Reviewerly/exHarmony huggingface dataset:
Analysis of Copyright Protection Issues for Characters in Works

The use of characters in various types of prior works in modern society has raised questions about whether characters can be regarded as subjects of copyright. Based on copyright legislation and jurisprudence, regardless of the type or scale, the determination of whether any intellectual creation is eligible for copyright protection rests solely on the criteria of originality and replicability, unrelated to factors such as the commercial value of the characters. Various standards applicable to the issue of copyrightability of characters, as practiced in both Chinese and American judicial contexts, emphasize the consideration of specific descriptive elements inherent in characters. Guided by the "idea-expression dichotomy" principle, deconstructing characters reveals that only when characters are concretely and adequately described, and when they have transcended the realm of conceptual ideas to constitute an expression of originality, can they independently become subjects of copyright separate from the work they originate in. The protection and use of characters in works should respect the objective laws of cultural creation, inheritance, and development, in order to ultimately achieve the fundamental principles and goals of copyright legislation.
Breaking down the Hierarchy: A New Approach to Leukemia Classification

The complexities inherent to leukemia, multifaceted cancer affecting white blood cells, pose considerable diagnostic and treatment challenges, primarily due to reliance on laborious morphological analyses and expert judgment that are susceptible to errors. Addressing these challenges, this study presents a refined, comprehensive strategy leveraging advanced deep-learning techniques for the classification of leukemia subtypes. We commence by developing a hierarchical label taxonomy, paving the way for differentiating between various subtypes of leukemia. The research further introduces a novel hierarchical approach inspired by clinical procedures capable of accurately classifying diverse types of leukemia alongside reactive and healthy cells. An integral part of this study involves a meticulous examination of the performance of Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs) as classifiers. The proposed method exhibits an impressive success rate, achieving approximately 90% accuracy across all leukemia subtypes, as substantiated by our experimental results. A visual representation of the experimental findings is provided to enhance the model’s explainability and aid in understanding the classification process.
More Than Meets the Eye: Physicians’ Visual Attention in the Operating Room

During surgery, the patient’s vital signs and the field of endoscopic view are displayed on multiple screens. As a result, both surgeons’ and anesthesiologists’ visual attention (VA) is crucial. Moreover, the distribution of said VA and the acquisition of specific cues might directly impact patient outcomes. Recent research utilizes portable, head-mounted eye-tracking devices to gather precise and comprehensive information. Nevertheless, these technologies are not feasible for enduring data acquisition in an operating room (OR) environment. This is particularly the case during medical emergencies. This study presents an alternative methodology: a webcam-based gaze target prediction model. Such an approach may provide continuous visual behavioral data with minimal interference to the physicians’ workflow in the OR. The proposed end-to-end framework is suitable for both standard and emergency surgeries. In the future, such a platform may serve as a crucial component of context-aware assistive technologies in the OR.
Investigating User Preferences for Conversation Design of Voice Assistant Systems using Linguistic Features

As the use of voice assistant (VA) systems is increasing, conversation design in the system is important for effective human-system interaction. The objective of the study was to investigate the level of user preference for VA outputs in terms of linguistics. Answers of three VA systems for each of the nine questions were collected and categorized for distinctive linguistic factors such as type of theme, thematic progression, number of predications, and ellipsis. The VA answers were evaluated through an online survey. Results show that linguistic factors and features significantly affect user preference for VA outputs. The results imply that the linguistic features need to be considered for designing voice interaction communications as a natural interaction method.
Design and Development of an Augmented Reality Interface for Space Exploration

The Holographic Augmented Reality Visualization Interface for Exploration (HARVIE) was developed for the 2022 NASA SUITS (Spacesuit User Interface for Students) challenge. HARVIE assists astronauts with elevated demands of the lunar surface through navigation, terrain sensing, and an optimal display of suit status elements (e.g., oxygen, battery, and heart rate). Considering environmental constraints, the system architecture promotes efficient cross modal communication between the mission control center, other astronauts, and the user interface. Currently, the system utilizes a hands-free modality such as speech recognition. Throughout the design process, we conducted heuristic evaluations on a low-fidelity prototype. Then, we implemented HARVIE into a high-fidelity prototype on the HoloLens 2 and utilized the Rapid Iterative Testing &amp; Evaluation (RITE) method for human-in-the-loop testing. Lastly, we evaluated our final design at NASA Johnson Space Center. Our interface serves as a novel approach to enhance how astronauts navigate on missions using augmented reality.
Mobile robot based on artificial potential field method Path planning method research

