Reviewerly/exHarmony huggingface dataset:
Invoke Artificial Intelligence and Machine Learning for Strategic-Level Games and Interactive Simulations

Computer games are an important sector of the digital economy, computer and entertainment industry are very sophisticated in many ways in the current context of technology. They’ve gone beyond entertainment needs, and the computer game paradigm and technology together are now increasingly used in education, training, storytelling, and wherever it’s necessary to create an appealing and engaging environment. More realism in virtual and artificial environments and more real interfaces to the users can be considered as two main advantages that we get using these techniques. Instead of pre-defined hard coded scripts driving these environments, we will be able to create just the environment and its relative mechanics, so the artificial intelligence could introduce tailored challenges and scenarios to the environment. This paper proposes a Behavioral Driven Procedural Content Generation methodology together with Ternary Neural Networks to be used in interactive strategy-based simulations for effective decision making. This is vital because current approaches like Experience Driven Procedural Content Generation algorithms can be very flexible and one small change could trigger complex changes in the system. Using another model created by using player behavior will be specifying the clamping conditions so the AI is capable of stabilizing itself.
Formulations and Rationales for Other Problems in Data Analysis

We shall now comment upon some other problems, considered in the broadly conceived domain of data analysis, as seen in the perspective of the bi-partial objective functionBi-partial objective function . We shall show the applicability of the concept of bi-partial objective function to these (and, indeed, yet other) problems with, whenever appropriate, illustrations for the potential form of the respective objective function.
Efficient and Effective Text-Annotation through Active Learning

The most commonly used active learning criterion is uncertainty sampling, where a supervised model is used to predict uncertain samples that should be labeled next by a human annotator. When using active learning, two problems are encountered: First, the supervised model has a cold-start problem in the beginning of the active learning process, and second, the human annotators might make labeling mistakes. In my Ph.D. research, I address the two problems with the development of an unsupervised method for the computation of informative samples. The informative samples are first manually labeled and then used for both: The training of the initial active learning model and the training of the human annotators in form of a learning-by-doing session. The planned unsupervised method will be based on word-embeddings and it will be limited to the area of text classification.
Aleatory Acoustics, Mechanics and Aesthetics

Sound for games has evolved to give more granular acoustic variation. Animated collisions of meshes trigger sounds that are more expressive of the unique characteristics of the materials and context in a game space. Sound design and music can reflect a wider presentation of possible states in a game. Game sound designers have made effective use of software that augments or replaces the sound controller of the game engine. Often called middleware or audio engine, the software system provides more sophisticated techniques and artistic practices that optimize a game sound’s combinatorial possibilities, and creates a more sonically complex game world for the player to experience.
Design Principles for Increasing the Comprehensibility of Icons in HMIs Through Motion Design

Animation is a significant game changer for designing icons that can communicate their action or status without any additional elements in any design language. This can increase the comprehensibility of Human-Machine-Interfaces (HMI). In this paper, we introduce design principles on how to animate icons for HMIs to increase the comprehensibility and to draw the users’ attention. This initial paper builds the foundation for further research on this topic. The results of an expert review on animated icons are presented. The results show that there is a highly expected positive effect of animated icons on comprehensibility and reduction of error rate.
Final Remarks and Challenging Problems

In this book, we studied the problem of data stream mining. Recently, it became a very important and challenging issue of computer science research. The reason is the enormous growth of data amounts generated in various areas of human activities. Data streams [1–3] are potentially of infinite size and often arrive at the system with very high rates. Therefore, it is not possible to store all the data in memory. Appropriate algorithms should use some synopsis structures to compress the information gathered from the past data. Moreover, data stream mining algorithms should be fast enough. Most often they have an incremental nature, i.e. each data element is processed at most once. Alternatively, the data stream can be analyzed in a block-based manner. Another feature of data streams is that the underlying data distribution may change over time. It is known in the literature as ‘concept drift’ [4, 5]. A good data stream mining method should be able to react to different types of changes. In this book, we studied various data stream mining algorithms. We focused on three groups of methods, based on decision trees, probabilistic neural networks, and ensemble methods. A separate part of the book was devoted to each group.
Basic Concepts of Probabilistic Neural Networks

