Reviewerly/exHarmony huggingface dataset:
Annotation

Annotation is a practice that is familiar to many of us, and yet it is a practice so natural that it is hard to pin down its characteristics, to find where its edges are, and identify what it does for us. In this piece, we use reflections on the practices of annotation in four fields of work: academia, software engineering, medical sonography and visual art as a point of departure to theorise annotation as a set of practices that bridge reading, writing and thinking. We think about annotation being performative and consider what and how it brings into being. Revealing hidden practices in our working lives, such as annotation, helps us to understand how knowledge comes to be created, disseminated, legitimated and popularised. To this end, we make the practices of annotation involved in writing the present piece visible in an effort to write differently in management and organisation studies, unpicking and exposing it as ever dialogical and unfinished.
Linked Lists

A linked list is a data structure that holds a group of data items which represents a sequence. Here each data item is referred to as a node. The nodes contain data and are interconnected to the next node in the sequence via their links. More complex forms of the list add additional links. The main advantage of a linked list is quick insertion and deletion. The linked list is a linear data structure like arrays, but unlike arrays, elements are not stored in a contiguous location. Like arrays, a linked list is a linear data structure (Figure 6-1).
Fingerprinting violating machines with in-memory protocol artefacts

Cyber crime has increased as a side effect of the dramatic growth in internet deployment. Identifying machines that are responsible about crimes is a vital step in an attack investigation. Tracking the IP address of the attacker to its origin is indispensable. However, apart from finding the attacker's (possible) machine, it is inevitable to provide supportive proofs to bind the attack to the attacker's machine, rather than depending solely on the IP address of the attacker, which can be dynamic. This paper proposes to implant such supportive proofs by utilising the internals of three well-known internet protocols: IP, TCP, and ICMP. Our results show that there can be potential proofs in the structures of these protocols. In addition, because a violator is unaware of (and has no control over) the involved protocols, the investigation process is empowered with stealth. To the best of our knowledge, we are the first to utilise protocol remnants in fingerprinting violating machines.
A fuzzified story point approach for agile projects

Effort estimation is very important for any software development organisation. A lot of work is present for effort estimation in traditional software development but, effort estimation in agile software development (ASD) poses a great research challenge as not much of work is done in this regard. This work proposes a fuzzy approach for effort estimation in ASD environment by identifying first the cost drivers affecting the effort of agile projects and then calculating the effort using fuzzified story point approach. These cost drivers are collected through meetings and by taking a web survey from the companies in Delhi-NCR which practise ASD. Statistical tests are also performed in order to show the dependency of effort on these cost drivers. Here, fuzzy interval type-2 is used which is compared with fuzzy type-1. It is found that, the effort calculated using fuzzy interval type-2 is better than fuzzy type-1.
Computational Preliminaries

This chapter presents the mathematical notation followed to represent the data and the computational measures defined on the data. Basics of matrix algebra and information theory are furnished as they form the building blocks of the computational model followed here. The standard procedure for preparing data sets of various types to perform outlier detection is also covered. In essence, the objective is to present the computational preliminaries necessary for developing various algorithmic methods for outlier detection in multi-dimensional record data as well as anomaly detection in network/graph data.
Factors Influencing the Adaption of Online Pharmacy in Oman: Literature Review (Preprint)

<sec> <title>BACKGROUND</title> There is low level of adoption and acceptance of online pharmacies in Oman; only very limited Omani Citizens are aware of the existence of online pharmacies and even fewer number of Omanis accessed and bought medicines using online pharmacy systems. Therefore, online pharmacy business is facing great challenge in adaption in Oman; despite many attempts on using web applications that mimic the actual pharmacy operations; such attempts still preliminary and need more theoretical modelling and practical enhancements to trust, reliability, efficacy, and other factors of success. </sec> <sec> <title>OBJECTIVE</title> the primary objective of the present study is to investigate the factors influencing on the adoption of online pharmacy in Oman. This study carries out under Unified Theory of Acceptance and Usage of Technology – 2 (UTAUT-2) to examine the perception of customers on adapting online pharmacy. </sec> <sec> <title>METHODS</title> The study has presented extensive review of existing books, journals, reports, newspapers, articles, and reputable online information on the issue at hand. The review of relevant literature is closely linked to UTAUT-2 variables and the adoption of online pharmacy in Oman </sec> <sec> <title>RESULTS</title> The most important factors such as performance expectancy, effort expectancy, and social influence, facilitating condition, habit, hedonic motivation, and price value were observed their influence on adoption of online pharmacy among Omanis. </sec> <sec> <title>CONCLUSIONS</title> The study addresses issues relevant in the context that help to examines all variables being investigated. Furthermore, it also focused on the review of related theories that support the relationship among the variables and the utilization of online digital pharmacy in the Oman. Finally, issues of relative importance were also discussed that could help broader the study. </sec>
Spam Detection Framework using ML Algorithm