In robot local path planning, the traditional artificial potential field method is a simple and feasible algorithm. However, it suffers from issues of target unreachability and local minima. To address the problem of target unreachability, we introduce the distance term between the mobile robot and the target point in the repulsive function model, gradually reducing the influence of the repulsive potential field on the robot's movement towards the target point. To mitigate the issue of local minima, we employ the methods of repulsive angle deflection and virtual target points. Subsequently, we conduct experimental simulations using MATLAB, which yield results consistent with the analysis. The introduction of the distance between the robot and the target in the repulsive force function effectively solves the problem of target unreachability. Additionally, the issue of local minima can be overcome through the application of repulsive force angle deflection and virtual target points.
Evaluating Alternative Metaheuristic Algorithms for Procedural Content Generation in Game Design

Procedural Content Generation (PCG) has emerged as a powerful approach for automating game content creation, offering significant benefits in terms of cost reduction and time efficiency compared to traditional game design and development processes (Zhang, Zhang, &amp; Huang, 2022). While Genetic Algorithms (GAs) have been widely used in PCG, alternative metaheuristic algorithms such as Particle Swarm Optimization (PSO) and Artificial Bee Colony (ABC) have demonstrated their effectiveness in delivering high-quality solutions and efficient optimization capabilities across different problem domains (Amato, 2017). However, their application in PCG remains limited. I aim to evaluate the performance of PSO and ABC in map layout generation, challenging the conventional use of GAs. By comparing three metaheuristic algorithms (GA, ABC, and PSO) I seek to assess the effectiveness of these approaches in generating game levels and identify any obvious differences in their performance characteristics. Comprehensive experiments are conducted, applying GA, ABC, and PSO to a map layout generation. Metrics like convergence speed and content quality are used to evaluate the generated game content. My findings reveal that both ABC and PSO demonstrate advantages over traditional GA implementations when generating game levels, indicating their potential for enhancing PCG. In this presentation, I will share the results of comparing three metaheuristic algorithms (GA, PSO, and ABC) in map layout generation for game levels, emphasizing the potential benefits of leveraging diverse algorithmic approaches to create more captivating and immersive game worlds. Also, I will conclude with a call for further research in this area to expose new possibilities in content generation. By considering varied metaheuristic approaches, game developers can improve content generation techniques and create more captivating and interactive player experiences.
Joint sparse latent representation learning and dual manifold regularization for unsupervised feature selection

As an effective dimensionality reduction method, unsupervised feature selection (UFS) focuses on the mutual correlations between high-dimensional data features but often overlooks the intrinsic relationships between instances. We also utilize pseudo-labels learned from the data to guide feature selection in UFS. However, the raw data space may contain noise and outliers, leading to a lower accuracy of the learned pseudo-label matrix. We propose a minimum-redundant UFS approach to tackle these problems through jointing sparse latent representation learning with dual manifold regularization (SLRDR). Firstly, SLRDR learns a subspace of latent representation by exploring the interconnection of original data. To enhance subspace sparsity, ℓ2,1-norm is applied to the residual matrix of latent representation learning. Pseudo-label matrix learning is then carried out in the high-quality latent space, resulting in effective pseudo-label information that can provide more useful guidance for sparse regression. Secondly, based on the manifold learning hypothesis, SLRDR exploits features’ local structural properties in feature space and explores the association between data and labels, allowing the model to learn richer and more accurate structural information. In addition, ℓ2,1/2-norm is imposed on the weight matrix to obtain a minimum-redundant solution and select more discriminative features. Finally, an alternating iterative method is used for SLRDR to solve the optimization problem of the objective function, and the convergence of the model is theoretically analyzed. Besides, a series of comparative experiments with ten existing algorithms on nine benchmark datasets are used to verify the model’s effectiveness.
Binary golden eagle optimizer combined with initialization of feature number subspace for feature selection

Feature Selection (FS) is a significant data preprocessing technique, whose purpose is to identify feature subset that can improve the prediction accuracy from the subsequent training model, while ensuring that the number of features is minimized. The Binary Golden Eagle Optimizer (BGEO) algorithm can efficiently search for the feature subset that satisfies the desired requirements on small and medium sized data. However, searching for subsets becomes much more difficult as the data dimension increases. To handle this problem, we present the Binary Golden Eagle Optimizer algorithm combined with Initialization of Feature Number Subspace (BGEO-IFNS) as the first application of BGEO for high-dimensional FS. Initialization of Feature Number Subspace (IFNS) consists of the following steps: First, the feature space is divided into several subspaces with different feature numbers. Next, new individuals are generated to correspond to the different subspaces. Finally, the quality of these new individuals is assessed using the original population. The initial population generated by this method improves the diversity while maintaining high quality, thus improving solving ability of the subsequent algorithm on high-dimensional data. To validate our approach, we conduct experiments on 14 small and medium dimensional datasets as well as 10 high-dimensional datasets. The experimental results show that BGEO-IFNS achieves a large improvement in effectiveness over BGEO and other state-of-the-art algorithms on the majority of datasets. In addition, BGEO-IFNS achieves optimal performance when compared with other metaheuristic algorithms utilizing IFNS. The results further validate the generalizability and adaptability of the new initialization method with BGEO.
Learning scalable dynamic filter in convolutional networks

