Reviewerly/exHarmony huggingface dataset:
Flood Risk Indices Mapping of Lebak Regency, Banten Province

Indonesia, as a developed country, has water-related disaster problems. Flood is one of them and it occurs in mostly province, including Banten province. Lebak Regency as the region of Banten Province has history of flood occurrences. According to the Banten Provincial BPBD (2021), among 2015-2020 period, twenty-nine flood events occurred in Lebak Regency. The severest flood of that period occurred in 2020. A total 2389 residents’ homes and forty-five units of public facilities were damaged. Flood in Lebak regency historically also resulted in nine deaths, and two people were declared missing. The objective of this study is to be figuring out the flood risk profile and which districts have the highest risk in Lebak Regency by develop the risk indices map. Risk indices map informs the risk of flooding in each sub-district, hence the policies taken can be delivered by the authority properly. The method used in this study used overlays based on Perka BNPB No. 2 of 2012. This research obtains the map of the flood hazard, vulnerability, and capacity level that generates through QGIS. According to the results of the study, the risk index of Lebak district is low with three states. The sub-districts with the non-risk category are seven sub-districts with a percentage of 25%, while the sub-districts that fall into the slightly risky category are 10 sub-districts with a percentage of 35.7% and the sub-districts that are in the fairly risky category are 11 sub-districts with a percentage of 39.3%. These results given the conclusion that Lebak Regency is not considerable risk on hydrological flood events. Moderate level on vulnerability should gives note to the authorities to enhance the disaster resiliences.
Minority-Oriented Vicinity Expansion with Attentive Aggregation for Video Long-Tailed Recognition

A dramatic increase in real-world video volume with extremely diverse and emerging topics naturally forms a long-tailed video distribution in terms of their categories, and it spotlights the need for Video Long-Tailed Recognition (VLTR). In this work, we summarize the challenges in VLTR and explore how to overcome them. The challenges are: (1) it is impractical to re-train the whole model for high-quality features, (2) acquiring frame-wise labels requires extensive cost, and (3) long-tailed data triggers biased training. Yet, most existing works for VLTR unavoidably utilize image-level features extracted from pretrained models which are task-irrelevant, and learn by video-level labels. Therefore, to deal with such (1) task-irrelevant features and (2) video-level labels, we introduce two complementary learnable feature aggregators. Learnable layers in each aggregator are to produce task-relevant representations, and each aggregator is to assemble the snippet-wise knowledge into a video representative. Then, we propose Minority-Oriented Vicinity Expansion (MOVE) that explicitly leverages the class frequency into approximating the vicinity distributions to alleviate (3) biased training. By combining these solutions, our approach achieves state-of-the-art results on large-scale VideoLT and synthetically induced Imbalanced-MiniKinetics200. With VideoLT features from ResNet-50, it attains 18% and 58% relative improvements on head and tail classes over the previous state-of-the-art method, respectively. Code and dataset are available at https://github.com/wjun0830/MOVE.
Analysis of The Influence of Reading Speed and Reading Reference on Reading Comprehension Skills With Reading Strategy as Mediation Variables for Students at State University of Padang

This study aims to analyze the analysis of the effect of reading speed and reading frequency on reading comprehension skills with reading strategy as a mediating variable for students of Universitas Negeri Padang. This research is a quantitative method with a descriptive type. The sampling technique in this study used the nonprobability sampling method, namely the purposive sampling technique. Data was collected using a questionnaire in the form of a Likert scale with five alternative answers. The data analysis technique used SEM-PLS with SmartPLS version 3 software. The results of the study found that reading speed had a direct and significant effect on reading comprehension skills. Reading frequency has a direct and significant effect on reading comprehension skills. Reading strategy have a direct and significant effect on reading comprehension skills. Reading speed has a direct and significant effect on reading strategy. Reading frequency has a direct and significant effect on reading strategy. There is an effect of reading speed on reading comprehension skills mediated by reading strategy. There is an effect of reading speed on reading comprehension skills mediated by reading strategy.
A Novel Calibration Method of Line Structured Light Plane Using Spatial Geometry

