Reviewerly/exHarmony huggingface dataset:
An Attentive Consensus Platform for Collaborative Reinforcement Learning Agents

This work integrates concurrent team learning into a deep reinforcement learning model for collaborative multiagent problems to reach a common goal. RL trains each agent to decide its best actions, and then the joint decision is made to accomplish the assigned team task based on the collaborative platform. Each agent learns individually and shares the observed feature with others. The collaborative platform makes the decision based on these features provided by agents. The collaborative platform consists of two attention mechanisms—soft and self-attention. The self-attention module is applied to distinguish local environmental features sensed by an individual agent for concurrent learning. The platform integrates collective information locally from agents through a soft attention interface. A termination network is introduced to the collaborative model to determine the number of elapsed sequences in collecting sufficient information to support the joint decision instead of a fixed number of iterations. Experiments on limit-sighted agent deployment for static image classifications, video game playing, and aerial image object detection of multiple airborne sensors have evaluated the performance of the proposed model. The experimental results show that the proposed model provides a more efficient collaborative mechanism for multiagent systems than the state-of-the-art methods.
Attributed Abnormality Graph Embedding for Clinically Accurate X-Ray Report Generation

Despite the recent success of deep learning models for text generation, generating clinically accurate reports remains challenging. More precisely modeling the relationships of the abnormalities revealed in an X-ray image has been found promising to enhance the clinical accuracy. In this paper, we first introduce a novel knowledge graph structure called an attributed abnormality graph (ATAG). It consists of interconnected abnormality nodes and attribute nodes for better capturing more fine-grained abnormality details. In contrast to the existing methods where the abnormality graph are constructed manually, we propose a methodology to automatically construct the fine-grained graph structure based on annotated X-ray reports and the RadLex radiology lexicon. We then learn the ATAG embeddings as part of a deep model with an encoder-decoder architecture for the report generation. In particular, graph attention networks are explored to encode the relationships among the abnormalities and their attributes. A hierarchical attention attention and a gating mechanism are specifically designed to further enhance the generation quality. We carry out extensive experiments based on the benchmark datasets, and show that the proposed ATAG-based deep model outperforms the SOTA methods by a large margin in ensuring the clinical accuracy of the generated reports.
Optimal Knowledge Component Extracting Model for Knowledge-Concept Graph Completion in Education

As people have become accustomed to non-face-to-face education because of the COVID-19 pandemic, adaptive and personalized learning is being emphasized in the field of education. Learning paths suitable for each student may differ from those normally provided by teachers. To support coaching based on the concept of adaptive learning, the first step is to discover the relationships among the concepts in the curriculum provided in the form of a knowledge graph. In this study, feature reduction for the target knowledge-concept was first performed using Elastic Net and Random Forest algorithms, which are known to have the best performance in machine learning. Deep knowledge tracing (DKT) in the form of a dual-net, which is more efficient because of the already slimmer data, was then applied to increase the accuracy of feature selection. The new approach, termed the optimal knowledge component extracting (OKCE) model, was proven to be superior to a feature reduction approach using only Elastic Net and Random Forest using both open and commercial datasets. Finally, the OKCE model showed a meaningful knowledge-concept graph that could help teachers in adaptive and personalized learning.
Understanding and Mitigating Overfitting in Prompt Tuning for Vision-Language Models

Pretrained vision-language models (VLMs) such as CLIP have shown impressive generalization capability in downstream vision tasks with appropriate text prompts. Instead of designing prompts manually, Context Optimization (CoOp) has been recently proposed to learn continuous prompts using task-specific training data. Despite the performance improvements on downstream tasks, several studies have reported that CoOp suffers from the overfitting issue in two aspects: (i) the test accuracy on base classes first improves and then worsens during training; (ii) the test accuracy on novel classes keeps decreasing. However, none of the existing studies can understand and mitigate such overfitting problems. In this study, we first explore the cause of overfitting by analyzing the gradient flow. Comparative experiments reveal that CoOp favors generalizable and spurious features in the early and later training stages, respectively, leading to the non-overfitting and overfitting phenomena. Given those observations, we propose Subspace Prompt Tuning (Sub PT) to project the gradients in back-propagation onto the low-rank subspace spanned by the early-stage gradient flow eigenvectors during the entire training process and successfully eliminate the overfitting problem. In addition, we equip CoOp with a Novel Feature Learner (NFL) to enhance the generalization ability of the learned prompts onto novel categories beyond the training set, needless of image training data. Extensive experiments on 11 classification datasets demonstrate that Sub PT+NFL consistently boost the performance of CoOp and outperform the state-of-the-art CoCoOp approach. Experiments on more challenging vision downstream tasks, including open-vocabulary object detection and zero-shot semantic segmentation, also verify the effectiveness of the proposed method. Codes can be found at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://tinyurl.com/mpe64f89</uri> .
Toward Robust Hierarchical Federated Learning in Internet of Vehicles

