Reviewerly/exHarmony huggingface dataset:
KWL (Know, Want To Know, Learned) Method As An Effort To Increasing The Reading Capacity Of Students With Learning Difficulties

Children who have learning difficulties really need to be considered, so that they can realize their dreams in the future. One of the initial steps in achieving this goal is that children must be able to read, because reading is a bridge so that children can get to know what they want to know. By finding out (know) what the child likes, the teacher can re-develop how to teach the child to read. Arouse children's curiosity by telling hero characters related to the problems they are facing. This study aims to accelerate students in learning, especially for students who cannot read. Research uses a literature method with a descriptive approach to reveal something as it is. Based on research conducted by children's experts in Indonesia, the interest in reading is still far from average. So that the culture of reading is still not very applied in Indonesia. Through this study, the researcher discussed how important it is for students to use the KWL method in learning so that teachers do not have difficulty in teaching reading students, especially low-grade elementary school students. This KWL method has three stages, namely the pre-reading, reading and post-reading stages. So that students can easily understand the lessons explained by the teacher in class. In addition to being easy to understand, the KWL method also makes students remember learning easily because the reading methods used are made interesting so that students' learning motivation increases.
Development of Students' Professional Skills and Institutions of Higher Education during the Pandemic

The article is devoted to evaluating the pedagogical process of students' professional skills development during the pandemic. The study aims to investigate the formation of students' professional skills and determine their level of studying during a pandemic. The author's team surveyed to obtain empirical data on students' professional skills development. Surveying students and teachers were carried out during distance learning during a pandemic. The questionnaire results show the advantages and disadvantages of distance learning from the student's point of view. It is essential for education, built on a student-centered learning model. The main advantages of distance learning are a convenient schedule, combining study with work, acquiring new skills, the ability to do favorite things, and increasing motivation for self-education. Distance education's disadvantages in a pandemic include technical problems, including the lack of Internet, the difficulty of learning self-organization, and the low information and computer skills among students and teachers. The study results showed that students and teachers need support and motivation for distance learning.
A Novel Embedding Model for Knowledge Graph Entity Alignment Based on Graph Neural Networks

Abstract The objective of the Entity Alignment (EA) task is to identify entities with identical semantics across distinct Knowledge Graphs (KGs) situated in the real world, which has garnered extensive recognition in both academic and industrial circles. Within this paper, a pioneering entity alignment framework named PCE-HGTRA is proposed. This framework integrates the relation and property information from varying KGs, along with the heterogeneity information present within the KGs. Firstly, by learning embeddings, this framework captures the similarity that exists between entities present across diverse KGs. Additionally, property triplets in KGs are used to generate property character-level embeddings, facilitating the transfer of entity embeddings from two distinct KGs onto an identical space. Secondly, the framework strengthens property character-level embeddings using the transitivity rule to increase the count of entity properties. Then, in order to effectively capture the heterogeneous features in entity neighborhoods, a heterogeneous graph transformer with relation awareness is designed to model the heterogeneous relations in KGs in the framework. Finally, comparative experimental results on four widely recognized real-world datasets demonstrate that PCE-HGTRA performs exceptionally well. In fact, its Hits@1 performance exceeds the best baseline by 7.94%, outperforming seven other state-of-the-art methods.
Wasmizer: Curating WebAssembly-driven Projects on GitHub

WebAssembly has attracted great attention as a portable compilation target for programming languages. To facilitate in-depth studies about this technology, we have deployed Wasmizer, a tool that regularly mines GitHub projects and makes an up-to-date dataset of WebAssembly sources and their binaries publicly available. Presently, we have collected 2 540 C and C++ projects that are highly-related to WebAssembly, and built a dataset of 8 915 binaries that are linked to their source projects. To demonstrate an application of this dataset, we have investigated the presence of eight WebAssembly compilation smells in the wild.
ESCAPE: Countering Systematic Errors from Machine's Blind Spots via Interactive Visual Analysis

