Miracle-dz/UniFAR-FTUs huggingface dataset:
Implementation of network moving target defense in embedded systems

Moving target defense provides opportunities for adaptive defense in embedded systems. A great deal of work has been done on incorporating moving target defense techniques into enterprise systems to increase the cost to attackers and level the playing field. A smaller body of work focuses on implementing these techniques in embedded systems, which can greatly benefit from adaptive self-defense techniques. This work implements a network shuffling proof of concept in the Zephyr real time operating system to tackle the challenge of incorporating shuffling techniques into embedded systems. A host-centric, high security implementation is provided which maximizes attacker uncertainty and minimizes the impact of host compromise. Identifiers are utilized at the datalink, network, and transport layers and rotated per connection using keys shared between host pairs. Existing shuffling schemes are explored, including those targeted to IoT contexts. Existing limitations in protecting embedded systems are considered along with the presented by moving target defense. The design details and implementation of incorporating a moving target defense module to in the Zephyr networking stack is provided. The protection provided by the scheme is evaluated and it is compared to existing address shuffling schemes. Future work in better handling data forwarding and collisions in the proof of concept scheme are considered. Options for adapting and building on the scheme to meet the needs of system designers are explored. This work provides system designers with insights into implementing address shuffling in embedded systems.
The Role of Remittances in Financial Development: Evidence from Nonlinear ARDL and Asymmetric Causality

This study’s impetus is to explore fresh evidence to answer the question, i.e., whether remittances asymmetrically influence financial development in Bangladesh from 1975 to 2019. The study employs several tests, i.e., nonlinear unit root test, Autoregressive Distributed Lagged (ARDL), NARDL, and asymmetric causality test for establishing the pattern of association. Nonlinear unit root tests confirm that variables follow a nonlinear system of being stationary after the first difference. nonlinearity among variables is investigated by performing the BDS test and nonlinear OLS. Directional causality is investigated through both linear and nonlinear effects of remittance inflows by following the non-granger casualty test. The test statistics of Fpass and tBDM showed the Long-run cointegration in the empirical model and positive effect running from remittances inflow to financial development both in the long-run and short-run. Furthermore, the results of a standard Wald test divulge the presence of long-run and short-run asymmetry. Asymmetry causality test established unidirectional causality due to positive and negative shocks in remittances inflows to Bank-based financial development and feedback hypothesis hold for explaining causality between positive and negative shocks in remittance inflows and Stock-based financial development.
Trapping cidal spray constituents using blend masks in agricultural practices

India ranks twelfth in the world for the use of pesticides. Exposure to pesticides causes tremendous health effects in people as well as in the environment. But the effect can be controlled when it is handled properly. The baseline survey was conducted to know the awareness status of the farmer, the hazardous effect faced, and the culprit spray responsible for mishaps. The various districts of Tamil Nadu, Telangana, Andhra Pradesh, Karnataka and the Union territory, Puducherry were under the survey area. The chi-square value (32.9938) suggests that there is a strong bearing between the awareness status of the farmer and the farmers undertaking safety measures. The toxicity information provided by the cide-seller is negatively correlated with farmers undertaking safety measures (r= -0.0382). There is no association was found between pre-incidence health issues and causalities (chi-square value= 151.9). The five culprit sprays such as Methoxychlor, Profenofos, Glyphosate, Diafenthiuron and Imidacloprid were selected for further study. These chemicals were docked against the target proteins. The target protein can be blended on the mask to trap the culprit spray. This would prevent the entry of pesticides into the respiratory tract of farmers and thereby prevent the mishaps.
VIRTUAL TESTING AND EXPERIMENTAL VERIFICATION OF SEAT COMFORT IN DRIVER’S SEAT FOR PASSENGER AUTOMOBILE

The choice of passenger automobile depends on a lot of factors, such as, vehicle type, brand, trend, security, performance, space inside in the vehicle, design of the vehicle interior, additional equipment and other. Also, the seat comfort is very important issue where the owners of automobiles are very careful. Because the long time they spend in their car, people complain on pain caused by the discomfort of the seats. The expectations of customers regarding the seat comfort are continuously increasing.
First principles study of structural and magnetic properties of transition metal nitrides TMN (TM = Cr, Mn)

ABSTRACT The structural stability of chromium nitride (CrN) and manganese nitride (MnN) is investigated among four different structures, namely, NaCl (Fm3m), zinc blende (F4-3m), orthorhombic (Pnma) and tetragonal (I4/mmm). It is found that the most stable phase is the zinc blende phase for CrN and MnN. The structural phase transition from zinc blende to orthorhombic phase is predicted at high pressure. At normal pressure, CrN and MnN are found to be antiferromagnetic. As the pressure is increased, antiferromagnetic-to-nonmagnetic phase transition is observed at the pressures of 169.5 GPa in CrN and 206 GPa in MnN. The elastic constants obey the Born--Huang criteria, suggesting that they are mechanically stable. The calculated B/G values indicate that CrN and MnN are ductile in nature.
Understanding Managers’ Tradeoffs between Exploration and Exploitation

