Reviewerly/exHarmony huggingface dataset:
Intelligent Librarians in the Metaverse

The field of library intelligence has seen significant attention in academic circles, with various perspectives offered on its future trajectory. However, there remains a dearth of research on the crucial role of intelligent librarians as a defining component of library intelligence, which is emerging as a novel profession. The advent of metaverse technology has not only sparked new opportunities for library intelligence, but also amplified the demands for the professional capabilities of intelligent librarians. This chapter endeavors to provide a comprehensive examination of the evolution, applications, and benefits of metaverse technology within the library context and delves into the concept of intelligent librarians in relation to metaverse, as well as identifying the necessary skills and training pathways for this emerging profession. Furthermore, this chapter highlights the importance of collaboration between library professionals, technology experts, and other stakeholders in the development and implementation of library intelligence and metaverse technology.
Topic Discovery and Hotspot Analysis of Sentiment Analysis of Chinese Text Using Information-Theoretic Method

Currently, sentiment analysis is a research hotspot in many fields such as computer science and statistical science. Topic discovery of the literature in the field of text sentiment analysis aims to provide scholars with a quick and effective understanding of its research trends. In this paper, we propose a new model for the topic discovery analysis of literature. Firstly, the FastText model is applied to calculate the word vector of literature keywords, based on which cosine similarity is applied to calculate keyword similarity, to carry out the merging of synonymous keywords. Secondly, the hierarchical clustering method based on the Jaccard coefficient is used to cluster the domain literature and count the literature volume of each topic. Thirdly, the information gain method is applied to extract the high information gain characteristic words of various topics, based on which the connotation of each topic is condensed. Finally, by conducting a time series analysis of the literature, a four-quadrant matrix of topic distribution is constructed to compare the research trends of each topic within different stages. The 1186 articles in the field of text sentiment analysis from 2012 to 2022 can be divided into 12 categories. By comparing and analyzing the topic distribution matrices of the two phases of 2012 to 2016 and 2017 to 2022, it is found that the various categories of topics have obvious research development changes in different phases. The results show that: ① Among the 12 categories, online opinion analysis of social media comments represented by microblogs is one of the current hot topics. ② The integration and application of methods such as sentiment lexicon, traditional machine learning and deep learning should be enhanced. ③ Semantic disambiguation of aspect-level sentiment analysis is one of the current difficult problems this field faces. ④ Research on multimodal sentiment analysis and cross-modal sentiment analysis should be promoted.
Three-Dimension Attention Mechanism and Self-Supervised Pretext Task for Augmenting Few-Shot Learning

The main challenge of few-shot learning lies in the limited labeled sample of data. In addition, since image-level labels are usually not accurate in describing the features of images, it leads to difficulty for the model to have good generalization ability and robustness. This problem has not been well solved yet, and existing metric-based methods still have room for improvement. To address this issue, we propose a few-shot learning method based on a three-dimension attention mechanism and self-supervised learning. The attention module is used to extract more representative features by focusing on more semantically informative features through spatial and channel attention. Self-supervised learning mainly adopts a proxy task of rotation transformation, which increases semantic information without requiring additional manual labeling, and uses this information for training in combination with supervised learning loss function to improve model robustness. We have conducted extensive experiments on four popular few-shot datasets and achieved state-of-the-art performance in both 5-shot and 1-shot scenarios. Experiment results show that our work provides a novel and remarkable approach to few-shot learning.
Integrated ICT Systems Over Sustainable Cloud Environment

In traditional education management systems, records of current or former users are maintained by public or private organizations. And these records are in the institution or ministry that students attend or attended. And these records are maintained conventionally. If a user wants to transfer from one organization to another, the candidate’s records may transfer along with the organization, but some education records may remain at the school or education department. Or when they apply for further delegation, the process of applying and verification of the certificate takes too much time. In the traditional education management system, every educational department maintains data with their own database for keeping the records. Now it is very difficult to know the complete previous or present record of a student in the different organizations. And if we want to, then we retrieve their entire data from their concern educational departments, which takes a lot of time. For better management, there are such software which are available in the market, but schools or institutions find it difficult to retrieve the exact information from the system and managing the system with tons of student records, technical management of the software and the more users makes the system more complicated. The purpose of this paper is to describe the design of a cloud base digitized academic system for improving current traditional education system.
AN EXPERIENCE OF IMPLEMENTING SOFTWARE DEVELOPMENT MANAGEMENT SYSTEM BY COMBINING CMMI WITH AGILE IN SMALL AND MEDIUM-SIZED ORGANIZATION