The line structured light plane calibration method using a plane target cannot produce satisfactory calibration results due to inaccurate positioning of the calibrated points. Field of view noise and sensor noise affect the target light stripe extraction and camera parameter calculation during the calibration process. These factors will cause the calculation of the coordinates of the calibrated point to deviate, and thus affect the light plane calibration. To solve this problem, we propose a new method to calculate the calibrated point based on spatial geometry. Firstly, for the projection line corresponding to the feature point on the light stripe and the corresponding line on the target, a common perpendicular of these two lines above is established, and since the sum of the squares of the distances from the midpoint to the two straight lines is the smallest, the midpoint of the common perpendicular is taken as the calibrated point. Secondly, the target is moved to different positions, and the non-collinear calibrated points are calculated. Finally, the parameters of the light plane are obtained by fitting these calibrated points. This method requires only a checkerboard target, and has a simple calibration process. The experimental results show that the average error of the calibration method proposed in this paper is 0.011 mm, which is less than the 0.031 mm of the calibration method based on the plane target with cross-ratio invariant.
Inter-image Contrastive Consistency for Multi-Person Pose Estimation

Multi-person pose estimation (MPPE) has achieved impressive progress in recent years. However, due to the large variance of appearances among images or occlusions, the model can hardly learn consistent patterns enough, which leads to severe location jitter and missing issues. In this study, we propose a novel framework, termed Inter-image Contrastive consistency (ICON), to strengthen the keypoint consistency among images for MPPE. Concretely, we consider two-fold consistency constraints, which include single keypoint contrastive consistency (SKCC) and pair relation contrastive consistency (PRCC). The SKCC learns to strengthen the consistency of individual keypoints across images in the same category to improve the category-specific robustness. Only with SKCC, the model can effectively reduce location errors caused by large appearance variations, but remains challenging with extreme postures (e.g., occlusions) due to lack of relational guidance. Therefore, PRCC is proposed to strengthen the consistency of pair-wise joint relation between images to preserve the instructive relation. Cooperating with SKCC, PRCC further improves structure aware robustness in handling extreme postures. Extensive experiments on kinds of architectures across three datasets (i.e., MS-COCO, MPII, CrowdPose) show the proposed ICON achieves substantial improvements over baselines. Furthermore, ICON under the semi-supervised setup can obtain comparable results with the fully-supervised methods using only 30% labeled data.
Literacy Class Program Management at Taman Lentera School Semarang

Managing the Literacy Class Program at Taman Lentera School in Semarang is an urgent priority to enhance students' reading, writing, and critical thinking skills, which will directly impact the improvement of education quality and empowerment of the younger generation in the future. This study aims to reveal the implementation of the literacy class program at Taman Lentera School related to management, supporting, and inhibiting factors. The design used in this study is qualitative, using a case study approach. The subjects of the study are founders, alumni volunteers, and active volunteers. Data were collected through (1) observation, (2) interviews, and (3) documentation. The data validity technique used in this study is source and technique triangulation, with data analysis using data collection, reduction, presentation, and drawing conclusions. The results revealed that program management includes six functions: (a) planning, (b) organizing, (c) mobilizing, (d) coaching, (e) assessment, and (f) development. Volunteers, community leaders, youth, parents, and children are supporting factors. Meanwhile, the inhibiting factors are consistency, communication, and coordination of volunteers, which are still lacking, limited literature owned by Taman Lentera School, and the Covid-19 pandemic.
Token Mixing: Parameter-Efficient Transfer Learning from Image-Language to Video-Language

Applying large scale pre-trained image-language model to video-language tasks has recently become a trend, which brings two challenges. One is how to effectively transfer knowledge from static images to dynamic videos, and the other is how to deal with the prohibitive cost of fully fine-tuning due to growing model size. Existing works that attempt to realize parameter-efficient image-language to video-language transfer learning can be categorized into two types: 1) appending a sequence of temporal transformer blocks after the 2D Vision Transformer (ViT), and 2) inserting a temporal block into the ViT architecture. While these two types of methods only require fine-tuning the newly added components, there are still many parameters to update, and they are only validated on a single video-language task. In this work, based on our analysis of the core ideas of different temporal modeling components in existing approaches, we propose a token mixing strategy to enable cross-frame interactions, which enables transferring from the pre-trained image-language model to video-language tasks through selecting and mixing a key set and a value set from the input video samples. As token mixing does not require the addition of any components or modules, we can directly partially fine-tune the pre-trained image-language model to achieve parameter-efficiency. We carry out extensive experiments to compare our proposed token mixing method with other parameter-efficient transfer learning methods. Our token mixing method outperforms other methods on both understanding tasks and generation tasks. Besides, our method achieves new records on multiple video-language tasks. The code is available at https://github.com/yuqi657/video_language_model.
Policy-Independent Behavioral Metric-Based Representation for Deep Reinforcement Learning

