Reviewerly/exHarmony huggingface dataset:
Securing Construction Workers’ Data Security and Privacy with Blockchain Technology

The construction industry, characterized by its intricate network of stakeholders and diverse workforce, grapples with the challenge of managing information effectively. This study delves into this issue, recognizing the universal importance of safeguarding data, particularly amid rising concerns around unauthorized access and breaches. Aiming to harness the potential of blockchain technology to address these challenges, this study used hypothetical biographical and safety data of construction workers securely stored on a Hyperledger Fabric blockchain. Developed within the Amazon Web Services (AWS) cloud platform, this blockchain infrastructure emerged as a robust solution for enhancing data security and privacy. Anchored in the core principles of data security, the model emerges as a potent defender against the vulnerabilities of traditional data management systems. Beyond its immediate implications, this study exemplifies the marriage of blockchain technology and the construction sector, and its potential for reshaping workforce management, especially in high-risk projects and optimizing risk assessment, resource allocation, and safety measures to mitigate work-related injuries. Practical validation through transaction testing using Hyperledger Explorer validates the model’s feasibility and operational effectiveness, thus serving as a blueprint for the industry’s data management. Ultimately, this research not only showcases the promise of blockchain technology in addressing construction data security challenges but also underscores its practical applicability through comprehensive testing, thus heralding a new era of data management that harmonizes security and efficiency for stakeholders’ benefit.
Design of an Immersive Virtual Reality Framework to Enhance the Sense of Agency Using Affective Computing Technologies

Virtual Reality is expanding its use to several fields of application, including health and education. The continuous growth of this technology comes with new challenges related to the ways in which users feel inside these virtual environments. There are various guidelines on ways to enhance users’ virtual experience in terms of immersion or presence. Nonetheless, there is no extensive research on enhancing the sense of agency (SoA), a phenomenon which refers to the self-awareness of initiating, executing, and controlling one’s actions in the world. After reviewing the state of the art of technologies developed in the field of Affective Computing (AC), we propose a framework for designing immersive virtual environments (IVE) to enhance the users’ SoA. The framework defines the flow of interaction between users and the virtual world, as well as the AC technologies required for each interactive component to recognise, interpret and respond coherently within the IVE in order to enhance the SoA.
On the (Im)plausibility of Public-Key Quantum Money from Collision-Resistant Hash Functions

Public-key quantum money is a cryptographic proposal for using highly entangled quantum states as currency that is publicly verifiable yet resistant to counterfeiting due to the laws of physics. Despite significant interest, constructing provably-secure public-key quantum money schemes based on standard cryptographic assumptions has remained an elusive goal. Even proposing plausibly-secure candidate schemes has been a challenge. These difficulties call for a deeper and systematic study of the structure of public-key quantum money schemes and the assumptions they can be based on. Motivated by this, we present the first black-box separation of quantum money and cryptographic primitives. Specifically, we show that collision-resistant hash functions cannot be used as a black-box to construct public-key quantum money schemes where the banknote verification makes classical queries to the hash function. Our result involves a novel combination of state synthesis techniques from quantum complexity theory and simulation techniques, including Zhandry’s compressed oracle technique.
Clinical prediction models and the multiverse of madness

Abstract Background Each year, thousands of clinical prediction models are developed to make predictions (e.g. estimated risk) to inform individual diagnosis and prognosis in healthcare. However, most are not reliable for use in clinical practice. Main body We discuss how the creation of a prediction model (e.g. using regression or machine learning methods) is dependent on the sample and size of data used to develop it—were a different sample of the same size used from the same overarching population, the developed model could be very different even when the same model development methods are used. In other words, for each model created, there exists a multiverse of other potential models for that sample size and, crucially, an individual’s predicted value (e.g. estimated risk) may vary greatly across this multiverse. The more an individual’s prediction varies across the multiverse, the greater the instability. We show how small development datasets lead to more different models in the multiverse, often with vastly unstable individual predictions, and explain how this can be exposed by using bootstrapping and presenting instability plots. We recommend healthcare researchers seek to use large model development datasets to reduce instability concerns. This is especially important to ensure reliability across subgroups and improve model fairness in practice. Conclusions Instability is concerning as an individual’s predicted value is used to guide their counselling, resource prioritisation, and clinical decision making. If different samples lead to different models with very different predictions for the same individual, then this should cast doubt into using a particular model for that individual. Therefore, visualising, quantifying and reporting the instability in individual-level predictions is essential when proposing a new model.
Quantum Algorithms for Charged Particle Track Reconstruction in the LUXE Experiment

