Reviewerly/exHarmony huggingface dataset:
Generative Label Enhancement with Gaussian Mixture and Partial Ranking

Label distribution learning (LDL) is an effective learning paradigm for dealing with label ambiguity. When applying LDL, the datasets annotated with label distributions (i.e., the real-valued vectors like the probability distribution) are typically required. Unfortunately, most existing datasets only contain the logical labels, and manual annotating with label distributions is costly. To address this problem, we treat the label distribution as a latent vector and infer its posterior by variational Bayes. Specifically, we propose a generative label enhancement model to encode the process of generating feature vectors and logical label vectors from label distributions in a principled way. In terms of features, we assume that the feature vector is generated by a Gaussian mixture dominated by the label distribution, which captures the one-to-many relationship from the label distribution to the feature vector and thus reduces the feature generation error. In terms of logical labels, we design a probability distribution to generate the logical label vector from a label distribution, which captures partial label ranking in the logical label vector and thus provides a more accurate guidance for inferring the label distribution. Besides, to approximate the posterior of the label distribution, we design a inference model, and derive the variational learning objective. Finally, extensive experiments on real-world datasets validate our proposal.
Metric Nearness Made Practical

Given a square matrix with noisy dissimilarity measures between pairs of data samples, the metric nearness model computes the best approximation of the matrix from a set of valid distance metrics. Despite its wide applications in machine learning and data processing tasks, the model faces non-trivial computational requirements in seeking the solution due to the large number of metric constraints associated with the feasible region. Our work designed a practical approach in two stages to tackle the challenge and improve the model's scalability and applicability. The first stage computes a fast yet high-quality approximate solution from a set of isometrically embeddable metrics, further improved by an effective heuristic. The second stage refines the approximate solution with the Halpern-Lions-Wittmann-Bauschke projection algorithm, which converges quickly to the optimal solution. In empirical evaluations, the proposed approach runs at least an order of magnitude faster than the state-of-the-art solutions, with significantly improved scalability, complete conformity to constraints, less memory consumption, and other desirable features in real applications.
Lifelong Person Re-identification via Knowledge Refreshing and Consolidation

Lifelong person re-identification (LReID) is in significant demand for real-world development as a large amount of ReID data is captured from diverse locations over time and cannot be accessed at once inherently. However, a key challenge for LReID is how to incrementally preserve old knowledge and gradually add new capabilities to the system. Unlike most existing LReID methods, which mainly focus on dealing with catastrophic forgetting, our focus is on a more challenging problem, which is, not only trying to reduce the forgetting on old tasks but also aiming to improve the model performance on both new and old tasks during the lifelong learning process. Inspired by the biological process of human cognition where the somatosensory neocortex and the hippocampus work together in memory consolidation, we formulated a model called Knowledge Refreshing and Consolidation (KRC) that achieves both positive forward and backward transfer. More specifically, a knowledge refreshing scheme is incorporated with the knowledge rehearsal mechanism to enable bi-directional knowledge transfer by introducing a dynamic memory model and an adaptive working model. Moreover, a knowledge consolidation scheme operating on the dual space further improves model stability over the long-term. Extensive evaluations show KRC’s superiority over the state-of-the-art LReID methods with challenging pedestrian benchmarks. Code is available at https://github.com/cly234/LReID-KRKC.
Attention-Based Depth Distillation with 3D-Aware Positional Encoding for Monocular 3D Object Detection

