Reviewerly/exHarmony huggingface dataset:
Valued rank-metric codes

In this paper, we study linear spaces of matrices defined over discretely valued fields and discuss their dimension and minimal rank drops over the associated residue fields. To this end, we take first steps into the theory of rank-metric codes over discrete valuation rings by means of skew algebras derived from Galois extensions of rings. Additionally, we model projectivizations of rank-metric codes via Mustafin varieties, which we then employ to give sufficient conditions for a decrease in the dimension.
Emotional irony detection based on multi-feature fusion

Irony is the use of counterwords, irony and other techniques to reflect the essential characteristics of people and things, and is a way to express emotions. Irony detection is an important research content in the field of sentiment analysis, and it is also one of the hot issues in the research of artificial intelligence in natural language processing. In this paper, a sentimental ironic detection model based on multi-feature fusion is proposed, which takes text features, image features and image attribute features as inputs, and image attribute features are represented by adjective-noun pairs abbreviated as ANPs. In this model, the neural network is first used to extract features from the three inputs, and then the text features and image features are fused through the shared fusion network, and the contrast fusion network fuses the text features and image attribute features, so as to express the contrast and difference of graphic and text information. Finally, satirical classification is carried out through the classification layer. Experimental results show that the model achieves good performance on the public multimodal irony detection dataset.
Unsupervised specific emitter identification of bispectrum features based on contrastive learning

Aiming at the problem that features of communication emitter data without label information are different to extract and the classification acquisition is not high, this paper cites the contrastive learning theory, constructs a residual network with two parameters sharing as the backbone network, conducts contrastive learning on the rectangular integral bispectral features of signal augmented samples, and further extracts the feature presentation with more differentiation. To this end, the feature separability between samples from different emitters is enhanced. Then, the new features extracted are used for contrastive learning at the cluster level to complete the tasks of classification and identification. Compared with other unsupervised learning algorithms, the proposed method achieves a better identification accuracy of about 78% through experiences on the dataset of measured ultra-short wave communication stations.
C4.5 Algorithm Implementation to Predict Student Satisfaction Level of Lecturer’s Performance in the Covid-19 Pandemic

Implementation of education during the emergency period of Covid-19 in Higher Education was carried out at home through online/distance learning. The lecturer is one of the key holders of success in the learning process. Lecturer performance is a main factor needed to improve education and service quality in online learning. In this study, the authors implemented the C4.5 algorithm using RapidMiner 9.10 app to predict student satisfaction with lecturer performance during the Covid-19 pandemic. The data in this study were obtained from a questionnaire distributed to active students in the Computer Science Study Program (class of 2016 - 2021) at the University of Nusa Cendana with 942 records. The attributes used in this study were the lecturer's age, gender, suitability of learning media (SLM), and the competencies of Pedagogic Competence (PeC), Professional Competence (PrC), Personal Competence (PsC), and social competence (SC), with the level of student satisfaction as the target class divided into two, namely Satisfied and Dissatisfied. The dataset is processed using RapidMiner and produces 11 decision rules which show that the attribute PeC has the most significant influence on the level of student satisfaction with lecturer performance during the Covid-19 pandemic and the test results of the decision tree model using cross-validation. The test results show that the C4.5 algorithm has a good performance in predicting levels of student satisfaction with an accuracy rate of 94.8%, precision for the prediction class Dissatisfied and Satisfied of 92.23 % and 95.52%, and recall of the actual Dissatisfied and Satisfied classes of 85.2% and 97.77%.
EHFP-GAN: Edge-Enhanced Hierarchical Feature Pyramid Network for Damaged QR Code Reconstruction

In practical usage, QR codes often become difficult to recognize due to damage. Traditional restoration methods exhibit a limited effectiveness for severely damaged or densely encoded QR codes, are time-consuming, and have limitations in addressing extensive information loss. To tackle these challenges, we propose a two-stage restoration model named the EHFP-GAN, comprising an edge restoration module and a QR code reconstruction module. The edge restoration module guides subsequent restoration by repairing the edge images, resulting in finer edge details. The hierarchical feature pyramid within the QR code reconstruction module enhances the model’s global image perception. Using our custom dataset, we compare the EHFP-GAN against several mainstream image processing models. The results demonstrate the exceptional restoration performance of the EHFP-GAN model. Specifically, across various levels of contamination, the EHFP-GAN achieves significant improvements in the recognition rate and image quality metrics, surpassing the comparative models. For instance, under mild contamination, the EHFP-GAN achieves a recognition rate of 95.35%, while under a random contamination, it reaches 31.94%, both outperforming the comparative models. In conclusion, the EHFP-GAN model demonstrates remarkable efficacy in the restoration of damaged QR codes.
Evaluating YJIT’s Performance in a Production Context: A Pragmatic Approach

