Reviewerly/exHarmony huggingface dataset:
Generalized hamming weights of linear codes from cryptographic functions

<p style='text-indent:20px;'>The generalized Hamming weights of linear codes have attracted scholars' attention since Wei used them to characterize the cryptography performance of a linear code over the wire-tap channel of type II in 1991. Generally speaking, it is hard to determine linear codes' generalized Hamming weights, especially the weight hierarchy. On the other hand, since Ding and Niederreiter presented a generic approach to construct linear codes with good properties by suitable defining sets in 2007, many linear codes with few weights have been obtained using cryptographic functions. However, there does not seem to be much research on the generalized Hamming weights of linear codes from cryptographic functions. <p style='text-indent:20px;'>In this paper, we first provide a general formula to compute the generalized Hamming weights of linear codes from the defining sets, which generalizes several known results. Next, some weight hierarchies of linear codes from three famous classes of Bent functions (the Maiorana-McFarland class, the class <inline-formula><tex-math id="M1">\begin{document}$ \mathcal{PS}_{ap} $\end{document}</tex-math></inline-formula>, the class <inline-formula><tex-math id="M2">\begin{document}$ H $\end{document}</tex-math></inline-formula>) and two classes of Semi-Bent functions (the Maiorana-McFarland class, the summation of <inline-formula><tex-math id="M3">\begin{document}$ \mathcal{PS}_{ap} $\end{document}</tex-math></inline-formula> and <inline-formula><tex-math id="M4">\begin{document}$ H $\end{document}</tex-math></inline-formula>) are considered. We hope this paper can attract interested scholars to find more results about the generalized Hamming weights of linear codes from cryptographic functions.
Development of ROV operator assistant system based on scan imaging sonar

This paper designs an underwater map construction algorithm for ROV. The algorithm takes ROS as the framework, uses imaging sonar and IMU sensor, and completes path display and map mosaic based on OpenCV. This algorithm obtains the real-time pose and moving path through IMU, and stitches the sonar imaging at each interval on the moving path, so as to obtain the map of the whole region. In the aspect of mileage calculation, this algorithm calculates the difference based on the distance measured by sonar and combines it with IMU angle. Compared with the direct integration calculation using inertial navigation, the error is smaller and the accuracy of the map is improved.
BLOCK PROGRAMMING AND TRAINING IN INFORMATION TECHNOLOGIES IN HIGH SCHOOL

For several years now, the education in informatics and programming in high school has been realized only as a profiled or elective subject. On the other hand, information technologies are studied in all forms and participate as a profiling subject in many professions and specialized classes. The problem related to the construction of digital competencies in coding is already successfully solved with the introduction of computer modelling from 3rd grade as a compulsory subject. However, most of today’s high school students have deficits in these competencies. The article considers an approach for application of block programming in elective modules in the profiled training in IT in the second-high school degree. Solving specific practical tasks through coding increases the activity and motivation of students and allows them to build the key competencies necessary for their successful professional realization in the digital age.
Student Self-Regulation during Online Lecture During the Covid-19 Pandemic at Muhammadiyah University of Sidoarjo

Self-regulation is an impulse from within (internal), which can lead to an individual's desire to determine or identify goals in life, plan strategies that will be carried out in achieving these goals, and evaluate and modify behavior. that will do. The purpose of this study was to find out how the description of self-regulation in students during online lectures at the Muhammadiyah University of Sidoarjo. The population selected in this study were all students from 5 faculties at the Muhammadiyah University of Sidoarjo, totaling 11.914 students with the characteristics of active students, semester I – VIII, and taking online lectures. The total population in this study was 11.914, so that when viewed from the table Krejcie and Morgan, a sample of 375 people was obtained. This study uses a data collection technique in the form of a psychological scale, namely a self-regulation scale developed by the researcher through aspects of self-regulation according to Zimmerman (Ghofar, 2014) with a total of 54 statements in the form of a Likert scale. From the results of the validity test, 46 items were valid from 54 items. The result of the reliability test is 0.939. The results showed that students' self-regulation during online lectures at Muhammadiyah University of Sidoarjo was generally in the moderate category. And the aspect of self-regulation that is most widely applied by students of Muhammadiyah University of Sidoarjo is the aspect of motivation.
Human location estimation in a room using the multiple nodes with optical distance sensors

In this paper, we proposed indoor human location estimation by the multiple sensor nodes for monitoring the elderly indoors naturally. Optical distance sensor nodes are equipped with Bluetooth Low Energy (BLE) communication function. These nodes gather the data that measure the distance to a person using distance sensors and transmit the data to the management system. Using this data, the management system estimates human location using machine learning as multi-class classification problem. The experiment showed the potential of these nodes and this method to be used as a location estimator.
2. The Life Spans of Genres

