Children-in-conflict- with-the-law

This study aimed to establish data on the impact of Alternative Learning System (ALS) to the learner-passers in the Division of Sagay City, Negros Occidental, Philippines, from 2003 to 2015. Mixedmethod research, particularly convergent parallel design was used. The respondents of this study were the 219 ALS passers sampled per year. For the qualitative investigation, 10 participants were chosen from among the classified learner-types. Findings showed that the educational attainment of the ALS learnerpassers improved wherein majority of them finished vocational courses and college degrees. The family monthly income of the respondents also increased along with the improvement in their employment status. Majority of the learner-passers were employed by other persons, business and government establishments. Still a majority of the learner-passers became professionals; there was a dramatic shift from physical labor to office and profession-based jobs. Thus, ALS programs and projects contributed to the improvement of the ALS learners in terms of their educational attainment, family monthly income, employment status, and nature of jobs. Moreover, supports of local government unit and other stakeholders helped the sustainability of ALS programs and projects implementation. This study advances knowledge for the ALS implementers especially on how the investments on the program pays off.
Polarized-Beam High-Energy EDXRF in Geological Samples

ABSTRACT Certified reference materials (NIST 1645, BCR 143, IAEA 7, BCR 141, NIES_CRM_02, and IAEA 375) were used for determining the performance of a secondary target energy-dispersive X-ray fluorescence (EDXRF) spectrometer, Epsilon 5 (PANalytical, Almelo, the Netherlands). For the evaluation of the EDXRF spectra with polarized-beam high-energy excitation, the WinAxil software package has been applied. The results showed that Epsilon 5, EDXRF spectrometry is favorable for the determination of elemental concentrations in geological samples, but the sample preparation has the largest influence on the precision. However, they presented good agreement with certified values for most of the elements.
Deriving Workload Expectations - Monitoring and Analysis Using HPC Job Proﬁles

—With the growing availability of time series metric data from High Performance Computing (HPC) machines, there is signiﬁcant potential for using this data to improve the monitoring and analysis of HPC workloads and the systems on which they run. In this paper, we detail the statistical methods for dynamically generating job proﬁles and workload expectations from this data. This establishes a basis for live job monitoring and enables various methods for detecting aberrant job performance. When a deviation is detected, it can be visualized by displaying the job’s metric series plotted on top of its expectation, which is represented as a cloud path. We also demonstrate how to identify system-wide anomalies by monitoring the entire set of jobs on a machine for acute, synchronized deviations. These tools can be applied to a variety of use cases such as shared resource scheduling, benchmark trending, system utilization and planning, and log analytics. In the case where jobs are not explicitly grouped by workload type, machine learning techniques can be employed to infer job types based on the shape of the time series data. Jobs are automatically classiﬁed, and when a user’s workload classiﬁcation changes, this indicates that something on the system (for benchmark jobs) or in the user’s workload has changed.
THE EFFECT OF SPRAYING NANO, CHEMICAL AND BIO FERTILIZER AND THEIR INTERACTION ON SOME GROWTH AND YIELD TRAITS OF EUROPEAN GRAPES VITIS VINIFERA L. CV. AL-KHALILI

