Reviewerly/exHarmony huggingface dataset:
Unsupervised Restoration of Weather-affected Images using Deep Gaussian Process-based CycleGAN

Existing approaches for restoring weather-degraded images follow a fully-supervised paradigm and they require paired data for training. However, collecting paired data for weather degradations is extremely challenging, and existing methods end up training on synthetic data. To overcome this issue, we describe an approach for supervising deep networks that is based on CycleGAN, thereby enabling the use of unlabeled real-world data for training. Specifically, we introduce new losses for training CycleGAN that lead to more effective training, resulting in high quality reconstructions. These new losses are obtained by jointly modeling the latent space embeddings of predicted clean images and original clean images through Deep Gaussian Processes. This enables the CycleGAN architecture to transfer the knowledge from one domain (weather-degraded) to another (clean) more effectively. We demonstrate that the proposed method can be effectively applied to different restoration tasks like de-raining, de-hazing and de-snowing and it outperforms other unsupervised techniques (that leverage weather-based characteristics) by a considerable margin.
Image Dehazing using Dark Channel Prior

Windstorms, foggy winters, and sand storms generally degrade image quality and have an impact on computer vision applications, which could be a safety concern for drivers because of light dispersing and retention by dust remnant or air molecules, making the view hazy or foggy. Our method relies on the Dark Channel Prior (DCP) methodology, which improves visual contrast too. Upon quantifying the ambient light, the value is utilized for calculating transmission estimate. The transmission map is fine-tuned afterwards, and even the image is recovered. By processing locally, we simplify the refining stage and localize the refinement zone by working with the coarse dark channel. This strategy will reduce blurriness while restoring the true brilliance of distant objects. To compare we have chosen different existing methods like All-in-One Dehazing Network (AOD-net), Convolutional Neural Network (CNN), High Resolution de-hazer(HR de-hazer) and make the comparison using metrics like Peak Signal to Noise Ratio(PSNR) and Structural Similarity Index (SSIM). Not only in terms of quality parameters but we have also checked over the latency period where we found DCP is giving a valuable output. Hence, the Peak Signal to Noise Ratio value of our proposed method is 20% greater than that of existing methods.
A UAV Video Data Generation Framework for Improved Robustness of UAV Detection Methods

Recent advances have facilitated the development and popularization of Unmanned Aerial Vehicles (UAVs) that can operate semi or fully autonomously. The real-time, accurate visual detection of UAVs is crucial for various tasks and applications including surveillance (e.g., detecting UAVs flying over restricted areas such as airports) or multi-robot systems (e.g., a swarm of UAVs that need to cooperate and avoid collisions between swarm members in GPS-denied environments). The small target-to-image ratio and large similarity with other flying objects makes the visual detection of UAVs a challenging task. In addition, data distribution shifts can have a major negative impact to UAV detection frameworks, often trained on a wide variety of datasets to achieve an adequate level of robustness. As an attempt to mitigate the effect of these issues, we present a method that can generate realistic annotated video data depicting flying UAVs, using as input real background videos and 3D UAV models. The conducted experimental evaluation showed that the synthetic data are both challenging and realistic and that detectors trained on a combination of real-world and synthetic data, exhibit an improved generalization performance, achieving better precision rates when evaluated on real datasets that are visually distinct from the corresponding real training data.
Metaverse: Virtual and Augmented Reality Presence

More and more, our daily lives are entwined with technology. Our interactions with other people increasingly take place in virtual encounters. Many different concepts have appeared to describe the various virtual experiences that we might have. Recently the idea of the Metaverse has become prominent as a way to describe the digital realm in which we interact with objects, environments, and other people. But there is disagreement and confusion about what the Metaverse actually is. This review aims to bring different concepts and meanings to technologies that sometimes work in a complementary way to enable the Metaverse. Virtual Reality, Augmented Reality, and Mixed Reality, the Web or the Internet, and Blockchain or Distributed Ledger Technology are some concepts discussed and compared in this paper. After a clarification of their differences and similarities, this study also presents the concept of the Metaverse, a complex virtual world where many activities, including commerce, can take place and where avatars may be used to facilitate social experiences and make them feel more like real interactions. The methodology used in this study was a mix of Literature Review and Critical Review and the results presented show characteristics and particularities that allow a better understanding of virtual technologies and the Metaverse.
GAN-Based Medical Images Synthesis