Classification models learn to generalize the associations between data samples and their target classes. However, researchers have increasingly observed that machine learning practice easily leads to systematic errors in AI applications, a phenomenon referred to as AI blindspots. Such blindspots arise when a model is trained with training samples (e.g., cat/dog classification) where important patterns (e.g., black cats) are missing or periphery/undesirable patterns (e.g., dogs with grass background) are misleading towards a certain class. Even more sophisticated techniques cannot guarantee to capture, reason about, and prevent the spurious associations. In this work, we propose ESCAPE, a visual analytic system that promotes a human-in-the-loop workflow for countering systematic errors. By allowing human users to easily inspect spurious associations, the system facilitates users to spontaneously recognize concepts associated misclassifications and evaluate mitigation strategies that can reduce biased associations. We also propose two statistical approaches, relative concept association to better quantify the associations between a concept and instances, and debias method to mitigate spurious associations. We demonstrate the utility of our proposed ESCAPE system and statistical measures through extensive evaluation including quantitative experiments, usage scenarios, expert interviews, and controlled user experiments.
Revisiting Image Reconstruction for Semi-supervised Semantic Segmentation

Autoencoding, which aims to reconstruct the input images through a bottleneck latent representation, is one of the classic feature representation learning strategies. It has been shown effective as an auxiliary task for semi-supervised learning but has become less popular as more sophisticated methods have been proposed in recent years. In this paper, we revisit the idea of using image reconstruction as the auxiliary task and incorporate it with a modern semi-supervised semantic segmentation framework. Surprisingly, we discover that such an old idea in semi-supervised learning can produce results competitive with state-of-the-art semantic segmentation algorithms. By visualizing the intermediate layer activations of the image reconstruction module, we show that the feature map channel could correlate well with the semantic concept, which explains why joint training with the reconstruction task is helpful for the segmentation task. Motivated by our observation, we further proposed a modification to the image reconstruction task, aiming to further disentangle the object clue from the background patterns. From experiment evaluation on various datasets, we show that using reconstruction as auxiliary loss can lead to consistent improvements in various datasets and methods. The proposed method can further lead to significant improvement in object-centric segmentation tasks.
SRFormer: Permuted Self-Attention for Single Image Super-Resolution

Previous works have shown that increasing the window size for Transformer-based image super-resolution models (e.g., SwinIR) can significantly improve the model performance but the computation overhead is also considerable. In this paper, we present SRFormer, a simple but novel method that can enjoy the benefit of large window self-attention but introduces even less computational burden. The core of our SRFormer is the permuted self-attention (PSA), which strikes an appropriate balance between the channel and spatial information for self-attention. Our PSA is simple and can be easily applied to existing super-resolution networks based on window self-attention. Without any bells and whistles, we show that our SRFormer achieves a 33.86dB PSNR score on the Urban100 dataset, which is 0.46dB higher than that of SwinIR but uses fewer parameters and computations. We hope our simple and effective approach can serve as a useful tool for future research in super-resolution model design.
Motion Planning for Autonomous Driving: The State of the Art and Future Perspectives

Intelligent vehicles (IVs) have gained worldwide attention due to their increased convenience, safety advantages, and potential commercial value. Despite predictions of commercial deployment by 2025, implementation remains limited to small-scale validation, with precise tracking controllers and motion planners being essential prerequisites for IVs. This paper reviews state-of-the-art motion planning methods for IVs, including pipeline planning and end-to-end planning methods. The study examines the selection, expansion, and optimization operations in a pipeline method, while it investigates training approaches and validation scenarios for driving tasks in end-to-end methods. Experimental platforms are reviewed to assist readers in choosing suitable training and validation strategies. A side-by-side comparison of the methods is provided to highlight their strengths and limitations, aiding system-level design choices. Current challenges and future perspectives are also discussed in this survey.
On the Effects of Self-supervision and Contrastive Alignment in Deep Multi-view Clustering

Self-supervised learning is a central component in recent approaches to deep multi-view clustering (MVC). However, we find large variations in the development of self-supervision-based methods for deep MVC, potentially slowing the progress of the field. To address this, we present DeepMVC, a unified framework for deep MVC that includes many recent methods as instances. We leverage our framework to make key observations about the effect of self-supervision, and in particular, drawbacks of aligning representations with contrastive learning. Further, we prove that contrastive alignment can negatively influence cluster separability, and that this effect becomes worse when the number of views increases. Motivated by our findings, we develop several new DeepMVC instances with new forms of self-supervision. We conduct extensive experiments and find that (i) in line with our theoretical findings, contrastive alignments decreases performance on datasets with many views; (ii) all methods benefit from some form of self-supervision; and (iii) our new instances outperform previous methods on several datasets. Based on our results, we suggest several promising directions for future research. To enhance the openness of the field, we provide an open-source implementation of DeepMVC, including recent models and our new instances. Our implementation includes a consistent evaluation protocol, facilitating fair and accurate evaluation of methods and components.
Dual-path Adaptation from Image to Video Transformers

