Reviewerly/exHarmony huggingface dataset:
Maintenance commitments: Conception, semantics, and coherence

Social commitments are recognized as an abstraction that enables flexible coordination between autonomous agents. This article introduces and formalizes a concept of a maintenance commitment, a kind of social commitment characterized by a maintenance condition whose truthhood an agent commits to maintain. This concept of maintenance commitments enables us to capture a richer variety of real-world scenarios than possible using achievement commitments with a temporal condition. Our second contribution is to develop a rule-based operational semantics, by which we study the relationship between agents' achievement and maintenance goals, achievement commitments, and maintenance commitments. Third, we motivate a notion of coherence between an agents' achievement and maintenance cognitive and social constructs, and prove that, under specified conditions, the goals and commitments of both rational agents individually and of a multiagent system altogether are coherent. Lastly, we illustrate with a detailed real-world scenario from an aerospace aftermarket domain.
Illuminating the darkness: A pyramid‐based approach with lightness attention and noise suppression

Abstract Abstract Images taken in low‐light conditions often suffer from reduced contrast, amplified noise, and colour distortions. Such degradation falls short of viewer expectations and negatively impacts high‐level tasks, such as object detection. In this paper, the above problems are addressed by leveraging the properties of the LAB colour space and a multi‐scale feature pyramid. Specifically, the model first generates a light attention map and a noise map, which are proficient at manipulating the luminance channel for brightness recovery while preserving the colour information and separating noise from the image content. Moreover, enriched contextual features is progressively extracted and then the low‐light images are refined by incorporating the light attention map and the noise map as priors into a coarse‐to‐fine architecture. The enhanced results are characterized by improved contrast, vibrant colour, and noise suppression.
Improving the accuracy of sentiment analysis using a linguistic rule-based feature selection method in tourism reviews

Sentiment Analysis technique involves extracting the relevant information from Unstructured User Reviews (UUR) dataset fetched from online and classifying them into appropriate positive and negative comments for making decisions. In UUR, data may be in noisy state, irrelevant features exist which creates high dimensional feature space. To design an effective sentiment learning model, users are required to extract the most relevant sentiment features from UUR. To overcome the issue, we proposed a Linguistic rule based feature selection method for extracting and selecting the sentiment features for Sentiment Analysis as it improves the predictive performance of classification algorithms. The proposed novel feature selection method involves identifying the various sentiment features in the review dataset by using filtering methods such as POS tags, n-grams. In the ensemble model, where the Random Forest classification algorithm is trained for textual sentiment classification, the chosen sentiment feature sets are used. Finally, we test our approach using the real-time review dataset that was collected from a multitude of sources, and the results demonstrate prediction accuracy that is superior to that of existing Sentiment analysis techniques.
Learning from safety science: A way forward for studying cybersecurity incidents in organizations

In the aftermath of cybersecurity incidents within organizations, explanations of their causes often revolve around isolated technical or human events such as an Advanced Persistent Threat or a “bad click by an employee.” These explanations serve to identify the responsible parties and inform efforts to improve security measures. However, safety science researchers have long been aware that explaining incidents in socio-technical systems and determining the role of humans and technology in incidents is not an objective procedure but rather an act of social constructivism: what you look for is what you find, and what you find is what you fix. For example, the search for a technical “root cause” of an incident might likely result in a technical fix, while from a sociological perspective, cultural issues might be blamed for the same incident and subsequently lead to the improvement of the security culture. Starting from the insights of safety science, this paper aims to extract lessons on what general explanations for cybersecurity incidents can be identified and what methods can be used to study causes of cybersecurity incidents in organizations. We provide a framework that allows researchers and practitioners to proactively select models and methods for the investigation of cybersecurity incidents.
Aristotle's "Catharsis" as an Inspiration for Modern Drama Therapy

This work is an attempt to decipher the therapeutic essence of the Hellenic theater through the prism of "catharsis", starting with the Athenian orgy, when theatrical performances turned into a tool for collective healing. The article deals with the theoretical views of Aristotle, in whose aesthetics catharsis has become the main concept that testifies to the healing abilities of the Greek theater to purify and harmonize the personality. The author shows how these ideas can be used in modern theatrical art, helping to identify and eliminate the mental problems of today's audience. Dramatic "catharsis", named and analyzed in the theoretical system of Aristotle, in turn, became a theoretical generalization of the healing abilities of the Greek theater. The scientific novelty of this work lies in the fact that the study of the essence and form of healing in the Greek theater can be used as a theoretical basis for the development of modern drama therapy. The work uses an interdisciplinary approach, a cultural-historical method and the principle of comparative analysis in the 70-80s. In the twentieth century, drama therapy began to be explored as a separate discipline that awakened creativity and imagination, contributed to the reunification of people with their inner world, removed the burden of difficult experiences, changed behavior and life circumstances, thus revealing the healing abilities of theatrical "catharsis". The rise of modern theater therapy reflects a new direction in the development of psychotherapy and theater, as well as a return to the ancient traditions of healing and artistic creativity and the development of fresh ideas and new creative ideas based on them.
Exploring the integration of art and technology