Medical images have always occupied a very important position in modern medical diagnosis. They are standard tools for doctors to carry out clinical diagnosis. However, nowadays, most clinical diagnosis relies on the doctors' professional knowledge and personal experience, which can be easily affected by many factors. In order to reduce the diagnosis errors caused by human subjective differences and improve the accuracy and reliability of the diagnosis results, a practical and reliable method is to use artificial intelligence technology to assist computer-aided diagnosis (CAD). With the help of powerful computer storage capabilities and advanced artificial intelligence algorithms, CAD can make up for the shortcomings of traditional manual diagnosis and realize efficient, intelligent diagnosis. This paper reviews GAN-based medical image synthesis methods, introduces the basic architecture and important improvements of GAN, lists some representative application examples, and finally makes a summary and discussion.
Sonar-Based Human Leg Localization Using Chaos Enhanced Dynamic Neighborhood Learning-Based GSA Aided sNDT Algorithm

The present paper proposes a new human leg localization algorithm using ultrasonic sensors in human-robot coexisting environments. The algorithm estimates the motion of a human leg pair between two successive sonar scans by using a new <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">static cluster elimination</i> method, an <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">edge feature based leg recognition</i> algorithm and an advanced scan matching technique. We also propose a novel, robust approach to overcome <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">bad initialization</i> problem in sonar scan matching, by introducing a metaheuristic search based optimization algorithm for the sonar NDT (sNDT) method. The recently proposed dynamic neighborhood learning-based GSA (DNLGSA) has been successfully utilized in real-life scenario to solve this problem. The work also proposes a new chaos enhanced DNLGSA (CEDNLGSA) to further improve real-life performance and the proposed novel variant of the sNDT method based on CEDNLGSA, called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Chaotic Metaheuristic Search Based sNDT</i> (CMHS-sNDT), has been demonstrated to achieve superior leg detection performance in various real-life case studies, compared to different contemporary state-of-the-art methods.
A Stem to Stern Sentiment Analysis Emotion Detection

Due to the Internet's rapid expansion, social networking sites have emerged as crucial tools for sharing emotions with the entire globe. Many people share their thoughts or opinions through text, images, audio, and video On the other hand, text communication over web-based network media can be a little overwhelming. Social media web sites generate a substantial volume of unstructured data on the Internet every second. It is essential to analyze data as soon as it is generated in order to understand human psychology. Sentiment analysis, which detects polarity in texts, can help with this. It evaluates the author's attitude toward a particular thing, administration, person, or place and determines if that attitude is negative, positive, or neutral. A person's emotional/mental condition can be reliably detected using emotion detection. This article describes different levels of sentiments, emotions models, and techniques for detecting sentiments from text. The study also emphasis the challenges associated with assessing mood and emotion. Last but not least, this paper also highlights the difficulties encountered during sentiment and emotion analysis.
Boosting Facial Emotion Recognition by Using GANs to Augment Small Facial Expression Dataset

Facial expression recognition (FER) is an important research field for human-machine interfaces, and FER by deep learning from small training datasets is still challenging. This paper proposes a new generative adversarial network (GAN) model to transfer neutral face images to images with diverse facial expressions to deal with the problem of expressional data scarcity in deep learning for FER based on a small set of training samples. To mitigate distortions and overfitting that often happen in training GANs with small training datasets, a novel GAN architecture is proposed, which consists of a generator with two encoders and two decoders, two discriminators, and a feature extractor. Specifically, a feature map mechanism is proposed to discover regional feature differences between images in the source domain and target domain, which makes the proposed GAN architecture be able to not only generate desirable facial expression images but also maintain the original characters in input neutral face images. Experimental results show that, by using the proposed GAN to augment a training dataset of images with up to 7 facial expressions, the FER accuracy of several deep neural networks tested in the experiments can be significantly improved by over 10%.
A Robust De-noising Method via Training Loss for Distantly Supervised Relation Extraction

Distant supervision (DS) is widely used in relation extraction which can automatically generate large-scale training data by aligning a knowledge base with an unlabeled corpus. However, it suffers from the label noise problem. In this paper, we propose a novel explicit training loss based DS relation extraction de-noising method to generate a cleansed dataset. Specifically, we firstly design a noise detector to select noisy bags using average loss during cyclical training, which is based on the idea that the noisy samples will have different loss variation process during training compared to the clean samples. Then we propose a label corrector to generate right labels for the selected samples, which can keep more useful information to a great extent. Finally, the experimental results show that our de-noising method is robust that can detect the label noise quite well on both large scale and small scale dataset and the generated cleansed dataset significantly improves the performance of previous distant supervision models.
Real-time earthquake detection using Twitter tweets

