Reviewerly/exHarmony huggingface dataset:
Multimodal Approach to Recommend Movie Genres Based on Multi Datasets

Objectives:The main purpose of this study is to implement the multimodal as well as the multilabel approach of accurate movie genre classification using the Actor, Director, Writer, Poster Images, and Synopsis data.The various kind of data collected from IMDB as well as existing datasets are used to train the models.Methods: The 5 deep learning models are created on Poster Images, Text Data, Actors Data, Directors Data, and Writers Data.These models are then combined using the weighted average ensemble model of deep learning.The final output gives the prediction scores of the top 3 genres for a given movie.The main deep learning model used for poster image model training is CNN.The LSTM model is used for text/synopsis model training.The multilabel along with the multimodal approach yielded good results in terms of accurate predictions than the existing models.Findings: The total F1 Score is 0.65.Along with good results, it has some limitations which are further discussed in the paper.Novelty: The novelty of the work lies in 3 major aspects, firstly the consideration of the Actor, Director, and Writer data for the genre classification, as usually, an actor does a particular kind of movie, a writer writes a particular type of movie and director directs a particular type or genre movie.Although there are many exceptions but usually this is the case.The second novelty lies in combining the models using the weighted average method wherein every model is assigned a weight for how much it will contribute to the final genre classification.And lastly, the novelty lies in how the weights are defined i.e., we used the correlation method to determine the weights of every individual model for genre classification.
An Optimization-Based Order-and-Cut Approach for Fair Clustering of Data Sets

Machine learning algorithms have been increasingly integrated into applications that significantly affect human lives. This surged an interest in designing algorithms that train machine learning models to minimize training error and imposing a certain level of fairness. In this paper, we consider the problem of fair clustering of data sets. In particular, given a set of items each associated with a vector of nonsensitive attribute values and a categorical sensitive attribute (e.g., gender, race, etc.), our goal is to find a clustering of the items that minimizes the loss (i.e., clustering objective) function and imposes fairness measured by Rényi correlation. We propose an efficient and scalable in-processing algorithm, driven by findings from the field of combinatorial optimization, that heuristically solves the underlying optimization problem and allows for regulating the trade-off between clustering quality and fairness. The approach does not restrict the analysis to a specific loss function, but instead considers a more general form that satisfies certain desirable properties. This broadens the scope of the algorithm’s applicability. We demonstrate the effectiveness of the algorithm for the specific case of k-means clustering as it is one of the most extensively studied and widely adopted clustering schemes. Our numerical experiments reveal the proposed algorithm significantly outperforms existing methods by providing a more effective mechanism to regulate the trade-off between loss and fairness. History: Rema Padman served as the senior editor for this article. Data Ethics &amp; Reproducibility Note: The code capsule is available on Code Ocean at https://doi.org/10.24433/CO.9556728.v1 and in the e-Companion to the this article (available at https://doi.org/10.1287/ijds.2022.0005 ).
When is argumentation deductive?

This paper discusses and compares various answers to the question when argumentation is deductive. This includes an answer to the questions when argumentation is defeasible and whether defeasible argumentation is a subclass of deductive argumentation or whether it is a distinct form of argumentation. It is concluded that deductive and defeasible argumentation as conceived by Philosophers like Pollock and Rescher and as formalised in the ASPIC+ framework and systems like Defeasible Logic Programming, are semantically different categories. For this reason, purely syntactic base logic approaches to formal argumentation are unsuitable for characterising this distinction.
Gaze estimation based on swin transformer

The direction of human eye gaze is an important human behavior information that reflects the level of attention and cognitive state of the gazer towards various visual information in the environment. Eye gaze estimation has wide application value in multiple fields such as medical care, market research, and human-computer interaction. In recent years, some studies have introduced Transformer into the task of eye gaze estimation and achieved advanced performance. Although Transformer has better global modeling ability, its structural characteristics are not suitable for multi-scale feature learning in visual tasks. In addition, the global self-attention calculation for images has high complexity. This paper introduces Swin Transformer into the field of eye gaze estimation, using self-attention mechanism to perform more flexible and effective global modeling of images. The self-attention calculation uses Windows Multi-head Self-Attention(W-MSA) and Shifted Windows Multi-head Self-Attention (SW-MSA), which greatly reduces the calculation of image self-attention. The experimental results demonstrate that the Swin Transformer can obtain good results in the task of eye gaze estimation
Defect in photonic time crystals

