Reviewerly/exHarmony huggingface dataset:
Prediction of Graduation for Students at the ISB Atma Luhur Faculty of Information Technology Using the C4.5 Algorithm

Higher Education is a level of education after secondary education which includes diploma programs, undergraduate programs, master programs, doctoral programs, professional programs, and specialist programs organized based on the culture of the Indonesian nation. Student graduation is one of the important factors to improve university accreditation. Students who graduate above 5 years and the number of students who drop out are important indicators in determining accreditation which then causes the difficulty of accrediting a college to rise. This research aims as an early warning for students who graduate on time and graduate late from the Faculty of Information Technology, Institute of Science and Business Atma Luhur using the C4.5 decision tree algorithm by implementing the Cross-Industry Standard Process for Data Mining (CRISP- DM) method. The initial data of this research amounted to 1,015 which was taken through a query in the database of the Atma Luhur Institute of Science and Business. However, the data that will be used becomes 694 after preprocessing due to the large number of record contents that do not have a graduation year, with a total of 641 graduates graduating on time and 53 graduates graduating late. Based on the application of the model using the C4.5 decision tree algorithm and the Confusion Matrix method, the accuracy is 93.94%, Recall is 98.59%, and Precision is 95.03%. So it can be concluded that the C4.5 decision tree algorithm is the most effective algorithm for predicting student graduation, because it has a high level of accuracy.
Emotion Mining User Review of the BRImo Mobile Banking Application Using the Decision Tree Algorithm

As consumer transaction preferences shifted from analog to digital, banks were compelled to develop digital transactions in the form of mobile banking. Users of mobile banking provide feedback regarding the application's usability. The opinions of users can be emotive. Emotions influence what a person emits or applies. Emotions are the behavioral response of a person when he is happy or unhappy. Thus, the manifestation of a person's emotions, whether in the form of facial expressions, verbal communication, written text, or judgment, can be used as a source of information to aid in decision making. The objective of this study is to apply emotion mining to the analysis of user evaluations of the BRImo application, one of the three most popular platforms in Indonesia as of August 2022, with a total of 800,000 reviews on the Play Store. Emotion Mining can be used to analyze the four categories of emotions expressed by users in the comments section: happy, angry, sad, and afraid. According to BRImo user evaluations, the decision tree algorithm is used to categorize happy, sad, afraid, and angry feelings. Using a decision tree to manage large data category sets is effective. The obtained dataset included 2959 happy classes, 2196 sad classes, 387 angry classes, and 81 scared classes. According to the findings of the analysis, a significant number of users of the BRImo application express positive sentiments in their evaluations, which are indicative of happy emotions. The Decision Tree algorithm yields results with a performance specification of 84.5%, sensitivity of 85.5%, and precision of 84.4%.
Determining Promotional Package Recommendations Using the Frequent Pattern Growth Algorithm at The Java Cafe

Data analysis and processing is very important to support business development. One example is The Javanese Café which requires analysis and processing to determine promotional menu package recommendations. To carry out data analysis and processing, of course you need technology to make these activities easier. The technology that can be used to overcome this problem is data mining. Data mining has an association rule method which functions to form association patterns. Researchers also use the FP-Growth algorithm to speed up the data processing process. The sales transaction data processing resulted in 14 association patterns with the highest confidence values and 9 menu items with the lowest support values. Then the results were analyzed again and produced 4 recommendations for promotional menu packages that could be used to support product marketing strategies.
A Study on Developing Truck Safety Education Program and Establishing Promotion Strategies

With the development of the logistics industry, there has been a rapid surge in truck traffic volume, giving rise to pronounced traffic congestion and a surge in traffic accidents. The fatality rate, which gauges the number of deaths per 100 traffic accidents, rising from 2.7 in 2020 to 2.8 in 2021. and was about twice higher than passenger cars and buses. This rate stood at nearly double that of passenger cars and buses. Although extant studies have underscored the significance of driver education as a pivotal factor in enhancing truck traffic safety, the realm of research concerning the formulation of comprehensive truck driver education programs remains underdeveloped. This study surveyed safety managers and truck drivers of freight transportation companies to identify the requirements for the development and operation of education programs such as the main contents, training period, and training method. Based on this, truck driver education programs (draft) are proposed by considering the truck drivers’ characteristics. In addition, 8 representative domestic freight transportation companies were selected to do in-depth interviews. Through this, the need for a truck driver traffic safety education program was confirmed. It was implied that legal and institutional support for education program’s development and management needs to be prepared. In light of these findings, this study elucidates a comprehensive strategy to foster the successful creation and streamlined operation of educational programs. The outcomes of this research are poised to serve as fundamental reference material for the formulation of targeted truck driver education programs within pertinent organizations, including the Korea Transportation Safety Authority, in the times ahead.
Comparison Analysis of Graph Theory Algorithms for Shortest Path Problem

