Reviewerly/exHarmony huggingface dataset:
DEVELOPMENT OF INDONESIAN-BASED TEACHING MATERIALS INTERACTIVE EDUCATIONAL GAME "PEBI "TO IMPROVE READING SKILLS IN THE BEGINNING OF GRADE II ELEMENTARY SCHOOL STUDENTS

Interactive Educational Game is a game that aims to provoke children's interest in learning the subject matter while playing, so that with a happy feeling it is hoped that children can more easily understand the subject matter presented. This study aims to develop Indonesian language teaching materials based on interactive educational games to improve the early reading skills of second grade elementary school (SD) students. This study uses the Research and Development Research method using the Dick and Carey Model. The Dick and Carey model is one of the learning design models with a systems approach. Based on the results of the research on the Development of Indonesian Language Teaching Materials Based on Interactive Educational Games "PEBI" Class II SDN Mekar Jaya Serang Regency, it can be concluded several things as follows: (1) Development of Indonesian Language Teaching Materials Based on Interactive Educational Games "PEBI" was developed based on an analysis of teacher needs and students through a needs questionnaire provided by the developer, (2) The results of the validation of material experts, media experts, and design experts on the development product of Indonesian Language Teaching Materials Based on the "PEBI" Interactive Educational Game with very feasible criteria to be developed, and (3) Based on the results Research on the development of Indonesian Language Teaching Materials Based on Interactive Educational Games "PEBI" can be concluded that the Digital Book learning media is used in developing abilities in designing creative, innovative and interesting learning.
Assessment of Semantic Segmentation Models for Landslide Monitoring Using Satellite Imagery in Peruvian Andes

In the domain of machine learning, one persistent challenge is the availability of ample data, especially pertinent to computer vision. Moreover, this challenge is amplified within the realm of remote sensing, where annotations for addressing problems are frequently scarce. This manuscript critically examines the daunting task of monitoring a geophysical phenomenon —landslides— within the Peruvian landscape, a nation profoundly impacted by such events on a global scale. In this paper, we present three contributions in that direction. Our first contribution is to expand a well-known satellite imagery dataset targeting landslides. The nucleus of this foundational dataset originates from Asian territories, comprising 3799 meticulously annotated images. However, recognizing the distinct geospatial dynamics of Peru, we embarked on a rigorous exercise to augment this dataset with 838 local scenarios. These additions maintain congruence with the original dataset in terms of attributes and configuration, thereby ensuring both replicability and scalability for future research endeavours. Our second contribution is an exhaustive assessment of various semantic segmentation models. At the heart of our experimentation lies the U-net architecture, bolstered by the Weighted Cross Entropy + Dice Loss —a loss function acclaimed for its efficacy in segmentation tasks with imbalanced data sets. The empirical findings are illuminating: a rudimentary U-net architecture exhibits a formidable F1-Score of 75.5%, transcending the benchmark score of 71.65% set by the original dataset. Our third and final contribution is the comprehensive research framework developed for data acquisition, processing pipeline and model training/evaluation. Given this framework has the potential to drive a general applicability of segmentation systems to landslide monitoring systems, and to have a broader reach to the academic community and governmental stakeholders in Latin America and worldwide, we will be making all scripts and experiment details available upon publication, thus, hoping to foster an environment for collaborative scrutiny, discourse, and further advancement.
Teacher Information Literacy for Inclusive Early Childhood Education (ECE) to Provide Literacy and Numeracy for Special Needs Children in Central Java-Indonesia

Digital teaching emphasises information literacy as one of the competencies critically required in the education community. It marks the starting point of information management to acquire comprehension and achieve learning objectives, as an attempt to support individual abilities in facing any challenges. The increasing number of special needs children at inclusive schools challenges teachers to provide the best education services. Teachers are urged to learn in diverse ways to acquire skills in handling special needs children in their classrooms. This study used a mixed methods approach, using quantitative data to present participants’ demographic data and observation on literacy-numeracy skills of special needs children and qualitative data on the teachers’ information literacy for facilitating literacy-numeracy activities for special needs children in inclusive classrooms. This study purposively selected 20 Early Childhood Education teachers from five regions in Central Java who were experienced in handling inclusive classrooms. The data were collected through interviews, observations and documentation studies. Meanwhile, the data analysis interpreted all aspects that correlated to each other. The findings confirmed that teachers with information literacy on special needs children could facilitate play-based activities for literacy-numeracy skill learning. Teachers used the outcome of this information literacy to manage learning, starting from identifying the learning needs, designing learning programmes, implementing programmes, to conducting evaluations. Therefore, teachers urgently require necessary training that supports their information literacy in managing classroom learning, especially for special needs children.
A little help from my friends: the moderating role of neurodiversity traits on perceptions of presence

