Reviewerly/exHarmony huggingface dataset:
SmartMeasurer: A Secure and Automated Bandwidth Measurement for Tor with Smart Contract

Tor is now using a centralized measurement system called Sbws to measure the bandwidth of relays to guide clients in selecting relays to balance traffic. Sbws has been proven to be vulnerable to multiple attacks because of the centralized structure and exposed measurements. We present SmartMeasurer, a secure and decentralized system for bandwidth measurement. Combining smart contract, Oracle Chainlink and ECC technology, we achieve decentralization while hiding the measurement circuits among the general circuits by exploiting the dual identity of randomly dynamic measurers and guards. We analyze the security of our system and demonstrate that it defends against three types of attacks. Our experiments on both private and public Tor networks show that our system is decentralized while keeping the error and the average of our measurements converges to a small interval of 0.30 Mbps. Compared to other existing methods, our system reduces trust assumptions and the costs of using smart contract, and enhances the practical feasibility of the solution.
Augmented Reality and Its Significance in Healthcare Systems

Augmented reality and virtual reality are terms often used together and even interchangeably sometimes without knowing their actual meaning. Augmented reality (AR) enhances the real world by mixing and overlapping digital objects with the real world whereas virtual reality (VR) is a completely different world created in a virtual space. VR can be experienced with wearables; but AR needs a device as simple as a phone and it's also wearable. In this chapter, AR is discussed and explored in a detailed manner. With its rapid evolving time, AR will be more common than it is now. It already is a part of everyone's life with the help of applications like Google Lens and Snapchat. AR has been experimented on for a while and the first spine surgery on a patient has been performed by John Hopkins neurosurgeon on June 8, 2020, using AR headsets. In this chapter, the types of extended reality (XR) and their differences. AR technology used in the study of anatomy, medical surgeries, pharma study, MedTech, and case studies of AR implementation in the field of medical surgeries is discussed.
A New Approach to Crime Scene Management: AR-VR Applications in Forensic Science

The crime scene, a place where a crime has been or is suspected to have occurred or where the evidence related to a crime was found, is a vital part of the investigation as it contains all the major information about a crime. A keen eye for the crime scene can determine the possible Modus Operandi of a crime and establish Corpus Delicti in a court of law. As per Indian law, we are allowed to visit a crime scene once only and if we want to visit again, we have to take permission but it is of no use. During a revisit to a crime scene, the chance or probability of finding any evidence is nearly 0%. Documenting a crime thus became a very crucial step. By using ordinary methods for documenting the crime scene we cannot give a visual or walk to the actual crime scene. It’s just a physical view of the documents. It is, therefore, critical to visually capture the crime scene and any potential evidence to aid the investigation. The current demand is for Augmented Reality and Virtual Reality. Virtual Reality is a wholly virtual view of the scenario, whereas Augmented Reality reflects a real-world context. VR is a type of advanced user interface that comprises a real-time simulation of a real-world environment with which the user interacts through numerous sensory channels: sight, hearing, touch, smell, and taste. The 3D reconstruction and visualization of crime situations such as criminal assaults, traffic accidents, and homicides is a new method of criminal investigation that has the potential to improve efficacy. To produce an accurate and immersive virtual environment, modern 3D recording and processing methods, such as AR and VR, are used. Immersion in a virtual environment, on the other hand, allows for various points of view.
Multi-Agent Based Decision Support Systems

Multi-Agent-Based Decision Support Systems (MADSS) have emerged as powerful tools for facilitating decision-making in complex and dynamic environments. This chapter provides an overview of MADSS, highlighting their fundamental concepts, key components, and applications. MADSS leverage the principles of multi-agent systems, artificial intelligence, and decision support systems to enable collaborative decision-making among multiple autonomous agents. The chapter begins by introducing the concept of multi-agent systems, emphasizing the advantages they offer in terms of adaptability, flexibility, and scalability. It then explores the integration of decision support systems within this framework, enabling agents to make informed decisions by analyzing vast amounts of data, evaluating various alternatives, and considering multiple criteria. The architecture of MADSS is discussed, focusing on the interactions among agents, the coordination mechanisms employed, and the information exchange protocols utilized. Various agent types, such as user agents, decision agents, and knowledge agents, are described, along with their roles and responsibilities within the system. The chapter further explores the different approaches and techniques used in MADSS, including rule-based systems, expert systems, machine learning, and optimization algorithms. It highlights the importance of agent learning and adaptation to improve decision-making capabilities over time. The applications of MADSS across various domains are presented, including finance, supply chain management, healthcare, and transportation. Case studies illustrate how MADSS can enhance decision-making processes, improve efficiency, and optimize resource allocation in complex real-world scenarios. Lastly, the chapter discusses the challenges and future directions of MADSS. Issues such as agent coordination, trust among agents, and handling uncertainty are addressed. The potential of integrating emerging technologies like blockchain, the Internet of Things (IoT), and big data analytics is also explored, envisioning more sophisticated MADSS capable of handling larger-scale problems.
Agent Interactions Environments

