Reviewerly/exHarmony huggingface dataset:
Cluster Attack: Query-based Adversarial Attacks on Graphs with  Graph-Dependent Priors

While deep neural networks have achieved great success in graph analysis, recent work has shown that they are vulnerable to adversarial attacks. Compared with adversarial attacks on image classification, performing adversarial attacks on graphs is more challenging because of the discrete and non-differential nature of the adjacent matrix for a graph. In this work, we propose Cluster Attack -- a Graph Injection Attack (GIA) on node classification, which injects fake nodes into the original graph to degenerate the performance of graph neural networks (GNNs) on certain victim nodes while affecting the other nodes as little as possible. We demonstrate that a GIA problem can be equivalently formulated as a graph clustering problem; thus, the discrete optimization problem of the adjacency matrix can be solved in the context of graph clustering. In particular, we propose to measure the similarity between victim nodes by a metric of Adversarial Vulnerability, which is related to how the victim nodes will be affected by the injected fake node, and to cluster the victim nodes accordingly. Our attack is performed in a practical and unnoticeable query-based black-box manner with only a few nodes on the graphs that can be accessed. Theoretical analysis and extensive experiments demonstrate the effectiveness of our method by fooling the node classifiers with only a small number of queries.
Encoder-decoder with Multi-level Attention for 3D Human Shape and Pose  Estimation

3D human shape and pose estimation is the essential task for human motion analysis, which is widely used in many 3D applications. However, existing methods cannot simultaneously capture the relations at multiple levels, including spatial-temporal level and human joint level. Therefore they fail to make accurate predictions in some hard scenarios when there is cluttered background, occlusion, or extreme pose. To this end, we propose Multi-level Attention Encoder-Decoder Network (MAED), including a Spatial-Temporal Encoder (STE) and a Kinematic Topology Decoder (KTD) to model multi-level attentions in a unified framework. STE consists of a series of cascaded blocks based on Multi-Head Self-Attention, and each block uses two parallel branches to learn spatial and temporal attention respectively. Meanwhile, KTD aims at modeling the joint level attention. It regards pose estimation as a top-down hierarchical process similar to SMPL kinematic tree. With the training set of 3DPW, MAED outperforms previous state-of-the-art methods by 6.2, 7.2, and 2.4 mm of PA-MPJPE on the three widely used benchmarks 3DPW, MPI-INF-3DHP, and Human3.6M respectively. Our code is available at https://github.com/ziniuwan/maed.
Application of The Fuzzy Promethee Method in Selecting Farmer Groups Verification for Candidates for Receiving Grants Regional Revenues And Expenditures Budget

The Bogor Regency Department of Fisheries and Livestock, in charge of the Breeding and Livestock Cultivation Section, has a work program to assist farmer groups, especially livestock, in acquiring aid funds from the government sourced from the Bogor Regency Regional Revenues and Expenditures Budget (APBD). As a result, the government allocates aid fundsfrom the Regional Revenues and Expenditures Budges (APBD) budget source to aid the community in managing livestock. Based on this, research will be conducted and methods used to determine which livestock farmer groups are eligible for government assistance will be implemented.A Decision Support System (DSS) will be used as a tool to recommend which farmer groups deserve assistance based on predetermined assessment criteria. By using the service, you will be able to provide a more accurate and objective evaluation. Because of its capacity to determine criteria values that contain uncertainty and select the best alternative based on these rankings and alternatives, the Fuzzy-PROMETHEE approach was chosen for handlingmulti criteria issues.
reading level in the cognitive development of the students of the upper basic

&#x0D; Reading is considered a cognitive activity that stimulates thought in order to make a pertinent judgment, it has also become a great challenge for students. Reading is one of the primary educational processes in education, it allows knowing the level of linguistic development and reading comprehension. The objective of the research is to investigate the reading level and its association with the cognitive development of the students, of higher basic education of the Fiscal Educational Unit "Carlos Julio Arosemena Tola, during the 2021-2022 school period. A questionnaire was applied that allowed to know the low reading level of the students. A mixed qualitative-quantitative methodology was applied. The results have shown that there are several factors that affect the low reading level, from demotivation to the lack of strategies such as extracting ideas and summarizing what has been read. However, there are tools to enhance critical thinking and analytical skills, in which the guide and strategies used by the teacher must be based on the student's level of prior knowledge to achieve a valuable level of reading comprehension.
An Underwater Image Semantic Segmentation Method Focusing on Boundaries  and a Real Underwater Scene Semantic Segmentation Dataset

