Reviewerly/exHarmony huggingface dataset:
Deep Learning Based Truth Discovery Algorithm for Research the Genuineness of Given Text Corpus

Lot of research has gone into Natural language processing and the state of the art algorithms in deep learning that unambiguously helps in converting an English text into a data structure without loss of meaning. Also with the advent of neural networks for learning word representations as vectors has helped a lot in revolutionizing the automatic feature extraction from text data corpus. A combination of word embedding and the use of a deep learning algorithm like a convolution neural network helped in better accuracy for text classification. In this era of Internet of things and the voluminous amounts of data that is overwhelming the users determining the veracity of the data is a very challenging task. There are many truth discovery algorithms in literature that help in resolving the conflicts that arise due to multiple sources of data. These algorithms help in estimating the trustworthiness of the data and reliability of the sources. In this paper, a convolution based truth discovery with multitasking is proposed to estimate the genuineness of the data for a given text corpus. The proposed algorithm has been tested on analysing the genuineness of Quora questions dataset and experimental results showed an improved accuracy and speed over other existing approaches.
Open Science for a Global Transformation: CODATA-coordinated submission to the UNESCO Open Science Consultation

Abstract UNESCO is preparing a Recommendation on Open Science to be adopted by Member States in 2021. To contribute to this process, UNESCO launched a global consultation gathering inputs from all regions and all interested stakeholders, through online consultations, regional and thematic meetings and numerous debates on implications, benefits and challenges of Open Science across the globe. Following exchanges on the CODATA International Discussion list, CODATA agreed to coordinate a joint response from a number of data and information organisations. That document—Open Science for a Global Transformation—was submitted to UNESCO on 15 June and, following minor editorial adjustments: https://doi.org/10.5281/zenodo.3935461
Neural Network Application for Event Detection in Hybrid Dynamical Systems

This contribution investigates a feed-forward neural network approach for event detection in hybrid dynamical models.Machine learning algorithms are commonly used in software development.In recent years these approaches have also been increasingly applied in modelling and simulation of physical systems.A significant amount of these models use artificial neural networks.However, hybrid dynamical systems describe a combination of different methods to describe a continuous process, which experiences behavioural changes at discrete events.Accordingly, the models of such systems are based on a combination of discrete and continuous methods and are often illustrated as automaton.Based on these two areas an approach, to predict the event time of the discrete processes, is presented.The different required elements are defined and a general approach is outlined.The feasibility of this concept is examined on the basis of one examples.If the given imbalanced data is resampled, training can be successful.Unfortunately, even then, the generalised classification of events often does not work sufficiently.The evaluation of the approximation results of the discrete events in hybrid systems suggests that neural networks are not suitable to classify the system states with regard to the occurrence of an event.In the outlook we suggest an alternative approach to predict the event with neural networks.
Barium-Rich, Olivine-Mica Lamprophyres with Affinities to Lamproites, from the Mt Bundey Area, Northern Territory, Australia

Potassic igneous rocks, transitional between lamproites of continental intra-plate settings and shoshonitic volcanic and lamprophyric rocks (e.g.minettes) of active continental margins, are known from several localities world-wide but their petrogenesis, tectonic setting and relationship to Ti-rich lamproites are poorly understood.Lack of detailed mineralogical and geochemical documentation of such 'transitional lamproites' has contributed to difficulties in distinguishing them from true lamproites.In this abstract we describe an occurrence of unusual olivine-mica lamprophyre dykes which intrude Early Proterozoic, low-grade metasediments and granitoids of the Mt Bundey area.Pine Creek Inlier, northern Australia.Their mineral assemblages and mineral chemistry are typical of shoshonitic (calc-alkaline) lamprophyres, but their whole-rock chemistry more closely resembles lamproites.The Mt Bundey lamprophyres are spatially and probably genetically associated with a post-tectonic, composite syenite-granite pluton.Numerous dykes of 0.5-3.0m thickness intrude the ,granite and syenite; contacts with
The Promises of Blockchain and Cryptocurrencies Technology for Architecture and Interaction Design

Blockchain technology is gaining prominence after its first famous implementation as part of the digital currency Bitcoin. It is made up of a series of blocks (the ledger) that give a secure, unchangeable, and auditable list of records. Once a block is recorded using encryption and a distributed database, it cannot be modified. The ledger in the case of Bitcoin stores the history of all monetary transactions. In three sections, this paper provides a review of blockchain technology for an interface design audience: (1) The fundamentals that enable block-chain technology are briefly discussed. (2) Specific examples include the use of HCI in blockchain research and development. (3) In the form of a fledgling research agenda, opportunities and difficulties for HCI and interface design are presented.
Differentiation of Sediment Source Regions in the Southern Benue Trough and Anambra Basin, Nigeria: Insights from Geochemistry of Upper Cretaceous Strata