The current use of social media has created incomparable amounts of social data, as it is a cheap and popular information sharing communication platform. Nowadays, a huge percentage of people depend on the accessible material on social networking in their choices (e.g. comments and suggestions about a subject or product). This feature on exchanging knowledge with a wide number of users has quickly prompted social spammers to exploit the network of confidence to distribute spam messages and support personal forums, advertising, phishing, scams and so on. Identifying these spammers and spam material is a hot subject of study, and while large amounts of experiments have recently been conducted to this end, so far the methodologies are only barely able to identify spam feedback, and none of them demonstrates the value of each derived function type. In this study, we have suggested a machine learning-based spam detection system that determines whether or not a specific message in the dataset is spam using a set of machine learning algorithms. Four main features have been used; including user-behavioral, user-linguistic, review-behavioral and review-linguistic, to improve the spam detection process and to gather reliable data.
EML: An Android Application for Electrical Education

This paper is to explain the development of android that will be used later in the mobile-based education system. Nowadays, it is important for schools, colleges, and universities to search for an innovative way of delivering information to students. Since the advancement of mobile-based technology, android has now become an important tool in teaching and learning processes. Several institutions have also proved that with this application, students are now able to gain more experience and be more engaging in classroom learning rather than the traditional learning method. In relation to that, the author has created a new application in mobile learning called Electrical Mobile Learning (EML), especially for Electrical Education. The purpose of this application is to positively contribute to users by providing them with a simple, convenient, reliable and a faster way of accessing information in Electrical Education either online or offline. EML application is developed by using the MIT Apps Inventor 2 platform and is mainly made with five function buttons containing electrical information as the user’s options. The EML has been successfully developed with more options and information with just one application.
Performance of Symmetric and Asymmetric Encryption Techniques for Attribute Based Encryption

The objective of this research article is to study performance of the the symmetric and asymmetric encryption based on the attributed encryption. In the current scenario, to protect the data in the distributed data base is the challenging task. Mainly to provide the privacy based on the specific parameter like attributed based encryption. In this algorithm dataset is provided to do the encryption for the selected sensitive attribute. Them for each symmetric and asymmetric encryption techniques encryption execution time and memory occupied after encryption is observed for ten different dataset. In the experiment and result analysis, each encryption techniques was discussed based on the comparion of encryption time and memory occupied after encryption. Result shows that RC2 encryption is less costlier as compare to AES, 3DES, Rijndael and RSA encryption. Result also indicates that 3DES and RC2 are almost need same encryption time and less costlier as compare to RSA, AES and Rijndael encryption and RSA encryption is more costlier as compare to AES, 3DES, Rijndael and RC2 encryption.
Implementation of Attribute Based Symmetric Encryption Through Vertically Partitioned Data in PPDM

The aim of this research article is to implement the symmetric encryption techniques on the vertically partitioned database table. In privacy-preserving data mining mainly sensitive attributes to be protected so that privacy will maintain during data mining. In this proposed algorithm the datasets partitioned in the three different sets. Then for each table for the selected attributes only one symmetrical encryption as well as different symmetric encryption is implemented. Initial data size, encryption execution time and data size after encryption are observed for each file of different data size. In result and analysis examined the performance of execution time and memory occupied after each encryption techniques is discussed and found that hybrid algorithm of using different symmetric encryption for each partitioned table is good as compared to implementing only one encryption on each partitioned table. This algorithm is mainly useful to provide privacy in PPDM in case of distributed data base of health care organizations.
Association Rules

Association rules seek to identify combinations of things that frequently occur together (affinity analysis). This is also the basis of market basket analysis, which we discussed in terms of correlation and Jaccard ratios. Association rules take things a step further by applying a form of machine learning, the most common of which is the apriori algorithm.
Enhancement of Shadow Region in an Image using Artificial Neural Network

