Reviewerly/exHarmony huggingface dataset:
Decentralization in the Digital Age

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.
Disruptive Business Models and the Shift Towards Decentralized Marketplaces

In today's rapidly evolving business landscape, the emergence of disruptive technologies and innovative business models have revolutionized traditional market structures. One such transformation is the shift towards decentralized marketplaces, driven by blockchain technology, smart contracts, and decentralized finance (DeFi) solutions. This chapter aims to explore the concept of disruptive business models and their profound impact on market dynamics, focusing on the rise of decentralized marketplace. This chapter addresses a pressing need for comprehensive, up-to-date, and insightful information on a topic that is rapidly changing the business landscape. It provides a valuable resource for a diverse audience, ranging from industry professionals to academics and policymakers. It can be highly beneficial for several reasons, given the ongoing evolution and significance of decentralized marketplaces.
Decision Support Framework for Parkinson's Disease Using Novel Handwriting Markers

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

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

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

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

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

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.
Oxidative fluids facilitate Cd enrichment in a magmatic-hydrothermal system: An example from the Kangjiawan Pb-Zn polymetallic deposit, South China

The Kangjiawan, as one of the representative magmatic-hydrothermal Pb + Zn polymetallic deposits in the central Qin-Hang Metallogenic Belt (QHMB), South China, contains 16.59 million tons of ore reserves with 0.5 Mt Pb metal reserves and 0.56 Mt Zn metal reserves. Episodic magmatic and associated hydrothermal ore fluids produced a variety of sphalerite generations with distinct mineral assemblages and compositions, making the Kangjiawan an excellent target for studying the controls on the enrichment of critical metals such as Cd, In, and Sn in Pb-Zn deposits. In this contribution, we systematically collected the samples from the different horizontal and vertical levels. Then, a series of microscopic identification, electron probe micro-analyzer (EPMA), and laser ablation inductively coupled plasm mass spectrometer (LA-ICP-MS) analyses were carried out. We identified four generations of sphalerite, that is, Sp1, Sp2, Sp3, and Sp4, with largely different mineral assemblages. A group of minor and trace elements such as Mn, Fe, Cd, Cu, Ag, and Pb with great variations in sphalerite are detected. It is notable that Cd in sphalerite displays an obvious elevation from Sp1 (mean = 2583 ppm), Sp2 (2489 ppm), and Sp3 (3249 ppm) to Sp4 (6324 ppm, a maximum up to 9356 ppm). Further temperature estimation via empirical GGIMFis geothermometer shows there is a clear drop from higher-temperature Sp1, Sp2, and Sp3 (mean = 320 ℃) to low-temperature Sp4 (160 ℃). Moreover, the concentrations of Mn sensitive to redox state also have a significant drop from early Sp1 (mean 3461 ppm), Sp2 (6373 ppm), and Sp3 (6763 ppm) to late Sp4 (5.28 ppm). Combined with a massive of pyrrhotite inclusions in Sp1, Sp2, and Sp3, against Sp4, we deduce that Sp1, Sp2, and Sp3 are formed in a relatively reductive condition, but Sp4 is strongly oxidative. Collectively, we conclude that the redox state, besides temperature, acts as an important factor for Cd enrichment, and the oxidized, low-temperature hydrothermal fluids facilitate Cd enrichment in the Kangjiawan magmatic-hydrothermal system.
Temperature as a major control on Cd enrichment in a skarn system: A case study of the Yiliu Pb-Zn-As deposit, South China