Abstract Disagreements about the history of genre are hard to resolve because contemporary readers inevitably see earlier works through a retrospective lens. We may call the novels of Mary Shelley "science fiction," for instance, but that phrase didn't exist when she wrote them, and it is difficult to be sure that the genealogical connections we perceive aren't shaped by a need to find ourselves reflected in the past. There may be no perfect solution to this dilemma, but statistical models of genre do provide a new kind of leverage on it, because a model defined only by nineteenth-century examples can be genuinely ignorant about the present, and can serve as a proxy for the literary standards of a vanished era. Using this method, the chapter examines the history of science fiction, detective fiction, and the Gothic. The generational divides predicted by Franco Moretti do not appear. Models trained only on pre-Gernsback "scientific romance" are capable of recognizing their kinship to contemporary science fiction. Detective fiction, similarly, is revealed as a tightly unified genre, with a continuity that stretches back to Edgar Allan Poe. The Gothic, on the other hand, falls apart into several different subgenres.
DGMLP: Deformable Gating MLP Sharing for Multi-Task Learning

With the development of deep learning, many dense prediction tasks have been significantly improved. In this work, we introduce a DGMLP model that jointly learns multiple dense prediction tasks in a unified multi-task learning architecture that is trained end-to-end. Specifically, the DGMLP consists of (i) a spatial deformable MLP to capture the valuable spatial information for different tasks and (ii) a spatial gating MLP to learn the shared feature across all the tasks. Deformable MLP can adaptively adjust the receptive field and sample valuable locations in this approach. In addition, the Gating MLP is adopted to learn task-relevant features for each task. We take advantage of the spatial deformable MLP and spatial gating MLP to build a new MLP-like architecture that is especially simple and effective for multiple visual dense prediction tasks. We provide extensive experiments and evaluations to verify the advantages of our approach, and the extensive experiments demonstrate the superiority of the proposed framework over state-of-the-art methods.
Specification of the Base Measure of Nonparametric Priors via Random Means

Gaffi, Francesco Lijoi, Antonio Prünster, IgorFunctionals of random probability measures are probabilistic objects whose properties are studied in different fields. They also play an important role in Bayesian Nonparametrics: understanding the behavior of a finite dimensional feature of a flexible and infinite-dimensional prior is crucial for prior elicitation. In particular distributions of means of nonparametric priors have been the object of thorough investigation in the literature. We target the inverse path: the determination of the parameter measure of a random probability measure giving rise to a fixed mean distribution. This direction yields a better understanding of the sets of mean distributions of notable nonparametric priors, giving moreover a way to directly enforce prior information, without losing inferential power. Here we summarize and report results obtained in [6] for the Dirichlet process, the normalized stable random measure and the Pitman–Yor process, with an application to mixture models.
Inovasi Bahan Ajar Baca Puisi melalui Multimedia 3D Flipbook

The purpose of this study is to describe the innovation of poetry reading teaching materials through 3D Flipbook multimedia. The method used in the research is descriptive procedural research method. The reference is ADDIE model development research. What is described is 3D multimedia Flipbook for learning to read poetry. Data collection was carried out using a closed questionnaire. There were 24 respondents, who were seventh semester students of the Indonesian Language and Literature Education Study Program, FKIP Jambi University. The results of the study illustrate that the 3D Flipbook multimedia when used by lecturers in teaching is an actual medium, brings innovation, has an appeal, and makes it easier to follow the lecture flow and examples. The average respondent's assessment for all aspects assessed is 4.60. Aspects of media appearance were assessed with an average of 4.70 (decent category); the content aspect of poetry reading material is assessed with an average of 4.50 (very decent category); and the learning aspect was assessed with an average of 4.60 (very feasible category).
Artefactual Skills for Simulating Impasto Effect in Digital Painting: An Adobe Photoshop Solution

Impasto is one of the fascinating techniques in studio painting that traces its foundation to the period between the 17 th and 19 th century Italian painters like van Gogh.It is characterised by thick application of paint in a plastering mode to artistically model forms on a canvas using either a bristle brush or a palette knife.When effectively executed, the technique finishes with the effect of ridges that cast shadows under grooves created by the thick application to give the painting its unique tactile contrasting appearance.In digital painting, the painterly effect of impasto is simply impossible to execute.Most of the well-known graphic application software cannot offer any applicator that can render the characteristic effect excellently.The author of this paper has developed a practical scheme for achieving this result using the Craquelure Filter in Adobe Photoshop version 21.The principal objective of the author is to employ the descriptive and demonstrational research methods to illustrate this artefactual scheme, which constitutes a practice-led exercise under the design-based research scheme.In this scheme, the author engages two of the major digital painting techniques: "Digital photo-art" and "Traditional digital painting" techniques to execute a figure painting composition in impasto style.
Research on Worksite’s Video Image Safety Helmet Detection Method Based on Cloud-edge Cooperation