Behavioral metrics can calculate the distance between states or state-action pairs from the rewards and transitions difference. By virtue of their capability to filter out task-irrelevant information in theory, using them to shape a state embedding space becomes a new trend of representation learning for deep reinforcement learning (RL), especially when there are explicit distracting factors in observation backgrounds. However, due to the tight coupling between the metric and the RL policy, such metric-based methods may result in less informative embedding spaces which can weaken their aid to the baseline RL algorithm and even consume more samples to learn. We resolve this by proposing a new behavioral metric. It decouples the learning of RL policy and metric owing to its independence on RL policy. We theoretically justify its scalability to continuous state and action spaces and design a practical way to incorporate it into an RL procedure as a representation learning target. We evaluate our approach on DeepMind control tasks with default and distracting backgrounds. By statistically reliable evaluation protocols, our experiments demonstrate our approach is superior to previous metric-based methods in terms of sample efficiency and asymptotic performance in both backgrounds.
AutoStegaFont: Synthesizing Vector Fonts for Hiding Information in Documents

Hiding information in text documents has been a hot topic recently, with the most typical schemes of utilizing fonts. By constructing several fonts with similar appearances, information can be effectively represented and embedded in documents. However, due to the unstructured characteristic, font vectors are more difficult to synthesize than font images. Existing methods mainly use handcrafted features to design the fonts manually, which is time-consuming and labor-intensive. Moreover, due to the diversity of fonts, handcrafted features are not generalizable to different fonts. Besides, in practice, since documents might be distorted through transmission, ensuring extractability under distortions is also an important requirement. Therefore, three requirements are imposed on vector font generation in this domain: automaticity, generalizability, and robustness. However, none of the existing methods can satisfy these requirements well and simultaneously. To satisfy the above requirements, we propose AutoStegaFont, an automatic vector font synthesis scheme for hiding information in documents. Specifically, we design a two-stage and dual-modality learning framework. In the first stage, we jointly train an encoder and a decoder to invisibly encode the font images with different information. To ensure robustness, we target designing a noise layer to work with the encoder and decoder during training. In the second stage, we employ a differentiable rasterizer to establish a connection between the image and the vector modality. Then, we design an optimization algorithm to convey the information from the encoded image to the corresponding vector. Thus the encoded font vectors can be automatically generated. Extensive experiments demonstrate the superior performance of our scheme in automatically synthesizing vector fonts for hiding information in documents, with robustness to distortions caused by low-resolution screenshots, printing, and photography. Besides, the proposed framework has better generalizability to fonts with diverse styles and languages.
Incremental Image De-raining via Associative Memory

While deep learning models have achieved the state-of-the-art performance on single-image rain removal, most methods only consider learning fixed mapping rules on the single synthetic dataset for lifetime. This limits the real-life application as iterative optimization may change mapping rules and training samples. However, when models learn a sequence of datasets in multiple incremental steps, they are susceptible to catastrophic forgetting that adapts to new incremental episodes while failing to preserve previously acquired mapping rules. In this paper, we argue the importance of sample diversity in the episodes on the iterative optimization, and propose a novel memory management method, Associative Memory, to achieve incremental image de-raining. It bridges connections between current and past episodes for feature reconstruction by sampling domain mappings of past learning steps, and guides the learning to trace the current pathway back to the historical environment without storing extra data. Experiments demonstrate that our method can achieve better performance than existing approaches on both inhomogeneous and incremental datasets within the spectrum of highly compact systems.
Decision Support System for Selection of Pesticides in Chili Plants Using the MAUT Method

Diseases in chili plants often cause farmers to experience crop failure. Diseases that are often encountered are leaf spot disease, wilt disease, and anthracnose disease caused by fungi. Therefore, farmers are trying to prevent and reduce the disease by using pesticides. The use of pesticides in agriculture plays a role in preventing and reducing diseases in chili plants. Pesticides for chili plants are quite widely spread on the market and have the advantages of each product offered to farmers, so farmers must be more careful and understanding in choosing pesticides to be used in preventing chili plant diseases. For that we need a decision support system (SPK). The purpose of this research is to apply the MAUT method in SPK which can help chili farmers in selecting pesticides. The data used in this study are 10 types of pesticides as an alternative. While the criteria consist of price, classification, number of diseases eradicated, method of action, shelf life, and formulation concentration. Data analysis uses the MAUT method with the steps of forming a decision matrix, normalizing the decision matrix, determining the utility matrix, calculating the final utility, and ranking. The research results obtained for the three best pesticides are Tridex 80 WP, Ziflo 76 WG, and Cabriotop 60 WG.
Dual Mutual Information Constraints for Discriminative Clustering

