Reviewerly/exHarmony huggingface dataset:
An Effectual Underwater Image Enhancement using Deep Learning Algorithm

Digital image processing domain is growing day-by-day by introducing novel technologies to provide assistance for several applications such as robotic activities, underwater network formation, and so on. In particular, underwater image processing is considered as the crucial task in image processing industry due to the flow of light waves that are not in the specific and expected range under the water level. While image restoration technology can adequately consider removing this same haze from source images, they need to obtain several images from a certain place that prevent it from being used in a real-time system. To overcome this issue, a deep study approach is developed by providing excellent outcomes of deep learning approaches in several other image analysis concerns such as colorizing images or object identification. A convolution neural network (CNN) model is trained to de-haze the individual images with image restoration in order to perform further with an image improvement. The proposed approach can produce images with image restoration quality by including a standard image input and here, the neural network is evaluated by using images and features, which are obtained from separate areas to prove its capacity to generalize. The efficiency of the proposed approach is high when compared to other existing methods.
Unbalanced Feature Transport for Exemplar-based Image Translation

Despite the great success of GANs in images translation with different conditioned inputs such as semantic segmentation and edge maps, generating high-fidelity realistic images with reference styles remains a grand challenge in conditional image-to-image translation. This paper presents a general image translation framework that incorporates optimal transport for feature alignment between conditional inputs and style exemplars in image translation. The introduction of optimal transport mitigates the constraint of many-to-one feature matching significantly while building up accurate semantic correspondences between conditional inputs and exemplars. We design a novel unbalanced optimal transport to address the transport between features with deviational distributions which exists widely between conditional inputs and exemplars. In addition, we design a semantic-activation normalization scheme that injects style features of exemplars into the image translation process successfully. Extensive experiments over multiple image translation tasks show that our method achieves superior image translation qualitatively and quantitatively as compared with the state-of-the-art.
Multi-Regularized Correlation Filter for UAV Tracking and Self-Localization

As a sort of model-free tracking approach, discriminative correlation filter (DCF)-based trackers have shown prominent performance in unmanned aerial vehicle (UAV) tracking. Nevertheless, typical DCFs acquire all samples oriented to filter training merely from the current frame by cyclic shift operation in the spatial domain but ignore the consistency between samples across the timeline. The lack of temporal cues restricts the performance of DCFs under object appearance variations arising from object/UAV motion, scale variations, and viewpoint changes. Besides, many existing methods commonly neglect the channel discrepancy in object position estimation and generally treat all channels equally, thus limiting the further promotion of the tracking discriminability. To these concerns, this work proposes a novel tracking approach based on a multi-regularized correlation filter (MRCF), i.e., MRCF tracker. By regularizing the deviation of responses and the reliability of channels, the tracker enables smooth response variations and adaptive channel weight distributions simultaneously, leading to favorable adaption to object appearance variations and enhancement of discriminability. Exhaustive experiments on five authoritative UAV-specific benchmarks validate the competitiveness and efficiency of MRCF against top-ranked trackers. Furthermore, we apply our proposed tracker to monocular UAV self-localization under air–ground robot coordination. Evaluations indicate the practicability of the presented method in UAV localization applications.
Universal Weighting Metric Learning for Cross-Modal Retrieval

Cross-modal retrieval has recently attracted growing attention, which aims to match instances captured from different modalities. The performance of cross-modal retrieval methods heavily relies on the capability of metric learning to mine and weight the informative pairs. While various metric learning methods have been developed for unimodal retrieval tasks, the cross-modal retrieval tasks, however, have not been explored to its fullest extent. In this paper, we develop a universal weighting metric learning framework for cross-modal retrieval, which can effectively sample informative pairs and assign proper weight values to them based on their similarity scores so that different pairs favor different penalty strength. Based on this framework, we introduce two types of polynomial loss for cross-modal retrieval, self-similarity polynomial loss and relative-similarity polynomial loss. The former provides a polynomial function to associate the weight values with self-similarity scores, and the latter defines a polynomial function to associate the weight values with relative-similarity scores. Both self and relative-similarity polynomial loss can be freely applied to off-the-shelf methods and further improve their retrieval performance. Extensive experiments on two image-text retrieval datasets, three video-text retrieval datasets and one fine-grained image retrieval dataset demonstrate that our proposed method can achieve a noticeable boost in retrieval performance.
Capturing Omni-Range Context for Omnidirectional Segmentation

