Reviewerly/exHarmony huggingface dataset:
A two-stream deep model for automated ICD-9 code prediction in an intensive care unit

Authors:
raw_author_name: Seniha Esen Yuksel Erdem
raw_affiliation_string: Department of Electrical and Electronics Engineering, Hacettepe University, Ankara, 06800, Türkiye

Abstract: <h2>Abstract</h2> Assigning medical codes for patients is essential for healthcare organizations, not only for billing purposes but also for maintaining accurate records of patients' medical histories and analyzing the outputs of certain procedures. Due to the abundance of disease codes, it can be laborious and time-consuming for medical specialists to manually assign these codes to each procedure. To address this problem, we discuss the automatic prediction of ICD-9 codes, the most popular and widely accepted system of medical coding. We introduce a two-stream deep learning framework specifically designed to analyze multi-modal data. This framework is applied to the extensive and publicly available MIMIC-III dataset, enabling us to leverage both numerical and text-based data for improved ICD-9 code prediction. Our system uses text representation models to understand the text-based medical records; the Gated Recurrent Unit (GRU) to model the numerical health records; and fuses these two streams to automatically predict the ICD-9 codes used in the intensive care unit. We discuss the preprocessing and classification methods and demonstrate that our proposed two-stream model outperforms other state-of-the-art studies in the literature.
A novel IoT trust model leveraging fully distributed behavioral fingerprinting and secure delegation

Authors:
raw_author_name: Marco Arazzi
raw_affiliation_string: Department of Electrical, Computer and Biomedical Engineering, University of Pavia, via A. Ferrata, 5 - 27100 - Pavia, Italy
raw_author_name: Serena Nicolazzo
raw_affiliation_string: Department of Computer Science, University of Milan, Via G. Celoria, 18 - 20133 - Milan, Italy
raw_author_name: Antonino Nocera
raw_affiliation_string: Department of Electrical, Computer and Biomedical Engineering, University of Pavia, via A. Ferrata, 5 - 27100 - Pavia, Italy

Abstract: The pervasiveness and high number of Internet of Things (IoT) applications in people’s daily lives make this context a very critical attack surface for cyber threats. The high heterogeneity of involved entities, both in terms of hardware and software characteristics, does not allow the definition of uniform, global, and efficient security solutions. Therefore, researchers have started to investigate novel mechanisms, in which a super node (a gateway, a hub, or a router) analyzes the interactions of the target node with other peers in the network, to detect possible anomalies. The most recent of these strategies base such an analysis on the modeling of the fingerprint of a node behavior in an IoT; nevertheless, existing solutions do not cope with the fully distributed nature of the referring scenario. In this paper, we try to provide a contribution in this setting, by designing a novel and fully distributed trust model exploiting point-to-point devices’ behavioral fingerprints, a distributed consensus mechanism, and Blockchain technology. In our solution we tackle the non-trivial issue of equipping smart things with a secure mechanism to evaluate, also through their neighbors, the trustworthiness of an object in the network before interacting with it. Beyond the detailed description of our framework, we also illustrate the security model associated with it and the tests carried out to evaluate its correctness and performance.
Customized Video Summarization with Thumbnail Containers and 2D CNN

Authors:
raw_author_name: None Shashank Reddy
raw_affiliation_string: Global Academy of Technology Bangalore, India

Abstract: This project focuses on acquiring customized video summaries using thumbnail container-based summarization framework and 2D CNN model to select and extract specific features from thumbnails. This framework creates a custom keyshot summary for two or more concurrent users by leveraging the computing power of the user’s device. The most advanced methods that collect and analyze complete video data to create video summaries consume a lot of computing power. In this context, we use the thumbnail containers framework which implements light thumbnails to manage the complex detection of events. This minimizes computational complexity and increases communication and storage performance by overcoming computational and privacy issues in resource constrained end-user environments. We aim on developing a user interactive customized video summarization tool which will be trained utilizing diverse datasets leading to the generation of personalized video summaries for feature-length videos.
Case-based selection of explanation methods for neural network image classifiers