Managers frequently explore new strategies, and exploit familiar ones, when making decisions on new product development, pricing, or advertising. Exploring for too long, or exploiting too soon, will generate inferior financial returns. Our research describes managers idiosyncratic exploration/exploitation tradeoffs, their link to psychometric traits, and their evolution across different learning environments. We conduct three incentive-aligned studies in which subjects play a multi-armed bandit experiment. Study 1 describes how subjects balance exploration and exploitation, linked to psychometric traits. Study 2 allows for repeated opportunities to learn. Study 3 manipulates the planning horizon of the learning environment. To formally describe exploration/exploitation tradeoffs, we develop a behavioral model that captures latent dynamics in learning behavior. Subjects transition between three unobserved states: exploration, exploitation, and inertia, updating their beliefs about expected payoffs. Our analysis suggests that managers over-explore low-performing options, forgoing over 30% of potential revenue. Managers heavily rely on recent experiences. Riskaverse decision-makers spend more time exploring, and maximizers are more sensitive to payoffs than satisficers. Offering repeated opportunities to learn and increasing the planning horizon appears beneficial. Our research builds the groundwork needed to devise remedial actions aimed at assisting managers find an optimal balance between exploration and exploitation.
Large Sample Problems

Variance is very important in test statistics as it measures the degree of reliability of estimates. It depends not only on the sample size but also on other factors such as population size, type of data and its distribution, and method of sampling or experiments. But here, we assume that these other fasctors are fixed, and that the test statistic depends only on the sample size. When the sample size is larger, the variance will be smaller. Smaller variance makes test statistics larger or gives more significant results in testing a hypothesis. Whatever the hypothesis is, it does not matter. Thus, the test result is often misleading because much of it reflects the sample size. Therefore, we discuss the large sample problem in performing traditional tests and show how to fix this problem.
Sex Identification of in vitro Plants of Carica papaya L. MSXJ Hybrid through Molecular Markers

Objective: To identify the sex of in vitro plants of papaya (Carica papaya L.) MSXJ hybrid obtained via somatic organogenesis, through SCAR type molecular markers. Design/Methodology/Approach: Eight-month old MSXJ papaya hybrid plants in the fructification stage were collected in Cotaxtla, Veracruz, Mexico. They were superficially disinfected with abundant running water, detergent during 30 min, and then alcohol at 70% was added for one minute, commercial chlorine at 30% for 30 min, and they were rinsed with sterile distilled water; then the meristems were cultivated in MS medium and after 30 d a subculture was made. The DNA extraction was made with the CTAB method, and the DNA PCR was done with the Deputy et al. (2002) method, and the primers T1, T12 and W11 were used. Results: The T1 primer was the positive control and the T12 and W11 primers allowed the amplification of fragments that identify hermaphrodite, feminine and male plants, while the T12 and W11 primers were specific for hermaphrodite plants. Study Limitations/Implications: It is required to standardize the method for it to be inexpensive. Findings/Conclusions: The sexuality of papaya plants can be differentiated until the stage of flowering, which is why the implementation of molecular markers would facilitate plant selection if it is implemented at a large scale. Costs, maintenance time and elimination of plants of unwanted sex are reduced this way.
Discovering diverse athletic jumping strategies

We present a framework that enables the discovery of diverse and natural-looking motion strategies for athletic skills such as the high jump. The strategies are realized as control policies for physics-based characters. Given a task objective and an initial character configuration, the combination of physics simulation and deep reinforcement learning (DRL) provides a suitable starting point for automatic control policy training. To facilitate the learning of realistic human motions, we propose a Pose Variational Autoencoder (P-VAE) to constrain the actions to a subspace of natural poses. In contrast to motion imitation methods, a rich variety of novel strategies can naturally emerge by exploring initial character states through a sample-efficient Bayesian diversity search (BDS) algorithm. A second stage of optimization that encourages novel policies can further enrich the unique strategies discovered. Our method allows for the discovery of diverse and novel strategies for athletic jumping motions such as high jumps and obstacle jumps with no motion examples and less reward engineering than prior work.
Monetary value of human capital disclosure for predicting future business earnings

