Reviewerly/exHarmony huggingface dataset:
A Novel Path Planning Approach for Mobile Robot in Radioactive Environment Based on Improved Deep Q Network Algorithm

The path planning problem of nuclear environment robots refers to finding a collision-free path under the constraints of path length and an accumulated radiation dose. To solve this problem, the Improved Dueling Deep Double Q Network algorithm (ID3QN) based on asymmetric neural network structure was proposed. To address the issues of overestimation and low sample utilization in the traditional Deep Q Network (DQN) algorithm, we optimized the neural network structure and used the double network to estimate action values. We also improved the action selection mechanism, adopted a priority experience replay mechanism, and redesigned the reward function. To evaluate the efficiency of the proposed algorithm, we designed simple and complex radioactive grid environments for comparison. We compared the ID3QN algorithm with traditional algorithms and some deep reinforcement learning algorithms. The simulation results indicate that in the simple radioactive grid environment, the ID3QN algorithm outperforms traditional algorithms such as A*, GA, and ACO in terms of path length and accumulated radiation dosage. Compared to other deep reinforcement learning algorithms, including DQN and some improved DQN algorithms, the ID3QN algorithm reduced the path length by 15.6%, decreased the accumulated radiation dose by 23.5%, and converged approximately 2300 episodes faster. In the complex radioactive grid environment, the ID3QN algorithm also outperformed the A*, GA, ACO, and other deep reinforcement learning algorithms in terms of path length and an accumulated radiation dose. Furthermore, the ID3QN algorithm can plan an obstacle-free optimal path with a low radiation dose even in complex environments. These results demonstrate that the ID3QN algorithm is an effective approach for solving robot path planning problems in nuclear environments, thereby enhancing the safety and reliability of robots in such environments.
Project technologies as a factor in the development of students’ creative potential

Importance. The demand for a creative approach in many fields of activity is revealed, and creativity is also considered as a necessary part of professional training. The actualization of active methods (using the example of project technologies) for the university students’ creative potential development is worked out. Research Methods. The research object is project technologies, the subject is project technologies possibilities to develop the higher educational institutions students’ creative potential. The theoretical basis of the research is an interdisciplinary approach, the research axiological foundations are the subject-activity and functional-activity approach to the educational process. The research empirical basis is the practice of using project technologies in the teaching process. Results and Discussion. The possibilities and features of project technologies for the development of students’ creative potential are considered. The characteristic of the main conditions and necessary components of this process is given. The practice of using project technologies in teaching such disciplines as “Philosophy”, “Cultural codes”, “Great Books”, “History of Sociology” is analyzed. Conclusion. Project activity, developing the individual creative potential, solves an important task of professional training – the development the ability to independently set professional goals and implement their ideas.
Healthcare Sustainability: Hospitalization Rate Forecasting with Transfer Learning and Location-Aware News Analysis

Monitoring and forecasting hospitalization rates are of essential significance to public health systems in understanding and managing overall healthcare deliveries and strategizing long-term sustainability. Early-stage prediction of hospitalization rates is crucial to meet the medical needs of numerous patients during emerging epidemic diseases such as COVID-19. Nevertheless, this is a challenging task due to insufficient data and experience. In addition, relevant existing work neglects or fails to exploit the extensive contribution of external factors such as news, policies, and geolocations. In this paper, we demonstrate the significant relationship between hospitalization rates and COVID-19 infection cases. We then adapt a transfer learning architecture with dynamic location-aware sentiment and semantic analysis (TLSS) to a new application scenario: hospitalization rate prediction during COVID-19. This architecture learns and transfers general transmission patterns of existing epidemic diseases to predict hospitalization rates during COVID-19. We combine the learned knowledge with time series features and news sentiment and semantic features in a dynamic propagation process. We conduct extensive experiments to compare the proposed approach with several state-of-the-art machine learning methods with different lead times of ground truth. Our results show that TLSS exhibits outstanding predictive performance for hospitalization rates. Thus, it provides advanced artificial intelligence (AI) techniques for supporting decision-making in healthcare sustainability.
A classifier model for prostate cancer diagnosis using CNNs and transfer learning with multi-parametric MRI

