Reviewerly/exHarmony huggingface dataset:
A P2P File Sharing Market Based on Blockchain and IPFS with Dispute Resolution Mechanism

Online merchandising plays an important role in internet users’ behavior. With the advent of Web3.0 and blockchain technologies, online markets can be adapted to these new changes. In traditional online markets, buying and selling are performed in a centralized manner, which, in addition to the problems of centralized systems, also brings the challenge of users’ trust. One of the challenges in online markets is maintaining the privacy and not revealing the exchanged file to unauthorized entities. Another challenge is to have a mechanism to detect fraud in the market. If the wrong file or key is sent by the seller, the fraud detection mechanism guaranties that the smart contract will be informed and the buyer will be refunded. On the other hand, the seller will be paid if a correct file and its corresponding key have been sent.Blockchain network is not suitable for file storing. Therefore, there are centralized or decentralized methods to store files in building a blockchain based file sharing market. In this work, we selected a distributed file system storage called IPFS (InterPlanetary File system). We integrated IPFS network with a suitable smart contract which is deployed on Ethereum blockchain. We provided a decentralized dispute resolution mechanism to resolve any dispute that happens between market users.
Social innovation in education

This article presents different perspectives of social innovation in education, focusing on formal education and problematizing the lack of concepts, recognition, integration and acceptance of social innovation in education. A definition of social innovation in education introduces three different dimensions: level of innovation, type of education where the innovation emerges and types of social innovation in education. This paper recognizes that an open question in social innovation in education is how to analyze educational innovation with a systemic approach. More empirical research on social innovation in education is crucial to better understand how innovation in education occurs, how actors co-create new models of education, how innovation networks in education are constructed and what new forms of governance in education are being implemented in different countries. This paper highlights the role of civil society in educational innovation as a means to modernize the education system through new social practices.
Effects of Volumetric Video Capture on Interactive Storytelling in Immersive Augmented Reality: A Short Film Experiment

Volumetric video capture is an emerging technology that opens up several fields of research and, in combination with immersive augmented reality, new opportunities for interactive storytelling. This paper describes an experiment in the conception and production process of an interactive story with volumetrically filmed characters experienced through an MS HoloLens, resulting in an interactive participatory theater application. We report on an initial experiment with a low-budget approach, describe the technical setup, story, and interaction concept, report on a user evaluation, and discuss our findings. The experiment illustrates that the benefit of the high production effort compared to a 2D film is important, as volumetric features need to be integrated into the narrative ideas. These include – in addition to working around technical issues – the sense of the actors’ presence in the same space with the audience, spatial affordances for interaction, and expanded opportunities for emotionally believable feedback from the characters.
How in-store sensor technologies can help retailers to understand their customers: overview on two decades of research

To compete with e-commerce, retailers are increasingly integrating smart technologies into their offline retail environment. This enables retailers to analyze their customers and provide them with a personalized shopping experience. However, research to date has mainly focused on the customer rather than the retailer. Building on a semi-systematic review of 75 studies using sensor technologies in retail environments between 2001 and 2021, this paper facilitates future research on the selection and combination of sensor technologies for in-store customer analysis from a retailer’s perspective. The literature was analyzed in terms of the technology used, the raw data collected, and the interpretation of the data. The results show that technologies were primarily used to gain insights into customers’ physical behavior, while few studies attempted to identify the customer or examine customers’ emotions. The identified studies rarely combined multiple technologies. Based on the analysis, this study provides recommendations for selecting and combining sensor technologies in stores. Current approaches to support technology selection take a broad perspective and compare entire store technology concepts, such as smart shelving or smart shopping carts, rather than basic sensor technologies. The findings are relevant to both research and retail practice.
Universal resources for quantum computing

Abstract Unravelling the source of quantum computing power has been a major goal in the field of quantum information science. In recent years, the quantum resource theory (QRT) has been established to characterize various quantum resources, yet their roles in quantum computing tasks still require investigation. The so-called universal quantum computing model (UQCM), e.g. the circuit model, has been the main framework to guide the design of quantum algorithms, creation of real quantum computers etc. In this work, we combine the study of UQCM together with QRT. We find, on one hand, using QRT can provide a resource-theoretic characterization of a UQCM, the relation among models and inspire new ones, and on the other hand, using UQCM offers a framework to apply resources, study relation among these resources and classify them. We develop the theory of universal resources in the setting of UQCM, and find a rich spectrum of UQCMs and the corresponding universal resources. Depending on a hierarchical structure of resource theories, we find models can be classified into families . In this work, we study three natural families of UQCMs in detail: the amplitude family, the quasi-probability family, and the Hamiltonian family. They include some well known models, like the measurement-based model and adiabatic model, and also inspire new models such as the contextual model that we introduce. Each family contains at least a triplet of models, and such a succinct structure of families of UQCMs offers a unifying picture to investigate resources and design models. It also provides a rigorous framework to resolve puzzles, such as the role of entanglement versus interference, and unravel resource-theoretic features of quantum algorithms.
The Role of Privacy Regulations in AI Development (A Discussion of the Ways in Which Privacy Regulations Can Shape the Development of AI)