Ruby is a dynamically-typed programming language with a large breadth of features which has grown in popularity with the rise of the modern web, and remains at the core of the implementation of widely-used online platforms such as Shopify, GitHub, Discourse, and Mastodon.
Don’t Trust Your Profiler: An Empirical Study on the Precision and Accuracy of Java Profilers (Poster Abstract)

To identify optimisation opportunities, Java developers often use sampling profilers that attribute a percentage of run time to the methods of a program. Even so these profilers use sampling, are probabilistic in nature, and may suffer for instance from safepoint bias, they are normally considered to be relatively reliable. However, unreliable or inaccurate profiles may misdirect developers in their quest to resolve performance issues by not correctly identifying the program parts that would benefit most from optimisations. With the wider adoption of profilers such as async-profiler and Honest Profiler, which are designed to avoid the safepoint bias, we wanted to investigate how precise and accurate Java sampling profilers are today. In this Poster, we investigate the precision, reliability, accuracy, and overhead of async-profiler, Honest Profiler, Java Flight Recorder, JProfiler, perf, and YourKit, which are all actively maintained. We assess them on the fully deterministic Are We Fast Yet benchmarks to have a stable foundation for the probabilistic profilers. We find that profilers are relatively reliable over 30 runs and normally report the same hottest method. Unfortunately, this is not true for all benchmarks, which suggests their reliability may be application-specific. Different profilers also report different methods as hottest and cannot reliably agree on the set of top 5 hottest methods. On the positive side, the average run time overhead is in the range of 1% to 5.4% for the different profilers. Future work should investigate how results can become more reliable, perhaps by reducing the observer effect of profilers by using optimisation decisions of unprofiled runs or by developing a principled approach of combining multiple profiles that explore different dynamic optimisations.
DEVELOPMENT OF BACKPROPAGATION ALGORITHM FOR ESTIMATING SOLAR RADIATION: A CASE STUDY IN TURKEY

Solar energy has an important role in achieving the objective of replacing fossil fuels and reducing greenhouse gas emissions with significant potential. Engineers, architects, and farmers require accurate information about solar radiation in order to develop solar energy systems. It is a common practice for meteorological services around the world to measure the duration of sunshine and air temperature. Despite this, worldwide measurements of solar radiation are extremely rare and some information is lacking. It becomes vitally critical to estimate solar radiation at sites that have not own station. In the literature, a variety of models have been developed to estimate solar radiation. The artificial neural network (ANN) model is commonly used for the estimation of global solar radiation. In this study, a backpropagation algorithm for throughout the year is generated to estimate global solar radiation in Adana by using the meteorological data obtained from Turkish State Meteorological Services. ANN model was developed by using the data for 2014, 2015, and 2016 years with the MATLAB program. The data for 2017 is used for testing the model. A comparison between the developed model and real data is performed depending on the R2 value. As a result of the study, the R2 obtained by training the data was calculated as 0.9019. The R2 value derived from test data was calculated as 0.7277. In light of these results, it can be said that the estimation study was satisfactory.
Non-intersecting curved paths using Bezier curves in the 2D-Euclidean plane for multiple autonomous robots

This paper proposes an algorithm for generating non-intersecting continuous paths for multiple robots having unique source and destination with convex polygonal obstacles in the 2D-Euclidean plane. It generates paths using Dijkstra's algorithm with the edges of the visibility graph of the map. Although Dijkstra's algorithm is used, the generated paths between the source-destinations will not be explicitly 'short paths' as an edge of the visibility graph cannot be used in multiple paths. The robots are prioritised with respect to the Euclidean distance between them. The discrete paths are converted into continuous paths using Bezier curves along the corners of the edges. The algorithm sequentially generate paths one after another based on a priority based on the Euclidean distance between their source and destination.
DRIM: Deviation Ratio Index Based on Medoids

