Reviewerly/exHarmony huggingface dataset:
Computational Analysis of the Role of Leadership Style for Its Context-Sensitive Control over Multilevel Organisational Learning

This chapter addresses formalisation and computational modelingComputational modeling of context-sensitive controlContext-sensitive control over multilevel organisational learningMultilevel organisational learning and in particular the role of the leadership styleLeadership style in influencing feed forward learningFeed forward learning flows. It addresses a realistic case study with focus on the role of managers for controlControl of multilevel organisational learningMultilevel organisational learning. To this end a second-order adaptive self-modeling network modelAdaptive self-modeling network model is introduced and an example simulation for the case study is discussed.
Energy-Positive Activity Recognition: Future Directions

This book has presented various mechanisms for self-powered activity recognition in IoT. Conventional activity recognition systems use various activity sensors such as accelerometers, magnetometers and gyroscopes for wearable-based activity recognition.
Adaptive Mental Model Aggregation in Organisational Learning Using Boolean Propositions of Context Factors

AggregationAggregation of developed individual mental modelsIndividual mental model to obtain shared mental modelsShared mental model for the organisationOrganisation is a crucial process for organisational learningOrganisational learning. This aggregationAggregation process usually does not only depend on the mental modelsMental model used as input for it, but also on several context factorsContext factor that may vary over circumstances and time. This means that for computational modelingComputational modeling of organisational learningOrganisational learning the aggregationAggregation process better can be modeled as an adaptive dynamical process where adaptationAdaptation is used to obtain a context-sensitiveContext-sensitive outcome of the aggregationAggregation. In this chapter it is explored how Boolean functionsBoolean functions of these context factorsContext factor can be used to model this form of adaptationAdaptation. Using self-modeling networksSelf-modeling network, mental modelMental model adaptationAdaptation by learningLearning, formation or aggregationAggregation can be modeled in an appropriate manner at a first-order self-modelFirst-order self-model level, whereas the controlControl over such processes can be modeled at a second-order self-modelSecond-order self-model level. Therefore, for adaptationAdaptation of aggregationAggregation of mental modelsMental model in particular, a second-order adaptive self-modeling network modelAdaptive self-modeling network model for organisational learningOrganisational learning can be used. In this chapter it is shown how in such a networkNetwork model at the second-order self-modelSecond-order self-model level, Boolean functionsBoolean functions can be used to express logical combinations of context factorsContext factor and based on this can exert context-sensitive controlContext-sensitive control over the mental model aggregationMental model aggregation process. Thus, a computational networkNetwork model of organisational learningOrganisational learning is presented in which the process of aggregationAggregation of individual mental modelsIndividual mental model to form shared mental modelsShared mental model takes place in an adaptive context-dependentContext-dependent manner based on any Boolean combinations of context factorsContext factor.
A Decentralized Solution for Secure Management of IoT Access Rights

As the Internet of Things (IoT) grows, there is a pressing need for secure and efficient management of device access rights. This paper presents Ureka, an open system that uses blockchain technology to manage IoT access rights in a transparent and bidirectional way. Ureka consists of a personal private system, a blockchain smart contract system, and a ticket processing system on an IoT device. Each user and IoT device are identified and authenticated through a unique asymmetric key pair, and access to devices is mediated through smart contracts and tickets. Ureka also implements regulation mechanisms to govern user behavior and prevent rule violations. Our evaluation shows that Ureka effectively manages IoT access rights and prevents unauthorized access. The contributions of this paper include the Ureka open standard, the implementation of a transparent and bidirectional smart contract and ticket system, and the regulation mechanisms for governing user behavior.
New teaching and learning strategies during the COVID-19 pandemic: implications for the new normal

The COVID-19 pandemic adversely impacted higher education by suddenly disrupting its delivery globally. The stringent government regulations and health guidelines meant that teachers and students could not meet physically in one place. Higher education responded with many innovative strategies to overcome the challenge and continue delivering education to its students. These responses varied across the countries based on the availability of resources and local access to the institutions. Undoubtedly, digital tools and platforms have become a lifeline for delivering classes and engaging students. As the world battled with the pandemic and adjusted to the new normal, digital technologies became critical in all areas of teaching and learning. The authors of this chapter teach in a business master’s program and share their experience of quickly shifting their courses to remote teaching. Using an autoethnographic research method, the authors reflect on the context of the program, delivery challenges, digital resources available, and collaborations deployed for online teaching. Further, they describe how the assessments were adapted to online teaching while ensuring their integrity and quality. The initial experience was characterized by extreme caution and trying different options to support learning by students who were isolated due to lockdown restrictions. However, in the last two years, the authors gained more confidence in delivering courses online using digital technologies, which have become popular across the globe. The authors describe using different digital tools that helped them teach effectively. The authors posit that given the dependence of higher education on digital tools and delivery platforms, it is expected that their use will be entrenched in higher education for teaching and learning beyond the pandemic as a new normal. Hopefully, the details in this chapter will resonate with peers who continue their journey in using digital technology and HyFlex educational approaches.
Security Culture and Security Education, Training and Awareness (SETA) Influencing Information Security Management