In order to ensure the safety of worksites staff, solve the problem that worksite monitoring video transmission occupies many network resources, has high time delay and cannot detect staff not wearing safety helmets in real time, a method for detecting safety helmets in video images of worksites based on cloud-edge cooperation is designed. The YOLOv3 algorithm is used for training the helmet detection model. Using the feature that the cloud-edge cooperation model can reduce processing latency, tasks such as model training and optimization, which are computationally intensive and have low real-time requirements, are handled by the cloud, and tasks such as target detection, which are relatively less computationally intensive and have high real-time requirements, are handled by the edge. Deploy the helmet detection model on the edge node to process the surveillance video images locally, and alarm in time when the behavior of not wearing a helmet is detected; the cloud server receives the surveillance video data and the processing results of the edge, and sends the information to the edge. The information and data are analyzed and calculated, the model is updated and optimized, and then the optimized model is pushed to the edge nodes, so as to realize the data interaction and automatic model optimization of the whole system. The experimental results show that this system can significantly reduce the surveillance video processing delay while maintaining a high recognition rate.
“Like, Subscribe, and Share”: Students' Views on Using YouTube Videos to Improve Their English Vocabulary

In the effort of learning speaking at school, oftentimes students should face difficulty to master the skill. Many EFL students struggle to speak in English because they have insufficient vocabulary to express their opinion. Hence, improving vocabulary has become one of the many focuses for teachers in teaching English. Oftentimes, teachers use YouTube as a media to improve students’ vocabulary. This paper aims to investigate and describe deeper insights on students’ perspective on using YouTube videos to improve vocabulary in speaking English. Employing a descriptive qualitative method, an open-ended questionnaire administered to 103 senior high school students. The result of the study showed that the majority of the students admitted that they have a positive perspective in using YouTube as a media to improve their vocabulary. Nevertheless, some students also prefer to learn vocabulary from the teachers in the usual classroom settings.
Tooled approach to interoperability monitoring with traceability for message-driven systems

Message-driven information systems allow for asynchronous data exchange of messages via a publish/subscribe mechanism. This is achieved by setting up data exchanges in form of a message-oriented-middleware (MOM) between the collaborating systems. Traceability provides a method for the persistence of the message intermediate states across the message lifecycle allowing for retransmission. This research proposes a model for traceability and metrics generation, not only from the message broker but from the producer and consumer clients interacting via HTTP. A messaging metamodel is proposed for aggregating interaction metadata. We develop this model using RabbitMQ an open-source message broker and Spring Framework enhanced to include traceability interfaces. A web application-compatible tool was designed, prototyped, and implemented based on this metamodel. This approach helps not only for monitoring interactions but for the detection of potential interoperability-related issues and improving overall message throughput. A case study was used to demonstrate the feasibility of this approach.
A STUDY OF READING HABITS OF SELECTED EXTENDED PROGRAMME STUDENTS AT A SOUTH AFRICAN UNIVERSITY

Ponte Academic JournalDec 2022, Volume 78, Issue 12 A STUDY OF READING HABITS OF SELECTED EXTENDED PROGRAMME STUDENTS AT A SOUTH AFRICAN UNIVERSITYAuthor(s): Stenford Matenda ,Rumbidzai NcubeJ. Ponte - Dec 2022 - Volume 78 - Issue 12 doi: 10.21506/j.ponte.2022.12.4 Abstract:Research on the importance of reading over the years has consistently shown that reading has social, psychological, moral, emotional, and cognitive benefits. In higher education contexts, reading has been found to be the substratum of critical and communication skills that are essential for a successful and rewarding life. Through reading, students acquire collective wisdom, and self-knowledge, organise human experience and enable students to prepare for life experiences. This study surveyed the reading habits of selected extended programme students at a South African University. The study was undertaken at one of South Africa’s historically disadvantaged institutions and most participants were enrolled in diploma-extended programmes in human resources management, management, public relations management, and a national higher certificate in accounting. A mixed methods design was used for this study and questionnaires and focus groups were used to collect data. The study established that while students acknowledge that there is a positive correlation between reading and academic performance, the average reading time per week, the frequency of visiting the library and the habit borrowing books is quite low. Furthermore, technology is shown to influence what is read, where reading occurs, and when reading takes place. Laziness and social life are largely blamed for the bad habits. It was that students’ reading habits do negatively affect students’ success and recommend the implementation of university interventions to improve reading cultures. Download full text:Check if you have access through your login credentials or your institution Username Password
Extracting Information about Research Resources from Scholarly Papers