Santonian units [3,[7][8][9].Besides the aforementioned hypotheses, further data and reports, support that more than one provenance region exists.Petters [28] opined that sediment contribution from the palaeo-River Niger and the Southern Benue Trough exists.This hypothesis has been somewhat reinforced by recent palaeogeographic and palaeo-drainage models [11][12].Tijani [8], who undertook textural and geochemical analysis of the Ajali Formation, hypothesized a sediment provenance in the Adamawa-Oban Massif highlands as well as from the pre-Santonian strata of the Southern Benue Trough.Our previous findings in the western segment of the Anambra basin [9] using high resolution multidisciplinary techniques suggested some detrital contribution from basement complex rocks in the southwest (minor) as well as the pre-Santonian rocks (major).It is against this background that we undertook this study, which seeks to investigate
“Dangerous” Cantemir

The paper is devoted to a facet of Cantemirʼs works that didn’t strike eye of the learned world – his relationship with the highest censorship. In 1852 A. Smirdin decided to republish the volume of Cantemir and Chemnitser’s volume, published in 1847 in the series “Complete Collection of Russian Authors”. The article presents the entire censorship “kitchen” in connection with the alleged publication. All the arguments “pro” and “contra” are absolutely universal and can explain a lot in the complex publication life of Cantemir’s works. The culmination is the peremptory resolution of Emperor Nicholas I, a carefully verified censorship presentation on the works of Cantemir. The hateful words of the monarch about the writer two centuries distaut from him testify to the relevance of Cantemirʼs satire.
Student scientific circle as a form of tutor’s support

The concept of a student scientific circle as a form of tutor training is presented. The history of the circle movement in Russian universities of imperial Russia is analyzed, starting with the circle organized by Professor Schwartz in 1781 and continued in the Soviet Union. The data on the state support of students research work in the post-war period, including state documents of title and measures of financial support, are presented. The traditional student scientific circle is a circle of those wishing to educate themselves in addition to the planned curriculum, by conducting their own research and publishing the results. As a result, an activity-level specialist is formed from a graduate of the circle. In contrast to the traditional one, the tutor-type training circle forms the circle member as a future specialist not only at the activity level, but also at the project and, preferably, conceptual levels. Methodologically, the task of a tutor-type training circle is not only to teach the circle members the practical skills of conducting scientific research, but, first of all, in personalized work to achieve anthropological goals, to create a reasonable, creative, effective personality who can create, plan and design. In the conditions of the rapid development of modern science, the construction of the future is proceeding at a rapid pace and is associated with the active implementation of the results of scientific experiments in all aspects of human life, which requires active suppression of everyday consciousness. At the same time, due to the risk of a landslide dehumanization, the circle member, as a scientist, needs to lay down humanitarian values that define the individual as a person who shares the ideals of goodness and justice. Thus, the student scientific circle is a system in which a new generation of honest and effective scientists and high class specialists is formed under the guidance of a tutor.
Preprocessing Methods for Unstructured Healthcare Text Data

At present, the amount unstructured text data is increasing exponentially from the past periodically. Information retrieval (IR) from these unstructured text data is challenging. As the data users foresee for particular/specific outcomes. Retrieval of the significant outcomes depends on the fashion, how they are associated/indexed. Unstructured text data like clinical data containing more health information requires challenging preprocessing methods, which also help to reduce the size of the dataset so that it will optimize the performance of the IR system. In this paper, we have proposed the pre-processing methods such as Data collection, Data Cleaning, Tokenization, Stemming, Removal of Stop words which will efficiently help the data users to find the specific patterns from the unstructured text data.
A new unsupervised feature selection algorithm using similarity‐based feature clustering

No abstract is available for this article.
Credit Card Fraud Analysis using Robust Space Invariant Artificial Neural Networks (RSIANN)

One of the impact factor for any organizations or banks revenue and service quality is credit card fraud activities. Hence, need of efficient approach for detect early potential fraud and/or prevent them. In this paper, we considered pre-processing and used deep convolution neural network called as Space Invariant Artificial Neural Networks for classifying fraudsters. Available Credit card fraud dataset may not have sufficient information hence need pre-processing. The proposed approach has pre-processing phrase to make as robust. This approach used leverage layers and suitable tuning parameters for getting good classification accuracy. In neural network applications, choosing of tuning parameters and model selection has great role in solving the problems. We have done careful analysis and selected leverage layers and corresponding parameter values. The proposed architecture tested with all possible tuning parameters to evaluate the performance on pre-processed credit card fraud records. We found the proposed robust SIANN (RSIANN) is outperformed other state-of-art machine learning (ML) algorithms (Support vector machine (SVM), random forest (RF), Navie bayes and deep convolution neural network (DCNN) in terms of accuracy (85%). Thus, this model analyses the transaction and decide it fraud or not.
A Survey on Binary Code Vulnerability Mining Technology