The integration of art and technology has emerged as a formidable force in the creative realm, presenting a wide array of opportunities and challenges. With the advent of the internet, collaborative artistic practices have flourished, while financial burdens on artists have been alleviated through democratized production software, reducing their reliance on corporate entities. Technology has empowered artists with increased creative freedom and direct engagement with their fans through social media platforms. Nevertheless, concerns have been raised regarding the authenticity of creative practices, sparking debates about the appropriate extent of technological intervention. The mainstream acceptance of artificial intelligence (AI) and augmentation technologies has further transformed the creative landscape. AI tools now cater to both professionals and amateurs, enabling diverse creative endeavours, ranging from the generation of visual art to assisting in music composition. This Special Issue delves into the profound changes that technology has brought to artistic and creative expression. The articles explore topics such as AI-generated communication, the role of AI in advertising campaigns, AI tools in music composition, technological innovations in art, light augmentation in urban spaces, augmented reality (AR) in literature and the integration of AR in design magazines. The overarching aim is to stimulate critical discussions regarding the future of art, creativity and the implications of technological advancements.
Recurring patterns: An ethnographic study on the adoption of AI music tools by practitioners of electroacoustic, contemporary and popular musics

The intersection of artificial intelligence (AI) and art has been a topic of great interest in recent times. Driven by greater visibility of accessible AI applications within mainstream media, artists have increased their uptake of such tools as means of exploring and expanding their creative expressions. With the music industry also displaying similar levels of curiosity for AI tools, practitioners and audiences voice diverging opinions on the topics of artistic authenticity, creative labour and the threats posed by thinking machines on the future of musicians’ careers. This article aims to explore these topics through an ethnographic study conducted through interviews with five composers active in the areas of electroacoustic music, contemporary composition and experimental electronic music. The discussions reveal some of the software and methodologies currently popular among composers, the challenges faced and avenues presented when adopting AI tools, as well as the attitudes and discourse that permeate the niche circles of AI-generated music. The findings point towards the swift uptake of new technologies by curious artists and the slow development of trust in AI applications by traditionalist makers and listeners, suggesting a continuation of the patterns of behaviour evident since the emergence of music technology.
Threaded Edition 21: Exploring Māori creation narratives with augmented reality-animated sonic experiences in publication design

In this article, we present the creative strategies implemented on the practice-based research project Threaded Edition 21 , an international design magazine published in Aotearoa New Zealand. The editorial model draws from a cultural and collaborative framework situated in Tikanga (customary practices of the Indigenous people of New Zealand), Mātauranga Māori ( Māori knowledge ) and kawa ( protocols ). In this Special Edition, the structural format was inspired by Māori Maramataka ( lunar cycles ) and whakapapa ( genealogy ), and incorporated queues to communicate the multi-layered Māori creation narrative of Te Kore ( nothingness ), Te Pō ( night realms and darkness ), Te Whē Ao ( coming in to being ) and Te Ao Mārama ( light realms ). These narratives resided primarily in the virtual realm of augmented reality (AR) (as a spiritual encounter), while the printed matter functioned as an enigmatic vessel, creating an ontological reversal where the ‘virtual’ (AR) answered to the ambiguities of ‘real’ (publication). AR-animated vignettes and sonic artistry were activated through printed motifs to incorporate hau ( breath of life ) into the pages. The AR-animated sonic experience expanded the connection with the audience that when synthesized, provided a sense of immersion into the domain of the unseen which is felt aurally and visually. Structurally, the AR pages open with a chanted karakia ( a welcoming prayer ) and end with a mihi ( closing speech ), creating a balance that acknowledges aspects of Māori cultural significance and dualities. When scanned, the AR-animated sonic experiences direct the audience to the accessibility of the Instagram platform, expanding the materiality of printed matter. This approach afforded distinct modes of cognitive processing that combined the visual and the linguistic, the spatial and the temporal, sound and virtual reality to graphically symbolize the many layers and dimensions of things unseen: the realm between being and non-being.
The future of advertising campaigns: The role of AI-generated images in advertising creative

