Reviewerly/exHarmony huggingface dataset:
Beyond games: a systematic review of neural Monte Carlo tree search applications

Abstract The advent of AlphaGo and its successors marked the beginning of a new paradigm in playing games using artificial intelligence. This was achieved by combining Monte Carlo tree search, a planning procedure, and deep learning. While the impact on the domain of games has been undeniable, it is less clear how useful similar approaches are in applications beyond games and how they need to be adapted from the original methodology. We perform a systematic literature review of peer-reviewed articles detailing the application of neural Monte Carlo tree search methods in domains other than games. Our goal is to systematically assess how such methods are structured in practice and if their success can be extended to other domains. We find applications in a variety of domains, many distinct ways of guiding the tree search using learned policy and value functions, and various training methods. Our review maps the current landscape of algorithms in the family of neural monte carlo tree search as they are applied to practical problems, which is a first step towards a more principled way of designing such algorithms for specific problems and their requirements.
Method of automated arrangement of devices in a spacecraft compartment taking into account connection to interface connectors of feed-through plates of an on-board cable system

The article proposes a method for automated arrangement of on-board equipment in the spacecraft compartment, connected to external devices through sealed adapters by the on-board cable system routes. The purpose is to create a program that allows automated placing of a part of the on-board equipment in the compartment, taking into account the existing practical principles for developing instrument placement schemes, one of which is to minimize the mass of the on-board cable system. The mathematical model is described using the matrix-topological layout method in a procedural form, as well as in a numerical form for calculating the coordinates of the placement of devices and estimating the number of cycles to find the final solution. Recommendations are provided to ensure that the full range of technical requirements is taken into account in the future. An outline flowchart of the method algorithm is presented with support in the high-level Python programming language. The software implementation of the method was carried out with emulators. Testing was carried drawing on the example of a compartment of one of the advanced space vehicles. The effectiveness of the method is evaluated by comparing the number of cycles needed to obtain the result and the total time spent on the layout relative to the control data known from the job statistics.
Deep Learning for Enhanced Marine Vision: Object Detection in Underwater Environments

This study leverages the Semantic Segmentation of Underwater Imagery (SUIM) dataset, encompassing over 1,500 meticulously annotated images that delineate eight distinct object categories. These categories encompass a diverse array, ranging from vertebrate fish and invertebrate reefs to aquatic vegetation, wreckage, human divers, robots, and the seafloor. The use of this dataset involves a methodical synthesis of data through extensive oceanic expeditions and collaborative experiments, featuring both human participants and robots. The research extends its scope to evaluating cutting-edge semantic segmentation techniques, employing established metrics to gauge their performance comprehensively. Additionally, we introduce a fully convolutional encoder-decoder model designed with a dual purpose: delivering competitive performance and computational efficiency. Notably, this model boasts a remarkable accuracy of 88%, underscoring its proficiency in underwater image segmentation. Furthermore, this model's integration within the autonomy pipeline of visually-guided underwater robots presents its tangible applicability. Its rapid end-to-end inference capability addresses the exigencies of real-time decision-making, vital for autonomous systems. This study elucidates the model's practical benefits across diverse applications like visual serving, saliency prediction, and intricate scene comprehension. Crucially, the utilization of the Enhanced Super-Resolution Generative Adversarial Network (ESRGAN) elevates image quality, enriching the foundation upon which our model's success rests. This research establishes a solid groundwork for future exploration in underwater robot vision by presenting the model and the benchmark dataset.
A Study on the Multi-Cyber Range Application of Mission-Based Cybersecurity Testing and Evaluation in Association with the Risk Management Framework

With the advancement of IT technology, intelligent devices such as autonomous vehicles, unmanned equipment, and drones are rapidly evolving. Consequently, the proliferation of defense systems based on these technologies is increasing worldwide. In response, the U.S. Department of Defense is implementing the RMF (Risk Management Framework) to ensure the cybersecurity of defense systems and conducting cybersecurity T&amp;E (test and evaluation) concurrently. However, RMF and cybersecurity T&amp;E conducted during the acquisition phase of defense systems often result in fragmented cybersecurity assessments, excluding the operational environment of the defense systems. This omission fails to account for the complex network integration, data exchange functionalities, and mission-specific requirements in actual cyber attack scenarios. For these reasons, vulnerabilities in defense systems that remain unidentified during the acquisition phase can potentially pose significant cybersecurity threats during operational phases, necessitating substantial costs and efforts for remediation. Therefore, this paper proposes a mission-based cybersecurity T&amp;E model using a Multi-Cyber Range to effectively apply these two systems in a practical manner. The Multi-Cyber Range integrates independently operated cyber ranges into a network to expand the evaluation environment, which better reflects the mission environment of defense systems. The proposed model’s effectiveness is validated using a cyber attack simulation system targeting a virtualized arbitrary defense system. This paper not only presents an enhanced model for mission-based cybersecurity T&amp;E, but also contributes to the advancement of cybersecurity T&amp;E methodologies by providing a concrete application process.
Application and Consequences of Service Robots in Tourism and Hospitality Scenarios: A Systematic Literature Review