Prostate cancer (PCa) is a major global concern, particularly for men, emphasizing the urgency of early detection to reduce mortality. As the second leading cause of cancer-related male deaths worldwide, precise and efficient diagnostic methods are crucial. Due to high and multiresolution MRI in PCa, computer-aided diagnostic (CAD) methods have emerged to assist radiologists in identifying anomalies. However, the rapid advancement of medical technology has led to the adoption of deep learning methods. These techniques enhance diagnostic efficiency, reduce observer variability, and consistently outperform traditional approaches. Resource constraints that can distinguish whether a cancer is aggressive or not is a significant problem in PCa treatment. This study aims to identify PCa using MRI images by combining deep learning and transfer learning (TL). Researchers have explored numerous CNN-based Deep Learning methods for classifying MRI images related to PCa. In this study, we have developed an approach for the classification of PCa using transfer learning on a limited number of images to achieve high performance and help radiologists instantly identify PCa. The proposed methodology adopts the EfficientNet architecture, pre-trained on the ImageNet dataset, and incorporates three branches for feature extraction from different MRI sequences. The extracted features are then combined, significantly enhancing the model's ability to distinguish MRI images accurately. Our model demonstrated remarkable results in classifying prostate cancer, achieving an accuracy rate of 88.89%. Furthermore, comparative results indicate that our approach achieve higher accuracy than both traditional hand-crafted feature techniques and existing deep learning techniques in PCa classification. The proposed methodology can learn more distinctive features in prostate images and correctly identify cancer.
Assessments of Order-Picking Tasks Using a Paper List and Augmented Reality Glasses with Different Order Information Displays

This study investigated the effects of wearing AR devices on users’ performance and comfort ratings while performing order-picking (OP) tasks. In addition to a picking-by-paper list, two AR devices combined with three order information display designs were examined. Thirty adult participants joined. They searched for and found the boxes in the order list, picked them up, and returned to the origin. The time to complete the task and the number of incorrect boxes picked up were analyzed to assess the performance of the tasks. The subjective ratings of the participants on the comfort rating scale (CRS) and the simulator sickness questionnaire (SSQ) were collected to assess the designs of both the AR devices and the order information displays. It was found that the participants could complete the OP tasks faster when adopting the order map (4.97 ± 1.57 min) or the 3D graph display (4.87 ± 1.50 min) using either one of the AR devices than when using a paper list (6.03 ± 1.28 min). However, they needed more time to complete the OP tasks when wearing both types of AR glasses when the Quick Response (QR) code option was adopted (10.16 ± 4.30 min) than when using a paper list. The QR code scanning and display design using either one of the AR devices guaranteed 100% accuracy but sacrificed efficiency in task completion. The AR device with a binocular display and hand gesture recognition functions had a significantly lower CRS score in the dimensions of attachment and movement (5.6 and 6.3, respectively) than the corresponding dimensions (8.0 and 8.3, respectively) of the other device with a monocular and hand touch input design. There were complaints of eye strain after using both AR devices examined in this study. This implies that these AR devices may not be suitable to wear for extended periods of time. Users should take off the AR device whenever they do not need to view the virtual image to avoid eye strain and other discomfort symptoms.
Considerations on the regulation of AI systems in the financial sector by the AI Act

The proposal for the Artificial Intelligence regulation in the EU (AI Act) is a horizontal legal instrument that aims to regulate, according to a tailored risk-based approach, the development and use of AI systems across a plurality of sectors, including the financial sector. In particular, AI systems intended to be used to evaluate the creditworthiness or establish the credit score of natural persons are classified as "high-risk AI systems". The proposal, tabled by the Commission in April 2021, is currently at the center of intense interinstitutional negotiations between the two branches of the European legislature, the European Parliament and the Council. Without prejudice to the ongoing legislative deliberations, the paper aims to provide an overview of the main elements and choices made by the Commission in respect of the regulation of AI in the financial sector, as well as of the position taken in that regard by the European Parliament and Council.
Алгоритм построения дерева синтаксических единиц русскоязычного предложения по дереву синтаксических связей

Automatic syntactic analysis of a sentence is an important computational linguistics task. At present, there are no syntactic structure parsers for Russian that are publicly available and suitable for practical applications. Ground-up creation of such parsers requires building of a treebank annotated according to a given formal grammar, which is quite a cumbersome task. However, since there are several syntactic dependency parsers for Russian, it seems reasonable to employ dependency parsing results for syntactic structure analysis. The article introduces an algorithm that allows to construct the constituency tree of a Russian sentence by a syntactic dependency tree. The formal grammar used by the algorithm is based on the D.E. Rosenthal’s classic reference. The algorithm was evaluated on 300 Russian-language sentences. 200 of them were selected from the aforementioned reference, and 100 from OpenCorpora, an open corpus of sentences extracted from Russian news and periodicals. During the evaluation, the sentences were passed to syntactic dependency parsers from Stanza, SpaCy, and Natasha packages, then the resulted dependency trees were processed by the proposed algorithm. The obtained constituency trees were compared with the trees manually annotated by experts in linguistics. The best performance was achieved using the Stanza parser: the constituency parsing F1–score was 0.85, and the sentence parts tagging accuracy was 0.93, that would be sufficient for many practical applications, such as event extraction, information retrieval and sentiment analysis.
Decentralized Oracle Networks (DONs) Provision for DAML Smart Contracts