A technique for estimating the number of clusters in a dataset that is flexible for various types and sizes of data, adaptive to any clustering methods, and easy to calculate is discussed in this chapter. A Deviation Ratio Index based on Medoids (DRIM) is the approach we propose. The object distance to the final \(\mathit{k}\)-medoids is utilized to calculate the DRIM technique. The block-based \(\mathit{k}\)-medoids algorithm (Block-KM) and the \(\mathit{k}\)-medoids constructed using the variance of distance (VarD-KM) were applied to obtain these final medoids. Before running the Block-KM and VarD-KM, we select a specific transformation for some datasets. We use ten real datasets to validate the DRI. These data include Vote, Soybean (small), Primary Tumor, Breast Cancer, Ionsphere, Iris, Wine, Zoo, Heart Disease Case 2, and Credit Approval data. The experimental results show that the DRIM technique predicts the number of clusters for the ten real datasets more precisely than other methods. Three types of artificial data to evaluate the proposed method resulted in 76.67% of experiments predicting correctly. Applying the new approach to grouping 62 universities in Indonesia based on data on human resources, education, research, organization, infrastructure, and cooperation produces three easily interpreted groups.
The Impact of Preprocessing Techniques Towards Word Embedding

In this study, we analyze the performance of various pre-processing methods and classification algorithms on health-related tweet data on Twitter. The data set consists of a number of different pre-processing methods, such as Z-score Scaling, Min-max Scaling, Decimal Scaling, Log Transformation, Percentage Scaling, and Log2 Scaling, as well as two main classification algorithms: Naive Bayes and Logistic Regression. The results of the analysis show that the pre-processing method has a significant effect on the performance of the classification algorithm. Z-score Scaling emerges as a stable option and provides good accuracy for both algorithms. However, Min-max Scaling is more suitable for Logistic Regression than Naive Bayes. In addition, Logistic Regression tends to provide higher accuracy in some pre-processing methods. We also suggest further exploration to understand how this pre-processing method might apply to different types of data and its impact on other classification algorithms. In addition, the selection of alternative models, hyperparameter optimization, and data enrichment are areas that can be improved to obtain better classification results. This study underscores the importance of careful pre-processing and selection of appropriate pre-processing methods in applying classification algorithms to text data. The results and recommendations in this study can be a guide for researchers and practitioners in making better decisions in the classification analysis of Twitter data about health.
Effectiveness of Students’ Vocabulary Mastery Using the Comic “Donald Duck” in Junior High School

This research was focused on investigating the effectiveness of students' vocabulary mastery using the comic "Donald Duck" for 7th-grade students in SMP N 25 Semarang. This research aimed to compare the findings of students' vocabulary mastery before and after being taught Donald Duck comic media and to see if there was a significant difference in student achievement in their vocabulary mastery taught before and after using Donald Duck comic media. This research used a quasi-experimental design with two classes: experimental and control. This study's population consisted of 261 students in the seventh grade at SMP N 25 Semarang, and the sample consisted of 64 students from two classes, VIG and VID. Data was collected by giving a pre-test and a post-test and then analyzed in the SPSS program using the t-test formula. The analysis results show that students not taught to use comics have a "poor" ability, as seen from their post-test average score of 60.13. Students taught to use comics had "good" abilities, as seen from their post-test average score of 75.50. So, a t-test of 13,054 is obtained with a t-table of 2,042 at a significance level of 5%. It can be concluded that the t-test is higher than the t-table (13.054&gt;2.042). This shows that there is a significant difference in vocabulary mastery between students who are taught using Donald Duck comics and those who are not taught using Donald Duck comics. This means the alternative hypothesis (Ha) is accepted, and the null hypothesis (Ho) is rejected. Learning vocabulary mastery using Donald Duck comics is effective.
Towards Building the Knowledge Graph for a Collection of Mathematical Articles

This paper describes the process of creating a knowledge graph for a collection of mathematical articles in the Russian language, gathered from the "Izvestiya VUZov. Matematika" journal. The collection consists of approximately 1100 documents in LaTex format. The work involves constructing an ontology for the collection of mathematical articles, which will serve as the basis for the created knowledge graph. Various article objects are extracted from the collection, including universal decimal classification codes, authors, titles, used formulas, articles publication dates, authors affiliations and references to other works. Each object is recorded through a specific relationship in the knowledge graph. Thematic modeling is also performed on the collection using the latent Dirichlet allocation method, for which optimal hyperparameters are selected. The document themes are recorded in the knowledge graph through relationships. An interesting approach is used for extracting mathematical terms. In this work, mathematical entities are identified in the documents using the OntoMathPRO ontology. During the knowledge graph construction process, tools were developed that allow the creation of a knowledge graph on any collection that meets the patterns of the original collection. The resulting knowledge graph can serve as a foundation for various research purposes and the development of intelligent systems, that can be used by researchers, journals, as well as students.
Visualization of argumentation by software