Monocular 3D object detection is a low-cost but challenging task, as it requires generating accurate 3D localization solely from a single image input. Recent developed depth-assisted methods show promising results by using explicit depth maps as intermediate features, which are either precomputed by monocular depth estimation networks or jointly evaluated with 3D object detection. However, inevitable errors from estimated depth priors may lead to misaligned semantic information and 3D localization, hence resulting in feature smearing and suboptimal predictions. To mitigate this issue, we propose ADD, an Attention-based Depth knowledge Distillation framework with 3D-aware positional encoding. Unlike previous knowledge distillation frameworks that adopt stereo- or LiDAR-based teachers, we build up our teacher with identical architecture as the student but with extra ground-truth depth as input. Credit to our teacher design, our framework is seamless, domain-gap free, easily implementable, and is compatible with object-wise ground-truth depth. Specifically, we leverage intermediate features and responses for knowledge distillation. Considering long-range 3D dependencies, we propose \emph{3D-aware self-attention} and \emph{target-aware cross-attention} modules for student adaptation. Extensive experiments are performed to verify the effectiveness of our framework on the challenging KITTI 3D object detection benchmark. We implement our framework on three representative monocular detectors, and we achieve state-of-the-art performance with no additional inference computational cost relative to baseline models. Our code is available at https://github.com/rockywind/ADD.
Teaching Reform and Practice of Embedded System Structure under OBE Background

According to the OBE concept, in the order of defining outcomes, designing courses, organizing teaching, implementing teaching, and evaluating learning achievements, the teaching reform is implemented for the students of Grade 2020 in Computer Science and Technology of Changchun Institute of Engineering.The whole teaching activity is carried out around the course objectives, emphasizing the principle of process supervision.Through the analysis of the degree of achievement of the course objectives, we will continue to track and improve the teaching effect in education and teaching to promote the quality of professional training.
Improving Named Entity Recognition in Bahasa Indonesia with Transformer-Word2Vec-CNN-Attention Model

Named entity recognition (NER) is one of the topics that get the attention of NLP (natural language processing) researchers.Most NER research uses English datasets in other languages, such as Bahasa Indonesia.However, NER is essential in recognizing corpus entities that can improve NLP performance.Deep learning approaches are currently a trend, including in NER research.In this article, we propose TWCAM (transformer-Word2Vec-CNN-attention model).Combining these models can improve NER performance in Bahasa Indonesia by obtaining better vector representations of words, extracting features in sentences, and notice to the surrounding context.The dataset in Bahasa Indonesia comes from several online news sites.Our annotation scheme is BIOLU (Begin, inside outside, last, unit).Using the learning-rate finder, the maximum F1-Score in the tests we conducted on the TWCAM was 0.8178, while the BiLSTM (Bidirectional long short-term memory) was only 0.7200.The following research opportunity is how to reduce computational complexity but not decrease overall NER performance.
Effective Workflow Scheduling in Cloud Platform Using Data Aware based Adaptive Gravitational Search Algorithm

In recent periods, cloud-computing environments are widely utilizing scientific workflows for executing large-scale applications.The workflow scheduling with the scientific standard for optimizing quality of service (QoS) parameters is a hard task.In the existing research studies, several metaheuristics optimization algorithms are employed for satisfying the QoS parameters such as resource utilization, cost, and makespan.Still, the existing metaheuristics optimization algorithms are insignificant for maintaining the balance between exploitation and exploration in a search space, because the algorithms are easily trapped in local optima.For addressing the above-stated issues, a data aware based adaptive gravitational search algorithm (DA-AGSA) technique is implemented to minimize the cost and makespan, and to schedule workflows in the cloud-computing platform.In the conventional GSA technique, a random coefficient is replaced by an adaptive weight function for improving convergence rate, and further, the weight function is multiplied with an acceleration term for facilitating quicker convergence.In this article, the performance of the DA-AGSA technique is validated by utilizing workflow sim for scheduling multiple workflows.An extensive experimental investigation showed that the DA-AGSA technique almost reduced 20% of the cost and 15% of the makespan compared to the conventional optimization algorithms on the Montage, CyberShake, and Epigenomics workflows with 1000 tasks.In addition, the DA-AGSA technique achieved a reliability of 0.99, 0.98, and 0.98 on the Montage, CyberShake and Epigenomics workflows with 1000 tasks.
Swarm Magnetic Optimizer: A New Optimizer that Adopts Magnetic Behaviour