Decentralized oracle networks (DONs) provide a mechanism for interoperability within blockchain networks, additionally, they can address potential security vulnerabilities associated with smart contracts reliant on external data feeds. Decentralized oracle networks (DONs), allow smart contracts to access data from multiple sources without the need for a centralized authority, which can reduce the risk of manipulation or data tampering. However, not all platforms support DONs, including Digital Asset Modelling Language (DAML). DAML is a privacy-focused smart contract language that is designed to support multi-party applications, but it does not have native support for external data feeds. Furthermore, its applicability is limited by the lack of an integrated trust mechanism for external oracle feeds. Hence, this study proposes a DAML-DONs system which is comprised of five components: DON Broker Contract, Service Registry, Ranking Engine, Web-service, and an Interactive User Interface with Chatbot Assistance. By combining these five components, the proposed DAML-DON system can provide a robust and trustworthy solution for integrating DAML Ledger with external oracle networks. Additionally, it increases the interoperability of DAML smart contracts and contributes to enhancing the effectiveness and quality of using the services provided by Oracles.
An Investigation of Scalability for Blockchain-Based E-Voting Applications

In the last years, a number of initiatives have developed and deployed new electronic voting (e-voting) systems, with the goal of improving the paper-based electoral systems. Traditional e-voting systems are vulnerable to manipulation concerns, such as altering election outcomes, due to their centralized nature. Blockchain is a decentralized system technology with a distributed ledger in which every participant in the network accesses the same data source. The immutability of blockchains makes them suited for e-voting systems as no one can alter data. Although blockchain technology has potential for e-voting systems, there are challenges, including the scalability for large-scale elections. We present a solution to scalability of e-voting systems using the Solana blockchain. Solana provides a fast and scalable blockchain infrastructure utilizing the Proof of History (PoH) consensus algorithm, making it easier for developers to create efficient decentralized applications. The evaluation of factors such as throughput, delay, and cost shows that a Solana implementation can finalize a vote 32 times faster than Ethereum 2.0, with a significantly lower cost per vote. The throughput of e-voting app on Solana indicates that it can deal with a large scale election entirely.
Deep Learning for the Pathologic Diagnosis of Hepatocellular Carcinoma, Cholangiocarcinoma, and Metastatic Colorectal Cancer

Diagnosing primary liver cancers, particularly hepatocellular carcinoma (HCC) and cholangiocarcinoma (CC), is a challenging and labor-intensive process, even for experts, and secondary liver cancers further complicate the diagnosis. Artificial intelligence (AI) offers promising solutions to these diagnostic challenges by facilitating the histopathological classification of tumors using digital whole slide images (WSIs). This study aimed to develop a deep learning model for distinguishing HCC, CC, and metastatic colorectal cancer (mCRC) using histopathological images and to discuss its clinical implications. The WSIs from HCC, CC, and mCRC were used to train the classifiers. For normal/tumor classification, the areas under the curve (AUCs) were 0.989, 0.988, and 0.991 for HCC, CC, and mCRC, respectively. Using proper tumor tissues, the HCC/other cancer type classifier was trained to effectively distinguish HCC from CC and mCRC, with a concatenated AUC of 0.998. Subsequently, the CC/mCRC classifier differentiated CC from mCRC with a concatenated AUC of 0.995. However, testing on an external dataset revealed that the HCC/other cancer type classifier underperformed with an AUC of 0.745. After combining the original training datasets with external datasets and retraining, the classification drastically improved, all achieving AUCs of 1.000. Although these results are promising and offer crucial insights into liver cancer, further research is required for model refinement and validation.
Generators and Relations for 2-Qubit Clifford+T Operators

We give a presentation by generators and relations of the group of Clifford+T operators on two qubits.The proof relies on an application of the Reidemeister-Schreier theorem to an earlier result of Greylyn, and has been formally verified in the proof assistant Agda.
An R package for automatically generating candidate correspondence tables between classifications