Convolutional Networks (ConvNets) excel at semantic segmentation and have become a vital component for perception in autonomous driving. Enabling an all-encompassing view of street-scenes, omnidirectional cameras present themselves as a perfect fit in such systems. Most segmentation models for parsing urban environments operate on common, narrow Field of View (FoV) images. Transferring these models from the domain they were designed for to 360° perception, their performance drops dramatically, e.g., by an absolute 30.0% (mIoU) on established test-beds. To bridge the gap in terms of FoV and structural distribution between the imaging domains, we introduce Efficient Concurrent Attention Networks (ECANets), directly capturing the inherent long-range dependencies in omnidirectional imagery. In addition to the learned attention-based contextual priors that can stretch across 360° images, we upgrade model training by leveraging multi-source and omni-supervised learning, taking advantage of both: Densely labeled and unlabeled data originating from multiple datasets. To foster progress in panoramic image segmentation, we put forward and extensively evaluate models on Wild PAnoramic Semantic Segmentation (WildPASS), a dataset designed to capture diverse scenes from all around the globe. Our novel model, training regimen and multi-source prediction fusion elevate the performance (mIoU) to new state-of-the-art results on the public PASS (60.2%) and the fresh WildPASS (69.0%) benchmarks. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>
Multi-shot Temporal Event Localization: a Benchmark

Current developments in temporal event or action localization usually target actions captured by a single camera. However, extensive events or actions in the wild may be captured as a sequence of shots by multiple cameras at different positions. In this paper, we propose a new and challenging task called multi-shot temporal event localization, and accordingly, collect a large-scale dataset called MUlti-Shot EventS (MUSES). MUSES has 31,477 event instances for a total of 716 video hours. The core nature of MUSES is the frequent shot cuts, for an average of 19 shots per instance and 176 shots per video, which induces large intra-instance variations. Our comprehensive evaluations show that the state-of-the-art method in temporal action localization only achieves an mAP of 13.1% at IoU=0.5. As a minor contribution, we present a simple baseline approach for handling the intra-instance variations, which reports an mAP of 18.9% on MUSES and 56.9% on THUMOS14 at IoU=0.5. To facilitate research in this direction, we release the dataset and the project code at https://songbai.site/muses/.
The effectiveness of smart home technologies to support the health outcomes of community-dwelling older adults living with dementia: A scoping review

Objectives: To map the current state-of-knowledge about the effectiveness of smart home technologies to support the health outcomes of community-dwelling older adults living with dementia.Design: A scoping review following the methodological frameworks described by Arksey and O'Malley (2005) and Levac et al. (2010).Data sources: Electronic databases and online sources were searched in April 2020 using database specific medical subject headings and keywords about 'smart homes' and 'dementia'.Methods: Empirical peer-reviewed articles were included if they were written in English; used a quantitative, qualitative, or mixed method design; and presented the effects of a smart home technology on the health outcomes of community-dwelling adults living with dementia.Methodological and reporting quality of studies was assessed using the Mixed Methods Appraisal Tool -Version 2018.Results: Five studies described evaluations of five smart home technology systems with a total of n = 617 community-dwelling people living with dementia.Collectively, studies showed potential effectiveness of the technologies on a range of health outcomes (physical activity, activities of daily living, sleep, anxiety, depression, agitation, irritability, risk of falls, cognitive functioning, night-time injury, unattended home exits).However, the overall methodological and reporting quality of studies was low and profiled a research field lacking in rigorous evaluation.Conclusions: Based on current evidence, the success of smart home technologies to support people with dementia to live at home remains unclear.Recommendations are provided to inform future research into smart home technologies for community-based dementia care.
Anycost GANs for Interactive Image Synthesis and Editing

Generative adversarial networks (GANs) have enabled photorealistic image synthesis and editing. However, due to the high computational cost of large-scale generators (e.g., StyleGAN2), it usually takes seconds to see the results of a single edit on edge devices, prohibiting interactive user experience. In this paper, inspired by quick preview features in modern rendering software, we propose Anycost GAN for interactive natural image editing. We train the Anycost GAN to support elastic resolutions and channels for faster image generation at versatile speeds. Running subsets of the full generator produce outputs that are perceptually similar to the full generator, making them a good proxy for quick preview. By using sampling-based multi-resolution training, adaptive-channel training, and a generator-conditioned discriminator, the anycost generator can be evaluated at various configurations while achieving better image quality compared to separately trained models. Furthermore, we develop new encoder training and latent code optimization techniques to encourage consistency between the different sub-generators during image projection. Anycost GAN can be executed at various cost budgets (up to 10× computation reduction) and adapt to a wide range of hardware and la tency requirements. When deployed on desktop CPUs and edge devices, our model can provide perceptually similar previews at 6-12× speedup, enabling interactive image editing. The ${\color{RubineRed}{code}}$ and ${\color{RubineRed}{demo}}$ are publicly available.
A Vision-based System for Traffic Anomaly Detection using Deep Learning and Decision Trees