The role of privacy regulations in AI development is very crucial nowadays, especially with a significant increase in the usage of AI technologies across various industries, such as facial recognition technology, biometric data, and decision-making processes. There are continuous concerns regarding the privacy of individuals and the potential misuse and abuse of such data by AI systems. The development of AI depends on learning from the processing of vast amounts of data, processing such data requires the establishment of adequate privacy regulations to ensure that individuals' data and rights are securely protected. In this context, various different regulations have been issued and introduced to address these concerns, such as the General Data Protection Regulation (GDPR) in Europe, the California Consumer Privacy Act (CCPA), and the California Privacy Rights Act (CPRA) in the United States. These regulations are a set of guidelines and rules for organizations to follow in their collecting and processing of data, which includes the usage of AI to establish a balance between the potential benefits of AI and the risks to people’s privacy. This involves issues that come out from data collection, storage, sharing, and processing, as well as the rights of individuals to access and control their data. Although, the implementation of these regulations in AI development is not always straightforward or consistent, and there are different ongoing discussions on how to balance privacy concerns with AI development. Therefore, it is essential to explore the role of privacy regulations in AI development to address new and emerging issues and to ensure that AI systems are developed and used in an ethical, and transparent way that protects individuals' privacy rights.The development of artificial intelligence (AI) has the potential to revolutionize a lot of industries and transform the way we live and work. However, as AI technology advances, concerns around privacy and data protection have become increasingly important. Therefore, the purpose of this paper is to explore the role of privacy regulations in the development of artificial intelligence (AI), as it will cover the current state of privacy regulations in AI, the potential implications of inadequate privacy regulations on AI development, and the strategies that can be used to incorporate privacy considerations into the AI development process. In addition, to discussing the importance of stakeholder engagement to balance competing priorities and provide examples of successful approaches for balancing privacy, innovation, and ethical considerations in AI development. The ultimate goal of this paper is to contribute to a better understanding of the critical role that privacy regulations play in the responsible and ethical development of AI systems.
Principal Component Analysis and t-Distributed Stochastic Neighbor Embedding Analysis in the Study of Quantum Approximate Optimization Algorithm Entangled and Non-Entangled Mixing Operators

In this paper, we employ PCA and t-SNE analyses to gain deeper insights into the behavior of entangled and non-entangled mixing operators within the Quantum Approximate Optimization Algorithm (QAOA) at various depths. We utilize a dataset containing optimized parameters generated for max-cut problems with cyclic and complete configurations. This dataset encompasses the resulting RZ, RX, and RY parameters for QAOA models at different depths (1L, 2L, and 3L) with or without an entanglement stage within the mixing operator. Our findings reveal distinct behaviors when processing the different parameters with PCA and t-SNE. Specifically, most of the entangled QAOA models demonstrate an enhanced capacity to preserve information in the mapping, along with a greater level of correlated information detectable by PCA and t-SNE. Analyzing the overall mapping results, a clear differentiation emerges between entangled and non-entangled models. This distinction is quantified numerically through explained variance in PCA and Kullback-Leibler divergence (post-optimization) in t-SNE. These disparities are also visually evident in the mapping data produced by both methods, with certain entangled QAOA models displaying clustering effects in both visualization techniques.
Enhancing Supply Chain Efficiency and Security: A Proof of Concept for IoT Device Integration With Blockchain