Authors:
raw_author_name: Humberto Parejas-Llanovarced
raw_affiliation_string: Department of Software Engineering and Artificial Intelligence, Instituto de Tecnologías del Conocimiento, Universidad Complutense de Madrid, Spain
raw_author_name: Marta Caro-Martínez
raw_affiliation_string: Department of Software Engineering and Artificial Intelligence, Instituto de Tecnologías del Conocimiento, Universidad Complutense de Madrid, Spain
raw_author_name: Mauricio G. Orozco-del-Castillo
raw_affiliation_string: Tecnológico Nacional de México/IT de Mérida, Departamento de Sistemas y Computación, Mexico
raw_author_name: Juan A. Recio-García
raw_affiliation_string: Department of Software Engineering and Artificial Intelligence, Instituto de Tecnologías del Conocimiento, Universidad Complutense de Madrid, Spain

Abstract: Deep learning is especially remarkable in terms of image classification. However, the outcomes of models are not explainable to users due to their complex nature, having an impact on the users’ trust in the provided classifications. To solve this problem, several explanation techniques have been proposed, but they greatly depend on the nature of the images being classified and the users’ perception of the explanations. In this work, we present Case-Based Reasoning as a learning-based solution to the problem of selecting the best explanation method for the image classifications obtained by models. We propose the elicitation of a case base that reflects the human perception of the quality of the explanations and how to reuse this knowledge to select the best explanation approach for a given image classification.
Constructing MRD codes by switching

Authors:
raw_author_name: Minjia Shi
raw_affiliation_string: Key Laboratory of Intelligent Computing Signal Processing, Ministry of Education, School of Mathematical Sciences Anhui University Hefei Anhui China
raw_author_name: Denis S. Krotov
raw_affiliation_string: Sobolev Institute of Mathematics Novosibirsk Russia
raw_author_name: Ferruh Özbudak
raw_affiliation_string: Faculty of Engineering and Natural Sciences Sabancı University  Istanbul Turkey

Abstract: Abstract Maximum rank‐distance (MRD) codes are (not necessarily linear) maximum codes in the rank‐distance metric space on ‐by‐ matrices over a finite field . They are diameter perfect and have the cardinality if . We define switching in MRD codes as the replacement of special MRD subcodes by other subcodes with the same parameters. We consider constructions of MRD codes admitting switching, such as punctured twisted Gabidulin codes and direct‐product codes. Using switching, we construct a huge class of MRD codes whose cardinality grows doubly exponentially in if the other parameters (, the code distance) are fixed. Moreover, we construct MRD codes with different affine ranks and aperiodic MRD codes.
The role of digital skills in the acceptance of artificial intelligence

Authors:
raw_author_name: Vanja Vitezić
raw_affiliation_string: Department of Management, Faculty of Tourism and Hospitality Management, University of Rijeka, Opatija, Croatia
raw_author_name: Marko Perić
raw_affiliation_string: Department of Management, Faculty of Tourism and Hospitality Management, University of Rijeka, Opatija, Croatia

Abstract: Purpose The service industry is facing the huge impact of digital transformation, in which artificial intelligence (AI) plays one of the most important roles. This study aims to expand the understanding of the AI acceptance framework and confirm whether consumers’ digital skills have a moderating effect on the research model. Design/methodology/approach Hypotheses were tested using a data set of 1,641 individuals. Partial least squares structural equation modeling and multi-group analysis were used to estimate the model. Findings The results indicate that antecedent factors influence consumers’ willingness to use AI devices in services. The two groups of different digitally savvy respondents differ because the influence of anthropomorphism, social influence and hedonic motivation on respondents’ perceived efforts to use AI devices in service delivery depends on respondents’ digital skills. Originality/value The novel contribution of this study is reflected in a comprehensive model that explains the moderating effect of individual digital skills on willingness to use AI devices. The attitudes of experienced and digitally skilled consumers are valuable and highlight some important theoretical, practical implications and future lines of research.
Decentralization in the Digital Age

