Reviewerly/exHarmony huggingface dataset:
Supply chain management and blockchain: bridging the antecedents of the technology with the &lt;i&gt;status quo&lt;/i&gt; of use case applications

Blockchain technology has been characterised as a disruptive technology in multiple business contexts, including supply chain and logistics management. However, being at the centre of the academic debate since the beginning, the unique value proposition levers of blockchain (i.e., durability, transparency, immutability and process integrity) have never been connected to real-world applications on an empirical ground. In addition, how and to what extent do these real-world applications make use of this innovative technology? Our paper, aimed at enriching the conversation about real use case scenarios specific to the supply chain industry, is presented as a multiple case study leveraging a coding strategy that is structured around the reference literature on the topic. In particular, we cover seven companies that are operating blockchain networks in different supply chain and logistics contexts. The paper ultimately addresses the need for both practitioners and researchers to 'connect the dots' between the blockchain networks and their empirical implementation within the supply chain industry.
Contraction Decomposition in Unit Disk Graphs and Algorithmic Applications in Parameterized Complexity

We give a new decomposition theorem in unit disk graphs (UDGs) and demonstrate its applicability in the fields of Structural Graph Theory and Parameterized Complexity. First, our new decomposition theorem shows that the class of UDGs admits a Contraction Decomposition Theorem. Prior studies on this topic exhibited that the classes of planar graphs [Klein, SICOMP, 2008], graphs of bounded genus [Demaine, Hajiaghayi and Mohar, Combinatorica 2010] and H-minor free graphs [Demaine, Hajiaghayi and Kawarabayashi, STOC 2011] admit a Contraction Decomposition Theorem. Even bounded-degree UDGs can contain arbitrarily large cliques as minors, therefore our result is a significant advance in the study of contraction decompositions. Additionally, this result answers an open question posed by Hajiaghayi (www.youtube.com/watch?v=2Bq2gy1N01w) regarding the existence of contraction decompositions for classes of graphs beyond H-minor free graphs.Second, we present a "parameteric version" of our new decomposition theorem. We prove that there is an algorithm that given a UDG G and a positive integer k, runs in polynomial time and outputs a collection of O(k) tree decompositions of G with the following properties. Each bag in any of these tree decompositions can be partitioned into O(k) connected pieces (we call this measure the chunkiness of the tree decomposition). Moreover, for any subset S of at most k edges in G, there is a tree decomposition in the collection such that S is well preserved in the decomposition in the following sense. For any bag in the tree decomposition and any edge in S with both endpoints in the bag, either its endpoints lie in different pieces or they lie in a piece which is a clique. Having this decomposition at hand, we show that the design of parameterized algorithms for some cut problems becomes elementary. In particular, our algorithmic applications include single-exponential (or slightly superexponential) algorithms for well-studied problems such as Min Bisection, Steiner Cut, s-Way Cut, and Edge Multiway Cut-Uncut on UDGs; these algorithms are substantially faster than the best known algorithms for these problems on general graphs.
Natural language processing systems for data extraction and mapping on the basis of unstructured text blocks

Modern natural language processing technologies allow you to work with texts without being a specialist in linguistics. The use of popular data processing platforms for the development and use of linguistic models provides an opportunity to implement them in popular geographic information systems. This feature allows you to significantly expand the functionality and improve the accuracy of standard geocoding functions. The article provides a comparison of the most popular methods and software implemented on their basis, using the example of solving the problem of extracting geographical names from plain text. This option is an extended version of the geocoding operation, since the result also includes the coordinates of the point features of interest, but there is no need to separately extract the addresses or geographical names of the objects in advance from the text. In computer linguistics, this problem is solved by the methods of extracting named entities (Eng. named entity recognition). Among the most modern approaches to the final implementation, the authors of the article have chosen algorithms based on rules, models of maximum entropy and convolutional neural networks. The selected algorithms and methods were evaluated not only from the point of view of the accuracy of searching for geographical objects in the text, but also from the point of view of simplicity of refinement of the basic rules or mathematical models using their own text bodies. Reports on technological violations, accidents and incidents at the facilities of the heat and power complex of the Ministry of Energy of the Russian Federation were selected as the initial data for testing the abovementioned methods and software solutions. Also, a study is presented on a method for improving the quality of recognition of named entities based on additional training of a neural network model using a specialized text corpus.
Blockchain Competition