Orientation: Human capital (HC) is a key dimension of intellectual capital (IC) that creates shareholder value when integrated with structural capital (SC) and relational capital (RC). The disclosure of HC reveals how the human resource (HR) practices support business growth. Research purpose: The primary focus of this study was to investigate the extent of how human capital disclosure (HCD) could predict organisational performance (expected future earnings) of the Johannesburg Stock Exchange (JSE)-listed companies. Motivation for the study: Human capital disclosure reduces information asymmetry, predicts future business performance and improves the investors’ buy-sell-hold decisions. Research approach/design and method: A causal, comparative design was applied quantitatively in a cross-sectional survey of 150 top- and low-performing JSE-listed companies sampled based on their market capitalisation. A self-constructed disclosure index with a seven-point scoring system was developed to examine the extent of disclosure across 81 items. Subsequently, multiple regression analysis was computed to test the relationship between the dependent and independent variables. Main findings: Human capital disclosure was associated with both the market capitalisation and book value. Overall, no mediation of human resource risk disclosure (HRRD) against organisational performance was found. Lastly, information disclosed on gender diversity yielded the interaction effect with both structural capital disclosure (SCD) and relational capital disclosure (RCD) but not with HCD. Practical/managerial implications: Human capital disclosure identifies key HR practices that drive organisational performance and create shareholder value. This information can potentially improve benchmarking and support investor decision-making. Contribution/value-add: The study provides a novel contribution to IC theory by demonstrating how HCD can be used to identify HR practices that leverage the monetary value of HC for business improvement.
Influence of sodium dodecyl sulfate on the morphology and performance of titanate nanotubes/polyethersulfone mixed-matrix membranes

The influence of sodium dodecyl sulfate (SDS) on the morphology and performance of mixed-matrix polyethersulfone (PES) membranes modified with titanate nanotubes (TNTs) was studied. The membranes were prepared via wet-phase inversion method using N,N-dimethylformamide (DMF) as PES (15 wt.%) solvent and SDS surfactant (0.25–5 wt.%) as a hydrophilic pore forming additive. The physicochemical properties, morphology and topography of the membranes were examined on a basis of scanning electron microscopy (SEM), atomic force microscopy and contact angle measurements. The effect of casting dope composition on water permeability, rejection characteristics and resistance to fouling of the membranes was also evaluated. The SDS loading significantly influenced all the above parameters. The water permeability was correlated with the hydrophilicity and the mean size of the pores on the membrane surface. The highest pure water flux was observed for the membrane modified with 2.5 wt. % SDS and 1 wt. % (vs. PES) of TNTs. All the hybrid membranes exhibited superior antifouling properties towards bovine serum albumin (BSA) and sodium alginate (SA) compared with the neat PES one. Moreover, the fouling by SA was found to be less significant than by BSA.
Using Recurrent Neural Networks to Predict Future Events in a Case with Application to Cyber Security

—Due to the number of cyber attacks targeting business organisations daily, anomaly detection software generates large numbers of alerts. While this information is invaluable to Incident Response Teams, one problem is to prioritize these alerts and to distinguish between those that signal a serious threat to network enterprises and low priority alerts. One approach is to use a model that relates an organisation’s missions, processes, services and infrastructure. By predicting future events in existing business processes, and subsequently using this model to identify associated services and infrastructure, cyber security personnel can prioritize critical alerts that threaten these assets. Long Short Term Memory based deep learning models are suited to modeling sequential data, and in particular can model long term dependencies in sequences. This paper evaluates the use of such models to predict subsequent events in ongoing cases. Two training techniques are applied to four data sets. The techniques are evaluated with respect to the accuracy of the predictions and their performance on predicting frequent and infrequent events.
A Novel Method for Image Encryption Using Modified Logistic Map

With the development of the internet and social networks, the multimedia data, particularly digital images, has been of increasing interest to scientists. Due to their advantages including high speed, high security, and complexity, chaotic functions have been broadly employed in image encryption. The present paper proposed a modified logistic map function which resulted in higher scattering in the obtained results. Confusion and diffusion functions, as the two main actions in cryptography, are not necessarily performed in order, i.e. each of these two functions can be applied on the image in either order, provided that the sum of total functions does not exceed 10. So, to calculate the sum of functions, confusion has the factor of 1 and diffusion has the factor of 2. To simulate this method, a binary stack was used. Application of binary stack and pseudo-random numbers obtained from the modified chaotic function increased the complexity of the proposed encryption algorithm. The security key length, entropy value, NPCR and UACI values, and correlation coefficient represented in the analytical results revealed the capability and validity of the proposed method. Analyzing the obtained results and comparing the algorithm to other investigated methods clearly verified high efficiency of the proposed method.
CorefUD 0 . 1 Coreference meets Universal Dependencies – a pilot experiment on harmonizing coreference datasets for 11 languages