Information security management (ISM) ensures the protection of organisations' data assets. Studying actual security events becomes more critical for ISM preparation. The Capital Market constitutes a wealth of data sources which react to various security incidents. ISM is an essential part of this industry due to high technology dependency. The previous literature emphasises the need for a holistic approach for ISM; therefore, there is necessary to investigate the current state of the ISM to develop a cybersecurity and ISM culture. Research should further explore the impact of national and organisational culture and its effects on ISM and explore ISM practices and initiatives that organisations implement to develop a security culture. This paper explores the factors in order to improve how employees' culture and IS awareness affect ISM implementation. A qualitative approach using the case study method was applied to understand the problem. Twenty-two semi-structured interviews were conducted in the Middle Eastern Capital Market. The thematic data analysis revealed that Middle Eastern culture is a dominant factor influencing ISM and the security culture and awareness significantly impact ISM. This suggests that organisations should focus on security culture and, even more, on IS awareness to improve ISM. This research identifies several challenges in current security practices in the Middle Eastern Capital Market industry, including the lack of attention to cultural effects, generic SETA programs that do not consider specific industry needs, and a lack of connection between culture and awareness programs.
Multi Factor Authentication as a Service (MFAaaS) for Federated Cloud Environments

Growing needs of organizations have left them looking for innovative solutions in cloud computing. One of the methods currently being used to meet those demands is federated cloud computing. Described as the linking of many service providers' cloud environments to load balance traffic and respond to spikes in server demand, this environment provides great solutions for those in need of them. However, cloud computing generally uses single factor authentication which has proven to be less effective. This paper proposes a multi factor authentication framework for federated cloud environments to address the security issues surrounding federated cloud computing. The proposed method will be implemented on a simulated cloud environment to evaluate its strength and ability of protection against various simulated attacks and all results will be recorded.
Project Manager Competences and University Educational Programs in Project Management

Project management is a dynamically developing direction of management in the context of the digital transformation of the economy, both in the private and public sectors. And the need for qualified specialists like project managers is very high. Nowadays Russian universities actively develop and implement project management higher education programs based on the Federal State Education Standards. At the same time there is no a unified educational approach in terms of project manager’s knowledge, technical and soft skills. This leads to the situation when graduates of different universities - future project managers - get a different set of competences, which creates certain difficulties in the practice of project management. A substantive analysis of the competences described in the recognized project management standards is carried out; different approaches and models of project management competences are taken in consideration to identify the range of competences recognized by all analyzed sources. On the other hand, based on consideration of the Federal State Educational Standards of Higher Education in combination with a study of some industry project management standards in Russia, it is possible to identify possible essentials of the discussed competences, taking into account the tasks of professional activity.
A Survey of Artificial Intelligence Tools in Project Management

Artificial intelligence today is one of the key technological drivers for project management digital transformation and evolution. In this paper we consider three main areas of artificial intelligence applications for project management – digital assistants, intelligent tools for various areas of project management and project management systems with artificial intelligence features. We also discuss the requirements of prospective artificial intelligence system for project management which is called iPMO – the requirements can be used both to develop a new intelligent project management system as well as extend the capabilities of existing solutions. Despite the impressive capabilities of artificial intelligence, project managers are responsible for decision-making. Artificial intelligence plays the roles of assistant and advisor. Artificial intelligence enhances the competence of a project manager, frees up time from routine and allows people to focus on collaborative work and complex management tasks. Project managers also should enhance leadership, communication skills and professional competencies, including managing hybrid project teams using digital assistants and intelligent tools.
Deep Learning Based Path-Planning Using CRNN and A* for Mobile Robots