Social networking sites have become a part of our daily lives. Due to the development of technology and usage of such networking sites, people come to know of the news alerts related to natural calamities very quickly. But the authenticity of such news needs to be taken care of. Deploying a physical sensor system in a residential area for earthquake detection is a tough and expensive solution. Hence, social network data can be used by humanitarian organisations to find help, and save lives. The primary goal of our work focus on near real-time earthquake detection.Twitter has become a potential data source to explore useful information mentioned by users. For instance, Twitter tweets related to earthquake can be used to detect temporal occurrence as well as location information by a humanitarian organisation. The proposed system will use deep learning techniques such as RNN/LSTM to check the validity of Twitter tweets and real-time detection of the given event. In this work, machine learning models trained from tweets related to the earthquake in the past labelled by crowdsourcing plays a role as the classifier to predict the validity of the tweets. The system will use Twitters API to listen to particular keyword like earthquake and take these tweets as inputs. The tweets are converted to word embeddings using BERT before giving them to the model. The proposed system can detect the earthquake after it happens in the level of tolerance and ensure earlier warning to the public than any websites.
Image Undistortion and Stereo Rectification Based on Central Ray-Pixel Models

Recently ray-pixel imaging models which work on pixels and their related incoming rays have attracted much attention in the computer vision community. Having many more parameters than traditional models, ray-pixel models are capable of describing cameras more accurately. Despite this, ray-pixel models are still rarely used in practice due to the lack of mature modules for image undistortion and stereo rectification. Perceived that optical centers can be used as the bridge between pinhole and central ray-pixel models, we thus propose algorithms for image undistortion and stereo rectification based on central ray-pixel models. Besides, we propose sector grids in the B-spline interpolation procedure, which significantly reduces the calibration time. With a focus on accuracy, we show that ray-pixel models outperform traditional models in terms of image undistortion, stereo rectification and 3D reconstruction. Our calibration pipeline has been released at https://github.com/painterdrown/raxel .
Real-time Shadow-aware Portrait Relighting in Virtual Backgrounds for Realistic Telepresence

While using virtual backgrounds has recently become a very popular feature in videoconferencing, there often exists a jarring mismatch between the lighting of the user and the illumination condition of the virtual background. Existing portrait relighting methods can alleviate the problem, but do not have the capacity to deal with difficult shadow effects. In this paper, we present a new shadow-aware portrait relighting system that can relight an input portrait to be consistent with a given desired background image with shadow effects. Our system consists of four major components: portrait neutralization, illumination estimation, shadow generation and hierarchical neural rendering, which are all based on deep neural networks, and the whole system is end-to-end trainable. In addition, we created a large-scale photorealistic synthetic dataset with shadow, illumination and depth annotations for training, which allows our model to generalize well to real images. The extensive experiments demonstrate that our shadow-aware relight system outperforms the state-of-the-art portrait relighting solutions in terms of producing more lighting-consistent relighted images with shadow effects.
A Non-Local Means Filters for Sar Speckle Reduction with Likelihood Ratio Test

SAR imagery is always accompanied by speckle because it is obtained with coherent illumination. Given the multiplica-tive property of speckle, ratio-based metrics are widely used in SAR image quality assessment. In this paper, using the hy-potheses testing principle, a likelihood ratio test (LRT) based metric was designed for similarity measure in intensity SAR imagery. Then, using non-local means (NLM) scheme, an LRT-based NLM filter was proposed for speckle reduction of SAR imagery. In the experiments, using simulated intensity speckled images, the performance of the designed method is analyzed.
Limitations of the Use of Neural Networks in Black Box Cryptanalysis

In this work, we first abstract a block cipher to a set of parallel Boolean functions. Then, we establish the conditions that allow a multilayer perceptron (MLP) neural network to correctly emulate a Boolean function. We extend these conditions to the case of any block cipher. The modeling of the block cipher is performed in a black box scenario with a set of random samples, resulting in a single secret key chosen plaintext/ciphertext attack. Based on our findings we explain the reasons behind the success and failure of relevant related cases in the literature. Finally, we conclude by estimating what are the resources to fully emulate 2 rounds of AES-128, a task that has never been achieved by means of neural networks. Despite the presence of original results and observations, we remark the systematization of knowledge nature of this work, whose main point is to explain the reason behind the inefficacy of the use of neural networks for black box cryptanalysis.
Towards reliable agile iterative planning via predicting documentation changes of work items

