Miracle-dz/UniFAR-FTUs huggingface dataset:
SHRUBS: simulating influencing human behaviour in security

An organisational requirement of no unauthorised personnel permitted in a restricted area may have a security policy such as all employees must wear identification badges and employees must challenge people who are not wearing a badge. An employee's choice to wear/not wear their badge can be strongly related to how they perceive the security policy, which we call the compliance attitude. Peoples behaviour towards a security policy can influence other peoples compliance attitudes, such as challenging those not wearing a badge influences the compliance attitudes of the people who are challenged and those observing the challenge. The exchange of a challenge by social interaction between two or more people can create a social influence, whereby a persons choice to wear their badge is nudged. For the organisation, the problem is assessing how these social influences propagate throughout the compliance attitudes for particular security policies of all employees. We present SHRUBS which is a work in progress. It is a tool evaluating global security compliance attitudes of human agents. SHRUBS simulates behaviour for security policies where social interaction is present. It is built from conclusions in psychology, behavioural economics and human factors in security. SHRUBS takes as input, a list of behaviour parameters describing agent behaviour and returns the global compliance attitude from a set of traces formed through simulation. We demonstrate the application of SHRUBS with a running example to illustrate how one might mitigate against poor compliance attitudes amongst agents. We then go onto discuss the validation possibilities and explain this with a real world data set that we have collected. We envision that future versions of the tool would enable organisations to make more informed security policy decisions about employee behaviour, such as the best behavioural intervention to use.
Analysis of Marine Planktonic Cyanobacterial Assemblages From Mooriganga Estuary, Indian Sundarbans Using Molecular Approaches

The ecological significance of marine planktonic cyanobacteria in mangrove ecosystems is not well understood from the context of community structure and functions. In this study, members of the marine planktonic cyanobacterial communities were elucidated based on 16S rRNA sequencing (clone libraries and Illumina sequencing) and microscopic approaches from the Sundarbans Biological Observatory Time Series (SBOTS) stations Stn1 and Stn 3 located in and around Mooriganga estuary, Indian Sundarbans over 6 months representing monsoon and post-monsoon seasons of 2012. In total 222 sequences were marine planktonic cyanobacteria-like and were dominated by three major clades of Synechococcus-like 16S rRNA sequences (160 sequences). There was clear dominance of Synechococcus sp. KORDI-78 like sequences (71 sequences) in both monsoon and post-monsoon seasons from the study area. Although in very low numbers, other cyanobacterial sequences showing taxonomic affiliation with members of Chroococcales, Pleurocapsales, Oscillatoriales, and Stigonematales were also encountered. The presence of some of the representative genera belonging to the above Orders were reconfirmed by bright field microscopy from both stations of SBOTS. Presence of four novel clades of planktonic cyanobacteria were also confirmed from the study area based on molecular phylogeny thereby indicating unexplored cyanobacterial diversity is high in Sundarbans. Illumina sequencing undertaken for both stations of SBOTS in one of the monsoon months of 2014 (July) revealed low abundance of cyanobacteria-like sequences (2349 out of 4,276,863 pair-end reads) and dominance of KORDI-78 like sequences. Principal component analysis (PCA) showed that dissolved nutrients, transparency and salinity influenced the observed marine planktonic cyanobacterial assemblages of Sundarbans. There was overall dominance of Synechococcus-like 16S rRNA sequences confirming the importance of small-sized picocyanobacterial cells and their role in export of carbon in mangrove ecosystems.
A New Cryptojacking Malware Classifier Model Based on Dendritic Cell Algorithm

A new threat known as "cryptojacking" has entered the picture where cryptojacking malware is the future trend for cyber criminals, who infect victim's device, install cryptojacking malware, and use the stolen resources for crytocurrency mining. Worse comes to worst by 2020, researchers estimate there will be 30 billion of IoT devices in the world. IoT devices are vulnerable to attacks because of their simple configuration, unpatched vulnerability and weak passwords. IoT devices also prone to be poorly monitored because of their nature. There is lack of studies that provide in depth analysis on cryptojacking malware classification using machine learning approach where the current research mostly focused on manual analysis of web-based cryptojacking attacks. As IoT devices requires small processing capability, a lightweight model are required for the cryptojacking malware detection algorithm to maintain its accuracy without sacrificing the performance of other process. As a solution, we propose a new lightweight cryptojacking classifier model based on machine learning technique that may fit in a low processing capability environment such as IoT and Cyber Physical Systems (CPS). This paper aim to disscuss a new approach based on dendritic cell algorithm in order to provide a lightweight cryptojacking classifier model. The output of this paper will be significant used in detecting cryptojacking malware attacks that benefits multiple industries including cyber security contractors, oil and gas, water, power and energy industries.
Patterns of Amino Acids near Signal‐Sequence Cleavage Sites

