Reviewerly/exHarmony huggingface dataset:
Random following ant colony optimization: Continuous and binary variants for global optimization and feature selection

Continuous ant colony optimization was a population-based heuristic search algorithm inspired by the pathfinding behavior of ant colonies with a simple structure and few control parameters. However, in the case of multimodal and high-dimensional optimization problems, it was often limited to local regions in the feasible domain space, negatively affecting the computational effort required to find the optimal solution point. To alleviate its limitations in this regard, a random following strategy is proposed to enhance communication among the ant colony search agent and other ant colony members within the search dimension. The proposed algorithm that incorporates this strategy is called Random Following Ant Colony Optimization. Then, to evaluate the global optimization performance of the proposed algorithm, the well-known numerical optimization problem, namely the Congress on Evolutionary Computation 2017 test suite, is used. First, the proposed algorithm’s parameters are analyzed for sensitivity, scalability experiments, and balanced diversity. Second, it is compared experimentally with 11 state-of-the-art algorithms in dimensions 10, 30, 50, and 100, respectively, and Wilcoxon signed-rank test, Friedman test, and Bonferroni-Dunn post-hoc statistical test are used to synthesize the experimental comparison results. Finally, to evaluate the ability of the proposed algorithm to handle discrete feature selection problems, comparative experiments are conducted on 24 datasets with eight well-known classification methods and five high-performance classification methods. The benchmark test results show that the global optimization performance of the proposed algorithm is comparable to the winners of the test suite in 50 and 100 dimensions. The results of the feature selection experiments show that the proposed algorithm is much stronger than the well-known and high-performance classification methods on high-dimensional datasets.
DRNet: Dual-stage refinement network with boundary inference for RGB-D semantic segmentation of indoor scenes

Semantic segmentation is a dense pixel prediction task, and its accuracy depends on the extraction of long-range contextual knowledge and refinement of segmentation boundaries. Most segmentation methods are based on feature extraction using a convolutional neural network, and layer-by-layer sampling and fusion are applied to solve inherent problems such as chaotic boundaries and scattered objects. Owing to the limited receptive field and loss of details during downsampling, the segmentation results may be unsatisfactory. To address existing shortcomings, we propose a dual-stage refinement network (DRNet) for semantic segmentation. In the first stage, we adopt an efficient spatiotemporal representation learning framework called UniFormer. We also use a novel boundary extractor and initial segmentation map generator to obtain rough segmentation results. In the second stage, we use the rough segmentation map and extracted boundary information in a graph reasoning module that restores the class boundary features while completing global modeling and local information inference. Benefiting from the acquisition of long-range dependencies between image pixels, contextual information promotes the distinction of pixel categories. In addition, edge information can increase the interclass distinguishability and refine the segmentation boundaries. Results from extensive experiments demonstrate that the proposed DRNet outperforms state-of-the-art semantic segmentation methods. The codes and results are available at: https://github.com/EnquanYang2022/DRNet.
Efficient Deep Models for Real-Time 4K Image Super-Resolution. NTIRE 2023 Benchmark and Report

This paper introduces a novel benchmark for efficient up-scaling as part of the NTIRE 2023 Real-Time Image Super-Resolution (RTSR) Challenge, which aimed to upscale images from 720p and 1080p resolution to native 4K (×2 and ×3 factors) in real-time on commercial GPUs. For this, we use a new test set containing diverse 4K images ranging from digital art to gaming and photography. We assessed the methods devised for 4K SR by measuring their runtime, parameters, and FLOPs, while ensuring a minimum PSNR fidelity over Bicubic interpolation. Out of the 170 participants, 25 teams contributed to this report, making it the most comprehensive benchmark to date and showcasing the latest advancements in real-time SR.
NTIRE 2023 Video Colorization Challenge

This paper reviews the video colorization challenge on the New Trends in Image Restoration and Enhancement (NTIRE) workshop, held in conjunction with CVPR 2023. The target of this challenge is converting grayscale videos into color videos with better colorization performance and temporal consistency. The challenge consists of two tracks. For Track 1, the goal is achieving the best FID (Fréchet Inception Distance) while being constrained to maintain or improve over the baseline method in terms of the temporal-consistency metric. The Color Distribution Consistency (CDC) index is used as the temporal consistency evaluation metric in this challenge. For Track 2, the target is to obtain a solution with the best CDC result while being constrained to maintain or improve over the baseline method in terms of FID. We use DeOldify-video as the baseline method for two tracks. For the final testing phase of both tracks, six teams submitted fact sheets and executable code of their solutions. This report brings together descriptions and discussions of all these solutions. Both tracks use the same data and the datasets are available at this url.
NTIRE 2023 Challenge on 360° Omnidirectional Image and Video Super-Resolution: Datasets, Methods and Results