Any intelligent traffic monitoring system must be able to detect anomalies such as traffic accidents in real time. In this paper, we propose a Decision-Tree enabled approach powered by deep learning for extracting anomalies from traffic cameras while accurately estimating the start and end times of the anomalous event. Our approach included creating a detection model, followed by anomaly detection and analysis. YOLOv5 served as the foundation for our detection model. The anomaly detection and analysis step entail traffic scene background estimation, road mask extraction, and adaptive thresholding. Candidate anomalies were passed through a decision tree to detect and analyze final anomalies. The proposed approach yielded an F1 score of 0.8571, and an S4 score of 0.5686, per the experimental validation.
Domain-Specific Suppression for Adaptive Object Detection

Domain adaptation methods face performance degradation in object detection, as the complexity of tasks require more about the transferability of the model. We propose a new perspective on how CNN models gain the transferability, viewing the weights of a model as a series of motion patterns. The directions of weights, and the gradients, can be divided into domain-specific and domain-invariant parts, and the goal of domain adaptation is to concentrate on the domain-invariant direction while eliminating the disturbance from domain-specific one. Current UDA object detection methods view the two directions as a whole while optimizing, which will cause domain-invariant direction mismatch even if the output features are perfectly aligned. In this paper, we propose the domain-specific suppression, an exemplary and generalizable constraint to the original convolution gradients in backpropagation to detach the two parts of directions and suppress the domain-specific one. We further validate our theoretical analysis and methods on several domain adaptive object detection tasks, including weather, camera configuration, and synthetic to real-world adaptation. Our experiment results show significant advance over the state-of-the-art methods in the UDA object detection field, performing a promotion of 10.2 ∼ 12.2% mAP on all these domain adaptation scenarios.
Practitioners’ Perceptions of the Goals and Visual Explanations of Defect Prediction Models

Software defect prediction models are classifiers that are constructed from historical software data. Such software defect prediction models have been proposed to help developers optimize the limited Software Quality Assurance (SQA) resources and help managers develop SQA plans. Prior studies have different goals for their defect prediction models and use different techniques for generating visual explanations of their models. Yet, it is unclear what are the practitioners' perceptions of (1) these defect prediction model goals, and (2) the model-agnostic techniques used to visualize these models. We conducted a qualitative survey to investigate practitioners' perceptions of the goals of defect prediction models and the model-agnostic techniques used to generate visual explanations of defect prediction models. We found that (1) 82%-84% of the respondents perceived that the three goals of defect prediction models are useful; (2) LIME is the most preferred technique for understanding the most important characteristics that contributed to a prediction of a file, while ANOVA/VarImp is the second most preferred technique for understanding the characteristics that are associated with software defects in the past. Our findings highlight the significance of investigating how to improve the understanding of defect prediction models and their predictions. Hence, model-agnostic techniques from explainable AI domain may help practitioners to understand defect prediction models and their predictions.
Cryptocurrency price volatility and investor attention

This study examines the relationship between the price volatility of cryptocurrencies and investor attention. Using a large dataset of approximately 25 million tweets about 23 of the largest cryptocurrencies, I show that investor attention, as proxied by the number of tweets, retweets, and favorites, corresponds to greater cryptocurrency price volatility. I use a Vector Autoregression (VAR) framework to show that investor attention predicts future price volatility. Additionally, days on which investors are "distracted" because of attention-grabbing events correspond to lower price volatility in cryptocurrency markets. The results suggest that increased investor attention to cryptocurrencies has the undesirable effect of increasing price volatility.
Toward Lightweight, Privacy-Preserving Cooperative Object Classification for Connected Autonomous Vehicles

Collaborative perception enables autonomous vehicles to exchange sensor data among each other to achieve cooperative object classification, which is considered an effective means to improve the perception accuracy of connected autonomous vehicles (CAVs). To protect information privacy in cooperative perception, we propose a lightweight, privacy-preserving cooperative object classification framework that allows CAVs to exchange raw sensor data (e.g., images captured by HD camera), without leaking private information. Leveraging chaotic encryption and additive secret sharing technique, image data are first encrypted into two ciphertexts and processed, in the encrypted format, by two separate edge servers. The use of chaotic mapping can avoid information leakage during data uploading. The encrypted images are then processed by the proposed privacy-preserving convolutional neural network (P-CNN) model embedded in the designed secure computing protocols. Finally, the processed results are combined/decrypted on the receiving vehicles to realize cooperative object classification. We formally prove the correctness and security of the proposed framework and carry out intensive experiments to evaluate its performance. The experimental results indicate that P-CNN offers exactly almost the same object classification results as the original CNN model, while offering great privacy protection of shared data and lightweight execution efficiency.
Deep Convolution Neural Network Model for Credit-Card Fraud Detection and Alert