According to the signal hypothesis, a signal sequence, once having initiated export of a growing protein chain across the rough endoplasmic reticulum, is cleaved from the mature protein at a specific site. It has long been known that some part of the cleavage specificity resides in the last residue of the signal sequence, which invariably is one with a small, uncharged side-chain, but no further specific patterns of amino acids near the point of cleavage have been discovered so far. In this paper, some such patterns, based on a sample of 78 eukaryotic signal sequences, are presented and discussed, and a first attempt at formulating rules for the prediction of cleavage sites is made.
Employee's CSR perceptions and their organizational commitment: an analysis of the moderating role of need to belong and the mediating role of Person Organization Fit

Corporate Social Responsibility (CSR) is an approach that is of great importance to many companies. It is part of their strategic objectives and their concerns about the overall performance. According to many theoreticians and professionals, CSR would have consequences for extra-financial outcomes, namely the behaviors and the attitudes of stakeholders. In order further promote the issues associated with CSR practices and deal with the risks that threaten the internal resources of the company, it would be important to look at employees' perceptions and their ultimate reactions, particularly their organizational commitment. The purpose of this research is to understand the consequences of CSR perceptions on the organizational commitment of employees in a new perspective, that of the underlying mechanisms. So, this study considers that the organizational commitment of employees can be anticipated with the help of psychological elements. By integrating deontic justice with other psychological mechanisms in mediation or moderation, we try to better understand the organizational commitment of employees. A model incorporating both a mediator and moderator is presented to show that limitative conditions can better explain the effects of CSR on organizational commitment. Our results show that employees are sensitive to CSR because it reinforces their sense of fit with the values of their organization. The theoretical and managerial implications of the model are discussed.
Automatic Inspection Drone with Deep Learning-based Auto-tracking Camera Gimbal to Detect Defects in Power Lines

The traditional drone inspection performed by human operators is unsuited for the purpose of inspecting power transmission lines, because steel towers and their spans are too high and wide to be inspected with a 250 m line of sight. For this reason, the KEPCO Research Institute developed a new inspection drone system that can automatically fly a predetermined flight path based on the GPS coordinates of steel towers, filming a video of power transmission lines with a high definition camera and a thermal imaging camera. In this system, a camera gimbal with the cameras was still controlled by a human operator from a long distance away. When the drone approached close to a steel tower, however, the camera gimbal was often unable to be controlled and real-time video transmission for the gimbal operator was sometimes interrupted due to radio-frequency interference from steel structure and energized power conductors. To solve such a control problem in the field, we also developed an auto-tracking camera gimbal that can automatically track and photograph power facilities on the basis of Deep Learning. With the automatic gimbal, the entire inspection process can be fully automated. The effectiveness of the developed overall system was confirmed through field tests.
Cross-Cultural Nomological Network of Gratitude: Findings From Midlife in the United States (MIDUS) and Japan (MIDJA)

Gratitude enhances prosocial behavior and is considered a positive trait in most cultures, yet relatively little is known about its relationship to other psychological constructs, nor how it varies across diverse cultural contexts. To investigate the cross-cultural consistency of the benefits of having a grateful disposition, the current study examined the nomological network of gratitude in the United States and Japan, using data from two longitudinal studies: Midlife in the United States (MIDUS Refresher Biomarker Project) and Midlife in Japan (MIDJA). Results showed significant positive bivariate associations between trait gratitude and positive psychological functioning (Satisfaction with Life, Sympathy, Anger Control, Cognition Control, and Support/Affectual Solidarity Given to Relational Network) in both the United States and Japan. On the other hand, trait gratitude was negatively correlated with constructs associated with maladaptive psychological processes (Perceived Stress, Social Anxiety, Loneliness, and Anger-In) in both countries. The present findings provide valuable guidance for the development and implementation of future interventions that may lead to positive outcomes in individuals from diverse cultural and ethnic backgrounds.
Presence of beta-cyanoalanine synthase in unimbibed dry seeds and its activation by ethylene during pre-germination

Evolution of HCN from both rice (Oryza sativa) and cocklebur (Xanthium pennsylvanicum) seeds increased during a pre-germination period and preceded the evolution of (C2H4). These two species were adopted as the representatives of starchy and fatty seeds, respectively. Ethylene promotes seed germination of many species. However, HCN evolution declined abruptly when the radicles emerged and before the peak in C2H4 evolution. More-over, both rice and soybean (Glycine max) seeds showed some activity of β-cyanoalanine synthase (CAS, EC 4.4.1.9) even in the unimbibed dry state. The activities of CAS in the lower seed of cocklebur and in soybean seeds increased rapidly after emergence of the radicle. However, the CAS of rice seeds, with high activity in the dry state, exhibited a bimodal change, gradually decreasing until radicle emergence had occurred, but then increaing. It is thus likly that HCN evolution during initial imbibition may be derived from cyanogenic reserves and controlled by both pre-existing and subsequently-developing CAS. The exogenous application of C2H4 stimulated the activities of CAS in both rice and upper cocklebur seeds and reduced their cyanogen contents. Therefore, the decline of HCN evolution after germination seems to be due to the increased activities of CAS by endogenously produced C2H4.
Treatment of 6 COVID-19 Patients with Convalescent Plasma

