Reviewerly/exHarmony huggingface dataset:
A Novel Light U-Net Model for Left Ventricle Segmentation Using MRI

MRI segmentation and analysis are significant tasks in clinical cardiac computations. A cardiovascular MR scan with left ventricular segmentation seems necessary to diagnose and further treat the disease. The proposed method for left ventricle segmentation works as a combination of the intelligent histogram-based image enhancement technique with a Light U-Net model. This technique serves as the basis for choosing the low-contrast image subjected to the stretching technique and produces sharp object contours with good contrast settings for the segmentation process. After enhancement, the images are subjected to the encoder–decoder configuration of U-Net using a novel lightweight processing model. Encoder sampling is supported by a block of three parallel convolutional layers with supporting functions that improve the semantics for segmentation at various levels of resolutions and features. The proposed method finally increased segmentation efficiency, extracting the most relevant image resources from depth-to-depth convolutions, filtering them through each network block, and producing more precise resource maps. The dataset of MICCAI 2009 served as an assessment tool of the proposed methodology and provides a dice coefficient value of 97.7%, accuracy of 92%, and precision of 98.17%.
Occurrences of a Chronicle

This chapter gives elements about how a chronicle occurs in a sequence of events. The seminal work defining the notion of chronicles was motivated by system monitoring applications. A chronicle defines a temporal arrangement of events whose occurrences have to be monitored (for instance, for firing alarms). This section exploits our formal account of chronicles to formalize the notion of occurrence of a chronicle in a sequence. In addition, we introduce the multiple occurrences of a chronicle in a sequence and how to count them.
A Conceptual Framework for Conversational Human-AI Interaction Design (CHAI)

Artificial intelligence is transforming the sociocultural and economic fabric of the world today, paving the way to a new era of Conversational Human-AI Interaction (CHAI). Being embryonic in its evolution, CHAI poses a broad range of challenges, opportunities, and perspectives, rendering any existing general design frameworks, models and approaches insufficient and ineffective. With the advent of generative technologies such as ChatGPT built on Large Language Models, AI has begun to emulate human cognitive ability in handling seamless multi-turn conversations while traversing interchangeable contexts with humanlike dexterity. At this backdrop, Human-AI interaction has become one of the most complex emerging fields of design today with an urgency to prioritize widespread research studies. As the field lacks requisite standards, guidelines, and benchmarks, we have undertaken an exploratory study through qualitative methods and proposed a conceptual framework for Conversational Human-AI Interaction (CHAI) considering the challenges, opportunities, and perspectives surrounding this nascent yet invigorating field. The framework identifies and integrates the principles of CHAI within the larger latitude of contextual, sociocultural, and psychological dimensions. The proposed conceptual framework attempts to act as a stepping-stone for further research in validating and extending it to a broader range of industries, applications, and usage scenarios.
Towards a New Multi-tasking Learning Approach for Human Fall Detection

Many fall detection systems are being used to provide real-time responses to fall occurrences. Automated fall detection is challenging because it requires near perfect accuracy to be clinically acceptable. Recent research has tried to improve the accuracy along with reducing the high rate of false positives. Nevertheless, there are still limitations in terms of having efficient learning approaches and proper datasets to train. To improve the accuracy, one approach is to include non-fall data from public datasets as negative examples to train the deep learning model. However, this approach could increase the imbalance of the training set. In this paper, we propose a multi-task deep learning model to tackle this problem. We divide datasets into multiple training sets for multiple tasks, and we prove this approach gives better results than a single-task model trained on all datasets. Many experiments are conducted to find the best combination of tasks for multi-task model training for fall detection.
Factors Associated with Cybersecurity Culture: A Quantitative Study of Public E-health Hospitals in South Africa

The healthcare sector has become a high target of cyber threats due to the nature of the industry and the potential of personal and confidential information. Human related factors have proven to be the major contributor to the challenges confronting cybersecurity across different domains. Addressing the human problem in cybersecurity calls for a coordinated and inclusive cybersecurity measure like Cybersecurity Culture (CSC). CSC has been argued as an essential cybersecurity measure that contributes to changing human behaviour in terms of their attitude, beliefs and values as well as their performance towards security that may impact positive security behaviour. Research work in CSC is limited in the healthcare sector as existing works focus on financial and insurance sectors. Following a quantitative research method, this paper conducted an empirical study to identify CSC factors that are associated with public e-health hospitals in South Africa. The findings revealed that under the component of preparedness are issues of awareness and competency as factors that are highly associated with CSC. Under management, lack of a cybersecurity team, top management support as well as rewards and punishment were identified. Factors relating to responsibility and environmental components were also identified to have an association with CSC among Information Technology users. Identifying the factors would assist in the development of a framework for establishing CSC in the hospitals which would form a base for hospitals in developing CSC in their settings.
RL-based path planning for controller performance validation