The experiment was conducted in one of the private orchards in the Al-Medhatia Al-Seyahi region in the Babylon province during the agriculture seasons 2019-2020 on Vitis vinifera L. cv. Al-Khalili grape cultivar. To study the effect of spraying with nano fertilizer at a concentration of (0, 10, 20) mg.L-1 and the chemical fertilizer at a concentration of (10, 20) mg.L-1 and bio fertilization with a concentration of (0, 5) mg.L-1 and their interaction In some characteristics of vegetative growth and the yield of the Al-Khalili grape cultivar. A factorial experiment was conducted according to the randomized complete block design (R.C.B.D ) with three factors distributed randomly with their concentrations in three replicates. The results were analyzed by using the Genstat program in data analysis to study the effect of each of the three factors and their interaction in the studied traits. Average treatments were tested according to the least significant difference test (L.S.D) and below at the probability level 5% . Nano fertilizer spray showed a significant effect on most studied traits, especially the concentration (20 mg.L-1) compared with (10 mg.L-1) and the control treatment where it excelled in (leaf area 168.2 cm2) , the relative content of chlorophyll 67.36 SPAD , the percentage of nitrogen 4.453% , the percentage of Phosphorous is 1.283%, the potassium percentage is 3.802%, the fresh weight is 7.016 g and the total yield 280.1 kg). The effect of spraying with chemical fertilizer was significant with the studied traits (leaf area 165.2 cm2, the relative content of chlorophyll 63.21 SPAD, the percentage of nitrogen 4.0720% and the total yield of 287.4 kg) and the use of biofertilizer concentrate (5 ml.L-1) has no effect except in the traits of fresh weight of leaves 6.877 g compared to the control treatment. The bi-interaction treatment between the nano fertilizer with a concentration of (20 mg.L-1) and the chemical fertilizer (20 mg.L-1) significantly affected in most of the studied traits ( leaf area of the vine was 175.2 cm and the total yield was 300.3 kg), while the treatment of the bi interaction between the nano fertilizer at a concentration of (20 mg.L-1) and chemical fertilizer (20 ml .L-1), a significant effect on the nitrogen percentage was 4.613% with the control treatment.
“GUI Based Home Automation System Operated By Voice Command”

: This paper presents the design and implementation of a Home Automation system Operated by Voice Command. It uses human-computer interaction to control different devices. In this paper, ASR (Automatic Speech Recognition) has been used. The automatic speech recognition (ASR) system is used to accurately and efficiently convert a speech signal into a text message by using MATLAB. The speech signal is captured by using microphone. This signal is pre-processed at front-end for feature extraction using Mel Frequency Cepstrum Coefficient (MFCC). The obtained features are compared at back end for pattern matching using Dynamic Time Wrapping (DTW). After comparing features of recorded voice with database, controlling command is send to microcontroller to control devices. This system is helpful for quadriplegia and blind people.
Treatment challenges in a very high-risk diabetic patient with coronary artery disease

Treatment challenges in a very high-risk diabetic patient with coronary artery disease Dragica Andrić1, Milenko Čanković1,2, Anastazija Stojšić-Milosavljević1,2, Dragan Tešić2,5, Stefan Andrić1, Milovan Petrović1,2, Jadranka Dejanović1,2, Tatjana Miljković1,2, Milan Nedeljković3,4 1Institute for Cardiovascular Diseases of Vojvodina, Cardiology Clinic, Sremska Kamenica, Serbia, 2University of Novi Sad, Faculty of Medicine, 3 Clinical Center of Serbia, Cardiology Clinic, Belgrade, Serbia, 4 University of Belgrade, Faculty of Medicine, 5 Clinical Center of Vojvodina, Diabetes and Endocrinology Clinic Case report
Purification of Anthocyanins with o-Dihydroxy Arrangement by Sorption in Cationic Resins Charged with Fe(III)

In the present work, a new purification method of anthocyanins with o-dihydroxy arrangement is proposed. This method is based on a ligand-exchange mechanism, using a cationic exchange resin loaded with metallic ions in order to increase the affinity of the resin to the anthocyanin(s) with o-dihydroxy arrangement. This method was used to purify the main anthocyanin (cyanidin-3-glucoside; Cy-3-glc) from the anthocyanic methanolic extract of blue corn. The best sorption result was using Fe(III) in its ion form. The purification procedure begins with the formation of a metal-anthocyanin complex (Cy-3-glc-Fe) which was optimal at pH 5, followed by a NaOH 0.1 M elution process in order to eliminate anthocyanins without o-dihydroxy arrangement, sugars, and organic acids. Finally, the pure anthocyanin is obtained by adding HCl 0.1 M which breaks the metal-anthocyanin complex.
A Survey Analysis of Recognition-Based Graphical Password Art-form Design Attributes