Digitization has a substantial impact on almost all facets of fashion, starting from the designing of a fashion item to its production and its usage by consumers. Fashion has always been evolving with emerging technologies. With the beginning of Artificial Intelligent (AI) enabled technologies, the fashion industry has become as dynamic as technology, emerging as a forward-looking trend giant. The impact of AI on the augmentation of fashion trends is unquestionable and the industry has witnessed its fast move from 4.0 to 5.0 with the use of advanced technology. Although fashion is changing at a very fast pace with AI, fashion professionals have raised the socio economic impact of AI on the fashion industry, including the Green Economy (GE) issues, thus, making the exploration of the phenomenon essential. This chapter explores how AI-enabled technology in the fashion industry and fashion environment, is influencing the GE status of the fashion industry, especially in the post-COVID-19 era of innovative e-commerce fashion.
An Intelligent UAV Path-Planning Method Based on the Theory of the Three-Dimensional Subdivision of Earth Space

With the rapid development of the big data era, Unmanned Aerial Vehicles (UAVs) are being increasingly adopted for various complex environments. This has imposed new requirements for UAV path planning. How to efficiently organize, manage, and express all kinds of data in complex scenes and intelligently carry out fast and efficient path planning for UAVs are new challenges brought about by UAV application requirements. However, traditional path-planning methods lack the ability to effectively integrate and organize multivariate data in dynamic and complicated airspace environments. To address these challenges, this paper leverages the theory of the three-dimensional subdivision of earth space and proposes a novel environment-modeling approach based on airspace grids. In this approach, we carried out the grid-based modeling and storage of the UAV flight airspace environment and built a stable and intelligent deep-reinforcement-learning grid model to solve the problem of the passage cost of UAV path planning in the real world. Finally, we designed multiple sets of experiments to verify the efficiency of the global subdivision coding system as an environmental organization framework for path planning compared to a longitude–latitude system and to demonstrate the superiority of the improved deep-reinforcement-learning model in specific scenarios.
Carbon and Phosphorus in Volcanic Soils of Mt. Kerinci (Sumatra) After Long Term Tea and Paddy Cultivation

ABSTRACTVolcanic soils are well-known to have a high agronomical value, and in Sumatra, Mt. Kerinci volcanic soils produce one of the highest quality tea and rice. Declining soil organic carbon (C) and high phosphorus (P) retention are two main issues in volcanic soils in the area. Thus, this study aimed to study the dynamic of C) and P content in volcanic soils and to find the main controls of their variation from geochemical signatures. The soils of Mt Kerinci present a unique environment as one part of the area was converted from forest to tea plantation for about 100 years, and a nearby field had been used for paddy rice cultivation for about 200 years. The study was conducted in the Mt Kerinci area covering 5,922 ha in the north comprise of paddy soils (ps) and north tea soils (nts) and the south slope (south tea soils= sts). Soil samples were collected on a grid of 500 × 500 m under tea and paddy (n = 206 samples) at 0–20 and 20–40 cm depth. All soils have similar pH in H2O, but pH in KCl paddy soils (ps) > north tea soils (nts) > south tea soils (sts). The average content of soil organic Carbon (SOC) in paddy soils was 50% less than in the soils of tea plantations. The labile organic carbon content in paddy soils were lower than in soils of tea plantations. Most C was chemically bound to the metal complex. Higher P retention occurred in all soils. The CaO/TiO2 index indicated a weathering sequence of ps > nts > sts. Soil weathering controlled P retention with an increasing P retention as the soils weathered. Overall, the study suggests that cultivation in volcanic soils influenced the geochemical properties of the soils and subsequently their C and P contents.KEYWORDS: Andisolsdiscriminant analysisland-use conversionphosphorus retentionsoil carbonweathering indices Disclosure statementThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.Author contributionDian Fiantis: Funding acquisition, Conceptualization, Methodology, Field works, Data Analysis, Visualization, Writing – review & editing. Rudiyanto: Methodology, Data analysis, Visualization, Writing – review & editing. Frisa Irawan Ginting: Field works, Remote sensing and GIS Data Analysis, Visualization. Gusnidar: Field work and laboratory analysis. Boby Arianto: Field work, laboratory analysis and Data collection. Diki Prayoga: Field work, laboratory analysis and Data collection. Rahmat Padrikal: Field work, laboratory analysis and Data collection. Rahmatul Fajrianeldi: Field work, laboratory analysis and Data collection. Malik Nelson: Field work and laboratory analysis.Additional informationFundingThis study was supported by the Ministry of Research, Technology and Higher Education of Indonesia with grant no.T/33/UN.16.17/PT.01.03/PD-PP/2019 awarded to DF.
Optimal one-shot entanglement sharing

