Reviewerly/exHarmony huggingface dataset:
Keefektifan Media Pembelajaran Scrapbook Terhadap Hasil Belajar Tematik Siswa Kelas V Tema 5 Ekosistem di SD Negeri 1 Getas

The background that drives this research is the low learning outcomes of thematic 2 subtheme 1 in grade II elementary school students. This is due to the lack of application of learning models that are not monotonous in the learning process.The problems in this study are: (1) The learning process is still dominated by the teacher. (2) The media used has not been varied and in accordance with the material. (3) In addition, students are less active in teaching and learning activities, resulting in low student learning outcomes. The goal to be achieved in this study is to determine the effectiveness of the use of scrapbook learning media on thematic learning outcomes of fifth grade students of SD Negeri 1 Getas. This type of research is quantitative research with quantitative experimental method and uses One Group Pretest Posttest Design research design. The population used is the entire sample of class II for the academic year 2022/2023. Data collection techniques based on observations and student learning outcomes.The results showed an increase in learning outcomes without being given treatment and learning outcomes using scrapbook media experienced differences. This can be proven from the results of classical learning completeness, which is 80% with good criteria and 28 students who complete with the highest score of 100. The percentage of complete student learning outcomes by implementing scrapbook learning media can be seen from the posttest score, which reaches 80% or already more than the percentage provisions, with the number of students who completed 28 and as many as 7 students who did not complete. The results of the cognitive pretest and posttest data showed differences, in the cognitive pretest test as many as 15 students who completed with the highest score of 90 and as many as 20 students who did not complete with the lowest score of 20, the percentage of classical pretest learning completeness reached 43%. While the cognitive posttest value using scrapbook learning media has reached KKM 65 as many as 28 students who completed with the highest score of 100 and 7 students did not complete with the lowest score of 50, the percentage of posttest classical learning completeness reached 80%. So, it can be said that scrapbook learning media can improve the learning outcomes of fifth grade students with the Ecosystem theme at SD Negeri 1 Getas. So it can be concluded that learning by using it can be concluded that it is proven that the effectiveness of scrapbook learning media in improving the learning outcomes of class V students with the Ecosystem theme at SD Negeri 1 Getas is said to have achieved good criteria.
A Taxonomy of Anomalies in Log Data

Log data anomaly detection is a core component in the area of artificial intelligence for IT operations. However, the large amount of existing methods makes it hard to choose the right approach for a specific system. A better understanding of different kinds of anomalies, and which algorithms are suitable for detecting them, would support researchers and IT operators. Although a common taxonomy for anomalies already exists, it has not yet been applied specifically to log data, pointing out the characteristics and peculiarities in this domain. In this paper, we present a taxonomy for different kinds of log data anomalies and introduce a method for analyzing such anomalies in labeled datasets. We applied our taxonomy to the three common benchmark datasets Thunderbird, Spirit, and BGL, and trained five state-of-the-art unsupervised anomaly detection algorithms to evaluate their performance in detecting different kinds of anomalies. Our results show, that the most common anomaly type is also the easiest to predict. Moreover, deep learning-based approaches outperform data mining-based approaches in all anomaly types, but especially when it comes to detecting contextual anomalies.
Tiny Pointers

This paper introduces a new data-structural object that we call the tiny pointer. In many applications, traditional $\log n $-bit pointers can be replaced with $o (\log n )$-bit tiny pointers at the cost of only a constant-factor time overhead. We develop a comprehensive theory of tiny pointers, and give optimal constructions for both fixed-size tiny pointers (i.e., settings in which all of the tiny pointers must be the same size) and variable-size tiny pointers (i.e., settings in which the average tiny-pointer size must be small, but some tiny pointers can be larger). If a tiny pointer references an element in an array filled to load factor $1 - 1 / k$, then the optimal tiny-pointer size is $\Theta(\log \log \log n + \log k) $ bits in the fixed-size case, and $ \Theta (\log k) $ expected bits in the variable-size case. Our tiny-pointer constructions also require us to revisit several classic problems having to do with balls and bins; these results may be of independent interest. Using tiny pointers, we revisit five classic data-structure problems: the data-retrieval problem, succinct dynamic binary search trees, space-efficient stable dictionaries, space-efficient dictionaries with variable-size keys, and the internal-memory stash problem. These are all well-studied problems, and in each case tiny pointers allow for us to take a natural space-inefficient solution that uses pointers and make it space-efficient for free.
Semantic-Guided Zero-Shot Learning for Low-Light Image/Video Enhancement