Over the current century, recognition-based graphical password models (RGPM) that embed art-forms in their interfaces have been designed to enable users to comply with fundamental usability and security requirements, yet users still find it a challenge to comply with these requirements. Though some studies have centered on humans as the weakest link in the security chain, recent assertions from the literature have shown that the choice of art-forms in graphical password models and allowing users to choose their own art-forms or click point within an art-form during authentication can have a direct effect on a system’s usability and security level. This study is a literature survey on RGPM. The occurrence of some important design attributes relating to RGPM are analyzed in order to determine unique design attributes that can be used as a framework to measure the quality RGPM from designers’ perspective. Our result shows that there exist 80 art-form design attributes that can be used as a framework to measure the quality of art-forms in RGPM.
Cylinder Detection From RGBD Data Based on Radius Estimation Using Number of Measurement Points

In this paper, we describe a method to estimate cylindrical parameters using RGBD data. As cylindrical shape is one of the shape elements that composes of a building, to know cylindrical parameters accurately is useful to robots aiming to grasp such part. Therefore, we propose a method to estimate the diameter and the inclination of a cylinder part. We formulate the relationship between cylinder diameter and the number of 3D points measured from the cylinder. It enables to estimate cylinder diameter from measured point cloud directly. Through proof experiments, we show that our method is more accurate than conventional methods. Moreover, as an example of the search activity at disaster environments, we report an experiment on grasping a ladder by an autonomous mobile manipulator and showed the effectiveness of the proposed method.
Do fiscal regimes matter for fiscal sustainability in South Africa? A Markov-switching approach

This paper empirically examines South Africa’s fiscal sustainability through a Markov-switching model which utilizes quarterly datasets for the period from 1960 to 2019. The results show that public debt responds positively, demonstrating a sustainable fiscal policy. Furthermore, considering the regime-specific feedback coefficients of the fiscal policy rule and the durations of fiscal regimes, the study finds that South Africa’s fiscal policy satisfies the No-Ponzi game condition. Therefore, from a policy perspective, the South African government should take measures such as pension reforms, reducing operational expenses, reducing subsidies, and funding micro and small enterprises to gain the double dividend on the expenditure side along with revenue-enhancing measures on consumption taxes to achieve stable public finances and lower debt levels.
The humoral response to BK polyomavirus in kidney transplant recipients is dominated by IgM antibodies that use a distinct repertoire compared to IgG against the same antigen

The BK polyomavirus (BKPyV) persists asymptomatically in the kidney and active replication is only seen in immunosuppressed individuals, such as kidney transplant (KTx) recipients, in whom BKPyV reactivation can cause significant morbidity. KTx recipients with BKPyV reactivation mount a robust humoral response, but this often fails to clear the virus. In order to characterize the BKPyV-specific B-cell receptor (BCR) repertoire in KTx recipients, we used fluorescence-labeled BKPyV virus-like particles (VLPs) to sort with BKPyV-specific B-cells, then single-cell RNAseq to obtain paired heavy and light chain antibody sequences, and gene transcriptome data. The BCR repertoire was highly diverse in terms of both V-gene usage and clonotype diversity, with approximately 3% repertoire overlap between patients. The BKPyV-specific response was characterized by the presence of both memory IgG and memory IgM B-cells with extensive somatic hypermutation, which expressed distinct BCR repertoires within the same patient. The gene expression profile of IgG and IgM memory B-cells was highly similar, with only 19 genes, including CD83, CD79A and PARP1 showing significant differential expression. These results confirm that the IgM memory B-cells are a significant component of the BKPyV-specific humoral response, and show for the first time that IgG and IgM repertoires directed against the same antigen can have significant differences.
Accelerate CNNs from Three Dimensions: A Comprehensive Pruning Framework