Sharing entanglement across quantum interconnects is fundamental for quantum information processing. We discuss a practical setting where this interconnect, modeled by a quantum channel, is used once with the aim of sharing high-fidelity entanglement. For any channel, we provide methods to easily find both this maximum fidelity and optimal inputs that achieve it. Unlike most metrics for sharing entanglement, this maximum fidelity can be shown to be multiplicative. This ensures a complete understanding in the sense that the maximum fidelity and optimal inputs found in our one-shot setting extend even when the channel is used multiple times, possibly with other channels. Optimal inputs need not be fully entangled. We find that the minimum entanglement in these optimal inputs can even vary discontinuously with channel noise. Generally, noise parameters are hard to identify and remain unknown for most channels. However, for all qubit channels with qubit environments, we provide a rigorous noise parametrization, which we explain in terms of no cloning. This noise parametrization and a channel representation that we call the standard Kraus decomposition have pleasing properties that make them useful more generally.
A Novel Approach for Clustering Large-scale Cloud Data using Computational Mechanism

In the present situation, with the enhancement of virtualization techniques, it is very essential to keep track of accumulated large-scaled heterogeneous data in every respect. In addition to that, it is also necessary to prioritize the processing mechanisms when being linked with clustered data. Sometimes it has been observed that the large scaled datasets are too complex and therefore, the normal computation mechanisms are not sufficient or adequate for the specific applications. But it is highly required to observe the significance of each individual dataset and focus on the responses being accumulated from other aspects to make a suitable decision and generation of efficient analytical clustered data. The main aim of such applications is to apply the clustering gaining merits from evolutionary computation to process the large-scaled data and based on optimality, the performance of datasets can be measured.
Secure and Privacy-Preserving Authentication Scheme Using Decentralized Identifier in Metaverse Environment

The metaverse provides a virtual world with many social activities that parallel the real world. As the metaverse attracts more attention, the importance of security and privacy preservation is increasing significantly. In the metaverse, users have the capability to create various avatars, which can be exploited to deceive or threaten others, leading to internal security issues. Additionally, users attempting to access the metaverse are susceptible to various external security threats since they communicate with service providers through public channels. To address these challenges, we propose an authentication scheme using blockchain, a decentralized identifier, and a verifiable credential to enable metaverse users to perform secure identity verification and authentication without disclosing sensitive information to service providers. Furthermore, the proposed approach mitigates privacy concerns associated with the management of personal information by enabling users to prove the necessary identity information independently without relying on service providers. We demonstrate that the proposed scheme is resistant to malicious security attacks and provides privacy preservation by performing security analyses, such as AVISPA simulation, BAN logic, and the real-or-random (ROR) model. We also show that the performance of our proposed scheme is better suited for the metaverse environment by providing greater security and efficiency when compared to competing schemes.
Experimental anonymous conference key agreement using linear cluster states

Multipartite entanglement enables secure and anonymous key exchange between multiple parties in a network. Greenberger-Horne-Zeilinger states have been introduced as resource states for anonymous key exchange protocols, in which an anonymous subset of parties within a larger network establishes a secret key. However, the use of other types of multipartite entanglement for such protocols remains relatively unexplored. Here, we demonstrate that linear cluster states can serve as a versatile and potentially scalable resource in such applications. We implemented an anonymous key exchange protocol with four photons in a linear cluster state and established a shared key between three parties in our network. We show how to optimize the protocol parameters to account for noise and to maximize the finite key rate under realistic conditions. As cluster states have been established as a flexible resource in quantum computation, we expect that our demonstration provides a first step towards their hybrid use for networked computing and communication.
Target-Network Update Linked with Learning Rate Decay Based on Mutual Information and Reward in Deep Reinforcement Learning