The Sumba region, Indonesia, is known for its extraordinary natural beauty and unique cultural richness. There are 19 interesting tourist attractions spread throughout the area, but tourists often face difficulties in planning efficient visiting routes. From this case, it can be solved by applying graph theory in terms of searching for the shortest distance which is completed using the shortest path search algorithm. Then these 19 tourist objects are used to build a weighted graph, where the nodes represent the tourist objects and the edges of the graph describe the distance or travel time between these objects. Therefore, this research aims to compare the shortest path search algorithm with parameters to compare the shortest distance results, algorithm complexity and execution time for tourism in the Sumba area. The results of this research involve a comparison of several shortest path search algorithms, with the aim of finding the shortest distance results, algorithm complexity, and execution time for tourism in the Sumba area. Based on the test results of the five algorithms with the parameters that have been prepared, and the findings show that each algorithm has its own characteristics, the results are as follows: Dijkstra's algorithm can be used to calculate the shortest route for single-source and single-destination types. This resembles the Bellman-Ford algorithm, only the Bellman-Ford algorithm can be used simultaneously on graphs that have negative weight values. Meanwhile, the Floyd-Warshall algorithm is suitable for use on the all-pairs type. Then, the Johnson Algorithm can be used to determine the shortest path from all pairs of paths where the destination node is located in the graph. Finally, the Ant Colony algorithm to compute from a node to each pair of destination nodes.
Early Detection of Alzheimer's Disease with the C4.5 Algorithm Based on BPSO (Binary Particle Swarm Optimization)

Alzheimer's disease is a degenerative disease associated with memory loss, communication difficulties, mental health, thinking skills, and other psychological disorders that affect a person's daily activities. Alzheimer's disease is a disease that causes disability for people aged 70 years and over and is the seventh highest contributor to death in the world. However, until now there has not been found an effective treatment to cure Alzheimer's disease. Thus, early detection of Alzheimer's disease is very important so that sufferers of Alzheimer's disease can immediately receive intensive medical care so as to reduce the death rate from Alzheimer's disease. One method that can be used to detect Alzheimer's disease is by utilizing a machine learning algorithm model. The machine learning model in this study was carried out using the Decision Tree C4.5 algorithm classification method based on Binary Particle Swarm Optimization (BPSO). The C4.5 Decision Tree algorithm is used to classify Alzheimer's disease, while the BPSO algorithm is used to perform feature selection. By performing feature selection with the BPSO algorithm, the results show that the BPSO algorithm can improve accuracy and can increase the performance of the C4.5 algorithm in the Alzheimer's disease classification process. The results of the accuracy of the C4.5 algorithm using the BPSO feature selection are greater, namely 98.2% compared to the C4.5 algorithm without BPSO feature selection, which is only 96.4%.
An Evaluation Study on Leadership Styles and its Impact on Academic Employees’ Job Satisfaction (Private Iraqi Universities as an Example)

Research confirms leadership's significant influence on employee retention and satisfaction via effective communication, fostering relationships that impact performance and loyalty. This study analyzes leadership styles in two Iraqi private universities, using quantitative methods and SPSS 26. Results show autocratic leadership dominance at (A) university and democratic leadership at (B) university, leading to substantial differences in employee job satisfaction. In summary, autocratic leadership negatively affects job satisfaction at (A) university, while democratic leadership positively impacts (B) university.&#x0D;
Research of Highway Vehicle Inspection Based on Improved YOLOv5

Vehicle detection has become an important detection target for highways, but the traditional vehicle detection technology has poor real-time performance and large model parameters. The algorithm is based on YOLOv5, which introduces the improved network structure Ghostnet-C in the backbone layer to simplify the network structure and while increasing the detection speed of it. Subsequently, for further optimize the structure of the model, GSConv + Slim-neck structure is introduced in the neck layer. Finally, the CAS attention mechanism is used in the neck layer, which is developed in this paper, to change the focus of model predictions and get better results from the model. Compared with original YOLOv5, W-YOLO we propose in the paper reduces the amount of parameters by about 58.6%, the size of storage space by 58%, and the computation by 72.2%, while the accuracy can reach 75.6%. From the final results of experiments, we can discover that W-YOLO can significantly reduce the amount of parameters, model size and computation while guaranteeing accuracy, which can satisfy the requirements of highway vehicle detection more easily.
A Deep Reinforcement Learning Based Facilitation Agent for Consensus Building Among Multi-Round Discussions