Computational creativity is a growing component of new artificial intelligence (AI) technologies that allow a machine to render creative constructs such as music, text and images. A rapidly growing area of computational creativity is AI text-to-image engines capable of producing realistic imagery that can now meet the standard of human quality outputs. DALL-E 2, built by OpenAI, is a leader in the field and offers commercial access to AI-produced images. To understand the impact of engines such as DALL-E 2 on advertising agencies and their creative workflows, we conducted a series of focus groups with Aotearoa, New Zealand-based advertising agencies exploring creative practitioners’ considerations on the capability of the DALL-E 2 text-to-image technology. An existing Volkswagen advertising campaign called ‘Small but Ferocious’ that used ‘blended’ animals as a visual metaphor for their economical yet powerful ‘TSI’ engines was expanded in a ‘faux’ continuation of the campaign. Four new images produced by DALL-E 2 were presented to creatives attending the focus groups. Participants were then asked about these new creative-AI assets concerning image quality, creative production and collaborative models. A thematic analysis of the comments from the focus groups was conducted and elicited three themes: aesthetics, creative practice and human vs. machine . Participants’ responses revealed that they were both excited and concerned about DALL-E 2’s capability in image production, its effect on creative workflows and the role of the human vs. machine in generating creative outputs. The result was a clear sense of inevitability for how creative roles will change as computational creativity systems, such as DALL-E 2, advance and are adopted into agency workflows.
Can readability formulae adapt to the changing demographics of the UK school-aged population? A study on reading materials for school-age bilingual readers

Due to the increase bilingual population in the UK, it is imperative that reading materials are made accessible for them in and out of school. This study begins by reviewing literature on bilinguals reading comprehension competence, discussing findings and impact on limiting academic achievement. Much literature criticises readability formulae as a tool for grading reading materials. Readability formulae, for example, do not account for differences in reader's dialects and cultural backgrounds. Therefore, this study looks to identify those discrepancies and assess the effectiveness of two well-known readability formulae. It then compares these to a readability formula for bilinguals to identify any consistencies between outcomes, enabling identification of any categories or factors crucial in identifying the reading difficulty of texts for bilinguals not included in well-known readability formulae, specifically for school-aged children. 20 randomly selected eBooks available for children aged 7-9 y were quantitatively evaluated using three readability formulae: Spache, Flesch-Kincaid and McAlpine EFLAW. Based on these results, it is inconclusive if the readability formulae are consistent with each other, as they did not appear to follow the same trend and assessed different criteria. Therefore, the findings suggest no readability formula used in this study can be confidently used on its own to successfully assess the readability of books to deem suitability for bilingual readers as it is paramount that non-text factors need to be incorporated when matching books for students. This study concludes that a formula or a new set of criteria needs to be created which incorporates the salient factors affecting reading comprehension of bilinguals to best allow educators and authors to select and modify reading materials for this growing population, to increase accessibility academically, enabling best outcomes to be achieved.
The dragon’s AR: Narrative augmentation in Russell’s The Dragon Defenders

The use of augmented reality (AR) in literature has predominantly focused on providing instructional tools for educators and learners, emphasizing its capacity of enhancing engagement and supporting immersive learning. Aside from its central literacy purpose, AR in children’s literature appears in the form of digital pop-up books or as add-on games, which are usually located outside of the main narrative. In this sense, AR risks disrupting the flow of a story instead of enriching the continuous imaginative space of the narrative. The Dragon Defenders series, by James Russell, uses AR as a narrative tool in ways previously unseen in children’s literature. Russell puts the reader into the shoes of his main characters, Paddy and Flynn, using AR to show the reader what the boys themselves see. There are moments in these books where the AR system and traditional narrative system fully overlap, integrate and enhance the narrative dimension of the story. While readers navigate through the book by turning physical pages, the addition of AR at specific moments in the story not only provides a more immersive reading experience but, in this case, advances the narrative to great effect. This case study asks how AR can be used to enhance the narrative structure and flow in a text-based novel, using formal gameplay analysis to examine how the AR and narrative systems interact and identifying which examples of this interaction work to the best effect. It analyses the AR moments in the context of the book, deconstructs the unusual first-person perspective from the protagonist’s point of view and explores how these AR experiences help drive and truly augment the core narrative. The larger context of this study seeks to emancipate AR from its predominantly technological ontology and contribute to the development of AR as a genuine narrative device in fiction storytelling.
Automated Detection of Breast Lump/ Masses through Mammogram Image Analysis