Low-light images challenge both human perceptions and computer vision algorithms. It is crucial to make algorithms robust to enlighten low-light images for computational photography and computer vision applications such as real-time detection and segmentation. This paper proposes a semantic-guided zero-shot low-light enhancement network (SGZ) which is trained in the absence of paired images, unpaired datasets, and segmentation annotation. Firstly, we design an enhancement factor extraction network using depthwise separable convolution for an efficient estimate of the pixel-wise light deficiency of an low-light image. Secondly, we propose a recurrent image enhancement network to progressively enhance the low-light image with affordable model size. Finally, we introduce an unsupervised semantic segmentation network for preserving the semantic information during intensive enhancement. Extensive experiments on benchmark datasets and a low-light video demonstrate that our model outperforms the previous state-of-the-art. We further discuss the benefits of the proposed method for low-light detection and segmentation. Code is available at https://github.com/ShenZheng2000/Semantic-Guided-Low-Light-Image-Enhancement
An Optimal Algorithm for Finding Champions in Tournament Graphs

A tournament graph is a complete directed graph, which can be used to model a round-robin tournament between $n$ players. In this paper, we address the problem of finding a champion of the tournament, also known as Copeland winner, which is a player that wins the highest number of matches. In detail, we aim to investigate algorithms that find the champion by playing a low number of matches. Solving this problem allows us to speed up several Information Retrieval and Recommender System applications, including question answering, conversational search, etc. Indeed, these applications often search for the champion inducing a round-robin tournament among the players by employing a machine learning model to estimate who wins each pairwise comparison. Our contribution, thus, allows finding the champion by performing a low number of model inferences. We prove that any deterministic or randomized algorithm finding a champion with constant success probability requires $Ω(\ell n)$ comparisons, where $\ell$ is the number of matches lost by the champion. We then present an asymptotically-optimal deterministic algorithm matching this lower bound without knowing $\ell$, and we extend our analysis to three variants of the problem. Lastly, we conduct a comprehensive experimental assessment of the proposed algorithms on a question answering task on public data. Results show that our proposed algorithms speed up the retrieval of the champion up to $13\times$ with respect to the state-of-the-art algorithm that perform the full tournament.
Immortal Tracker: Tracklet Never Dies

Previous online 3D Multi-Object Tracking(3DMOT) methods terminate a tracklet when it is not associated with new detections for a few frames. But if an object just goes dark, like being temporarily occluded by other objects or simply getting out of FOV, terminating a tracklet prematurely will result in an identity switch. We reveal that premature tracklet termination is the main cause of identity switches in modern 3DMOT systems. To address this, we propose Immortal Tracker, a simple tracking system that utilizes trajectory prediction to maintain tracklets for objects gone dark. We employ a simple Kalman filter for trajectory prediction and preserve the tracklet by prediction when the target is not visible. With this method, we can avoid 96% vehicle identity switches resulting from premature tracklet termination. Without any learned parameters, our method achieves a mismatch ratio at the 0.0001 level and competitive MOTA for the vehicle class on the Waymo Open Dataset test set. Our mismatch ratio is tens of times lower than any previously published method. Similar results are reported on nuScenes. We believe the proposed Immortal Tracker can offer a simple yet powerful solution for pushing the limit of 3DMOT. Our code is available at https://github.com/ImmortalTracker/ImmortalTracker.
Effectiveness of Detection-based and Regression-based Approaches for Estimating Mask-Wearing Ratio