With the development of underwater object grabbing technology, underwater object recognition and segmentation of high accuracy has become a challenge. The existing underwater object detection technology can only give the general position of an object, unable to give more detailed information such as the outline of the object, which seriously affects the grabbing efficiency. To address this problem, we label and establish the first underwater semantic segmentation dataset of real scene(DUT-USEG:DUT Underwater Segmentation Dataset). The DUT- USEG dataset includes 6617 images, 1487 of which have semantic segmentation and instance segmentation annotations, and the remaining 5130 images have object detection box annotations. Based on this dataset, we propose a semi-supervised underwater semantic segmentation network focusing on the boundaries(US-Net: Underwater Segmentation Network). By designing a pseudo label generator and a boundary detection subnetwork, this network realizes the fine learning of boundaries between underwater objects and background, and improves the segmentation effect of boundary areas. Experiments show that the proposed method improves by 6.7% in three categories of holothurian, echinus, starfish in DUT-USEG dataset, and achieves state-of-the-art results. The DUT- USEG dataset will be released at https://github.com/baxiyi/DUT-USEG.
YANMTT: Yet Another Neural Machine Translation Toolkit

In this paper, we present our open-source neural machine translation (NMT) toolkit called "Yet Another Neural Machine Translation Toolkit" abbreviated as YANMTT 1 which is built on top of the HuggingFace Transformers library.YANMTT aims to enable pre-training and fine-tuning of sequence-to-sequence models with ease.It can be used for training parameter-heavy models with minimal parameter sharing and efficient, lightweight models via heavy parameter sharing.Additionally, efficient fine-tuning can be done via finegrained tuning parameter selection, adapter and prompt tuning.Our toolkit also comes with a user interface that can be used to demonstrate these models and visualize the attention and embedding representations.Apart from these core features, our toolkit also provides other advanced functionalities such as but not limited to document/multi-source NMT, simultaneous NMT, mixtures-of-experts and model compression.
Generating Superpixels for High-resolution Images with Decoupled Patch  Calibration

Superpixel segmentation has recently seen important progress benefiting from the advances in differentiable deep learning. However, the very high-resolution superpixel segmentation still remains challenging due to the expensive memory and computation cost, making the current advanced superpixel networks fail to process. In this paper, we devise Patch Calibration Networks (PCNet), aiming to efficiently and accurately implement high-resolution superpixel segmentation. PCNet follows the principle of producing high-resolution output from low-resolution input for saving GPU memory and relieving computation cost. To recall the fine details destroyed by the down-sampling operation, we propose a novel Decoupled Patch Calibration (DPC) branch for collaboratively augment the main superpixel generation branch. In particular, DPC takes a local patch from the high-resolution images and dynamically generates a binary mask to impose the network to focus on region boundaries. By sharing the parameters of DPC and main branches, the fine-detailed knowledge learned from high-resolution patches will be transferred to help calibrate the destroyed information. To the best of our knowledge, we make the first attempt to consider the deep-learning-based superpixel generation for high-resolution cases. To facilitate this research, we build evaluation benchmarks from two public datasets and one new constructed one, covering a wide range of diversities from fine-grained human parts to cityscapes. Extensive experiments demonstrate that our PCNet can not only perform favorably against the state-of-the-arts in the quantitative results but also improve the resolution upper bound from 3K to 5K on 1080Ti GPUs.
Occlusion-Aware Video Object Inpainting

Conventional video inpainting is neither object-oriented nor occlusion-aware, making it liable to obvious artifacts when large occluded object regions are inpainted. This paper presents occlusion-aware video object inpainting, which recovers both the complete shape and appearance for occluded objects in videos given their visible mask segmentation. To facilitate this new research, we construct the first large-scale video object inpainting benchmark YouTube-VOI to provide realistic occlusion scenarios with both occluded and visible object masks available. Our technical contribution VOIN jointly performs video object shape completion and occluded texture generation. In particular, the shape completion module models long-range object coherence while the flow completion module recovers accurate flow with sharp motion boundary, for propagating temporally-consistent texture to the same moving object across frames. For more realistic results, VOIN is optimized using both T-PatchGAN and a new spatio-temporal attention-based multi-class discriminator. Finally, we compare VOIN and strong baselines on YouTube-VOI. Experimental results clearly demonstrate the efficacy of our method including inpainting complex and dynamic objects. VOIN degrades gracefully with inaccurate input visible mask.
Exploring Head-based Mode-Switching in Virtual Reality

Mode-switching supports multilevel operations using a limited number of input methods. In Virtual Reality (VR) head-mounted displays (HMD), common approaches for mode-switching use buttons, controllers, and users' hands. However, they are inefficient and challenging to do with tasks that require both hands (e.g., when users need to use two hands during drawing operations). Using head gestures for mode-switching can be an efficient and cost-effective way, allowing for a more continuous and smooth transition between modes. In this paper, we explore the use of head gestures for mode-switching especially in scenarios when both users' hands are performing tasks. We present a first user study that evaluated eight head gestures that could be suitable for VR HMD with a dual-hand line-drawing task. Results show that move forward, move backward, roll left, and roll right led to better performance and are preferred by participants. A second study integrating these four gestures in Tilt Brush, an open-source painting VR application, is conducted to further explore the applicability of these gestures and derive insights. Results show that Tilt Brush with head gestures allowed users to change modes with ease and led to improved interaction and user experience. The paper ends with a discussion on some design recommendations for using head-based mode-switching in VR HMD.
ConvNets vs. Transformers: Whose Visual Representations are More  Transferable?

