Reviewerly/exHarmony huggingface dataset:
Design and Implementation of a Markings Assisted Guide Robot for Art Museums

As nowadays people are attracted by technology in all domains and fields of activity, museums are in danger of gradually losing their visitors, unless they find ways to attract younger people with new technologies and new ways of presenting their heritage. One of the most appealing solutions is the employment of robot guides to complement or replace a human guide. This would increase the novelty factor of a museum along with mitigating the problem of lack of personnel or experience. Considering the actual state of guided tours made by museum staff, a robot that guides visitors would be of a real interest and would offer experienced assistance any time. The robot proposed and implemented in this paper is catered to visitors or groups of visitors that want to participate in a guided tour where they could get streamlined information about presented works of art.
The Three-Dimensional Model for a Community

The community cyber security maturity model (CCSMM) was designed and developed to provide communities with an action plan to build a viable and sustainable cybersecurity program focused on improving their overall cybersecurity capability. Not long after the initial development of the model, it was realized that there are intertwined relationships that needed to be addressed. This drove the creation of the three-dimensional model broadening the scope to include individuals, organizations, communities, states, and the nation. This chapter will provide an overview of the development and importance of the 3-D model and will describe the scope areas that were included.
Fraud Detection Based on Graph Neural Networks with Self-attention

With the rapid development of electronic payment, fraud cases occur frequently, and fraud detection is becoming more and more important. Traditional fraud detection model is not very good at processing information interaction between users. Furthermore, it could not handle the importance of each feature well. In order to solve this problem, this paper proposes a fraud detection model based on graph neural networks with self-attention mechanism. First of all, based on transaction data, the complex networks of interaction between user nodes and surrounding relationship nodes are reflected on the network modeling of social relationships between users. Second, by introducing graph neural networks model with self-attention mechanism based on node centrality structure characteristic index, a fraud detection model with coupling information of network characteristics and transaction characteristics is proposed. The experimental results show that this method can respond to fraud more accurately and improve the quality of judgment for the traditional fraud detection methods.
On the Use of Bayesian Network in Crime Suspect Modelling and Legal Decision Support

Predictive policing (Pp) relates to identifying potentially related offences, similar criminal attributes and potential criminal activity, in order to take actionable measures in deterring crime. Similarly, Legal Decision Making Process (LDMP) considers some level of probabilistic reasoning in deriving logical evidence from crime incidents. Bayesian Networks (BN) have great potential in contributing to the area of Pp and LDMP. Being based on probabilistic reasoning, they can assess uncertainty in crime related attributes and derive useful evidence based on crime incident observations or evidential data. For example, in a particular context of crime investigation, BN based inference could help collect useful evidence about a crime scenario or incident. Such evidence promotes effective legal decision making process and can assist public safety and security agencies in allocating resources in an optimal fashion. This chapter reports on various application areas of BN in the crime domain, highlights the potential of BN and presents “thought experiments” on how offender characteristics could be modelled for decision support in legal matters. The chapter further reports on the performance of empirical analysis in the legal decision support process, in order to elucidate the practical relevance and challenges of using BN in the crime domain.
Perspective and Challenges of Blockchain Technology in the Accountability of Financial Information

The accounting information system could be improved by blockchain technology, but some potential risk could arise. Thus, it is worth considering such risks. The accounting research and academic literature regarding the impact of this technology on the accounting system are in an initial stage of this emergent field. The purpose of this chapter is to go a step further on this topic and to spur additional research regarding accounting and blockchain technology. The contribution of this study is twofold. On the one hand, it shows the main technologies comprising blockchain and their main consequences understood as sources of improvement. On the other hand, it assesses said effects applied to different processes of the accounting information system. Not only does this work show implications for the accounting profession, but the effects on the primary stakeholders are also brought to light.
Answering Why-not Questions on Top $k$ Queries with Privacy Protection