The manufacturing industry comprises various departments, and the supply chain is crucial in ensuring uninterrupted commodity flow during production. However, traditional supply chain systems face transparency, data visibility, and security challenges. This scientific article presents a Proof of Concept (PoC) that explores integrating IoT devices with blockchain technology to address these issues. Our PoC focuses on enabling IoT devices to autonomously sign transactions to the blockchain using IoT devices’ authenticated private keys, eliminating the need for external wallets. This approach offers scalability, efficiency, and real-time responsiveness benefits. Leveraging the devices’ transaction processing capabilities enhances scalability, allowing for a higher transaction volume. Automation of transaction signing streamlines the process, improving efficiency and eliminating delays caused by human interaction. Real-time responsiveness is ensured, eliminating any latency introduced by external wallets. We provide a detailed workflow of the use case and simulation results, making our research findings more accessible. Through this work, we demonstrate the feasibility and advantages of this method in scenarios where continuous, automated interaction with the blockchain is required through IoT devices. The PoC code is publicly available on GitHub.
Micro-Expression Spotting Based on VoVNet, Driven by Multi-Scale Features

Micro-expressions are a type of real emotional expression, which are unconscious and difficult to hide. Identifying these expressions has great potential applications in areas such as civil aviation security, criminal interrogation, and clinical medicine. However, because of their characteristics such as short duration, low intensity, and sparse action units, this makes micro-expression spotting difficult. To address this problem and inspired by object detection methods, we propose a VoVNet-based micro-expression spotting model, driven by multi-scale features. Firstly, VoVNet is used to achieve the extraction and reuse of different scale perceptual field features to improve the feature extraction capability. Secondly, multi-scale features are extracted and fused using the Feature Pyramid Network module, incorporating optical flow features, and by realizing the interactive fusion of fine-grained feature information and semantic feature information. Finally, the model is trained and optimized on CAS(ME)2 and SAMM Long Video. The experimental results show that the F1 score of the proposed model is improved by 0.1963 and 0.2441 on the two datasets compared with the baseline method, which outperforms the most popular spotting methods.
Prompt Engineering in Medical Image Segmentation: An Overview of the Paradigm Shift

Foundation AI models have emerged as powerful pre-trained models on a large scale, capable of seamlessly handling diverse tasks across multiple domains with minimal or no fine-tuning. These models, exemplified by the impressive achievements of GPT-3 and BERT in natural language processing (NLP), as well as CLIP and DALL-E in computer vision, have garnered considerable attention for their exceptional performance. A noteworthy addition to the realm of image segmentation is the Segment Anything Model (SAM), a foundation AI model that revolutionizes image segmentation. With a single click or a natural language prompt, SAM exhibits the remarkable ability to segment any object within an image, marking a significant paradigm shift in medical image segmentation. Unlike conventional approaches that rely on labeled data and domain-specific knowledge, SAM breaks free from these constraints. Deep convolutional neural network (DCNN)-based, SAM comprises an image encoder, a prompt encoder, and a mask decoder, showcasing its efficient and flexible architecture. Medical image segmentation, in particular, benefits from SAM’s exceptional speed and high-quality segmentation. In this paper, we delve into the effectiveness of SAM for medical image segmentation shedding light on its capabilities. Moreover, our investigation explores the strengths and limitations of prompt engineering in medical computer vision applications, not only encompassing SAM but also other foundation AI models. Through this exploration, we unravel their immense potential in catalyzing a paradigm shift in the field of medical imaging.
An empirical analysis of the (Un)readability of inmate handbooks

All 50 state Departments of Corrections (DOC) provide some form of handbook to inmates upon their incarceration. Handbooks specify, in part, the rules, regulations, responsibilities, and consequences of behavior critical for upholding safety, security, and order within correctional institutions. Considering the vital importance of these documents to the inmate and the institution, it is essential they be written at a level accessible and understandable to incarcerees. The present multi-method study first assessed the purpose of inmate handbooks. Qualitative results suggest handbooks serve as informational guides regarding the facility’s rules. Moreover, a significant portion of the handbooks emphasize that inmates bear the responsibility of understanding and adhering to the instructions outlined in the provided guide. Next, quantitative analysis of 74 inmate handbooks from DOCs in 49 US states was completed, utilizing three established readability metrics (the Flesch Readability Scale, the Flesch-Kincaid Grade Level, and the SMOG index), to determine whether handbooks are suitably written for their intended inmate audience. All 74 reviewed handbooks significantly exceeded a reasonable expectation of readability given the likely literacy and education levels of inmates. Recommendations are provided for the improvement of inmate handbook readability drawing on the empirical findings of the present study.
Microwave Photon Emission in Superconducting Circuits

