Reviewerly/exHarmony huggingface dataset:
Integrating Semi-Open Data in a Criminal Judicial Setting

Judiciary systems comprise various partner organizations (e.g., police, public prosecutor, courts, and rehabilitation centres) that collaboratively resolve criminal cases. These partner organizations have their own data administration and management systems, which are setup/operated separately and integrated barely. This chapter explains the approach of the authors' organization for integrating the data sets of the Dutch judiciary systems, and for opening the data integration outcomes to the public and/or to specific groups. These outcomes (e.g., data sets and reports) are meant to provide useful insights into (the performances of) the partner organizations individually and collectively. Such data opening efforts do not comply with all Open Data requirements, mainly due to the quality, (privacy) sensitivity and interoperability issues of the raw data. Nevertheless, since these initiatives aim at delivering some benefits of Open Data, the chapter introduces the new paradigm of Semi-Open Data for acknowledging such data opening initiatives.
IFRS, Foreign Investment, and Prevailing Institutional Structure in Africa

We revisit the effectiveness of IFRS adoption for FDI attractiveness by considering the adopting country's institutional structure. Attention was on the extent of corruption control (as a measure of institutional structure). Data was tested on 42 African countries for the period 2001-2012. Using the Panel Corrected Standard Error (PSCE) estimation technique, our result suggest that the effect of IFRS adoption on foreign investment differs based on the level of corruption control instituted in the sample countries. When the variable – IFRS adoption, was tested for the sample with corruption control below the median value, the coefficient was either negative or insignificant. However, the opposite was seen for the category of countries with corruption control above the median value. This result was robust to the inclusion of alternative measures of corruption, foreign investment, and control of global financial crisis and legal origin of the country.
A Social Knowledge Management Platform for Universal Primary Education Online

Social knowledge management is a framework that allows its users to create and modify content collaboratively using social media and web 2.0 technologies. Social knowledge management can also be defined as the management of social knowledge where it is aimed towards social development. India today is facing a huge rural-urban divide with regard to poor learning achievement at the elementary level in remote rural areas. Remote rural schools are engulfed with problems such as the unavailability of good quality teachers and a poor learning environment. In this context, the chapter seeks to utilize a robust social knowledge management framework to impart universal education in the form of both formal and informal knowledge to the masses in remote rural areas by mobilising free online knowledge resources and the dormant knowledge capital of educated senior citizens. It further illustrates the design and development of a social media based knowledge management platform named OwlishOracle that addresses issues of exclusion and unequal educational attainment through “connecting generations”.
The Next Generation of Scientific-Based Risk Metrics

One of the major challenges to an organization achieving a certain level of preparedness to “effectively” combat existing and future cyber threats and vulnerabilities is its ability to ensure the security and reliability of its networks. Most of the existing efforts are quantitative, by nature, and limited solely to the networks and systems of the organization. It would be unfair to not acknowledge that for sure some progress has been achieved in the way that organizations, as a whole, are now positioning themselves to address the threats (GAO 2012). Unfortunately, so have the skill sets and resource levels improved for attackers--they are increasingly getting better at achieving the unwanted access to organizations' information assets. In large part the authors believe that some of this is due to the failure by methods to assess the overall vulnerability of the networks. In addition, significant levels of threats and vulnerabilities beyond organizations' networks and systems are not being given the level of attention that is warranted. In this paper, the authors propose a more comprehensive approach that enables an organization to more realistically assess its “cyber maturity” level in hope of better positioning itself to address existing and new cyber threats. The authors also propose the need to better understand another missing critical piece to the puzzle--the reliability and security of networks in terms of scientific risk-based metrics (e.g., the severity of individual vulnerabilities and overall vulnerability of the network). Their risk-based metrics focus on the probability of compromise due to a given vulnerability; employee non-adherence to company cyber-based policies; insider threats. They are: (1) built on the CVSS Base Score which is modified by developing weights derived from the Analytic Hierarchy Process (AHP) to make the overall score more representative of the impact the vulnerability has on the global infrastructure, and (2) rooted in repeatable quantitative characteristics (i.e., vulnerabilities) such as the sum of the probabilities that devices will be compromised via client-side or server-side attacks stemming from software or hardware vulnerabilities. The authors will demonstrate the feasibility of their method by applying their approach to a case study and highlighting the benefits and impediments which result.
Graph Theory