Autonomous vehicles (AVs) will be part of everyday life in the near future. In order to accelerate this process, many subsystems need to be optimised and validated. One of the most important subsystem of AVs is the steering controller. It’s task is to keep the vehicle on track, which is the reason, why many steering controllers have been designed for a large variety of applications. However, the validation of such controllers is a labour-intensive task, which is why in this paper, an Artificial Intelligence (AI) is trained to find an edge case path that brings the steering controller to its limits. This path is a sufficient substitute for a large set of paths and enables fast validation of steering controllers. This contribution describes the development of a reinforcement learning (RL) based path planner using the PPO-Algorithm to train a so called agent. Comparing the resulting key feature maps shows that the agent adapts to each controllers characteristics during the learning process. The result is demonstrated for three different state of the art path tracking controllers. For each controller the agent finds a path that leads to the controllers failure within seconds.
User Experience of Virtual Reality Showcasing Sham Sunder Das Archive: A Case Study of Digital Preservation of the Archival Artifact

In the past two decades, Virtual Reality (VR) technology has been employed in a broad spectrum from entertainment to the engineering and medical field. However, the recent development in digital technology provides new opportunities for curatorial practices, museum experience, learning methods, and public education. This paper presents the virtual photographic exhibition Timeless memory: Chamba through the Lens of Sham Sunder Das, conceptualized and designed in an interactive and innovative method. This pilot exhibition is created using VR technology to demonstrate the 120-year-old photographic collection of an Indian photographer. The research focuses on usability aspects of building an interactive environment that integrates diverse data into one space. The paper brings out the potential approaches about how this interactive method allows sharing the data in a more tangible and informative mode that could be reachable to a wider audience at any time. In order to be able to assess the structure, presentation, and navigation of the VR exhibition the user study was conducted. That enabled us to optimize the exhibition in a more efficient and comprehensible way. Along with the pragmatic qualities the hedonic qualities were taken in account to examine how participants perceive the usability and aesthetic of the virtual environment. The results of the AttrakDiff questionnaire reflect the positive graph of user experience. However, the qualitative content questions showed the scope for the revision of the features.
Bangla Toxic Comment Classification and Severity Measure Using Deep Learning

Online users nowadays leave a lot of comments on various social networks, news websites, and forums. Certain comments are harmful or abusive. Since it is impractical to manually monitor so many comments, the majority of systems employ some form of a machine learning model to automatically identify harmful content. Much of the work has been done for the English language. However, only a few approaches have been taken to classify toxic comments in the Bangla language. A multi-label classification technique for Bangla comments is offered in the study that follows to classify the numerous toxic comments into six categories: toxic, severe toxic, obscene, threat, insult, and identity hate, and also measure the severity of these comments. For the experiment, we compiled a dataset of 12634 comments from TikTok and other sources. By incorporating knowledge from earlier proposed studies, the proposed classification model was developed utilizing deep learning techniques, specifically Long Short-Term Memory (LSTM) and Long Short-Term Memory-Convolutional Neural Networks (LSTM-CNNs), as well as word embeddings and obtained a maximum accuracy of 83.66% and 1.61 MSE loss for the severity measure. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> https://github.com/imbodrulalam/Toxic-Comment-Detection-BN
Creative Approaches to Teach Regional Languages Using Non-linguistic Cues