Photonic time crystals (PTCs) provide a completely new platform exhibiting light wave amplification owing to periodically varying electromagnetic properties. The need to control this amplification is becoming increasingly important, especially with the emergence of metasurface-based practical realization of PTCs. This paper investigates the introduction of isolated temporal defect in PTCs to establish an increased degree of control over the amplification. We find that in the presence of the defect, the transmittance and reflectance become close to unity for a specific value of momentum ${k}_{d}$ within the band gaps accompanied by a significant impact on the amount of amplification. We show the impact of the temporal defect on the exponential growth of the intensity with PTC periods. The effect primarily depends on the Floquet frequency of the PTC, which becomes real at ${k}_{d}$ giving rise to four pulses instead of two as an outcome of gap propagation. We further demonstrate that by manipulating the temporal and dielectric properties of the defect, the defect state in the momentum can be tuned to serve the design interest for specialty applications.
A mobile package recommendation method based on grid search combined with XGBoost model

The package recommendation has always been an important issue in the marketing of mobile operators, and machine learning provides a new solution for operators. Aiming at the problem that too many training times of dirty data in the existing methods lead to the reduction of prediction accuracy and the tedious manual setting of model parameters, this paper propose a model combining grid search and XGBoost, and use the exhaustive search method to find the parameter value with the highest accuracy in the validation set within the parameter range of the given XGBoost. Compared with Random Forest and XGBoost default parameter values, experiments show that the proposed model has higher prediction accuracy and can effectively avoids the error caused by manual adjustment of parameters.
Privacy preservation for federated learning based on Gaussian noise scrambling

This article discusses a privacy preservation for federated learning based on gaussian noise scrambling. The main objective of this model is to protect user data privacy and security by aggregating model parameters from multiple parties instead of raw data sets. However, attackers may still obtain sensitive information from the model parameter information transmitted during federated learning training through certain means. To address this issue, we propose a differential privacy noise addition scheme for federated learning. By adding noise to transmitted model parameters to some extent, it prevents attackers from inferring participant information through reverse inference. We also study Gaussian noise mechanism in differential privacy protection and prove it by combining characteristics of federated learning and blockchain technology for adding Gaussian noise size according to differential privacy requirements. This article tests their proposed differential privacy protection mechanism using the NSL-KDD dataset and finds that after adding differential privacy protection, models still have good anomaly recognition performance. This study contributes to the field of federated learning by proposing a novel approach for protecting user data privacy and security during training. The proposed approach can be used in various applications such as healthcare, finance, and social media where data privacy is crucial.
Evolution of response time and accuracy on online mastery practice assignments for introductory physics students

We have investigated the temporal patterns of algebra (N=606) and calculus (N=507) introductory physics students practicing multiple basic physics topics several times throughout the semester using an online mastery homework application called science, technology, engineering, and mathematics (STEM) fluency aimed at improving basic physics skills. For all skill practice categories, we observed an increase in measures of student accuracy, such as a decrease in the number of questions attempted to reach mastery, and a decrease in response time per question, resulting in an overall decrease in the total time spent on the assignments. The findings in this study show that there are several factors that impact a student’s performance and evolution on the mastery assignments throughout the semester. For example, using linear mixed modeling, we report that students with lower math preparation for the physics class start with lower accuracy and slower response times on the mastery assignments than students with higher math preparation. However, by the end of the semester, the less prepared students reach similar performance levels to their more prepared classmates on the mastery assignments. This suggests that STEM fluency is a useful tool for instructors to implement to refresh student’s basic math skills. Additionally, gender and procrastination habits impact the effectiveness and progression of the student’s response time and accuracy on the STEM fluency assignments throughout the semester. We find that women initially answer more questions in the same amount of time as men before reaching mastery. As the semester progresses and students practice the categories more, this performance gap diminishes between males and females. In addition, we find that students who procrastinate (those who wait until the final few hours to complete the assignments) are spending more time on the assignments despite answering a similar number of questions as compared to students who do not procrastinate. We also find that student mindset (growth vs fixed mindset) was not related to a student’s progress on the online mastery assignments. Finally, we find that STEM fluency practice improves performance beyond the effects of other components of instruction, such as lectures, group-work recitations, and homework assignments.5 MoreReceived 29 April 2022Revised 23 March 2023Accepted 15 June 2023DOI:https://doi.org/10.1103/PhysRevPhysEducRes.19.020111Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article’s title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasInstructional strategiesLearning environmentStudent preparationProfessional TopicsLower undergraduate studentsPhysics Education Research
A dense formation control method for UAVs based on improved ant colony algorithm