In this chapter, a review of some aspects of graph theory that are important for probabilistic graphical models are presented. After providing a definition of directed and undirected graphs, some basic theoretical graph concepts are introduced, including types of graphs, trajectories and circuits, and graph isomorphism. A section is dedicated to trees, an important type of graph. Some more advanced theoretical graph aspects required for inference in probabilistic models are introduced, including cliques, triangulated graphs and perfect orderings. The chapter concludes with the description of the maximum cardinality search and graph filling algorithms.
Simple Screen Locking Method Using Randomly Generated Number Grid on Image

Smartphones have become part and parcel of our daily life. Due to the more and more advanced features incorporated, its role has extended beyond calls and text messages, and it has become a place where important personal information is being stored. Thus, it needs to be protected from unauthorised users, which is usually achieved by using screen locking. However, improved screen locking security often compromises other aspects, such as usability and cost. In this article, a new screen locking method (SyS) that is both simple, secure from shoulder surf and smudge attacks, and not expensive to implement is presented. It uses an image and a chosen number to create the screen locking code. Evaluation results show that the SyS screen lock method scores best in terms of usability and security compared to the other three common screen locking methods, namely personal identification number or PIN, password, and pattern. It also performs well theoretically when compared against existing screen locking methods that aim at defending against similar attacks.
Hypothesis Testing

Given a sample from a stationary ergodic distribution, it is required to tell whether the distribution belongs to a set H 0 or to a set H 1. This general formulation encompasses a variety of problems, including model verification, such as testing that a process is Markov versus it is stationarity ergodic but not Markov, and property testing, such as testing for independence or homogeneity. This chapter is concerned with the general problem of characterizing those pairs H 0, H 1 for which a consistent test exists. The main result is a criterion that provides such a characterization in terms of the topology of the distributional distance. Some examples and applications are considered. In addition, a variety of open problems concerning hypothesis testing for stationary ergodic processes is presented. Unlike the preceding chapters, here the attention is restricted to processes with a finite alphabet.
An Exploration of the Critical Need for Formal Training in Leadership for Cybersecurity and Technology Management Professionals

For many cybersecurity professionals it is often their technical skills, certifications, and technical academic education that gets them hired and even promoted from a line employee to a management role in technical departments and technical organizations. Being in management roles requires the development of new leadership soft skills that include personality traits, attitudes, habits, and behaviors you display when working with leading, coaching, empowering and developing others. While good soft skills are also important for employees, they are critical for managers - and for those who want to be managers. This article explores that nature of those skills and approaches to help organizations develop leaders in these areas.
Attention-based Feature Fusion Network for Fake Reviews Detection

Neural networks are widely used in the field of fake reviews detection. To extract features more comprehensively, we propose a novel recurrent neural network with feature fusion. The network models the reviews more accurately in many aspects and portrays the differences in meanings of the same text across reviewers and review objects. Specifically, we incorporate artificially set non-semantic features into word vectors through the attention mechanism and neural networks. We then apply an attention-based bidirectional long short-term memory model to obtain a fused vector representation of the review text. Seventeen features are set, among which three features are cross-platform features to measure the difference of the same review object across different review platforms. Experiments show that the detection performance of the model proposed in this paper, which combines the novel feature fusion approach and cross-platform features, is improved compared with the current general models.
“Ya bloody drongo!!!”

This sutdy explores impoliteness-related discourse on Facebook as a form of expressing situated moral judgement. The analysis focuses on negative and aggressive comments as a response to one public post that claims the non-existence of Australia. The content of the post indicates the threat to national identity of anyone who associates themselves with the country. As a result, a large number of impolite comments were made. A qualitative analysis of the dataset (limited to one month from the post’s publication) reveals four main strategies that are used in order to express the judgement of the post’s inappropriateness and repair the wrongdoing. Those range from insults of the author’s mental abilities and suspicion of drug use to violence-related discourse (e.g., death threats) and counter-attack of the author’s national identity. This study, thus, aims to contribute to a growing area of research into online impoliteness.
SC-RegNet: Unsupervised Self-Calibrated 3D Biomedical Image Registration Network

Deformable image registration is critical in many biomedical image analysis tasks. Traditional registration algorithms involve a time-consuming iterative optimization process. Most deep learning-based registration networks follow the U-Net shape encoding-decoding architecture, and their downsampling layers are shallow, making it difficult to obtain sufficient semantic information with a small receptive field. In this work, we extend the self-calibrated convolution applied to tasks such as 2D target detection to the encoding stage of 3D image registration networks. The proposed SC-RegNet can get more semantic information to make up for the existing registration networks in an unsupervised training manner while not introducing additional parameters. We evaluated SC-RegNet on two public available human Brain MRI datasets: LPBA40 and IBSR18, and a private mouse brain dataset. The experimental results show the superiority of SC-RegNet over the SOTA image registration methods with no extra time-consuming.
SpSiSb: The Technique to Identify Forgery in Legal Handwritten Documents

