Reviewerly/exHarmony huggingface dataset:
A Recursive System Identification With Non-Uniform Temporal Feedback Under Coprime Collaborative Sensing

Abstract We present a system identification method based on recursive least-squares (RLS) and coprime collaborative sensing, which can recover system dynamics from non-uniform temporal data. Focusing on systems with fast input sampling and slow output sampling, we use a polynomial transformation to reparameterize the system model and create an auxiliary model that can be identified from the non-uniform data. We show the identifiability of the auxiliary model using a Diophantine equation approach. Numerical examples demonstrate successful system reconstruction and the ability to capture fast system response with limited temporal feedback.
GLF-Net: A Semantic Segmentation Model Fusing Global and Local Features for High-Resolution Remote Sensing Images

Semantic segmentation of high-resolution remote sensing images holds paramount importance in the field of remote sensing. To better excavate and fully fuse the features in high-resolution remote sensing images, this paper introduces a novel Global and Local Feature Fusion Network, abbreviated as GLF-Net, by incorporating the extensive contextual information and refined fine-grained features. The proposed GLF-Net, devised as an encoder–decoder network, employs the powerful ResNet50 as its baseline model. It incorporates two pivotal components within the encoder phase: a Covariance Attention Module (CAM) and a Local Fine-Grained Extraction Module (LFM). And an additional wavelet self-attention module (WST) is integrated into the decoder stage. The CAM effectively extracts the features of different scales from various stages of the ResNet and then encodes them with graph convolutions. In this way, the proposed GLF-Net model can well capture the global contextual information with both universality and consistency. Additionally, the local feature extraction module refines the feature map by encoding the semantic and spatial information, thereby capturing the local fine-grained features in images. Furthermore, the WST maximizes the synergy between the high-frequency and the low-frequency information, facilitating the fusion of global and local features for better performance in semantic segmentation. The effectiveness of the proposed GLF-Net model is validated through experiments conducted on the ISPRS Potsdam and Vaihingen datasets. The results verify that it can greatly improve segmentation accuracy.
MultiNet-GS: Structured Road Perception Model Based on Multi-Task Convolutional Neural Network

In order to address the issue of environmental perception in autonomous driving on structured roads, we propose MultiNet-GS, a convolutional neural network model based on an encoder–decoder architecture that tackles multiple tasks simultaneously. We use the main structure of the latest object detection model, the YOLOv8 model, as the encoder structure of our model. We introduce a new dynamic sparse attention mechanism, BiFormer, in the feature extraction part of the model to achieve more flexible computing resource allocation, which can significantly improve the computational efficiency and occupy a small computational overhead. We introduce a lightweight convolution, GSConv, in the feature fusion part of the network, which is used to build the neck part into a new slim-neck structure so as to reduce the computational complexity and inference time of the detector. We also add an additional detector for tiny objects to the conventional three-head detector structure. Finally, we introduce a lane detection method based on guide lines in the lane detection part, which can aggregate the lane feature information into multiple key points, obtain the lane heat map response through conditional convolution, and then describe the lane line through the adaptive decoder, which effectively makes up for the shortcomings of the traditional lane detection method. Our comparative experiments on the BDD100K dataset on the embedded platform NVIDIA Jetson TX2 show that compared with SOTA(YOLOPv2), the mAP@0.5 of the model in traffic object detection reaches 82.1%, which is increased by 2.7%. The accuracy of the model in drivable area detection reaches 93.2%, which is increased by 0.5%. The accuracy of the model in lane detection reaches 85.7%, which is increased by 4.3%. The Params and FLOPs of the model reach 47.5 M and 117.5, which are reduced by 6.6 M and 8.3, respectively. The model achieves 72 FPS, which is increased by 5. Our MultiNet-GS model has the highest detection accuracy among the current mainstream models while maintaining a good detection speed and has certain superiority.
Self-efficacy-based change in educational administration style and public management model

Abstract At present, the public management model is relatively backward, and the way of education administration needs urgent change. This paper explores the characteristics of the higher education administrative body operation mode in different periods and analyzes and summarizes the problems of the current educational administrative style. In response to these problems, an educational administrative management style based on self-efficacy is proposed, and the mathematical expression of three characteristic dimensions of self-efficacy is established through the modeling study of the factors influencing self-efficacy. The impact of characteristic self-efficacy dimensions on the efficiency and fairness of educational administration was analyzed as a strategy to propose management model changes. The impact factors of self-efficacy magnitude on input-output efficiency reached 0.43, self-efficacy breadth on problem-solving efficiency reached 0.40, and self-efficacy intensity on planning goal completion efficiency reached 0.27. The impact factors of the three characteristic dimensions of self-efficacy on educational equity reached 0.31, 0.35, and 0.34. This study provides an effective reference path for educational administration. This study provides an effective reference path for optimizing educational administration and helps promote the scientific construction of the public management model.
A Formalized Extension of the Substitution Lemma in Coq