Researchers have investigated the impact of social presence and avatars across various contexts. While many studies highlight the positive outcomes associated with increased social presence for students, prior research has not delved into how heightened presence may affect students exhibiting traits of neurodiversity. This study (N = 291) assesses participants' perceptions of avatars in terms of anthropomorphism and two presence categories: other-copresence and self-copresence. Additionally, the study explores the moderating role of neurodiversity traits, specifically attention switching and attention to detail, on perceptions of self-copresence. The analysis reveals that more anthropomorphic avatars enhance perceptions of other-copresence. However, moderation analysis indicates that individuals struggling with attention-switching experience reduced self-copresence when other-copresence is higher.
Research on the Path of College Teachers' Role Transformation in the Smart Education Environment

With the in-depth application of artificial intelligence, AR/VR, big data, 5G and other emerging technologies in the field of education and teaching, human education is transforming and evolving to the stage of smart education. The connotation and characteristics of "smart education" require teachers to improve their professional ability under the premise of mastering the theoretical concepts such as constructivism and connectionism, and finally realize the transformation of their roles in the teaching of "wisdom education". On the basis of combing the relevant education and teaching theories under the smart education environment, this paper summarizes the transformation of teaching characteristics, the characteristics and direction of teachers' role transformation and the difficulties faced by teachers' role transformation brought by smart education, and puts forward the realistic path of teachers' role transformation under the background of smart education in combination with the reality of our school.
Service Resources Mining Based on Association Rules

In recent years, the common service resources mining for cloud business operation under micro-service architecture has been a key problem in the intelligent operation and maintenance of cloud platforms. How to identify system resource anomaly to complete comprehensive monitoring and analysis of the system is an predominant challenge to most existing methods. In this paper, we propose an improved Apriori algorithm based on comparing transaction ID sets, which addresses the bottleneck problems of the classic Apriori algorithm requiring multiple scans of the transaction database and generating a large number of candidate sets during processing. The algorithm utilizes the minimum support threshold to remove non frequent items, thereby achieving the goal of reducing the generated candidate set. Based on the improved Apriori algorithm, analyze the correlation between various attributes, standardize the relevant attributes first, and then conduct discretization processing. The combination of discretization attributes forms the chromosome sequence under the normal operation of the business system, and then analyze whether the monitoring data is within the existing chromosome range through a fixed time window (such as 1 hour). If not, judge whether there is an exception.
Identifying Mobile Application Over Shadowsocks with Single-Direction Traffic

An anonymous proxy system, such as Shadowsocks, creates a secure tunnel between the user and the proxy server, effectively concealing the raw traffic from any network elements along the transmission path. While this enhances user privacy, it also presents a challenge for traffic inspection. Identifying the application associated with the traffic is crucial, but it can be a challenging task when dealing with encrypted traffic over Shadowsocks. Current research on encrypted traffic analysis assumes that bi-directional traffic can be used for testing. However, in reality, traffic inspection is typically performed on gateways, and in a large cross-regional network environment there are often multiple ingress and egress gateways. This means that inbound and outbound traffic usually travel through different gateways, making it essential to be able to identify the application with single-direction Shadowsocks encrypted traffic. This paper presents a novel method for identifying mobile applications through Shadowsocks encrypted single-direction traffic. To achieve this, we have designed a sliding-window based feature extraction mechanism that can effectively extract features from single-direction traffic. Additionally, we have constructed three machine learning models, including random forest, XGBoost, and lightGBM, to classify the application type. To handle high data dimension and feature redundancy, we have also employed a feature selection scheme that keeps prediction accuracy while reducing the computational cost. We have conducted experiments using popular social network mobile applications, and our results demonstrate that our method achieves promising classification accuracy.
GAN-IVDS: An Intrusion Detection System for Intelligent Connected Vehicles Based on Generative Adversarial Networks

