Reviewerly/exHarmony huggingface dataset:
Domain-Aware Universal Style Transfer

Style transfer aims to reproduce content images with the styles from reference images. Existing universal style transfer methods successfully deliver arbitrary styles to original images either in an artistic or a photo-realistic way. However, the range of "arbitrary style" defined by existing works is bounded in the particular domain due to their structural limitation. Specifically, the degrees of content preservation and stylization are established according to a predefined target domain. As a result, both photo-realistic and artistic models have difficulty in performing the desired style transfer for the other domain. To overcome this limitation, we propose a unified architecture, Domain-aware Style Transfer Networks (DSTN) that transfer not only the style but also the property of domain (i.e., domainness) from a given reference image. To this end, we design a novel domainness indicator that captures the domainness value from the texture and structural features of reference images. Moreover, we introduce a unified framework with domainaware skip connection to adaptively transfer the stroke and palette to the input contents guided by the domainness indicator. Our extensive experiments validate that our model produces better qualitative results and outperforms previous methods in terms of proxy metrics on both artistic and photo-realistic stylizations. All codes and pre-trained weights are available at Kibeom-Hong/Domain-Aware-Style-Transfer.
Causal Attention for Unbiased Visual Recognition

Attention module does not always help deep models learn causal features that are robust in any confounding context, e.g., a foreground object feature is invariant to different backgrounds. This is because the confounders trick the attention to capture spurious correlations that benefit the prediction when the training and testing data are IID (identical & independent distribution); while harm the prediction when the data are OOD (out-of-distribution). The sole fundamental solution to learn causal attention is by causal intervention, which requires additional annotations of the confounders, e.g., a "dog" model is learned within "grass+dog" and "road+dog" respectively, so the "grass" and "road" contexts will no longer confound the "dog" recognition. However, such annotation is not only prohibitively expensive, but also inherently problematic, as the confounders are elusive in nature. In this paper, we propose a causal attention module (CaaM) that self-annotates the confounders in unsupervised fashion. In particular, multiple CaaMs can be stacked and integrated in conventional attention CNN and self-attention Vision Transformer. In OOD settings, deep models with CaaM outperform those without it significantly; even in IID settings, the attention localization is also improved by CaaM, showing a great potential in applications that require robust visual saliency. Codes are available at https://github.com/Wangt-CN/CaaM.
UAV path optimization with an integrated cost assessment model considering third-party risks in metropolitan environments

Various applications of unmanned aerial vehicles (UAVs) in urban environments facilitate our daily life and public services. However, UAV operations bring third party risk (TPR) issues, as UAV may crash to pedestrians and vehicles on the ground. It may also cause property damages to critical infrastructures and noise impacts to the public. Path planning is an effective method to mitigate these risks and impacts by avoiding high-risk areas before flight. However, most of the existing path planning methods focus on minimizing flight distance or energy cost, rarely considered risk cost. This paper develops a novel flight path optimization method that considers an integrated cost assessment model. The assessment model incorporates fatality risk, property damage risk, and noise impact, which is an extension of current TPR indicators at modeling and assessment levels. To solve the proposed integrated cost-based path optimization problem, a hybrid estimation of distribution algorithm (EDA) and CostA* (named as EDA-CostA*) algorithm is proposed, which provides both global and local heuristic information for path searching in cost-based environments. A downtown area in Singapore is selected for the case study. Simulation results demonstrate the effectiveness of the developed cost-based path optimization model in reducing the risk cost. The statistical analysis for 100 sampled environments also shows the reliability of the proposed method, which reduced the cost by [42.64%, 44.15%] at 95% confidence level.
Labels4Free: Unsupervised Segmentation using StyleGAN

We propose an unsupervised segmentation framework for StyleGAN generated objects. We build on two main observations. First, the features generated by StyleGAN hold valuable information that can be utilized towards training segmentation networks. Second, the foreground and background can often be treated to be largely independent and be swapped across images to produce plausible composited images. For our solution, we propose to augment the StyleGAN2 generator architecture with a segmentation branch and to split the generator into a foreground and background network. This enables us to generate soft segmentation masks for the foreground object in an unsupervised fashion. On multiple object classes, we report comparable results against state-of-the-art supervised segmentation networks, while against the best unsupervised segmentation approach we demonstrate a clear improvement, both in qualitative and quantitative metricsProject Page : https:/rameenabdal.github.io/Labels4Free
Glitch epistemologies for computational cities