With the exponential increase in the usage of the internet, numerous organisations, including the financial industry, have operationalized online services. The massive financial losses occur as a result of the global growth in financial fraud. Henceforth, devising advanced financial fraud detection systems can actively detect the risks such as illegal transactions and irregular attacks. Over the recent years, these issues are tackled to a larger extent by means of data mining and machine learning techniques. However, in terms of unknown attack pattern identification, big data analytics and speed computation, several improvements must be performed in these techniques. The Deep Convolution Neural Network (DCNN) scheme based financial fraud detection scheme using deep learning algorithm is proposed in this paper. When large volume of data is involved, the detection accuracy can be enhanced by using this technique. The existing machine learning models, auto-encoder model and other deep learning models are compared with the proposed model to evaluate the performance by using a real-time credit card fraud dataset. Over a time duration of 45 seconds, a detection accuracy of 99% has been obtained by using the proposed model as observed in the experimental results.
BABEL: Bodies, Action and Behavior with English Labels

Understanding the semantics of human movement – the what, how and why of the movement – is an important problem that requires datasets of human actions with semantic labels. Existing datasets take one of two approaches. Large-scale video datasets contain many action labels but do not contain ground-truth 3D human motion. Alternatively, motion-capture (mocap) datasets have precise body motions but are limited to a small number of actions. To address this, we present BABEL, a large dataset with language labels describing the actions being performed in mocap sequences. BABEL consists of language labels for over 43 hours of mocap sequences from AMASS, containing over 250 unique actions. Each action label in BABEL is precisely aligned with the duration of the corresponding action in the mocap sequence. BABELalso allows overlap of multiple actions, that may each span different durations. This results in a total of over 66000 action segments. The dense annotations can be leveraged for tasks like action recognition, temporal localization, motion synthesis, etc. To demonstrate the value of BABEL as a benchmark, we evaluate the performance of models on 3D action recognition. We demonstrate that BABEL poses interesting learning challenges that are applicable to real-world scenarios, and can serve as a useful benchmark for progress in 3D action recognition. The dataset, baseline methods, and evaluation code are available and supported for academic research purposes at https://babel.is.tue.mpg.de/.
SAND

With the increasing demand for real-time analytics and decision making, anomaly detection methods need to operate over streams of values and handle drifts in data distribution. Unfortunately, existing approaches have severe limitations: they either require prior domain knowledge or become cumbersome and expensive to use in situations with recurrent anomalies of the same type. In addition, subsequence anomaly detection methods usually require access to the entire dataset and are not able to learn and detect anomalies in streaming settings. To address these problems, we propose SAND, a novel online method suitable for domain-agnostic anomaly detection. SAND aims to detect anomalies based on their distance to a model that represents normal behavior. SAND relies on a novel steaming methodology to incrementally update such model, which adapts to distribution drifts and omits obsolete data. The experimental results on several real-world datasets demonstrate that SAND correctly identifies single and recurrent anomalies without prior knowledge of the characteristics of these anomalies. SAND outperforms by a large margin the current state-of-the-art algorithms in terms of accuracy while achieving orders of magnitude speedups.
Chatbot application in a 5th grade science course

This study aims to investigate the effect of chatbots that work with artificial intelligence on the success of students and their opinions about chatbots in the 'Matter and the changing state of matter' unit in the 5th grade science course. In addition to text-based functions, the designed chatbot includes a video accessed on the web to support students visually and aurally. The chatbot was designed using the Dialogflow program and an instant messaging program made available to students through a group created on Telegram. The study, which used a quasi-experimental pretest-posttest design, included 41 participants (n = 20 for the experimental, n = 21 for the control group) studying in the 5th grade of a state secondary school in the 2020-2021 academic year. Results suggest that although there was no significant difference between the experimental and control groups in terms of academic achievement, it was determined that the chatbot application positively affected the online learning experience of the experimental group students. Students' opinions about the chatbot included that it was useful and fun, they would like to use it for other courses, it provided useful assistance in learning outside the classroom, and it allowed them to repeat the course again. The results showed that, especially during the Covid-19 pandemic, such applications could contribute positively to students' learning.