This paper introduces a novel swarm-based metaheuristic called swarm magnetic optimizer (SMO).SMO imitates the behaviour of two magnets close to each other: pushing toward or pulling away from each other.This pushpull mechanism is then adopted to become a novel search in SMO.SMO is set up as a swarm of magnets that move autonomously.In the early iteration, the pull strategy is dominant.Meanwhile, it declines during the iteration and is replaced by a push strategy.The determination between these two strategies is calculated stochastically.SMO consists of three sequential phases in every iteration where the corresponding magnet and its reference run a push or pull search in each phase.The reference used in each phase is the global best magnet, a randomly selected magnet, and a randomly generated magnet.This paper evaluates SMO through simulation to find the optimal solution for 23 functions.The performance of SMO is compared with five latest metaheuristics: mixed leader-based optimizer (MLBO), guided pelican algorithm (GPA), zebra optimization algorithm (ZOA), coati optimization algorithm (COA), and clouded leopard optimizer (CLO).The result shows that SMO is better than MLBO, GPA, ZOA, COA, and CLO in consecutively 22, 15, 11, 13, and 14 functions.The superiority of SMO, especially, is in solving high dimension functions.Meanwhile, in the fixed-dimension multimodal functions, SMO is still superior to MLBO but less superior to GPA, ZOA, COA, and CLO.
ILSGAN: Independent Layer Synthesis for Unsupervised Foreground-Background Segmentation

Unsupervised foreground-background segmentation aims at extracting salient objects from cluttered backgrounds, where Generative Adversarial Network (GAN) approaches, especially layered GANs, show great promise. However, without human annotations, they are typically prone to produce foreground and background layers with non-negligible semantic and visual confusion, dubbed "information leakage", resulting in notable degeneration of the generated segmentation mask. To alleviate this issue, we propose a simple-yet-effective explicit layer independence modeling approach, termed Independent Layer Synthesis GAN (ILSGAN), pursuing independent foreground-background layer generation by encouraging their discrepancy. Specifically, it targets minimizing the mutual information between visible and invisible regions of the foreground and background to spur interlayer independence. Through in-depth theoretical and experimental analyses, we justify that explicit layer independence modeling is critical to suppressing information leakage and contributes to impressive segmentation performance gains. Also, our ILSGAN achieves strong state-of-the-art generation quality and segmentation performance on complex real-world data.
Anonymization for Skeleton Action Recognition

Skeleton-based action recognition attracts practitioners and researchers due to the lightweight, compact nature of datasets. Compared with RGB-video-based action recognition, skeleton-based action recognition is a safer way to protect the privacy of subjects while having competitive recognition performance. However, due to improvements in skeleton recognition algorithms as well as motion and depth sensors, more details of motion characteristics can be preserved in the skeleton dataset, leading to potential privacy leakage. We first train classifiers to categorize private information from skeleton trajectories to investigate the potential privacy leakage from skeleton datasets. Our preliminary experiments show that the gender classifier achieves 87% accuracy on average, and the re-identification classifier achieves 80% accuracy on average with three baseline models: Shift-GCN, MS-G3D, and 2s-AGCN. We propose an anonymization framework based on adversarial learning to protect potential privacy leakage from the skeleton dataset. Experimental results show that an anonymized dataset can reduce the risk of privacy leakage while having marginal effects on action recognition performance even with simple anonymizer architectures. The code used in our experiments is available at https://github.com/ml-postech/Skeleton-anonymization/
Hybrid Learning: Russian and International Practice