We present a novel approach for solving path-planning problems using a state-of-the-art data-driven deep learning technique. Although machine learning has been previously utilized for path planning, it has proven to be challenging due to the discrete nature of search algorithms. In this study, we propose a deep learning-based algorithm for path planning, which incorporates a Convolutional Recurrent Neural Network (CRNN) to create an end-to-end trainable neural network planner. This planner is then combined with the A* algorithm through an adaptive autonomy concept to autonomously select the best path planning strategy for increasing time efficiency and completeness. To train the CRNN, a labeled data set is generated autonomously from various maps by changing the starting and endpoints. The trained CRNN can find the shortest path from the starting point to the goal point by evaluating map images in one go. Additionally, the CRNN can predict way-points on image inputs. Our simulation results demonstrate that our proposed strategy is capable of finding the shortest path much faster than the A* algorithm in sparse environments, achieving a speed-up of up to 831 in some cases, which is exceptional.
Privacy-Aware IoT Based Fall Detection with Infrared Sensors and Deep Learning

Falls among the elderly are a major worry for both the elderly and their care-takers, as falls frequently result in severe physical injury. Detecting falls using Internet of Things (IoT) devices can give elderly persons and their care-takers peace of mind in case of emergency. However, due to usability and intrusive nature of wearable and vision-based fall detection has limited acceptability and applicability in washroom and privacy sensitive locations as well as older adults with mental health condition. Privacy-aware infrared array sensors have great potential to identify fall in a non-intrusive way preserving privacy of the subject. Using a secondary dataset, we have utilised and tuned time series based deep learning network to identify fall. Experiments indicate that the time-series based deep learning network offers accuracy of 96.4% using 6 infrared sensors. This result provides encouraging evidence that low-cost privacy-aware infrared array sensor-based fall monitoring can enhance safety and well-being of older adults in self-care or aged care environment.
Machines as Thought Partners: Reflections on 50 Years of Prolog

In 1972, Kowalski and Colmerauer started a revolution with the advent of the Prolog programming language. As with LISP, the language enabled us to think previously impossible thoughts, and ushered in both logic programming and the declarative programming paradigm. Since that time, a number of descendants of Prolog have been brought into the world, among them constraint logic programming and answer-set prolog. In this paper, we celebrate the 50th anniversary of the Prolog language, and give a brief introduction to a new member of the Prolog family of languages — the logic programming language Cogent.
Enterprise and Business Process Automation

Organizations have long struggled with a lack of horizontal and vertical integration and the need to achieve high degrees of coordination across the enterprise. The solution to this challenge was the emergence of Enterprise Systems (ES) and then Enterprise Resource Planning (ERP) systems, which came along with the promise of a common database and a single source of “truth” to enable management to make data-driven decisions. In this chapter, we explore the relationship between how enterprises, technology, and competitive dynamics changed over time, enabling the emergence of ERP I, ERP II, and now ERP III – three eras of the ERP movement. We also look forward at blockchain-based, cloud-based, and AI-based ERP systems and the impact that these systems will have on business processes and workflow automation for enterprises of the future.
The Design and Implementation of a Weapon Detection System Based on the YOLOv5 Object Detection Algorithm

In recent years, as the increases of people's concerns on environmental and body safety, various image-based detection techniques and research have gained wide attention. Currently, object-detection algorithms can be generally divided into two categories: traditional ones which extract features manually, and deep learning-based approaches that automatically extract features from images. Since the former requires a lot of manpower, material resources, and financial costs and consumes a lot of time to screen abnormal images, it no longer meets the urgent needs by our societies. In other words, more intelligent identification systems are required. For society security reason, if different types of weapons, such as sticks, knives, and guns, can be detected in surveillance images, this can effectively prevent the chance of gangsters carrying weapons and acting fiercely or seeking revenge. To identify weapons, we need to distinguish them from other surveillance objects and images in a real-time manner. But most cameras have limited computing power, and images captured in the real world have their own problems, such as noise, blur, and rotation jitter, which need to be solved if we want to correctly detect weapons. Therefore, in this study, we develop a weapon detection system for surveillance images by employing a deep learning model. The intelligent tool used for image detection is YOLO (You Only Look Once)-v5, a lightweighting architecture of YOLO series and Sohas (Small Objects Handled Similarly to a weapon) dataset are adopted for image detection comparison. According to our simulation results, we successfully reduced the number of parameters in the YOLOv5s model by substituting the backbone with Shufflenetv2, replacing the PANet upsample module in the neck with the CARAFE (Content-Aware ReAssembly of Features upsample) upsample module, and replacing the SPPF (Spatial Pyramid Pooling-Fast) module with three lightweight options of simp PPF. These changes resulted in a 16.35% reduction in the parameter size of the YOLOv5s model, a 30.38% increase in FLOPS computational efficiency, and a decrease of 0.024 in mAP@0.5.
Role of Technology and Cybersecurity in Academic Integrity: Introduction