Quantum computing requires a novel approach to store data as quantum states, opposite to classical bits. One of the most promising candidates is entangled photons. In this manuscript, we show the photon emission in the range of microwave frequencies of three different types of superconducting circuits, a SQUID, a JPA, and a JTWPA, often used as low-noise parametric amplifiers. These devices can be operated as sources of entangled photons. We report the experimental protocol used to produce and measure microwave radiation from these circuits, as well as data simulations. The collected spectra are obtained by performing single-tone measurements with a direct rf pump on the devices; the output spectra at low powers (below −100 dBm) are well interpreted by the dynamical Casimir model, while at high powers (above −100 dBm) the system is well described by the Autler–Townes fluorescence of a three-level atom.
An Efficient Approach for Recognition of Motor Imagery EEG Signals Using the Fourier Decomposition Method

This research paper presents an approach for recognizing motor imagery (MI) movements through brain signals, which has essential applications in assisting people with mobility disorders. One of the critical challenges in this field is that such individuals should be exposed to their surroundings with the help of exact motion recognition. This article represents an algorithm where Fourier-based filters are used for obtaining sub-bands of EEG signals for motion recognition and brain computer interface (BCI) application. Specifically, we segment motor imagery signals into eight orthogonal Fourier intrinsic band functions (FIBFs) and extract statistical feature matrices from each FIBF. We then propose a methodology derived from the k-nearest neighbor (kNN) classifier which is also compared with state-of-the-art classifiers like decision tree (DT), support vector machine (SVM), and naive Bayes (NB), to classify the extracted features and to establish its outperforming nature. Our experimental results show that our proposed approach achieves the highest classification accuracy of 96% and 84.03% with the kNN classifier on the BCI III IVa and BCI IV 2a datasets, outperforming state-of-the-art methods. These results demonstrate the potential of our approach in enabling accurate motion recognition and assisting people with mobility disorders.
Unsupervised Multiview Fuzzy C-Means Clustering Algorithm

The rapid development in information technology makes it easier to collect vast numbers of data through the cloud, internet and other sources of information. Multiview clustering is a significant way for clustering multiview data that may come from multiple ways. The fuzzy c-means (FCM) algorithm for clustering (single-view) datasets was extended to process multiview datasets in the literature, called the multiview FCM (MV-FCM). However, most of the MV-FCM clustering algorithms and their extensions in the literature need prior information about the number of clusters and are also highly influenced by initializations. In this paper, we propose a novel MV-FCM clustering algorithm with an unsupervised learning framework, called the unsupervised MV-FCM (U-MV-FCM), such that it can search an optimal number of clusters during the iteration process of the algorithm without giving the number of clusters a priori. It is also free of initializations and parameter selection. We then use three synthetic and six benchmark datasets to make comparisons between the proposed U-MV-FCM and other existing algorithms and to highlight its practical implications. The experimental results show that our proposed U-MV-FCM algorithm is superior and more useful for clustering multiview datasets.
Improving cloud storage and privacy security for digital twin based medical records

Abstract As digital transformation progresses across industries, digital twins have emerged as an important technology. In healthcare, digital twins are created by digitizing patient parameters, medical records, and treatment plans to enable personalized care, assist diagnosis, and improve planning. Data is core to digital twins, originating from physical and virtual entities as well as services. Once processed and integrated, data drives various components. Medical records are critical healthcare data but present unique challenges for digital twins. However, directly storing or encrypting medical records has issues. Plaintext risks privacy leaks while encryption hinders retrieval. To address this, we present a cloud-based solution combining post-quantum searchable encryption. Our system includes key generation using Physical Unable Functions (PUF). It encrypts medical records in cloud storage, verifies records using blockchain, and retrieves records via cloud. By integrating cloud encryption, blockchain verification and cloud retrieval, we propose a secure and efficient cloud-based medical records system for digital twins. Our implementation demonstrates the system provides users efficient and secure medical record services, compared to related designs. This highlights digital twins’ potential to transform healthcare through secure data-driven personalized care, diagnosis and planning.
VR for Diversity. The Seven Lives of a Research Project

This paper gives an overview of the research project VR for Diversity that focused on the impact of interaction in VR on the user. For this purpose, a distinction is made between physical interaction, that can be achieved by using controllers, and para-social interaction, that refers to interacting with characters that inhabit the virtual world. In line with the perceived affordance of VR to serve as a perspective shifter, the topic of diversity was chosen as the theme for the narratives that were to be created for this the research project. The paper describes a number of different projects that all evolved around this theme and concludes by presenting insights on the impact of interactive VR.