This paper proposes an anomaly detection algorithm based on Generative Adversarial Networks (GANs) for intrusion detection in Intelligent Connected Vehicles (ICVs). To address the imbalance between normal network traffic and attack traffic in vehicular networks, as well as the difficulty in collecting attack data, we train the neural network solely with normal traffic. We employ the Gini index to perform data dimensionality reduction, selecting 34 features with the highest purity of information and enhancing model training speed via normalization functions. Our algorithm combines the generative capabilities of GANs and the reconstructive power of autoencoders, detecting abnormal network data by learning the distribution of normal network data, thus eliminating the need to analyze packet payloads and safeguarding user privacy. We also develop a vehicular network traffic data collection scheme to collect network traffic data from ICVs. Experimental results indicate that our algorithm demonstrates high accuracy in detecting anomalous network traffic in ICVs, with an accuracy rate of 94.9%.
Improved OpenPose for multi-person pose estimation and behavior recognition

In response to the sluggishness of multi-person pose estimation algorithms, rendering real-time pose estimation unattainable and restricting the output to human keypoints alone, practical behavior recognition applications remain unfeasible. Propose a lightweight attitude estimation method that introduces a deep separable convolutional structure and improves its pre-feature extraction module to improve target detection speed. Simultaneously, an adopted dual-stream adaptive graph convolutional network, Two-Stream Adaptive Graph Convolutional Networks (2s-AGCN) , leverages the key point information extracted by OpenPose for skeleton-based action recognition. On the MSCOCO dataset, in comparison to prevalent algorithms like OpenPose, AlphaPose, and HRNet, this algorithm attains an 85.2% accuracy rate in human key point recognition. While upholding detection precision, it reduces the computational complexity of the base model by 33.7×109 and trims down the parameter count by 83.1%. On the NTU-RGB+D dataset, the algorithm achieves a 95.3% accuracy rate on the X-View benchmark set, demonstrating a slight advantage over its counterparts. And it can effectively detect behavior recognition for human pose estimation in real-time.
A sequential recommendation algorithm combining attributes and long-term and short-term preferences

In recent years, sequence recommendation has played an important role in solving information overload problems in many online services.Most of the existing sequential recommendation methods focus on the dynamic preferences in the time series of user interaction, and ignore the long-term preferences of users and the characteristics and attributes of users and projects, which limit the recommendation ability.To solve these problems, a novel sequential recommendation algorithm CASLRec is proposed in this paper, that incorporates both long-term and short-term preference and attribute information. This model embedded the user and the item into the vector respectively and added multi-head self-attention mechanism to extract the deeper connection between items from the order pattern of users. At the same time, considering the long-term and short-term preference of users with various weights assigned, it took the embedding of similar users as a supplement to enhance the user representation. At the same time, in order to improve the performance of recommendation, the embedded learning deeply integrates user and project attribute information to simulate user interaction more comprehensively. Experiment results show that the proposed algorithm on ML-100K, ML-1M, and ML-HetRec datasets is superior than other baseline models on HR@50 and MRR.
A novel security framework based on blockchain for IoT networks

Internet of Things (IoT) devices are indispensable components of smart cities, smart homes, and industrial control systems. Using a blockchain (BC)-based security framework in IoT security is extremely necessary and a current research trend. However, most current BC-based decentralized security frameworks have not yet reached the optimal performance of miners in transactions verification and data consensus in the BC ledger. In this paper, we present a novel BC-based security framework for ensuring the correctness and reliability of the data in the BC ledger and optimizing miners’ performance over an IoT network. We propose the process of verifying transactions and data consensus based on two assumptions about miners in a BC network: (1) all miners are trusted nodes, and (2) some miners are untrusted nodes but less than one-third of the total number of miners. The evaluation results show that the more miners join the network, the more verified transactions increase and the lower the average mining time for a new block for assumption 1. For assumption 2, transactions only need to be verified once even if an untrusted miner is selected to propose a new block at a mining round. The proposed framework is more eﬀective than other decentralized frameworks in the process of verifying transactions and data consensus.
Pengelompokan Penerimaan Mahasiswa Baru Dengan Algoritma K-Means Untuk Meningkatkan Potensi Pemasaran

Utilization of the existing PMB dataset through the clustering method approach can be applied in analyzing the rate of acceptance of new students. The K-Medoid Cluster algorithm model that is applied has results that show a new insight, namely the grouping of new student acceptance rates based on 3 clusters, cluster 1 (C0) is a high level consisting of 49 data from 86 datasets tested and cluster 2 (C1) is a low level consisting of 11 data from 86 datasets tested and cluster 3 (C2) is a medium level consisting of 26 data from 86 datasets tested. The results of the Davies Bouldin Index or DBI value are based on the RapidMiner Studio application obtained from data testing, with a Davies-Bouldin Index evaluation value of 0.769.&#x0D; Keywords: Data Mining, K-Medoid Cluster, Klastrer, PMB
A Fast GP Regression Method Using Banded Sparsification of Inverse Covariance