The rapid growth of the Internet of Vehicles (IoV) paradigm sparks the generation of large volumes of distributed data at vehicles, which can be harnessed to build models for intelligent applications. Federated learning has recently received wide attentions, which allows model training over distributed datasets without requiring raw datasets to be shared out. However, federated learning is known to be vulnerable to poisoning attacks, where malicious clients may manipulate the local datasets or model updates to corrupt the global model. Such attacks have to be countered when federated learning is adopted in IoV systems, given that the training process is distributed among a large number of vehicles in an open environment. In addition, IoV systems present a hierarchical architecture in practice where other types of nodes sit between the cloud server and vehicles, allowing intermediate aggregation for reducing overall training latency. Yet the intermediate aggregation nodes may also pose threats. In this paper, we propose a robust hierarchical federated learning framework named RoHFL, which allows hierarchical federated learning to be suitably applied in the IoV with robustness against poisoning attacks. We develop a robust model aggregation scheme that contains a logarithm-based normalization mechanism to cope with scaled gradients from malicious vehicles. We integrate the notion of reputation into the aggregation process and develop a scheme for reputation updating. We provide a formal analysis of RoHFL’s convergence guarantees. Experiment results over several popular datasets demonstrate the promising performance of RoHFL, which is superior to prior work in the robustness against poisoning attacks.
Learning Consistency From High-Confidence Pseudo-Labels for Weakly Supervised Object Localization

Weakly supervised object localization (WSOL) tasks aim to classify and locate a single object under the supervision of only image-level labels.Pseudo-supervised learning methods have been shown to be effective for WSOL, which divided WSOL tasks into two decoupled subtasks: classification and localization.The decoupled framework has been proven to be effective in improving the performance of the localization subtask, but the predicted localizations are not robust enough due to the noise of pseudolabels.Based on the assumption that the localization model should have similar predictions on different versions of the same image, we propose an additional refinement stage to learn more consistent localization.Specifically, in the refinement stage, we propose a simple and effective method for evaluating the confidence of pseudo-labels based on classification discrimination, and by learning consistency from high-confidence pseudo-labels, we further refine the localization model to get better localization performance.Besides, in the initialization stage, we propose a mask-based pseudo-label generator to initialize the localization model.We conduct experiments on two benchmark datasets: CUB-200-2011 and ImageNet-1k.Experimental results show that our two-stage approach achieves 94.01%GT-Konwn localization accuracy on the CUB-200-2011 testing dataset, and 65.23% GT-Konwn localization accuracy on the ImageNet-1k validation dataset.Moreover, when directly applied to the pseudo-supervised localization model, our refinement stage could achieve 94.05% and 67.13% GT-Konwn localization accuracy on CUB-200-2011 and ImageNet-1k datasets, respectively, which outperforms the corresponding pseudo-supervised localization model with 3.34% and 2.34% accuracy.
CFRLA-Net: A Context-Aware Feature Representation Learning Anchor-Free Network for Pedestrian Detection

High resolution and strong semantic representation are both vital for feature extraction networks of pedestrian detection. The existing high-resolution network (HRNet) has presented a promising performance for pedestrian detection. However, we observed that it still has some significant shortcomings for heavily occluded and small-scale pedestrians. In this paper, we propose to address the shortcomings by extracting semantic and spatial context from HRNet. Specifically, we propose a Context-aware Feature Representation Learning Module (CFRL-Module), which combines a Multi-scale Feature Context Extraction Parallel Block for Convolution and Self-attention (CEPCA-Block) with two parallel paths and an Equivalent FFN (EFFN) Block. The core CEPCA-Block adopts a parallel design to integrate convolution and multi-head self-attention (MHSA) with low parameter computational cost, which can obtain the deep semantic context by convolution path and precise context by MHSA path. Furthermore, to overcome the inefficiency of global MHSA in high-resolution pedestrian detection, we propose a novel local window MHSA, which can significantly reduce memory consumption but barely affect the detection performance. Cascading the proposed CFRL-Module with the anchor-free detection head constitutes our Context-aware Feature Representation Learning Anchor-Free Network (CFRLA-Net). The proposed CFRLA-Net can catch a high-level understanding of the heavily occluded and small-scale pedestrian instances based on HRNet, which can effectively solve the limitation of the insufficient feature extraction ability of HRNet for the hard samples. Experimental results show that CFRLA-Net achieves state-of-the-art performance on CityPersons, Caltech, and CrowdHuman benchmarks.
HDR Map Reconstruction From a Single LDR Sky Panoramic Image for Outdoor Illumination Estimation