Abstract The LUXE experiment is a new experiment in planning in Hamburg, which will study quantum electrodynamics at the strong-field frontier. LUXE intends to measure the positron production rate in this unprecedented regime using, among others, a silicon tracking detector. The large number of expected positrons traversing the sensitive detector layers results in an extremely challenging combinatorial problem, which can become computationally expensive for classical computers. This paper investigates the potential future use of gate-based quantum computers for pattern recognition in track reconstruction. Approaches based on a quadratic unconstrained binary optimisation and a quantum graph neural network are investigated in classical simulations of quantum devices and compared with a classical track reconstruction algorithm. In addition, a proof-of-principle study is performed using quantum hardware.
The Future of Artificial Intelligence in Blockchain Applications

The integration of artificial intelligence (AI) and blockchain technologies represents a powerful synergy with the potential to revolutionize various industries. This chapter explores the promising future of AI in blockchain applications, shedding light on the significant impacts, challenges, and opportunities it offers. AI's capabilities in data analysis, pattern recognition, and automation find natural alignment with blockchain's immutable, transparent, and decentralized ledger technology. The chapter examines several key use cases where AI and blockchain intersect, including supply chain management, healthcare, finance, and smart contracts. It also discusses the challenges of scalability, data privacy, and regulatory compliance, and how AI can address or mitigate these issues. Furthermore, the chapter highlights the opportunities for innovation and disruption in emerging AI-powered blockchain applications, such as self-executing smart contracts, fraud detection, and identity verification.
One-to-Many Simultaneous Secure Quantum Information Transmission

This paper presents a new quantum protocol designed to transmit information from one source to many recipients simultaneously. The proposed protocol, which is based on the phenomenon of entanglement, is completely distributed and is provably information-theoretically secure. Numerous existing quantum protocols guarantee secure information communication between two parties but are not amenable to generalization in situations where the source must transmit information to two or more recipients. Hence, they must be executed sequentially two or more times to achieve the desired goal. The main novelty of the new protocol is its extensibility and generality to situations involving one party that must simultaneously communicate different, in general, messages to an arbitrary number of spatially distributed parties. This is achieved in the special way employed to encode the transmitted information in the entangled state of the system, one of the distinguishing features compared with previous protocols. This protocol can prove expedient whenever an information broker, say, Alice, must communicate distinct secret messages to her agents, all in different geographical locations, in one go. Due to its relative complexity compared with similar cryptographic protocols, as it involves communication among n parties and relies on |GHZn⟩ tuples, we provide an extensive and detailed security analysis so as to prove that it is information-theoretically secure. Finally, in terms of its implementation, the prevalent characteristics of the proposed protocol are its uniformity and simplicity, because it only requires CNOT and Hadamard gates and the local quantum circuits are identical for all information recipients.
Teachers’ Competencies in Developing Digital Educational Escape Rooms

The development of digital educational escape rooms (DEERs) by teachers presents many advantages for student learning and engagement. By developing DEERs, teachers have the opportunity to customize the learning experience and align it with specific curricular goals and with the students’ needs. Teachers can select the content that is relevant to the subject matter, and ensure that the puzzles and challenges within the DEERs promote the desired learning outcomes. Furthermore, a digital platform of DEERs enables a large group of students to play simultaneously, or to access the game remotely, and by this allows adjusting diverse learning environments to students’ needs. For these reasons, it is crucial to understand the necessary competencies teachers need to possess to develop a successful DEER. This paper highlights these competencies, in order to promote dedicated training programs for teachers and encourage the adoption of the DEER educational approach in teaching.
Near Real-Time Detection and Moment Tensor Inversion of the 11 May 2022, Dharchula Earthquake

On 11 May 2022, an earthquake of Mw 5.2 occurred in the Dharchula region of Uttarakhand Himalayas, India. The Uttarakhand State Earthquake Early Warning System (UEEWS) detected and transmitted the warning within 11.61 s from the origin time, taking only 4.26 s for processing, location, and magnitude estimation and warning dissemination. The complete analysis was performed using three seconds of waveforms. Using the initial earthquake parameters provided by the UEEWS, moment tensor inversion was performed using the broadband seismometers network installed in northern India. The moment tensor (MT) inversion was performed for the event using both the body waves and the surface waves. The first motion polarity was used along with waveform data to enhance the solution’s stability. This paper discusses the importance of real-time event detection and efforts towards real-time MT source inversion of earthquakes in the Uttarakhand Himalayas. Relocation of two past earthquakes near Dharchula is also undertaken in this study. The outcome of this study provides insights into mitigating seismic hazards, understanding earthquake source mechanisms, and enhancing knowledge of local fault structures.
AI Content Generation Technology based on Open AI Language Model