In agile iterative development, an agile team needs to analyze documented information for effort estimation and sprint planning. While documentation can be changed, the documentation changes after sprint planning may invalidate the estimated effort and sprint plan. Hence, to help the team be aware of the potential documentation changes, we developed DocWarn to estimate the probability that a work item will have documentation changes. We developed three variations of DocWarn, which are based on the characteristics extracted from the work items (DocWarn-C), the natural language text (DocWarn-T), and both inputs (DocWarn-H).
Structure of requests for services of documents electronic delivery in the Vasyl Stefanyk National Scientific Library of Ukraine in Lviv during the Covid-19 pandemic

The article describe the features of servicing library’s remote users during the Covid-19 pandemic. The library staff faced a number of complex tasks, as such challenges have not yet arisen. The situation required prompt adoption of non-standard and creative decisions. The way of providing the service “Electronic delivery of documents” was worked out by methods of appeals, consultations, exchange of experience with colleagues in Ukraine, study of world practice. The main methods of comparison, synthesis and analysis are used in the research. The information on the main stages of “Electronic delivery of documents” in the library is presented. The new forms and directions of work in remote service of remote readers have been introduced. Here is shown the dynamics of growth and territorial distribution of requests for the service of electronic delivery of documents in the library and information services of remote users during the introduction of quarantine restrictions Covid-19 in 2020–2021. During the Covid-19 pandemic, this service enabled librarians to quickly reformat and establish remote work with remote readers, provide appropriate sanitary and hygienic conditions for the library, focus on mostly electronic communication, remote customer service. Electronic delivery of documents was introduced in the library in 2016 as a paid service, the cost of which is determined by the “List of paid services provided in the V. Stefanyk National Scientific Library of Ukraine in Lviv and tariffs for them as of 08.09.2021”. This service provided an opportunity to simultaneously form an insurance fund for digital copies of Library documents. The resource of the insurance fund is formed to preserve digital documentary information. The basis of the service has the technological instruction “Way to perform electronic delivery of documents to remote users”, which regulates the sequence of operations of the process of electronic delivery of documents in compliance with applicable law and without violating copyright laws. The structure of requests for EDD service of remote users for the whole period of service is given. The prospects of using this service to meet the information needs of users are determined. The Library improves organizational measures and practical models of “Electronic delivery of documents” in conditions of limited access in order to implement the operational needs of remote users and to prevent the spread of acute respiratory disease in Ukraine Covid-19. Keywords: library, information space, electronic delivery of documents, remote reader, service of remote users, paid services, electronic communication, Vasyl Stefanyk National Scientific Library of Ukraine in Lviv, Covid-19.
AMixer: Adaptive Weight Mixing for Self-attention Free Vision Transformers

Vision Transformers have shown state-of-the-art results for various visual recognition tasks. The dot-product self-attention mechanism that replaces convolution to mix spatial information is commonly recognized as the indispensable ingredient behind the success of vision Transformers. In this paper, we thoroughly investigate the key differences between vision Transformers and recent all-MLP models. Our empirical results show the superiority of vision Transformers mainly comes from the data-dependent token mixing strategy and the multi-head scheme instead of query-key interactions. Inspired by this observation, we propose a computationally and parametrically efficient operation named adaptive weight mixing to generate attention weights without token-token interactions. Based on this operation, we develop a new architecture named as AMixer to capture both long-term and short-term spatial dependencies without self-attention. Extensive experiments demonstrate that our adaptive weight mixing is more efficient and effective than previous weight generation methods and our AMixer can achieve a better trade-off between accuracy and complexity than vision Transformers and MLP models on both ImageNet and downstream tasks. Code is available at https://github.com/raoyongming/AMixer .
Reading Problems in Grade II Elementary School Students

The low reading ability of low grade students, especially in grade II elementary school students, will lead students to face several obstacles during the learning process. In fact, reading ability is regarded as the key to the success of the learning process. Moreover, all activities at school also require reading abilities. This study was intended to explore the problematic phenomenon of reading in grade II elementary school students. To achieve this goal, this study was conducted by using a qualitative descriptive study design, with the type of descriptive study. This study involved grade II elementary school students with low reading skills as study subjects. The samples of this study were determined by means of a purposive sampling technique. The students selected as the sample were 5 grade II students from each elementary school. Consequently, the total number of test subjects in this study was amounted to 40 students. Moreover, the data of this study were collected by utilizing the triangulation method, including observation, interviews, and document studies. The data collection instruments used in this study were observation sheets, interview guidelines, and documentation study sheets. Furthermore, the data in the study were further analyzed by using thematic analysis techniques. Based on the results of the study, six obstacles were found as the cause of reading problems in grade II elementary school students. Thus, it can be concluded that reading problems in grade II elementary school students are caused by various obstacles.