Reviewerly/exHarmony huggingface dataset:
Unsupervised Confidence Approximation: Trustworthy Learning from Noisy Labelled Data

Training neural networks with noisy labels presents a challenge due to inherent errors in label annotations. Concurrently, selectively predicting outputs from neural networks involves identifying confidently predicted results. These challenges are particularly important in the medical domain, as they often occur jointly. Existing techniques address either the training of models with noisy labels or the task of selective prediction in isolation, often neglecting their intrinsic interdependence. We establish a relationship between these challenges and propose a novel framework called Unsupervised Confidence Approximation (UCA) to address them simultaneously. UCA facilitates the concurrent training of neural networks for a main task such as image segmentation and classification while also predicting confidence levels. This is all done while accommodating datasets containing noisy labels. Remarkably, UCA operates autonomously, eliminating the need for labelled confidence information and qualifying as an unsupervised solution. Furthermore, UCA is versatile, integrating with diverse network architectures. Our evaluation of UCA’s efficacy covers the general CIFAR-10N dataset as well as the medical image datasets CheXpert and Gleason-2019. In our experiments, incorporating UCA into existing networks enhances performance in both aspects of noisy label training and selective prediction. Moreover, networks equipped with UCA demonstrate comparable performance to state-of-the-art methods for noisy label training when operating in the conventional full coverage mode. By design, these UCA-equipped networks incorporate a risk-management mechanism, as evidenced by flawless risk-coverage curves. Additionally, UCA-equipped networks outperform existing selective prediction techniques, leading to substantial performance improvements and reinforcing its utility and impact within the context of trustworthy medical deep learning.
Iterative Robust Visual Grounding with Masked Reference based Centerpoint Supervision

Visual Grounding (VG) aims at localizing target objects from an image based on given expressions and has made significant progress with the development of detection and vision transformer. However, existing VG methods tend to generate false-alarm objects when presented with inaccurate or irrelevant descriptions, which commonly occur in practical applications. Moreover, existing methods fail to capture fine-grained features, accurate localization, and sufficient context comprehension from the whole image and textual descriptions. To address both issues, we propose an Iterative Robust Visual Grounding (IRVG) framework with Masked Reference based Centerpoint Supervision (MRCS). The framework introduces iterative multi-level vision-language fusion (IMVF) for better alignment. We use MRCS to ahieve more accurate localization with point-wised feature supervision. Then, to improve the robustness of VG, we also present a multi-stage false-alarm sensitive decoder (MFSD) to prevent the generation of false-alarm objects when presented with inaccurate expressions. Extensive experiments demonstrate that IR-VG achieves new state-of-the-art (SOTA) results, with improvements of 25% and 10% compared to existing SOTA approaches on the two newly proposed robust VG datasets. Moreover, the proposed framework is also verified effective on five regular VG datasets. Codes and models will be publicly at https://github.com/cv516Buaa/IR-VG.
Robust Asymmetric Loss for Multi-Label Long-Tailed Learning

In real medical data, training samples typically show long-tailed distributions with multiple labels. Class distribution of the medical data has a long-tailed shape, in which the incidence of different diseases is quite varied, and at the same time, it is not unusual for images taken from symptomatic patients to be multi-label diseases. Therefore, in this paper, we concurrently address these two issues by putting forth a robust asymmetric loss on the polynomial function. Since our loss tackles both long-tailed and multi-label classification problems simultaneously, it leads to a complex design of the loss function with a large number of hyper-parameters. Although a model can be highly fine-tuned due to a large number of hyper-parameters, it is difficult to optimize all hyper-parameters at the same time, and there might be a risk of overfitting a model. Therefore, we regularize the loss function using the Hill loss approach, which is beneficial to be less sensitive against the numerous hyper-parameters so that it reduces the risk of overfitting the model. For this reason, the proposed loss is a generic method that can be applied to most medical image classification tasks and does not make the training process more time-consuming. We demonstrate that the proposed robust asymmetric loss performs favorably against the long-tailed with multi-label medical image classification in addition to the various long-tailed single-label datasets. Notably, our method achieves Top-5 results on the CXR-LT dataset of the ICCV CVAMD 2023 competition. We opensource our implementation of the robust asymmetric loss in the public repository: https://github.com/kalelpark/RALoss.
The quantum house of cards

Quantum computers have been proposed to solve a number of important problems such as discovering new drugs, new catalysts for fertilizer production, breaking encryption protocols, optimizing financial portfolios, or implementing new artificial intelligence applications. Yet, to date, a simple task such as multiplying 3 by 5 is beyond existing quantum hardware. This article examines the difficulties that would need to be solved for quantum computers to live up to their promises. I discuss the whole stack of technologies that has been envisioned to build a quantum computer from the top layers (the actual algorithms and associated applications) down to the very bottom ones (the quantum hardware, its control electronics, cryogeny, etc.) while not forgetting the crucial intermediate layer of quantum error correction.
A survey on fuzzy control for mechatronics applications