This intervention advances glitches as epistemological vectors for apprehending and engaging the significance of digitally-mediated spatialities that appear nonperformative against normative scripts of urban computational paradigms. Drawing on two strands of contemporary thinking about glitches as systemic design features of digital systems and as generative fissures within them, we mobilize a queer orientation that stays with the generative tensions of urban spatialities that present as idiosyncratic and as interrupting. We mobilize this epistemological approach through illustrative U.S. based examples of seemingly abandoned shared e-bikes, performatively ‘ugly’ homes, and wilful property dilapidation wrought through the registers of desire and aesthetics. In so doing, we show how glitch empistemologies render visible how the technocapitalist manufacturing of normative spatial desires for particular kinds of urban sociospatialities and aesthetic visual signatures are both secured and interrupted on digitally-mediated and -mediatized terrains. Glitch epistemologies establish the significance of small-scale disorientations in digital urban mediations, engaging these nonperformativities and non-computes as unexceptional openings onto everyday possibilities for politics in computational cities.
ColloSSL

A major bottleneck in training robust Human-Activity Recognition models (HAR) is the need for large-scale labeled sensor datasets. Because labeling large amounts of sensor data is an expensive task, unsupervised and semi-supervised learning techniques have emerged that can learn good features from the data without requiring any labels. In this paper, we extend this line of research and present a novel technique called Collaborative Self-Supervised Learning (ColloSSL) which leverages unlabeled data collected from multiple devices worn by a user to learn high-quality features of the data. A key insight that underpins the design of ColloSSL is that unlabeled sensor datasets simultaneously captured by multiple devices can be viewed as natural transformations of each other, and leveraged to generate a supervisory signal for representation learning. We present three technical innovations to extend conventional self-supervised learning algorithms to a multi-device setting: a Device Selection approach which selects positive and negative devices to enable contrastive learning, a Contrastive Sampling algorithm which samples positive and negative examples in a multi-device setting, and a loss function called Multi-view Contrastive Loss which extends standard contrastive loss to a multi-device setting. Our experimental results on three multi-device datasets show that ColloSSL outperforms both fully-supervised and semi-supervised learning techniques in majority of the experiment settings, resulting in an absolute increase of upto 7.9% in F1 score compared to the best performing baselines. We also show that ColloSSL outperforms the fully-supervised methods in a low-data regime, by just using one-tenth of the available labeled data in the best case.
Three-dimensional path planning for autonomous underwater vehicles based on a whale optimization algorithm

Due to the influence of the complex underwater environment, safety, smoothness, real-time requirements and poor integrity, the path of autonomous underwater vehicles is mainly determined by perception, decision-making and control systems. These not only achieve object detection and environmental investigation but also ensure the safety and reliability of autonomous navigation systems. This paper proposes a whale optimization algorithm based on forward-looking sonar to tackle the three-dimensional path planning of autonomous underwater vehicles. The purpose of path planning is to effectively avoid threatened regions and obtain the shortest and safest paths with minimal fuel and time. The whale optimization algorithm based upon bubble-net hunting behavior imitates the contraction surrounding mechanism, logarithmic spiral position updating mechanism and stochastic searching mechanism to effectively solve the complex problem in the solution space. The whale optimization algorithm not only avoids premature convergence and avoids falling into the local optimum to achieve the global optimal solution but also utilizes exploration and exploitation to enhance the convergence speed and calculation accuracy. To verify the superiority and overall optimization performance of the whale optimization algorithm, the optimization results of the algorithm are compared with other algorithms by minimizing the fitness value. The experimental results reveal that the whale optimization algorithm has better planning efficiency, shorter execution time, faster convergence speed and higher solution precision, which is effective and efficient for path planning.
Mapping the rapid development of photovoltaic power stations in northwestern China using remote sensing