With the increase of software complexity, the security threats faced by the software are also increasing day by day.So people pay more and more attention to the mining of software vulnerabilities.Although source code has rich semantics and strong comprehensibility, source code vulnerability mining has been widely used and has achieved significant development.However, due to the protection of commercial interests and intellectual property rights, it is difficult to obtain source code.Therefore, the research on the vulnerability mining technology of binary code has strong practical value.Based on the investigation of related technologies, this article firstly introduces the current typical binary vulnerability analysis framework, and then briefly introduces the research background and significance of the intermediate language; with the rise of artificial intelligence, a large number of machine learning methods have been tried to solve the problem of binary vulnerability mining.This article divides the current related binary vulnerabilities mining technology into traditional mining technology and machine learning mining technology, respectively introduces its basic principles, research status and existing problems, and briefly summarizes them.Finally, based on the existing research work, this article puts forward the prospect of the future research on the technology of binary program vulnerability mining.
An Ameliorated method for Fraud Detection using Complex Generative Model: Variational Autoencoder

Perpetrating fraud for financial gain is a known phenomenon, in this fast-growing adoption of smart phones and increased internet penetration, embracing digital technology. Evolution of financial transactions over the years, from paper currency to electronic media, leading the way in the form of credit cards or interbank electronic transactions. Consumers trending towards e-commerce hasn't deterred criminals, but considered this as the opportunity to make money through defrauding methods. Criminals are rapidly improving their fraud abilities. The current Supervised and Unsupervised Machine Learning Algorithm approaches to the discovery of fraud are their inability to learn and explore all possible information representation. The proposed system, VAE based fraud detection, which uses a variational autoencoder for predicting and detecting of fraud detection. The VAE based fraud detection model consists of three major layers, an encoder, a decoder and a fraud detector element. The VAE-based fraud detection model is capable of learning latent variable probabilistic models by optimizing the average value of the information observed. The fraud detector uses the latent representations obtained from the variational autoencoder to classify whether transactions are fraud or not. The model is applied on real time credit card fraud dataset. The experimental results show that, implemented model perform better than supervised Logistic Regression, unsupervised Autoencoders or Random Forest ensemble model.
Differentiation of Mamdani and Sugeno Fuzzy Inference System in developing Automatic Plant Watering Systems for Domestic Use

Water management systems are an essential requirement for maintaining plants. One of the major problems encountered in growing plants in houses, offices or buildings with in-house kitchen gardens is irregular irrigation patterns. This leads to over irrigation of pots or elongated durations of dry soil, both conditions ill for plant growth. This problem is further aggravated when manual irrigation ignores the environmental conditions and plant specific requirements. Utilizing Fuzzy logic for developing Automatic Plant watering systems provide flexibility in manipulating input parameters such as Temperature, Humidity, Soil Moisture and Plant Growth to determine optimum flow rate of the irrigation system. In this study, multiple fuzzy systems are developed for different environments and parameters. MATLAB is used for designing the fuzzy logic controllers using both Mamdani and Sugeno Models. The requirement for the system is to adjust the flow rate in accordance with the environmental conditions and plant requirements. The paper draws comparison between Mamdani and Sugeno methods on the basis of their performance characteristics for different environments. It also provides development of an effective controller for a watering system for household plantations.
An Efficient Proof-of-Work Mechanism for Computational Feasibility in Blockchain

Recently, Blockchain technology created a buzz in technological world and gained importance as a solution that offers the realization of multiple authoritative domains, where economic transactions are guaranteed. It is very important for such technology to validate the transactions performed by different users flawlessly. To maintain such a trust, Blockchain uses different consensus protocols, where different miners follow them to validate transactions and mine a new block. One such consensus protocol is proof-of-work, where a miner has to solve a puzzle with his computational capabilities. The existing proof-of work mechanisms discriminates a normal user to participate in the mining procedure, as he needs to maintain a specialized hardware which is expensive. In this paper, we propose a new algorithm on basis of crypto-puzzle Integer Prime Factorization, for proof-of-work consensus, which makes a user with minimal hardware capabilities to participate in the mining procedure
The Design and Implementation of Second-hand Market Trading Platform in Campus Based on Web