We describe a pilot experiment aimed at harmonizing diverse data resources that contain coreferencerelated annotations. We converted 17 existing datasets for 11 languages into a common annotation scheme based on Universal Dependencies, and released a subset of the resulting collection publicly under the name CorefUD 0.1 via the LINDAT-CLARIAH-CZ repository (http://hdl.handle.net/ 11234/1-3510). All the datasets included in CorefUD 0.1 are enriched with automatic morphological and syntactic annotations which are fully compliant with the standards of the Universal Dependencies project. The datasets are stored in the CoNLL-U format, with coreferenceand bridging-specific information captured by attribute-value pairs located in the MISC column. This report describes our current knowledge and the results of the harmonization procedure valid in March 2021; see https://ufal.mff. cuni.cz/corefud for future updates.
Quality of mangabeira fruits from different maturation stages treated with CaCl2

Hancornia speciosa is a fruit tree, popularly known as mangabeiras. The mangaba, fruits of this tree, are quite appreciated for their organoleptic characteristics. Because it is a climacteric fruit, this fruit has very high perishability. The use of products that extend the useful life is necessary. Calcium chloride (CaCl2) has been shown to be an alternative in post-harvest because it promotes few changes in fruit quality and increases the storage period. Thus, the objective of this work was to evaluate quality attributes of mangaba fruits in two stages of maturation, 'Immature' and 'Mature', submitted to CaCl2 application, in four storage times (0, 2, 4 and 6 days) under ambient atmosphere. During the experiment, the loss of fresh weight, color, pH, titratable acidity, soluble solids and SS/TA ratio were evaluated. It was verified that the 'mature' fruits showed a higher acidity and soluble solids content, even with the application of CaCl2, the difference that the loss with the application of CaCl2 was smaller. Unlike '‘Immature’ and ‘Immature’ fruits with CaCl2 in which these characteristics were acquired as the experiment was conducted, in addition to presenting lower values for weight loss, pH and color.
DQSGD: D YNAMIC Q UANTIZED S TOCHASTIC G RA - DIENT D ESCENT FOR C OMMUNICATION - EFFICIENT D ISTRIBUTED L EARNING

bits adaptively by taking into account the norm of gradients, the communication budget, and the remaining number of iterations. We validate our theoretical analysis through extensive experiments on large-scale Computer Vision (CV) and Natural Language Processing (NLP) tasks, including image classiﬁcation tasks on CIFAR-10 and CIFAR-100 and text classiﬁcation tasks on AG-News. Numerical results show that our proposed DQSGD signiﬁcantly outperforms the baseline quantization methods.
Image Texture Analysis for Medical Image Mining: A Comparative Study Direct to Osteoarthritis Classification using Knee X-ray Image

— Knee Osteoarthritis (OA) is one of the most prominent diseases in an ageing society and has affected over 10 million people in Thailand. When people suffer from OA, it is very difficult to recover. Therefore, early detection and prevention are important. The typical way to detect OA is by using X-ray imaging. This research study is focused on early detection of OA by applying image processing and classification techniques to knee X-ray imagery. The fundamental concept is to find a region of interest, use feature extraction techniques and build a classifier that can classify between OA or non-OA imageries. There are four regions of interest obtained from each image: (i) Medial Femur (MF), (ii) Lateral Femur (LF), (iii) Medial Tibia (MT), and (iv) Lateral Tibia (LT). The ten texture analysis techniques are then adopted to generate the embedded properties of the bone surface. Once the feature vector has been generated the variety of techniques of machine learning mechanisms are applied to generate the desired classifiers, which can be used to distinguish between OA and non-OA images. From the conducted experiments, a total of 131 images (68 OA cases and 63 non-OA cases) was used, the results obtained show that LF region with Local Binary Pattern descriptor produced the most appropriate classifier with an AUC value of 0.912.
Proceedings of the Society for Computation in Linguistics Proceedings of the Society for Computation in Linguistics

Constituency parsing is generally evaluated superﬁcially, particularly in a multiple language setting, with only F-scores being re-ported. As new state-of-the-art chart-based parsers have resulted in a transition from traditional PCFG-based grammars to span-based approaches (Stern et al., 2017; Gaddy et al., 2018), we do not have a good understanding of how such fundamentally different approaches interact with various treebanks as re-sults show improvements across treebanks (Ki-taev and Klein, 2018), but it is unclear what inﬂuence annotation schemes have on various treebank performance (Kitaev et al., 2019). In particular, a span-based parser’s capability of creating novel rules is an unknown factor. We perform an analysis of how span-based parsing performs across 11 treebanks in order to examine the overall behavior of this parsing approach and the effect of the treebanks’ spe-ciﬁc annotations on results. We ﬁnd that the parser tends to prefer ﬂatter trees, but the approach works well because it is robust enough to adapt to differences in annotation schemes across treebanks and languages.