Reviewerly/exHarmony huggingface dataset:
Intelligent understanding algorithm of aerial visual inspection image based on deep learning technology

Abstract At present, there is a problem that the efficiency and accuracy of the safety belt wearing detection method for aerial patrol workers are not ideal. In order to solve this problem, based on the deep learning technology, a safety belt specification wearing detection model is constructed. First, in view of the low quality of image data, wavelet transform and Gaussian curvature filter are used to preprocess the image. Aiming at the defect of poor performance of convolutional neural network (CNN), Gabor local features and Momentum algorithm are used to improve it. Finally, combined with the above content, a safety belt specification wearing detection model based on improved CNN is constructed. The results show that the loss value of the model is 0.51, the accuracy rate is 98.14%, the Recall value is 95.04%, and the AUC value is 0.971. Therefore, the model built in the study can detect the wearing of safety belt with high efficiency and accuracy, and ensure the safety of staff.
Book Reviews

Welcome to the chapter on implementing review functionality in our application. In this chapter, we will explore how to enable users to create and update reviews for books. Reviews provide valuable insights and feedback, allowing users to share their opinions and experiences with others.
Classical and Quantum 3 and 4-Sieves to Solve SVP with Low Memory

The Shortest Vector Problem (SVP) is at the foundation of lattice-based cryptography. The fastest known method to solve SVP in dimension d is by lattice sieving, which runs in time $$2^{td+o(d)}$$ with $$2^{md+o(d)}$$ memory for constants $$t,m \in \varTheta (1)$$ . Searching reduced vectors in the sieve is a problem reduced to the configuration problem, i.e. searching k vectors satisfying given constraints on their pairwise scalar products. In this work, we present a framework for k-sieve algorithms: we filter the input list of lattice vectors using a code structure modified from [Bec+16] to get lists centred around k codewords summing to the null-vector. Then, we solve a simpler instance of the configuration problem in the k filtered lists. Based on this framework, we describe classical sieves for $$k=3$$ and 4 that introduce new time-memory trade-offs. We also use the k-Lists algorithm [Kir+19] inside our framework, and this improves the time for $$k=3$$ and gives new trade-offs for $$k=4$$ .
Analysis of Casual Relationships between Social Determinants of Health in Iran: Using Fuzzy Cognitive Map

Background: Health is a fundamental issue in recent years, highlighting the importance of harmonizing the policies of any sector with health strategies. The present study aims to identify the socio-economic factors affecting health and to provide a cognitive map of the social determinants of health in Iran. Study Design: A retrospective cohort study. Methods: This study follows a developmental process with an exploratory sequential mixed methods approach. First, a meta-synthesis qualitative method determines the most critical health determinants by reviewing 54 studies from 2000 to 2019. Then, the fuzzy cognitive map (FCM) is drawn based on interviews with six experts to derive the causal relationships among the social determinants of population health in Iran. Results: Stage 1 introduces 170 factors as the health determinants, classified into four levels: individual, local, national, and global levels, with 7, 4, 13, and 3 branches, respectively. According to the causal relationships and the out-degree (od) index, the most effective factors are the economic system (18.24), governance and policy-making (17.13), and national policies (16.93). According to the degree of centrality, these factors are the economic system (33.27), health system (30.37), and governance and policy-making (30.15). Conclusion: Considering health as a complex and comprehensive system, the resulting FCM displays that the policies developed in other sectors than health are profoundly affecting population’s health in Iran. Specifically, the comparative analysis of this research shows that policies regarding the economic system and people’s livelihood are more effective than the policies regarding the health system itself on the population’s health in Iran.
Generative Data Augmentation for Aspect Sentiment Quad Prediction

Aspect sentiment quad prediction (ASQP) analyzes the aspect terms, opinion terms, sentiment polarity, and aspect categories in a text.One challenge in this task is the scarcity of data owing to the high annotation cost.Data augmentation techniques are commonly used to address this issue.However, existing approaches simply rewrite texts in the training data, restricting the semantic diversity of the generated data and impairing the quality due to the inconsistency between text and quads.To address these limitations, we augment quads and train a quads-to-text model to generate corresponding texts.Furthermore, we designed novel strategies to filter out low-quality data and balance the sample difficulty distribution of the augmented dataset.Empirical studies on two ASQP datasets demonstrate that our method outperforms other data augmentation methods and achieves state-of-the-art performance on the benchmarks. 1
Large Language Models and Psychoeducation