The paper delivers the results of the study analyzing the established advanced practices of combining distance and face-to-face forms of learning in higher education. The key criterion for hybrid learning is the ratio of synchronous and asynchronous online formats in the learning process, and its essential basis - the learner's agency. The praximetric analysis of the sources and cases of foreign and Russian universities resulted in peculiarities of hybrid learning models. In-depth interviews with representatives of 14 universities allowed us to specify organizational and didactic features of the hybrid learning as well as its advantages and its limitations. The geography of respondents from foreign universities (Harvard Business School, Stockholm School of Economics, University of Arizona) and leading Russian universities (St. Petersburg State University, ITMO University, Higher School of Economics, Tomsk State University, Moscow City University, Ural Federal University, Moscow School of Management SKOLKOVO, Skolkovo Institute of Science and Technology, University of Tyumen) makes the study representative. Based on the comprehensive analysis of the interviewees' answers, the authors consider the hybrid format not as a universal solution for higher education, but as an opportunity to solve specific educational goals, individualize learning and attract new target groups. The study focuses on the flexible hybrid format (HyFlex), which gives students the choice of attending the class on campus (face-to-face), participation of classes via videoconferencing, or studying the course completely asynchronously, i.e., from class recordings and other materials.
Difference of anisotropic and isotropic TV for segmentation under blur and Poisson noise

In this paper, we aim to segment an image degraded by blur and Poisson noise. We adopt a smoothing-and-thresholding (SaT) segmentation framework that finds a piecewise-smooth solution, followed by k -means clustering to segment the image. Specifically for the image smoothing step, we replace the least-squares fidelity for Gaussian noise in the Mumford-Shah model with a maximum posterior (MAP) term to deal with Poisson noise and we incorporate the weighted difference of anisotropic and isotropic total variation (AITV) as a regularization to promote the sparsity of image gradients. For such a nonconvex model, we develop a specific splitting scheme and utilize a proximal operator to apply the alternating direction method of multipliers (ADMM). Convergence analysis is provided to validate the efficacy of the ADMM scheme. Numerical experiments on various segmentation scenarios (grayscale/color and multiphase) showcase that our proposed method outperforms a number of segmentation methods, including the original SaT.
Evolution of Rényi-2 quantum correlations in a double cavity–magnon system

This work reports on the stationary evolution of three different kinds of quantum correlations between two distant magnon modes. The system at hand consists of two spatially separated cavities, where each cavity has a microwave (MW) cavity and a magnon mode. The two cavities are coupled to each other via the photon-tunneling process, and both are exposed to the output field of a squeezed vacuum MW fields source. By calculating the covariance matrix fully describing the state of the two magnon modes, we give the explicit expressions of the measures of correlations defined via the Rényi-2 entropy, i.e. the Gaussian quantum steering [Formula: see text], Gaussian Rényi-2 entanglement [Formula: see text] and Gaussian Rényi-2 mutual information [Formula: see text]. We find that, the quantum steering is always upper bounded by the entanglement, which is in turn always upper bounded by half of the total correlations. The obtained results are consistent with the hierarchical Rényi-2 quantum correlations established in [L. Lami, C. Hirche, G. Adesso and A. Winter, Phys. Rev. Lett. 117, 220502 (2016)], i.e. they fulfill the hierarchical inequality [Formula: see text]. The influences of the squeezing parameter, the environmental temperature, the cavity–magnon coupling and the cavity–cavity coupling on the correlations are studied in detail.
Combating Mode Collapse via Offline Manifold Entropy Estimation

Generative Adversarial Networks (GANs) have shown compelling results in various tasks and applications in recent years. However, mode collapse remains a critical problem in GANs. In this paper, we propose a novel training pipeline to address the mode collapse issue of GANs. Different from existing methods, we propose to generalize the discriminator as feature embedding and maximize the entropy of distributions in the embedding space learned by the discriminator. Specifically, two regularization terms, i.e., Deep Local Linear Embedding (DLLE) and Deep Isometric feature Mapping (DIsoMap), are introduced to encourage the discriminator to learn the structural information embedded in the data, such that the embedding space learned by the discriminator can be well-formed. Based on the well-learned embedding space supported by the discriminator, a non-parametric entropy estimator is designed to efficiently maximize the entropy of embedding vectors, playing as an approximation of maximizing the entropy of the generated distribution. By improving the discriminator and maximizing the distance of the most similar samples in the embedding space, our pipeline effectively reduces the mode collapse without sacrificing the quality of generated samples. Extensive experimental results show the effectiveness of our method which outperforms the GAN baseline, MaF-GAN on CelebA (9.13 vs. 12.43 in FID) and surpasses the recent state-of-the-art energy-based model on the ANIMEFACE dataset (2.80 vs. 2.26 in Inception score).
Multi-Modal Knowledge Hypergraph for Diverse Image Retrieval

