Reviewerly/exHarmony huggingface dataset:
Novel Multi-Criteria Group Decision Making Method for Production Scheduling Based on Group AHP and Cloud Model Enhanced TOPSIS

Optimized production scheduling can greatly improve efficiency and reduce waste in the steel manufacturing industry. With the increasing demands on the economy, the environment, and society, more and more factors need to be considered in the production scheduling process. Currently, only a few methods are developed for the comprehensive evaluation and prioritization of scheduling schemes. This paper proposes a novel MCGDM (multi-criteria group decision making) method for the ranking and selection of production scheduling schemes. First, a novel indicator system involving both qualitative and quantitative indicators is put forward. Diverse statistical methods and evaluation functions are proposed for the evaluation of quantitative indicators. The evaluation method of qualitative indicators is proposed based on heterogeneous data, cloud model theory, and group decision-making techniques. Then, a novel Group AHP model is proposed to determine the weights of all evaluation indicators. Finally, a novel cloud-model-enhanced TOPSIS (technique for order of preference by similarity to ideal solution) method is proposed to rank alternative production scheduling schemes. A practical example is presented to show the implementation details and demonstrate the feasibility of our proposed method. The results and comparative analysis indicate that our hybrid MCGDM method is more reasonable, flexible, practical, and effective in evaluating and ranking production scheduling schemes in an uncertain environment.
Specific Emitter Identification Based on Attractor Feature Space of System under Blind Equalization

In the process of the individual identification of radiation sources, the effective extraction of fine features of target radiation sources can be regarded as being crucial for the subsequent individual identification. However, in the complex electromagnetic environment, the effective extraction of the radiation source features is still facing great challenges. To solve this problem, we propose an algorithm for constructing the attractor feature space of the radiation source system based on blind equalization to solve this problem. Firstly, we use blind equalization to process the target signal. Secondly, we use the phase-space reconstruction technique to construct the system attractor feature space of the target signal processed, and explore the adaptive relationship between feature-space-embedding dimensions, the delay time and the neural network, finding the optimal values. Finally, the feature space is used as the input of the neural network for the subsequent individual discrimination decision. Experimentally, it is proved that our proposed algorithm improves the individual recognition rate of the target radiation source under the complex electromagnetic environment to a certain extent, which is of practical application value.
Queuing Model with Customer Class Movement across Server Groups for Analyzing Virtual Machine Migration in Cloud Computing

The advancement of cloud computing technologies has positioned virtual machine (VM) migration as a critical area of research, essential for optimizing resource management, bolstering fault tolerance, and ensuring uninterrupted service delivery. This paper offers an exhaustive analysis of VM migration processes within cloud infrastructures, examining various migration types, server load assessment methods, VM selection strategies, ideal migration timing, and target server determination criteria. We introduce a queuing theory-based model to scrutinize VM migration dynamics between servers in a cloud environment. By reinterpreting resource-centric migration mechanisms into a task-processing paradigm, we accommodate the stochastic nature of resource demands, characterized by random task arrivals and variable processing times. The model is specifically tailored to scenarios with two servers and three VMs. Through numerical examples, we elucidate several performance metrics: task blocking probability, average tasks processed by VMs, and average tasks managed by servers. Additionally, we examine the influence of task arrival rates and average task duration on these performance measures.
Privacy-Preserving Deep Learning Framework Based on Restricted Boltzmann Machines and Instance Reduction Algorithms

The combination of collaborative deep learning and Cyber-Physical Systems (CPSs) has the potential to improve decision-making, adaptability, and efficiency in dynamic and distributed environments. However, it brings privacy, communication, and resource restrictions concerns that must be properly addressed for successful implementation in real-world CPS systems. Various privacy-preserving techniques have been proposed, but they often add complexity and decrease accuracy and utility. In this paper, we propose a privacy-preserving deep learning framework that combines Instance Reduction Techniques (IR) and the Restricted Boltzmann Machine (RBM) to preserve privacy while overcoming the limitations of other frameworks. The RBM encodes training data to retain relevant features, and IR selects the relevant encoded instances to send to the server for training. Privacy is preserved because only a small subset of the training data is sent to the server. Moreover, it is sent after encoding it using RBM. Experiments show that our framework preserves privacy with little loss of accuracy and a substantial reduction in training time. For example, using our framework, a CNN model for the MNIST dataset achieves 96% accuracy compared to 99% in a standard collaborative framework (with no privacy measures taken), with training time reduced from 133.259 s to 99.391 s. Our MLP model for MNIST achieves 97% accuracy compared to 98% in the standard collaborative framework, with training time reduced from 118.146 s to 87.873 s. Compared to other studies, our method is a simple approach that protects privacy, maintains the utility of deep learning models, and reduces training time and communication costs.
Data Augmentation for Regression Machine Learning Problems in High Dimensions