The classical Ant Colony Optimization (ACO) has some problems such as slow convergence speed and easy to fall into local optimum, a control method for dense formation of UAVs is proposed in this thesis. Firstly, cubic mapping is used to initialize the ACO distribution for making full use of map information and avoiding falling into local optimum; Secondly, the pheromone concentration updated by reward and punishment mechanism. Adaptive pheromone volatility factor is used to balance the global search and local search, as well as accelerate the convergence of the algorithm; Then, the artificial potential field is combined with the ACO to optimize the obstacle avoidance path. Finally, the state information from the dense formation of UAVs is corrected according to the consistency control theory, and the formation consistency control model is designed to realize the dense formation control. The experimental results shows that the proposed algorithm can achieve obstacle avoidance while maintaining tight formation, the formation can converge to the desired formation quickly after the obstacle avoidance is completed.
Multi-label topic classification model of COVID-19 literature

One of the most significant aims of natural language processing is automatic knowledge extraction. The amount of COVID-19 literature is increasing by ten thousand each month, which greatly complicates manual annotation and downstream activities. In this paper, we describe a system for biomedical multic-label topic classification. Firstly, BERT is pre-trained on PMC and PubMed biomedical corpora which helps to capture deep semantic information. Additionally, we fine-tune the pre-trained BERT using the COVID-19 literature from the LitCovid Database. Finally, we predict the topic of LitCovid scientific literature using the novel model. The experimental results of our model on the BioCreative LitCovid corpus achieves a micro F-score of 91.14%, which is 1.29 percentage points higher than BERT. The F-scores of the our model are 1.33%, 2.32%, 0.27%, 0.44%, 6.91%, 14.14% higher than BERT on Treatment, Diagnosis, Prevention, Mechanism, Transmission, Epidemic Forecasting topics respectively, which demonstrates the potential and effectiveness of the proposed framework.
A comprehensive energy services knowledge graph completion method based on a filter-feedback mechanism

In order to achieve a balanced breadth and depth of knowledge graph in the energy domain and alleviate the high complexity of information sources during energy service, a comprehensive energy service knowledge graph construction and completion method based on a filter-feedback mechanism is proposed. Firstly, the domain filtering mechanism is designed to filter the information in the input text unrelated to the energy domain by calculating the entity commonality. Then, a feedback mechanism is constructed using third-party information to complete the missing entities or relations in the triplet. The method is validated on energy domain and public datasets, and experimental results demonstrate that the accuracy of domain knowledge discrimination exceeds 90%. Moreover, its performance is comparable to that of the public dataset.
Multi-turn automatic question answering based entity-relation extraction method for power technology standards

The goal of entity relation extraction is to extract entities and the relations between entities from unstructured texts. Most of the existing research approaches are oriented towards common entity labels in general-purpose domains (e.g., time, place, person, institution, etc.) and simple texts in specialized domains (texts consisting of single sentences with low knowledge density). The existence of long-distance dependencies of entity pairs (cross-sentence entity pairs) and the phenomenon of overlapping relations (different relations sharing the same entity) in complex texts are ignored. However, complex texts are common in practical applications, especially in professional fields such as power technology standards, where the knowledge density of texts is high and the phenomenon of entity-pair cross-sentence dependency is significant. To solve the above problems, this paper proposes a novel multi-hop automatic question-and-answer-based entity relation extraction method, which combines the current well-established machine reading comprehension framework with the automatic question construction mechanism proposed in this paper, and uses the a priori knowledge provided by the question as the extraction type guide, and uses the multi-hop question-and-answer mechanism to reason about the answer span of the question, effectively alleviating the phenomena of overlapping relations and entity dependence on crosssentences in complex texts. We conducted extensive comparative experiments on the power technology standard dataset self-constructed in this paper, and the results show that the MT-auQA model proposed in this paper achieves optimal performance
An inpainting method for paper-based photos

To enhance the quality of paper-based photographic images, this paper proposes an image optimization method based on Criminisi and CLAHE techniques. To eliminate the impact of plastic film and other background factors on the restoration of paper-based photographs during contrast enhancement, geometric transformations such as cropping and flipping are first applied to the image. The preprocessed image is then subjected to channel separation, and the CLAHE method is used to enhance the contrast of the R, G, and B channels separately. After channel merging, an enhanced image is obtained. The Scharr operator is used to accurately obtain the boundary of the image to be restored, and the Criminisi algorithm is employed to implement inpainting of the selected area. The experiments show that the method proposed in this paper can achieve optimization of paper-based photographic images to a certain extent, and can be further applied to inpainting tasks for old photographs and early videos.
Method of insulator defect detection based on the improved YOLOv5s