Probabilistic neural networks (PNN), introduced by Specht [1, 2] have their predecessors in the theory of statistical pattern classification. In the fifties and sixties, problems of statistical pattern classification in the stationary case were accomplished by means of parametric methods, using the available apparatus of statistical mathematics (e.g. [3–7]). The knowledge of the probability density to an accuracy of unknown parameters was assumed and the parameters were estimated based on the learning sequence. Having observed tendencies present in the literature within the next decades, we should say that these methods have been almost completely replaced by the non-parametric approach (see e.g. [8–24]). In the non-parametric approach it is assumed that a functional form of probability densities is unknown.
Introduction, Scope of Work and Summary of Results

The possibility to capture arbitrarily formed object surfaces in space in its full, three-dimensional shape, has become omnipresent. Although 3D measurement techniques have been used for many years in industry, medicine, forensics, or art, they have only recently found their way into public awareness—not least because of the introduction of 3D printers and the steadily increasing computing power of personal computers. The latter facilitated the large scale marketing of ‘virtual reality’ (VR) currently taking place.
Automated Login Method Selection in a Multi-modal Authentication System : Login Method Selection based on User Behavior

An intelligent model is proposed to automate selection of login authentication method in a multi-model authentication system based on user behavior profile. The proposed protocol analyzes the user behavior data to minimize the process in real time and prevent untrusted attempt, meanwhile, it able to facilitate frictionless user experience in a multi-modal authentication system. The proposed system determines a user at a log-in interface and retrieves the user's behavioral historical data as its determinant factors, matching user behavior based on data retrieval from database. Wherein the behavioral data includes the user's login sessions time, geolocation, accessed application and type of user-agent used. The system processes the behavioral data to generate a user profile by a profiling module, at that point, evaluates the user's profile by factoring in environmental parameters of the user terminal to select an authentication method by an evaluation module. The selected authentication method will be displayed for the user to complete the authentication process. On the other hand, the system will updating the user profile with data relating to a new successful login session for future evaluation.
Spam Detection in Social Media: A Bayesian Scheme Based on Social Activity Over Content

Spam is an enemy of our resources and mood. In this paper spam detection is examined in case of social media content. In particular a typical Bayesian classifier is setup and trained on real data. Additionally a social computing method is also introduced, which analyses the attention that content receives. Finally a novel hybrid approach is implemented, where content is characterized as spam when both the Bayesian classifier and the social attention model agree on content’s evaluation. Experimental results on real world Twitter content, exhibit the promising performance of the proposed scheme.
Comparison Analysis of Multivariate Process Capability Indices

Recently, the manufacturing process system in the industrial field has become more and more complex and has been influenced by many and various...
AHP-based Security Decision Making: How Intention and Intrinsic Motivation Affect Policy Compliance

Analytic hierarchy process is a multiple-criteria tool used in applications related to decision-making.In this paper, analytic hierarchy process is used as guidance in information security policy decision-making by identifying influencing factors and their weights for information security policy compliance.The weights for intrinsic motivators are identified based on self-determination theory as essential criteria, namely, autonomy, competence, relatedness, along with behavioural intention towards compliance; and use four awareness focus areas.A survey of cyber-security decisionmakers at a Fortune 600 organisation provided data.The results suggest that behavioural intention (52% of the weight of influencing factors) is more important than autonomy (21%), competence (21%) or relatedness (6%) in influencing behaviour towards information security policy compliance.Determining weights of intrinsic motivation, intention, and awareness focus areas can help security decision-making and compliance with policy, and support design of effective security awareness programmes.However, these weights may in turn be affected by local organisational and cultural factors.
The Cross-Language Query Expansion Algorithm Based on Hybrid Clustering

To propose a cross-language query expansion algorithm based on hybrid clustering aiming at problems such as theme shift, word dismatch, translation ambiguity and polysemy etc. in the cross-language information retrieval. The HPH-CLQE for cross-language query expansion is put forward by combining advantages of partition clustering and hierarchical clustering. This algorithm can achieve a good clustering effect by avoiding selection of the initial cluster center and cluster number. Then, the CLQE-HPH based on hybrid clustering is presented. This algorithm applies HPH-CLQE to conduct clustering analysis on top documents selected at the initial cross-language retrieval aiming to obtain the expansion word that is highly relevant with user query to achieve the cross-language query post-translation expansion. The experimental results based on NTCIR-5 CLIR data set indicate that MAP, P@5 and P@10 related to the algorithm proposed in this paper get improved greatly with the maximal increase of 20.52% by comparing with three standard algorithms. This algorithm can reduce problems such as theme shift and word dismatch etc. in the cross-language information retrieval effectively and improve the cross-language information retrieval performance.
Cross-Modal Entity Resolution Based on Co-Attentional Generative Adversarial Network