Most existing neural networks use static filters with fixed model capacity to process all samples. To cover the large variation among different samples, these networks contain considerable redundant computational budgets, thereby limiting their inference efficiency. To reduce these redundant computation for higher inference speed, in this paper, we propose a scalable dynamic filter that can customize its parameters and shapes conditioned on the input sample. Specifically, our scalable dynamic filter uses a kernel prediction head to predict dynamic kernels and employs a channel mask head to adaptively prune redundant channels. As a result, a kernel with dynamic parameters and an adaptive shape is produced for efficient inference. Extensive experiments on both image classification and object detection tasks demonstrate that our method achieves superior performance than previous methods with low computational complexity and high inference efficiency.
LGI Net: Enhancing local-global information interaction for medical image segmentation

Medical image segmentation is a critical task used to accurately extract regions of interest and pathological areas from medical images. In recent years, significant progress has been made in the field of medical image segmentation using deep learning and neural networks. However, existing methods still have limitations in terms of fusing local features and global contextual information due to the complex variations and irregular shapes of medical images. To address this issue, this paper proposes a medical image segmentation architecture called LGI Net, which improves the internal computation to achieve sufficient interaction between local perceptual capabilities and global contextual information within the network. Furthermore, the network incorporates an ECA module to effectively capture the interplay between channels and improve inter-layer information exchange capabilities. We conducted extensive experiments on three public medical image datasets: Kvasir, ISIC, and X-ray to validate the effectiveness of the proposed method. Ablation studies demonstrated the effectiveness of our LGAF, and comparative experiments confirmed the superiority of our proposed LGI Net in terms of accuracy and parameter efficiency. This study provides an innovative approach in the field of medical image segmentation, offering valuable insights for further improvements in accuracy and performance. The code and models will be available at https://github.com/LiuLinjie0310/LGI-Net.
Pyramidal temporal frame prediction for efficient anomalous event detection in smart surveillance systems

Video anomaly detection plays a pivotal role within contemporary intelligent surveillance systems, holding significant utility in the identification and mitigation of anomalies within smart environments. The task of anomaly detection is made difficult by the presence of complex environments and varying perspectives, even in diverse situations. Furthermore, the lack of labeled data in this field presents additional challenges. In this article, we present a robust pyramidal frame prediction architecture coupled with an efficient anomaly detection mechanism. The foundation of the frame prediction module rests upon a combination of localized predictors, incorporating bidirectional sampling techniques and a proficient discriminator-sharing mechanism. For the purpose of anomaly detection, an innovative loss function is employed, tailored to accommodate multi-instance learning scenarios. Departing from the conventional treatment of anomalies as a binary classification problem, we adopt a regression-oriented approach, thereby enabling a nuanced understanding of disruptive occurrences at a granular level, whilst facilitating an assessment of the severity associated with anomalous events. The amalgamation of these dual modules culminates in a distinctive and robust framework adept at comprehending anomalies within intricate scenarios. Empirical assessments conducted across benchmark datasets within this domain serve to underscore the efficacy of the proposed architectural paradigm across a spectrum of environments.
Geology, mineralogy, and alteration of the Cu-Pb veins, and their significance in mineral exploration: A case study in the Khajeh-Nezam area, SE Iran