Outsourcing data services to the public cloud will help data owners save administrative costs, while it may bring privacy concerns. To guarantee the confidentiality of sensitive data against the untrusted cloud and unauthorized users, one way is to have the cloud perform the query processing with encrypted query requests over outsourced encrypted data. However, it is of great challenge to support various types of query processing, especially those of answering why-not questions, under data encryption settings without sensitive information leakage. In this paper, we define and solve the problem of answering why-not questions on top- <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$k$</tex> queries with privacy protection. To address the problem, we propose two Secure Why-Not top <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$k$</tex> Query (SWN <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$k$</tex> Q) processing approaches. In the basic approach, we adopt the Paillier cryptosystem to guarantee the semantic security of data and queries and propose two new secure protocols to support computations on ciphertext in SWN <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$k$</tex> Q processing. Then, we propose a secure weighing space generation method for obtaining the best approximate refined query. To solve the efficiency problem of the basic approach, we further propose an optimized approach, in which dominance-based secure data pruning and early stopping conditions are presented to improve the query efficiency by pruning searching space in the retrieval of refined queries. Thorough analysis shows the security and computational complexity of our approaches, and extensive experimental results on real datasets further demonstrate the query performance of our approaches.
Predicting the Masses of Theelectron, the Neutrino, the Proton, The Neutrino and Possibly the W-and Z-Bosons&amp; Additional Prediction of a Small Non-Zero Mass for Gluons

According to space particle dualism, gravity is a side effect of cardinal hypercharge [1]. Could something similar apply to inertia? If that is the case, then one could try to predict the masses of different particles by simply looking at their charge. The proton has the same electric charge as the positron, but besides electric charge (intermediate by virtual photons), it also has a strong ‘charge’ or hypercharge, usually called ‘flavor’ (intermediate by gluons), and a weak charge (intermediate by W- and Z-bosons). That could be what makes it heavier than an electron.
Medical Image Thresholding Using Genetic Algorithm and Fuzzy Membership Functions

Thresholding is one of the important steps in image analysis process and used extensively in different image processing techniques. Medical image segmentation plays a very important role in surgery planning, identification of tumours, diagnosis of organs, etc. In this article, a novel approach for medical image segmentation is proposed using a hybrid technique of genetic algorithm and fuzzy logic. Fuzzy logic can handle uncertain and imprecise information. Genetic algorithms help in global optimization, gives good results in noisy environments and supports multi-objective optimization. Gaussian, trapezoidal and triangular membership functions are used separately for calculating the entropy and finding the fitness value. CPU time, Root Mean Square Error, sensitivity, specificity, and accuracy are calculated using the three membership functions separately at threshold levels 2, 3, 4, 5, 7 and 9. MRI images are considered for applying the proposed method and the results are analysed. The experimental results obtained prove the effectiveness and efficiency of the proposed method.
Finding service compositions in complex homecare service network

With the development of pension service industry, there have been numerous homecare services. The demands of the elderly are becoming increasingly complex and a single service is difficult to meet the demands of the elderly, so homecare service composition becomes particularly important. According to the homecare service features, this paper builds a complex network model with homecare services as nodes and relationship between services as edges. Then, the search strategies are used to search in the complex network to obtain service composition. This method can find high quality service composition which meets the complex demands of the elderly.
The Relationship between Legal and Design Cultures: Tension and Resolution

The central thesis of legal design is that service design principles and methods can be usefully applied to legal processes. This necessarily involves lawyers commissioning, participating in, and implementing design projects. This chapter examines anthropological and sociological accounts of professional practice and posits that there are some cultural hurdles to developing this law/design relationship.There are identifiable lawyerly attributes and mindsets derived from legal culture. This culture is not homogeneous but at its heart are themes of precedent, formality, and textual/verbal articulacy. Design has its own cultural tropes including flexibility, experimentation, and visual literacy. The cultural centres of law and design are some distance apart and this tension may be problematic for those seeking design interventions into law.These cultural constructs have significant power, but they also exhibit plasticity. The chapter concludes by examining the potential of both cultural change and exchange to ease the process of introducing human-centred design characteristics into legal practices.
The Cloud in Education

This chapter is based on a review of literature written as part of the project “School on the Cloud” (SoC) launched by 57 project partners in March 2014 in Athens, Greece. The SoC project sought to provide a better understanding of potential impact that Cloud Computing can have on education in Europe. It presents leadership and management issues related to the adoption and implementation of the Cloud in education. It has been developed through a mixed-research approach involving desk-based review, analysis and policy guidance for educational stakeholders. E-leadership issues and support measures are identified for implementing the Cloud in education organisations.
Awareness Based Recommendation