Nowadays, the research on improvements of software development processes has been developing extensively in software organizations. Currently these researches are focusing on how to combine CMMI with Agile. In addition, most of the software development organizations all over the world are small or medium, not large. Such small and medium-sized organizations play a great role in the software industry. However, there is lack of case studies that small and medium-sized organizations implement CMMI process areas to establish their development management systems, especially practical and helpful case studies that such organizations may apply to combine with Agile for improving their management systems. Our goal is to share our experience in successful improvements of development process under the industrial environment from an empirical point of view. And these results cannot be generalized, but they point out that under some circumstances in small and medium-sized organizations, a combination strategy of CMMI and Agile can be a possible option for software professionals. In this paper we present our experience of implementing a DMS (Development Management System), in which CMMI is combined with Agile, for a small and medium-sized software development aiming at CMMI level 3. Moreover, we give the result that our organization applied this DMS.
The Effect of Learning Leadership Managerial at the State of Cikande Elementary School

This study purpose is determined how to describe or provide an overview and relationship between learning leadership managerial at the state of Cikande Elementary School. The research method use a descriptive correlational research. It reveals whether there is a relationship between various variables based on the size of the correlation coefficient. The data obtained in the form of descriptive and numerical processing using statistical methods used and then interpreted. In this study, the population or research targets were all teachers at State Elementary Schools in Cikande District, Serang Regency. Based on data from the Department of Education and Culture of Serang Regency, there are 32 public elementary schools in the education sector, consisting of 306 teachers. The sample size calculation uses a table developed. From this calculation, 171 samples were obtained. The total population of 464 and the sampling technique used is proportional random sampling. This study uses SEM analysis tools using the Lisrel program. The methodologically of the research design is causal and simultaneous, has a relatively complicated relationship, so testing using this method is to be able to test what the objectives of this study are.The result show the managerial ability of the principal affects the quality of the school, learning leadership affects school quality and the feasibility of the facilities affects the quality of the school.
Pleobot: a modular robotic solution for metachronal swimming

Metachronal propulsion is widespread in aquatic swarming organisms to achieve performance and maneuverability at intermediate Reynolds numbers. Studying only live organisms limits our understanding of the mechanisms driving these abilities. Thus, we present the design, manufacture, and validation of the Pleobot-a unique krill-inspired robotic swimming appendage constituting the first platform to study metachronal propulsion comprehensively. We combine a multi-link 3D printed mechanism with active and passive actuation of the joints to generate natural kinematics. Using force and fluid flow measurements in parallel with biological data, we show the link between the flow around the appendage and thrust. Further, we provide the first account of a leading-edge suction effect contributing to lift during the power stroke. The repeatability and modularity of the Pleobot enable the independent manipulation of particular motions and traits to test hypotheses central to understanding the relationship between form and function. Lastly, we outline future directions for the Pleobot, including adapting morphological features. We foresee a broad appeal to a wide array of scientific disciplines, from fundamental studies in ecology, biology, and engineering, to developing new bio-inspired platforms for studying oceans across the solar system.
NUMSnet: Nested-U Multi-Class Segmentation Network for 3D Medical Image Stacks

The semantic segmentation of 3D medical image stacks enables accurate volumetric reconstructions, computer-aided diagnostics and follow-up treatment planning. In this work, we present a novel variant of the Unet model, called the NUMSnet, that transmits pixel neighborhood features across scans through nested layers to achieve accurate multi-class semantic segmentation with minimal training data. We analyzed the semantic segmentation performance of the NUMSnet model in comparison with several Unet model variants in the segmentation of 3–7 regions of interest using only 5–10% of images for training per Lung-CT and Heart-CT volumetric image stack. The proposed NUMSnet model achieves up to 20% improvement in segmentation recall, with 2–9% improvement in Dice scores for Lung-CT stacks and 2.5–16% improvement in Dice scores for Heart-CT stacks when compared to the Unet++ model. The NUMSnet model needs to be trained with ordered images around the central scan of each volumetric stack. The propagation of image feature information from the six nested layers of the Unet++ model are found to have better computation and segmentation performance than the propagation of fewer hidden layers or all ten up-sampling layers in a Unet++ model. The NUMSnet model achieves comparable segmentation performance to previous works while being trained on as few as 5–10% of the images from 3D stacks. In addition, transfer learning allows faster convergence of the NUMSnet model for multi-class semantic segmentation from pathology in Lung-CT images to cardiac segmentation in Heart-CT stacks. Thus, the proposed model can standardize multi-class semantic segmentation for a variety of volumetric image stacks with a minimal training dataset. This can significantly reduce the cost, time and inter-observer variability associated with computer-aided detection and treatment.
Application of mathematical expression-based recommendation models in user behaviour modelling