The representation of argumentation is based on various forms of structured and non-structured presentation of arguments. Among them, graphic ones deserve special attention, since they are more expressive and, in addition to practical application, can be used for methodological purposes when teaching argumentation. With the development of information and communication technologies, since the mid-noughties of the XXI century, software has been developed designed to represent argumentation and evidence-based reasoning. Since the middle of the XX century, within the framework of research conducted by argumentation theorists, theoretical approaches to the representation of argumentation have been developed, which form the basis of the representation of argumentation by means of software. The graphical capabilities of modern hardware and software are used for visual representation of argumentation. This study examines the possibilities of existing software for visualizing argumentation. The connection of visualization with the theoretical foundations of the representation of argumentation is analyzed, its features are revealed.
Automatic replenishment of metadata of digital publications using semantic services of the Internet

The article describes approaches to supplementing metadata by means of semantic networks. A method has been developed for supplementing affiliation metadata with the help of the ROR registry - an open registry of identifiers of scientific organizations and a means of making links between ROR and other semantic networks.
Modelling the process of development of a contract soldier’s value attitude towards military service

The aim of the study is to substantiate the importance and necessity of developing value attitude towards military service among contract servicemen. The paper sheds light on the notion of “a contract soldier’s value attitude towards military service”; identifies the specifics of using the modelling method in the context of a pedagogical research on developing value attitude towards professional activities among contract servicemen; presents a model for the process of development of a contract soldier’s value attitude towards military service and describes the main blocks of the model. The scientific novelty of the study lies in the fact that it is the first to present a model for the process of development of a contract soldier’s value attitude towards military service. As a result of the study, a theoretical model for the process of development of a contract soldier’s value attitude towards military service was elaborated. The model includes the main four blocks: the theoretical and methodological block, containing the goal, methodological approaches, principles; the structural and content-related block, consisting of the main structural-content components: motivational-target, content-procedural, emotional-evaluative, resultant and sense-bearing; the operational and activity block, within the framework of which the course programme “Development of a contract soldier’s value attitude towards military service” was implemented and the corresponding forms and methods of value attitude development were used; the evaluative and effective block, reflecting the main criteria, indicators and levels of development of a contract soldier’s value attitude towards military service.
An Enhanced Privacy-Preserving Hierarchical Federated Learning Framework for IoV

The intelligent Internet of Vehicles (IoV) can help alleviate road security issues. However, increasing requirements for data privacy make it difficult for centralized machine learning paradigms to collect sufficient training data, which hinders the development of intelligent IoV. Federated Learning (FL) has emerged as a promising method to overcome this gap. However, traditional FL may leak privacy when encountering attacks such as the Membership Inference Attack. Existing approaches to address this issue either bring significant additional overhead or reduce the accuracy of FL, which are not suitable for the IoV. Therefore, we present a novel hierarchical FL framework called EPHFL. It leverages the Diffie-Hellman algorithm and pseudorandom technology to enhance the privacy of FL while bringing little additional overhead and not reducing the accuracy. Its hierarchical architecture can effectively schedule devices in the IoV to accomplish FL and reduce the communication overhead of each device, dramatically improving our system’s scalability. Moreover, we design a method based on Blockchain and Distributed Hash Table to detect malicious tampering and offset its impact, further guaranteeing FL’s data integrity. Finally, we perform experiments to demonstrate the performance of EPHFL. The results show that our method does not reduce accuracy, and our computation overhead on the user side is much lower than the classic baseline.
Learning to Detect Deepfakes via Adaptive Attention and Constrained Difference

Since facial forgery techniques have made remarkable progress, the area of forgery detection attracts a significant amount of attention due to security concerns. Existing methods attempt to utilize convolutional neural networks (CNNs) to mine discriminative clues for forgery detection. However, most of these coarse-grained and vanilla methods struggle to extract subtle and multiscale clues in forgery detection. To address such problems, we propose a well-designed deep learning framework, named SCA-Net, to exploit subtle, multiscale and multiview clues. Specifically, our framework consists of a skipped channel attention module (SCM), a constrained difference module (CDM) and an adaptive attention module (AAM). First, the skipped channel attention module is used as the backbone to extract sufficient different information, including low-level and high-level features. Then, the constrained difference module captures manipulation clues from the input image based on constrained characteristics. Finally, the adaptive attention module captures multiscale features represented by facial forgery. Moreover, we introduce a combined loss to address the learning difficulty of our framework. The experimental results demonstrate that the proposed model has great detection performance compared with other face forgery detection methods in most cases.