Forgery activity is the fastest growing crime across the globe. In the field of document image analysis, especially the legal handwritten documents that can be considered as the object of forgery include bank cheque, bills, legal contracts, wills, and so on. The loss due to these crimes not only causes a huge loss to a person or an organization but also demolishes the financial condition of the commonwealth. Identification of ink used in the handwritten documents remains the key priority to identify the fraud in a questioned document by the forensic document examiners. The different techniques used to identify fraudulent alteration in handwritten legal documents are presented in this research paper. Such fraudulent alterations in the documents are performed by adding an extra letter to the existing word. A non-destructive approach named SpSiSb has been proposed in the research paper to identify the forgery in the existing word in the legal handwritten documents under a different scenario.
The Emerging Future

This chapter draws together the various chapters of the book, summarising the high-level points from each. It highlights how Blockchain and other frontier technologies will be an important tool for social impact globally. A renewed focus and promise on emerging economies is highlighted as they now have a way to access knowledge, talent, capital, and to share their talent and ideas and to seek global investment in ways that were not possible before. Some of the policy and governance challenges which will emerge from Blockchain economies are raised as well as the need for more research and discovery. It reinforces the links to the Sustainable Development Goals (SDGs) and the ways that Blockchain and frontier technologies can exponentiate impact towards the SDGs and should be a focus of governments, international institutions, and indeed, the entire ecosystem.
Cryptographic Algorithms for Next Generation Wireless Networks Security

At present a majority of computer and telecommunication systems requires data security when data is transmitted the over next generation network. Data that is transient over an unsecured Next Generation wireless network is always susceptible to being intercepted by anyone within the range of the wireless signal. Hence providing secure communication to keep the user's information and devices safe when connected wirelessly has become one of the major concerns. Quantum cryptography algorithm provides a solution towards absolute communication security over the next generation network by encoding information as polarized photons, which can be sent through the air security issues and services using cryptographic algorithm explained in this chapter.
Detecting fake Videos using Block Chain and Smart Contracts

The Rapid growth of agile gadgets has led to tremendous increase in digital media utilization, mostly for mobile video in ease of marketing. As encryption provides better user confidentiality and perseveration, greater number of online movement is associated with end-to-end encryption form. Irrevlant content such as unreal, violent and unconstitutional videos are being circulated online without being identified its truthfulness creating a platform for intruders and attackers. It is necessary for users to identify and report the contents of the video. Sometimes these videos act as evidence in courts to prove the guilty and the proper state and contents recorded in it .We aim a system to detect and classify the video's truthfulness to solve these problems. A detailed collection of studies has been toted to demonstrate the efficacy of new program over current literature.
Blueprint of Blockchain for Land Registry Management in India

Land ownership in the country is validated through the basic register like ownership register and plot registers (khewat, khasra, kahtoni etc.). Though the legal ownership is established through the Records of Right (ROR), it is still presumptive in nature. The conclusive titling is initiated by Government of India through the national programme like Digital India Land Records Modernization Programme (DILRMP). Further the record details are managed by various departments like survey and settlement, registration, revenue department and various courts etc. The lack of coordination between these departments has lead to numerous disputes. In this paper, different layout plans have been proposed to solve the problem of trust, security and immutability of the records through the blockchain technology. Layout of the ledgers for different systems, inclusion of channels, formation of smart contracts, regulatory requirements, security and privacy requirements, scalability and performance issues are described so that decision makers can take a fast decision for implementation of blockchain in the operational projects.
PDSA Computing

As materials are getting softer, we need to interact with the object directly to find out what it is and how we should handle it. Thus, movement becomes increasingly important. Human movements are divided into two. One is Motion observed from outside and the other is Motor inside of us. In the case of Motion, our trajectories vary widely at the early stage but when we get close to the object, muscles harden and move together with skeleton. So, we can apply mathematical approaches and control the trajectory. Why trajectories vary widely at the early stage is because we need to mobilize many body parts to balance our body to adapt to the environment and situation. So, coordination is important, but the way to secure coordination is not established yet. Further, muscles are analog, so their degrees of freedom are tremendously large. Therefore, we cannot solve the problem mathematically. We need another way of computing to find out how we should handle the object. Thus, strategic decision making is called for. So Pragmatic computing, which repeats the Plan $$\to$$ Do $$\to$$ Study $$\to$$ Act cycle is needed. This paper proposed quantitative performance evaluation tool using Mahalanobis Distance and pattern. The uniqueness and usefulness of this approach is it allows us focus our attention on any location where we wish to, and it helps us understand how we can improve our movements. This is made possible by making the most of instinct and proprioception, which have been ignored in regular computing.
Bayesian Networks: Representation and Inference