Estimating the mask-wearing ratio in public places is important as it enables health authorities to promptly analyze and implement policies. Methods for estimating the mask-wearing ratio on the basis of image analysis have been reported. However, there is still a lack of comprehensive research on both methodologies and datasets. Most recent reports straightforwardly propose estimating the ratio by applying conventional object detection and classification methods. It is feasible to use regression-based approaches to estimate the number of people wearing masks, especially for congested scenes with tiny and occluded faces, but this has not been well studied. A large-scale and well-annotated dataset is still in demand. In this paper, we present two methods for ratio estimation that leverage either a detection-based or regression-based approach. For the detection-based approach, we improved the state-of-the-art face detector, RetinaFace, used to estimate the ratio. For the regression-based approach, we fine-tuned the baseline network, CSRNet, used to estimate the density maps for masked and unmasked faces. We also present the first large-scale dataset, the ``NFM dataset,'' which contains 581,108 face annotations extracted from 18,088 video frames in 17 street-view videos. Experiments demonstrated that the RetinaFace-based method has higher accuracy under various situations and that the CSRNet-based method has a shorter operation time thanks to its compactness.
Explaining machine-learned particle-flow reconstruction

The particle-flow (PF) algorithm is used in general-purpose particle detectors to reconstruct a comprehensive particle-level view of the collision by combining information from different subdetectors. A graph neural network (GNN) model, known as the machine-learned particle-flow (MLPF) algorithm, has been developed to substitute the rule-based PF algorithm. However, understanding the model's decision making is not straightforward, especially given the complexity of the set-to-set prediction task, dynamic graph building, and message-passing steps. In this paper, we adapt the layerwise-relevance propagation technique for GNNs and apply it to the MLPF algorithm to gauge the relevant nodes and features for its predictions. Through this process, we gain insight into the model's decision-making.
Improvement in Machine Translation with Generative Adversarial Networks

In this paper, we explore machine translation improvement via Generative Adversarial Network (GAN) architecture. We take inspiration from RelGAN, a model for text generation, and NMT-GAN, an adversarial machine translation model, to implement a model that learns to transform awkward, non-fluent English sentences to fluent ones, while only being trained on monolingual corpora. We utilize a parameter $\lambda$ to control the amount of deviation from the input sentence, i.e. a trade-off between keeping the original tokens and modifying it to be more fluent. Our results improved upon phrase-based machine translation in some cases. Especially, GAN with a transformer generator shows some promising results. We suggests some directions for future works to build upon this proof-of-concept.
Training on Making Learning Videos through Bandicam and Droidcam Applications for Teachers Elementary Schools

This community service program was initiated in response to the issue of teachers’ lack of skills in conducting online learning using online media. The program was held at SDN 1 Gerdu Pecangaan Jepara, involving eight teachers as participants. Through demonstrations and direct training for each participant, this activity was aimed to improve the teachers’ creativity in teaching with Bandicam and Droidcam applications. It is expected that the teachers can make use of both applications effectively, utilizing laptops or computers and smartphones to further support their students’ learning. The program outcomes showed that the assistance provided in the video productions using the Bandicam application could enhance the quality of the learning process at SDN 1 Gerdu. The teachers’ understanding of the learning applications was observed to improve, with the results of the post-test showing that 75% of the teachers were able to make learning videos using Bandicam and Droidcam applications well. The program has benefitted the teachers in that they can now make learning videos using Bandicam and Droidcam applications to improve their lessons for various materials, which will eventually help their students learn more effectively.
Enrichment of Anti Hoax Competency among Secondary School Students

