Reviewerly/exHarmony huggingface dataset:
CHFL: A Collaborative Hierarchical Federated Intrusion Detection System for Vehicular Networks

Wireless interfaces, remote control schemes, and increased autonomy have raised the attacks surface of vehicular networks. As powerful monitoring entities, intrusion detection systems (IDS) must be updated and customised to respond to emerging networks' requirements. As server-based monitoring schemes were prone to significant privacy concerns, new privacy constrained learning methods such as federated learning (FL) have received considerable attention in designing IDS. However, to alleviate the efficiency and enhance the scalability of the original FL, this paper proposes a novel collaborative hierarchical federated IDS, named CHFL for the vehicular network. In the CHFL model, a group of vehicles assisted by vehicle-to-everything (V2X) communication technologies can exchange intrusion detection information collaboratively in a private format. Each group nominates a leader, and the leading vehicle serves as the intermediate in the second level detection system of the hierarchical federated model. The leader communicates directly with the server to transmit and receive model updates of its nearby end vehicles. By reducing the number of direct communications to the server, our proposed system reduces network uplink traffic and queuing-processing latency. In addition, CHFL improved the prediction loss and the accuracy of the whole system. We are achieving an accuracy of 99.10% compared with 97.01 % accuracy of the original FL.
Threat-Aware Selection for Target Engagement

This paper investigates the scheduling problem related to engaging a swarm of attacking drones with a single defensive turret. The defending turret must turn, with a limited slew rate, and remain facing a drone for a dwell time to eliminate it. The turret must eliminate all the drones in the swarm before any drone reaches the turret. In 2D, this is an example of a Traveling Salesman Problem with Time Windows (TSPTW) where the turret must visit each target during the window. In 2D, the targets and turret are restricted to a plane and the turret rotates with one degree of freedom. In 3D, the turret can pan and tilt, while the drones attempt to reach a safe zone anywhere along the vertical axis above the turret. This 3D movement makes the problem more challenging, since the azimuth angles of the turret to the drones vary as a function of time. This paper investigates the theoretical optimal solution for simple swarm configurations. It compares heuristic approaches for the path scheduling problem in 2D and 3D using a simulation of the swarm behavior. It provides results for an improved heuristic approach, the Threat-Aware Nearest Neighbor.
A SOCIOLINGUISTIC APPROACH TO ELT: PERSPECTIVES FROM ENGLISH TEACHERS

The use of English seldom makes students have a bad ability to speakand comprehend English as their teacher wants. It caused thestudents can not understand what their teacher has explainedbecause they just have little vocabulary in English. Besides, the text in the students' book used foreign narratives, and foreign activities so that the students can not understand and comprehend the context of the book itself. Because of the condition, the teacher needs to approach to inform their students about English itself. That is whythe sociolinguistic approach is needed in that condition because it can make students understand and comprehend easily the contextoftheir English book. Therefore, this study is the gap to know teachers' perspectives on the use of the sociolinguistic approach in ELT and hindering that the teachers found in the implementation sociolinguistic approach. This study used descriptive qualitative research with 26 participants of English teachers. The findings of this study showed that sociolinguistics gave positive perspectives and benefits to the students.
Pengembangan Lembar Kerja Siswa sebagai Materi Tambahan untuk Kelas Sembilan di SMP NU Gondanglegi

This research is aimed to develop worksheet as supplementary material is to increase understanding and insight into the material that is being or has been studied so that students can learn optimally. In addition, worksheet as supplementary material learning can provide opportunities for students to get new learning to optimize the development of their interests, talents, and skills. This study also examines the feasibility of learning. The procedures carried out in research and development are based on research and development steps according to Borg and Gall (in Sukmadinata, 2013: 169), namely: research and data collection, planning, product draft development, initial field trials, revising test results, field trials, product refinement from field tests, field implementation tests, final product improvements as well as dissemination and implementation. However, in this research and development, only four steps were adapted, namely to the revised product trial step. Learning media is made using Microsoft word and Canva. At development stage, the feasibility of learning media is validated by media and material experts. Based on the data from the results of the validation of the language feasibility above, This can be seen from the percentage of validation results obtained from the media construct aspect average of 76.08%, the material aspect of 78,26%, and from the language aspect of 64%. With this percentage, it can be concluded that the validation of the worksheet material carried out by the validator can be said to be good and feasible. can be used as a medium of learning in the teaching and learning process in schools. Thus, the researcher suggests that future implementation will be needed by teachers, practitioners and researchers.
DIGITALLY REVISED PRECAMBRIAN BASEMENT MAP OF WYOMING

