Reviewerly/exHarmony huggingface dataset:
Vision Robotic Manipulator Combined with Neural Radiance Field for Autonomous Real-time 3D Reconstruction

We present a new method to achieve accurate and real-time 3D scene reconstruction based on monocular cameras on a vision robotic manipulator platform. To realize this, we use the initialization part of SLAM and the latest advances in N eRF (Neural Radiance Field), Instant-NGP, and, most importantly, the information from the robotic manipulator to acquire real-time camera pose. This work uses keyframe images with camera real-time pose obtained from the visual robotic manipulator. Thus, the problem that instant-NGP needs images with pose are solved on the platform of the robotic manipulator, reducing the time of secondary processing of pure images and, consequently, realizing real-time 3D reconstruction.
Real-time VaR Calculations for Crypto Derivatives in KDB /Q

Cryptocurrency market is known for exhibiting significantly higher volatility than traditional asset classes. Efficient and adequate risk calculation is vital for managing risk exposures in such market environments where extreme price fluctuations occur in short timeframes. The objective of this thesis is to build a real-time computation workflow that provides VaR estimates for non-linear portfolios of cryptocurrency derivatives. Many researchers have examined the predictive capabilities of time-series models within the context of cryptocurrencies. In this work, we applied three commonly used models - EMWA, GARCH and HAR - to capture and forecast volatility dynamics, in conjunction with delta-gamma-theta approach and Cornish-Fisher expansion to crypto derivatives, examining their performance from the perspectives of calculation efficiency and accuracy. We present a calculation workflow which harnesses the information embedded in high-frequency market data and the computation simplicity inherent in analytical estimation procedures. This workflow yields reasonably robust VaR estimates with calculation latencies on the order of milliseconds.
Decentralized Evidence Storage System Using Blockchain and IPFS

During legal proceedings, producing untampered evidence is crucial. Evidence may be viewed by several parties involved in the investigation who temporarily assume ownership from the time it is collected until it is used in a court. Many times, digital evidence is stored on pen drives, hard disks, etc. Due to recent developments in computer technology, evidence integrity can be compromised by hackers. During this entire process from evidence collection to using them in courts, authorities must ensure that evidence must not get tampered with or lost. In this paper, we proposed a blockchain-based evidence storage system to decentralize the entire process of evidence handling. This system is made using Ethereum blockchain and IPFS which ensures that evidence is immutable, secure, accessible, and auditable. Blockchain is a digital ledger that keeps track of transactions between number of computers without central authority. It is made up of chain of blocks, each containing record of multiple transactions, and once a block is added to the chain, the information cannot be altered. Additionally, blockchain technology reduces the cost of storing and securing evidence by eliminating intermediaries and centralized authority. Interplanetary File System (IPFS) is a protocol that enables users to share files in a decentralized way. Instead of storing evidence in one central location, IPFS works by breaking large files into smaller chunks and spreading those files across different computers or nodes in a network.
Virtual Machine Consolidation Techniques to Reduce Energy Consumption in Cloud Data Centers: A Survey

Due to the growing demand for computational power, cloud computing is becoming one of the most attractive emerging technologies. It provides on-demand access to massive computing resources and services for the user through the data center. However, cloud data centers consume enormous electric energy to provide various services. Therefore, energy-efficient resource management in distributed cloud data centers is inevitable from economic and environmental perspectives. The challenge of delivering services with efficient resource utilization and low power consumption opens up a new direction. Virtual machine consolidation (VMC) is a powerful tool that ensures the provision of services without compromising QoS with a lesser active physical server. This paper presents a chronology of recent improvements through a systematic and comprehensive survey of existing VMC approaches and their implementation techniques, competitive algorithms, and performance metrices. Further, the performance of benchmark heuristics is evaluated on CloudSim.
Mitigating Vanishing Gradient in SGD Optimization in Neural Networks

Deep structured learning has become a brand-new topic because of its many applications in signals and data processing. Two main elements of deep learning are often highlighted in high-level characterizations: its structural frameworks comprising many layers or stages designed for the nonlinear processing of information and its optimization strategies for either supervised or unsupervised training of feature extraction at successively higher abstract levels. The vanishing gradient problem (VGP) is a crucial issue when training on multilayer neural nets employing the backpropagation technique with SGD. Sigmoid function and ReLU are typical neural network (NN) activation functions. Even though several researchers have proposed solutions to this issue, there has yet to be an effective, workable answer. When utilizing sigmoid and tanh, Batch Normalization alone cannot address the issue of gradients that disappear. ReLU is superior at solving VGP since it does not saturate, and its gradients are constant and more significant. The most effective solution for VGP is combining ReLU with Batch Normalization. In this paper, a unifying review of solutions for VGP is explained.
Machine Learning-Based Human Body Mass Index Prediction Using Facial Features