This report introduces two high-quality datasets Flickr360 and ODV360 for omnidirectional image and video super-resolution, respectively, and reports the NTIRE 2023 challenge on 360° omnidirectional image and video super-resolution. Unlike ordinary 2D images/videos with a narrow field of view, omnidirectional images/videos can represent the whole scene from all directions in one shot. There exists a large gap between omnidirectional image/video and ordinary 2D image/video in both the degradation and restoration processes. The challenge is held to facilitate the development of omnidirectional image/video super-resolution by considering their special characteristics. In this challenge, two tracks are provided: one is the omnidirectional image super-resolution and the other is the omnidirectional video super-resolution. The task of the challenge is to super-resolve an input omnidirectional image/video with a magnification factor of ×4. Realistic omnidirectional downsampling is applied to construct the datasets. Some general degradation(e.g., video compression) is also considered for the video track. The challenge has 100 and 56 registered participants for those two tracks. In the final testing stage, 7 and 3 participating teams submitted their results, source codes, and fact sheets. Almost all teams achieved better performance than baseline models by integrating omnidirectional characteristics, reaching compelling performance on our newly collected Flickr360 and ODV360 datasets.
MIC: Masked Image Consistency for Context-Enhanced Domain Adaptation

In unsupervised domain adaptation (UDA), a model trained on source data (e.g. synthetic) is adapted to target data (e.g. real-world) without access to target annotation. Most previous UDA methods struggle with classes that have a similar visual appearance on the target domain as no ground truth is available to learn the slight appearance differences. To address this problem, we propose a Masked Image Consistency (MIC) module to enhance UDA by learning spatial context relations of the target domain as additional clues for robust visual recognition. MIC enforces the consistency between predictions of masked target images, where random patches are withheld, and pseudo-labels that are generated based on the complete image by an exponential moving average teacher. To minimize the consistency loss, the network has to learn to infer the predictions of the masked regions from their context. Due to its simple and universal concept, MIC can be integrated into various UDA methods across different visual recognition tasks such as image classification, semantic segmentation, and object detection. MIC significantly improves the state-of-the-art performance across the different recognition tasks for synthetic-to-real, day-to-nighttime, and clear-toadverse-weather UDA. For instance, MIC achieves an un-precedented UDA performance of 75.9 mIoU and 92.8% on GTA→Cityscapes and VisDA-2017, respectively, which corresponds to an improvement of +2.1 and +3.0 percent points over the previous state of the art. The implementation is available at https://github.com/lhoyer/MIC.
VideoMAE V2: Scaling Video Masked Autoencoders with Dual Masking

Scale is the primary factor for building a powerful foundation model that could well generalize to a variety of downstream tasks. However, it is still challenging to train video foundation models with billions of parameters. This paper shows that video masked autoencoder (VideoMAE) is a scalable and general self-supervised pre-trainer for building video foundation models. We scale the VideoMAE in both model and data with a core design. Specifically, we present a dual masking strategy for efficient pre-training, with an encoder operating on a subset of video tokens and a decoder processing another subset of video tokens. Although VideoMAE is very efficient due to high masking ratio in encoder, masking decoder can still further reduce the overall computational cost. This enables the efficient pre-training of billion-level models in video. We also use a progressive training paradigm that involves an initial pre-training on a diverse multi-sourced unlabeled dataset, followed by a post-pre-training on a mixed labeled dataset. Finally, we successfully train a video ViT model with a billion parameters, which achieves a new state-of-the-art performance on the datasets of Kinetics (90.0% on K400 and 89.9% on K600) and Something-Something (68.7% on V1 and 77.0% on V2). In addition, we extensively verify the pre-trained video ViT models on a variety of downstream tasks, demonstrating its effectiveness as a general video representation learner.
GALIP: Generative Adversarial CLIPs for Text-to-Image Synthesis

Synthesizing high-fidelity complex images from text is challenging. Based on large pretraining, the autoregressive and diffusion models can synthesize photo-realistic images. Although these large models have shown notable progress, there remain three flaws. 1) These models require tremendous training data and parameters to achieve good performance. 2) The multi-step generation design slows the image synthesis process heavily. 3) The synthesized visual features are challenging to control and require delicately designed prompts. To enable high-quality, efficient, fast, and controllable text-to-image synthesis, we propose Generative Adversarial CLIPs, namely GALIP. GALIP leverages the powerful pretrained CLIP model both in the discriminator and generator. Specifically, we propose a CLIP-based discriminator. The complex scene understanding ability of CLIP enables the discriminator to accurately assess the image quality. Furthermore, we propose a CLIP-empowered generator that induces the visual concepts from CLIP through bridge features and prompts. The CLIP-integrated generator and discriminator boost training efficiency, and as a result, our model only requires about 3% training data and 6% learnable parameters, achieving comparable results to large pretrained autoregressive and diffusion models. Moreover, our model achieves ~120×faster synthesis speed and inherits the smooth latent space from GAN. The extensive experimental results demonstrate the excellent performance of our GALIP. Code is available at https://github.com/tobran/GALIP.
V2V4Real: A Real-World Large-Scale Dataset for Vehicle-to-Vehicle Cooperative Perception