Machine learning approaches are currently used to understand or model complex physical systems. In general, a substantial number of samples must be collected to create a model with reliable results. However, collecting numerous data is often relatively time-consuming or expensive. Moreover, the problems of industrial interest tend to be more and more complex, and depend on a high number of parameters. High-dimensional problems intrinsically involve the need for large amounts of data through the curse of dimensionality. That is why new approaches based on smart sampling techniques have been investigated to minimize the number of samples to be given to train the model, such as active learning methods. Here, we propose a technique based on a combination of the Fisher information matrix and sparse proper generalized decomposition that enables the definition of a new active learning informativeness criterion in high dimensions. We provide examples proving the performances of this technique on a theoretical 5D polynomial function and on an industrial crash simulation application. The results prove that the proposed strategy outperforms the usual ones.
Automated Mapping Methodology and Case Study of Healthcare User Activities and Spaces from a Sustainable Perspective

Operational inefficiencies in healthcare buildings and sustainability issues have become increasingly prominent. The root cause of this phenomenon is the irregular and incomplete structured data related to hospital user activities and spaces that are difficult to resolve using traditional manual methods. In order to solve this problem, an automated mapping approach is presented for hospital user activity and space. Using the Revit API technology and the C# language on the Visual Studio platform, a program for automated mapping is developed. It achieves the automatic mapping of hospital user activities and spaces, along with automatic calculation of utilization rates. An illustrative case study is conducted, focusing on the activity data and building space utilization of healthcare users at Shanghai Renji Hospital. The results indicate that (1) This method can realize the automatic combination of hospital user activities and hospital spaces, and hospital administrators can optimize the utilization rate of hospital spaces quickly and accurately. (2) This approach is highly adaptable, especially to changes in hospital user activities and hospital spatial data. (3) This method promotes the sustainable development of healthcare buildings and has great practical application value.
The findings of the marketing analysis academic library’s services

The author accomplished the marketing analysis of academic library services as the case study of Cherepovets State University Library, a flagship university of Vologda Region. She characterizes the status of library services, strengths and weaknesses, opportunities and threats, challenges and expectations of modern library users. The novelty of the article lies in the use of marketing analysis tools and the results obtained. The practical value of library services for users is revealed. With four criteria, namely demographic, geographic, psychographic and behavioral, the target user of university library services is characterized in detail. The author applies the research methods of analysis, comparison, observation, Google survey, in-depth interview, and empathy mapping. Four levels of university library services aree analyzed based on the “service pyramid”. The quantitative findings of marketing research are provided. The author demonstrates that despite most students prefer electronic communications, they do not ignore the hybrid, online and offline, mode of interaction with the library. She also shows that modern academic library users are emotionally neutral towards the library services. Recommendations are offered for the further development of academic library services.
Grants for the libraries: Where to look

The authors discuss the challenges and ways of grant support of librarianship, and review the most popular Russian sponsor foundations. The grants make the commonly available way to get investment support that enables noncommercial organizations to accomplish their socially and profes- sionally valuable projects. To win a grant, the applicant has to identify the “right” sponsor among different categories, their specialization and recommendations on application and documents to be submitted for the contest. The authors characterize Russian charity foundations and classify them by the founder type, e. g. governmental, private, corporate, and agencies. The foundations provide grants along the lines corresponding to projects and programs of non-profit organizations within the information and library sphere. The authors describe the key thematic vectors for grants awarding; indicate the websites, and provide brief recommendations on how to prepare grant applications.
Lossless data hiding technique reducing cover data size for compressed videos

In recent years, multimedia techniques have rapidly advanced. Video discs with massive storage capacities exemplify widely used multimedia storage mediums. However, online multimedia services face strict storage limits, and compression rates impact streaming quality. This poses a dilemma—data hiding risks exceeding storage limits and affecting the quality of video streams. To address this, we propose a lossless data hiding technique focusing on cover data size. The technique embeds new data into compressed cover video data, decreasing data size post-embedding. Extraction requires no key. In addition, the processing time is short enough that the technique can be used in real-time applications. We verified the technique through experiments with MPEG-2 videos. Video data size decreased after hiding data, and quality degradation remained acceptable. Results confirm the technique's efficacy.
KDP-Net: An Efficient Semantic Segmentation Network for Emergency Landing of Unmanned Aerial Vehicles