Private Academic Consultant Samraong, Cambodia [email protected] Chandigarh University, Punjab, India Joesph Ayobabalola University, Ikeji-Arakeji, Nigeria. Authors' contribution: A.K. participated in the ideas, writing, analyzing, approval. V.W. participated in the ideas, supervision, approval. The authors declare no conflict of interest.
The relationship between digital literacy and academic performance of college students in blended learning: the mediating effect of learning adaptability

Blended learning is one of the main learning modes for college students nowadays.However, the relationship between college students' digital literacy, learning adaptability, and their academic performance in the blended learning environment has not been revealed in known studies.This study explores the impact of digital literacy on college students' academic performance, as well as the mediating role of college students' learning adaptability between the two variables.Based on previous literature, this study uses a structural equation model to verify the corresponding hypotheses and tests the mediating effect based on data from 357 participants.This study reveals that in blended learning environment, college students' digital literacy has a significant direct impact on their academic performance, college students' learning adaptability can also positively influence their academic performance, and learning adaptability plays a partially mediating role between digital literacy and academic performance in the blended learning environment.These results are discussed and provided suggestions for college students' blended learning and future research.
Kara Nisan Öğrencilerinin Gambiya'daki Gösterisi

Two isolated incidents in The Gambia spurred a student demonstration on April 10, and 11, 2000. The Gambia Students’ Union felt the government handled the issues which involve their members nonchalantly. The student body’s futile attempt to engage the government to redress the grievances compelled its members to vent their anger in the streets against a society they felt treated their colleagues unjustly. Government’s highhanded reaction escalated the demonstration to a full-blown riot that resulted in unprecedented casualties. Through qualitative method, the undercurrents which propelled the highhanded measures in quashing the riot constitutes the subject of this paper. It also questions, within the legal framework of Vicarious Liability and with the application of Karl Marx Conflict Theory, the motives propping the recommended punitive measures against some key law enforcement officers, a decision for which the conclusion of the article recommended adjudication. Viewed against the background of the protracted inherent security situation of the country, the article examines the entrenched causes of the riot received attention. In conclusion, the underlying causes is ascribed to social conflict instigated by class struggle in the politically polarized society.
A Recurrent Neural Network Optimization Method for Anticipation of Hierarchical Human Activity

Human activity anticipation is a pillar of new interactive multimedia technologies. It enables better human-computer interaction and creative sensory experience in all types of virtual reality-based multimedia applications. Human behaviors are hierarchically constituted in terms of low-level and high-level activities. Fine-grained activities play a substantial role in the early recognition of simple and complex human movements in real-time with low latency, allowing longer predictions in the future. In this paper, a new objective function is proposed to anticipate fine-grained human activities using IMU data, which still suffers from an imbalance problem. Four customized objective surrogate functions are applied in unidirectional and bidirectional recurrent neural networks and compared to efficiently optimize the model considering the loss of individual classes. The experiments on five datasets across proposed loss functions show that the proposed model significantly outperforms the counterpart methods and advances the state-of-the-art. The proposed model shows accuracy scores of up to 98% and 96% for high-level and low-level activities, respectively. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —There are a large number of methods that can be used for the recognition of human activity in assisted living, smart home technologies, and pervasive healthcare applications. However, studies on these methods tend to focus on recognition rather than anticipation of human activity. Furthermore, they use either vision-based or egocentric video data, which can make it challenging to collect and evaluate in virtual reality-based scenarios. To enable more natural interaction with physical and virtual reality environments, virtual reality-based technologies require built-in algorithms capable of consistently identifying complex and varied human movements. Fine-grained activities are paramount in predicting activities at an early stage to adequately characterize human behaviors. In this paper, a study is conducted to anticipate fine-grained human activity based on IMU (Inertial Measurement Unit) data, which are still subject to the negative effects of an imbalanced sample distribution. The proposed model is evaluated on different benchmarks and compared with baseline methods. The results show that the proposed model outperforms all baselines. These results have important implications for human activity prediction problems.
The roles of servant leadership style, LMX style and leadership effectiveness on employees' engagement in Medina hotels