The substitution lemma is a renowned theorem within the realm of lambda-calculus theory and concerns the interactional behaviour of the metasubstitution operation. In this work, we augment the lambda-calculus's grammar with an uninterpreted explicit substitution operator, which allows the use of our framework for different calculi with explicit substitutions. Our primary contribution lies in verifying that, despite these modifications, the substitution lemma continues to remain valid. This confirmation was achieved using the Coq proof assistant. Our formalization methodology employs a nominal approach, which provides a direct implementation of the alpha-equivalence concept. The strategy involved in variable renaming within the proofs presents a challenge, specially on ensuring an exploration of the implications of our extension to the grammar of the lambda-calculus.
Symmetric Functions over Finite Fields

The number of linear independent algebraic relations among elementary symmetric polynomial functions over finite fields is computed. An algorithm able to find all such relations is described. It is proved that the basis of the ideal of algebraic relations found by the algorithm consists of polynomials having coefficients in the prime field F_p.
Addressing challenges in multi-label text classification: A joint attention and shared semantic space approach

The task of multi-label text classification involves assigning a set of related labels to a given document. However, there are three main problems with this task. Firstly, the joint modeling of label-text and label-label relationships is inadequate. Secondly, the semantic mining of the label itself is insufficient. Lastly, the utilization of the internal structure information of the label is ignored. To address these issues, a new multi-label text classification method has been proposed. This method is based on joint attention and shared semantic space. The joint multi-head attention mechanism models the relationship between labels and documents as well as the relationship between labels simultaneously. This helps to avoid error transmission and utilizes the interaction information between them. The decouple shared semantic space embedding method improves the method of using labels semantic information and reduces deviation in the phase of modeling correlation. The hierarchical hinting method based on prior knowledge relies on the prior knowledge in the pre-trained model to exploit the labels hierarchy information. Experimental results have shown that this proposed method is superior to existing multi-label text classification methods in public datasets.
Fuzzy comprehensive evaluation model in the evaluation of English teaching quality

Evaluating English teaching quality is vital for improving knowledge-based developments through communication for different aged students. Teaching quality assessment relies on the teachers’ and students’ features for constructive progression. With the development of computational intelligence, optimization and machine learning techniques are widely adapted for teaching quality assessment. In this article, a Quality-centric Assessment Model aided by Fuzzy Optimization (QAM-FO) is designed. This optimization approach validates the student-teacher features for a balanced model assessment. The distinguishable features for improving students’ oral and verbal communication from different teaching levels (basic, intermediate, and proficient) are extracted. The extracted features are the crisp input for the fuzzy optimization such that the recurring fuzzification detains the least fit feature. Such features are replaced by the level-based teaching and performance feature that differs from the previous fuzzy input. This replacement is pursued until a maximum recommendable feature (performance/ learning) is identified. The identified feature is applicable for different teaching levels for improving the quality assessment. Therefore, the proposed optimization approach provides different feasible recommendations for teaching improvements.
Research on Sparrow Search Optimization Algorithm for multi-objective task scheduling in cloud computing environment

In cloud computing, optimizing task scheduling is crucial for improving overall system performance and resource utilization. To minimize cloud service costs and prevent resource wastage, advanced techniques must be employed to efficiently allocate cloud resources for executing tasks. This research presents a novel multi-objective task scheduling method, BSSA, which combines the Backtracking Search Optimization Algorithm (BSA) and the Sparrow Search Algorithm (SSA). BSA enhances SSA’s convergence accuracy and global optimization ability in later iterations, improving task scheduling results. The proposed BSSA is evaluated and compared against traditional SSA and other algorithms using a set of 8 benchmark test functions. Moreover, BSSA is tested for task scheduling in cloud environments and compared with various metaheuristic scheduling algorithms. Experimental results demonstrate the superiority of the proposed BSSA, validating its effectiveness and efficiency in cloud task scheduling.
Infrared image enhancement based on adaptive non-local filter and local contrast

High-quality infrared images urgently needed in military and civilian fields are always associated with appropriate contrast. However, the main challenge to obtain high-quality infrared images lies in enhancement of the contrast effectively without over-enhancement of the background and noise. Hereby, an effective infrared image enhancement approach is proposed and based on an adaptive non-local filter and local contrast. An input infrared image is detected by noisy adaptive detection to separate noisy pixels, which are filtered by non-local means filter to acquire denoised image. The grayscale histogram of the denoised image is divided into foreground and background based on the local minima value. The foreground part is enhanced by local contrast weighted distribution and max entropy gamma correction. The background part is processed by linear mapping to project the grayscale to an appropriate region. Finally, the enhanced infrared image is obtained by remapping the processed foreground and background histogram. Extensive experiments on public and homemade datasets demonstrate that our method achieves 46.8183 on image clarity, which expresses its superiority for infrared image enhancement.
Improved meet-in-the-middle attacks on reduced-round tweakable block ciphers Kiasu-BC and Deoxys-BC