Outdoor low dynamic range (LDR) panoramic images that contain the sun and sky are generally over-saturated because the sun is 10,000 times brighter than the regions that surround it. Because the luminance information in the region that contains the sun in these images is lost, it is difficult to identify the sun’s position and generate high dynamic range (HDR) environment maps that can be used to realistically relight virtual objects. Previous methods to reconstruct HDR maps did not consider that the sun covers a small area in an image but contains extremely high luminance values. These methods are therefore insufficient for estimating scene illumination. We propose a multi-faceted approach to reconstructing HDR maps from a single LDR sky panoramic image that considers the sun and sky regions separately. We encode an input image and transfer a multi-dimensional latent representation to two decoders, which reconstruct the luminance information in the sky and sun regions separately. To plausibly model sun illumination, we introduce two networks (Sunpose-net and Sunrad-net) that estimate the position and radiance of the sun. The generated sun radiance map is then merged with the output of the decoder that is responsible for sun regions. We demonstrated that the proposed method more plausibly reconstructs HDR maps than previous methods using the HDR-VDP-2.2 which measures the visual quality of reconstructed HDR maps against ground truth. The accuracy of the overall sun and sky illumination distribution in HDR maps reconstructed using the proposed method was evaluated using histogram distance measures.
Learning Camera Parameters With Weighted Edge Attention From Single-View Images

This paper presents a novel Deep Learning (DL) model that estimates camera parameters, including camera rotations, field of view, and distortion parameter, from single-view images. The classical approach often analyzes geometric cues such as vanishing points, but is constrained only when geometric cues exist in images. To alleviate such constraints, we use DL, and employ implicit geometric cues, which can reflect the inter-image changes of camera parameters and be observed more frequently in images. Our geometric cues are inspired by two important intuitions: 1) geometric appearance changes caused by camera parameters are the most prominent in object edges; 2) spatially consistent objects (in size and shape) better reflect the inter-image changes of camera parameters. To realize our approach, we propose a weighted edge-attention mechanism that assigns higher weights onto the edges of spatially consistent objects. Our experiments prove that our edge-driven geometric emphasis significantly improves the estimation accuracy of the camera parameters than the existing DL-based approaches.
Probabilistic Estimation of Instantaneous Frequencies of Chirp Signals

We present a continuous-time probabilistic approach for estimating the chirp signal and its instantaneous frequency function when the true forms of these functions are not accessible. Our model represents these functions by non-linearly cascaded Gaussian processes represented as non-linear stochastic differential equations. The posterior distribution of the functions is then estimated with stochastic filters and smoothers. We compute a (posterior) Cram\'er--Rao lower bound for the Gaussian process model, and derive a theoretical upper bound for the estimation error in the mean squared sense. The experiments show that the proposed method outperforms a number of state-of-the-art methods on a synthetic data. We also show that the method works out-of-the-box for two real-world datasets.
Hierarchical Model Selection for Graph Neural Networks

Node classification on graph data is a major problem in machine learning, and various graph neural networks (GNNs) have been proposed.Variants of GNNs such as H2GCN and CPF outperform graph convolutional networks (GCNs) by improving on the weaknesses of the traditional GNN.However, there are some graph data which these GNN variants fail to perform well than other GNNs in the node classification task.This is because H2GCN has similar feature values on graph data with high average degree, and CPF gives rise to a problem about label-propagation suitability.Accordingly, we propose a hierarchical model selection framework (HMSF) that selects an appropriate GNN model to predict the class of nodes for each graph data.HMSF uses average degree and edge homophily ratio as indicators to decide the useful model based on our analyses.In the experiment, we show that the model selected by our HMSF achieves high performance on node classification for various types of graph data.
Integration of SMILE++ software system into the Salome platform

Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Twitter Facebook Reddit LinkedIn Tools Icon Tools Reprints and Permissions Cite Icon Cite Search Site Citation Pavel Vashchenkov, Alexey Litvintsev, Yevgeniy Bondar; Integration of SMILE++ software system into the Salome platform. AIP Conference Proceedings 16 February 2023; 2504 (1): 030066. https://doi.org/10.1063/5.0132730 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAIP Publishing PortfolioAIP Conference Proceedings Search Advanced Search |Citation Search
THE EFECTIVENESS OF PICTORIAL READING IN IMPROVING STUDENTS’ READING INTEREST TO THE 7TH-GRADE STUDENTS OF MTS N 1 GROBOGAN IN THE ACADEMIC YEAR 2022-2023

