Reviewerly/exHarmony huggingface dataset:
Financial transaction fraud detector based on imbalance learning and graph neural network

Fraud detection is a vital and challenging task in the financial domain. Traditional machine learning approaches have limitations in dealing with the high imbalance and complexity of transaction data and the stealthy behavior of fraudulent entities. To address these issues, we propose HHLN-GNN, a graph neural network-based fraud detection model that leverages subgraph generators and neighborhood samplers to handle category imbalance. Furthermore, the model employs a self-attentive module and differentiates between homogeneous and heterogeneous connections to reduce the artificiality of fraudulent nodes and exploit the hidden information of transaction data more fully. We conduct experiments on three real-world public benchmark datasets, YelpChi, Amazon and Elliptic, and demonstrate that our model significantly outperforms existing benchmark methods on various performance metrics, such as F1-macro, AUC and GMean, by 10%, 12.5%, and 17.3% on YelpChi, 0.7%, 2.5% and 4% on Amazon, respectively.
RestorNet: An efficient network for multiple degradation image restoration

Even though Single Degradation Image Restoration (SDIR) has made significant progress and achieved remarkable performance, Multiple Degradation Image Restoration (MDIR) remains a long-term and arduous challenge to achieve the similar levels of success. To further improve the performance and efficiency of MDIR, we propose a novel MDIR method named RestorNet, which comprises an unsupervised degradation encoder for the learning of multi-scale degradation representations and a Multi-scale Degradation-assisted Restoration Module (MDRM) for image reconstruction. Our RestorNet aims to remove noise, rain, and haze in a unified network from the following three aspects. Firstly, to better distinguish among different degradations and learn the corruption information more accurately, we introduce a degradation-specific contrastive loss based on contrastive learning. Next, we develop a multi-scale degradation representation learning method to improve preservation of the spatial structure and distribution of inputs, and to extract multi-scale information to satisfy the diverse requirements of restoring different degraded images. Finally, to make a more reasonable use of degradation representation, we present a novel semi-guided strategy for effective feature transformation, where the multi-scale degradation representations are only incorporated into the MDRM encoder. For image denoising, deraining, and dehazing, by integrating the approaches above, RestorNet not only outperforms the recent state-of-the-art MDIR algorithms with lower computational complexity, but also achieves impressive performance in SDIR. Extensive experiments demonstrate the effectiveness and superiority of our proposed method.
Pendampingan Kegiatan EDM Dan ERKAM Madrasah Di Kecamatan Sangkapura

The use of e-RKAM and EDM applications is a concrete effort in realizing more effective, efficient, transparent and corruption-free management of the education budget. The purpose of the Assistance Activities is to ensure that each madrasah is able to carry out and use EDM instruments in accordance with the guidelines, as well as develop a RKAM based on EDM results using the e-RKAM application with the aim of Assessing madrasah performance based on SNP, Mapping madrasah strengths, weaknesses, opportunities and challenges, Helping determine the priorities of programs and activities that are in accordance with the needs of the madrasah. In this mentoring, several methods were used, including: lecture method, The Question and Answer method, Discussion method, and Simulation and Practice Methods, which are used to demonstrate the use of the EDM and ERKAM Applications. The results of the mentoring Madrasas were able to carry out EDM according to the established guidelines and compile RKAM using the e-RKAM application and all madrasas stated that mentoring activities were very useful and EDM work could be completed more quickly and on time because if there were difficulties they could be asked directly
Generative adversarial mediation network: A novel generative learning approach to causal mediation analysis

Casual mediation analysis (CMA) plays an essential role in various fields of social sciences. However, traditional models have restrictive parametric settings and strong random assumptions, which can be inflexible due to general nonlinearity, heterogeneity, and complex noise effects in many applications. Motivated by the similarities between the CMA and image-to-image translation that were thought to be unrelated initially, this paper proposes a novel prototype called the Generative Adversarial Mediation Network (GAMN), to explore the generative learning approach in the context of CMA. Thanks to a new encoding scheme for random terms, carefully designed partially linear architecture and inherent advantages of the generative learning framework, GAMN can flexibly handle nonlinear covariate effects and effectively model complex noise and heterogeneous mediating mechanisms with minimal model assumptions. Thus, when encountering intricate data patterns, the counterfactuals relating to treatment effects in CMA can be efficiently inferred, providing more promising mediation results. Experiments conducted on both synthetic and realistic datasets demonstrate that, compared with state-of-the-arts, GAMN can achieve notably more accurate estimations of out-of-sample predictions and treatment/mediation effects, which further illustrate the utility and advantages of our method. With the novel reinterpretations and solid theoretical results, this study also substantially broadens insights into developing mediation models from a machine-learning perspective.
Pendampingan Pembelajaran Bahasa Inggris kepada Siswa Kelas VI MI Mambaul Ulum Banjarejo Pagelaran Malang