The Open AI language model is a powerful tool for generating AI content. A Large amount of text data is trained through the language model, which can generate new text that is similar in style and tone to the training data. This language model can assist writers in generating high-quality content by offering suggestions and insights for improving language usage, sentence structure, and overall readability. This study represents the development of a content generation tool based on the open AI language model by utilising GPT 3 in the backend as an API to generate the necessary information for the model. With the help of this tool, businesses and individuals can produce high-quality, engaging content more efficiently than ever before. This content generation tool uses a recurrent neural network (RNN) architecture, which enables it to make more accurate predictions than rule-based chatbots. All the features, like Facebook ads, LinkedIn posts, Amazon product descriptions, blogs, company bios, chat bots, and so on, will be presented in the dashboard. This tool is powered by advanced machine learning algorithms that can analyse and understand natural language, allowing them to produce content that is grammatically correct, free of errors, and tailored to specific audiences. They can also help optimize content for search engines, ensuring that it reaches a wider audience and generates more traffic with fine-tuning templates.
Facial Expression Analysis Interpreting Emotion in Multicultural Settings

Facial expressions are a vital part of nonverbal communication and are used to convey a wide range of emotions. Recent works have suggested that cultural and societal factors may influence how people express and interpret facial expressions of emotion. This study explores image features of facial expressions of the same emotion in two different cultures, Thai and Japanese, to identify features that are common and different among expressions across cultures. To achieve this goal, we collected facial expression samples from entertainment media in Thailand and Japan. The subspace techniques are applied to create feature spaces that represent facial expressions from these samples and to extract image features that can reveal differences in facial expressions between pairs of emotions in both cultures. Our study finds evidence that suggests sufficient distinctions between expressions of some emotions in both cultures, particularly happy and disgust classes, while the character of the surprise class is similar across Thai and Japanese samples.
Sequence to Sequence Pre-Trained Model for Natural Language Processing

Pre-trained model is a transfer learning model trained on huge dataset. These model can be reused to solve new problems. Pre -trained model uses the encoder structure. Sequence - to Sequence model uses the encoder - decoder structure. Deep learning model can do the better task whenever large dataset is available but it may not be Sequence - to - Sequence pre - trained model (SSPT). SSPT model takes the chunks of random text and it will try to decode the clean text. For the natural language processing both BERT and BART can be used. BERT is more kind of bidirectional encoder structure with an objective of mask filling language, whereas BART is both encoder and decoder structure with left to right language modelling task. T5 is the text to text transformer which is aims to achieve state of the art result. mT5 is the massively multilingual text to text transformers which is the variant of T5. This paper focus more on SSPT model such as BART and T5 Model, The result indicates that BART performance is better over the other trained model. Prompting and fine tuning shall be used to improve the performance of the existing model.
An ontology of 3D environment where a simulated manipulation task takes place (ENVON)

Thanks to the advent of robotics in shopfloor and warehouse environments, control rooms need to seamlessly exchange information regarding the dynamically changing 3D environment to facilitate tasks and path planning for the robots. Adding to the complexity, this type of environment is heterogeneous as it includes both free space and various types of rigid bodies (equipment, materials, humans etc.). At the same time, 3D environment-related information is also required by the virtual applications (e.g., VR techniques) for the behavioral study of CAD-based product models or simulation of CNC operations. In past research, information models for such heterogeneous 3D environments are often built without ensuring connection among different levels of abstractions required for different applications. For addressing such multiple points of view and modelling requirements for 3D objects and environments, this paper proposes an ontology model that integrates the contextual, topologic, and geometric information of both the rigid bodies and the free space. The ontology provides an evolvable knowledge model that can support simulated task-related information in general. This ontology aims to greatly improve interoperability as a path planning system (e.g., robot) and will be able to deal with different applications by simply updating the contextual semantics related to some targeted application while keeping the geometric and topological models intact by leveraging the semantic link among the models.
Named Entity Recognition in Classical Chinese by Lexicon Enhancement

The significant differences between classical Chinese and modern Chinese make the NER methods that are applicable to modern Chinese not applicable to classical Chinese. In this paper, we propose a NER model for classical Chinese based on a lexicon-enhanced BERT model, where lexical features are integrated into the encoder layer. Experiment results show that lexicon enhancement on the classical Chinese BERT model can improve the performance of NER. Additionally, the effectiveness of fusing lexical information depends on the quality of word embeddings.
Multiparty entanglement routing in quantum networks