Many leading countries are boosting renewables, especially solar energy, as a major way to mitigate future energy crises and climate change. Particularly, in China, the number and scale of photovoltaic (PV) power stations have grown unprecedentedly in the last decade. There is an urgent need to monitor the PV power development in order to accurately estimate national renewable potentials and understand the ecological impacts. However, there are few efforts towards providing spatially explicit and time-series datasets of PV development at the regional and national scales. To fill the gap, this study proposes an integrated remote sensing approach for PV power stations mapping by combining image segmentation and object-based classification (ISOC) techniques. We took five northwestern provinces of China as an illustration and produced 30-m medium-resolution PV power station distribution maps from 2007 to 2019. Our analysis shows that the total area of PV power stations in the five provinces increased to 722 km 2 in 2019, with producer, user and overall accuracies of 86%, 100% and 93%. Of the 309 PV station clusters (hereafter, PV parks), the top 7% largest ones account for 61% of the total area of PV power stations, indicating that PV power stations in the Northwest tend to be developed in the form of large-scale centralized PV parks. The land used for PV power stations was mainly converted from four land cover types: Gobi Desert, sandy land, sparse grassland, and moderate grassland. The central government policy on facilitating clean energy played a major role in driving the rapid expansion of PV parks across the country. The methodology and results of this study will help policymakers, researchers, and practitioners to develop corresponding industrial standards and environmental regulations to mitigate the potential environmental impacts, and promote more sustainable development of solar energy in China and beyond.
Privacy-Preserving Federated Learning for Internet of Medical Things Under Edge Computing

Edge intelligent computing is widely used in the fields, such as the Internet of Medical Things (IoMT), which has advantages, including high data processing efficiency, strong real-time performance and low network delay. However, there are many problems including privacy disclosure, limited calculation force, as well as scheduling and coordination issues. Federated learning can greatly improves training efficiency. However, due to the sensitive nature of the healthcare data, the aforementioned approach of transferring the patient's data to the servers may create serious security and privacy issues. Therefore, this article proposes a Privacy Protection Scheme for Federated Learning under Edge Computing (PPFLEC). First of all, we propose a lightweight privacy protection protocol based on a shared secret and weight mask, which is based on a random mask scheme of secret sharing. It is more accurate and efficient than,homomorphic encryption. It can not only protect gradient privacy without losing model accuracy, but also resist equipment dropping and collusion attacks between devices. Second, we design an algorithm based on a digital signature and hash function, which achieves the integrity and consistency of the message, as well as resisting replay attacks. Finally, we propose a periodic average training strategy, compared with differential privacy to prove that our scheme is 40 % faster in efficiency than in deferential privacy. Meanwhile, compared with federated learning, we can achieve the same efficiency under the condition of ensuring safety. Therefore, our scheme can work well in unstable edge computing environments such as smart healthcare.
Underwater image enhancement method with light scattering characteristics

Light is absorbed and scattered when propagating in water, which results in low quality and poor visibility of underwater optical images. Furthermore, the absorption of light by water causes color distortion, whereas the scattering of light by small particles suspended in water results in low image contrast. To enhance the underwater image quality, we developed a method based on light scattering characteristics. Firstly, we group the color cast into five categories according to the proportion of the average of the RGB channels. Then, we use the optical attenuation characteristics to calculate the color loss rate of the RGB channels of underwater images in different scenes and develop a multi-scene color restoration method to correct the color cast of underwater images. While keeping the color constant, we set a 64-block multi-contrast factor histogram stretching to enhance the contrast of the underwater image. The experimental results verify that the developed method has achieved image quality improvement via qualitative and quantitative evaluation. Our approach is proved to effectively improve the quality of underwater images with color distortion, low contrast, and detail loss.
Feature optimization and identification of ovarian cancer using internet of medical things

Abstract Ovarian cancer (OC) is one kind of tumour that impacts women's ovaries and is hard to diagnose in the initial phase as a primary cause of cancer death. The ovarian cancer information generated by the Clinical Network has been used, and the Self Organizing Map (SOM) and Optimized Neural Networks have suggested a new method for the distinction between ovarian cancer and remaining cancer. Feature optimization and identification of the ovarian cancer (FOI‐OV) framework are proposed in this research. The SOM algorithm has also been used separately to improve the functional subset, with understandable and intriguing information from participants' health information steps. The SOM‐based collection appears to be tolerable in guided learning strategies due to the lack of different classifiers, which would direct the quest for knowledge specific to the classification algorithm. The classification technique will classify data from ovarian cancer as benign/malignant. By optimizing Neural Network configuration, Advanced Harmony Searching Optimization (AHSO) can enhance the ovarian cancer detection method compared with other methods. This research's suggested model can also diagnose cancer with high precision, and low root means square error (RMSE) early. With 94% precision and 0.029%, RMSE, SOM, and NN techniques have shown identification and precision in ovarian cancer. Optimization (AHSO) has provided an efficient classification approach with a better failure rate.
Eye gaze estimation: A survey on deep learning-based approaches