Achieving consensus among diverse opinions through multi-round discussions can be a complex process. The advent of large language models (LLMs) offers promising avenues for resolving this challenge, given their prowess in understanding and analyzing human sentiments. However, existing approaches typically focus on single-round discussion, limiting their effectiveness in real-world discussion scenarios. In response to this, we proposes a two-layer facilitation agent modeled a multi-round discussion as a Markov decision process (MDP) to foster efficient agreement. The model comprises a high-level reinforcement learning-based agent, deciding the optimal facilitation action such as facilitation time and facilitation prompt. In the low-level, a large language model that generates the facilitation message based on the facilitation action. Our agent dynamically chooses facilitation moments, generates novel content, and directs the discussion towards consensus. Our methodology was validated across several different topic-based discussions, demonstrating excellent performance in achieving agreement swiftly across all.
Traffic Sign Recognition Model Based on Small Object Detection

Traffic sign recognition is a popular task in the field of computer vision, but it faces challenges such as small object size, complex scenes, and real-time requirements. In this paper, a model framework is proposed to comprehensively improves the detection and recognition of traffic signs. To address small objects, FlexCut data augmentation is introduced, which generates non-repetitive sub-images through the strategy of maximizing sample region cropping. This approach enhances the detection ability of small objects. Additionally, PIoU loss function based on keypoints is also investigated, which accurately guides the position and shape of the bounding boxes by considering factors such as overlap area, distance, aspect ratio, and geometric size. Furthermore, the YOLOv5s network is enhanced by integrating the TransformerBlock module, SimAM attention mechanism, and Decoupled detection head to enhance the receptive field and feature extraction capability. In the experiments conducted on the TT100K dataset,, the proposed YOLOv5T achieves significant performance with an mAP@0.5 of 87.5% and an mAP@0.5:0.95 of 66.1%. These results validate the effectiveness of the proposed approach in addressing traffic sign recognition problems.
SVFNeXt: Sparse Voxel Fusion for LiDAR-Based 3D Object Detection

Voxel-based 3D object detection methods have gained more popularity in autonomous driving. However, due to the sparse nature of LiDAR point clouds, voxels from conventional cubic partition lead to incomplete representation of objects in farther range. This poses significant challenges to 3D object perception. In this paper, we propose a novel 3D object detector dubbed SVFNeXt, a Sparse Voxel Fusion Network that performs cross-representation (X) feature learning. It is because cylindrical voxel representation considers the rotational or radial scanning of LiDAR that we can better explore the inherent 3D geometric structure of point clouds. To further enchance cubic voxel features, we innovatively integrates the features of cylindrical voxels into cubic voxels, incorporating both local and global features. We particularly attend to informative voxels by two additional losses, striking a good speed-accuracy tradeoff. Extensive experiments on the WOD and KITTI datasets demonstrate consistent improvements over baselines. Our SVFNeXt achieves competitive results compared to state-of-the-art methods, especially for small objects(e.g., cyclist, pedestrian).
Multilayer Vision and Language Augmented Transformer for Image Captioning

Image Captioning task is one of the important tasks in computer vision. In this paper, we propose a Multilayer Vision and Language Augmented Transformer (MVLAT) method for improving the correctness of image description statements. In MVLAT, we have matched image and text features by adding regional object image features as well as grid image features and introduced them into the image description task by cross-modal retrieval. We refer to this process as the visual contextual relationship module (VRM) to enhance the visual and contextual features of the image description model. In addition, to focus attention more on the focused information, we propose an attention enhancement module (AEM) that enhances the attention weight of important information while weakening the attention weight of non-essential information. Finally, we propose a multi-label Focal loss in the image description task that balances the positive and negative sample imbalance in the training model. Experiments on the MSCOCO image description baseline dataset show that the present method can obtain good performance, and the overall performance of the model is better than many existing state-of-the-art methods. The improvement over the baseline model is 7.7 on the CIDEr score and 1.5 on the BLEU-1 score.
A Heuristic Framework for Personalized Route Recommendation Based on Convolutional Neural Networks