Distributing entanglement among multiple users is a fundamental problem in quantum networks, requiring an efficient solution. In this work, a protocol is proposed for extracting maximally entangled ($|{\text{GHZ}}_{n}\ensuremath{\rangle}$) states for any number of parties in a quantum network. It is based on the graph-state formalism and requires minimal assumptions on the network state. The protocol only requires local measurements at the network nodes and just a single-qubit memory per user. Existing protocols on bipartite entanglement routing are also improved for specific nearest-neighbor network architectures. To this end, the concept of majorization is utilized to establish a hierarchy among different paths in a network based on their efficacy. This approach utilizes the symmetry of the underlying graph state to obtain better-performing algorithms.
Do users want platform moderation or individual control? Examining the role of third-person effects and free speech support in shaping moderation preferences

This study examines social media users’ preferences for the use of platform-wide moderation in comparison to user-controlled, personalized moderation tools to regulate three categories of norm-violating content—hate speech, sexually explicit content, and violent content. Via a nationally representative survey of 984 US adults, we explore the influence of third-person effects and support for freedom of expression on this choice. We find that perceived negative effects on others negatively predict while free speech support positively predicts a preference for having personal moderation settings over platform-directed moderation for regulating each speech category. Our findings show that platform governance initiatives need to account for both actual and perceived media effects of norm-violating speech categories to increase user satisfaction. Our analysis also suggests that users do not view personal moderation tools as an infringement on others’ free speech but as a means to assert greater agency over their social media feeds.
Topology-aware universal adversarial attack on 3D object tracking

Abstract 3D object tracking based on deep neural networks has a wide range of potential applications, such as autonomous driving and robotics. However, deep neural networks are vulnerable to adversarial examples. Traditionally, adversarial examples are generated by applying perturbations to individual samples, which requires exhaustive calculations for each sample and thereby suffers from low efficiency during malicious attacks. Hence, the universal adversarial perturbation has been introduced, which is sample-agnostic. The universal perturbation is able to make classifiers misclassify most samples. In this paper, a topology-aware universal adversarial attack method against 3D object tracking is proposed, which can lead to predictions of a 3D tracker deviating from the ground truth in most scenarios. Specifically, a novel objective function consisting of a confidence loss, direction loss and distance loss generates an atomic perturbation from a tracking template, and aims to fail a tracking task. Subsequently, a series of atomic perturbations are iteratively aggregated to derive the universal adversarial perturbation. Furthermore, in order to address the characteristic of permutation invariance inherent in the point cloud data, the topology information of the tracking template is employed to guide the generation of the universal perturbation, which imposes correspondences between consecutively generated perturbations. The generated universal perturbation is designed to be aware of the topology of the targeted tracking template during its construction and application, thus leading to superior attack performance. Experiments on the KITTI dataset demonstrate that the performance of 3D object tracking can be significantly degraded by the proposed method.
UNet-2022: Exploring Dynamics in Non-isomorphic Architecture

In this paper, we first analyze the differences between the weight allocation mechanisms of the self-attention and convolution. Based on this analysis, we propose to construct a parallel non-isomorphic block that takes the advantages of self-attention and convolution with simple parallelization. We name the resulting U-shape segmentation model as UNet-2022. In experiments, UNet-2022 obviously outperforms its counterparts in a range segmentation tasks, including abdominal multi-organ segmentation, automatic cardiac diagnosis, neural structures segmentation, and skin lesion segmentation, sometimes surpassing the best performing baseline by 4%. Specifically, UNet-2022 surpasses nnUNet, the most recognized segmentation model at present, by large margins. These phenomena indicate the potential of UNet-2022 to become the model of choice for medical image segmentation.
Data Augmentation for Semantic Segmentation Using a Real Image Dataset Captured Around the Tsukuba City Hall

We are exploring the use of semantic scene understanding in autonomous navigation for the Tsukuba Challenge. However, manually creating a comprehensive dataset that covers various outdoor scenes with time and weather variations to ensure high accuracy in semantic segmentation is onerous. Therefore, we propose modifications to the model and backbone of semantic segmentation, along with data augmentation techniques. The data augmentation techniques, including the addition of virtual shadows, histogram matching, and style transformations, aim to improve the representation of variations in shadow presence and color tones. In our evaluation using images from the Tsukuba Challenge course, we achieved the highest accuracy by switching the model to PSPNet and changing the backbone to ResNeXt. Furthermore, the adaptation of shadow and histogram proved effective for critical classes in robot navigation, such as road, sidewalk, and terrain. In particular, the combination of histogram matching and shadow application demonstrated effectiveness for data not included in the base training dataset.