Current technology is playing a key role in the field of health care. As everything is getting automated here is an attempt to automate the identification of lumps in the breast. Lump in the breast can be a sign of breast cancer. The uncontrolled growth of breast cells is the reason for lumps or cancer in the breast. Not all lumps in the breasts are cancer. Still early identification and staging of the disease is critical in planning the treatment of breast cancer. In late stages malignancy can extend beyond the breast and spread to surrounding structures. Mammogram is the gold standard procedure in the diagnosis of carcinoma of breast cancer. In this article we have made an attempt to spot out the lump in the breast with the aid of technology. We have also attempted to extend our work to identify the stage of the cancer.
Risk Management Evaluation in Hospital Management Information Systems Using Framework COBIT 2019 - Case Study: Ernaldi Bahar South Sumatera Hospital

Hospital Management Information System (SIMRS) is a system to assist service performance, reporting and data retrieval at hospitals that have been required by the government to be implemented in all hospitals in Indonesia. The existence of SIMRS is certainly an inseparable part of the service process and hospital data management, but it can also cause various IT risks to arise. Therefore, a good risk management is needed to minimize any possible IT risks that have not or have occurred. The performance of an IT risk management can be indicated from its capability levels. This study aims to determine how high the capability levels and the gap value from each process of the IT risk management at Ernaldi Bahar Hospital. The framework used as a reference in the assessment of the risk management process is COBIT 2019 which has 3 stages, namely the mapping process, capability level assessment, and conclusions. This study resulted in the value of capabilities in each process in IT risk management, the gap value, and recommendations for improvement that can be applied to SIMRS Ernaldi Bahar. The results of the measurement of the IT risk management capability of SIMRS Ernaldi Bahar in the EDM03 and DSS03 processes are at level 3, while the APO12 and DSS05 processes are at level 1. The gap values for the EDM03 and DSS03 processes is 1 level, while the gap values for the APO12 and DSS05 processes are 3 levels. Process improvement recommendations refer to COBIT 2019 best practices.
Optimization of the Cluster-Variant Method of Constructing a Multi-Position Direction Finding System for Conditions of a Priori Uncertainty

The possibility of constructing a multi-position direction finding system for the case of a priori uncertainty, based on the application of the principles of multiplication of single marks of the location of the emitting target (multistructure principle) and their subsequent partition into classes (clustering principle) is considered. The criteria and algorithms for detecting the resulting cluster and for constructing the optimal estimation of target location stable to anomalous measurement errors are presented, taking into account the time costs of their computer realization. Practical recommendations and results of comparative analysis of different algorithms are given.
Personal Federated Learning via Momentum Target with Self-Improvement

Federated learning (FL) is a new artificial intelligence concept that enables Internet-of-Things (IoT) devices to learn a collaborative model without sending the raw data to centralized nodes for processing. Despite numerous advantages, Federated Learning faces huge challenges from model overfitting due to the lack of data and statistical diversity among clients. To address these challenges, we propose a novel personalized federated learning method via momentum adaptation, the so-called pFLTI. Specifically, pFLTI generates the target model by adapting from the temporal ensemble of the meta-learner, i.e., the momentum network. This momentum network and its task-specific adaptations enjoy a favorable generalization performance, enabling self-improving of the meta-learner through knowlodels to find the across task relationsedge distillation. Moreover, we found that perturbing parameters of the meta-learner, e.g., dropout, further stabilize this self-improving process by preventing fast convergence of the distillation loss during meta-training. Our experimental results demonstrate that our algorithm is awesome.
Real-Time Multi-vessel Classification and Tracking Based on StrongSORT-YOLOv5

Vessel detection, classification, and tracking are very important problems in maritime surveillance systems. In recent years, the field of computer vision has significantly developed, which allows its application to these systems. Accordingly, in this paper, a method based on a YOLOv5-based deep neural network combined with the Strong Simple Online Real-time Object Tracking (StrongSORT) algorithm is proposed for vessel detection, classification, and tracking. Specifically, the YOLOv5 model is trained by using a dataset of diverse images, which are collected from various public sources. The dataset contains several popular vessel types for the purpose of classification. Experimental results show that the proposed model gives high accuracy of vessel classification and high-speed tracking of approximately 16 frames per second, which is near real-time. The model has been embedded into a real small demonstrator to verify the potential implementation in maritime surveillance systems.
Optimal Navigation Based on Improved A* Algorithm for Mobile Robot

Path planning is one of the core research direction of mobile robot navigation. First, a grid-based method transfers the complex environment to a simple grid-based map. Hence, the mobile robot’s position is definitely determined in the grid map. To solve simultaneously the problems of the traditional A∗ path finding algorithm such as close distance from the obstacle, the shortest path, and path corners, the paper introduces the idea of the improved algorithm is to eliminate A* unnecessary nodes as for the purpose of reducing the computational scale. Then, the obtained path is smoothed by B-spline transition method. Hence, AMR’s optimal obstacle avoidance strategy based on A* algorithm will be completely constructed. Finally, simulated results are shown to prove the feasibility of the proposed method.