&#x0D; Hoax and misinformation are common occurrences in the current era of social media. Nurturing critical thinking is necessary to combat the proliferation of hoaxes, especially in the younger generation. In this activity, we conducted a workshop for high school students and teachers to build awareness of hoaxes and how to identify them. This in-person workshop was held at Canisius College High School in a three-hour session. After taking part in the workshop, participants were expected to be able to explain the definition of a hoax, explain why hoaxes exist and are spread out among the community, and analyze the credibility of new information. For this purpose, the workshop consisted of three sessions. In the first session, the facilitators walked all participants through a lively discussion on what hoaxes are and how they get passed on from one person to another. In the second session, we introduced the concept of credibility to the participants. In the final session, the participants practiced evaluating the credibility of news. All sessions were delivered as paired or group discussions using authentic articles or postings from various social media platforms. The evaluation survey at the end of the activity showed that the participants agreed that the topic covered by this workshop was necessary and useful; however, they felt that the event was too short. This opportunity provided insights into how to improve similar hoax awareness-raising programs in the future.&#x0D;
Multi-Modal Mutual Information Maximization: A Novel Approach for Unsupervised Deep Cross-Modal Hashing

In this paper, we adopt the maximizing mutual information (MI) approach to tackle the problem of unsupervised learning of binary hash codes for efficient cross-modal retrieval. We proposed a novel method, dubbed Cross-Modal Info-Max Hashing (CMIMH). First, to learn informative representations that can preserve both intra- and inter-modal similarities, we leverage the recent advances in estimating variational lower-bound of MI to maximize the MI between the binary representations and input features and between binary representations of different modalities. By jointly maximizing these MIs under the assumption that the binary representations are modelled by multivariate Bernoulli distributions, we can learn binary representations, which can preserve both intra- and inter-modal similarities, effectively in a mini-batch manner with gradient descent. Furthermore, we find out that trying to minimize the modality gap by learning similar binary representations for the same instance from different modalities could result in less informative representations. Hence, balancing between reducing the modality gap and losing modality-private information is important for the cross-modal retrieval tasks. Quantitative evaluations on standard benchmark datasets demonstrate that the proposed method consistently outperforms other state-of-the-art cross-modal retrieval methods.
Multi-User Blind Symmetric Private Information Retrieval from Coded Servers

The problem of Multi-user Blind $X$-secure $T$-colluding Symmetric Private Information Retrieval from Maximum Distance Separable (MDS) coded storage system with $B$ Byzantine and $U$ unresponsive servers (U-B-MDS-MB-XTSPIR) is studied in this paper. Specifically, a database consisting of multiple files, each labeled by $M$ indices, is stored at the distributed system with $N$ servers according to $(N,K+X)$ MDS codes over $\mathbb{F}_q$ such that any group of up to $X$ colluding servers learn nothing about the data files. There are $M$ users, in which each user $m,m=1,\ldots,M$ privately selects an index $\theta_m$ and wishes to jointly retrieve the file specified by the $M$ users' indices $(\theta_1,\ldots,\theta_M)$ from the storage system, while keeping its index $\theta_m$ private from any $T_m$ colluding servers, where there exists $B$ Byzantine servers that can send arbitrary responses maliciously to confuse the users retrieving the desired file and $U$ unresponsive servers that will not respond any message at all. In addition, each user must not learn information about the other users' indices and the database more than the desired file. An U-B-MDS-MB-XTSPIR scheme is constructed based on Lagrange encoding. The scheme achieves a retrieval rate of $1-\frac{K+X+T_1+\ldots+T_M+2B-1}{N-U}$ with secrecy rate $\frac{K+X+T_1+\ldots+T_M-1}{ N-(K+X+T_1+\ldots+T_M+2B+U-1)}$ on the finite field of size $q\geq N+\max\{K, N-(K+X+T_1+\ldots+T_M+2B+U-1)\}$ for any number of files.
Digital Transformation Planning Based on Big Data Technology (Case Study: XYZ Bank)