Vision transformers have attracted much attention from computer vision researchers as they are not restricted to the spatial inductive bias of ConvNets. However, although Transformer-based backbones have achieved much progress on ImageNet classification, it is still unclear whether the learned representations are as transferable as or even more transferable than ConvNets' features. To address this point, we systematically investigate the transfer learning ability of ConvNets and vision transformers in 15 single-task and multi-task performance evaluations. Given the strong correlation between the performance of pre-trained models and transfer learning, we include 2 residual ConvNets (i.e., R-101x3 and R-152x4) and 3 Transformer-based visual backbones (i.e., ViT-B, ViT-L and Swin-B), which have close error rates on ImageNet, that indicate similar transfer learning performance on downstream datasets. We observe consistent advantages of Transformer-based backbones on 13 downstream tasks (out of 15), including but not limited to fine-grained classification, scene recognition (classification, segmentation and depth estimation), open-domain classification, face recognition, etc. More specifically, we find that two ViT models heavily rely on whole network fine-tuning to achieve performance gains while Swin Transformer does not have such a requirement. Moreover, vision transformers behave more robustly in multi-task learning, i.e., bringing more improvements when managing mutually beneficial tasks and reducing performance losses when tackling irrelevant tasks. We hope our discoveries can facilitate the exploration and exploitation of vision transformers in the future.
Penggunaan Classroomscreen dan Google Jamboard untuk Meningkatkan Kemampuan Pemahaman Konsep Matematis Peserta Didik di Sekolah Dasar

The purpose of this study is to find out the difference in the ability to understand mathematical concepts between students who use classroomscreen media and google jamboard media. The population in this study was a student of class V of SD Negeri Blok C Kota Cilegon Academic Year 2021/2022. The selection of samples by means of total sampling, obtained class V students consisting of V-A as a control class and V-B into an experimental class. The experimental class conducted learning with classroomscreen media, while the control class did learning with google jamboard media. Data retrieval is obtained by test methods to determine the ability to understand the mathematical concepts which are then analyzed with average difference tests. The result of the research obtained is the ability to understand the mathematical concepts of experimental class better than the ability to understand the mathematical concepts of control class.
Virtual Tour Campus Sebagai Media Promosi di Masa Pandemik Covid-19

The use of Virtual Tours is now widely used to introduce infrastructure facilities owned by a campus. For almost a year, the COVID-19 pandemic has made prospective students and college students hesitant to continue their studies. This raises concerns if you come directly to campus to see all the facilities in it. The purpose of this research is to create a virtual campus tour application as an alternative media promotion during the COVID-19 pandemic. This study uses the Multimedia Development Life Cycle (MDLC) method using augmented reality. The result of this research is an application in the form of a virtual game, where before exploring the study programs, the user will be asked to have the appropriate character, then with the help of controlling the joystick from the keyboard, the user can enter the rooms and the information in it, either in the form of direct templates. or use the barcode to go directly to the information in it. Thus, in addition to users becoming more familiar with the Computer Science Study Program environment and the infrastructure in it, it is also an inspirational tool, that to study or create applications it can be done if you enter as a student.
Online Transformation of Learning during Covid-19 at Higher Education Institutions

The objective of this study was to see how the shift from conventional to online teaching effected classroom environment which overall linked with academic performance of students.Data were collected from students of B. Ed Hons from semester six who learned through online mode.Purposive sampling technique was used to select the sample.Descriptive method was used and a questionnaire at 5 point likert scale was used to know the students' perceptions during transition from traditional to online mode.Study findings shows that students were not ready to receive online education due to inefficient use of technological facilities.A significant effect of online teaching was seen on students' learning at higher education level during COVID-19.This has been recommended that online teaching needs more practicable and reasonable other than COVID days as well so as to make students ready to be used to meet odd situation for learning.
Checking the Validity of Ricardian Equivalence Hypothesis: Analysis from Developed and Developing Countries