Body mass index (BMI) is widely used as a general health indicator. BMI determination using non-invasive measurements is of interest, and recent advances in the availability of digital imaging sensors have paved the way for quick and automatic measurements. In this study, we used a dataset of 1026 individuals gathered from the Internet. To categorize persons based on BMI data, machine learning methods like random forest and linear regression were applied. Face features were extracted from photographs in the VIP attribute dataset, and the resulting data is being used for training and testing with the linear regression and random forest algorithms. Once after training, we can test by giving the face input image. The mean squared error, average error, and accuracy are used to evaluate the algorithms.
An Ensemble for Satellite Image to Map Layout Translation

Mapping and updating maps tend to be a tedious and arduous job requiring human supervision. Accurate and up-to-date maps are necessary for the proper functioning of mobility services, urban planning, navigation, etc. Since generative adversarial networks (GANs) are a cutting-edge method to translate satellite-to-map images, streamlining is a forward-looking technique for satellite-to-map image translation. Furthermore, the introduced work utilizes generative adversarial networks to automatically update the map layout, which will ultimately facilitate the process of maintaining proper map layouts. To decrease the noisy patterns created during translation, a controller or manager that oversees the meaning and interpretation of information at a conceptual or abstract level is presented in the study. The cGAN converts the aerial and satellite images into broad map images without the help of any human annotated data. The paper proposes conditional GAN-based approach, an adversarial learning-based unsupervised domain mapping model with conditioning variable to create high-quality images.
The Impact of Information System and Technology of Courier Service During Pandemic

Courier services are a means of transport that can be used to deliver orders to customers or claim orders from them, and customers can use technology from the courier service like track order to track their order. The use of courier service nowadays is common that every day, people use courier service as their daily means of transport for delivering goods. However, in situations like pandemic, the courier services need to put more effort into their services, so that customers can use courier services with trust. The courier service must be more careful to deliver customer orders or send orders to customers. For this research methodology, the data collection technique using Google Forms and collecting data directly from people about their opinion toward the research have been employed. The result of the data collecting technique is depicted as the usage of the courier service during COVID-19 pandemic starting from March 2020. The results vary every month because not everyone uses the courier service every day. Moreover, the conclusion on why the months starting from March to June do not show high usage of courier service is that the pandemic has not been that worse than the later months, and so, people were going out to deliver or to do activities like shopping or buying groceries on their own.
InsuraChain: A Blockchain-Based Parametric Health Insurance Platform

The healthcare sector has emerged a lot in the last few decades. From making the process fast and digital to deliver the requirements in good quality, this industry is growing on a day-to-day basis. But one issue is still unresolved: the time-consuming process of insurance claims. This research study attempts to develop a solution to reduce the time-consuming process of claiming insurance in healthcare systems. This study begins by exploring the evolution of the healthcare industry, then explores the InsuraChain platform’s architecture, and finally presents the results that have been achieved by implementing the blockchain-based parametric health insurance platform. The purpose of InsuraChain is to streamline the claim process and reduce the required retrieval time by utilizing smart contracts. Moreover, this innovative platform provides users with the ability to access and share their Electronic Medical Records (EMRs) or Electronic Health Records (EHRs) with any doctor across the world, to result in a more accurate diagnosis. The use of blockchain technology ensures the security and privacy of medical records.
Pareto Optimization Technique for Protein Motif Detection in Genomic Data Set

Protein sequence motifs are defined as short, frequent fixed-length patterns in nucleic acid and protein sequences that may reflect significant structural or functional characteristics. Although finding the gapped consensus motif is crucial and is difficult due to the wide variety of combinatorial depictions and spaces. The proposed method, de Bruijn-based Pareto optimization for Emerging Substring (DPES), follows enumerative approach using Bruijn as a structure for developing the emerging substring, based on pangenome and Pareto optimization on protein sequences for cervical cancer and detects the different instances of (l, d) motif instances. Multiple values for (l, d) were considered, and de Bruijn node ensures that all the possible subsets are considered from the emerging substrings. Experimental results on the data set exhibit that (i) DPES can identify (l,d) motifs effectively and is faster than other contemporary state of the art (l, d) motif uncovering algorithms and (ii) DPES can identify subtle consensus that is concealed by the stronger patterns in the data.
Novel Visual Effects in Computer Vision of Animation Film Based on Artificial Intelligence