Background. India, a country renowned for its cultural heritage, leads the world in endangered languages, with 197 endangered languages among the 600 or so languages spoken. When a language dies, it is not simply a loss of vocabulary, grammar, and literature; it is a loss of culture and identity. The study began with a premise about Generation Z’s lack of interest in learning their native language. The goal of this study was to identify the barriers that inhibit young adults in India’s metropolitan areas from adopting their native language. Using design research techniques, the collected primary and secondary data was analysed, generating actionable insights to devise a strategy to reverse the trend of language loss. The most crucial takeaway was the social dilemma confronted by young adults and their parents in the age of globalisation: they want to appear upmarket while still feeling guilty for abandoning their culture and language. Upgraded lifestyles due to urbanisation and increasing economic standards lead to gradual language loss. In a globalised society where technology drives most advancements, the perception of being socially reputed is frequently connected with trend adoption. The absence of regional languages in considered prestigious elements of one’s life creates an inferiority complex, leading to language abandonment. Since language is typically taught in isolation, it becomes difficult to relate to and use in real context, resulting in dissatisfaction in the learner. Through the study, a framework was evolved to teach regional languages using non-linguistic variables such as contextual cultural and social cues. The use of emerging technology was explored and ideated upon as a means of increasing engagement and retention among the target audience by serving as a motivator and offering sociocultural confluence to fit into their social interactions. The framework, creatively designed using these strategies, makes language learning participatory, experiential, and fun.
Conclusion

This chapter is the conclusion of the book. It simply gives a brief summary of its content and opens up toward new perspectives.
Awareness of the consequences of right-skewed effort and productivity distributions in software development

The available evidence suggests that many characteristics of software development are not symmetrically distributed but follow a right-skewed distribution, meaning that their value distributions have a long tail toward higher values. This right-skew may have important consequences for the effective management of software development work. In this paper, we focus on two right-skewed distributions relevant to software management processes, i.e., the right-skewed distributions of software development effort usage and productivity, and software professionals' awareness of important consequences of the right-skew. Our analysis, which is based on data from four empirical studies with software professionals, reveals that although most software professionals are aware of the right-skew of these two distributions, they may not fully appreciate several of the important management consequences of it. This includes its consequences for the communication of effort estimates, for the use of correct methods for adding effort estimates, and for team productivity. We argue that increasing the awareness of the management consequences of right-skewed distributions among software professionals may play a role in the improvement of software development management.
eduroam in Africa

eduroam is the secure, world-wide roaming access service developed for the international research and education community to address the increasing need for fast and secure Internet connection everywhere. With over 6.4 billion international authentications to date stretching across 106 countries, eduroam is expanding in Africa where National Research and Education Networks (NRENs) are going beyond the traditional campus connectivity aiming at bridging the digital divide. Uganda, South Africa and Kenya are leading the way and connecting users off campus at bus stops, libraries, cafes, and hospitals. NRENs are at the helm of Africa’s digital transformation and eduroam is a tangible solution to bridging the digital divide. This paper is therefore a guidebook detailing the technology behind eduroam, the benefits and the challenges for its four main target groups in Africa: users, institutions, NRENs and municipalities. The call for action is to further deploy eduroam hotspots and provide connectivity everywhere on the continent, including remote areas and unprivileged communities where students, researchers and teachers are present.
Cryptocurrencies and Lucky Factors: The value of technical and fundamental analysis

Abstract This study explores the effectiveness of technical and fundamental analysis in predicting and trading the returns of 12 cryptocurrencies, namely Bitcoin, Ethereum, Ripple, Dash, Cardano, Avalanche, Binance Coin, Dogecoin, Polkadot, Litecoin, Terra and Solana. A universe of 7846 technical rules, five log moving average‐based ratios and 59 fundamental factors are used to test predictability and profitability through the Lucky Factors methodology and Superior Predictive Ability test. We observe predictability for a small set of technical and fundamental rules, while only the short‐term log moving average‐based ratio and Hashrate Index demonstrate genuine in‐sample and out‐of‐sample profitability. Our findings question the value of both technical and fundamental analysis on cryptocurrencies.
What Goes Around Comes Around; Effects of Unclear Questionnaire Items in Information Security Research

The credibility of research on information system security is challenged by inconsistent results and there is an ongoing discussion about research methodology and its effect on results within the employee non-/compliance to information security policies literature. We add to this discussion by investigating discrepancies between what we cl/aim to measure (theoretical properties of variables) and what we actually measure (respondents’ interpretations of our operationalized variables). The study asks: (1) How well do respondents’ interpretations of variables correspond to their theoretical definitions? (2) What are the characteristics and causes of any discrepancies between variable definitions and respondent interpretations? We report a pilot study including interviews with seven respondents to understand their interpretations of the variable Perceived severity from the Protection Motivation Theory (PMT). We found that respondents’ interpretations differ substantially from the theoretical definitions which introduces error in measurement. There were not only individual differences in interpretations but also, and more importantly, systematic ones; When questions are not well specified, or do not cover respondents’ practice, respondents make interpretations based on their practice. Our results indicate three types of ambiguities, namely (i) Vagueness in part/s of the measurement item causing inconsistencies in interpretation between respondents, (ii) Envision/Interpret ‘new’ properties not related to the theory, (iii) ‘Misses the mark’ measurements whereby respondents misinterpret the fundamentals of the item. The qualitative method used proved conducive to understanding respondents’ thinking, which is a key to improving research instruments.
The educational image of a modern-day university: conceptual, methodological and pedagogical aspects