This paper presents a method to extract information about research resource metadata from scholarly papers to expand research resource repositories using an academic knowledge graph. Here, we considered the hypothesis that information extracted from scholarly papers is beneficial to expand research resource repositories from two perspectives: (1) the amount of information in existing metadata, and (2) the number of entries in existing repositories. We constructed an academic knowledge graph, where the nodes and directed edges in the graph correspond to the entities and their relations in scholarly papers, respectively. To verify our hypothesis, we constructed a knowledge graph using 15,721 papers published in international conferences. We then investigated the expandability of a language resource metadata repository using the constructed knowledge graph. The experimental results demonstrated that the constructed knowledge graph could be used to enrich the descriptions of metadata and increase the number of entries in the repository.
Optimization of Solar PV Power Forecasting Using Bootstrap Techniques and the Feed-Forward Neural Network Model

The outbreak of the COVID-19 disease has exerted a deep and extensive influence on the energy sector. The work modality and lifestyle caused by the confinement policy have increased electricity consumption in the residential sector. In such a way that the application of photovoltaic solar energy (PV) is rapidly evolving to mitigate the problems caused. However, due to the variability and uncertainty of solar irradiance, several technical challenges are created to produce PV energy. To reduce these adverse effects, forecasting of energy production at multiple scales is used. In this sense, the objective of this study is to determine the forecast performance of a hybrid model through the application of a Feed-Forward Neural Network (FFNN), together with the application of the moving block bootstrap technique (MBB), using the real data of the production of a PV system. The results show that the FFNN method combined with MBB techniques consistently outperform the original FFNN method in terms of forecast accuracy. That is, the original model presents a performance of 4.48% percentage forecast error (MAPE), compared to 3.14% fo r the proposed hybrid model. Finally, through the Ljung-Box test it isshown that the results are not correlated; therefore, the recommended model is validated.
Analisis Sentimen Ulasan Aplikasi Transportasi Online Menggunakan Multinomial Naïve Bayes dan Query Expansion Ranking

The rapid development of the transportation industry in recent years has led to a new innovation in the field of transportation, namely the application of online transportation services. To facilitate the translation of user satisfaction, in addition to users being able to provide reviews, the Google Play Store uses a rating system consisting of a rating of 1 to 5. However, users often do not provide a rating that is in accordance with the review so that this is not enough to determine the sentiment of the review. This research is focused on evaluating the performance of the features selection using Query Expanison Ranking on the Multinomial Naïve Bayes method in the problem of sentiment analysis on the two of most popular online transportation service applications in Indonesia, namely Gojek and Grab. From the results of the performance evaluation using k-fold cross validation, it was found that the best feature selection ratio was 20% with the best performance in terms of precision.
A Study on Japanese Text Multi-classification with ALBERT-TextCNN

Text classification is an essential task in the domain of natural language processing (NLP), which involves assigning a sentence or document to an appropriate category. This paper mainly focuses on using ALBERT-TextCNN for Japanese text classification. First, the data files from Japanese Wikipedia pages are collected and then divided into 31 categories. Next, the ALBERT-TextCNN model for Japanese text classification is built with two steps: 1) select the ALBERT model as the pre-training model; 2) use TextCNN to further extract semantic features from texts. We conducted experiments to compare the ALBERT-TextCNN model using the Sentencepiece tokenizer with other state-of-the-art models. The results show that the performance is improved by about 14.5%, 11.6%, 13.8%, and 13.3% in value evaluation metrics like Accuracy, Precision, Recall, and F1-score, which shows that the ALBERT-TextCNN model can be used to classify Japanese text effectively.
An adapted model for assessing the maturity of a higher education organization

The article is devoted to solving the problem of assessing the level of maturity of a higher education institution. The article considers both a methodology for assessing maturity for one period (year) and tracking the history of changes in maturity levels. The object of the study was the directorate of the Institute of Informatics and Telecommunications of the University of Science and Technology named after M.F. Reshetnev. Self-assessment is carried out based on surveys of teachers and employees of university management services, including former ones, for different periods. The structure of the study is based on the Voyager Plant Optimization method; the questionnaires were developed following the McKinsey model and adapted to the system of higher professional education in Russia. The results obtained reflect the stable, progressive development of the university management system over review.