Reviewerly/exHarmony huggingface dataset:
KLASIFIKASI HASIL BELAJAR SISWA MENGGUNAKAN METODE C4.5 BERDASARKAN RIWAYAT AKADEMIK DI SMP XYZ

This research was conducted to classify student learning outcomes at XYZ Middle School based on academic history during learning and student learning interests. This study aims to provide information to students and teaching staff regarding student learning outcomes. With the available information, it is hoped that teaching staff can develop methods for conveying material in order to obtain better results. The method used in solving this problem is the C.45 algorithm method. Starting from collecting data consisting of assignment scores, daily tests, UTS, and UAS. Then the formation of a decision system as initial data that has condition and decision attribute values. Then calculate the entropy value of each attribute. Calculating the highest gain value which will then be used as a node. Then, determine the decision from the results of the decision tree process by starting from the highest root to the lowest root to determine the decision criteria.
PENERAPAN METODE DECISION TREE UNTUK PRAKIRAAN CUACA KOTA BEKASI

To predict the weather requires a lot of weather data variables, many influencing factors such as the large amount of data. Because this makes prediction accuracy and speed less accurate. So with that made a way to make predictive model research using several data mining techniques. The data to be used in this study were obtained from timeanddate.com, timeanddate.com is a site that provides data and information about daily weather conditions. The data used ranges from December 2021 to August 2022. The research aims to obtain weather classification patterns using the data mining classification algorithm, namely the C4.5 algorithm. The results of testing the C4.5 algorithm using the php website and program get an accuracy of 82%.
E-Portfolio Effectiveness in Seeking IT T Jobs

The fact that graduate students usually have no experience and similar backgrounds might cause job vacancy placement and applicant evaluation to be complicated and stressful tasks for employers.This research project focuses on the role of e-portfolios in facilitating the hiring process and assisting job seekers in finding IT jobs.By using eportfolios, employers would be able to assess IT applicants during the evaluation process more efficiently than using conventional CVs.A hybrid method was used to assess the e-portfolio effectiveness involving HR/IT interviewees after the experimentation of using simulated e-portfolios.Accordingly, Interviewees previewed the results of compression between a CV and an e-portfolio conducted in a focus group experimentation to get their feedback.The findings of this research indicate a high probability of acceptance to use the e-portfolio for seeking jobs and evaluation purposes by both employers and job seekers.
THE DECISION TREE ANALYSIS MODEL ON A REMOTE POPULAR SCIENCE LEARNING SYSTEM FOR IN-SERVICES EDUCATION OF TEACHERS

The in-service training learning system for primary and secondary school teachers in Taiwan breaks through the time and space limitations and uses a distance learning method to promote primary and secondary school and kindergarten teachers on understanding the scientific development, cultivating scientific knowledge literacy and life-long learning habits, and then giving feedback to students, making popular science knowledge activities a part of teachers' in-service training and social rational cultural activities.Nowadays, Taiwan's largest teachers' in-service education distance science learning website is "Knowledge Lecture Hall" (https://knowledge.colife.org.tw/), which was built by the Co-Life team of National Center for High-performance Computing (NCHC) of NAR Labs.The purpose of this study was to establish a data repository based on the in-service training data of 5,291 teachers participating in distance science education learning in the past five years (2018-2022) collected by the Knowledge Lecture website.The data contains 16 domains of variables, including basic information about teachers and the schools they work for, online viewing time, and information about teachers' in-service training courses.In the process of knowledge discovery in the database, the decision tree algorithm C5.0 was used for modeling, the AUC value was used to evaluate the predictive ability, and the concept of tree branch was used as the decision model.According to the class and relationship of variables, the explanation model of good prediction results was found, and the following research objectives were achieved as follows: First, analyze the preferences and trends of in-service teachers' participation in learning courses during the past five years (2018)(2019)(2020)(2021)(2022).Second, explore the decision tree model of the key predictors of in-service faculty participation in learning courses in the Knowledge Forum over the past five years (2018)(2019)(2020)(2021)(2022).Finally, recommendations for improvement based on the findings are provided as a reference for future management of in-service distance learning courses on the Knowledge Lecture website and for future research by future researchers.
PENERAPAN APLIKASI KAHOOT PADA MATA PELAJARAN MATEMATIKA UNTUK MENINGKATKAN MOTIVASI DAN MINAT BELAJAR SISWA DIKELAS VII SMP NEGERI 38 PADANG