This study comprehensively reviews the literature on robotics’ uses in the tourism and hospitality industries. Through descriptive and content analyses, 121 articles on robotics applications were examined. The results of this investigation showed that the majority of studies were carried out in the setting of hospitality. This article identified potential directions for future academic research. The adoption of robots was discussed in terms of its practical ramifications for industry practitioners.
Event design method: New meanings, typology, pedagogical potential

The paper aims to provide a theoretical substantiation for the “event design method” notion and identify its types and the pedagogical potential of the phenomenon. The paper reflects the results of a theoretical study, which amount to inferring the essence of the event design method by integrating the theories of event-based pedagogy and instructional design; substantiating the typologization of event design by means of the “series of information criteria” method and the promising possibility of applying the identified types in the training of teacher students as counselors of children’s health improvement camps. The scientific novelty of the research lies in putting forward and substantiating the idea of combining eventivity and instructional design as the leading trends in modern educational practices to identify new meanings of the event design method and determine its types based on the parameter “student activities” using the triadic methodology (V. I. Razumov), which includes a triad as the first stable state of an object, a necessary and sufficient number of its components (attributes), providing an understanding of its essence and nature. The research findings are as follows: the definition of the “event design method” notion drawing upon the ideas of event-based pedagogy and the theory of instructional design was clarified; the types of the event design method determined by the triadic methodology were identified; the pedagogical potential of the phenomenon under study was outlined through a set of functions performed and tasks solved.
Sustaining the experience: students’ perceptions of online field trips

COVID-19 caused significant disruption for all industries, including education. Many studies, including tourism, utilise field trips to support student’s learning, which were moved online in response to the pandemic. Understanding students’ perceptions of virtual field trips is necessary to improve their usage. A total of seven focus groups were conducted with undergraduate students in Qatar, the UAE, and the Netherlands to explore students experience with field trips. Students were split into groups based on their familiarity with i) physical; ii) virtual; or iii) physical and virtual field trips. Physical field trips were preferred because of the out-of-class learning, socialisation, and networking opportunities. Virtual field trips had some noteworthy advantages, such as improved flexibility, repeatability, and access to otherwise off-limit sites, but unless improvements could be made to the social and networking opportunities, virtual field trips may always appear inferior. Through comparing students’ experiences with physical and virtual field trips, this research provided notable insights into what can be done to improve the field trip experience. This could include better planning and structuring virtual field trips to provide networking experiences, including AR or recordings of physical field trips to facilitate repeatability, and supplementing a few physical with several virtual field trips.
A Recognition Model Incorporating Geometric Relationships of Ship Components

Ship recognition with optical remote sensing images is currently widely used in fishery management, ship traffic surveillance, and maritime warfare. However, it currently faces two major challenges: recognizing rotated targets and achieving fine-grained recognition. To address these challenges, this paper presents a new model called Related-YOLO. This model utilizes the mechanisms of relational attention to stress positional relationships between the components of a ship, extracting key features more accurately. Furthermore, it introduces a hierarchical clustering algorithm to implement adaptive anchor boxes. To tackle the issue of detecting multiple targets at different scales, a small target detection head is added. Additionally, the model employs deformable convolution to extract the features of targets with diverse shapes. To evaluate the performance of the proposed model, a new dataset named FGWC-18 is established, specifically designed for fine-grained warship recognition. Experimental results demonstrate the excellent performance of the model on this dataset and two other public datasets, namely FGSC-23 and FGSCR-42. In summary, our model offers a new route to solve the challenging issues of detecting rotating targets and fine-grained recognition with remote sensing images, which provides a reliable foundation for the application of remote sensing images in a wide range of fields.
Disabling Tracing in Black-Box-Traceable CP-ABE System: Alert Decryption Black Box