Taking a dynamic and reflexive perspective on the interaction between technology, law, and economics, this chapter focuses on the role of competition in shaping the economic, and regulatory, ecosystem in which blockchain technology becomes embedded. There is the promise that the technology and the social space it will foster will give rise to more competitive structures in the organization of economic activity in the digital economy, in comparison to the current, highly centralized paradigm of digital platforms. The narrative of disruption has indeed been important in promoting the greater use of blockchain technology and has also framed the socio-technical ‘agencements’ that have so far guided regulatory action in this area. However, this narrative of disruption has also led to some hasty conclusions made in the literature as to the impotence of competition law to deal with an emergent digital space dominated by blockchain technology, predicting its eventual demise … The chapter critically engages with this rhetoric and explains why it does not stand serious scrutiny and relies on a superficial analysis of blockchain competition, as it does not consider the various ways in which the competitive strategies of economic actors shape the rules of the competitive game, the broader ‘industry architectures’ that structure competitive interactions. It unveils how blockchain can contribute to competitive advantage, and thus shape the distribution of surplus value in various industries. It then draws lessons for the work of regulators, in particular competition law enforcers. Finally, it provides a detailed analysis of competition law implications of blockchain technology.
Behavioural Based Online Comment Spammers in Social Media

With the development of mobile Internet, it is changing the way we communicate with others. Internet media plays important application for information dissemination and user communication, including online news and social networks. However, the present advancement in technology provides opportunities to raise large number of spammers, who release false speech, advertisements and phishing websites on the media to gain commercial benefits, which seriously affects the experience of normal users. Therefore, in order to reduce the harm of false information, our paper focuses on the identification of spammers from normal users. However, the existing technologies of identifying spammers involve high data costs and poor effects, and most of them are concentrated in the field of social networks, while less research is carried out in the field of online news. In this paper, we propose an effective technology of identifying online news comment spammers based on the comment propagation algorithm (CPA), making full use of the user comment behaviours and contents. First of all, we extract few amount of information using scraping tool and label some users in the data as spammers or normal users manually to construct a labelled dataset. Next, we propose the identification technology based on the CPA. Finally, the set of values is input into the proposed technology in different combinations, and experiments and evaluations are carried out to determine possible spammers using behavioural features.
Generalized Information

Generalized Information (GI) is a measurement of the degree to which a program can be said to generalize a dataset. It is calculated by creating a program to model the data set, measuring the Active Information in the model, and subtracting out the size of the model. Active Information allows GI to be usable with both exact and inexact models.
Determining Customer Perceptions using Text Mining for an FMCG Product “Maggi” In India

Purpose: With the popularity of social media, blogging, documents in the web, multiple text information is being generated every moment. Companies can gauge consumers’ sentiments by conducting analysis of tweets or Facebook posts and can take timely action to tweak promotional campaigns. In the beginning of 2015, Maggi noodles was banned by the then government. To track the sentiments of the people on the coming back of Maggi, the widely accepted micro blogging site, Twitter, is used. Twitter continuously generates different points of view on any given subject, relating to social issues, marketing issues etc. The challenge lies in understanding and analyzing these unstructured texts, figuring out the relevant information and transforming it into actionable cognizance. Methods: The paper extracts set of 500 Twitter posts containing “Maggi”. 500 tweets were extracted to avoid heavy computation. The data was extracted by creating an interface between a twitter account and the statistical software R where we used the graphical user interface RStudio. This paper analyses tweets on this popular packed food item “MAGGI” by using statistical software like R and Excel. The methodology performed sentiment analysis using text mining approach following steps of Data Extraction, Text Transformation, Analyze the data, Data representation and validation. Results: The paper conducts sentiment analysis on social media and examines consumer perception of “Maggi”. There are few negative tweets like “Tired of #Maggi”, “Hesitant to take the first bite of #Maggi #marketingmoves” but most of the tweets were in the favor of Maggi. Tweets like, “This new #Maggi ad will surely make you go nostalgic!!”, “3am with beloved curry maggi + boiled chicken”, “When you wanna eat healthy but you low on cash. #Yasssss #Maggi #Broccoli #Sausages #Protein” show strong positive sentiment. From the sentiment analysis conducted on Maggi noodles, there were more positive rather than negative responses towards Maggi’s reentry into the Indian market. Thus, the concept of sentiment analysis can give marketers quick, preliminary insights into the consumers psyche which can later be followed up by traditional market research techniques.
Software Reusability: Approaches and Challenges