Tweakable block ciphers Kiasu-BC and Deoxys-BC are both the internal block ciphers of the candidates for the CAESAR authenticated encryption competition. The cryptanalysis of Kiasu-BC and Deoxys-BC is helpful to promote the cryptanalysis process of tweakable block ciphers. At present, under the condition of related-tweak single-key model, the results of the meet-in-the-middle attacks on Kiasu-BC and Deoxys-BC are the best attacks among all the traditional cryptanalysis methods. Hence, in this paper, we improve the results of the meet-in-the-middle attacks on Kiasu-BC and Deoxys-BC. We study the properties of MixCloumn operation to compute several linear equations. Combining the linear equations with the differential cancellation technique and the differential enumeration technique, as results, we construct the first 6-round meet-in-the-middle distinguisher for Kiasu-BC, the first 7-round meet-in-the-middle distinguisher for Deoxys-BC-256 and the first 8-round meet-in-the-middle distinguisher for Deoxys-BC-384. Based on the new distinguishers, we present favorable results of the 8-round meet-in-the-middle attack on Kiasu-BC, 9-round meet-in-the-middle attack on Deoxys-BC-256 and 11-round meet-in-the-middle attack on Deoxys-BC-384. The favorable results of the attacks improve the time complexity of the current meet-in-the-middle attacks on Kiasu-BC and Deoxys-BC.
Virtual selfhood and consumer behavior: Exploring avatar attachment and consumption patterns in Second Life's metaverse

In the vast digital landscape of the Metaverse, users can create and personalize their avatars as virtual representations of themselves. This study delves into the emotions users experience in relation to their avatars and examines how this attachment influences their consumption behaviors within the virtual world. The research employed a sample of 214 active users participating in Second Life, a prominent virtual world platform. By analyzing survey data, we explore the dynamics of self-presentation and attachment between users and their virtual personas across this well-established platform. Our research offers valuable contributions to the existing literature on the Metaverse, providing empirical evidence on how virtual reality platforms like Second Life foster avatar customization and how this, in turn, affects consumer behavior. As the Metaverse gains prominence in the business world, understanding the habits and preferences of virtual reality users is increasingly crucial. We aim to enhance our understanding of consumer behavior by incorporating attachment theory into our research on long-standing virtual environments like Second Life. Our study of Second Life provides valuable insights into the dynamics of consumer behavior within a well-established virtual world, which can be applied to the emerging Metaverse platforms. This knowledge helps businesses identify consumer profiles, address their needs, and enhance their virtual presence and success.
Continuity bounds on observational entropy and measured relative entropies

We derive a measurement-independent asymptotic continuity bound on the observational entropy for general positive operator valued measures measurements, making essential use of its property of bounded concavity. The same insight is used to obtain continuity bounds for other entropic quantities, including the measured relative entropy distance to a convex set of states under a general set of measurements. As a special case, we define and study conditional observational entropy, which is an observational entropy in one (measured) subsystem conditioned on the quantum state in another (unmeasured) subsystem. We also study continuity of relative entropy with respect to a jointly applied channel, finding that observational entropy is uniformly continuous as a function of the measurement. But we show by means of an example that this continuity under measurements cannot have the form of a concrete asymptotic bound.
Visual prior-based cross-modal alignment network for radiology report generation

Automated radiology report generation is gaining popularity as a means to alleviate the workload of radiologists and prevent misdiagnosis and missed diagnoses. By imitating the working patterns of radiologists, previous report generation approaches have achieved remarkable performance. However, these approaches suffer from two significant problems: (1) lack of visual prior: medical observations in radiology images are interdependent and exhibit certain patterns, and lack of such visual prior can result in reduced accuracy in identifying abnormal regions; (2) lack of alignment between images and texts: the absence of annotations and alignments for regions of interest in the radiology images and reports can lead to inconsistent visual and textual features of the abnormal regions generated by the model. To address these issues, we propose a Visual Prior-based Cross-modal Alignment Network for radiology report generation. First, we propose a novel Contrastive Attention that compares input image with normal images to extract difference information, namely visual prior, which helps to identify abnormalities quickly. Then, to facilitate the alignment of images and texts, we propose a Cross-modal Alignment Network that leverages the cross-modal matrix initialized by the features generated by pre-trained models, to compute cross-modal responses for visual and textual features. Finally, a Visual Prior-guided Multi-Head Attention is proposed to incorporate the visual prior into the generation process. The extensive experimental results on two benchmark datasets, IU-Xray and MIMIC-CXR, illustrate that our proposed model outperforms the state-of-the-art models over almost all metrics, achieving BLEU-4 scores of 0.188 and 0.116 and CIDEr scores of 0.409 and 0.240, respectively.
Exploration of overlap volumes for radiotherapy plan evaluation with the aim of healthy tissue sparing