Objectives To describe the efficacy of convalescent plasma transfusion for COVID-19 patients. Methods Through the review of the electronic medical records of Guizhou Jiangjunshan Hospital, clinical data of 6 patients were obtained. Three of the patients used convalescent plasma therapy for the first treatment, while the other three patients were used during in the recurrence. The efficacy of convalescent plasma depends on the relief of symptoms, changes in laboratory indicators and chest imaging abnormalities. Results The PaO2 / FiO2 and lymphocyte count of patients 1, 2 and 3 treated with convalescent plasma treatment for the first treatment period were changed from abnormal to normal. The levels of inflammation markers CRP and IL-6 of the patients decreased significantly. Chest imaging examination showed that the lung lesions gradually subsided. The relapsed patients (No. 4 and No. 6), after using convalescent plasma therapy, turned negative on two consecutive throat swab tests on Day 24 and Day 3, respectively. The basic condition of patient 4 with in the recurrence was too poor, and only one round of convalescent plasma transfusion was performed, so the laryngeal swab test paper has been consistently positive so far. Conclusions Six patients with COVID-19 were treated with antivirals and systemic corticosteroids combined with appropriate rounds of convalescent plasma therapy, and their clinical conditions were effectively improved.
Feature Selection Methods in Sentiment Analysis: A Review

The development of digital tecnnologies nowadays assists people by suggesting opinion, choices, preferences and feelings. This opinion is useful for company's engagement to make certain analysis to know their potential users and personalized their need. However, the information needs extraction to make further analysis. Thus, sentiment analysis is used to extract opinion and others and transform it into meaningful data. During the process of analysis, feature selection method is required to select a subset which consists of relevant features to construct a predictive model. This process requires some conditions during the selection of feature subset. The required conditions for feature selection are that the selected feature subset must be small and relevant for a high dimensional dataset which considers the presence of noise plus there are no redundant features. However, some of the feature selection methods unable to fulfill all conditions. In this research, 40 papers were collected, classified and reviewed. We discussed on the feature selection methods in sentiment analysis based on its level of analysis and make comparison between these methods to know its limitation and advantages. The comparison made between methods are based on its accuracy and CPU performance. Finally, suggest the best/benchmark method for feature selection. The findings obtained from this research shows that hybrid methods obtain the best accuracy and CPU performance compared to the other methods.
Classification and Predictive Analysis of the Stocks Listed with NIFTY50

Indian stock market has its prominent position in the globe. In 2018, the healthy economic growth in India has supported its stock market and become one of the largest stock market in the world. India's ascent reflects the growing blow of emerging markets. It also indicates its economy is positioned for sustained growth, even if the manufacturing sector is not firing on all cylinders. SENSEX and NIFTY are considered as the barometers of Indian stock market. Approximately 1600 companies are listed on National Stock Exchange of India Ltd. (NSE), from which fifty companies are listed with the prestigious index NIFTY50.The NIFTY50, is the leading index on the NSE, which is commonly known as NIFTY. It is derived from economic research and is created for the interest of investors, who wants to invest and trade in Indian equities. The NIFTY 50 stocks comprises of leading Indian companies from various sectors. The stocks of listed companies are relatively less volatile and offer a rather steady return. The NIFTY 50 covers major sectors of the Indian economy and offers great exposure to the investment managers to Indian stock market in one's competent portfolio. The companies listed with NIFTY50, show significantly diversified behavior with respect to their price movements. Thus, the risk and returns associated with the stocks found to be wide-ranging in nature. Also, the range of the beta factors of these stocks is significantly varied. The present study is an attempt to analysis the fifty stocks of NIFTY50 based on the returns offered by the stocks, risk associated with these stocks and their respective beta factors. The weekly data of past years have been collected and used to calculate the returns, risk and beta factors associated with the fifty stocks listed in NIFTY50. Using cluster analysis, the fifty stocks of NIFTY50 are classified into segments based on their respective returns, risk and beta values. Further for each segment, a predictive model for returns is Proposed.
Register Automata with Extrema Constraints, and an Application to Two-Variable Logic