In application scenarios such as cloud storage, a symmetric algorithm can be used to encrypt massive data. However, using the symmetric operation and keys in encryption and decryption, it is very difficult to realize an efficient access control system. The asymmetric encryption algorithm ciphertext-policy attribute-based encryption (CP-ABE), as a versatile one-to-many encryption algorithm, is considered an ideal access control tool to solve the user’s distrust of the service provider of cloud storage. However, in a traditional CP-ABE system, the malicious users may deliberately leak their attribute keys to others or build a decryption black box (DB) to provide illegal decryption services for benefits, while no one can determine their identities. To deal with the problems, especially the problem of tracing the DB builder, CP-ABE schemes with black-box traceability have been proposed in the past few years. But in this paper, we point out that for now, the tracing algorithms in all existing schemes actually work on an impractical assumption. That is, whenever a DB receives a decryption request, it always performs the decryption algorithm honestlycorrectly. We argue that if a DB finds the decryption request comes from the tracing algorithm, it may intentionally output incorrect decryption results to counter the tracing. Thus, we present the structural defect of the tracing algorithm applied to all known traceable CP-ABE schemes. We describe the construction of an alert decryption black box (ADB) that is capable of distinguishing a tracing ciphertext from a normal one. We also show how an ADB frustrates the tracing algorithm, and furthermore, malicious users can even apply an ADB to frame innocent users.
Strategies to Reimagine the Stadium Experience

Despite the challenges presented by seemingly limitless sports and entertainment options, increasing ticket cost, and transportation issues, Shields and Rein argue that the in-stadium sports experience is important and worth fighting for. To persuade fans to spend their time and money attending sporting events, they contend that sports organizations will need to rethink their core proposition. They offer four strategies to solve the problem of fan attendance in the future: First, introducing scarcity. Second, eventizing the sports calendar. The third and fourth strategies, making the stadium experience frictionless and utilizing satellite stadiums, respectively, will require integrating metaverse-related technologies including augmented reality and virtual reality. Embracing these strategies may not be easy, but they will be essential to preserving and reinvigorating stadium attendance going forward.
Taxonomy of Sportstech

In this chapter, the authors provide a snapshot of the opportunities, challenges, and development of the sportstech industry and propose a sportstech taxonomy comprised of the definition of sportstech and the SportsTech Matrix. Their goal is to provide a common understanding and a useful tool for researchers and practitioners alike. In so doing, they define sportstech based on established understanding of sports and technology, introduce the SportsTech Matrix, and exemplify how to apply it with use cases for a variety of stakeholders. The SportsTech Matrix provides an all-encompassing structure for the field of sportstech along two angles: The user and tech. Together, the two angles capture how different types of technologies provide solutions to different user groups.
Adaptive Global Power-of-Two Ternary Quantization Algorithm Based on Unfixed Boundary Thresholds

In the field of edge computing, quantizing convolutional neural networks (CNNs) using extremely low bit widths can significantly alleviate the associated storage and computational burdens in embedded hardware, thereby improving computational efficiency. However, such quantization also presents a challenge related to substantial decreases in detection accuracy. This paper proposes an innovative method, called Adaptive Global Power-of-Two Ternary Quantization Based on Unfixed Boundary Thresholds (APTQ). APTQ achieves adaptive quantization by quantizing each filter into two binary subfilters represented as power-of-two values, thereby addressing the accuracy degradation caused by a lack of expression ability of low-bit-width weight values and the contradiction between fixed quantization boundaries and the uneven actual weight distribution. It effectively reduces the accuracy loss while at the same time presenting strong hardware-friendly characteristics because of the power-of-two quantization. This paper extends the APTQ algorithm to propose the APQ quantization algorithm, which can adapt to arbitrary quantization bit widths. Furthermore, this paper designs dedicated edge deployment convolutional computation modules for the obtained quantized models. Through quantization comparison experiments with multiple commonly used CNN models utilized on the CIFAR10, CIFAR100, and Mini-ImageNet data sets, it is verified that the APTQ and APQ algorithms possess better accuracy performance than most state-of-the-art quantization algorithms and can achieve results with very low accuracy loss in certain CNNs (e.g., the accuracy loss of the APTQ ternary ResNet-56 model on CIFAR10 is 0.13%). The dedicated convolutional computation modules enable the corresponding quantized models to occupy fewer on-chip hardware resources in edge chips, thereby effectively improving computational efficiency. This adaptive CNN quantization method, combined with the power-of-two quantization results, strikes a balance between the quantization accuracy performance and deployment efficiency in embedded hardware. As such, valuable insights for the industrial edge computing domain can be gained.
Linear polarization state encoding for Ising computing with optically injection-locked VCSELs