Deep clustering is a fundamental task in machine learning and data mining that aims at learning clustering-oriented feature representations. In previous studies, most of deep clustering methods follow the idea of self-supervised representation learning by maximizing the consistency of all similar instance pairs while ignoring the effect of feature redundancy on clustering performance. In this paper, to address the above issue, we design a dual mutual information constrained clustering method named DMICC which is based on deep contrastive clustering architecture, in which the dual mutual information constraints are particularly employed with solid theoretical guarantees and experimental validations. Specifically, at the feature level, we reduce the redundancy among features by minimizing the mutual information across all the dimensionalities to encourage the neural network to extract more discriminative features. At the instance level, we maximize the mutual information of the similar instance pairs to obtain more unbiased and robust representations. The dual mutual information constraints happen simultaneously and thus complement each other to jointly optimize better features that are suitable for the clustering task. We also prove that our adopted mutual information constraints are superior in feature extraction, and the proposed dual mutual information constraints are clearly bounded and thus solvable. Extensive experiments on five benchmark datasets show that our proposed approach outperforms most other clustering algorithms. The code is available at https://github.com/Li-Hyn/DMICC.
Layout Representation Learning with Spatial and Structural Hierarchies

We present a novel hierarchical modeling method for layout representation learning, the core of design documents (e.g., user interface, poster, template). Existing works on layout representation often ignore element hierarchies, which is an important facet of layouts, and mainly rely on the spatial bounding boxes for feature extraction. This paper proposes a Spatial-Structural Hierarchical Auto-Encoder (SSH-AE) that learns hierarchical representation by treating a hierarchically annotated layout as a tree format. On the one side, we model SSH-AE from both spatial (semantic views) and structural (organization and relationships) perspectives, which are two complementary aspects to represent a layout. On the other side, the semantic/geometric properties are associated at multiple resolutions/granularities, naturally handling complex layouts. Our learned representations are used for effective layout search from both spatial and structural similarity perspectives. We also newly involve the tree-edit distance (TED) as an evaluation metric to construct a comprehensive evaluation protocol for layout similarity assessment, which benefits a systematic and customized layout search. We further present a new dataset of POSTER layouts which we believe will be useful for future layout research. We show that our proposed SSH-AE outperforms the existing methods achieving state-of-the-art performance on two benchmark datasets. Code is available at github.com/yueb17/SSH-AE.
Delving Deep into Pixel Alignment Feature for Accurate Multi-View Human Mesh Recovery

Regression-based methods have shown high efficiency and effectiveness for multi-view human mesh recovery. The key components of a typical regressor lie in the feature extraction of input views and the fusion of multi-view features. In this paper, we present Pixel-aligned Feedback Fusion (PaFF) for accurate yet efficient human mesh recovery from multi-view images. PaFF is an iterative regression framework that performs feature extraction and fusion alternately. At each iteration, PaFF extracts pixel-aligned feedback features from each input view according to the reprojection of the current estimation and fuses them together with respect to each vertex of the downsampled mesh. In this way, our regressor can not only perceive the misalignment status of each view from the feedback features but also correct the mesh parameters more effectively based on the feature fusion on mesh vertices. Additionally, our regressor disentangles the global orientation and translation of the body mesh from the estimation of mesh parameters such that the camera parameters of input views can be better utilized in the regression process. The efficacy of our method is validated in the Human3.6M dataset via comprehensive ablation experiments, where PaFF achieves 33.02 MPJPE and brings significant improvements over the previous best solutions by more than 29%. The project page with code and video results can be found at https://kairobo.github.io/PaFF/.
Quality Measurement of Accurate-5 Accounting Software Using the Iso 9126 Model

This study aims to evaluate the degree of excellence of the Accurate-5 accounting software by employing the ISO-9126 model, which assesses six dimensions (primary characteristics) and associated attributes (secondary characteristics) of software quality. The researchers measured a sample of 41 participants who attended a workshop at STMIK Mardira Indonesia Bandung using the Accurate-5 software. The researchers gathered primary data using a questionnaire that utilized the Likert scale. The validity test results supported the validity of the items, as a value greater than 0.316 was found. The reliability test results demonstrated that all attributes have a high level of reliability, as each item had a Cronbach's alpha value greater than 0.7. The software quality evaluation findings utilizing the ISO 9126 standard indicate that all dimensions and attributes satisfy the "Good" criteria. The researchers found that the attributes of Compliance, Installability, and Coexistence satisfy the criteria of "Very Good." Based on the data, we can infer that the Accurate-5 software complies with the ISO 9126 standard as it achieved an average percentage score of 77.53%, indicating that it meets the "Good" criteria.