We introduce a model of register automata over infinite trees with extrema constraints. Such an automaton can store elements of a linearly ordered domain in its registers, and can compare those values to the suprema and infima of register values in subtrees. We show that the emptiness problem for these automata is decidable. As an application, we prove decidability of the countable satisfiability problem for two-variable logic in the presence of a tree order, a linear order, and arbitrary atoms that are MSO definable from the tree order. As a consequence, the satisfiability problem for two-variable logic with arbitrary predicates, two of them interpreted by linear orders, is decidable.
Crytojacking Classification based on Machine Learning Algorithm

The rise of cryptocurrency has resulted in a number of concerns. A new threat known as cryptojacking" has entered the picture where cryptojacking malware is the trend for future cyber criminals, who infect computers, install cryptocurrency miners, and use stolen information from victim databases to set up wallets for illicit funds transfers. Worst by 2020, researchers estimate there will be 30 billion of IoT devices in the world. Majority of the devices are highly vulnerable to simple attacks based on weak passwords and unpatched vulnerabilities and poorly monitored. Thus it is the best projection that IoT become a perfect target for cryptojacking malwares. There are lacks of study that provide in depth analysis on cryptojacking malware especially in the classification model. As IoT devices requires small processing capability, a lightweight model are required for the cryptojacking malware detection algorithm to maintain its accuracy without sacrificing the performance of other process. As a solution, we propose a new lightweight cryptojacking classifier model based on instruction simplification and machine learning technique that can detect the cryptojacking classification algorithm. This research aims to study the features of existing cryptojacking classification algorithm, to enhanced existing algorithm and to evaluate the enhanced algorithm for cryptojacking malware classification. The output of this research will be significant used in detecting cryptojacking malware attacks that benefits multiple industries including cyber security contractors, oil and gas, water, power and energy industries which align with the National Cyber Security Policy (NCSP) which address the risks to the Critical National Information Infrastructure (CNII).
Temporal variation in the reproductive success of Cacicus haemorrhous (Linnaeus) (Aves, Icterinae) in an Atlantic Forest reserve in Southeast Brazil

For colonial bird species, egg hatching synchrony has been considered an important factor in their reproductive success. The Red-rumped Cacique, Cacicus haemorrhous (Linnaeus, 1766), is a Passeriformes species (Icterinae) that reproduces in colonies in which groups of females lay their eggs at different periods during the reproductive season. The objective of this study was to evaluate if there was a variation in the reproductive success among C. haemorrhous females groups that lay eggs in different periods along the reproductive season. A total of 192 nests from four colonies were monitored at Parque Estadual do Rio Doce, Minas Gerais state, Brazil, and used to calculate the nest survival probability in different periods (early and late) of the 2001 reproductive season. The results showed that females that reproduced at the beginning of the reproductive period had higher reproductive success than those that reproduced later. Predation was the main cause of nests loss (48,4%), and an increase in predation rate was observed as the reproductive period advanced.
A Question answering System with a sequence to sequence grammatical correction

Dialogue or conversational systems including chatbots, and personal assistants are becoming ubiquitous in modern society. Here we present a question answering system that's help a customer to search a product in an ecommerce website and inquiring about it. This article describes a question answering bot, which is based on deep learning. We develop a set of deep learning models for natural language retrieval and generation --- including recurrent neural networks, sequence-to-sequence models and attention mechanism --- and evaluate them in the context of natural language processing. Thus; we have used those technologies in a grammatical correction system, and a question answering system by passing the first model result as input to the second one, in order to improve result.
Ring-based Trust Modeling in Online Education Platform

The development of online education has provided a more convenient and integrated platform for both teachers and learners. A large number of resources could be accessed as an aid to learning. However, personalized services in this intelligent education area are still limited, such as descriptions for the behavior of learners and the recommendation of resources. This paper, based on the interaction relationship between online learners, with the idea of ring theory, analyzes the reliability as well as trust relationship to build the trust network model for learners. Experiments are designed to evaluate the performance of the algorithms and the interpretability of the models we implement and build.
SCoEmbeddings: encoding sentiment information into contextualized embeddings for sentiment analysis

Contextualized word representations such as ELMo embeddings, can capture rich semantic information and achieve impressive performance in a wide variety of NLP tasks. However, as problems found in Word2Vec and GloVe, we found that ELMo word embeddings also lack enough sentiment information, which may affect sentiment classification performance. Inspired by previous embedding refinement method with sentiment lexicon, we propose an approach that combines contextualized embeddings (ELMo) of the pre-trained model with sentiment information of lexicon to generate sentiment-contextualized embeddings, called SCoEmbeddings. Experimental results show that our SCoEmbeddings achieve higher accuracy than ELMo embeddings, Word2Vec embeddings, and refined Word2Vec embeddings on the SST-5 dataset. Meanwhile, we also visualize embeddings and weights of SCoEmbeddings, demonstrating the effectiveness of our SCoEmbeddings.