In this paper, we efficiently transfer the surpassing representation power of the vision foundation models, such as ViT and Swin, for video understanding with only a few trainable parameters. Previous adaptation methods have simultaneously considered spatial and temporal modeling with a unified learnable module but still suffered from fully leveraging the representative capabilities of image transformers. We argue that the popular dual-path (two-stream) architecture in video models can mitigate this problem. We propose a novel DualPath adaptation separated into spatial and temporal adaptation paths, where a lightweight bottleneck adapter is employed in each transformer block. Especially for temporal dynamic modeling, we incorporate consecutive frames into a grid-like frameset to precisely imitate vision transformers' capability that extrapolates relationships between tokens. In addition, we extensively investigate the multiple baselines from a unified perspective in video understanding and compare them with DualPath. Experimental results on four action recognition benchmarks prove that pretrained image transformers with DualPath can be effectively generalized beyond the data domain.
TeSLA: Test-Time Self-Learning With Automatic Adversarial Augmentation

Most recent test-time adaptation methods focus on only classification tasks, use specialized network architectures, destroy model calibration or rely on lightweight information from the source domain. To tackle these issues, this paper proposes a novel Test-time Self-Learning method with automatic Adversarial augmentation dubbed TeSLA for adapting a pre-trained source model to the unlabeled streaming test data. In contrast to conventional self-learning methods based on cross-entropy, we introduce a new test-time loss function through an implicitly tight connection with the mutual information and online knowledge distillation. Furthermore, we propose a learnable efficient adversarial augmentation module that further enhances online knowledge distillation by simulating high entropy augmented images. Our method achieves state-of-the-art classification and segmentation results on several benchmarks and types of domain shifts, particularly on challenging measurement shifts of medical images. TeSLA also benefits from several desirable properties compared to competing methods in terms of calibration, uncertainty metrics, insensitivity to model architectures, and source training strategies, all supported by extensive ablations. Our code and models are available on GitHub.
An evaluation framework for dimensionality reduction through sectional curvature

Unsupervised machine learning lacks ground truth by definition. This poses a major difficulty when designing metrics to evaluate the performance of such algorithms. In sharp contrast with supervised learning, for which plenty of quality metrics have been studied in the literature, in the field of dimensionality reduction only a few over-simplistic metrics has been proposed. In this work, we aim to introduce the first highly non-trivial dimensionality reduction performance metric. This metric is based on the sectional curvature behaviour arising from Riemannian geometry. To test its feasibility, this metric has been used to evaluate the performance of the most commonly used dimension reduction algorithms in the state of the art. Furthermore, to make the evaluation of the algorithms robust and representative, using curvature properties of planar curves, a new parameterized problem instance generator has been constructed in the form of a function generator. Experimental results are consistent with what could be expected based on the design and characteristics of the evaluated algorithms and the features of the data instances used to feed the method.
More Robust Schema-Guided Dialogue State Tracking via Tree-Based Paraphrase Ranking

The schema-guided paradigm overcomes scalability issues inherent in building task-oriented dialogue (TOD) agents with static ontologies. Instead of operating on dialogue context alone, agents have access to hierarchical schemas containing task-relevant natural language descriptions. Fine-tuned language models excel at schema-guided dialogue state tracking (DST) but are sensitive to the writing style of the schemas. We explore methods for improving the robustness of DST models. We propose a framework for generating synthetic schemas which uses tree-based ranking to jointly optimise lexical diversity and semantic faithfulness. The generalisation of strong baselines is improved when augmenting their training data with prompts generated by our framework, as demonstrated by marked improvements in average joint goal accuracy (JGA) and schema sensitivity (SS) on the SGD-X benchmark.
On decidable extensions of Propositional Dynamic Logic with Converse

We describe a family of decidable propositional dynamic logics, where atomic modalities satisfy some extra conditions (for example, given by axioms of the logics K5, S5, or K45 for different atomic modalities). It follows from recent results (Kikot, Shapirovsky, Zolin, 2014; 2020) that if a modal logic $L$ admits a special type of filtration (so-called definable filtration), then its enrichments with modalities for the transitive closure and converse relations also admit definable filtration. We use these results to show that if logics $L_1, \ldots , L_n$ admit definable filtration, then the propositional dynamic logic with converse extended by the fusion $L_1*\ldots * L_n$ has the finite model property.
Individual differences in knowledge network navigation