Learning is the main activity of the educational process in schools that aims to produce behavioral changes. These changes include cognitive, affective and psychomotor. This learning activity requires active learning, participation and interactive communication between teachers and students. Learning activities are designed in such a way that they can achieve learning objectives, one of which is learning media. Learning media is designing learning and planning teaching and learning activities. The benefits of learning media clarify the presentation of material and information so as to clarify and improve learning processes and outcomes. This is a challenge for teachers to create fun, interesting and active learning and achieve learning objectives. Against new adaptations that must be accepted in such a way. Learning media that can be used by teachers is Kahoot. Kahoot is an online learning media that contains quizzes and games. Kahoot is also able to help learning evaluation activities become interesting, interactive, conducive and easy to monitor learning outcomes, so that they can provide and increase students' motivation and interest in learning.
ENGLISH SKILL IMPROVEMENT AT GRADE 11 OF HOSPYTALITY MAJOR AT SMK TUNAS MUDA BERKARYA BATAM

This activity is carried out to improve knowledge and skills in English for grade 11 SMK Tunas Muda Berkarya Batam, the department of Hospitality. Students were given material about speaking development, vocabulary field, correct pronunciation and tenses usage. The activity was carried out for four meetings. The methods of developing the skills of participants that were applied consisted of methods of discussion, presentation, conversation, group in pairs, describing pictures and practicing roles as a staff and a guest. The result of this training is the participant's ability can be measured from the observations of 65% and the results after the training increased to 90% there were improments in participants' knowledge and skills in speaking, vocabulary, pronunciation and use of grammar.
P-Sketch: A Fast and Accurate Sketch for Persistent Item Lookup

In large data streams consisting of sequences of data items, those appearing over a long period of time are regarded as persistent. Compared with frequent items, persistent items do not necessarily hold large amounts of data and thus may hamper the effectiveness of vanilla volume-based detectors. Identifying persistent items plays a crucial role in a range of areas such as fraud detection and network management. Fast detection of persistent items in massive streams is however challenging due to the inherently high data rates, while state-of-the-art persistent item lookup solutions routinely require large enough memory to attain high accuracy, which questions the feasibility of deploying them in practice. In this paper, we introduce P-Sketch, a novel approach to persistent item lookup that achieves high accuracy even with small memory (L1 Cache) budgets and maintains high update speed across different settings. Specifically, we introduce the concept of arrival continuity <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">(hotness)</i> that counts the number of consecutive windows in which an item appears, to effectively protect persistent items from being wrongly replaced by non-persistent ones. Through meticulous data analysis, we also reveal that items with higher persistence tend to possess a stronger hotness than non-persistent ones. Thus, we harness the information of persistence and hotness, and employ a probability-based replacement strategy to achieve a good balance between memory efficiency, lookup accuracy, and update speed. We also present a theoretical analysis of the performance of the proposed P-Sketch. Through trace-driven emulations, we demonstrate that our P-Sketch yields average F1 score and update throughput gains of up to 10.32 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\times$</tex-math> </inline-formula> and respectively 2.9 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\times$</tex-math> </inline-formula> , over existing schemes. Lastly, we show how to further boost the P-Sketch’s update speed with Single Instruction Multiple Data (SIMD) instructions.
An Efficient Implementation of the Extended Object Trajectory PMB Filter Using Blocked Gibbs Sampling

This paper presents an efficient implementation of the trajectory Poisson multi-Bernoulli (PMB) filter for multiple extended object tracking (EOT), which directly estimates a set of object trajectories. The trajectory PMB filter propagates a PMB density on the posterior of sets of trajectories through the filtering recursions over time, where the multi-Bernoulli (MB) mixture in the PMB mixture (PMBM) posterior after each update step is approximated as a single MB. The efficient MB approximation is achieved by first running a blocked Gibbs sampler on the joint posterior of the set of trajectories and the measurement association variables. The single-object measurement model is assumed to be a Poisson point process which enables us to parallelize the sampling across all objects and association variables, respectively. Then, samples of object states are utilized to form the approximate MB density via Kullback-Leibler divergence minimization. Simulation results on EOT with known and constant elliptical shapes show that the TPMB implementation using blocked Gibbs sampling outperforms the state-of-the-art TPMB implementation using loopy belief propagation with significantly reduced runtime.
Delay-Based Dynamic Clustering Method for the IoT Cluster