This English learning assistance was carried out at MI Mambaul Ulum Banjarejo because at that school English was not practiced (not included in the curriculum) as a general lesson or Mulok. The purpose of this English learning assistance is to provide basic knowledge of English. The learning assistance method used is drilling. In this mentoring activity, there are two skills tests as evaluation material, namely in the middle of the mentoring implementation and at the end of the mentoring meeting. In the end, it was found that based on the scores obtained in the final test of the material presented, there were differences in mastery of the basic material in English; low, fair, and medium
Event-based Motion Deblurring with Modality-Aware Decomposition and Recomposition

Event camera responds to the brightness changes at each pixel independently with microsecond accuracy. Event cameras offer attractive property that can record well high-speed scene but ignore static and non-moving areas, while conventional frame cameras are able to acquire the whole intensity information of the scene but suffer from motion blur. Therefore, it would be desirable to combine the best of two cameras for reconstructing high quality intensity frame with no motion blur. The human visual system presents a two-pathway procedure for non-action-based representation and objects motion perception, which corresponds well to the hybrid frame and event. In this paper, inspired by the two-pathway visual system, a novel dual-stream based framework is proposed for motion deblurring (DS-Deblur), which flexibly utilizes the respective advantages from frame and event. A complementary-unique information splitting based feature fusion module is firstly proposed to adaptively aggregate the frame and event progressively at multiple levels, which is well-grounded on the hierarchical process in twopathway visual system. Then, a recurrent spatio-temporal feature transformation module is designed to exploit relevant information between adjacent frames, in which features of both current and previous frames are transformed in a global-local manner. Extensive experiments on both synthetic and real motion blur datasets demonstrate our method achieves state-of-the-art performance. Project website: https://github.com/wyang-vis/Motion-Deblurringwith-Hybrid-Frames-and-Events.
TIRDet: Mono-Modality Thermal InfraRed Object Detection Based on Prior Thermal-To-Visible Translation

Cross-modality images that combine visible-infrared spectra can provide complementary information for object detection. In particular, they are well-suited for autonomous vehicle applications in dark environments with limited illumination. However, it is time-consuming to acquire a large number of pixel-aligned visible-thermal image pairs, and real-time alignment is challenging in practical driving systems. Furthermore, the quality of visible-spectrum images can be adversely affected by complex environmental conditions. In this paper, we propose a novel neural network called TIRDet, which only utilizes Thermal InfraRed (TIR) images for mono-modality object detection. To compensate for the lacked visible-band information, we adopt a prior Thermal-To-Visible (T2V) translation model to obtain the translated visible images and the latent T2V codes. In addition, we introduce a novel attention-based Cross-Modality Aggregation (CMA) module, which can augment the modality-translation awareness of TIRDet by preserving the T2V semantic information. Extensive experiments on FLIR and LLVIP datasets demonstrate that our TIRDet significantly outperforms all mono-modality detection methods based on thermal images, and it even surpasses most State-Of-The-Art (SOTA) multispectral methods using visible-thermal image pairs. Code is available at https://github.com/zeyuwang-zju/TIRDet
Zero-Shot Object Detection by Semantics-Aware DETR with Adaptive Contrastive Loss

Zero-shot object detection (ZSD) aims to localize and recognize unseen objects in unconstrained images by leveraging semantic descriptions. Existing ZSD methods typically suffer from two drawbacks: 1) Due to the lack of data on unseen categories during the training phase, the model inevitably has a bias towards the seen categories, i.e., it prefers to subsume objects of unseen categories to seen categories; 2) It is usually very tricky for the feature extractor trained on data of seen categories to learn discriminative features that are good enough to help the model transfer the knowledge learned from data of seen categories to unseen categories. To tackle these problems, this paper proposes a novel zero-shot detection method based on a semantics-aware DETR and a class-wise adaptive contrastive loss. Concretely, to address the first problem, we develop a novel semantics-aware attention mechanism to mitigate the bias towards seen categories and integrate it into DETR, which results in a new end-to-end zero-shot object detection approach. Furthermore, to handle the second problem, a novel class-wise adaptive contrastive loss is proposed, which considers the relevance between each pair of categories according to their semantic description in order to learn separable features for better visual-semantic alignment. Extensive experiments and ablation studies on benchmark datasets demonstrate the effectiveness and superiority of the proposed method.
OraclePoints: A Hybrid Neural Representation for Oracle Character