With the rapid accumulation of online information, efficient web navigation has grown vital yet challenging. To create an easily navigable cyberspace catering to diverse demographics, understanding how people navigate differently is paramount. While previous research has unveiled individual differences in spatial navigation, such differences in knowledge space navigation remain sparse. To bridge this gap, we conducted an online experiment where participants played a navigation game on Wikipedia and completed personal information questionnaires. Our analysis shows that age negatively affects knowledge space navigation performance, while multilingualism enhances it. Under time pressure, participants' performance improves across trials and males outperform females, an effect not observed in games without time pressure. In our experiment, successful route-finding is usually not related to abilities of innovative exploration of routes. Our results underline the importance of age, multilingualism and time constraint in the knowledge space navigation.
Where and What do Software Architects blog? An Exploratory Study on Architectural Knowledge in Blogs, and their Relevance to Design Steps

Software engineers share their architectural knowledge (AK) in different places on the Web. Recent studies show that architectural blogs contain the most relevant AK, which can help software engineers to make design steps. Nevertheless, we know little about blogs, and specifically architectural blogs, where software engineers share their AK. In this paper, we conduct an exploratory study on architectural blogs to explore their types, topics, and their AK. Moreover, we determine the relevance of architectural blogs to make design steps. Our results support researchers and practitioners to find and re-use AK from blogs.
ShaRPy: Shape Reconstruction and Hand Pose Estimation from RGB-D with Uncertainty

Despite their potential, markerless hand tracking technologies are not yet applied in practice to the diagnosis or monitoring of the activity in inflammatory musculoskeletal diseases. One reason is that the focus of most methods lies in the reconstruction of coarse, plausible poses, whereas in the clinical context, accurate, interpretable, and reliable results are required. Therefore, we propose ShaRPy, the first RGB-D Shape Reconstruction and hand Pose tracking system, which provides uncertainty estimates of the computed pose, e.g., when a finger is hidden or its estimate is inconsistent with the observations in the input, to guide clinical decision-making. Besides pose, ShaRPy approximates a personalized hand shape, promoting a more realistic and intuitive understanding of its digital twin. Our method requires only a light-weight setup with a single consumer-level RGB-D camera yet it is able to distinguish similar poses with only small joint angle deviations in a metrically accurate space. This is achieved by combining a data-driven dense correspondence predictor with traditional energy minimization. To bridge the gap between interactive visualization and biomedical simulation we leverage a parametric hand model in which we incorporate biomedical constraints and optimize for both, its pose and hand shape. We evaluate ShaRPy on a keypoint detection benchmark and show qualitative results of hand function assessments for activity monitoring of musculoskeletal diseases.
A Comparison of Dijkstra's Algorithm Using Fibonacci Heaps, Binary Heaps, and Self-Balancing Binary Trees

This paper describes the shortest path problem in weighted graphs and examines the differences in efficiency that occur when using Dijkstra's algorithm with a Fibonacci heap, binary heap, and self-balancing binary tree. Using C++ implementations of these algorithm variants, we find that the fastest method is not always the one that has the lowest asymptotic complexity. Reasons for this are discussed and backed with empirical evidence.
Fuzziness-tuned: Improving the Transferability of Adversarial Examples

With the development of adversarial attacks, adversairal examples have been widely used to enhance the robustness of the training models on deep neural networks. Although considerable efforts of adversarial attacks on improving the transferability of adversarial examples have been developed, the attack success rate of the transfer-based attacks on the surrogate model is much higher than that on victim model under the low attack strength (e.g., the attack strength $\epsilon=8/255$). In this paper, we first systematically investigated this issue and found that the enormous difference of attack success rates between the surrogate model and victim model is caused by the existence of a special area (known as fuzzy domain in our paper), in which the adversarial examples in the area are classified wrongly by the surrogate model while correctly by the victim model. Then, to eliminate such enormous difference of attack success rates for improving the transferability of generated adversarial examples, a fuzziness-tuned method consisting of confidence scaling mechanism and temperature scaling mechanism is proposed to ensure the generated adversarial examples can effectively skip out of the fuzzy domain. The confidence scaling mechanism and the temperature scaling mechanism can collaboratively tune the fuzziness of the generated adversarial examples through adjusting the gradient descent weight of fuzziness and stabilizing the update direction, respectively. Specifically, the proposed fuzziness-tuned method can be effectively integrated with existing adversarial attacks to further improve the transferability of adverarial examples without changing the time complexity. Extensive experiments demonstrated that fuzziness-tuned method can effectively enhance the transferability of adversarial examples in the latest transfer-based attacks.