A person appreciates more spiritually fulfilling entertainment as his worldly life advances. Spiritually themed films and TV shows are attracting more viewers. However, the film industry has proliferated, and animated film production has increased yearly. There’s an urgent need to create a mechanism to locate consumers’ favourite anime movies in enormous databases fast and reliably. This work uses artificial intelligence and machine learning (ML) to explore new visual expressions in animation films. This study provides a backdrop for the sensible development and upgrading that has been taking place, which uses ML technology as a basis for researching new approaches and models to improve movie visual representation and suggests that it is crucial to film’s creative development. Convolution Neural Network investigates Hollywood’s “Kung Fu Panda news” visual language. The study found that when the model parameters were identified, the test set’s accuracy remained at 57%. The enhancing film works’ innovative and visual quality and promoting a balanced and long-term film industry expansion are crucial.
Investigation of Assessment Methodologies in Information Security Risk Management

Information security risk management is a crucial component of every organization's security plan. It comprises the identification, assessment, and prioritization of potential security risks to an organization's information assets as well as the implementation of protective measures or risk management plans. For this method to work, a detailed understanding of an organization's assets, threats, vulnerabilities, and potential impacts of security incidents is required. Effective information security risk management ensures business continuity, safeguards critical information assets, and prevents data breaches. In this study, the key concepts, practices, and tools of information security risk management are discussed. It also looks at the most effective strategies to set up a successful risk management program and identifies emerging trends.
Conditional Cube Key Recovery Attack on Round-Reduced Xoodyak

Since the announcement of the NIST call for a new lightweight cryptographic standard, a lot of schemes have been proposed in response. Xoodyak is one of these schemes and is among the finalists of the NIST competition with a sponge structure very similar to the Keccak hash function – the winner of the SHA3 NIST competition. In this paper with conditional cube attack technique, we fully recover the key of Xoodyak reduced to 6 and 7 rounds with time complexity resp. $$2^{42.58}$$ and $$2^{76.003}$$ in the nonce-reusing scenario. In our attack setting, we import the cube variables in the absorbing associated data phase, which has higher degree of freedom in comparison to data absorption phase. We use MILP tool for finding enough cube variables to perform the conditional key recovery attack. The 6-round attack is practical and has been implemented. To the best of our knowledge, this is the first proposed attack on 7-round Xoodyak.
A Security Policy Engine for Building Energy Management Systems

This paper presents a Policy Engine for securing building energy management systems (BEMSs), a class of industrial control systems (ICSs) requiring additional protection due to their complex and interconnected nature. The Policy Engine supports multiple deployment modes for legacy compliance and features seamless integration of new security policies. We have implemented a provenance verification solution and integrated it into the Policy Engine. To evaluate the effectiveness of the proposed solution, we have established a testbed that utilizes real BEMS equipment. We compared the security features and performance of the Policy Engine with a state-of-the-art security solution in BEMS, i.e., MQTT using TLS. Results indicate that the Policy Engine with provenance verification policy can secure BEMSs against signal injection, duplication, and remote attacks, with minimal operational impact.
Society with Trust: A Scientometrics Review of Zero-Knowledge Proof Advanced Applications in Preserving Digital Privacy for Society 5.0

Society 5.0 focuses on human productivity in the midst of advanced technological services. While the concept has human trust at its core, technology development is now leading to zero-trust architecture. In this scientometrics review, 107 selected papers were analyzed to obtain the nature of the Zero-knowledge Proof (ZKP) theory and its advanced applications to favor digital privacy in greater society. With the common literature review strategy, it was found that the most citations in this field appeared in 2018, with IEEE, Springer, and Elsevier as the top three publishers. By affiliations, China and United States are both leading countries where the researcher comes from. And as the positive trend emerged, the ZKP mechanism will keep being leveraged as one of the cryptographic toolsets in the cloud services, Internet of Things (IoT), smart contracts, healthcare, electronic systems, and many other industrial needs.
The Impact of Different Modes of Augmented Reality Information in Assisted Aircraft Cable Assembly

Aircraft avionics systems are complicated systems which involves high number of components and complex cable assembly procedure. To deal with this challenge, Augmented Reality (AR) has been proposed to be an effective method to improve assembly efficiency and to reduce error rates. In this paper, the authors developed AR-based guidance cable assembly and compare the impact of different modes of AR information. The results show that although AR outperformed the paper document assembly guidance, the design of AR information has distinct influence on the successful completion of the cables assembly process.
Church Online Queuing System Based-On Android