Most neural network pruning methods, such as filter-level and layer-level prunings, prune the network model along one dimension (depth, width, or resolution) solely to meet a computational budget. However, such a pruning policy often leads to excessive reduction of that dimension, thus inducing a huge accuracy loss. To alleviate this issue, we argue that pruning should be conducted along three dimensions comprehensively. For this purpose, our pruning framework formulates pruning as an optimization problem. Specifically, it first casts the relationships between a certain model's accuracy and depth/width/resolution into a polynomial regression and then maximizes the polynomial to acquire the optimal values for the three dimensions. Finally, the model is pruned along the three optimal dimensions accordingly. In this framework, since collecting too much data for training the regression is very time-costly, we propose two approaches to lower the cost: 1) specializing the polynomial to ensure an accurate regression even with less training data; 2) employing iterative pruning and fine-tuning to collect the data faster. Extensive experiments show that our proposed algorithm surpasses state-of-the-art pruning algorithms and even neural architecture search-based algorithms.
The phenomenology of dynamical neutron star tides

We introduce a phenomenological, physically motivated, model for the effective tidal deformability of a neutron star, adding the frequency dependence (associated with the star's fundamental mode of oscillation) that comes into play during the late stages of the binary inspiral. Testing the model against alternative descriptions, we demonstrate that it provides an accurate representation of the dynamical tide up to close to merger. The simplicity of the prescription makes it an attractive alternative for a gravitational-wave data analysis implementation, facilitating an inexpensive construction of a large number of templates covering the relevant parameter space.
Security and Privacy for Artificial Intelligence: Opportunities and Challenges

The increased adoption of Artificial Intelligence (AI) presents an opportunity to solve many socio-economic and environmental challenges; however, this cannot happen without securing AI-enabled technologies. In recent years, most AI models are vulnerable to advanced and sophisticated hacking techniques. This challenge has motivated concerted research efforts into adversarial AI, with the aim of developing robust machine and deep learning models that are resilient to different types of adversarial scenarios. In this paper, we present a holistic cyber security review that demonstrates adversarial attacks against AI applications, including aspects such as adversarial knowledge and capabilities, as well as existing methods for generating adversarial examples and existing cyber defence models. We explain mathematical AI models, especially new variants of reinforcement and federated learning, to demonstrate how attack vectors would exploit vulnerabilities of AI models. We also propose a systematic framework for demonstrating attack techniques against AI applications and reviewed several cyber defences that would protect AI applications against those attacks. We also highlight the importance of understanding the adversarial goals and their capabilities, especially the recent attacks against industry applications, to develop adaptive defences that assess to secure AI applications. Finally, we describe the main challenges and future research directions in the domain of security and privacy of AI technologies.
A modular framework for extreme weather generation

Extreme weather events have an enormous impact on society and are expected to become more frequent and severe with climate change. In this context, resilience planning becomes crucial for risk mitigation and coping with these extreme events. Machine learning techniques can play a critical role in resilience planning through the generation of realistic extreme weather event scenarios that can be used to evaluate possible mitigation actions. This paper proposes a modular framework that relies on interchangeable components to produce extreme weather event scenarios. We discuss possible alternatives for each of the components and show initial results comparing two approaches on the task of generating precipitation scenarios.
Leptogenesis in an extended seesaw model with U(1)
 B-L
 symmetry

We have explored an extended seesaw model accommodating a keV sterile neutrino adopting U(1)B-L symmetry. This model provides a natural platform for achieving resonant leptogenesis to account for the observed baryon asymmetry of the Universe. The required lepton asymmetry is sourced by the CP violating decay of the lightest heavy right handed neutrino to Standard Model leptons and Higgs. The presence of the light sterile neutrino in the model brings out an enhancement in the ﬁnal lepton asymmetry through an additional self-energy contribution. Adopting a proper treatment for all the washout processes this framework strictly favours a strong washout regime thereby protecting the low energy neutrino mass parameters in agreement with the present neutrino and cosmology data. This framework of extended seesaw scheme oﬀers the source of matter-antimatter asymmetry without any severe ﬁne tuning of the Yukawa couplings governing the tiny neutrino mass. We also comment on the half-life period for the neutrino less double beta decay process in the background of having a keV sterile neutrino satisfying all the constraints which guide the explanation for the observed baryon asymmetry of the Universe.