This research was conducted to know the effectiveness of using pictorial reading in improving students’ reading interest in MTS N 1 Grobogan. This research was a quantitative design method with a quasi-experimental design. There were 2 classes used in this research, the experimental group which was taught with pictorial reading, and the control group which was taught without pictorial reading. The result of this research shows that there was a significant difference between both classes. It can be seen from the data analysis by using a t-test. T-value is 11,3430600014, with a level of significance of 5% with df 58, the t_table is 2,001. It shows that t-value&gt;t-table. So, Na is rejected, and Ha is accepted. The mean of students’ post-test scores in the control group was 62,83, and the mean of students’ post-test scores in the experimental group was 80. So, it can be concluded that there is a significant difference between the students’ reading interests who were taught with pictorial reading and the students’ reading interests who were taught without pictorial reading.
Diff Feature Matching Network in Refactoring Detection

During software maintenance, it is often important to understand the reasons for code changes, so tools are being developed to automatically detect changes due to refactoring. Among these, RefDiff supports multiple programming languages by representing code changes (so-called diffs) by means of a language-independent abstract syntax tree containing nodes for the code parts removed and added during the change. Corresponding nodes, i.e., nodes participating in a refactoring, are matched based on text similarity, which leads to good precision, but the algorithmic limitation of computing text similarity also entail a large number of false negatives. To overcome this, we trained a neural network to classify features in diffs to be used for identifying refactorings. The main contribution of this paper is an approach for encoding differences between nodes in the syntax trees into image data for neural network matching. We have shown that the diff feature matching network not only improves the precision of RefDiff 2.0 to 98.6% and recall to 93.2%, but also is able to support detection tasks in multiple programming languages, with excellent robustness.
Reusing My Own Code: Preliminary Results for Competitive Coding in Jupyter Notebooks

The reuse of already existing code is widely considered a popular software development practice, that provides both benefits and drawbacks for all stakeholders involved. Prior work reports on how code reuse is a common practice in software development projects and data science projects such as machine learning pipelines. Recently, there has been much code reuse work in the context of competitive programming. Although there is work such as detecting plagiarism, there is no work that studies how a competitor will reuse their own code. In this paper, we present a preliminary study on the code reuse behavior of three grandmasters’ Jupyter notebooks in the Kaggle Competitions, an online competition platform for data scientists, and report the types of code they often reuse. Grandmasters are the highest level reached in competitions (novice, expert, master, and grandmaster). We find that Grandmasters are less likely to reuse specialized code, but instead, tend to reuse common functions like importing packages (importing the pandas library). They are most likely to reuse common abstractions like importing packages, configurations, file IO operations, show data, plotting graphs, defining functions, and exploring files. The work opens up new research potential into recommending how developers can reuse their own code.
Balancing between Creativity and Efficiency in Software Engineering Project Courses

Practical software engineering courses incorporate industrial clients to present a more realistic environment for the students. Clients introduce problem statements to students in the predevelopment phase. These documents describe the project’s vision, scope, requirements, and acceptance criteria. An unreasonable trade-off between efficiency and creativity can lead to unfulfilled client expectations or very constrained creative space for students. In problem statements, a good balance between freedom and efficiency helps foster such a creative-friendly environment that facilitates creative thinking and innovative approaches. This paper describes a case study in a software engineering project course among 17 projects (with over 100 students) in the past two semesters with real industrial clients. We develop criteria to classify problem statements into three main types. Innovation and creativity are measured through self-evaluation surveys and project documentation. The findings show that a problem statement with a known problem, fewer than 15 requirements, and fewer than 30 constraint word occurrences has the highest potential to strike the balance to encourage creativity and achieve project success.
DigiSoC – Digital Software City – toward Software 3R: Reduce, Reuse, and Recycle

The expansion of the development of software systems has been continuing in companies for years. That causes higher maintenance effort for the companies. To address the situation, exploring how reducing, reusing, and recycling software, which we call software 3R, is assumed to be the right direction. This paper proposes a new software visualization technique, Digital Software City (DigiSoC) by combining two approaches: process mining and program comprehension. By analyzing digital data (log and source code), it creates a 3D visualization of both software structure and behavior. For promoting software 3R, DigiSoC can help software architects to comprehend the actual, comprehensive status of the usage of the software in operation.