The pandemic COVID 19 case in the world changes the way of life for most people. Indonesia’s government has declared a disaster emergency status related to this outbreak. One of the policies includes the limitation of capacity in the house of worship with a maximum of 50%. The manager of the religious events has the challenge to limit the participants who want to join the events. The system is needed to support the managers of the house of worship to manage a queue. The study aims to design and develop a queue system to resolve the challenges. The waterfall methods, object-oriented analysis and design using Unified Modeling Language (UML) were used to develop the system. The application can be utilized to manage the queue of the congregation and reduce the pile of worshipers who attend places of worship without knowing whether the worship quota is still available. The evaluation results found that all application functions can run according to their duties. Previously, there is no research to explore this specific case, hence it can contribute to the academic by exploring the functions and technologies, and to the community by extending this system to the other environment, such as hospital, education, or pharmacy queuing system.
Business Plan Globetrot Plus Blockchain B2C Artificial Intelligence Solution for Tourism Industry

Travel is the ideal outcome for online diffusion. Unlike buying in most industries, the travel trade implicates the exchange, not physical inventory; however, it demands information. The tourist must select from many imaginable products, services, and fees that may differ with the minor adjustment of any parameters. Suppose, for example, a one-way airplane ticket. That ticket, for the same seat on an equivalent airplane, could be significantly more or less costly should the consumer decide to take delivery of the product (i.e., departure date or time) only a few hours earlier or later. The price for the same effect could likewise vary if the tourist was to complete the acquisition a day before or later. The special dynamics of travel shopping and buying are ideally fitted to the Internet, and the result of this creative medium has been nothing less than stunning. In this regard, the article is based on direct and indirect methods—being the literature review the most used technique. Contextually, in this study, it is possible to verify that the AI-combined Blockchain solution may seem a bit advanced at this stage; however, with further innovations, entering the applied field will certainty make this concept a reality.
Rudiments of First-Order Logic (FO)

In this chapter we present basic results on the second classical logic, first-order logic in many topics discussed here reduced to the predicate logic, i.e, logic without function symbols, yet with the full power of FO when deepest results like the Gödel completeness theorem, Gödel incompleteness theorems, the Rosser incompleteness theorem, and the Tarski theorem on non-definability of truth are discussed.
Modal and Intuitionistic Logics

Modal logics addressLogicModal Sentential SML uncertainty about truth values of statements by introducing and discussing the notion of possibility of truth in addition to the necessity of truth. In this, modal logics trespass the boundary between the realm of dichotomy true-false into the less transparent realm of certainly true-possibly true. As the latter is less rigorous about truth and the notions of necessity and possibility are open to various interpretations, the result is the existence of many variants of modal logics. We will follow in this chapter some line of more and more complex interpretations of necessity and possibility as well as their mutual relations.
Investigating the Impact of Augmented Reality and BIM on Retrofitting Training for Non-Experts

Augmented Reality (AR) tools have shown significant potential in providing on-site visualization of Building Information Modeling (BIM) data and models for supporting construction evaluation, inspection, and guidance. Retrofitting existing buildings, however, remains a challenging task requiring more innovative solutions to successfully integrate AR and BIM. This study aims to investigate the impact of AR+BIM technology on the retrofitting training process and assess the potential for future on-site usage. We conducted a study with 64 non-expert participants, who were asked to perform a common retrofitting procedure of an electrical outlet installation using either an AR+BIM system or a standard printed blueprint documentation set. Our findings indicate that AR+BIM reduced task time significantly and improved performance consistency across participants, while also decreasing the physical and cognitive demands of the training. This study provides a foundation for augmenting future retrofitting construction research that can extend the use of [Formula: see text] technology, thus facilitating more efficient retrofitting of existing buildings. A video presentation of this article and all supplemental materials are available at https://github.com/DesignLabUCF/SENSEable_RetrofittingTraining.
Injured Avatars: The Impact of Embodied Anatomies and Virtual Injuries on Well-Being and Performance

Human cognition relies on embodiment as a fundamental mechanism. Virtual avatars allow users to experience the adaptation, control, and perceptual illusion of alternative bodies. Although virtual bodies have medical applications in motor rehabilitation and therapeutic interventions, their potential for learning anatomy and medical communication remains underexplored. For learners and patients, anatomy, procedures, and medical imaging can be abstract and difficult to grasp. Experiencing anatomies, injuries, and treatments virtually through one's own body could be a valuable tool for fostering understanding. This work investigates the impact of avatars displaying anatomy and injuries suitable for such medical simulations. We ran a user study utilizing a skeleton avatar and virtual injuries, comparing to a healthy human avatar as a baseline. We evaluate the influence on embodiment, well-being, and presence with self-report questionnaires, as well as motor performance via an arm movement task. Our results show that while both anatomical representation and injuries increase feelings of eeriness, there are no negative effects on embodiment, well-being, presence, or motor performance. These findings suggest that virtual representations of anatomy and injuries are suitable for medical visualizations targeting learning or communication without significantly affecting users' mental state or physical control within the simulation.