Oracle Bone Inscriptions (OBI) are ancient hieroglyphs originated in China and are considered one of the most famous writing systems in the world. Up to now, thousands of OBIs have been discovered, which require deciphering by experts to understand their contents. Experts typically need to restore, classify, and compare each character with previous inscriptions. Although existing research can assist with one of these operations, their performance falls short of practical requirements. In this work, we propose the OraclePoints framework, which represents OBI images as hybrid neural representations comprising features of images and point sets. The image representation provides inscription appearance and character structure, while the point representation makes it easy and effective to distinguish characters and noises. In addition, we demonstrate that OraclePoints can be easily integrated with existing models in a plug-and-play manner. Comprehensive experiments demonstrate that the proposed hybrid neural representation framework supports a range of OBI tasks, including character image retrieval, recognition, and denoising. It is also demonstrated that OraclePoints is helpful for deciphering OBIs by linking ancient characters to modern Chinese characters. Our codes are available at https://ddghjikle.github.io/.
Semi-supervised Deep Multi-view Stereo

Significant progress has been witnessed in learning-based Multi-view Stereo (MVS) under supervised and unsupervised settings. To combine their respective merits in accuracy and completeness, meantime reducing the demand for expensive labeled data, this paper explores the problem of learning-based MVS in a semi-supervised setting that only a tiny part of the MVS data is attached with dense depth ground truth. However, due to huge variation of scenarios and flexible settings in views, it may break the basic assumption in classic semi-supervised learning, that unlabeled data and labeled data share the same label space and data distribution, named as semi-supervised distribution-gap ambiguity in the MVS problem. To handle these issues, we propose a novel semi-supervised distribution-augmented MVS framework, namely SDA-MVS. For the simple case that the basic assumption works in MVS data, consistency regularization encourages the model predictions to be consistent between original sample and randomly augmented sample. For further troublesome case that the basic assumption is conflicted in MVS data, we propose a novel style consistency loss to alleviate the negative effect caused by the distribution gap. The visual style of unlabeled sample is transferred to labeled sample to shrink the gap, and the model prediction of generated sample is further supervised with the label in original labeled sample. The experimental results in semi-supervised settings of multiple MVS datasets show the superior performance of the proposed method. With the same settings in backbone network, our proposed SDA-MVS outperforms its fully-supervised and unsupervised baselines.
StyleEDL: Style-Guided High-order Attention Network for Image Emotion Distribution Learning

Emotion distribution learning has gained increasing attention with the tendency to express emotions through images. As for emotion ambiguity arising from humans' subjectivity, substantial previous methods generally focused on learning appropriate representations from the holistic or significant part of images. However, they rarely consider establishing connections with the stylistic information although it can lead to a better understanding of images. In this paper, we propose a style-guided high-order attention network for image emotion distribution learning termed StyleEDL, which interactively learns stylistic-aware representations of images by exploring the hierarchical stylistic information of visual contents. Specifically, we consider exploring the intra- and inter-layer correlations among GRAM-based stylistic representations, and meanwhile exploit an adversary-constrained high-order attention mechanism to capture potential interactions between subtle visual parts. In addition, we introduce a stylistic graph convolutional network to dynamically generate the content-dependent emotion representations to benefit the final emotion distribution learning. Extensive experiments conducted on several benchmark datasets demonstrate the effectiveness of our proposed StyleEDL compared to state-of-the-art methods. The implementation is released at: https://github.com/liuxianyi/StyleEDL.
Filling the Information Gap between Video and Query for Language-Driven Moment Retrieval

This paper addresses the challenging task of language-driven moment retrieval. Previous methods are typically trained to localize the target moment corresponding to a single sentence query in a complicated video. However, this specific moment generally delivers richer contents than the query, i.e., the semantics of one query may miss certain object details or actions in the complex foreground-background visual contents. Such information imbalance between two modalities makes it difficult to finely align their representations. To this end, instead of training with a single query, we propose to utilize the diversity and complementarity among different queries corresponding to the same video moment for enriching the textual semantics. Specifically, we develop a Teacher-Student Moment Retrieval (TSMR) framework to fill this cross-modal information gap. A teacher model is trained to not only encode a certain query but also capture extra complementary queries to aggregate contextual semantics for obtaining more comprehensive moment-related query representations. Since the additional queries are inaccessible during inference, we further introduce an adaptive knowledge distillation mechanism to train a student model with a single query input by selectively absorbing the knowledge from the teacher model. In this manner, the student model is more robust to the cross-modal information gap during the moment retrieval guided by a single query. Experimental results on two benchmarks demonstrate the effectiveness of our proposed method.
Uncertainty-Aware Variate Decomposition for Self-supervised Blind Image Deblurring