Fuzzy control has become one of the most effective tools for dealing with complex engineering processes. Over the years, research on fuzzy control systems has continuously evolved, witnessing numerous theoretical contributions and successful real-world achievements. The concept of model-free or data-driven fuzzy control was initially introduced with specific heuristics incorporated into the design. Due to the lack of a systematic framework for stability analysis in model-free fuzzy control, the significance of model-based fuzzy control has grown extensively. This approach ensures systematic design based on precise fuzzy models of the process. This survey focuses on the fundamental aspects of three prominent classes of fuzzy control. First, the paper commences with a review of Takagi–Sugeno fuzzy control systems. This includes discussions on stability analysis and controller design, exploring techniques to derive less conservative and/or complex results from a numerical burden perspective. Second, various aspects of data-driven fuzzy control are analysed in detail including a classification of the most popular data-driven control techniques and their combination with fuzzy control; a representative Iterative Feedback Tuning-based fuzzy controller is described. Third, this survey explores the fundamental aspects of evolving fuzzy control, with a particular emphasis on the significance of stability and control laws, which are not usually the primary focus of evolving intelligent systems research. For each discussed class of fuzzy control, the paper provides a selective list of mechatronics applications to illustrate their performance effectiveness, emphasising research papers published after 2011. Finally, drawing from recent advances in fuzzy control theory and mechatronics applications, future research directions and associated challenges are discussed.
Ponzi scheme detection in smart contracts using the integration of deep learning and formal verification

Abstract Blockchain smart contracts are codes that can execute and enforce rules for blockchain digital transactions. However, smart contracts may contain numerous subtle vulnerabilities, among which Ponzi vulnerabilities are notable. Existing Ponzi scheme contract detection approaches often rely on machine learning models trained on manually extracted features to achieve satisfactory classification results. Nonetheless, the code of a smart contract potentially harbours elusive semantics and characteristics, which compromises the precision and accuracy of vulnerability detection. Therefore, this paper proposes a method of converting operation codes into sequences to process data to avoid losing unnecessary important information, and uses a one‐dimensional convolutional neural network combined with formal verification. This method is named PZ‐C1DZ3(Ponzi‐Conv1D‐Z3) and is used for Ponzi scheme detection. Four types of machine learning models, namely Conv1D, Conv1D‐LSTM, Conv1D‐MLP, and Conv1D‐transformer, are employed for improvement and comparative validation experiments. Additionally, formal verification tool Z3 solver is utilized to conduct formal security verification on the final model, ensuring its safety. Experimental results demonstrate that the improved Conv1D model outperforms other existing models in terms of detection efficiency and accuracy while also meeting the requirements of formal security verification.
Named Entity Recognition Using BERT Model for Kannada Language

Named Entity Recognition (NER) is a vital technique in Natural Language Processing (NLP) used to discern and classify words in a text into corresponding predefined categories. These categories often inlude entities such as names of people, organizations, locations, dates, and more. These are termed as Named Entities. The goal of NER is to extract and label these named entities automatically from unstructured text data. It has many applications, especially in information extraction, machine translation, text-to-speech synthesis, natural language understanding, question answering, text summarization, etc. This paper presents an approach based on Bidirectional Encoder Representations from Transformers (BERT) for identifying and recognizing named entities in Kannada, an Indian language.
Influence of Social Media Platform Trends on Academic anagement and Leadership in Higher Education Institutions: A Systematic Review

The growth of influencers in social media with respective delivery topics and their followers' reactions towards leader profiles and characters will lead to some extent to soft skills that can influence the performance of organizations as a whole. However, limited knowledge exists of how it is defined, how it operates, and how it relates to institutional leadership, including both administration and teaching. Therefore, this study aims to review current social media platform trends in Malaysia, the relationship between social media, management, and leadership, as well as how social media trends affect academic leadership and administration. The descriptive content analysis method was applied to examine 1,727 empirical, review, conceptual, and commentary papers published by Scopus and Web of Science journals. After considering other irrelevant information, the authors only reviewed a total of 494 from both websites. Considering Malaysia’s scholars, only 27 were chosen to be reviewed for title and abstract. Of these 27, only three articles were chosen for detailed review. All the information relevant to the research was extracted from each article and analysed using Excel software. Results showed that in 1,727, only three articles were related to social media and Malaysian scholars. According to these three articles, most organizations use Twitter, Instagram, and other digital technologies like digital communications, smartphones, and many others for social interaction. This is because workplace wellbeing can be achieved through human connections on social media. This workplace well-being can be related to leadership and work engagement in organizations. Hashtags in social media, especially Twitter, can be used to express sentiment, and this sentiment can influence human emotional state and well-being. In conclusion, the paper will add to recent efforts on how to improve the current situation, especially for new leaders with prospective approaches to the field, and contribute to the global knowledge base.
STATE POLICY IN THE FIELD OF COMPUTER TECHNOLOGY DEVELOPMENT IN THE 1950S: ACHIEVEMENTS AND BLOCKING FACTORS