Modern perception systems of autonomous vehicles are known to be sensitive to occlusions and lack the capability of long perceiving range. It has been one of the key bottlenecks that prevents Level 5 autonomy. Recent research has demonstrated that the Vehicle-to-Vehicle (V2V) cooperative perception system has great potential to revolutionize the autonomous driving industry. However, the lack of a real-world dataset hinders the progress of this field. To facilitate the development of cooperative perception, we present V2V4Real, the first large-scale real-world multi-modal dataset for V2V perception. The data is collected by two vehicles equipped with multi-modal sensors driving together through diverse scenarios. Our V2V4Real dataset covers a driving area of 410 km, comprising 20K LiDAR frames, 40K RGB frames, 240K annotated 3D bounding boxes for 5 classes, and HDMaps that cover all the driving routes. V2V4Real introduces three perception tasks, including cooperative 3D object detection, cooperative 3D object tracking, and Sim2Real domain adaptation for cooperative perception. We provide comprehensive benchmarks of recent cooperative perception algorithms on three tasks. The V2V4Real dataset can be found at research.seas.ucla.edu/mobility-lab/v2v4real/.
Towards resilience in Industry 5.0: A decentralized autonomous manufacturing paradigm

Manufacturers are increasingly aware of the importance of system resilience against unexpected disruptive occurrences, such as the recent global Covid-19 pandemic and geopolitical wars in Europe. Meanwhile, Decentralized Autonomous Organization (DAO) is recently envisioned as the Blockchain 3.0 stage, in which enabling DAO in the manufacturing domain could be a promising attempt and will lead to decentralized autonomous manufacturing. Blockchain-enabled smart contracts and decentralized applications have the characteristics of verifiability, decentralization, transparency, autonomy, and tamper-proofing, which can enable a mass individualization paradigm to realize the promising Industry 5.0 vision of resilience. DAO provides a probable manner to regulate cross-prosumer activities under the Industry 5.0 context. Inspired by this vision, our paper reviews the literature on Decentralized Manufacturing (DM) and Autonomous Manufacturing (AM). Then, these two streams of efforts are unified, and a manufacturing paradigm, named Decentralized Autonomous Manufacturing (DAM), is defined towards resilience in Industry 5.0. In this paper, a reference architecture of the DAM is given. Followed by a comprehensive investigation of the key enablers, challenges, and barriers in the implementation of DAM, based on the insights from this analysis, future research directions of DAM are highlighted. We believe that our effort can lay a foundation for positioning DAM in futuristic Industry 5.0 research and engineering practice.
Image encryption for offshore wind power based on 2D-LCLM and Zhou Yi eight trigrams

Offshore wind power is an important part of the new power system. Due to the complex and changing situation in the oceans, its normal operation and maintenance cannot be done without information such as images; therefore, it is especially important to transmit the correct image in the process of information transmission. In this paper, we propose a new encryption algorithm for offshore wind power based on two-dimensional lagged complex logistic mapping (2D-LCLM) and Zhou Yi eight trigrams. Firstly, the initial value of the 2D-LCLM is constructed by the Sha-256 to associate the 2D-LCLM with the plaintext. Secondly, a new encryption rule is proposed from the Zhou Yi Eight Trigrams to obfuscate the pixel values and generate the round key. Then, 2D-LCLM is combined with the Zigzag to form an S-box. Finally, the simulation experiment of the algorithm is accomplished. The experimental results demonstrate that the algorithm is resistant to common attacks and has prefect encryption performance.
Human-like systematic generalization through a meta-learning neural network

The power of human language and thought arises from systematic compositionality-the algebraic ability to understand and produce novel combinations from known components. Fodor and Pylyshyn1 famously argued that artificial neural networks lack this capacity and are therefore not viable models of the mind. Neural networks have advanced considerably in the years since, yet the systematicity challenge persists. Here we successfully address Fodor and Pylyshyn's challenge by providing evidence that neural networks can achieve human-like systematicity when optimized for their compositional skills. To do so, we introduce the meta-learning for compositionality (MLC) approach for guiding training through a dynamic stream of compositional tasks. To compare humans and machines, we conducted human behavioural experiments using an instruction learning paradigm. After considering seven different models, we found that, in contrast to perfectly systematic but rigid probabilistic symbolic models, and perfectly flexible but unsystematic neural networks, only MLC achieves both the systematicity and flexibility needed for human-like generalization. MLC also advances the compositional skills of machine learning systems in several systematic generalization benchmarks. Our results show how a standard neural network architecture, optimized for its compositional skills, can mimic human systematic generalization in a head-to-head comparison.
Digital Image Forgery Detection