With technology now omnipresent inside and outside classrooms, it is important to examine how it is evolving and influencing the education sector. This chapter takes a close look at the contributions by international experts on various areas of academia and technological advances, some that have become part of common conversations and some that are emerging, but all that present numerous opportunities and threats surrounding academic integrity.
Proposal of a Real-Time Video Avatar Generation Method for Metaverse Environment

Virtual avatars are commonly used as characters for communication in the Metaverse. However, they fall short in nonverbal communication, such as sensing facial expressions and the atmosphere of conversation partners. Therefore, in this study, we propose a real-time video generation method as a new advanced communication tool that allows information sharing and decision-making. This method realizes communication that allows the user to feel the human presence in the Metaverse space.
Digital image correlation: Advancing mechanical property characterization of adhesive joints

Digital image correlation (DIC) techniques bring a versatile and robust platform, along with unique capabilities, to experimental methods for measuring full-field deformations across a wide range of applications. Researchers and practitioners in the field are increasingly implementing DIC techniques to enhance their understanding and characterization of adhesively bonded joints. Following a brief overview of the method, this chapter will review some of the recent applications and emerging methods relevant to the adhesion field. Reported studies include constitutive behavior characterization of both bulk and in situ adhesive layers, implementation to reveal stress concentrations in strength test specimens and prototypes, determination of local deformations for correlations with finite element models, and possible extensions to the nondestructive inspection of bonded systems. DIC is also making significant contributions to our understanding of fracture processes and characterization of fracture behavior in bonded joints. The improved toughness of modern adhesives offers added capabilities, reliability, and robustness, but larger strain capabilities and higher fracture energies also complicate their evaluation using traditional linear elastic fracture mechanics approaches. Using DIC, researchers have developed improved methods, adapting testing techniques, data reduction, and model correlation with the evolving performance of tougher adhesive materials. A simple method combining DIC results with cohesive zone models is shown to be a robust method for calibrating fracture properties and traction-separation laws for adhesively bonded joints. This chapter will provide an overview of these DIC applications and how they are advancing our ability to test, characterize, model, and design bonded systems.
General Dynamic Difficulty Adjustment System for Major Game Genres

Dynamic difficulty adjustment (DDA) is a research hotspot in the field of artificial intelligence (AI) in games. This paper designs and implements a general DDA system based on the flow to improve the status of the existing DDA methods highly specialized. The system proposed can be widely applied to major game genres in the game industry effectively. Improvement of parameters after introducing DDA proves the system’s high adaptability. Advantages on mean total score greater demonstrate that the system has a better player experience by inviting players to participate in the Game Engagement Questionnaires (GEQ). The soundness and generality of the system can provide a complete solution for optimizing the player’s gaming experience, thus supporting game designers and developers to achieve or port core AI systems of games more efficiently.
Activity Recognition in IoT

Due to the advancements in technology and microelectromechanical systems, there is an exceptional development in the capabilities of sensors and smart devices. Nowadays people interact with these devices regularly in their daily lives due to the enhanced computational power, compact size, user-friendly interface and reduced cost of these devices.
Measuring the Agile Mindset on Individual, Team and Organizational Levels - Results of an Empirical Study

In recent years, agility has become an increasingly relevant aspect of practitioner approaches as well as research on organizational endeavours. While techniques are often in focus, governance aspects as well as mindsets are hypothesized to be more effective aspects or agile organizing, however. As of today, there is little quantitative empirical research aimed at illuminating agile mindsets, and non that aims at mindsets of temporary teams or entire companies. This article presents the findings of a study conducted in a major German IT services company. 79 valid results obtained from 500 polled operative employees allow for the quantitative analysis of agile mindset concepts introduced by Miler and Gaida. Multiple methods like PCA, cluster analysis and SEM were used to explore into the potential structure of the agile mindset on individual, team and organizational levels. This article adds knowledge to the still much unexplored concept of organizational agility and concludes to propose future research for making use of the obtained insights in longitudinal, temporary settings. It is the first study to present factor as well as principal component analysis, and structural equation modelling for a concept that was up to then largely unexplored and subject to qualitative research at most.