A dynamic clustering method can reconstruct IoT nodes into logical group units when using several groups of IoT nodes. However, massive control at a logical group level does not guarantee optimal control performance. To efficiently deliver and execute a massive control command within a certain communication delay, it is necessary to cluster each logical group into subgroups and execute massive control commands by each subgroup. This paper proposes the DBDC(Delay-Based Dynamic Clustering) method, a clustering method based on DBSCAN that can optimize the communication delay of a massive control within a logical group. Through several simulations, the parameters of the DBSCAN algorithm create different clustering cases, and the DBDC method can find the optimal parameter value that satisfies the optimization condition. Therefore, this study shows that the DBDC method can effectively design a cluster system so collective IoT clusters have the best massive control performance.
A Multi-scale Network Based on Compound Extended Coding UNet++ for Polyp Segmentation

Computer-aided segmentation has been widely used in clinic since it helps to quickly locate the region of interest (ROI) in medical images. At present, a large number of segmentation algorithms have been proposed based on convolutional neural networks in past decades. However, they are difficult to popularize in segmentation of complex and diverse targets such as Intestinal polyps. In this paper, an efficient segmentation model of parallel multi-scale networks based on compound extended coding UNet++ is proposed. Specifically, a multi-scale parallel strategy is designed to extract multi-level features from multiple branches of the segmentation network for fusion, in which each branch network is constructed by combining EfficientNet encoder and UNet++ decoder. The proposed model is applied on the public CVC-ClinicDB dataset and achieves the mean Dice of 0.9344, IoU of 0.8440, sensitivity of 0.9113 and precision of 0.9211, exhibiting more promising performance than some state-of-the-art methods in polyp segmentation from colonoscopy images.
Open-Vocabulary Multi-label Image Classification with Pretrained Vision-Language Model

We design an open-vocabulary multi-label image classification model to predict multiple novel concepts in an image based on a powerful language-image pretrained model i.e. CLIP. While CLIP achieves a remarkable performance on single-label zero-shot image classification, it only utilizes global image feature which is less applicable for predicting multiple labels. To address the problem, we propose a novel method that contains an Image-Text attention module to extract multiple class-specific image features from CLIP. In addition, we introduce a new training method with contrastive loss to help the attention module find diverse attention masks for all classes. During testing, the class-specific features are interpolated with CLIP features to boost the performance. Extensive experiments show that our proposed method achieves state-of-the-art performance on zero-shot learning tasks for multi-label image classifications on two benchmark datasets.
Aerial-Ground Person Re-ID

Person re-ID matches persons across multiple non-overlapping cameras. Despite the increasing deployment of air-borne platforms in surveillance, current existing person re-ID benchmarks’ focus is on ground-ground matching and very limited efforts on aerial-aerial matching. We propose a new benchmark dataset - AG-ReID, which performs person re-ID matching in a new setting: across aerial and ground cameras. Our dataset contains 21,983 images of 388 identities and 15 soft attributes for each identity. The data was collected by a UAV flying at altitudes between 15 to 45 meters and a ground-based CCTV camera on a university campus. Our dataset presents a novel elevated-viewpoint challenge for person re-ID due to the significant difference in person appearance across these cameras. We propose an explainable algorithm to guide the person re-ID model’s training with soft attributes to address this challenge. Experiments demonstrate the efficacy of our method on the aerial-ground person re-ID task. The dataset will be published and the baseline codes will be open-sourced at https://github.com/huynguyen792/AG-ReID to facilitate research in this area.
Forensics Forest: Multi-scale Hierarchical Cascade Forest for Detecting GAN-generated Faces

We describe a simple and effective method called ForensicsForest to detect GAN-generate faces. Instead of using the commonly used CNN models, we describe a novel multi-scale hierarchical cascade forest, which takes semantic and frequency features as input, and hierarchically cascades different levels of features for authenticity prediction. We then propose a multi-scale ensemble, which comprehensively considers different levels of information to improve the performance further. Our method is validated on state-of-the-art GAN-generated face datasets in comparison with several CNN models, which demonstrates the surprising effectiveness of our method in detecting GAN-generated faces.
Robots in the Hotel Kitchen

The adoption of robotics and automation tools in the hospitality sector is growing fast with robots programmed to perform a human-operated task. The shift towards the acceptance of robots in hotel kitchens with robot chefs as a part of the chefs team is a reality in some establishments. Robots are preferred by catering establishments as they reduce errors in food preparation and improve productivity; however, the initial investment for robots is high. Chefs do believe that humans could not be replaced by robots as the sensitivity of taste, and flavor may not be replicated by the robots even though standardization of recipes allows them to prepare distinctive food repeatedly. This study is about the customer's response to robots as chefs in various food establishments, and observation of robots in multi-cuisine establishments through secondary resources. The findings of the study will help hospitality chains to be better prepared for robot chefs and automation in the hotel kitchen.
AdaML: An Adaptive Meta-Learning model based on user relevance for user cold-start recommendation