With the growing challenges in authenticity and integrity of images, image manipulation has crumbled assurance over digital image. The major motivation of the forgery in image is manipulating the image in such a way that it cannot be distinguished to the naked eye. Image manipulation has increased the demand to assess the trustworthiness of digital images when used in crime investigation, as witness of law and for surveillance purposes. In this paper, various types of image forgery and detection techniques have been explained. Initially different kinds of forgery attacks are categorized and summary of passive approach is discussed
What the Language You Tweet Says About Your Occupation

Many aspects of people's lives are proven to be deeply connected to their jobs. In this paper, we first investigate the distinct characteristics of major occupation categories based on tweets. From multiple social media platforms, we gather several types of user information. From users' LinkedIn webpages, we learn their proficiencies. To overcome the ambiguity of self-reported information, a soft clustering approach is applied to extract occupations from crowd-sourced data. Eight job categories are extracted, including Marketing, Administrator, Start-up, Editor, Software Engineer, Public Relation, Office Clerk, and Designer. Meanwhile, users' posts on Twitter provide cues for understanding their linguistic styles, interests, and personalities. Our results suggest that people of different jobs have unique tendencies in certain language styles and interests. Our results also clearly reveal distinctive levels in terms of Big Five Traits for different jobs. Finally, a classifier is built to predict job types based on the features extracted from tweets. A high accuracy indicates a strong discrimination power of language features for job prediction task.
Photon-pair source working in a silicon-based detector wavelength range using tapered micro/nanofibers

The development of quantum photonic information technology demands high-quality photon sources. Here we demonstrate a low-noise and high-speed photon source generated by the spontaneous four-wave mixing process in a micro/nanofiber (MNF). The pair generation in a MNF is tailorable by controlling its diameter and designed for creating signal and idler photons in the silicon-based detector wavelength range, yielding high detection efficiency and coincidence count rate. This MNF photon source can be coupled to other fiber systems with negligible coupling loss and can be efficiently exploited as fiber-based quantum light sources for quantum information applications.
Impact of Prior Knowledge and Data Correlation on Privacy Leakage: A Unified Analysis

It has been widely understood that differential privacy can guarantee rigorous privacy against adversaries with arbitrary prior knowledge. However, recent studies demonstrate that this may not be true for correlated data, and indicate that three factors could influence privacy leakage: the data correlation pattern, prior knowledge of adversaries, and sensitivity of the query function. This poses a fundamental problem: what is the mathematical relationship between the three factors and privacy leakage? In this paper, we present a unified analysis of this problem. A new privacy definition, named prior differential privacy (PDP), is proposed to evaluate privacy leakage considering the exact prior knowledge possessed by the adversary. We use two models, the weighted hierarchical graph and the multivariate Gaussian model, to analyze discrete and continuous data, respectively. We demonstrate that positive, negative, and hybrid correlations have distinct impacts on privacy leakage. Considering general correlations, a closed-form expression of privacy leakage is derived for continuous data, and a chain rule is presented for discrete data. Our results are valid for general linear queries, including count, sum, mean, and histogram. Numerical experiments are presented to verify our theoretical analysis.
Classifying White Blood Cells Using Machine Learning Algorithms

Blood and its components have an important place in human life and are the best indicator tool in determining many pathological conditions. In particular, the classification of white blood cells is of great importance for the diagnosis of hematological diseases. In this study, 350 microscopic blood smear images were tested with 6 different machine learning algorithms for the classification of white blood cells and their performances were compared. 35 different geometric and statistical (texture) features have been extracted from blood images for training and test parameters of machine learning algorithms. According to the results, the Multinomial Logistic Regression (MLR) algorithm performed better than the other methods with an average 95% test success. The MLR can be used for automatic classification of white blood cells. It can be used especially as a source for diagnosis of diseases for hematologists and internal medicine specialists.
The Machine as Artist as Myth

The essay proposes an art–historical contextualisation of the notion of the “machine as artist”. It argues that the art–theoretical tropes raised by current speculations on artworks created by autonomous technical systems have been inherent to debates on modern and postmodern art throughout the 20th century. Moreover, the author suggests that the notion of the machine derives from a mythological narrative in which humans and technical systems are rigidly figured as both proximate and antagonistic. The essay develops a critical perspective onto this ideological formation and elucidates its critique in a discussion of a recent series of artworks and a text by US American artist Trevor Paglen.