Human gaze estimation plays a major role in many applications in human–computer interaction and computer vision by identifying the users’ point-of-interest. Revolutionary developments of deep learning have captured significant attention in gaze estimation literature. Gaze estimation techniques have progressed from single-user constrained environments to multi-user unconstrained environments with the applicability of deep learning techniques in complex unconstrained environments with extensive variations. This paper presents a comprehensive survey of the single-user and multi-user gaze estimation approaches with deep learning. State-of-the-art approaches are analyzed based on deep learning model architectures, coordinate systems, environmental constraints, datasets and performance evaluation metrics. A key outcome from this survey realizes the limitations, challenges and future directions of multi-user gaze estimation techniques. Furthermore, this paper serves as a reference point and a guideline for future multi-user gaze estimation research.
AnoViT: Unsupervised Anomaly Detection and Localization With Vision Transformer-Based Encoder-Decoder

Image anomaly detection problems aim to determine whether an image is abnormal, and to detect anomalous areas. These methods are actively used in various fields such as manufacturing, medical care, and intelligent information. Encoder-decoder structures have been widely used in the field of anomaly detection because they can easily learn normal patterns in an unsupervised learning environment and calculate a score to identify abnormalities through a reconstruction error indicating the difference between input and reconstructed images. Therefore, current image anomaly detection methods have commonly used convolutional encoder-decoders to extract normal information through the local features of images. However, they are limited in that only local features of the image can be utilized when constructing a normal representation owing to the characteristics of convolution operations using a filter of fixed size. Therefore, we propose a vision transformer-based encoder-decoder model, named AnoViT, designed to reflect normal information by additionally learning the global relationship between image patches, which is capable of both image anomaly detection and localization. While existing vision transformers perform image classification using only a class token, the proposed approach constructs a feature map that maintains the existing location information of individual patches by using the embeddings of all patches passed through multiple self-attention layers. Subsequently, the feature map, which has been transformed into three dimensions, is used to perform decoding. This design preserves the spatial information sufficiently by excluding the fully-connected layer, which extracts latent vectors in existing convolution-based encoder-decoders. The proposed AnoViT model performed better than the convolution-based model on three benchmark datasets. In MVTecAD, which is a representative benchmark dataset for anomaly localization, it showed improved results on 10 out of 15 classes compared with the baseline. Furthermore, the proposed method showed good performance regardless of the class and type of the anomalous area when localization results were evaluated qualitatively.
Develop New Algorithm To Improve Safety On WMSN In Health Disease Monitoring

The WSN was already commonly used in many applications because of the rapid growth of wireless communication networks including semiconductor technology. The basic elements of healthcare monitoring were Wireless Medical Sensor Networks (WMSNs). The WMSN, as a WSN application, has grown in importance in the current health industry and also can enhance value. Physiological information is retrieved by devices implanted in the body of the patient as well as wirelessly transmitted to healthcare providers’ portable devices in the WMSN. The person’s condition might then be obtained by health experts at any time and any place. This study identifies the much more difficult security issues in contemporary remote patient monitoring authentication methods as well as proposed a light general populace authentication protocol for MSNs. The networks of MSN were divided into the detectors, which provide measures about the person’s body, as well as actuation, which accept instructions from health workers as well as act in a certain way. Authenticating those instructions would be a crucial safety concern, as any change could have severe repercussions. The suggested method was implemented on the enhanced Rabin-Karp authentication algorithm, which has been tweaked in this study to enhance the signatory verification procedure, making it acceptable to delay-sensitive MSN systems. To demonstrate the enhanced Rabin-Karp method’s effectiveness, researchers used Tmote Sky motes to construct the method with various device configurations and also coded the method on an Field Programmable Gate Arrays (FPGA) to examine its quality and construction. The findings show that medical, health doctors secure, direct, instant, and authenticated instructions to MSN nodes. The suggested method outperforms current protocols in terms of safety as well as systems were working. As a result, it’s better suited to WMSN-based healthcare applications.