Khajeh-Nezam area is situated within the Bam 1:100,000 geological sheet, SE Iran. The area was the subject of silt geochemical studies and consists of highly prospective Cu, Pb, Ba, and Fe veins and veinlets that demonstrate a northwest-southeast trend. A total of 149 silt samples were collected from the area and analyzed for 7 elements (As, Cu, Mo, Pb, Zn, Sb, Ba) using Inductively Coupled Plasma-Optical Emission Spectrometry. Univariate-multivariate statistical analysis the majority of variables demonstrate a distribution that is slightly too moderately positively skewed, which can be accurately explained using a log-normal model. Only Cu, Zn, and Pb demonstrate a normal distribution. The number-size (N-S) fractal model show that. The variables can be classified into two main groups. The first group comprises the main ore-forming elements, namely Cu, Ba, As, and Sb, which are associated with the epithermal system. On the other hand, Mo, Pb, and Zn demonstrate a stronger affiliation with vein and veinlet mineralization that could be linked to the margin of a possible hidden porphyry system. The geochemical halo in the southwestern part of the study area, which overlaps with volcanic rocks, has experienced significant alterations attributed to local intrusions, leading to a level of modification. A secondary weak anomaly, of relatively lesser significance, is observed in Pb, Zn, and Mo. Although these elements are not the primary constituents of the ore, they serve as pathfinder elements in marginal copper porphyry. The results obtained from the study have been utilized to identify concealed ore bodies using the classes defined by N-S classification model. Fieldwork conducted in the area confirms the presence of Fe oxide veins and veinlets that contain copper mineralization, along with carbonate Pb and Cu minerals. Various types of alteration have been identified, including argillic alteration accompanied by quartz and hematite in the vicinity of the veins and veinlets. The findings serve as probable evidence for the existence of a new copper porphyry system in the study area.
BPG-based compression analysis of Poisson-noisy medical images

The subject matter is lossy compression using the BPG encoder for medical images with varying levels of visual complexity, which are corrupted by Poisson noise. The goal of this study is to determine the optimal parameters for image compression and select the most suitable metric for identifying the optimal operational point. The tasks addressed include: selecting test images sized 512x512 in grayscale with varying degrees of visual complexity, encompassing visually intricate images rich in edges and textures, moderately complex images with edges and textures adjacent to homogeneous regions, and visually simple images primarily composed of homogeneous regions; establishing image quality evaluation metrics and assessing their performance across different encoder compression parameters; choosing one or multiple metrics that distinctly identify the position of the optimal operational point; and providing recommendations based on the attained results regarding the compression of medical images corrupted by Poisson noise using a BPG encoder, with the aim of maximizing the restored image’s quality resemblance to the original. The employed methods encompass image quality assessment techniques employing MSE, PSNR, MSSIM, and PSNR-HVS-M metrics, as well as software modeling in Python without using the built-in Poisson noise generator. The ensuing results indicate that optimal operational points (OOP) can be discerned for all these metrics when the compressed image quality surpasses that of the corresponding original image, accompanied by a sufficiently high compression ratio. Moreover, striking a suitable balance between the compression ratio and image quality leads to partial noise reduction without introducing notable distortions in the compressed image. This study underscores the significance of employing appropriate metrics for evaluating the quality of compressed medical images and provides insights into determining the compression parameter Q to attain the BPG encoder’s optimal operational point for specific images. Conclusions. The scientific novelty of the findings encompasses the following: 1) the capability of all metrics to determine the OOP for images of moderate visual complexity or those dominated by homogeneous areas; MSE and PSNR metrics demonstrating superior results for images rich in textures and edges; 2) the research highlights the dependency of Q in the OOP on the average image intensity, which can be reasonably established for a given image earmarked for compression based on our outcomes. The compression ratios for images compressed at the OOP are sufficiently high, further substantiating the rationale for compressing images in close proximity to the OOP.
Prompting and Fine-tuning Pre-trained Generative Language Models

There has been an explosion of available pre-trained and fine-tuned Generative Language Models (LM). They vary in the number of parameters, architecture, training strategy, and training set size. Aligned with it, alternative strategies exist to exploit these models, such as Fine-tuning and Prompt Engineering. However, many questions may arise throughout this process: Which model to apply for a given task? Which strategies to use? Will Prompt Engineering solve all tasks? What are the computational and financial costs involved? This tutorial will introduce and explore typical modern LM architectures with a hands-on approach to the available strategies.
Re-Think, Retrieve and Re-Ranking: A New Complementary Two-stage Retrieving and Re-ranking Pipeline for Extreme Multi-Label Text Classification

Extreme multi-label text classification (XMTC) involves assigning relevant labels to text from a huge space of labels. Addressing the core challenges of XMTC (volume, imbalance and quality), we propose xCoRetriev, a two-stage pipeline migrating from a classification perspective to an information retrieval (IR) approach. We address the volume challenge by efficiently combining IR methods; we face the imbalance challenge by better capturing the text-label relationship and; we've improved quality by enriching tag names with pseudo-labels. Our results demonstrate the strengths of xCoRetriev when compared to baselines in terms of: (i) scalability for large spaces of labels and amount of text; (ii) effectiveness in the face of high imbalance, especially for predicting infrequent labels – with gains of up to 40% in MRR and NDCG –; and (iii) ability to handle poor quality annotated text and labels.