The structural configuration of basement rock, both at the surface and in the subsurface, is fundamental to understanding the complete geology of a region. In Wyoming, the igneous and metamorphic Precambrian rocks that comprise the “basement” are exposed at the surface in basement-cored mountain ranges that reach elevations greater than 13,000 feet above sea level. Despite being covered by thick (locally up to 40,000 feet) sequences of Phanerozoic strata in the adjacent basins, the basement structural configuration influences many aspects of basin geology ranging from sediment accumulation, to faulting and folding, to formation and alteration of hydrocarbon and mineral deposits. For these reasons, the Precambrian basement map of Wyoming, by D.L. Blackstone, Jr., has been a primary and authoritative reference for Wyoming geology since initial publication in 1993. With partial funding from the National Geological and Geophysical Data Preservation Program, which is aimed at identifying critical-mineral resources throughout the United States, the Wyoming State Geological Survey revised and updated the Precambrian basement map of Wyoming with the intention of providing a similarly useful product that considers new information and modernizes the map in a digital format. All available public information, especially including wells, seismic lines, cross sections, geologic maps, and digital elevation models, were used to refine and, in places, reinterpret the statewide Precambrian basement elevation, relative to mean sea level, at the scale of 1:500,000. The resulting dataset and associated metadata are organized and publicly available in a U.S. Geological Survey standardized geodatabase schema and in a Wyoming State Geological Survey online map.
Covid-19: Sentiment Analysis of Students Readiness on Open Distance Learning (ODL) via Twitter

The widespread of Corona virus in Malaysia has led to open distance learning (ODL) for every student in continuing their study. However, the student readiness in ODL is unknown for the university. Thus, university institutions must always monitor what the university says about social media and the student readiness on ODL. This project aimed at developing a sentiment classification by means of a Naïve Bayes algorithm in displaying the readiness index of students by extracting Twitter tweets. A standard sentiment analysis performance measurement was used in evaluating the developed sentiment analysis model. About 98.8% of the extracted tweet are negative tweet about the online learning which indicate that the students are not ready for the ODL. The classifier model's precision, recall and f1-measure for each category on online learning readiness are obtained. The best f1-score of the classifier model is in online category with 29.0% for negative tweets and 31.7% for positive tweets compare to the other category with a precision of 27.9% for negative tweets and 30.7% for positive tweets while for the recall value is 29.6% for negative tweets and 32.2% for positive tweets. However, there are room for improvements by implement of the autocorrect function for the sentiment analysis model, implementation of a live update function in dynamically for student readiness index live when new tweets appear, implement a machine classification model, providing dynamic database application and used higher performance computer for data scrapping.
Heterogeneous Adaptive Denoising Networks for Recommendation

Due to the complexity and diversity of real-world relationships, recommender systems are better suited to represent complex data using heterogeneous information networks (HINs), called HIN-based recommendations. However, it is a challenge to efficiently obtain the embedding and remove the noise from the dataset. In our work, we innovatively propose a recommendation model called Heterogeneous Graph Convolutional Network Recommendation with Adaptive Denoising Training (HGCRD). Our model uses a random walk strategy based on meta-path to obtain a valid sequence of nodes. Then for the generated node networks, we use graph convolutional networks (GCNs) to learn the node embeddings. Also, to eliminate the noise in the dataset, we incorporate an adaptive denoising training (ADT) strategy in the training. Experimental results on three public datasets show that HGCRD performs significantly better than the competitive baseline.
Human Pose Estimation Based on Multi-Spectral Attention and High Resolution Network

In view of the problem of feature information loss during multi-resolution feature fusion in human pose estimation, a lightweight high resolution human pose estimation network named Lite MSA-HRNet is designed based on Lite-HRNet and multi-spectral attention mechanism, which integrates multi-spectral attention mechanism into Lite-HRNet. Multiple frequency components are used to extract richer feature information, contributing to the repeated fusion of different resolution feature. A deconvolution module is used behind the main network to fuse the higher resolution features generated by itself with the high resolution features generated by the main network. Channel shuffle, pointwise group convolutions and depthwise separable convolution are introduced to lighten the residual block in the deconvolution module and improve the speed of network positioning key points. The experimental results on the COCO2017 data set show that Lite MSA-HRNet achieves a better balance between the accuracy and complexity of human posture estimation compared with other networks.
Thematic Development in Students’ Argumentative Essay