In Artificial Intelligence and Robotics, one of the important issues is to design Human interface. There are two issues, one is the machine-centered interaction design to adapt humans for operating the robots or systems. Another one is the human-centered interaction design to make it adaptable for humans. This research aims at latter issue. This paper presents the interactive learning system to assist positive change in the preference of a human toward the true preference, then evaluation of the awareness effect is discussed. The system behaves passively to reflect the human intelligence by visualizing the traces of his/her behaviors. Experimental results showed that subjects are divided into two groups, heavy users and light users, and that there are different effects between them under the same visualizing condition. They also showed that the authors' system improves the efficiency for deciding the most preferred plan for both heavy users and light users.
Educational Modeling of a New Smart Learning Management System

The problems of most LMSs are first of all of a pedagogical nature and then of a technical one. Studying these interrelated problems provides a useful conceptual reference that enables us to design a new model for a more relevant solution. In this chapter, a new conceptual model of an LMS is presented. The model, which stems from a methodological and systemic approach, is based on the hybridization between four learning theories, namely the traditional pedagogy, behaviorism, cognitivism, and social constructivism. The authors present at first each of these learning theories by discussing both their advantages and limits. The main principles of these learning theories and the technical functionalities of the proposed LMS that result from the hybridization of these principles are outlined. They finally discuss the implications of the new conceptual model for how to design and develop a new LMS that fits the needs of their final users, in particular learners.
Finding the outliers in scanpath data

In this paper, we describe the design of an interactive visualization tool for the comparison of eye movement data with a special focus on the outliers. In order to make the tool usable and accessible to anyone with a data science background, we provide a web-based solution by using the Dash library based on the Python programming language and the Python library Plotly. Interactive visualization is very well supported by Dash, which makes the visualization tool easy to use. We support multiple ways of comparing user scanpaths like bounding boxes and Jaccard indices to identify similarities. Moreover, we support matrix reordering to clearly separate the outliers in the scanpaths. We further support the data analyst by complementary views such as gaze plots and visual attention maps.
An Improved Fuzzy Actor-critic Learning Approach for Path Planning with Position Constraints

In the last decades, path planning with position constraints attracts many attentions. In this paper, we propose an innovative approach named improved fuzzy actor-critic learning (IFACL) to solve this problem without modelling the map containing obstacles and complex calculation. Specifically, only the initial position, target position and obstacle position are needed as inputs for the algorithm to learn a desired path. Based on FACL, a penalty factor is added to enable agents obtaining the ability to avoid obstacles through punishing agents when exceeding position constraints. Then, to optimize the path planned, an excessive coordinate point which can be updated iteratively during the training process is utilized to calculate the reward with penalty factor. The simulation results prove the superiority and effectiveness of this algorithm in different scenarios with regular obstacles and hypothetical irregular obstacles. Due to the flexibility of FACL, this approach may be easily extended to path planning with velocity constraints and dynamic constraints.
Future Directions for Public Service Delivery in the Digital Era

This chapter discusses emerging issues and technologies, such as ethical responsibilities in a changing technological environment, the use of analytics and artificial intelligence, the evolution of communications technology, and the growth of block chain and mobile apps technology. Mobile apps technology is a very exciting development because the nature of the applications is very personal and target specific customer needs, hence gradually resolving the issue of explicitly knowing the customer and meeting its personal needs through the concept of personas. This chapter provides numerous examples of how the various technological developments can be specifically implemented to enhance public service delivery in the digital era. In this context, Chapter 12 has two important implications, namely the impact of the technology trends to revolutionise public service delivery on the operations of government entities and users of government services in the future.
Using Machine Learning to Predict Serial Killers’ Vital Goal

Serial killers' criminal behavior and goal are sometimes challenging to find from criminals or very few cases before they are arrested. It is difficult to analyze some unique relationships through massive data that is a challenge to disclose the facts to the public. According to the study from Bartol, C. R., & Bartol, A. M. (2017)[1], there are still more than 30% of criminals who clearly commit crimes in a wider geographical area. Serial killers are mainly men, and they tend to prefer one sex over another. Therefore, through data visualization and hypothesis testing, we can get a strong linear relationship between killers and victims. This paper analyzes a data set based on a sample of American serial killers from the Radford/FGCU Serial Killer Database, visualizes American serial killers' behaviors and uses machine learning to classify and predict whether the serial killers have a specific goal of victims.
Introduction