Authors:
raw_author_name: Swaty Sharma
raw_affiliation_string: Lovely Professional University, India

Abstract: This chapter explores how digital technologies are reshaping decentralization in our fast-evolving technological era. It defines decentralization and examines its diverse aspects in politics, economics, and technology. The chapter emphasizes blockchain technology, highlighting its immutability, decentralized consensus, and smart contracts as pivotal elements driving decentralized applications and disrupting traditional paradigms. To provide context, it conducts a thorough literature review, tracing decentralization's historical evolution, exploring its impact on various sectors like decentralized finance, governance, financial inclusion, and system resilience. The chapter also addresses challenges such as regulatory uncertainties, scalability issues, and security concerns, drawing from authoritative sources and academic research. In conclusion, it sets the stage for a deeper exploration of decentralization's applications and impact across sectors, acknowledging both its potential and complexity as a transformative paradigm shift.
Decision Support Framework for Parkinson's Disease Using Novel Handwriting Markers

Authors:
raw_author_name: Deepak Panwar
raw_affiliation_string: Max Super Speciality Hospital, Vaishali, India

Abstract: Parkinson's disease (PD) is a neurological disorder that affects millions of people worldwide. Among its motor symptoms are bradykinesia, tremors, rigidity, and postural instability. Handwriting variations known as “Parkinsonian handwriting” or “micrographia” are frequently linked to Parkinson's disease. The condition is characterized by tiny, cramped handwriting that gets smaller with time. The illness may make writing challenging and frequently results in less readable text. Innovative handwriting markers are becoming more and more popular as methods for diagnosing and monitoring PD. A new handwriting marker-based Parkinson's disease decision support system is covered in this chapter, with an emphasis on how useful it may be for early detection and tracking.
DeFi's Transformative Influence on the Global Financial Landscape

Authors:
raw_author_name: M. Nandhini
raw_affiliation_string: Karpagam Academy of Higher Education, Coimbatore, India

Abstract: The rise of decentralized finance (DeFi) has fundamentally reshaped the financial industry, challenging traditional banking systems and opening up a world of possibilities in global finance. This chapter explores the multifaceted impact of DeFi on the global economic landscape, addressing critical themes through a series of subtitles. DeFi is disrupting traditional banking models by offering alternative financial services directly on blockchain networks, such as lending, borrowing, and trading. One of the remarkable achievements of DeFi is its ability to provide financial services to previously underserved and unbanked populations. Tokenization is a crucial aspect of DeFi, enabling the representation of real-world assets as digital tokens on the blockchain. DeFi offers numerous advantages but poses security challenges, including smart contract vulnerabilities and hacks. This chapter provides an overview of the major themes and implications of DeFi's influence on finance, highlighting its opportunities and challenges.
The Digital Disruption of Distribution

Authors:
raw_author_name: Sridhar Manohar
raw_affiliation_string: Chitkara Business School, Chitkara University, Punjab, India

Abstract: Digital transformation across human activity has emerged as a critical evolutionary trend. It has not only led to the emergence of neo-business models but has disrupted traditional industries like automobile manufacturers and stakeholders. A ripple effect is visible in the distribution phase of the automobile value chain due to the rapidly evolving needs of automobile customers, awareness and access to digital platforms, and the need to enhance customer lifetime value by manufacturers. In this chapter, the digitalization of automobile distribution has been discussed in three aspects – customer acquisition, retail experience, and customer life-cycle management. Digitalization has not only helped automobile dealers reduce their customer acquisition costs but also driven higher customer engagement and satisfaction. As more customers become tech-savvy and digitally aware, automobile dealers and manufacturers will need to quickly adapt to the new normal of digitalization in order to remain relevant to customers, innovate sustainable business models, and unlock customer lifetime value.
Unpacking the Role of Service Quality of AI Tools in Catalyzing Digital Transformation