With the development of technology, internet technology is becoming more and more mature, so that users can choose different categories of information according to their needs and suitable for their own characteristics and preferences when surfing the internet, instead of having to passively obtain it. Therefore, when browsing information, users will choose websites that interest them or suit their characteristics and interests according to their own needs, and search engines will calculate the corresponding ranking according to the user's browsing information, and finally recommend the website to the target user. The application of recommendation technology in the field of information retrieval not only helps users to obtain useful content from the huge amount of data, but also improves the effect of search engine optimization. This paper introduces a recommendation model based on mathematical expressions and focuses on its role in modelling user behaviour, while the algorithmic process is also described in this paper.
Developing a Comprehensive Reading Test for Undergraduate Students: An LMS-Based Reading Test

There has been a significant increase in the development of reading assessment methods by educators and researchers. However, very few of these assessments have been integrated into a Learning Management System (LMS) platform, with current LMS usage primarily focused on course content delivery. This research aims to address this gap by developing a comprehensive LMS-based reading test to measure students' reading competence. Additionally, the study seeks to explore students' perspectives on the use of the developed instrument. The development of the instrument followed a Research &amp; Development (R&amp;D) approach using the 4D model, which consists of defining, designing, developing, and disseminating stages. The participants of this study were undergraduate English as a Foreign Language (EFL) students from Universitas Madako Tolitoli. In total, 50 reading test items were successfully developed. Both validity and reliability tests were conducted: the validity test was performed by experts in the field and followed by a pilot test with a small sample of students. The results indicated that the test items were reliable, with a Cronbach's Alpha score of 0.75. However, 6 of the items (12%) were deemed to need improvement or were considered invalid, while the remaining 44 items (88%) were judged to be valid. Students' perceptions of the LMS-based reading test were generally positive across three aspects: usability and accessibility, engagement and motivation, and the impact of the LMS on language learning outcomes. In light of the limitations of the current study, suggestions for improvement were also provided. These will serve to guide future research in this area, with the aim of refining the LMS-based reading test and further enhancing its potential benefits for student learning.
Learning to Summarize Chinese Radiology Findings With a Pre-Trained Encoder

Automatic radiology report summarization has been an attractive research problem towards computer-aided diagnosis to alleviate physicians' workload in recent years. However, existing methods for English radiology report summarization using deep learning techniques cannot be directly applied to Chinese radiology reports due to limitations of the related corpus. In response to this, we propose an abstractive summarization approach for Chinese chest radiology report. Our approach involves the construction of a pre-training corpus using a Chinese medical-related pre-training dataset, and the collection of Chinese chest radiology reports from Department of Radiology at the Second Xiangya Hospital as the fine-tuning corpus. To improve the initialization of the encoder, we introduce a new task-oriented pre-training objective called Pseudo Summary Objective on the pre-training corpus. We then develop a Chinese pre-trained language model called Chinese medical BERT (CMBERT), which is used to initialize the encoder and fine-tuned on the abstractive summarization task. In testing our approach on a real large-scale hospital dataset, we observe that the performance of our proposed approach achieves outstanding improvement compared with other abstractive summarization models. This highlights the effectiveness of our approach in addressing the limitations of previous methods for Chinese radiology report summarization. Overall, our proposed approach demonstrates a promising direction for the automatic summarization of Chinese chest radiology reports, offering a viable solution to alleviate physicians' workload in the field of computer-aided diagnosis.
Detection and Quantization of Data Drift in Image Classification Neural Networks

An unforeseen change in the input data is called drift and may impact the accuracy of machine-learning models. A novel scheme for diagnosing data drift in the input stream of image classification neural networks is presented. The proposed drift detection and quantization method uses a threshold dictionary for the prediction probabilities of each class in the neural network model. The method is applicable to any drift type in images such as noise, and weather effects, among others. Experimental results on various datasets, drift types, and neural network models show that the proposed method estimates the drift magnitude with high accuracy, especially when the level of drift impacts the model's performance significantly.
Noise in the Clouds: Influence of Network Performance Variability on Application Scalability