Personalization of travel routes significantly impacts people’s quality of life and production efficiency. The personalized route recommendation (PRR) problem has personalized requirements and the goal of providing users with personalized route suggestions. Most of the existing work focuses on improving either the personalization or the availability of recommended routes, rather than both. In response to the above problems, a Personalized-Neural-Network-Heuristic framework (PNNH) is proposed, which can improve the personalization degree of the recommended routes and ensure their availability simultaneously. The PNNH framework consists of two stages: preference modeling and route recommendation. In the preference modeling stage, a prediction component with Graph Convolutional Network (GCN) as the core is constructed to learn the potential preference characteristics in the user’s historical travel information, and then a heuristic algorithm is constructed by using the evaluation value output by the prediction component reflecting the transition probability, thus introducing the user preference characteristics into its cost evaluation function. In the route recommendation stage, an improved heuristic algorithm is used for route planning, and the route planning results are recommended to users. A strategy of narrowing the search scope for the heuristic algorithm is proposed, which can ensure that the route reaches its destination. Based on the PNNH framework, a set of algorithms can be constructed. The NeuroMLR-Dijkstra-A* algorithm (NDA*) is constructed and used in the experiment to evaluate the performance of the PNNH framework. Experimental results demonstrate the superiority of the PNNH framework.
Bootstrap Diffusion Model Curve Estimation for High Resolution Low-Light Image Enhancement

Learning-based methods have attracted a lot of research attention and led to significant improvements in low-light image enhancement. However, most of them still suffer from two main problems: expensive computational cost in high resolution images and unsatisfactory performance in simultaneous enhancement and denoising. To address these problems, we propose BDCE, a bootstrap diffusion model that exploits the learning of the distribution of the curve parameters instead of the normal-light image itself. Specifically, we adopt the curve estimation method to handle the high-resolution images, where the curve parameters are estimated by our bootstrap diffusion model. In addition, a denoise module is applied in each iteration of curve adjustment to denoise the intermediate enhanced result of each iteration. We evaluate BDCE on commonly used benchmark datasets, and extensive experiments show that it achieves state-of-the-art qualitative and quantitative performance.
VIFST: Video Inpainting Localization Using Multi-view Spatial-Frequency Traces

Video inpainting techniques based on deep learning have shown promise in removing unwanted objects from videos. However, their misuse can lead to harmful outcomes. While current methods excel in identifying known forgeries, they struggle when facing unfamiliar ones. Thus, it is crucial to design a video inpainting localization method that exhibits better generalization. The key hurdle lies in devising a network that can extract more generalized forgery features. A notable observation is that the forgery regions often exhibit disparities in forgery traces, such as boundaries, pixel distributions, and region characteristics, when contrasted with the original areas. These traces are prevalent in various inpainted videos, and harnessing them could bolster the detection’s versatility. Based on these multi-view traces, we introduce a three-stage solution termed VIFST: 1) The Spatial and Frequency Branches capture diverse traces, including edges, pixels, and regions, from different viewpoints; 2) local feature learning via CNN-based MaxPoolFormer; and 3) global context feature learning through Transformer-based InterlacedFormer. By integrating local and global feature learning networks, VIFST enhances fine-grained pixel-level detection performance. Extensive experiments demonstrate the effectiveness of our method and its superior generalization performance compared to state-of-the-art approaches. The source code for our method has been published on GitHub: https://github.com/lajlksdf/UVL .
Generative Model of Suitable Meme Sentences for Images Using AutoEncoder

This paper proposes a new image caption generative model for Memes called GUMI-AE. Meme denotes a humorous short sentence suitable for the given image in this paper. An Image caption generative model usually consists of an image encoder and a sentence decoder. Furthermore, most conventional models use a pre-trained neural network model for the image encoder, e.g., ResNet152 trained using ImageNet. However, pre-trained ResNet152 may not be effective as an encoder for extracting features from arbitrary images. Because the training samples for the meme generative model can be obtained from the website “Bokete” (in Japanese) which is a website that provides a system for people to post images and humorous short sentences associated with these images. Images posted on Bokete include a wide variety of images such as illustrations and text-only images which may be outside of the training images of ImageNet. This paper proposes an image caption generative model incorporating AutoEncoder (AE) as the image encoder. AE can be trained with the training samples obtained from Bokete without the image annotation. This enables the proposed method to generate short sentences with humor for memes. Finally, the proposed model is compared with the conventional one, and the evaluation of the proposed GUMI-AE will be discussed.
Knowledge Graph Augmentation with Entity Identification for Improving Knowledge Graph Completion Performance

A knowledge graph often lacks some existent triples. Knowledge graph completion is a technique for complementing such triples and its performance can be improved by augmenting triples from other external databases. However, entity names often differ between the original knowledge graph and an external database, which reduce the augmentation’s efficiency. In this study, we identify the same entities that have different names (orthographic variants) that come from different sources, merge them into one entity, and augment the knowledge graphs. Our proposed method exploits in the original knowledge graph and the external database the similarity of triples, which were embedded using BERT. Experimental evaluation on our knowledge graph completion performance showed that our proposed method with graph information effectively outperformed two baselines.