The Yiliu Pb-Zn-As deposit is located in the Nanling Metallogenic Belt (NMB), South China, representing an extremely As-enriched skarn system, but the endowment of critical metals, such as Cd and In, and their recording metallogenic physicochemical condition have never been investigated. To address these questions, we conducted a series of field survey, microscopic observation, electron probe micro-analyzer (EPMA) and laser ablation inductively coupled plasm mass spectrometer (LA-ICP-MS) studies. The lenticular orebodies are closely associated with the altered Devonian limestone and Yanshanian Baoshan and Benggangling granitic intrusions, and the massive and disseminated ores comprise arsenopyrite, pyrite, sphalerite, chalcopyrite, galena and tetrahedrite, in a decreasing order. We identified three generations of sphalerite, that is, Sp1, Sp2 and Sp3, with different colors and mineral assemblages. EPMA and LA-ICP-MS data show that two groups of minor and trace elements are hosted in the Yiliu sphalerites: (1) Fe, Cd, Mn and In mainly occurring as lattice-bound (solid-solution) or homogeneously distributed nanoparticles, and (2) Cu, Sn, Pb, Sb and Ag mainly occurring as micro-mineral inclusions. It is notable that a relatively high Cd (4814–10149 ppm, mean = 6133 ppm) against low In (10–301 ppm, mean = 67 ppm) concentration is identified in all the three sphalerite generations. Calculation of sphalerite crystallization temperatures by GGIMFis geothermometer show that a clearly decreasing trend from the earlier Sp1 (273–319 ℃; median = 296 ℃) to the later Sp2 (267–301 ℃; median = 286 ℃) and Sp3 (278–290 ℃; median = 282 ℃). And there is a decrease for Cd concentrations from Sp1 to Sp2 and Sp3, i.e., 4903–10149 ppm (median 6964 ppm) for Sp1, and 4814–7078 ppm (median 5435 ppm) for Sp2 and 5756–6894 ppm (median 6420 ppm) for Sp3, respectively. Additionally, the enrichment of Cd is also affected by the content of Fe whose enrichment is controlled by the relatively high temperature. Collectively, we deduce that the temperature acts as the major control for Cd enrichment in the sphalerite of the Yiliu Pb-Zn-As deposit, and the higher temperature facilitate Cd enrichment in a skarn system.
Reinforcement learning tutor better supported lower performers in a math task

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

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
Sensor-Based Authentication in Smartphone; a Systematic Review

With the widespread usage of smartphones, users not just using their smartphone for calls and messaging only, but they are using it for a variety of purposes such as banking transactions, e-mailing, chatting, online shopping, video conferencing, health monitoring, and many more. Most of these activities store personal and sensitive data of the user on the device, reaching this confidential information by unauthorized person may cause huge losses and bad consequences. Therefore, securing the smartphone’s accessibility from unauthorized people became an extremely essential. With the advanced development in the area of sensor-based smartphone authentication methods, different methods were developed to be easily used by users through specific criteria. However, many aspects haven’t been discovered yet in the area of sensor-based smartphone authentication methods. On the basis of that, this study aims to review the area of sensor-based smartphone authentication methods systematically to provide a comprehensive understanding of this new area and to discuss the current challenges and issues. To conduct this systematic review, four scientific digital databases were utilized; ScienceDirect, IEEE Xplore, Web of Science, and Scopus. The total number of studies found was n=256 articles, only n=46 articles were included based on our inclusion criteria. To provide a comprehensive understanding of the included studies, taxonomy was drawn. Also, this study provides a discussion on the current motivations, challenges and issues, and recommendations in sensor-based smartphone authentication methods. This review enlightens, encourage, and direct researchers to develop authentication solutions and reduce the current gaps in this area.
Location-Aware Deep Interaction Forest for Web Service QoS Prediction

With the rapid development of the web service market, the number of web services shows explosive growth. QoS is an important factor in the recommendation scene; how to accurately recommend a high-quality service for users among the massive number of web services has become a tough problem. Previous methods usually acquired feature interaction information by network structures like DNN to improve the QoS prediction accuracy, but this generates unnecessary computations. Aiming at addressing the above problem, inspired by the multigrained scanning mechanism in a deep forest, we propose a location-aware deep interaction forest approach for web service QoS prediction (LDIF). This approach offers the following innovations: The model fuses the location similarity of users and services as a latent feature representation of them. In addition, we designed a scanning interaction structure (SIS), which obtains multiple local feature combinations from the interaction between user and service features, uses interactive computing to extract feature interaction information, and concatenates the feature interaction information with original features, which aims to enhance the dimension of the features. Equipped with these, we compose a layer-by-layer cascade by using SIS to fuse low- and high-order feature interaction information, and the early-stop mechanism controls the cascade depth to avoid unnecessary computation. The experiments demonstrate that our model outperforms eight other state-of-the-art methods on MAE and RMSE common metrics on real public datasets.
Federated Freeze BERT for text classification