This study investigates the impact of servant leadership, with respect to leader-member exchange (LMX), on leadership effectiveness and employee engagement. This research was conducted in hotels in the city of Medina in Saudi Arabia. As a quantitative study, both probability and non-probability sampling methods were used to select the 220 participants, who were hotel employees from a variety of backgrounds. A survey data collection tool was used alongside a questionnaire, and the data was processed with SPSS software. The findings showed that the servant leadership style places a high value on helping followers instead of trying to align their interests with that of the company. Servant leadership plays a major role in influencing LMX. LMX was identified as a strong mediator of servant leadership. Also, it was found that LMX is an important mediator of follower engagement where leadership effectiveness is positively related to follower engagement.
Make Text Unlearnable: Exploiting Effective Patterns to Protect Personal Data

This paper addresses the ethical concerns arising from the use of unauthorized public data in deep learning models and proposes a novel solution.Specifically, building on the work of Huang et al. ( 2021), we extend their bilevel optimization approach to generate unlearnable text using a gradient-based search technique.However, although effective, this approach faces practical limitations, including the requirement of batches of instances and model architecture knowledge that is not readily accessible to ordinary users with limited access to their own data.Furthermore, even with semantic-preserving constraints, unlearnable noise can alter the text's semantics.To address these challenges, we extract simple patterns from unlearnable text produced by bilevel optimization and demonstrate that the data remains unlearnable for unknown models.Additionally, these patterns are not instanceor dataset-specific, allowing users to readily apply them to text classification and questionanswering tasks, even if only a small proportion of users implement them on their public content.We also open-source codes to generate unlearnable text and assess unlearnable noise to benefit the public and future studies.
Fair Rewards in Federated Learning: A Novel Approach with Adjusted OR-TMC Shapley Value Approximation Algorithm

Federated Learning (FL), a new private and secure Machine Learning (ML) approach, faces a big difficulty when it comes to sharing profits with data producers. Shapley Values (SV) have been proposed as a fair incentive system to remedy this, but it is challenging to determine the SV with accuracy. Therefore, SV calculation is problematic since the number of necessary federated models rises exponentially with the number of data sources. As a result, an effective approximation approach is required. The One Round Model Reconstruction (OR) and Truncated Monte Carlo Shapley (TMC) approaches for SV approximation in FL are being improved and combined in this study. The proposed approach, Adjusted OR-TMC, combines TMC principles with OR and achieves a comparable level of accuracy over a shorter period. Because of this, Adjusted OR-TMC is the perfect OR replacement. The performance outcomes and underlying causes are covered in the study.
I-Cubid: a nonlinear cubic graph-based approach to visualize and in-depth browse Flickr image results

The existing image search engines allow web users to explore images from the grids. The traditional interaction is linear and lookup-based. Notably, scanning web search results is horizontal-vertical and cannot support in-depth browsing. This research emphasizes the significance of a multidimensional exploration scheme over traditional grid layouts in visually exploring web image search results. This research aims to antecedent the implications of visualization and related in-depth browsing via a multidimensional cubic graph representation over a search engine result page (SERP). Furthermore, this research uncovers usability issues in the traditional grid and 3-dimensional web image search space. We provide multidimensional cubic visualization and nonlinear in-depth browsing of web image search results. The proposed approach employs textual annotations and descriptions to represent results in cubic graphs that further support in-depth browsing via a search user interface (SUI) design. It allows nonlinear navigation in web image search results and enables exploration, browsing, visualization, previewing/viewing, and accessing images in a nonlinear, interactive, and usable way. The usability tests and detailed statistical significance analysis confirm the efficacy of cubic presentation over grid layouts. The investigation reveals improvement in overall user satisfaction, screen design, information &amp; terminology, and system capability in exploring web image search results.
Photon number dependent afterpulsing in superconducting nanostrip single-photon detectors

Superconducting nanostrip single-photon detectors (SNSPDs) are widespread tools in photonic quantum technologies. Here, we study the afterpulsing phenomenon in commercial SNSPDs exhibiting significant levels of afterpulses even at low counting rates. We find different contributions, where the probability of an afterpulse is not a constant but depends on the mean number of photons per light pulse including mean numbers much less than 1. Our observations exclude the electrical circuit as the primary cause of the main contribution to the observed afterpulsing probability, which exhibits a strong dependence on mean photon number. We propose a phenomenological model that qualitatively explains our findings via the introduction of slowly relaxing ``afterpulsing centers,'' storing the absorbed photons' energy for several tens of nanoseconds. We conjecture that two-level systems in amorphous materials are physical candidates for the role of such afterpulsing centers.
Film Music by A. Schnittke: The Range of Literary Images