Aiming at the problem of low accuracy in detecting small defects in insulator aerial images using the current YOLOv5s target detection network, this paper designs an insulator defect detection algorithm based on improved YOLOv5s. Firstly, in the feature extraction stage, a PST attention module is embedded to suppress redundant interfering feature information; The guidance network grasps effective features for learning during the training process. Secondly, a feature fusion module with a bilateral structure is used in the feature fusion process, making full use of contextual semantic information to improve the accuracy of small-scale target detection. The experimental results show that the average accuracy of the improved YOLOv5 algorithm is 81.82%, which is 4.71 percentage points higher than YOLOv5s algorithm and 1.2 percentage points higher than YOLOX-S. This indicates that the improved YOLOv5s network model used in this paper has good results in the detection of insulator defects and has certain application value in actual power inspection tasks.
Research hotspots and trend analysis of text generation for explainable recommendation based on CiteSpace

Bibliometric analysis was applied to retrieve the international core journals on the subject of text generation for an explainable recommendation from 2000 to 2022. CiteSpace, a visualization-based analysis tool, was used to analyze the research status and recent development of text generation for explainable recommendations by mapping the co-occurrence of high-frequency keywords and citation bursts. The results show that in the past 22 years, the number of international articles on text generation for an explainable recommendation has been on the rise with more publications especially compared with that in China, calling for further exchange and collaboration among scholars and institutions over the world to facilitate the research progress. “Personalized” recommendation has been exerting its influence in text generation for explainable recommendations, which effectively gain the trust of users, and increase the persuasiveness and satisfaction of the recommendation system by providing recommendations with explainable texts. Text processing in Deep Learning has now been widely used for explainable recommendations and will throw its weight further in the future.
Optimized Implementation of Argon2 Utilizing the Graphics Processing Unit

In modern information technology systems, secure storage and transmission of personal and sensitive data are recognized as important tasks. These requirements are achieved through secure and robust encryption methods. Argon2 is an advanced cryptographic algorithm that emerged as the winner in the Password Hashing Competition (PHC), offering a concrete and secure measure. Argon2 also provides a secure mechanism against side-channel attacks and cracking attacks using parallel processing (e.g., GPU). In this paper, we analyze the existing GPU-based implementation of the Argon2 algorithm and further optimize the implementation by improving the performance of the hashing function during the computation process. The proposed method focuses on enhancing performance by distributing tasks between CPU and GPU units, reducing the data transfer cost for efficient GPU-based parallel processing. By shifting several stages from the CPU to the GPU, the data transfer cost is significantly reduced, resulting in faster processing times, particularly when handling a larger number of passwords and higher levels of parallelism. Additionally, we optimize the utilization of the GPU’s shared memory, which enhances memory access speed, especially in the computation of the hash value generation process. Furthermore, we leverage the parallel processing capabilities of the GPU to perform efficient brute-force attacks. By computing the H function on the GPU, the proposed implementation can generate initial blocks for multiple inputs in a single operation, making brute-force attacks in an efficient way. The proposed implementation outperforms existing methods, especially when processing a larger number of passwords and operating at higher levels of parallelism.
A Multi-Behavior Recommendation Method for Users Based on Graph Neural Networks

Most existing recommendation models only consider single user–item interaction information, which leads to serious cold-start or data sparsity problems. In practical applications, a user’s behavior is multi-type, and different types of user behavior show different semantic information. To achieve more accurate recommendations, a major challenge comes from being able to handle heterogeneous behavior data from users more finely. To address this problem, this paper proposes a multi-behavior recommendation framework based on a graph neural network, which captures personalized semantics of specific behavior and thus distinguishes the importance of different behaviors for predicting the target behavior. Meanwhile, this model establishes dependency relationships among different types of interaction behaviors under the graph-based information transfer network, and the graph convolutional network is further used to capture the high-order complexity of interaction graphs. The experimental results of three benchmark datasets show that the proposed graph-based multi-behavior recommendation model displays significant improvements in recommendation accuracy compared to the baseline method.
VERNADSKY’S SCIENTIFIC PARADIGM AS THE BASIS FOR THE EVOLUTIONARY VARIABILITY IN EDUCATION

The importance and content of the ideas of V.I. Vernadsky for the evolution of mankind is considered. Social transformation gives rise to the need for an accelerated reformation of education. The concept of evolutionary education based on the implementation of Vernadsky’s ideas and observance of the world development laws is proposed.