Blind image deblurring remains challenging due to the ill-posed nature of the traditional blurring function. Although previous supervised methods have achieved great breakthrough with synthetic blurry-sharp image pairs, their generalization ability to real-world blurs is limited by the discrepancy between synthetic and real blurs. To overcome this limitation, unsupervised deblurring methods have been proposed by using natural priors or generative adversarial networks. However, natural priors are vulnerable to random blur artifacts, while generators of generative adversarial networks always produce inaccurate details and unrealistic colors. Consequently, previous methods easily suffer from slow convergence and poor performance. In this work, we propose to formulate the traditional blurring function as the composition of multiple variates, thus allowing us explicitly define characteristics of residual images between blurry and sharp images. We also propose a multi-step self-supervised deblurring framework to address the slow convergence issue. Our framework continuously decomposes and composes input images, thus utilizing the uncertainty of blur artifacts to obtain diverse pseudo blurry-sharp image pairs for self-supervised learning. This framework is more efficient than previous methods, as it does not rely on natural priors or GANs. Extensive comparisons demonstrate that the proposed framework outperforms state-of-the-art unsupervised methods on both dynamic scene, human-aware centric motion, real-world and out-of-focus deblurring datasets. The codes are available at https://github.com/ddghjikle/MM-2023-USDF.
Exploring Correlations in Degraded Spatial Identity Features for Blind Face Restoration

Blind face restoration aims to recover high-quality face images from low-quality ones with complex and unknown degradation. Existing approaches have achieved promising performance by leveraging pre-trained dictionaries or generative priors. However, these methods may fail to exploit the full potential of degraded inputs and facial identity features due to complex degradation. To address this issue, we propose a novel method that explores the correlation of degraded spatial identity features by learning a general representation using memory network. Specifically, our approach enhances degraded features with more identity by leveraging similar facial features retrieved from memory network. We also propose a fusion approach that fuses memorized spatial features with GAN prior features via affine transformation and blending fusion to improve fidelity and realism. Additionally, the memory network is updated online in an unsupervised manner along with other modules, which obviates the requirement for pre-training. Experimental results on synthetic and popular real-world datasets demonstrate the effectiveness of our proposed method, which achieves at least comparable and often better performance than other state-of-the-art approaches.
Personalized Single Image Reflection Removal Network through Adaptive Cascade Refinement

In this paper, we aim to restore a reflection-free image from a single reflection-contaminated image captured through the glass. Many deep-learning-based methods attempt to solve the challenging problem by utilizing a uniform model obtained from training data for all test images. Hence, the distinctive characteristics of the test images are not considered. Besides, several methods use a cascade structure in image restoration to refine the results. But they blindly cascade modules with the same weights, improving the model's performance only to a certain extent. To address these problems, we propose a personalized single-image reflection removal network through adaptive cascade refinement (PNACR) based on meta-learning and self-supervised learning. While meta-learning can rapidly adapt to a new task with a few samples, PNACR can remove reflections of a new image with its distinctive characteristics learned by self-supervised learning. Furthermore, the proposed adaptive cascade model can adjust the weights of the model at the next iteration according to the output of the model at the current iteration, significantly improving the model's performance. Hence, the proposed model can learn information from both external training data and the new input image to provide a personalized reflection removal model for each new input image. Extensive comparison and ablation experiments on publicly available datasets demonstrate the validity of the proposed method in quantitative evaluation metrics and qualitative visualization.
Peering into The Sketch: Ultra-Low Bitrate Face Compression for Joint Human and Machine Perception

We propose a novel face compression framework that leverages the external priors for joint human and machine perception under ultra-low bitrate scenarios. The proposed framework leverages the semantic richness of face images by representing the faces into sketches and thumbnails, resulting in improved bitrate utility for both human and machine vision. At the decoder side, the framework introduces a two-stage generative reconstruction, which faithfully enhances the reconstructed image via semi-parametric modeling and retrieved guidance from the external database. In particular, this coarse-to-fine strategy also results in improved identity consistency and analysis performance of the reconstructed image. Extensive evaluations of the proposed method have been conducted on the public face dataset by comparing it with end-to-end image compression techniques as well as traditional image compression standards. The experimental results demonstrate the effectiveness of the proposed method via superior perceptual and analytical performance under ultra-low bitrate conditions.