In this study, a target-network update of deep reinforcement learning (DRL) based on mutual information (MI) and rewards is proposed. In DRL, updating the target network from the Q network was used to reduce training diversity and contribute to the stability of learning. If it is not properly updated, the overall update rate is reduced to mitigate this problem. Simply slowing down is not recommended because it reduces the speed of the decaying learning rate. Some studies have been conducted to improve the issues with the t-soft update based on the Student’s-t distribution or a method that does not use the target-network. However, there are certain situations in which using the Student’s-t distribution might fail or force it to use more hyperparameters. A few studies have used MI in deep neural networks to improve the decaying learning rate and directly update the target-network by replaying experiences. Therefore, in this study, the MI and reward provided in the experience replay of DRL are combined to improve both the decaying learning rate and the target-network updating. Utilizing rewards is appropriate for use in environments with intrinsic symmetry. It has been confirmed in various OpenAI gymnasiums that stable learning is possible while maintaining an improvement in the decaying learning rate.
Machine learning meets photonics

Machine learning (ML) is becoming a ubiquitous and powerful tool helping to address challenges in countless fields. Applications of ML addressing optics challenges have been extensively studied in recent years opening up new research directions. In particular, here, we review some of our current efforts and provide examples of successful applications of ML to the characterization of photonic devices, design, and modeling of optical subsystems, and complete end-to-end optical system optimization. ML and statistical tools can yield additional insight from measurement data, e.g. by targeted filtering of noise sources. They have also been shown to assist complex or inaccurate physics-based models through black and grey-box modeling of photonics components or subsystems. Such ML-aided models have enabled easier optimization and design (including inverse design) of optical systems.
U-Net-Based Semi-Automatic Semantic Segmentation Using Adaptive Differential Evolution

Bone semantic segmentation is essential for generating a bone simulation model for automatic diagnoses, and a convolution neural network model is often applied to semantic segmentation. However, ground-truth (GT) images, which are generated based on handwriting borderlines, are required to learn this model. It takes a great deal of time to generate accurate GTs from handwriting borderlines, which is the main reason why bone simulation has not been put to practical use for diagnosis. With the above in mind, we propose the U-net-based semi-automatic semantic segmentation method detailed in this paper to tackle the problem. Moreover, bone computed tomography (CT) images are often presented in digital imaging and communications in medicine format, which consists of various parameters and affects the image quality for segmentation. We also propose a novel adaptive input image generator using an adaptive differential evolution. We evaluate the proposed method compared to conventional U-net and DeepLabv3 models using open bone datasets, the spine and the femur, and our artificial bone data. Performance evaluations show that the proposed method outperforms U-net and DeepLabv3 in terms of Dice, IoU, and pairwise accuracy, and DeepLabv3 show the lowest performance, due to a lack of training data. We verify that the U-net-based model is effective for bone segmentation, where a large quantity of training data are available. Moreover, we verify that the proposed method can effectively create proper GTs and input images, resulting in increased performance and reduced computational costs. We believe that the proposed method enhances the wide use of bone simulation based on CT images for practical use.
Breast Cancer Detection Using Machine Learning Concepts

Machine learning is applied in medical diagnosis to do early prediction of diseases, for increasing the possibility of recoverability around the globe. Cancer is a disease, which spreads quickly and would be difficult to control in advanced stages. The idea is to diagnose the disease at an early stage, so as to increase the chances of fast recovery. Breast cancer is common in women, and is a disease that causes the death of women in the age of fifty years or older. The purpose is to apply machine learning concepts to do early detection of disease. The system is fed with the images of all stages of cancer patients and the classification tools are used to train the system with the cases. This helps to predict the stage of cancer. After the prediction of the stage, the patient is prescribed with the medication or other appropriate treatment processes by the doctor. The right time diagnoses help to improve the prognosis and increase the chances of survival. The type of the tumour, size and its re-occurring nature need to be monitored from time to time to check it in control. The Data Mining algorithm in collaboration with Deep learning or Machine learning concepts can be used to design a system for early predictions. The proposal is to use the machine learning concepts to do performance comparison using different classifiers, such as Support Vector Machine (SVM), Decision Tree and K-Nearest Neighbour (KNN) on the Wisconsin Diagnostic Breast Cancer (WDBC) dataset [1]. The main aim of cancer detection is to classify tumours into malignant or benign, thus we use machine learning techniques to improve the accuracy of diagnosis. The main objective is to assess the efficiency, effectiveness and correctness of the algorithm using performance metrics like Accuracy, Precision, F1 score and Recall Experimentation is done using Jupyter Notebook.