Authors:
raw_author_name: Urvashi Tandon
raw_affiliation_string: Chitkara Business School, Chitkara University, Punjab, India

Abstract: The aim of this chapter is to do a comprehensive examination of the existing scholarly literature pertaining to artificial intelligence (AI) along with the service quality of AI tools within the framework of digital transformation, managing the effects of deglobalization, and fostering the use of technology. An in-depth study was carried out on a dataset consisting of 156 articles that were taken from the Scopus database. For the purpose of conducting this inquiry, bibliometric methods were utilized, and more specifically, VOSviewer software was utilized, in order to assess the performance analysis and science mapping of the literature that was under investigation. The findings of the study imply that there is a taxonomical representation of current scientific research on the incorporation of artificial intelligence with technological adoption across a variety of fields, with a particular emphasis on online shopping and the level of service quality provided by AI tools.
Exploring the Educational Value of an Immersive Virtual Reality Method Within a Continuing Education Module in Nursing: A Mixed Methods Study

Authors:
raw_author_name: Paul Best

Abstract: Background: Few studies have explored the potential educational value of immersive 360° video in continuing education. This study explored the potential value of immersive 360° video as an acceptable educational method in a continuing education module in nursing. Method: A convergent parallel mixed methods design was adopted. The setting was a nursing and midwifery school at a university. The 11 participants were RNs. Data were collected at three time points with surveys and focus groups. Results: Participants found educational value in the triggering of a deep reflective process, supported by a subsequent classroom discussion. Further, there were nuances and complexities to be considered, with a need to tailor material toward high-acuity, low-frequency, or challenging clinical events when considering content. Conclusion: Immersive 360° videos are a potentially useful method for providing continuing education; however, the content must be tailored to students' learning needs. A reflective model may provide a valuable structure for discussions after the use of immersive 360° video. [ J Contin Educ Nurs . 202x;5x(x):xx–xx.]
A Semantic Framework to Detect Problems in Activities of Daily Living Monitored through Smart Home Sensors

Authors:
raw_author_name: Lampros Mpaltadoros
raw_affiliation_string: Information Technologies Institute, Centre for Research & Technology Hellas, 6th Km Charilaou-Thermi, 57001 Thessaloniki, Greece
raw_author_name: Vasilis Alepopoulos
raw_affiliation_string: Information Technologies Institute, Centre for Research & Technology Hellas, 6th Km Charilaou-Thermi, 57001 Thessaloniki, Greece
raw_author_name: Margarita Grammatikopoulou
raw_affiliation_string: Information Technologies Institute, Centre for Research & Technology Hellas, 6th Km Charilaou-Thermi, 57001 Thessaloniki, Greece
raw_author_name: Thanos G. Stavropoulos
raw_affiliation_string: Information Technologies Institute, Centre for Research & Technology Hellas, 6th Km Charilaou-Thermi, 57001 Thessaloniki, Greece
raw_author_name: Spiros Nikolopoulos
raw_affiliation_string: Information Technologies Institute, Centre for Research & Technology Hellas, 6th Km Charilaou-Thermi, 57001 Thessaloniki, Greece
raw_author_name: Ioulietta Lazarou
raw_affiliation_string: Information Technologies Institute, Centre for Research & Technology Hellas, 6th Km Charilaou-Thermi, 57001 Thessaloniki, Greece
raw_author_name: Magda Tsolaki
raw_affiliation_string: Department of Neurology I, Medical School, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece; Greek Association of Alzheimer’s Disease and Related Disorders (GAADRD), 54643 Thessaloniki, Greece; Laboratory of Neurodegenerative Diseases, Center for Interdisciplinary Research and Innovation (CIRI-AUTh), Balkan Center, Buildings A & B, 57001 Thessaloniki, Greece
raw_author_name: Ioannis Kompatsiaris
raw_affiliation_string: Information Technologies Institute, Centre for Research & Technology Hellas, 6th Km Charilaou-Thermi, 57001 Thessaloniki, Greece