In writing argumentative essay, the writer should mind complete parts of the essay and other linguistic elements. The two important linguistic elements are theme and rheme. Since theme and rheme are crucial in clause structure, thematic development is regarded important in the development of text. There are three patterns of thematic development proposed by Eggins, i.e., theme reiteration, zig-zag pattern, and multiple-theme pattern. This research states its objective as to analyze the thematic development occurred in students’ argumentative essay. The occurrence of thematic development is analyzed and elaborated into the finding and discussion. This research is a qualitative research. There are 13 essays written by students. The essays are chosen randomly to maintain the objectiveness of the analysis. Those essays consist of 323 clauses. They are analyzed based on the concept of thematic development proposed by Eggins. The analysis of thematic development of the clauses shows that theme reiteration emerges as the most preferable thematic development pattern. It can be seen that 217 clauses (67,2%) in students’ essay are in forms of theme reiteration. 84 clauses (26,0%) of the essay apply zig-zag pattern in thematic development. Multiple-theme pattern comes as the least preferred thematic development pattern. It only comes up in 22 clauses (6,8%) out of 323 clauses. It can be concluded that students understand the way to develop their essays by implementing combination of thematic development. It marks a successful phase of students’ writing skill. Nevertheless, students need writing practice intensively to make their writing skill grow better.
FCI: Feature Cross and User Interest Network

Recommendation systems are widely used on Internet platforms and the Recommendation system is one of the most mature scenarios for data mining applications. Ranking algorithms in recommender systems are the most effective means to increase user clicks. At present, the recommendation system ranking algorithm is mainly divided into two categories. One is based on data mining, such as user features, product features, and scene features and crosses these functions manually or automatically. The second method, by mining the user’s behavior sequence, obtains the user’s interests and displays the product to the user. The first approach does not mine useful behavioral data. The second method cannot effectively perform feature crossover and loses some crossover information between users and products. This paper proposes a new ranking method for recommender systems. Feature Cross and User Interest Network FCI. It can efficiently and automatically discover cross features based on users, products, and scenarios. At the same time, mining the historical behavior information of users. In this algorithm, firstly our put-forward model resembles a wide-depth structure. Wide structure puts feature intersection network, deep structure puts user historical behavior mining network. Secondly experimental comparisons are made by changing different feature intersection networks and user behavior sequence networks. Finally, by adding the pre-training vector of the product, compare different pre-training methods. This approach greatly improves the CTR in practical recommender systems.
Mode Switching in Multi-Modality Pen Interface

Aiming at the low efficiency of mode switching in pen user interface, mode switching techniques are designed and evaluated. First, 6 mode switching techniques which are pen-pressure-trajectory, pen-tilt-trajectory, pen-azimuth-trajectory, pen-pressure-discretization, pen-tilt-discretization and pen-azimuth-discretization are designed according to pen-modality-trajectory and pen-modality-discretization by using pen pressure, pen tilt and pen azimuth modalities. Second, the comparative experiment of color and thickness adjustment in the process of painting is designed and carried out on the Wacom Intuos 3 to evaluate the performance of the mode switching techniques and the referencing technique. The experimental results showed that the pen-pressure-trajectory technique has the best comprehensive performance. Its time consumption is reduced by 20.13% and its comprehensive performance is improved by 37.68% compared to the referencing technique. The time consumption of pen-tilt-trajectory technique, pen-azimuth-trajectory technique and pen-azimuth-discretization technique are reduced by 11.12%, 2.65% and 10.70% respectively compared to the referencing technique. The error rates of pen-tilt-discretization technique and pen-azimuth-discretization technique are reduced by 62.82% and 32.38% respectively compared to the referencing technique. Last, the performance of mode switching techniques are analyzed, and the improvement measures of mode switching technique such as setting reasonable threshold, analyzing context environment and selecting appropriate switching level are given.
Immersive education for entrepreneurship and management disciplines: Metaverse’s experience

&lt;p&gt;The purpose of this study is to analyze the application of immersion edu cation in the subject of entrepreneurship and management in the unified general high school of jadan education unit, jadan Parish, guaracio, azuai province. Hybrid methodology. 88.5% of the students agreed to use immersive technology in the process of learning. Teachers said that it was difficult for them to use technology, so they needed training in information and communication technology, virtual platform, use and management of these digital educational resources, such as meta-database, in order to achieve immersive education in educational institutions.&lt;/p&gt;
Knowledge Base Entity Typing From Text via Entity-Aware Heterogeneous Graph Attention Network