Many statistical classifications exist in a statistical ecosystem, where they are interlinked with other classifications. When statistics on the same topic are compiled using different classifications, they need to be transformed in order to become comparable by means of a correspondence table – but sometimes, no correspondence table between the two classifications involved exists. This paper presents the newly developed ‘correspondenceTables’ R package, available on CRAN, which automates much of the ‘mechanical’ work required for developing a correspondence table (thus allowing statistical classification experts to focus on tasks with higher value added). Moreover, the paper presents lessons learned along the way, including unforeseen quality issues with the input data (that required considerable efforts to be successfully tackled), and outlines areas for future improvement.
Optimizing 3D UAV Path Planning: A Multi-strategy Enhanced Beluga Whale Optimizer

The goal of 3D UAV path planning problem is to assist the UAV in planning a flight path with the lowest total overhead cost. In this paper, we present a novel approach to address the problem by incorporating flight distance, threat cost, flight altitude and path smoothness constraints into a comprehensive cost function. The current popular metaheuristic algorithm is utilized to solve for the closest globally optimal UAV flight path. To overcome the challenges of local optima and slow convergence associated with the conventional Beluga Whale Optimizer (BWO), this paper proposes a modified beluga whale optimizer (OGGBWO) based on random opposition-based learning strategy, adaptive Gauss variational operator and elitist group genetic strategy. Extensive experiments conducted on the CEC2022 test set and four distinct terrain scenarios of varying complexity demonstrate that the OGGBWO algorithm outperforms classical and state-of-the-art metaheuristics. It achieves superior optimization performance across all 12 CEC2022 test functions and exhibits exceptional convergence in generating flight paths with the lowest total cost function in diverse terrain scenarios.
Preserving Change Information in Multi-temporal Choropleth Maps Through an Extended Data Classification Method

Diverse user requirements has led to an increasing availability of multi-temporal data, the analysis of which often requires visualization, e.g. in multi-temporal choropleth maps. However, if using standard data classification methods for the creation of these maps, problems arise: significant changes can be lost by data classification (change loss) or non-significant changes can be emphasized (change exaggeration). In this paper, an extended method for data classification is presented, which can reduce these effects as far as possible. In the first step, class differences are set for important or necessary changes. The actual data classification considers these class differences in the context of a sweep line algorithm, whose optimal solution is determined with the help of a measure called Preservation of Change Classes (POCC). By assigning weights during computation of this measure, different tasks or change analyses (e.g. emphasize only highly significant changes) can be processed.
Constructing an E-Commerce Model Using the Framework of Blockchain

Blockchain technology is gaining immense popularity in the realm of online e-commerce, advertising, and consumerism. This study aims to develop a model that comprehends how blockchain can act as a significant disruptor for various functions and applications related to e-commerce. The research explores the possibility that this technological advancement has the potential to revolutionize the core operations of e-commerce industry by providing trade connections devoid of trust or need for specialized intermediaries or central authority in case of permission less blockchains. Furthermore, it could facilitate equitable access to immutable data across supply chains which may result in a major reorganization pertaining to information and value exchanged between businesses involved in electronic commerce and their customers.
The role of ethical leadership to employees work engagement: a social learning theory perspective

Purpose This study examines the role of ethical leadership in building employee trust, knowledge sharing (KS), job satisfaction and then influencing employee engagement in the workplace. Design/methodology/approach The sample included 610 employees of Indonesia Islamic Bank, obtained through an online survey. Structural equation modelling was used to test the research hypotheses. Findings Ethical leadership actively contributes to the growth of employee trust, exchange knowledge frequent and job satisfaction and then become key points to enhance employees’ engagement. Research limitations/implications Future research is required to validate across regions and organisations to in light of the findings of the topic study. Practical implications Organisational leaders and employees obtain a better understanding of ethics and organisation management field, hence employees and leaders must encourage ethical values as code of conduct in the workplace. Originality/value This study demonstrated the extent of the Khan concept for a combination of employee engagement, ethical leadership and KS. It also incorporates employee job satisfaction and the organisational engagement among employees. Peer review The peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-03-2023-0218
Ignore-and-Recalculate Strategy for a Lossless and Low-Cost Secret Image Sharing Scheme

The field of secret image sharing is of utmost importance in safeguarding the confidentiality of images while sharing them among numerous participants. This article proposes a simple, low-cost and lossless secret image-sharing method based on 'ignore-and-recalculate' strategy. Even if any of the shares falls outside the intensity range of an image, the proposed scheme prevents pixel tampering while allowing the process of sharing to be repeated. Thus proposed method reduces the overhead of sharing process compared to the existing schemes. This method is capable of preventing information leakage, either individually or collectively, up to $$(n-1)$$ shares without any encryption techniques before sharing. The efficacy of the proposed scheme has been successfully demonstrated through qualitative, quantitative, and experimental security analyses.