Software reuse is used to aid the software development process which in recent times can improve the resulting quality and productivity of software development, by assisting software engineers throughout various software engineering phases to enhance the quality of software, provide quick turnaround time for software development using few people, tools, and methods, which creates a good software quality by enhancing integration of the software system to provide a competitive advantage.This paper examines the concept of software reuse, the approaches to be considered for software reuse, which is broadly shared into three categories: component-based software reuse, domain engineering and software product lines, architecture-based software reuse and challenges that affect the software reuse development process.
Indexing documents with reliable indexing techniques using Apache Lucene in Hadoop

Mostly 85% of the data is presented in the form of text, which is the human-readable format. Present educational, business, medical organisations, etc. making use of big data analytics for storage of data and processing that stored data by using information retrieval. Often time's text documents have been transferred from one system to another system without any restrictions like, structured, unstructured and semi-structured data. Systems are well performed with high speed and less complexity only when it has all the data arranged in an orderly way. This paper describes how documents of text data are being Indexed using Apache Lucene with approaches in Hadoop. Most of the applications that deal with huge data over the internet are completely lacking. Use of effective analysis and techniques allow users in resulting high-performance and a challenging option in leading big data analytics.
An enterprise perspective of web content analysis research: a strategic road-map

Participating in social networks to create and share opinion content has become a ubiquitous part of our everyday life. Understanding social media content is at the top of the agenda for many firms today. Business analysts and quants are trying harder to discover ways in which enterprises can be benefited by comprehending the content generated through social media such as Facebook, Wikipedia, Blogs, Youtube and Twitter. This pioneering work may aid business analysts and data scientists with insights into ways to adapt the stable content analysis (CA) techniques to analyse web page contents containing user-generated data. In this paper, we develop an integrated enterprise framework that defines web content analysis (WCA) as a comprehensive and functional layered architecture, and consequently, this framework can be used in various levels of the decision-making process. Further, a four dimensional view of comparative analysis of various WCA systems is presented. Based on the critical analysis of the literature survey, the study explores many open and challenging issues for further research in this domain.
Time Complexity Reduction for the Application of Stream Cipher System Based XOR Free Operation

Providing security to user information are the most important aspects of today’s internet applications. Due to the very fast increase in the internet applications, the value of data exchange also became important. Hence, the better security and fast processing of the system is at high demand in the communication-related communities. Cryptography is one such area of providing data security. The two major criteria that specify the significance of a cryptographic algorithm are the security provided against the various attacks and the time to perform the operations. The proposed system is a solution to the cryptographic system to reduce the time complexity by retaining the security of the algorithm as it is. The solution proposed is to reduce the processing time required for the XOR function. This solution is by generating; XOR values for the vector space mentioned by the tuple, later replace the same wherever required. These values of XOR depend on the tuple, which represents the vector. Algorithm and time complexity for the generation of the vector space of XOR values for the proposed space evaluated. The result of the work compared based on two Standard symmetric-key cipher systems, Advanced Encryption Standard (AES) and Blowfish. The results are also tested and analyzed for different data size for the time complexity.
Electronic data preservation and storage of evidence by blockchain

In the face of the era of big data, how to ensure the authenticity of electronic data has become an important practical issue. Online preservation methods based on hash operation, time stamp, and blockchain technology can effectively guarantee the authenticity and security of electronic data. Blockchains are inherently traceable, de-centralized, and difficult to tamper with, which can be enough served as the basic technology of electronic data certification. Preservation by blockchain mainly includes the following links: platform registration and identity authentication, generation and transmission of electronic data, and storage of electronic data. Preservation by blockchain has higher security than centralized online security because of distributed storage. However, it should be acknowledged that the preservation by blockchain is not without risk. The court should adhere to the principle of individual review of each case and review the electronic data from its generation, transmission, reception, storage, and collection.
Recognition of Emotional State Based On Handwriting Analysis and Psychological Assessment