Cross-modal entity resolution aims to find semantically similar items from objects of different modalities(e.g. image and text). The core way to solve the problem is to construct a shared space where multi-modal examples can be represented uniformly. In this paper, we propose a novel Co-Attentional Generative Adversarial Network(CAGAN) method for solving cross-modal entity resolution, which seeks an effective space based on co-attention mechanism and adversarial learning. The generative adversarial network that we design contains two parts, Generator and Discriminator, the generator aims to generate a shared space through intra-modal loss and inter-modal loss, while discriminator is a classifier which tries to discriminate the modalities based on the generated representation. In order to eliminate the imbalance of information between modalities, generate more consistent representation and accelerate the convergence speed of the network, co-attention mechanism is introduced into the network. Experimental results performed on two cross-modal datasets demonstrated the outstanding performance of the proposed method for cross-modal entity resolution.
A Standard MIDI File Steganography Based on Music Perception in Note Duration

Steganography aims to deliver messages in the recording space of a cover media without being noticed. Supported by the theory of music perception, note duration can be used as the recording space for a steganography method. This paper proposes a steganography method that embeds information in the duration of notes in a MIDI file and generates a new MIDI file in which the only difference lies in the note duration. Under several restrictions, the available varying range of each note is calculated, and then secret information is mapped on the change in note duration within the available range. A listening test and an evaluation on capacity are conducted to measure the transparency and capacity of the proposed method. The results reveal that the proposed method has perfect transparency with no perceptible difference in hearing, zero expansion in file size, and an effective capacity of 3.5%.
The Effects of Cybercrime on the Banking Sector in ASEAN

The main objective of this chapter is to investigate the effects of cybercrime on the banking sector in ASEAN. Global challenges on the evolution of cybercrime are in continuous dynamics in the case of emerging or developing countries, so that sustainable development plays an essential role. Moreover, the propagation effects can generate significant damages in the banking sector. Efficient bank management is essential in the context of providing advanced techniques for cyber security. Traditional cyber security measures are insufficient to ensure data protection and online information privacy. Consequently, investigations of cyber-criminal activity must become a priority especially in the context of globalization.
Cybersecurity Challenges Facing Sub Saharan Africa: Botswana Context

The Global Cybersecurity Index (GCI) of Botswana dropped from position 23 in 2014 to position 69 in 2017 with GCI scores of .176 and .430 respectively.  The mediocre GCI performance of Botswana resulted in modest GCI scores across all GCI competitive measures namely: legal, technical, and organizational structure, capacity building, and international cooperation. Generally, cybercrime exploits critical infrastructure systems, thereby placing the nation’s security, economy, public safety and health at risk.  The absence of a national cybersecurity policy framework that describes the current security posture, identifies and prioritizes opportunities for improvement, and communicates to stakeholders about cybersecurity risk, may exacerbate the delay in the execution of Botswana National Cybersecurity Strategy, which has been under development for more than 3 years.  The purpose of this qualitative multiple case study was to explore policy frameworks developing countries use to guide the development of cybersecurity policy and strategies organizations use to safeguard and combat cybercrime.  Fifteen senior managers from the University of Botswana, Ministry of Transport and Communication, Botswana Police Service, Attorney General’s Chambers, and representatives from the private sector participated in a focus group interview during the 3 rd International Conference on Internet, Cybercrime, and Information Systems hosted by University of Botswana on 1 st to 2 nd November 2018.  Themes that emerged included awareness and training, fast tracking the approval of the National Cybersecurity policy, protecting government ICT infrastructure from incidents of cybercrime, building national computer emergency response teams and national security operations centers with appropriate governance structure, and the development of National Cybersecurity Policy to improve Botswana’s security posture and GCI performance.
Privacy vs. Utility: An Enhanced K-coRated

In recommender systems, collaborative filtering (CF) techniques are becoming increasingly popular with the evolution of the Internet. Such techniques are based on filtering or evaluating items through the opinions of online consumers. They use patterns learned from their behavior or preferences to make recommendation. In this context, it is of great importance to protect users’ privacy when there is a need to publish data for a specific purpose which conduct to the usefulness of collaborative recommender systems. However, too much protection to individual privacy will lead to the loss of data utility. How to balance between privacy and utility is challenging. In this paper, we propose a privacy-preserving method based on k-means and k-coRating privacy-preserving model. First, we evaluate the k-coRated model by privacy and utility. Then, according to the drawbacks of it, we introduce our solutions to address the problem. Finally, we make a comparison between our model and k-coRated model in different aspects. As a result, our model outperforms k-coRated model with respect to utility as well as privacy.
New Science, New Stories: Quantum Physics as a Narrative Trope in Contemporary Fiction