The existence of the Ricardian Equivalence Hypothesis (REH) in the case of the Group of Seven (G7) and the South Asian Association of Regional Cooperation (SAARC) nations is investigated in this study.The objective of this study is check the idea that combining the cases of industrialized and emerging economies will demonstrate REH.General Government Revenue, Government Budget Deficit, General Government Gross Debt, Household Final Consumption Expenditure, Disposable Income, General Government Total Expenditure, and Wealth are all measured using a panel data set from 2001 to 2021.Five distinct unit root tests were used to validate the stationarity of selected variables.Considering the Hausman test, researcher estimate the random effect model before Panel Least Squares (PLS).The Wald test has rejected all restrictions applied to verify the presence of REH in developed and emerging economies after applying PLS.According to the Findings the Fiscal policy should be one of the stabilizing measures in this understudied economic world to regulate income and expenditures.
Evaluasi Skor Pre-Test dan Post-Test Peserta Pelatihan Teknis New SIGA di Perwakilan BKKBN Provinsi Lampung

Abstract: Purpose: This study aims to evaluate pre-test and post-test results of participants in SIGA's new technical training for BKKBN representatives in Lampung. Research methodology: The research method used in this study is the quantitative method. The data used is the primary data obtained from the pre- and post-test scores of 30 respondents. During this time, the data analysis techniques used were descriptive analysis and paired t-test. Results: Through the Wilcoxon Matched-Pairs statistical test, it is known that the significance value (p-value) for the pretest and posttest data is 0.000 (&lt;0.05). This shows that there is a statistically significant difference between pre-test and post-test results for New Siga Technical Training 2022 participants. Therefore, we can conclude that the knowledge of the participants are increasing after participating in the 2022 New Siga Technical Training. Limitations: For further research, it is hoped that researchers can take a larger number of samples to form a normal curve. Moreover, it is better if the Kirkpatrick evaluation model is fully implemented, the evaluation of the performance of the education and training implementation (at level 1 and level 2) as well as post-training evaluation (level 3 and level 4). Thus, the evaluation of education and training will obtain more comprehensive results. Contribution: The results of this study may be helpful to BKKBN Lampung for conducting proper training especially in evaluating learning outcomes. Keywords: 1. Training evaluation 2. Pretest posttest score
Oriental Language Recognition (OLR) 2021: Summary and Analysis

The fifth Oriental Language Recognition (OLR) Challenge focuses on language recognition in a variety of complex environments to promote its development. The OLR 2020 Challenge includes three tasks: (1) cross-channel language identification, (2) dialect identification, and (3) noisy language identification. We choose Cavg as the principle evaluation metric, and the Equal Error Rate (EER) as the secondary metric. There were 58 teams participating in this challenge and one third of the teams submitted valid results. Compared with the best baseline, the Cavg values of Top 1 system for the three tasks were relatively reduced by 82%, 62% and 48%, respectively. This paper describes the three tasks, the database profile, and the final results. We also outline the novel approaches that improve the performance of language recognition systems most significantly, such as the utilization of auxiliary information.
SRF-Net: Selective Receptive Field Network for Anchor-Free Temporal  Action Detection

Temporal action detection (TAD) is a challenging task which aims to temporally localize and recognize the human action in untrimmed videos. Current mainstream one-stage TAD approaches localize and classify action proposals relying on pre-defined anchors, where the location and scale for action instances are set by designers. Obviously, such an anchor-based TAD method limits its generalization capability and will lead to performance degradation when videos contain rich action variation. In this study, we explore to remove the requirement of pre-defined anchors for TAD methods. A novel TAD model termed as Selective Receptive Field Network (SRF-Net) is developed, in which the location offsets and classification scores at each temporal location can be directly estimated in the feature map and SRF-Net is trained in an end-to-end manner. Innovatively, a building block called Selective Receptive Field Convolution (SRFC) is dedicatedly designed which is able to adaptively adjust its receptive field size according to multiple scales of input information at each temporal location in the feature map. Extensive experiments are conducted on the THUMOS14 dataset, and superior results are reported comparing to state-of-the-art TAD approaches.
Statistical Evaluation of Privacy-preserving Publication and Sharing of  Three Types of COVID-19 Pandemic Data: Methods and Case Studies

A huge amount of data of various types are collected during the COVID-19 pandemic, the analysis and interpretation of which have been indispensable for curbing the spread of the coronavirus. As the pandemic moves to an endemic state, the data collected during the pandemic will continue to be rich sources for further studying and understanding the impacts of the pandemic on various aspects of our society. On the other hand, na\"{i}ve release and sharing of the information can be associated with serious privacy concerns. In this study, we use three common but distinct data types collected during the pandemic (case surveillance tabular data, case location data, and contact tracing networks) to demonstrate publication and sharing of granular information or individual-level pandemic data in a privacy-preserving manner. We leverage and build upon the concept of differential privacy to generate and release privacy-preserving data for each data type. All the approaches employed in the study are straightforward to apply. We investigate the inferential utility of the privacy-preserving information through simulation studies at different levels of privacy guarantees and demonstrate the applications of approaches with examples and real-life pandemic data. Our study generates statistical evidence on the practical feasibility of sharing pandemic data with privacy guarantees and on how to balance the statistical utility of released information during this process.