Emotions describe the physiological states of an individual and are generated subconsciously. They motivate, organize, and guide perception, thought, and action. Emotions can be positive or negative. Negative emotions manifest in the form of depression, anxiety and stress. It is necessary to identify negative emotions of an individual who might be in the need for counseling or psychological treatment. Body signal analysis, handwriting analysis, and psychological assessment are some mechanisms to measure them. In this paper, emotional state is being measured through the person’s handwriting sample analysis and psychological assessment. Psychological assessment is done by using the results of DASS questionnaire attempted by the individual. Convolutional Neural Network (CNN) algorithm is used to find the emotional state of an individual from his/her handwriting sample. Comparative analysis is performed to suggest counseling/medication if required. The final CNN model is formed by using the ensemble method over cross-validation models. The accuracy achieved by the CNN model over the test dataset is 91.25%.
A weighted sum method MCDM approach for recommending product using sentiment analysis

In recent times, reviews of products by customers have been proliferating on the online platform. Majority of the reviews are lengthy, and going through the reviews before making a decision can be a tedious task for the user. In this paper, we extract the popular features from customers' reviews to analyse the possible opinions of these features. Choosing a product from the different combination of opinions for these features is treated as a multi-criteria decision making (MCDM) problem. Weighted sum method, a MCDM approach, is used to evaluate the priority score for each product. The product with the highest score is recommended to the user. Real-time dataset from Amazon is used to evaluate our system's performance. The experimental result shows that our proposed method produces a promising result which can help the user in the decision making process.
Introduction

Abstract This introduction provides a short overview on the book “The Ethics of Cybersecurity”. The volume explains the foundations of cybersecurity, ethics and law, outlines various problems of the domain such as ethical hacking and cyberwar, and it lists recommendations and best practices for cybersecurity professionals working in various application areas. Furthermore, the introduction outlines the background of the European CANVAS project, from which this volume emerged.
A Novel Authentication Mechanism for Cognitive Radio Network

By employing an efficient authentication protocol the security of the Cognitive radio Network can be upgraded. This paper portrays a strategy for recognizing the Primary client Emulation Attacks in a CRN using cross layer approach. A few kinds of attacks have been discussed in this paper and an authentication scheme to detect PUEA has been presented. It is identified that the physical layer authentication method can serve as a faster authentication method but with low accuracy in detection.And a cryptography based authentication protocol can serve as a slower authentication method but with high accuracy in detection. In this paper a cross layer confirmation plot that joins both the physical and higher layer authentication is defined. The proposed method can provide good accuracy in detection without sacrificing the speed of the detection.A detecting procedure to detect PUEA in a CRN for a single user has been defined in this paper.
Organizational Justice and Turnover Intention: The Mediating Role of Quality of Working Life

Turnover intention (TI) is one of the critical issues that confront organizations and cost high capital investment. Most of previous studies focus on this issue among business organization while few examined it in public sector. Accordingly, this paper aims to examine the effect of organizational justice (OJ) on turnover intention among police personnel in United Arab Emirates (UAE). Further, quality of working life (QWL) is examined as a mediator between OJ and TI. A quantitative approach was deployed using a questionnaire. Data was collected from police in the capital of UAE. A total of 101 respondents participated in this study. The analysis was conducted using Smart Partial Least Square (PLS-SEM). The findings showed that OJ has a negative effect on TI. In addition, the IJ is the most important dimension of OJ followed by DJ and both have negative effect on TI. PJ has insignificant effect on TI. The QWL mediated partially the effect of OJ on TI. Decision makers are suggested to focus on OJ and QWL to reduce TI and there is an urgent need for more studies in public sector.
Application of TOPSIS in the Diagnosis of Vector Borne Diseases

“Intuitionistic Fuzzy Set” (IFS) is used to manage nebulousness and indecision. In current investigation, another intuitionistic fuzzy TOPSIS method is proposed for decision making by utilizing entropy weight. Current model permits estimating the degree of membership and non-membership of various alternatives assessed over a criterion set. A case study has been carried out to diagnosis of vector borne disease. Criteria’s have been selected according to relevant disease and weight has been assigned to them by medical expert’s committee. It has been established that TOPSIS method can diagnose the VBD diseases using specific symptoms as criteria and VBDs as alternatives. The suggested methodology can help in correct and timely diagnosis of VBDs and provides doctors an innovative diagnostic tool (WHO, 2004; WHO, 2014). The result is validated by applying fuzzy VIKOR method.