The task of keyword-based diverse image retrieval has received considerable attention due to its wide demand in real-world scenarios. Existing methods either rely on a multi-stage re-ranking strategy based on human design to diversify results, or extend sub-semantics via an implicit generator, which either relies on manual labor or lacks explainability. To learn more diverse and explainable representations, we capture sub-semantics in an explicit manner by leveraging the multi-modal knowledge graph (MMKG) that contains richer entities and relations. However, the huge domain gap between the off-the-shelf MMKG and retrieval datasets, as well as the semantic gap between images and texts, make the fusion of MMKG difficult. In this paper, we pioneer a degree-free hypergraph solution that models many-to-many relations to address the challenge of heterogeneous sources and heterogeneous modalities. Specifically, a hyperlink-based solution, Multi-Modal Knowledge Hyper Graph (MKHG) is proposed, which bridges heterogeneous data via various hyperlinks to diversify sub-semantics. Among them, a hypergraph construction module first customizes various hyperedges to link the heterogeneous MMKG and retrieval databases. A multi-modal instance bagging module then explicitly selects instances to diversify the semantics. Meanwhile, a diverse concept aggregator flexibly adapts key sub-semantics. Finally, several losses are adopted to optimize the semantic space. Extensive experiments on two real-world datasets have well verified the effectiveness and explainability of our proposed method.
CALIP: Zero-Shot Enhancement of CLIP with Parameter-Free Attention

Contrastive Language-Image Pre-training (CLIP) has been shown to learn visual representations with promising zero-shot performance. To further improve its downstream accuracy, existing works propose additional learnable modules upon CLIP and fine-tune them by few-shot training sets. However, the resulting extra training cost and data requirement severely hinder the efficiency for model deployment and knowledge transfer. In this paper, we introduce a free-lunch enhancement method, CALIP, to boost CLIP's zero-shot performance via a parameter-free attention module. Specifically, we guide visual and textual representations to interact with each other and explore cross-modal informative features via attention. As the pre-training has largely reduced the embedding distances between two modalities, we discard all learnable parameters in the attention and bidirectionally update the multi-modal features, enabling the whole process to be parameter-free and training-free. In this way, the images are blended with textual-aware signals and the text representations become visual-guided for better adaptive zero-shot alignment. We evaluate CALIP on various benchmarks of 14 datasets for both 2D image and 3D point cloud few-shot classification, showing consistent zero-shot performance improvement over CLIP. Based on that, we further insert a small number of linear layers in CALIP's attention module and verify our robustness under the few-shot settings, which also achieves leading performance compared to existing methods. Those extensive experiments demonstrate the superiority of our approach for efficient enhancement of CLIP. Code is available at https://github.com/ZiyuGuo99/CALIP.
Learning Fractals by Gradient Descent

Fractals are geometric shapes that can display complex and self-similar patterns found in nature (e.g., clouds and plants). Recent works in visual recognition have leveraged this property to create random fractal images for model pre-training. In this paper, we study the inverse problem --- given a target image (not necessarily a fractal), we aim to generate a fractal image that looks like it. We propose a novel approach that learns the parameters underlying a fractal image via gradient descent. We show that our approach can find fractal parameters of high visual quality and be compatible with different loss functions, opening up several potentials, e.g., learning fractals for downstream tasks, scientific understanding, etc.
Automated program and software defect root cause analysis using machine learning techniques

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