Modeling and prediction using Gaussian processes (GPs) have widespread applications in machine learning, information processing, automatic control, biology, and other fields. However, GP regression requires a computational complexity of $O(N^{3})$. To overcome this issue, this paper proposes a fast GP regression method based on a banded sparsification of the inverse covariance matrix. It follows that we first propose an approximate inversion algorithm rooted in the Kullback-Leibler (KL) divergence, which exhibits approximately linear computational complexity. Furthermore, we demonstrate it is essentially a higher-order Gaussian Markov process (GMP) model and minimum mean square error (MMSE) estimation. Next, by transforming the inverse covariance matrix into a banded sparse structure, we derive a more efficient GP regression algorithm, of which the core is to employ a high-order GMP model to effectively approximate the general GP.
Utilize Pre-Trained PhoBERT to Compute Text Similarity and Rerank Documents for Question-Answering Task

Open-domain Question Answering (QA) is a crucial task in natural language processing. QA systems typically follow two main steps: (i) identifying relevant passages and (ii) generating answer sentences from these passages. Among these steps, identifying relevant passages poses a greater challenge and requires further refinement. In this paper, we introduce two novel strategies to improve the performance of this step, including: (i) a new method for computing the similarity between questions and text passages, and (ii) the integration of pretrained and fine-tuned models. Empirical evaluations conducted on the Zalo 2022 dataset demonstrate the efficacy of our proposed methods, manifesting a notable 10% increase in recall compared to using the BM25 method alone, and a 6% increase in recall compared to relying solely on a fine-tuned cross-encoder model.
Integration of Intelligent Driver Model with Interaction-Aware LMB (IA-LMB) Filter for Vehicle tracking

Vehicle tracking is one of the most common applications of multi-object tracking. In the context of Bayesian filtering, including accurate information about the tracking scenario tends to improve tracking performance. However, in some cases, including more information increases the complexity of the tracking system. This paper presents a novel method of direct mathematical incorporation of accurate motion models for vehicles, specifically the intelligent driver model (IDM), within the interaction-aware labeled multi-Bernoulli (IA-LMB) filter. The proposed method provides the inclusion of a more accurate vehicle motion model into the IA-LMB filter without increasing the complexity of the IA-LMB filter. The proposed filter outperforms traditional methods, which do not consider interactions between targets such as the PHD filter and the LMB filter. Moreover, the proposed method outperforms the original IA-LMB filter for the interacting vehicles owing to more accurate modeling of vehicle motion, and hence the inclusion of interactions. The performance of the proposed method has been evaluated using the optimal sub-pattern assignment (OSPA) metric for the overall simulation and the mean square error (MSE) for the interacting vehicles.
An Information-Theoretic Approach to Complementary Information Fusion

This paper presents the challenges of divergence-based sensor fusion methods in multi-target tracking applications where sensors have different fields of view. We propose an alternative approach to GCI fusion that is particularly devised for the fusion of labeled multi-Bernoulli densities. In our approach, we present the observation that fusion could be separately performed for each object label over the single-Bernoulli densities associated with that label, and the fusion weights could then be tuned according to the detectability of the object by each sensor. The resulting solution is tested in a challenging numerical experiment where up to 10 vehicles are moving and tracked by various dynamic and static sensors. The results show the advantages of our proposed method when compared to GCI fusion and the complementary fusion methods.
Analisis Maraknya Warga Miskin yang Kecanduan Judi Online di Indonesia

Gambling is a criminal act by risking a certain amount of money where the party who wins will get all the betting money. Gambling can be detrimental to society and damage the nation's morals. Gambling behavior will become a problem when it starts to approach the diagnostic criteria for pathological gambling, where the behavior is mal-like. -adaptive and sustainable, giving rise to a series of further problems, such as continuing to try to bet even though you have lost many times. According to all religious views, gambling is something that is prohibited. Online gambling games played by people are the result of social interactions that occur between them. Social interaction can be likened to a currency that has two sides: positive and negative. One example of the negative impact is what happens to people who take part in online gambling games. As technology develops, gambling is now moving to a place that is a little more elite. Technological advances too. changing the way of playing gambling, along with developments in technology, now gambling has moved to a slightly more elite place. Advances in technology have also changed the way of playing gambling. Now, gambling doesn't have to be hidden like in the past, just by sitting relaxed in front of a computer or cellphone that is connected to the internet network, we can play this haram game.