The paper is devoted to the characterisation of the range of artistic images from classical and modern literature of the XX century in Alfred Schnittke’s musical score for feature films. His work in cinema led the composer to a dialogue with such major poets and writers as A. Pushkin, A. Chekhov, L. Tolstoy, M. Bulgakov and contemporaries V. Rasputin, O. Bergholz, V. Astafyev, V. Panova, Yu. Bondarev, V. Grossman. The novelty of the research lies in determining the peculiarities of A. Schnittke’s interpretation of literary works in connection with their film adaptations. As a result, the main themes of Schnittke’s film music creative work, concordant with his views on the themes of “the artist and the world”, “life and death”, “man and his choice”, “Epoch – time – Man”, have been presented.
Academic Writing and Publication Support Services: The Role of Librarians in Higher Learning Institutions

Despite the presence of well-equipped libraries and committed librarians in higher learning institutions, the rate at which academicians produce and publish their work has slowed significantly in recent years. This is concerning since publication is important in decisions about promotion and contracts in academic and specialized settings. The purpose of this study is to investigate the role of librarians in the publishing process within academic institutions, the level of awareness among academicians regarding the research support services provided by librarians, the factors impeding academicians from actively engaging in writing and publishing, academicians' perceptions of the services offered in academic libraries, and, finally, academicians' recommended services that could improve their publication rate.
SPIDEN: deep Spiking Neural Networks for efficient image denoising

In recent years, Deep Convolutional Neural Networks (DCNNs) have outreached the performance of classical algorithms for image restoration tasks. However, most of these methods are not suited for computational efficiency. In this work, we investigate Spiking Neural Networks (SNNs) for the specific and uncovered case of image denoising, with the goal of reaching the performance of conventional DCNN while reducing the computational cost. This task is challenging for two reasons. First, as denoising is a regression task, the network has to predict a continuous value (i.e., the noise amplitude) for each pixel of the image, with high precision. Moreover, state of the art results have been obtained with deep networks that are notably difficult to train in the spiking domain. To overcome these issues, we propose a formal analysis of the information conversion processing carried out by the Integrate and Fire (IF) spiking neurons and we formalize the trade-off between conversion error and activation sparsity in SNNs. We then propose, for the first time, an image denoising solution based on SNNs. The SNN networks are trained directly in the spike domain using surrogate gradient learning and backpropagation through time. Experimental results show that the proposed SNN provides a level of performance close to the state of the art with CNN based solutions. Specifically, our SNN achieves 30.18 dB of signal-to-noise ratio on the Set12 dataset, which is only 0.25 dB below the performance of the equivalent DCNN. Moreover we show that this performance can be achieved with low latency, i.e., using few timesteps, and with a significant level of sparsity. Finally, we analyze the energy consumption for different network latencies and network sizes. We show that the energy consumption of SNNs increases with longer latencies, making them more energy efficient compared to CNNs only for very small inference latencies. However, we also show that by increasing the network size, SNNs can provide competitive denoising performance while reducing the energy consumption by 20%.
A Survey on Dataset Distillation: Approaches, Applications and Future Directions

Dataset distillation is attracting more attention in machine learning as training sets continue to grow and the cost of training state-of-the-art models becomes increasingly high. By synthesizing datasets with high information density, dataset distillation offers a range of potential applications, including support for continual learning, neural architecture search, and privacy protection. Despite recent advances, we lack a holistic understanding of the approaches and applications. Our survey aims to bridge this gap by first proposing a taxonomy of dataset distillation, characterizing existing approaches, and then systematically reviewing the data modalities, and related applications. In addition, we summarize the challenges and discuss future directions for this field of research.
Quantitative Reasoning and Structural Complexity for Claim-Centric Argumentation

Argumentation is a well-established formalism for nonmonotonic reasoning and a vibrant area of research in AI. Claim-augmented argumentation frameworks (CAFs) have been introduced to deploy a conclusion-oriented perspective. CAFs expand argumentation frameworks by an additional step which involves retaining claims for an accepted set of arguments. We introduce a novel concept of a justification status for claims, a quantitative measure of extensions supporting a particular claim. The well-studied problems of credulous and skeptical reasoning can then be seen as simply the two endpoints of the spectrum when considered as a justification level of a claim. Furthermore, we explore the parameterized complexity of various reasoning problems for CAFs, including the quantitative reasoning for claim assertions. We begin by presenting a suitable graph representation that includes arguments and their associated claims. Our analysis includes the parameter treewidth, and we present decomposition-guided reductions between reasoning problems in CAF and the validity problem for QBF.