The purpose of the publication, based on currently declassified documents, is to investigate the actual problem of the evolution of state policy in the field of computer technology development in the USSR in the 1950s. The conclusion is formulated that the «cold war», the unfolding scientific and technological revolution stimulated the implementation of their own projects in the field of information technology and hardware platforms in the USSR. A significant factor that determined the creation of various domestic computers in the early 1950s was the support at the state level of developments independent of Western analogues. The continuous growth in demand for calculating machines and the release in 1956 of a number of government decisions on the creation of new types of such machines forced the leadership of various departments to take urgent measures to accelerate the development of the mathematical engineering industry. In the period from 1955 to 1957, samples of high-performance computing and mathematical machines were created. A number of them were sent to mass production. Computer centers were created on the basis of new computer technology. However, in general, the development of computer technology in the country lagged behind the United States. The documents allow us to identify a number of factors that determined the braking of this process: the industrial management reform being carried out at that time, the departmental approach, the lack of understanding by the country's leadership of the special significance of this area of industrial and scientific and technical policy, the absence of a single center coordinating this work. The Scientific Council on Computer Technology and Control Systems of the State Committee of the USSR Academy of Sciences for Science and Technology and the Presidium of the USSR Academy of Sciences was established only in 1966.
A Conceptual Integrated Persuasive Behavioral Change Framework Based on Maqasid al-Shariah to Encourage Green Practices of Municipal Solid Waste (MSW) Disposal

Waste disposal has been a worldwide concern for several decades and the situation is worsening in developing countries including Malaysia due to the progressive increase in waste generation in recent years. Improper waste disposal causes a detrimental effect on the environment and human health. In many developing countries, environmentally friendly practices (reduce, reuse and recycle) have been introduced to reduce the amount of waste generated and alternative methods of final waste disposal are increasingly used to reduce environmental impact. However, most Malaysians have weak awareness of such green practices of MSW disposal despite numerous efforts made by the government. Therefore, how to motivate Malaysians to adopt green practices has been of concern. Within sustainable HCI, persuasive technology has been used to intentionally change user’s behaviors and attitudes through persuasion. However, one of the major limitations of persuasive technologies is, it does not consider how one’s culture and religious views shapes their behavior. As Malaysia is a Muslim-majority country and largely influenced by religious views in guiding and making a positive change in their attitudes and behaviors, we believed integrating persuasive technology with Maqasid al-Shariah would enhance its persuasive power at encouraging green practices of solid waste disposal. Accordingly, in this paper we are proposing a conceptual integrated persuasive behavioral change framework based on Maqasid al- Shariah aiming to encourage green practices of MSW disposal among Malaysians. We hypothesized this framework will be beneficial to increase Malaysian community awareness on the importance of disposing MSW based on Maqasid al-Shariah and eventually reduce risks to health and the environment due to improper waste disposal
Synthesizing Diverse Human Motions in 3D Indoor Scenes

We present a novel method for populating 3D indoor scenes with virtual humans that can navigate in the environment and interact with objects in a realistic manner. Existing approaches rely on high-quality training sequences that contain captured human motions and the 3D scenes they interact with. However, such interaction data are costly, difficult to capture, and can hardly cover the full range of plausible human-scene interactions in complex indoor environments. To address these challenges, we propose a reinforcement learning-based approach that enables virtual humans to navigate in 3D scenes and interact with objects realistically and autonomously, driven by learned motion control policies. The motion control policies employ latent motion action spaces, which correspond to realistic motion primitives and are learned from large-scale motion capture data using a powerful generative motion model. For navigation in a 3D environment, we propose a scene-aware policy with novel state and reward designs for collision avoidance. Combined with navigation mesh-based path-finding algorithms to generate intermediate waypoints, our approach enables the synthesis of diverse human motions navigating in 3D indoor scenes and avoiding obstacles. To generate fine-grained human-object interactions, we carefully curate interaction goal guidance using a marker-based body representation and leverage features based on the signed distance field (SDF) to encode human-scene proximity relations. Our method can synthesize realistic and diverse human-object interactions (e.g., sitting on a chair and then getting up) even for out-of-distribution test scenarios with different object shapes, orientations, starting body positions, and poses. Experimental results demonstrate that our approach outperforms state-of-the-art human-scene interaction synthesis methods in terms of both motion naturalness and diversity. Code, models, and demonstrative video results are available at: https://zkf1997.github.io/DIMOS.
HSR-Diff: Hyperspectral Image Super-Resolution via Conditional Diffusion Models