Image captured in darker region increases complexities in processing and extracting vital information. Enhancement of such images helps us to retrieve important data and various tools are available for the same. Proposed system uses multi layer feed forward artificial neural network. Error Back Propagation algorithm is used in training process. Desired data is obtained using log transformation method. The proposed model is trained to enhance only shadow part of an image. The results shows enhancement in the darker region and is expected to improve more by changing different parameters in the above methodology.
Virtual Citation Proximity (VCP): Learning a Hypothetical In-Text Citation-Proximity Metric For Uncited Documents

The relatedness of research articles, patents, court rulings, webpages, and other document types is often calculated with citation or hyperlink-based approaches like co-citation (proximity) analysis. The main limitation of citation-based approaches is that they cannot be used for documents that receive little or no citations. We propose Virtual Citation Proximity (VCP), a Siamese Neural Network architecture, which combines the advantages of co-citation proximity analysis (diverse notions of relatedness / high recommendation performance), with the advantage of content-based filtering (high coverage). VCP is trained on a corpus of documents with textual features, and with real citation proximity as ground truth. VCP then predicts for any two documents, based on their title and abstract, in what proximity the two documents would be co-cited, if they were indeed co-cited. The prediction can be used in the same way as real citation proximity to calculate document relatedness, even for uncited documents. In our evaluation with 2 million co-citations from Wikipedia articles, VCP achieves an MAE of 0.0055, i.e. an improvement of 20% over the baseline, though the learning curve suggests that more work is needed.
CLOUD COMPUTING A SYSTEMATIC LITERATURE REVIEW AND FUTURE AGENDA

The global competition have extended the scope of trade and extended the supply chains causing the information management to be an essential part of the businesses. Many new technologies have recently been adapted by organizations in the era of digitalization. Cloud computing had an important place in these technologies and has been integrated widely to organizations. In recent days unexpected and risky periods such as the global pandemic increased the interest towards cloud computing topic in both academically and practically. The purpose of this study is to analyze and classify the contributions of the studies published in cloud computing field. The cloud literature is analyzed systematically from the management and business point of view. The review is limited with journal articles and papers published between 2014 and 2019. This research summarizes the current research attempts, discovers the research gaps and provides a research agenda for the future research on cloud computing within the context of information management in business and management by a global perspective.
Dynamic software management practices using genetically augmented neural networks

The aim of research is to investigate real time projects and the utilisation of Ppc software packages for educational institutions. The main motivation behind the study is to resolve the scheduling problem in Ppc software projects, which is considered useful for students' curriculum. The scheduling problem is been a prominent issue in software projects, which creates a trade-off between project duration and skills. Hence, this work removes the trade-off between project duration and skills using genetic augmented neural network algorithm (GANN). The process runs in a structured way to obtain a better solution in software project duration over education sector. The result with GANN over software validation seems flexible and accurate and can work as a flexible tool for automated software management practices.
Detection and Monitoring of the Destructive Impacts in the Social Networks Using Machine Learning Methods

With the growing importance of the digital world, it becomes more and more important to ensure people’s, especially young people’s, security in the digital world as a whole and in the social networks, particularly. In this paper the authors introduce for the first time the developed full-cycle methodology for detection and monitoring of the presence of destructive impacts via their manifestation in young people profiles in the social network. The research uses information technology methods together with psychological methods. The paper describes the proposed methodology and the techniques included in it as well as the results of the experiments. The methodology should help to determine the features of destructive impacts for further development of recommendations for young people on how to identify and resist them.
Software Defect Prediction u sing Support Vectorised Data and Intelligent Techniques

Software enhances the working capability of any business. Developing such a software entrusts the developing organization to build defect free software. In this context we have used PC1 dataset(NASA dataset) which has sufficient parameters for analysis. Intelligent techniques using different methodologies have been applied exhaustively on the PC1 data to find out the best intelligent technique for software defect. As the PC1 data is highly imbalanced data, there was biasness in the prediction of the intelligent techniques. Hence, to overcome this issue, in this paper we tried to propose best balancing method along with the intelligent technique to predict the software defect accurately.
InnoDB Locks

The locks studied in the previous chapter were all, apart from the InnoDB intention locks, general for MySQL. InnoDB has its own sophisticated locking system that allows for a highly concurrent access to the data. In online transaction processing (OLTP) workloads, benchmarks show that depending on the workload, InnoDB handles up to over 100 concurrent queries well. This is not only related to the record-level locks but also low-level semaphores, and the latter is an area of ongoing improvement which is the main reason that newer versions of MySQL handle concurrency better than old versions.
4239 Published By: Blue Eyes Intelligence Engineering &amp; Sciences Publication Retrieval Number: E6720018520/2020©BEIESP DOI:10.35940/ijrte.E6720.018520 Journal Website: www.ijrte.org Data Obfuscation Technique for Security in Cloud Computing