Bank XYZ is one of the regional government-owned banks which as of August 16, 2021 has a total percentage of customers who have dormant accounts reached 50.43%. This happens because currently the development of the marketing strategy used by Bank XYZ is still based on the products and services owned by Bank XYZ, not referring to customer needs. For this reason, the purpose of this research is to make a digital transformation plan that can be used by Bank XYZ in order to become a customer-centric bank using Big Data technology. The method used to carry out this research is to use the Big Data Framework for Agile Business (BDFAB). The results of this study includes mapping data requirements and Big Data technology for Bank XYZ, the impact to business processes, proposals for changes in information system architecture, planning for quality control mechanisms, to changes in organizational structure so that the benefits obtained by Bank XYZ are a more personalized marketing approach to customers, increased accuracy in offering bank products and services based on customer profiles, needs, behavior, and interests, as well as increased customer engagement and customer satisfaction.
Peningkatan Ketersediaan Darah Sesuai Segmentasi Umur Menggunakan K-Means Clustering

Availability of blood at blood banks in Malang city fluctuates unevenly every month and every year. This greatly affects the demand for blood which is very high every day, especially during the current pandemic. To meet the high demand for blood, a program is needed that aims to attract Malang city residents, especially the productive age segment to donate blood so that the availability of blood every month can be met. This study aims to look at blood stock data from BPS (Central Statistics Agency) Malang City every month for three years (2018-2020) which will be clustered / grouped using K-Means method according to the productive age segmentation (17, 18-24, 25-44, 45-59 years) so that it will produce information when there will be an increase in blood donor raising program according to age segmentation by maximizing the month that is the favorite month of the age segmentation and the month that is quiet for donors. This research resulted in cluster groups based on months with analysis on the best number of clusters so that the evaluation resulted in knowledge that the highest month with many donors was October, while the lowest month with few donors was June and December. The government of Malang and related parties can make a program to increase and maximize the blood donor program in that month.
Prediksi Kualitas Air Menggunakan Metode Seasonal Autoregressive Integrated Moving Average (SARIMA)

Water conservation is very necessary to support the creation of clean water quality that is free from harmful substances that can disturb the environment. So a system is needed to monitor water quality to determine the level of pollution that occurs. This system will work to see water quality in real time with several quality parameters such as pH, temperature, and water turbidity. The purpose of this research is to produce a predictive model and find out the prediction results of a data mining-based system. The method used to predict water quality uses the Seasonal Autoregressive Integrated Moving Average (SARIMA) method, because the water quality data is thought to contain seasonal patterns. The results of this study indicate that the SARIMA model can be applied to the dataset used and obtain the accuracy of the forecasting results on each of the tested parameter data. The results of water quality forecasting with this parameter are the result data for testing at a dataset of a depth of 30 cm and a depth of 60 cm for temperature parameters, namely MSE&lt;0.1, and RMSE&lt;0.02. For pH parameters, MSE&lt;0.1, and RMSE&lt;0.1. As well as the turbidity parameter, the results of MSE&lt;0.02, and RMSE&lt;0.13. From these results indicate that this system can predict water quality with past data.
Shared Latent Space of Font Shapes and Their Noisy Impressions

Styles of typefaces or fonts are often associated with specific impressions, such as heavy, contemporary, or elegant. This indicates that there are certain correlations between font shapes and their impressions. To understand the correlations, this paper realizes a shared latent space where a font and its impressions are embedded nearby. The difficulty is that the impression words attached to a font are often very noisy. This is because impression words are very subjective and diverse. More importantly, some impression words have no direct relevance to the font shapes and will disturb the realization of the shared latent space. We, therefore, use DeepSets for enhancing shape-relevant words and suppressing shape irrelevant words automatically while training the shared latent space. Quantitative and qualitative experimental results with a large-scale font-impression dataset demonstrate that the shared latent space by the proposed method describes the correlation appropriately, especially for the shape-relevant impression words.