User cold-start recommendation is a critical problem due to the sparse user–item​ interactions of new users. To address this issue, an emerging popular direction employs the meta-learning paradigm, which can quickly adapt to new tasks with sparsely labeled data. Early meta-learning recommendation works use the same global prior to initialize the parameters for each user’s preference learning task, which may be insufficient to discern users’ complex behaviors and leads to poor generalization. As a result, recent works have proposed the concept of relevance-based meta-learning recommendation, which ensures that similar priors are locally shared by related users with similar preferences, and have yielded promising results. However, as an important factor influencing the performance of relevance-based meta-learning recommendation, related user information is still not sufficiently explored and exploited in existing works. To that end, in this paper, we present a novel adaptive meta-learning model based on user relevance (AdaML) to further improve the user cold-start recommendations. Specifically, AdaML firstly proposes a related user recognition component, which recognizes related users with similar preferences by integrating the meta-path-based and the clustering-based strategies. The retrieved high-quality information of related users is then effectively utilized by a well-designed user relevance enhanced recommender, on one hand, to enhance user representations and tailor relevance-specific priors from the globally shared prior, on the other. Finally, extensive experiments on several public datasets demonstrate AdaML’s superior performance over state-of-the-art approaches in the user cold-start scenario.
Microservices Scheduling Algorithms: A Survey Study

The development of many large and complex applications has led to the need to come up with a better solution to manage those applications from one large system into a combination of small services that work together in a cohesive way for a widely used application where it can be easy to deploy, configure, and scale.The popularity of microservices architecture in different fields has made it susceptible to development, especially in task scheduling.We report on the scheduling algorithms that have been used by many researchers and discuss their approaches.This report will help us ameliorate the flexibility of the system in future studies.
Bell Inequalities with Overlapping Measurements

Which nonlocal correlations can be obtained, when a party has access to more than one subsystem? While traditionally nonlocality deals with spacelike separated parties, this question becomes important with quantum technologies that connect devices by means of small shared systems. Here, we study Bell inequalities where measurements of different parties can have overlap. This allows us to accommodate problems in quantum information such as the existence of quantum error correction codes in the framework of nonlocality. The scenarios considered show an interesting behavior with respect to Hilbert space dimension, overlap, and symmetry.
The conceptual development of quantum mechanics and its interdisciplinary application

Quantum mechanics has evolved over the last hundred years and has had a profound impact on other fields. It is poised as a very powerful tool that can drastically improve our ability to carry out scientific research. This paper uses some concepts and examples to show how quantum mechanics influences many interdisciplinary applications. This paper first introduces the development of quantum mechanics to pave the way for the rest of the article. Then, the author selects two inventions to reflect the specific role of quantum mechanics. Finally, this paper also introduces two relatively advanced research areas, one is quantum computing and the other is quantum biology, with the basic framework and a simple and intuitive explanation provided, enabling the reader to see the recent study based on quantum mechanics. Through this paper, relevant researchers can not only draw a framework of quantum mechanics but also clarify some areas of future development.
Generation and Detection of Deepfakes

Generative adversarial networks have made remarkable progress in generating realistic images of high quality. While video generation is the natural sequel, it entails a number of challenges w.r.t. complexity and computation, associated to the simultaneous modeling of appearance, as well as motion. I will talk about our work related to design of generative models, which allow for realistic generation of face images and videos. We have placed emphasis on disentangling motion from appearance and have learned motion representations directly from RGB, without structural representations such as facial landmarks or 3D meshes. In our latest work, we have aimed at constructing motion as linear displacement of codes in the latent space. Based on this, our model LIA (Latent Image Animator) is able to animate images via navigation in the latent space. While highly intriguing, video generation has thrusted upon us the imminent danger of deepfakes, which can offer unprecedented levels of increasingly realistic manipulated videos. Deepfakes pose an imminent security threat to us all, and to date, deepfakes are able to mislead face recognition systems, as well as humans. Hence, we design generation and detection methods in parallel. In the second part of my talk, I will discuss our associated work, where in our latest work, we explore attention mechanisms in 3D CNNs.