Cloud computing is at present a day's changes into the most crushed in marvels to use for enormous scale alliance or for person who need unmistakable structure organizations with least expense. Usually person's data is dealt with on open Cloud which is accessible to everybody for get to. These focal raises some issue in reverse to adaptable organizations gave by cloud suppliers, similar to Confidentiality, Integrity, Availability, Authorization and some more. Starting late, Lots of decisions are open to guarantee the data and most perfect ways is to use encryption. Encryption can't give enough confirmation while contemplating heaps of customers' fragile data. It in like manner eats up greater chance to perform encryption and translating process for every single inquiry. Moreover it is definitely not an OK practice to contemplate customer driven considering the way that once customer data is moved on Cloud premises, customer doesn't have direct control over this data. To empty load of Cloud server likewise to give acceptable security to customer's data, we propose a methodology by joining the two procedures viz. Jumbling and Encryption in this assessment paper. Customer data may be scrambled if protection is needed for its records or reports and the SaaS Cloud organization is reviewed using obscurity techniques. Using this two-way approach, we may assume that the proposed agreement provides sufficient protections for darkened access and safety of even open data on cloud servers. Our point is additionally to give appropriate assessment on data disordering for security in cloud computing.
Forum size and content contribution per person: a field experiment

Promoting contribution of content is a key challenge for platforms that support the collective creation or transfer of knowledge. We study the role of forum size (number of people in a forum) on contribution of content per person with a field experiment on a massive open online course (MOOC). We find that larger forums elicit more contribution per person. The number of questions and other help-seeking threads posted per person was unchanged by size, but replies and other more conversational posts increased sharply. Most of the positive effect of size was in a subset of socially responsive subjects. The implication of social responsiveness driving our results is that the unequal distribution of contribution on online platforms is unlikely to be easily changed: if more contributions are elicited from infrequent contributors, the greatest contributors would contribute even more due to there being more to respond to.
Efficient Multilevel Polarity Sentiment Classification Algorithm using Support Vector Machine and Fuzzy Logic

This paper discusses an efficient algorithm for sentiment classification of online text reviews posted in social networking sites and blogs which are mostly in unstructured and ungrammatical in nature. Model proposed in this paper utilizes support vector machine supervised learning algorithm and fuzzy inference system for enhancing the degree of sentiment polarity of text reviews and providing multilevel polarity categories. Model is also able to predict degree of sentiment polarity of online reviews. The model accuracy is validated on twitter data set and compared with another earlier model.
Suppressing ripple distortions and spurious pistons in phase-shifting interferometry

In this work, we deduce explicit conditions to design phase shifting algorithms (PSAs) in order to suppress errors in phase measurement, the double-frequency ripple distortion and the spurious piston. These errors arise when non-uniform phase-shifting interferograms are processed with conventional PSAs which assume uniform phase shifts. By modeling the non-uniform phase shifts as a polynomial of the unperturbed phase shift value !0, we show that the conditions for eliminating those errors are associated to the m-th derivative of the PSA's frequency transfer function (FTF). Thus, we propose an approach to design robust algorithms based on the FTF formalism, and we present a ready-to-apply eight-frame PSA. Finally, our conclusions are supported by computer simulations.
Analysing Cohesion Device of Monologue Texts in Students’ Text Book for Eight Grade in Academic Year 2017/2018

Cohesive term deals with the unity of paragraph or passage that relates between sections of a text or speech. This study aimed to describe the types of cohesive devices that were mostly used and also to find out which one is the most cohesive text among students’ texts book (When English Rings a Bell) for Junior High School at Eight grade in the academic year of 2017/2018. The primary data of this study was monologue texts that are found in students’ texts book for Junior High School at Eight grade in the academic year of 2017/2018. This study used a qualitative approach; First, the data were analyzed by to divide and numbering sentences into clauses; The second, to put the number of cohesive devices into tables based on types; The third, to put the number of cohesive devices into the form of percentage; The last, to make an interpretation based on the result. The objective findings are three kinds of cohesive; those were reference, ellipsis, and conjunction. Then, most of the cohesive device was a reference, personal reference, which mostly realized in every single clause, then followed by the conjunction "additive and adversative." This study was practically implicated to teaching and learning which can introduce functions and samples of cohesive to their students in order that students knew and then utilized its concept in their writing.