As the application of UAVs becomes more and more widespread, accidents such as accidental injuries to personnel, property damage, and loss and destruction of UAVs due to accidental UAV crashes also occur in daily use scenarios. To reduce the occurrence of such accidents, UAVs need to have the ability to autonomously choose a safe area to land in an accidental situation, and the key lies in realizing on-board real-time semantic segmentation processing. In this paper, we propose an efficient semantic segmentation method called KDP-Net for characteristics such as large feature scale changes and high real-time processing requirements during the emergency landing process. The proposed KDP module can effectively improve the accuracy and performance of the semantic segmentation backbone network; the proposed Bilateral Segmentation Network improves the extraction accuracy and processing speed of important feature categories in the training phase; and the proposed edge extraction module improves the classification accuracy of fine features. The experimental results on the UDD6 and SDD show that the processing speed of this method reaches 85.25 fps and 108.11 fps while the mIoU reaches 76.9% and 67.14%, respectively. The processing speed reaches 53.72 fps and 38.79 fps when measured on Jetson Orin, which can meet the requirements of airborne real-time segmentation for emergency landing.
Twitter’s pulse on hydrogen energy in 280 characters: a data perspective

Abstract Uncovering the public discourse on hydrogen energy is essential for understanding public behaviour and the evolving nature of conversations over time and across different regions. This paper presents a comprehensive analysis of a large multilingual dataset pertaining to hydrogen energy collected from Twitter spanning a decade (2013–2022) using selected keywords. The analysis aims to explore various aspects, including the temporal and spatial dimensions of the discourse, factors influencing Twitter engagement, user engagement patterns, and the interpretation of conversations through hashtags and ngrams. By delving into these aspects, this study offers valuable insights into the dynamics of public discourse surrounding hydrogen energy and the perceptions of social media users.
More-than-human and more-than-digital collecting among young children in Norway

This study aims to advance our understanding of young children’s contemporary collecting. Collecting is a prevalent practice among young children. However, extant research typically highlights children of school age and emphasizes functions and motives of collecting, but rarely considers the relevant affective dimensions. Furthermore, young children today collect on digital interfaces through gaming, and their play tends to unsettle digital–analog binaries. Still, no explicit attention has been paid to young children’s collecting in these broader contemporary playscapes. In this study, I plug into ethnographic accounts of young children’s collecting in Norway, sociomaterial affect theorizing of early childhood play and literacy, and the key concept of ‘answering the world.’ The children in this case are found to enact relational sensibilities to their surroundings and collect in ways that leave the unfolding of the activity up to chance. They put less stress on the acquisition of a set collection than on moving with and feeling the collecting. The world answered refers to broad playscapes composed of spaces of tangible, fleshy, organic forest floors, and shiny, blocky, vividly colored Super Mario Worlds. The relational sensibilities enacted are found to be entangled with the tension and discord of young children’s material–discursive conditions.
Simulating the operation of a quantum computer in a dissipative environment

The operations of current quantum computers are still significantly affected by decoherence caused by interaction with the environment. In this work, we employ the non-perturbative hierarchical equations of motion (HEOM) method to simulate the operation of model quantum computers and reveal the effects of dissipation on the entangled quantum states and on the performance of well-known quantum algorithms. Multi-qubit entangled states in Shor’s factorizing algorithm are first generated and propagated using the HEOM. It is found that the failure of factorization is accompanied by a loss of fidelity and mutual information. An important challenge in using the HEOM to simulate quantum computers in a dissipative environment is how to efficiently treat systems with many qubits. We propose a two-dimensional tensor network scheme for this problem and demonstrate its capability by simulating a one-dimensional random circuit model with 21 qubits.
Learner experience and motivational beliefs for a VR Lab in advanced undergraduate biology