Despite the proven significance of hyperspectral images (HSIs) in performing various computer vision tasks, its potential is adversely affected by the low-resolution (LR) property in the spatial domain, resulting from multiple physical factors. Inspired by recent advancements in deep generative models, we propose an HSI Super-resolution (SR) approach with Conditional Diffusion Models (HSR-Diff) that merges a high-resolution (HR) multispectral image (MSI) with the corresponding LR-HSI. HSR-Diff generates an HR-HSI via repeated refinement, in which the HR-HSI is initialized with pure Gaussian noise and iteratively refined. At each iteration, the noise is removed with a Conditional Denoising Transformer (CDFormer) that is trained on denoising at different noise levels, conditioned on the hierarchical feature maps of HR-MSI and LR-HSI. In addition, a progressive learning strategy is employed to exploit the global information of full-resolution images. Systematic experiments have been conducted on four public datasets, demonstrating that HSR-Diff outperforms state-of-the-art methods.
FateZero: Fusing Attentions for Zero-shot Text-based Video Editing

The diffusion-based generative models have achieved remarkable success in text-based image generation. However, since it contains enormous randomness in generation progress, it is still challenging to apply such models for real-world visual content editing, especially in videos. In this paper, we propose FateZero, a zero-shot text-based editing method on real-world videos without per-prompt training or use-specific mask. To edit videos consistently, we propose several techniques based on the pre-trained models. Firstly, in contrast to the straightforward DDIM inversion technique, our approach captures intermediate attention maps during inversion, which effectively retain both structural and motion information. These maps are directly fused in the editing process rather than generated during denoising. To further minimize semantic leakage of the source video, we then fuse self-attentions with a blending mask obtained by cross-attention features from the source prompt. Furthermore, we have implemented a reform of the self-attention mechanism in denoising UNet by introducing spatial-temporal attention to ensure frame consistency. Yet succinct, our method is the first one to show the ability of zero-shot text-driven video style and local attribute editing from the trained text-to-image model. We also have a better zero-shot shape-aware editing ability based on the text-to-video model [52]. Extensive experiments demonstrate our superior temporal consistency and editing capability than previous works.
NDDepth: Normal-Distance Assisted Monocular Depth Estimation

Monocular depth estimation has drawn widespread attention from the vision community due to its broad applications. In this paper, we propose a novel physics (geometry)-driven deep learning framework for monocular depth estimation by assuming that 3D scenes are constituted by piece-wise planes. Particularly, we introduce a new normal-distance head that outputs pixel-level surface normal and plane-to-origin distance for deriving depth at each position. Meanwhile, the normal and distance are regularized by a developed plane-aware consistency constraint. We further integrate an additional depth head to improve the robustness of the proposed framework. To fully exploit the strengths of these two heads, we develop an effective contrastive iterative refinement module that refines depth in a complementary manner according to the depth uncertainty. Extensive experiments indicate that the proposed method exceeds previous state-of-the-art competitors on the NYU-Depth-v2, KITTI and SUN RGB-D datasets. Notably, it ranks 1st among all submissions on the KITTI depth prediction online benchmark at the submission time. The source code is available at https://github.com/ShuweiShao/NDDepth.
Distilling Large Vision-Language Model with Out-of-Distribution Generalizability

Large vision-language models have achieved outstanding performance, but their size and computational requirements make their deployment on resource-constrained devices and time-sensitive tasks impractical. Model distillation, the process of creating smaller, faster models that maintain the performance of larger models, is a promising direction towards the solution. This paper investigates the distillation of visual representations in large teacher vision-language models into lightweight student models using a small- or mid-scale dataset. Notably, this study focuses on open-vocabulary out-of-distribution (OOD) generalization, a challenging problem that has been overlooked in previous model distillation literature. We propose two principles from vision and language modality perspectives to enhance student’s OOD generalization: (1) by better imitating teacher’s visual representation space, and carefully promoting better coherence in vision-language alignment with the teacher; (2) by enriching the teacher’s language representations with informative and fine-grained semantic attributes to effectively distinguish between different labels. We propose several metrics and conduct extensive experiments to investigate their techniques. The results demonstrate significant improvements in zero-shot and few-shot student performance on open-vocabulary out-of-distribution classification, highlighting the effectiveness of our proposed approaches. Code released at this link.