Abstract: Activities of daily living (ADLs) are fundamental routine tasks that the majority of physically and mentally healthy people can independently execute. In this paper, we present a semantic framework for detecting problems in ADLs execution, monitored through smart home sensors. In the context of this work, we conducted a pilot study, gathering raw data from various sensors and devices installed in a smart home environment. The proposed framework combines multiple Semantic Web technologies (i.e., ontology, RDF, triplestore) to handle and transform these raw data into meaningful representations, forming a knowledge graph. Subsequently, SPARQL queries are used to define and construct explicit rules to detect problematic behaviors in ADL execution, a procedure that leads to generating new implicit knowledge. Finally, all available results are visualized in a clinician dashboard. The proposed framework can monitor the deterioration of ADLs performance for people across the dementia spectrum by offering a comprehensive way for clinicians to describe problematic behaviors in the everyday life of an individual.
Reinforcement learning tutor better supported lower performers in a math task

Authors:
raw_author_name: Sherry Ruan
raw_affiliation_string: Work done while at Stanford University, Stanford, CA, 94305, USA
raw_author_name: Allen Nie
raw_affiliation_string: Computer Science, Stanford University, Stanford, CA, 94305, USA
raw_author_name: Meng Guo
raw_affiliation_string: Computer Science, Stanford University, Stanford, CA, 94305, USA
raw_author_name: Yao Liu
raw_affiliation_string: Work done while at Stanford University, Stanford, CA, 94305, USA
raw_author_name: Rui Ying
raw_affiliation_string: Work done while at Stanford University, Stanford, CA, 94305, USA
raw_author_name: James A. Landay
raw_affiliation_string: Computer Science, Stanford University, Stanford, CA, 94305, USA
raw_author_name: Emma Brunskill
raw_affiliation_string: Computer Science, Stanford University, Stanford, CA, 94305, USA

Abstract: Abstract Resource limitations make it challenging to provide all students with one of the most effective educational interventions: personalized instruction. Reinforcement learning could be a pivotal tool to decrease the development costs and enhance the effectiveness of intelligent tutoring software, that aims to provide the right support, at the right time, to a student. Here we illustrate that deep reinforcement learning can be used to provide adaptive pedagogical support to students learning about the concept of volume in a narrative storyline software. Using explainable artificial intelligence tools, we extracted interpretable insights about the pedagogical policy learned and demonstrated that the resulting policy had similar performance in a different student population. Most importantly, in both studies, the reinforcement-learning narrative system had the largest benefit for those students with the lowest initial pretest scores, suggesting the opportunity for AI to adapt and provide support for those most in need.
IoT Based Railway Track Faults Detection and Localization using Acoustic Analysis and Signalling

Authors:
raw_author_name: None Harsha V
raw_affiliation_string: Global Academy of Technology, Bengaluru, India

Abstract: Ensuring the safety and efficiency of rail transportation is paramount, necessitating timely inspection of railway tracks to prevent potential hazards. This research introduces an innovative approach by implementing an Internet of Things (IoT)-based autonomous railway track fault detection system. Various track components, such as rail surface defects, broken sleepers, missing fasteners, and irregular ballast levels, can lead to hazardous situations if not properly monitored. This master's thesis proposes an efficient method for classifying track components using image processing techniques and deep learning algorithms. Specifically, it focuses on detecting missing fasteners by analyzing images captured by a line camera. Experimental results demonstrate the method's effectiveness and robustness in complex environments. Future work aims to develop a comprehensive model capable of classifying all track components simultaneously. Image processing is utilized to extract individual components such as fasteners, rail, ballast, and sleepers, with the model trained to classify fastener states. This research contributes to advancing railway inspection methods, mitigating operational costs, preventing accidents, and enhancing passenger experience