Vertical cavity surface-emitting (VCSEL) arrays offer an attractive platform to develop a photonic Ising computer due to their scalability and compact physical size. Ising interactions can be encoded between VCSELs through mutual optical injection locking, with the polarity of the interaction determined by the presence or absence of a half-wave plate in the optical path, and the bit itself represented by polarization state. The performance of this approach is investigated computationally by extending the spin-flip model to describe a system of mutually injection locked VCSELs for 2-, 3-, and 4-bit Ising problems. Numerical simulations demonstrate that the modeled system solves the given Ising problems significantly better than chance, with critical parameters in the model identified as crucial for achieving an unbiased Ising solver. The quantum well gain anisotropy parameter as well as the ratio of phase anisotropy to decay rate of the local carrier number causes the system to favor particular Ising configurations over others, but this may not prohibit the system from reaching the ground state.
A Double-Blind Trial Platform Based on Distributed Ledger Technology

In the pharmaceutical research and development process, the third phase of clinical trials involves double-blind trials to mitigate the influence of human subjective awareness on the experimental results and ensure the efficacy and safety of drugs or vaccines. However, conventional double-blind trials are often overseen by a single institution responsible for the entire trial process. Researchers centrally manage data, introducing risks of data loss and tampering. Furthermore, since researchers have full access to all experimental data, there is a potential for premature unblinding and leakage of results to specific individuals, giving rise to bias and conflicts of interest. To address these problems, this paper proposes a distributed-ledger-based double-blind trial platform called “BlindBox”. This platform leverages the immutability and decentralization of distributed ledgers to enhance the security of experimental data. On the other hand, the platform employs smart contracts to restrict personnel’s access to experimental data, coupled with RFID technology to encode and shuffle the experimental drugs discreetly. This ensures that no one can ascertain the pairing between test subjects and drug groups, preventing collusion and result manipulation. When the trial period concludes, smart contracts automatically unblind the results and publish them on the IOTA platform. By utilizing IOTA’s zero-value transactions, the platform reduces the cost of repeated data access and ensures information openness and transparency. This approach prevents deliberate premature unblinding or insider trading stemming from monopolized information. The platform proposed in this study is expected to enhance the credibility and accuracy of double-blind trials while promoting the willingness and convenience of public participation in experiments.
Blockchain-Based Data Breach Detection: Approaches, Challenges, and Future Directions

In cybersecurity, personal data breaches have become one of the significant issues. This fact indicates that data breaches require unique detection systems, techniques, and solutions, which necessitate the potential to facilitate precise and quick data breach detection. Various research works on data breach detection and related areas in dealing with this problem have been proposed. Several survey studies have been conducted to comprehend insider data breaches better. However, these works did not examine techniques related to blockchain and innovative smart contract technologies to detect data breaches. In this survey, we examine blockchain-based data breach detection mechanisms developed so far to deal with data breach detection. We compare blockchain-based data breach detection techniques based on type, platform, smart contracts, consensus algorithm language/tool, and evaluation measures. We also present a taxonomy of contemporary data breach types. We conclude our study by outlining existing methodologies’ issues, offering ideas for overcoming those challenges, and pointing the way forward.
A Nature-Inspired Method to Mine Top-k Multi-Level High-Utility Itemsets

High-Utility Itemset Mining (HUIM) is designed to discover sets of itemsets that can bring high profits from the database. However, HUIM encounters several challenges in picking a suitable minimum utility threshold for each database. A class of algorithms that select the top-k itemsets based on their utility has been proposed to address this issue. Although traditional top-k HUI mining algorithms do not require a specific threshold, they tend to be very time-consuming and memory-intensive when dealing with large datasets. To tackle the combinational complexity involved in HUIM algorithms, nature-inspired methods have been suggested and adopted. Nonetheless, these algorithms have traditionally focused on handling conventional, often overlooking critical data structures like product hierarchies. Consequently, they fail to extract crucial insight from this novel database format. Thus, our research introduces a heuristic-based algorithm designed to leverage top-k itemsets from databases enriched with item taxonomy data. We propose a technique involving the early pruning of unpromising items to enhance mining efficiency. Experimental evaluations are conducted on several datasets to assess the method’s performance, both with and without adopting this strategy, demonstrating its effectiveness.