Cloud computing represents an appealing opportunity for cost-effective deployment of HPC workloads on the best-fitting hardware. However, although cloud and on-premise HPC systems offer similar computational resources, their network architecture and performance may differ significantly. For example, these systems use fundamentally different network transport and routing protocols, which may introduce network noise that can eventually limit the application scaling. This work analyzes network performance, scalability, and cost of running HPC workloads on cloud systems. First, we consider latency, bandwidth, and collective communication patterns in detailed small-scale measurements, and then we simulate network performance at a larger scale. We validate our approach on four popular cloud providers and three on-premise HPC systems, showing that network (and also OS) noise can significantly impact performance and cost both at small and large scale. The full paper of this abstract can be found at https://doi.org/10.1145/3570609.
Content-Aware Latent Semantic Direction Fusion for Multi-Attribute Editing

For facial attribute editing, significant progress has been made in discovering semantic directions in the latent space of StyleGAN, and the manipulation is performed by mapping an input image to a latent code and then moving along a direction associated with a target attribute. In this case, multi-attribute editing typically needs a sequential transformation process, which may cause ineffective manipulation or the cumulative effect on irrelevant attribute deviation. In this work, we aim to simultaneously manipulate multiple attributes through a single transformation. Toward this end, we propose a StyleGAN-based latent semantic direction fusion model, referred to as StyleLSF. There are two learnable components: a content-aware direction predictor learns to infer the latent directions, which are associated with preset attributes. A fusion network fuses the directions with respect to target attributes and yields a single translation vector. We further ensure irrelevant attribute preservation by imposing an attribute-aware feature consistency regularization approach.
Overcoming the Long Horizon Barrier for Sample-Efficient Reinforcement Learning with Latent Low-Rank Structure

Reinforcement learning (RL) methods have been increasingly popular in sequential decision making tasks due to its empirical success. However, large state and action spaces in real-world problems modeled as a Markov decision processes (MDPs) limit the use of RL algorithms. Given a standard finite-horizon MDP (S, A, P, R, H) with state space S, action space A, transition kernel P = {Ph} ∈ []H, reward function R = {R h} ∈ [H] bounded between zero and one, and time horizon H, one needs Ω (|S||A|H3/∈2 samples given a generative model to learn an optimal policy [3], which can be impractical when S and A are large. The above tabular RL framework does not capture the fact that many real-world systems in fact have additional structure that if exploited should improve computational and statistical efficiency. Moreover, [1] empirically verifies that optimal and near-optimal action-value functions (both viewed as |S|-by-|A| matrices) of classical stochastic control tasks have low rank. Thus, the critical question is what are the minimal low rank structural assumptions that allow for computationally and statistically efficient learning?
Real-time Spread Burst Detection in Data Streaming

Data streaming has many applications in network monitoring, web services, e-commerce, stock trading, social networks, and distributed sensing. This paper introduces a new problem of real-time burst detection in flow spread. It is a challenging problem because estimating flow spread requires us to remember all past data items and detecting bursts in real time requires us to minimize spread estimation overhead, which was not the priority in most prior work. This paper provides the first efficient, real-time solution for spread burst detection. It is designed based on a new real-time super spreader identifier, which outperforms the state of the art in terms of both accuracy and processing overhead. The super spreader identifier is in turn based on a new sketch design for real-time spread estimation, which outperforms the best existing sketches.
Dynamic deformable transformer for end‐to‐end face alignment

Abstract Heatmap‐based regression (HBR) methods have dominated for a long time in the face alignment field while these methods need complex design and post‐processing. In this study, the authors propose an end‐to‐end and simple enough coordinate‐based regression (CBR) method called Dynamic Deformable Transformer (DDT) for face alignment. Unlike general pre‐defined landmark queries, DDT uses Dynamic Landmark Queries (DLQs) to query landmarks' classes and coordinates together. Besides, DDT adopts a deformable attention mechanism rather than a regular attention mechanism which has a faster convergence speed and lower computational complexity. Experiment results on three mainstream datasets 300W, WFLW, and COFW demonstrate DDT exceeds the state‐of‐the‐art CBR methods by a large margin and is comparable to the current state‐of‐the‐art HBR methods with much less computational complexity.
RoboArt: DALL-E and KUKA Robot Shake Hands

The use of artificial intelligence (AI) to create graphics has shown high popularity in the digital industry. Creating visuals, however, is not as stunning as creating AI-based human-like drawings on canvas, which is much more appealing in the world of aesthetics. In this article, we set a goal of teaching the robot to draw like a human artist. In this effort, the boundaries of modern art are pushed by integrating a ready-made DALL-E neural network with a KUKA robot through a CAD software module. To create 2D drawings on the canvas, a novel sort of end-effector is devised and manufactured on a 3D printer. The technique proved to be effective, as evidenced by the demonstration of a robot that draws unique images on canvas using human-like gestures.