WSN has a variety of multimedia-based information like image, video and secret data transmission process. For this process, the quality and the security of sensor nodes are critical. This chapter examined the background and difficulties of image security in WSN and Lightweight Cryptography (LWC) methods. Lightweight encryption strategy envelops quicker encryption and by analyzing the computing time, it expands the general lifetime of the sensor network. The fundamental reason for LWC in WSN is that its unique communication has been mixed or enciphered whereas the outcome is known as the cipher content or cryptogram. It is incorporated into block; the stream ciphers along with hash function are made to deliver the sturdy security for WSN image transmission process. Besides improving the nature of the images and security, the LWC optimization techniques also resemble PSO, GWO, and CSA with steganography, information data hiding and watermarking models. Toward the end of this chapter, the author discussed the vital performance measures utilized to analyze the security level of images in the network system.
Visualization technologies for material development in professional training of future specialists

The article presents the experience of using visualization technology in the professional training of future engineers-teachers of computer profile, in particular video lectures on the example of training IT specialists. It is emphasized that it is necessary to change the paradigm of teaching computer disciplines in the context of visualization of didactic materials as a basis for the abstraction of material. Practical aspects of the development of didactic and methodical materials of technology of students training for computer science' school course with the video materials used are considered. It is proved that a large number of different methods and tools of teaching is an effective mechanism that can solve both educational and methodological problems for the modern educational innovations’ application in higher education, optimize the educational process and implement new approaches to lifelong learning. It is established that the use of e-learning systems with the implementation of visualization contributes to a more successful perception and memorization of educational material. This is due to the work intensification at the same time both hemispheres: the left hemisphere usually works in traditional learning, the right hemisphere, the responsibility for the image-emotional perception of the proposed information, is activated by visualization. As a result, there is a creative and professional development of personality. The classical methods of teaching the discipline "School course of informatics" with lecture-practical forms of teaching with text didactic materials that have a low level of abstraction compared to the use of video materials are analysed. It is proved that, due to the technical advantages of the use of video materials, the quality and efficiency of the educational process is improved. That is, visualization technologies have significant potential, which guarantees high-quality professional training of students.
Semi-supervised Robust Dual-graph Concept Factorization via L<sub>2,1</sub> Norm

Recently, as an effective paradigm for representation learning, Concept Factorization (CF) has attracted a great deal of interests in the areas of machine learning and data mining. However, it is difficult for existing CF related algorithms to effectively learn data manifold and feature manifold while utilizing the label information of data to learn more discriminative features. To overcome this problem, we propose a novel method called semi-supervised robust dual-graph CF (named RGCCF), which considers the manifold of data and features and combines the limited label information. Furthermore, an effective optimized method is proposed to solve the RGCCF problem. The experimential results on real-world datasets shows that RGCCF can achieve better clustering performance compared with the other CF-based methods.
COMPARATIVE ANALYSIS OF APPROXIMATION METHODS USING REGRESSION RELATIONSHIPS AND NEURAL NETWORKS FOR LINEAR MODELS

The paper examines comparative analysis of approximation methods using regression dependencies and neural networks for linear models.
Validity

In order to elucidate the understanding of validity that underlies logic, it is useful to introduce some symbols that belong to the vocabulary of set theory. A set is a collection of things, called its elements. We will write a ∈ A to say that a is an element of A, and a∉A to say that a is not an element of A. The main thing to bear in mind about sets is that their identity is determined by their elements. If A and B have the same elements, then A = B. The converse also holds: if A = B, then A and B have the same elements, for identical objects cannot have different properties.
Residue Number Systems

Residue Number Systems have probed their potential for computation-intensive applications, especially those related to signal processing. Their main advantage is the absence of carry propagation between channels in addition, subtraction and multiplication. Thus, high-performance systems may be built for applications involving only these operations using Residue Number Systems.
Blind image separation for document restoration using plug-and-play approach

In this paper we propose a new method for document image restoration based on Blind Source Separation. The existing separation methods rely on the general properties of source images such as independence, sparsity, and non-negativity. In this work we show that by exploiting some characteristics of image denoising methods in a play-and-plug scheme, efficient BSS results could be achieved. In particular, we show that the use of BM3D and Non-local Means denoising methods as ingredients in the proposed scheme, which exploits the non-local properties of the image, leads to better image separation in terms of convergence rate and perceptual image quality. We also propose to use the dictionary-learning approach to take the concept of visual chirality into consideration. Finally, we apply the proposed scheme to document image restoration problem and show its advantage through experiments and objective performance evaluation.