Subject matter: the conceptual, methodological and pedagogical aspects of forming the educational image of Russian universities in the context of modern socio-economic changes. Aim: to formulate conceptual and methodological approaches to forming an educational image for modern-day universities by analyzing practices thereof. Competition on the services market and the current socio-economic situation in the country have demonstrated that there exists a need for constant improvements to an educational institution’s image, and have brought to light the necessity of further development of conceptual, methodological and pedagogical aspects thereof. Russian universities have accumulated enough experience in forming an educational image, however it has not yet been sufficiently analyzed, generalized and integrated into the higher education system. A revision and an modernization of views on the role, aim, goals and functions of a university’s educational image are required, as well as a rationale behind the organizational, informational and methodical elements of this process which would be adequate to modern educational and socio-economic circumstances and to development prospects of Russian higher education. The results of the study may be utilized for the purposes of developing a model of creating an educational image, for developing technological support for the forming and the development of educational image.
Decentralized Consensus Blockchain-Based Authentication Algorithm Using in Secure Internet of Vehicles

With the development of communication network and technology, Internet of Vehicles (IoV) is gradually widely used in the transportation network, and subsequent problems are also increasing. Nowadays, most of the Internet of Vehicles systems use centralized trusted units for information storage, processing and transmission, which will lead to many security risks, such as message leakage, high cost and high latency of the central unit. Therefore, ensuring the communication security in the network is the key in the field of IoV. By introducing the concept of blockchain in the scene, the decentralized system based on the IoV becomes possible. Blockchain technology is considered as the key technology for efficiently and securely storing, processing and sharing data. To overcome the limitations of existing works, we propose a vehicle-based blockchain security consensus authentication algorithm. Compared with the existing technologies, our method is better in authentication processing delay, and key analysis time.
Issues of Effective Study of Semiconductor Device Properties in Engineering Educational Institutions

The article justifies the importance of developing new interactive pedagogical technologies for the study of semiconductor physics in higher and secondary technical educational institutions. Interactive teaching methods developed by humanities teachers have been shown to be ineffective due to their lack of understanding of technical tools, structures, and machines. It is shown that semiconductor devices are present in the construction of equipment intended for any task, and the physical phenomena occurring in their volume are shown to affect the accurate and efficient operation of the equipment. A new innovative method was proposed based on the teaching of technical sciences, the physics of semiconductors. The interactive methods “Internet-zombie” and “Scheme fragment method” developed for lectures and laboratory sessions were recorded. The results of the experimental lessons were analysed. It has been shown how important it is to develop new teaching methods by experienced pedagogues of technical universities.
The Role of the Human-in-the-Loop in Industrial Digitalization and Automation

With the advancement of technologies that are increasingly better at capturing, cleansing, and deriving insights from data, as well as closed-loop systems that achieve near-complete automation, it is understandable that there is a perception that the role of the human in industrial processes is diminished. However, despite decades of such advances, a great portion of such systems in the present and the foreseeable future are not going to achieve practically viable solutions by ignoring the role of the human in the process. This paper makes the case for the human-in-the-loop, where systems are expected to be designed to either continuously perceive feedback from the human or to serve the functionary who is a human. The scope of these discussions is set in the context of industrial digitalization and automation, where the influencing factors, and hence, the need for working with and for the human is presented. There are certain strengths that make the case for automation, and there are unique, irreplaceable skills and knowledge that a human possesses. For example, in digitalization, the automated systems are great at extracting raw data from huge number of documents. But the semantic richness and domain relevance of the data can only be realized in completion by a human expert. Similarly, industrial automation benefits immensely from artificial intelligence for detection and prognosis; however, course corrections needed, and interpretation of the outcomes is better left to the human to derive actionable directions. The underlying theme in the paper is the need to consider how modern artificial intelligence systems will need to work seamlessly with the expert human operator/maintainer while designing industrial digitalization and automation systems, to enhance productivity and address human factors.