This chapter investigates the use of quantum physics in contemporary literature and its influence on both form and narrative, taking into account its conceptual and mathematical complexity and the proliferation of mutually exclusive interpretations. Adhering to a specific interpretation of quantum physics opens up distinct possibilities for the narrative; intersecting with contemporary reading technologies, it opens up new possibilities for interactive media to frame these narratives. The case studies in this chapter make use of different interpretations of quantum physics to explore questions of focalization, narrator, and structure: Greg Egan (Quarantine. London: Legend, 1992) and Ted Kosmatka (The Flicker Men. London: Penguin, 2016) use the conscious collapse interpretation; David Walton (Superposition/Supersymmetry. Amherst, NY: Pyr, 2015) questions identity by means of the Copenhagen interpretation; and Iain Pears bases the structure of his app-novel Arcadia (2015) on the many-worlds interpretation.
Analytical Comparison of DLT Platforms Activity

This paper describes a method for analyzing and comparing the activity of various distributed ledger platforms. The presented topics are the criteria for evaluating platforms, the method of their ranking and methods for automated collection information about them. A system that allows automatic assessment and ranking of distributed ledger technologies (DLT) with an open GitHub repository has been developed. Visualization of the collected information by means of Elasticsearch and Kibana is described.
INFOGRAPHICS IN THE TRAINING FOR THE UNIFIED STATE EXAMINATION ON INFORMATICS (THE SECTION “NUMBER SYSTEMS”)

The article is devoted to the use of infographics as one of the methods of visualization of educational material in training for the exam on informatics. The section “Number systems” is taken as a basis, for the tasks of which general and particular methods of solution are demonstrated. Examples of infographics are given.
Good Plot Symbols by Default

Scatterplots require different symbols for different purposes. For presentation, aesthetically pleasing symbols are popular. For analysis, highly discriminable symbols aid pattern detection. This study identifies a default symbol set suitable for both presentation and analysis. This is achieved by using popular symbols with preattentive differences. Supplemental materials for this article are available online.
Application of the Bayesian Model in Expert Systems

In this paper, we studied the basic elements of the Bayesian model, the elementary application of the rule of Bayes in the field of medical diagnosis, the Bayesian model in expert systems and finally the advantages and inconveniences of the Bayesian model. In fact that, the Bayesian methods been based on the theory of probabilities, nowadays they are popular in the artificial intelligence world as methods to treat the uncertainty. However, during many years it was thought that these methods were impracticable in real problems due to great amount of probabilities that is necessary to obtain in order to build a base of knowledge but this situation changed with the arrival of the networks of belief or Bayesian networks because the use of these networks allows to build bases of knowledge consistent probabilistic. The result of investigation demonstrate that the main advantage of the Bayesian methods resides in that they are strongly based in the theory of the probability, however, its main difficulty is in the great quantity of probabilities that is necessary to obtain in order to build a base of knowledge.
Sound and Relaxed Behavioural Inheritance

Object-oriented (OO) inheritance establishes taxonomies of OO classes. Behavioural inheritance (BI), a strong version, emphasises substitutability: objects of child classes replace objects of their ascendant classes without any observable effect difference on the system. BI is related to data refinement, but refinement’s constrictions rule out many useful OO subclassings. This paper revisits BI at the light of Z and the theory of data refinement. It studies existing solutions to this problem, criticises them, and proposes improved relaxations. The results are applicable to any OO language that supports design-by-contract (DbC). The paper’s contributions include three novel BI relaxations supported by a mathematical model with proofs carried out in the Isabelle proof assistant, and an examination of BI in the DbC languages Eiffel, JML and Spec $$\sharp $$ .
Exploring Customer Influence on the Agile Transformation of Service Providers

Transitioning to agile software development (ASD) practices is a long and difficult journey. Most existing literature assumes that agile transformation is solely the service provider's task, attributing only a passive role to the customer. This study explores customer influence on the agile transformation of service providers, based on an exploratory qualitative field study at a multinational Danish software firm. Our preliminary results show that a customer's risk tolerance and collaborative routines influence their service provider's transition to agile teamwork practices, planning routines, and customer involvement practices. We outline a conceptual model of customer-mediated agile transformation.