Knowledge base entity typing from the description text (KBET-X) has become an important research direction, which takes semantically richer descriptive text as input to obtain better typing results. However, existing approaches either consider all sentences in the text but ignore the entities themselves or consider only the sentences in which the entities are mentioned without considering the other sentences in the text. To address these issues, we propose a novel framework for KBET-X based on an entity-aware heterogeneous graph attention network that makes full use of all sentences in the description text and considers the entities themselves. Specifically, we construct a heterogeneous graph for the description text with the node being a word or sentence. Node embeddings are initialized with an entity-aware encoder. Then we use a context encoder to obtain a contextual node representation of each word and sentence, consisting of a heterogeneous graph attention network and a gated recurrent unit (GRU) network. Finally, we use a type decoder based on a multilayer perceptron (MLP) network to obtain the types of each entity. Experiments conducted on the DBpedia dataset show that our method achieves the new state-of-the-art performance. We also conduct an ablation study to demonstrate that each component plays an essential role in our framework.
Fast Few Shot Self-attentive Semi-supervised Political Inclination Prediction

With the rising participation of the common mass in social media, it is increasingly common now for policymakers/journalists to create online polls on social media to understand the political leanings of people in specific locations. The caveat here is that only influential people can make such an online polling and reach out at a mass scale. Further, in such cases, the distribution of voters is not controllable and may be, in fact, biased. On the other hand, if we can interpret the publicly available data over social media to probe the political inclination of users, we will be able to have controllable insights about the survey population, keep the cost of survey low and also collect publicly available data without involving the concerned persons. Hence we introduce a self-attentive semi-supervised framework for political inclination detection to further that objective. The advantage of our model is that it neither needs huge training data nor does it need to store social network parameters. Nevertheless, it achieves an accuracy of 93.7% with no annotated data; further, with only a few annotated examples per class it achieves competitive performance. We found that the model is highly efficient even in resource-constrained settings, and insights drawn from its predictions match the manual survey outcomes when applied to diverse real-life scenarios.
Predictive Model for Object Classification and Detection using Deep Learning

In the agricultural field, there are large number of objects that roam inside the field and tend to develop an unfavourable condition that may damage the crop and degrades the production. Therefore, the possibility of unavoidable situation is very high which may result into loss of human resources, agriculture assets, financial loss, and crop damage. In this paper, tiny-YOLOv3 is used to classify and detect object in real time environment, however its performance is very high, but the accuracy degrades. Thus, an enhanced model is proposed by modifying the network architecture which amplifies the real time performance, processing speed and reduces processing time. The empirical conclusion shows that the proposed model gives approximately double precision, recall, IoU, mAP, compared to actual Tiny-YOLOv3 with an improvement of 69.01%. However, the testing is performed on multiple images which also demonstrates that the proposed model gives much higher result in comparison to Tiny-YOLOv3.
DESAIN SISTEM INFORMASI MANAJEMAN PENERIMAAN SISWA BARU BERBASIS WEB MOBILE DI SMA NEGERI 1 PAGELARAN

The process of admission of new students at SMAN 1 Performances are still done manually so often have problems in entering data, do not provide the speed of service is better, the difficulty in registering due to come directly to the school to sign up, and less efficiency in the implementation of PPDB (Registration Admission Students New). Along with the development of mobile web-based programming technology, increasingly sophisticated, the researchers gave the solution to the problem. Researchers using SDLC (System Development Life Cycle) by designing wake a new student information system that can help prospective students to enroll. A new student information system is built with PHP programming language, HTML and database utilizing MySQL as the database server. The results of this research are new admissions application design mobile web based useful to allow potential students to enroll without difficulty so that the registration process can be run more effectively and efficiently.
Automatic selection of latent variables in variational auto-encoders

Variational auto-encoders (VAEs) are powerful generative neural networks based on latent variables. They aim to capture the distribution of a dataset, by building an informative space composed of a reduced number of variables. However, the size of this latent space is both sensitive and difficult to adjust. Thus, most state-of-the-art architectures experience either dis-entanglement issues, or, at the opposite, posterior collapse. Both phenomena impair the interpretability of the latent variables. In this paper, we propose a variant of the VAE which is able to automatically determine the informative components of the latent space. It consists in augmenting the vanilla VAE with auxiliary variables and defining a hierarchical model which favors that only a subset of the latent variables are used for the encoding. We refer to it as NGVAE. We compare its performance with other auto-encoder based architectures.
Implementation of the entanglement measure in quantum gates language

Entanglement is an important resource for quantum technologies that allow us to go beyond the classical world. Quantum systems can be entangled in various ways. Also, the degree of entanglement between the subsystems of a quantum system can be calculated in different ways. In our recent paper [Prog. Theor. Exp. Phys. (2022)], we directly measure the three-tangle of a pure three-qubit quantum state. In this paper, we extend this method to pure N-qubit quantum states (N is any odd number greater than 1) by considering polynomial invariant of degree 4 as a measure of entanglement. In this algorithm, we use quantum gates and output probabilities to calculate polynomial invariant of degree 4. The importance of this method lies in the possibility of experimental implementation of quantum calculations in the language of quantum gates.