Development of a novel interactive visualization approach for the exploration of radiotherapy treatment plans with a focus on overlap volumes with the aim of healthy tissue sparing.We propose a visualization approach to include overlap volumes in the radiotherapy treatment plan evaluation process. Quantitative properties can be interactively explored to identify critical regions and used to steer the visualization for a detailed inspection of candidates. We evaluated our approach with a user study covering the individual visualizations and their interactions regarding helpfulness, comprehensibility, intuitiveness, decision-making and speed.A user study with three domain experts was conducted using our software and evaluating five data sets each representing a different type of cancer and location by performing a set of tasks and filling out a questionnaire. The results show that the visualizations and interactions help to identify and evaluate overlap volumes according to their physical and dose properties. Furthermore, the task of finding dose hot spots can also benefit from our approach.The results indicate the potential to enhance the current treatment plan evaluation process in terms of healthy tissue sparing.
A Comprehensive Review of Sentiment Analysis on Indian Regional Languages: Techniques, Challenges, and Trends

Sentiment analysis (SA) is the process of understanding emotion within a text. It helps identify the opinion, attitude, and tone of a text categorizing it into positive, negative, or neutral. SA is frequently used today as more and more people get a chance to put out their thoughts due to the advent of social media. Sentiment analysis benefits industries around the globe, like finance, advertising, marketing, travel, hospitality, etc. Although the majority of work done in this field is on global languages like English, in recent years, the importance of SA in local languages has also been widely recognized. This has led to considerable research in the analysis of Indian regional languages. This paper comprehensively reviews SA in the following major Indian Regional languages: Marathi, Hindi, Tamil, Telugu, Malayalam, Bengali, Gujarati, and Urdu. Furthermore, this paper presents techniques, challenges, findings, recent research trends, and future scope for enhancing results accuracy.
WEBSITE-BASED LIBRARY DATA PROCESSING DESIGN

The main problem Indonesia faces, especially in education in the era of globalization, is the low quality of human resources. One of the efforts to improve the quality of human resources is to increase interest in reading and the habit of reading. From this fact, the library is expected to be the center of activities to develop interest in reading and reading habits. Libraries have a great responsibility to increase and generate interest in reading. Library data management is one of the essential activities in running a library. Librarians must be able to process and manage book data efficiently and effectively to avoid losing library property. This study uses PHP to create a website design to assist librarians in processing and storing data about existing books. This study designs a library data processing system that contains library book loans, such as recording books, transactions, and student data collection in the library. The results of this website design can facilitate library staff in organizing and tracking library management quickly and efficiently. Keywords: Digital Library, Library Website, Electronic Journal
Probabilistic XGBoost Threshold Classification with Autoencoder for Credit Card Fraud Detection

Due to the imbalanced data of outnumbered legitimate transactions than the fraudulent transaction, the detection of fraud is a challenging task to find an effective solution. In this study, autoencoder with probabilistic threshold shifting of XGBoost (AE-XGB) for credit card fraud detection is designed. Initially, AE-XGB employs autoencoder the prevalent dimensionality reduction technique to extract data features from latent space representation. Then the reconstructed lower dimensional features utilize eXtreame Gradient Boost (XGBoost), an ensemble boosting algorithm with probabilistic threshold to classify the data as fraudulent or legitimate. In addition to AE-XGB, other existing ensemble algorithms such as Adaptive Boosting (AdaBoost), Gradient Boosting Machine (GBM), Random Forest, Categorical Boosting (CatBoost), LightGBM and XGBoost are compared with optimal and default threshold. To validate the methodology, we used IEEE-CIS fraud detection dataset for our experiment. Class imbalance and high dimensionality characteristics of dataset reduce the performance of model hence the data is preprocessed and trained. To evaluate the performance of the model, evaluation indicators such as precision, recall, f1-score, g-mean and Mathews Correlation Coefficient (MCC) are accomplished. The findings revealed that the performance of the proposed AE-XGB model is effective in handling imbalanced data and able to detect fraudulent transactions with 90.4% of recall and 90.5% of f1-score from incoming new transactions.