Abstract Pre-trained BERT models have demonstrated exceptional performance in the context of text classification tasks. Certain problem domains necessitate data distribution without data sharing. Federated Learning (FL) allows multiple clients to collectively train a global model by sharing learned models rather than raw data. However, the adoption of BERT, a large model, within a Federated Learning framework incurs substantial communication costs. To address this challenge, we propose a novel framework, FedFreezeBERT, for BERT-based text classification. FedFreezeBERT works by adding an aggregation architecture on top of BERT to obtain better sentence embedding for classification while freezing BERT parameters. Keeping the model parameters frozen, FedFreezeBERT reduces the communication costs by a large factor compared to other state-of-the-art methods. FedFreezeBERT is implemented in a distributed version where the aggregation architecture only is being transferred and aggregated by FL algorithms such as FedAvg or FedProx. FedFreezeBERT is also implemented in a centralized version where the data embeddings extracted by BERT are sent to the central server to train the aggregation architecture. The experiments show that FedFreezeBERT achieves new state-of-the-art performance on Arabic sentiment analysis on the ArSarcasm-v2 dataset with a 12.9% and 1.2% improvement over FedAvg/FedProx and the previous SOTA respectively. FedFreezeBERT also reduces the communication cost by 5 $$\times$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mo>×</mml:mo> </mml:math> compared to the previous SOTA.
Social construction of XAI: Do we need one definition to rule them all?

<h2>Summary</h2> In this opinion, Upol Ehsan and Mark Riedl argue why a singular monolithic definition of explainable AI (XAI) is neither feasible nor desirable at this stage of XAI's development.
Stability of a quantum skyrmion: Projective measurements and the quantum Zeno effect

Magnetic skyrmions are vortexlike quasiparticles characterized by long lifetime and remarkable topological properties. That makes them a promising candidate for the role of information carriers in magnetic information storage and processing devices. Although considerable progress has been made in studying skyrmions in classical systems, little is known about the quantum case: quantum skyrmions cannot be directly observed by probing the local magnetization of the system, and the notion of topological protection is elusive in the quantum realm. Here we explore the potential robustness of quantum skyrmions in comparison to their classical counterparts. We theoretically analyze the dynamics of a quantum skyrmion subject to local projective measurements and demonstrate that the properties of the skyrmionic quantum state change very little upon external perturbations. We further show that by performing repetitive measurements on a quantum skyrmion, it can be completely stabilized through an analog of the quantum Zeno effect.
Quantum state tomography of undetected photons

The measurement of quantum states is one of the most important problems in quantum mechanics. We introduce a quantum state tomography technique in which the state of a qubit is reconstructed, while the qubit remains undetected. The key ingredients are (i) employing an additional qubit, (ii) aligning the undetected qubit with a known reference state by using path identity, and (iii) measuring the additional qubit to reconstruct the undetected qubit state. We establish theoretically and demonstrate experimentally the method with photonic polarization states. The principle underlying our method could also be applied to quantum entities other than photons.
Enhancing Pedestrian Tracking in Autonomous Vehicles by Using Advanced Deep Learning Techniques

Effective collision risk reduction in autonomous vehicles relies on robust and straightforward pedestrian tracking. Challenges posed by occlusion and switching scenarios significantly impede the reliability of pedestrian tracking. In the current study, we strive to enhance the reliability and also the efficacy of pedestrian tracking in complex scenarios. Particularly, we introduce a new pedestrian tracking algorithm that leverages both the YOLOv8 (You Only Look Once) object detector technique and the StrongSORT algorithm, which is an advanced deep learning multi-object tracking (MOT) method. Our findings demonstrate that StrongSORT, an enhanced version of the DeepSORT MOT algorithm, substantially improves tracking accuracy through meticulous hyperparameter tuning. Overall, the experimental results reveal that the proposed algorithm is an effective and efficient method for pedestrian tracking, particularly in complex scenarios encountered in the MOT16 and MOT17 datasets. The combined use of Yolov8 and StrongSORT contributes to enhanced tracking results, emphasizing the synergistic relationship between detection and tracking modules.