Recently, interest in understanding the prospects of virtual environments, specifically virtual reality laboratories (VR Lab) – those that involve a first-person practica experience via a desktop computer or head-mounted display – in science education has increased. Seemingly ingrained in the process of implementing a VR Lab is a supposition of interest, intrinsic motivation, and confidence on the part of participants. These attributes influence the participant's ability to start and complete the task and support sensemaking and meaningful learning. Thus, this study used expectancy-value theory with the intent of learning experience design to assess the relationship between task value beliefs and those for usability of a VR Lab. Beliefs were also compared to those for physical laboratories, where only a higher cost for the VR Lab was found. Increased cost beliefs suggest that participants perceived the VR Lab as requiring more from them, a negative consequence, which would decrease their potential for seeing it as a viable learning alternative. This finding suggests that a student's level of content knowledge may influence thier VR Lab experience. Cost belief was significantly related to utility value, giving credence to the theoretical model where cost is a value component. The VR Lab was determined to be marginally usable, but usability was not related to any motivational beliefs.
TSE-GAN: strain elastography using generative adversarial network for thyroid disease diagnosis

Over the past 35 years, studies conducted worldwide have revealed a threefold increase in the incidence of thyroid cancer. Strain elastography is a new imaging technique to identify benign and malignant thyroid nodules due to its sensitivity to tissue stiffness. However, there are certain limitations of this technique, particularly in terms of standardization of the compression process, evaluation of results and several assumptions used in commercial strain elastography modes for the purpose of simplifying imaging analysis. In this work, we propose a novel conditional generative adversarial network (TSE-GAN) for automatically generating thyroid strain elastograms, which adopts a global-to-local architecture to improve the ability of extracting multi-scale features and develops an adaptive deformable U-net structure in the sub-generator to apply effective deformation. Furthermore, we introduce a Lab-based loss function to induce the networks to generate realistic thyroid elastograms that conform to the probability distribution of the target domain. Qualitative and quantitative assessments are conducted on a clinical dataset provided by Shanghai Sixth People’s Hospital. Experimental results demonstrate that thyroid elastograms generated by the proposed TSE-GAN outperform state-of-the-art image translation methods in meeting the needs of clinical diagnostic applications and providing practical value.
Modified Playfair for Text File Encryption and Meticulous Decryption with Arbitrary Fillers by Septenary Quadrate Pattern

Abstract Cryptography secures data and serves to ensure the confidentiality of records. Playfair is a cryptographic symmetrical algorithm that encrypts statistics based on key costs. This secret is shared with an authorized person to retrieve data. In the conventional pattern, there is an area complexity and deficiency in letters, numbers, and special characters. This hassle has been overcome in previous studies by editing pattern dimensions. The fillers used throughout the enciphering were not eliminated during the retrieval process, which resulted in the indiscrimination of the retrieved statistics. The proposed method uses a separate quadrate pattern that strengthens the Playfair cipher and guarantees that the fillers are eliminated to ensure the authentic retrieval of records. The fillers indiscriminate and strengthen the set of rules in opposition to brute force and avalanche impact. The proposed algorithm was evaluated with a minimal change in the key, and was found to have an avalanche effect between 65% and 93.7%. The encrypted document is further encoded using the Lempel–Ziv–Markov chain algorithm (LZMA) to provide compressed second-level secured text with a compression ratio of 0.75 for a file size of 100 KB. The pattern was designed to subsidize the integrated characters found on the keyboard.
Being on the inside: a research methodology for data collection within the inner circle of the domain of video game translation/localization in Thailand

Purpose The Thai video game domain has witnessed substantial growth in recent years. However, many games enjoyed by Thai players are in foreign languages, with only a handful of titles translated/localized into the Thai locale. Some Thai video game enthusiasts have taken on the role of unofficial translators/localizers, contributing to a localization domain that accommodates both official and unofficial translation/localization efforts. This general review paper aims to outline the author's experiences in collecting data within the domain of video game translation/localization in Thailand. Design/methodology/approach Using a descriptive approach, this general review paper employs the netnography method. It sheds light on the complexities of video game translation/localization in Thailand and incorporates semi-structured interviews with a snowball sampling technique for the selection of participants and in-game data collection methods. Findings The netnography method has proved instrumental in navigating the intricacies of this evolving landscape. Adopting the netnography method for data collection in this research contributes to establishing more robust connections with the research sites. “Inside” professionals and individuals play a significant role in data gathering by recommending additional sources of information for the research. Originality/value While netnography is conventionally applied in the market and consumer research, this paper demonstrates its efficacy in unraveling the dynamics of video game translation/localization in Thailand.