This chapter introduces Bayesian networks, covering representation and inference. The basic representational aspects of a Bayesian network are presented, including the concept of D-Separation and the independence axioms. With respect to parameter specification, the two main alternatives for a compact representation are described, one based on canonical models and the other on graphical representations. Then the algorithms for probabilistic inference are introduced, including belief propagation, variable elimination, conditioning, junction trees, loopy propagation and stochastic simulation. The chapter concludes by illustrating the application of Bayesian networks in information validation and system reliability analysis.
A Comprehensive Literature Review on Nature-Inspired Soft Computing and Algorithms

Soft Computing techniques are capable of identifying uncertainty in data, determining imprecision of knowledge, and analyzing ill-defined complex problems. The nature of real world problems is generally complex and their common characteristic is uncertainty owing to the multidimensional structure. Analytical models are insufficient in managing all complexity to satisfy the decision makers' expectations. Under this viewpoint, soft computing provides significant flexibility and solution advantages. In this chapter, firstly, the major soft computing methods are classified and summarized. Then a comprehensive review of eight nature inspired – soft computing algorithms which are genetic algorithm, particle swarm algorithm, ant colony algorithms, artificial bee colony, firefly optimization, bat algorithm, cuckoo algorithm, and grey wolf optimizer algorithm are presented and analyzed under some determined subject headings (classification topics) in a detailed way. The survey findings are supported with charts, bar graphs and tables to be more understandable.
A New Smart Recommendation System of Learning Management Systems

This chapter presents the functional and technical architecture of a recommendation system of free learning management systems that the authors have implemented to facilitate the choice of the most suitable LMS to meet the objectives, specifications, and criteria chosen by the institution. Thus, any random choice entails a loss of money, effort, and time loss for porters and device designers, and this is for various reasons (cost, utility, usability, etc.). Notably, this system takes into account more than 20 LMSs. The choice of these LMSs is based on a methodical and systemic approach that identifies the adequate criteria to the objectives and specifications chosen by the institution, depending on the objects and pedagogical tools related to the recommended teaching and learning device, to retain the most suitable LMS. This chapter is motivated by the desire to clarify and support users in their choice of the most suitable LMS to meet their needs and to get maximum benefit from the potential offered by technologies in pedagogy.
A Novel Type-2 Fuzzy Directed Hybrid Post-Filtering Technique for Efficient JPEG Image Artifact Reduction

Image and video compression becomes very popular and intense field in recent decades, due to their fast and quality communication demand. To cope up with the high speed communication demand of image and videos, over the communication channel, higher compression (ie. low bit rate compression) is now preferred at the cost of quality degradation. Currently JPEG and JPEG2000 are the most popularly used codec’s for achieving quality image compression. Practically, on the low bit rate compression, it has been observed that, the JPEG standard compressed images suffer from multifarious visual distortions. To address this problem effectively, this paper proposed an innovative type-2 fuzzy directed hybrid post filtering technique, which is framed to suppress the artifacts generated in JPEG compressed images at low bit rate compression. The proposed technique addresses all three types of JPEG compressed image artifacts: blocking, edges blurring, and aliasing. Furthermore the proposed technique is structured with two stages, to enhance the quality of JPEG compressed images. The first stage, removes blocking artifacts using boundary smoothing and guided filtering techniques. The second stage reduces blurring and aliasing around the edges through type-2 fuzzy directed local edge regeneration. To prove higher efficiency of proposed work, a complete comparative performance evaluation with other existing JPEG artifact removal techniques, has also presented. This extensive performance analysis includes visual quality assessment of post-filtered images along with subjective quality assessment on the basis of, Peak Signal to Noise Ratio (PSNR) and Mean Square Error (MSE). Performance evaluation illustrates that the, proposed approach is efficient, provides PSNR improvement of approximately 1 dB along with higher reduction in MSE values as compared to state of the art algorithms for all the bit-rate compressions.