In recent years, with the application and popularization of internet technologies, people can make online shopping, online booking and online payment anytime, anywhere, which greatly facilitates people’s lives, saves time, and accelerates the pace of the times. Due to the convenience of online shopping, a group of young people like students may purchase a lot of things that they do not particularly need, which results in the idleness of items. Coupled with the lack of economic ability of students, buying a lot of new items can not afford. Based on this type of demand, a second-hand market has been created that can sell and purchase various idle items. The Second-hand Commodity Trading Platform of this project is a trading website designed for users and based on the C2C model. C2C is Customer to Customer, which means transactions between individuals and individuals. Taobao is using this model, each user can freely publish and purchase goods, has been achieved unimaginable success. The system is also designed and implemented using this model. Through this system, users can register and log in, quickly publish their own information on idle items, and also browse and purchase a variety of products by category, providing users with a fast and convenient online trading platform. The system is mainly used for teacher and student second-hand goods transactions, so that idle items can play their due value, and here you can get the satisfaction and cheap second-hand items you need. This has not only achieved social and economic health and environmental protection, but also promoted social progress.
A game-based virtual machine pricing mechanism in federated clouds

In a federated cloud environment, diverse pricing schemes among different IaaS service providers (ISPs) form a complex economic landscape that nurtures the market of cloud brokers. Although pricing mechanisms have been proposed in the past few years, few of them address the issue of competitive and cooperative behaviours among different ISPs. In this paper, we employ the learning curve to model the operation cost of ISPs, and introduce a novel algorithm that determines the cooperative pricing mechanism among different ISPs. The cooperation decision algorithm uses the operation cost computed based on the learning curve model and price policies obtained from the competition part as parameters to calculate the final revenue when outsourcing or locally satisfying users' resource requests. Extensive experiments are conducted in a real-world federated cloud platform, and the experimental results are compared with three existing pricing mechanisms. Our experimental results show that the proposed pricing mechanism is effective to improve resource utilisation as well as reduce the profit loss caused by request rejection.
NAMED ENTITY RECOGNITION AND INFORMATION EXTRACTION FOR ARABIC MEDICAL TEXT

The article discusses the possibilities of solving NER (Named Entity Recognition) problem for medical texts in Arabic with limited availability of labeled datasets, as well as computational and specialized linguistic resources.To overcome them, it is proposed to use recurrent neural networks.In our experiments, we used "BERT-Base, Multilingual Cased" from Google and Pooled-GRU with Multi-lingual Universal Sentence Encoder (MUSE) from Facebook.Each network was fine-tuned with our dataset.The used dataset was obtained from three medical volumes issued by Arabic Encyclopedia.We experimentally evaluated the effectiveness of tuned models on real NLP (Natural Language Processing) task -medical entities recognition from the Arabic Medical Encyclopedia and obtained encouraging results.
The Data Encryption Standard

In this chapter we give a brief description of the block cipher DES [1]. Both differential cryptanalysis and linear cryptanalysis were successfully applied to the DES. Differential cryptanalysis was the first chosen-plaintext attack that was theoretically more efficient than an exhaustive key search for the DES.
Detection of Abnormalities in Kidneys Using Image Processing

This paper proposes a method to process advanced medical image models that provide the automation of kidneys abnormal deetction.In this paper, it has been designed in a way to identify the organ structures based on their features and look for abnormalities using erode and corrode image filling.The proposed method also invokes the segmentation algorithm on rendering filtering process for removal of noise resulting in recognition of image, and then connectivity analysis is performed, which differentiates the relation between neighborhood pixels that is used to detect boundaries.And based on the analysis and classification, we can calculate the desired probabilistic estimations of relevant images.The provoked method enables the increase in automation and higher accuracy of organs from the input stage of MRI datasets.The proposed method is evaluated with the available dataset and it shows a vast improvement in the performance classification.
The impact of digital transformation strategy on customer satisfaction in the Palestinian telecom industry

This paper aims to identify the impact of digital transformation strategy on customer satisfaction in the Palestinian telecom industry. One of the major roles of digital transformation strategies is to enlighten business ventures on finding new ways to sell and promote products, which will eventually lead to better meeting customers' needs and expectations. The findings show that there is a significant impact of digital transformation strategy on customers' satisfaction in Palestinian telecom industry. In addition, it found that perception of digital transformation strategy dimensions are significantly correlated to customers' satisfaction in Palestinian telecom industry. Study recommended telecommunications companies in Palestine to modify their business models and adopt new models that focus on customers and new digital products. Also they were advised to adopt new technologies that can expand their business such as adopting internet of things (IoT) technologies and to engage customers in the process of product development.