Reviewerly/exHarmony huggingface dataset:
Learning from Multiple Instances: A Two-Stage Unsupervised Image Denoising Framework Based on Deep Image Prior

Supervised image denoising methods based on deep neural networks require a large amount of noisy-clean or noisy image pairs for network training. Thus, their performance drops drastically when the given noisy image is significantly different from the training data. Recently, several unsupervised learning models have been proposed to reduce the dependence on training data. Although unsupervised methods only require noisy images for learning, their denoising effect is relatively weak compared with supervised methods. This paper proposes a two-stage unsupervised deep learning framework based on deep image prior (DIP) to enhance the image denoising performance. First, a two-target DIP learning strategy is proposed to impose a learning restriction on the DIP optimization process. A cleaner preliminary image, together with the given noisy image, was used as the learning target of the two-target DIP learning process. We then demonstrate that adding an extra learning target with better image quality in the DIP learning process is capable of constraining the search space of the optimization process and improving the denoising performance. Furthermore, we observe that given the same network input and the same learning target, the DIP optimization process cannot generate the same denoised images. This indicates that the denoised results are uncertain, although they are similar in image quality and are complemented by local details. To utilize the uncertainty of the DIP, we employ a supervised denoising method to preprocess the given noisy image and propose an up- and down-sampling strategy to produce multiple sampled instances of the preprocessed image. These sampled instances were then fed into multiple two-target DIP learning processes to generate multiple denoised instances with different image details. Finally, we propose an unsupervised fusion network that fuses multiple denoised instances into one denoised image to further improve the denoising effect. We evaluated the proposed method through extensive experiments, including grayscale image denoising, color image denoising, and real-world image denoising. The experimental results demonstrate that the proposed framework outperforms unsupervised methods in all cases, and the denoising performance of the framework is close to or superior to that of supervised denoising methods for synthetic noisy image denoising and significantly outperforms supervised denoising methods for real-world image denoising. In summary, the proposed method is essentially a hybrid method that combines both supervised and unsupervised learning to improve denoising performance. Adopting a supervised method to generate preprocessed denoised images can utilize the external prior and help constrict the search space of the DIP, whereas using an unsupervised method to produce intermediate denoised instances can utilize the internal prior and provide adaptability to various noisy images of a real scene.
Knowledge Graph-Based Framework for Decision Making Process with Limited Interaction

In this work, we present an algorithmic framework that supports a decision process in which an end user is assisted by a domain expert to solve a problem. In addition, the communication between the end user and the domain expert is characterized by a limited number of questions and answers. The framework we have developed helps the domain expert to pinpoint a small number of questions to the end user to increase the likelihood of their insights being correct. The proposed framework is based on the domain expert’s knowledge and includes an interaction with both the domain expert and the end user. The domain expert’s knowledge is represented by a knowledge graph, and the end user’s information related to the problem is entered into the graph as evidence. This triggers the inference algorithm in the graph, which suggests to the domain expert the next question for the end user. The paper presents a detailed proposed framework in a medical diagnostic domain; however, it can be adapted to additional domains with a similar setup. The software framework we have developed makes the decision-making process accessible in an interactive and explainable manner, which includes the use of semantic technology and is, therefore, innovative.
Implementation of Blended Learning during COVID-19

Blended learning (BL) is a teaching model that combines face-to-face activities in the classroom with activities outside the classroom through the introduction of technology that is computer-based, distance, or mobile learning, among others. There are several BL models to adopt, depending on the importance and extent to which the technology is used. It brings great benefits to the learner and involves teachers in the design of new teaching methods.
Motion Compensation-based Low-Complexity Decoder Side Depth Estimation for MPEG Immersive Video

Decoder-Side Depth Estimation (DSDE) is a system firstly enabled in the novel MPEG Immersive Video (MIV) coding standard. In DSDE, only texture components are coded, while the depth is estimated at the decoder-side. This is motivated by previous work, which has shown high coding gain and pixel rate savings in DSDE. However, the computational complexity remains a concern, as high quality depth search has a high runtime and memory requirement. In this work we extend the concept of depth estimation to depth recovery. Using this mode, the decoder-side depth information is recovered through motion compensation utilizing the displacement vectors contained in the texture bitstream. This strategy enables us to replace most of the complex depth estimation processes with a simple motion compensation step, a decision that is drawn on the encoder-side and signaled per coding unit. With only minor losses in terms of synthesis PSNR and similar perceptual quality in terms of MS-SSIM, the complexity is significantly reduced. Depending on the acceptable loss, up to 80 % of the moving objects depth may be motion compensated instead of estimated by a depth estimator translating into a speed-up of a factor of 104 for inter-frames compared to the reference depth estimator.
A study of students' attention levels while watching educational videos with the use of neurofeedback

Currently, the use of neurotechnologies in education is not of practical nature; however, attempts are made to introduce them into pedagogical science. One of such areas is the use of EEG method which allows to evaluate educational materials in terms of brain reactions and to interpret them in different mental states (concentration, tranquility, attention, etc.). The article presents the results of the study focused on the use of neurotechnology methods for pedagogical purposes. The work supposed analysing the level of learners' attention while they were watching an educational video. In addition, the experimental group involved the screen display of the learners' attention scale in real-time - thus neurofeedback was used. The “attention” scale was assessed on the basis of analysis of beta and theta rhythms activity. The preliminary study results obtained for 20 trainees showed that the use of neurofeedback by those watching educational videos may improve one's perception of information. The difference in attention span between the control and experimental groups was 14%. This method of learning educational videos requires further exploration and may contribute to better perception of information in the e-learning process.
Impact of Adversarial Training on the Robustness of Deep Neural Networks

Recently, adversarial attack has received a decent degree of attention in the image domain after showing a high attack success rate due to their continuous characteristic. Several experiments have demonstrated a sharp drop in classification accuracy after evaluating the model with adversarial examples. While the number of research papers on different types of attacks has been flourishing, the method of adversarial training is still not established as the “classic” way of training the model. Most pre-trained models that could be downloaded easily from various websites are trained with clean data, which means such models will have high vulnerability to real-life images that mostly contain small noises. In this paper, we experimented with different types of adversarial training methods and observe how each one of them influenced the robustness of the deep learning model. Experiments demonstrate the comparison between each method in defending the modified Resnet-18 against perturbed histopathological images on diagnosing metastatic cancer.
Reasoning about Moving Target Defense in Attack Modeling Formalisms

Since 2009, Moving Target Defense (MTD) has become a new paradigm of defensive mechanism that frequently changes the state of the target system to confuse the attacker. This frequent change is costly and leads to a trade-off between misleading the attacker and disrupting the quality of service. Optimizing the MTD activation frequency is necessary to develop this defense mechanism when facing realistic, multi-step attack scenarios. Attack modeling formalisms based on DAG are prominently used to specify these scenarios. Our contribution is a new DAG-based formalism for MTDs and its translation into a Price Timed Markov Decision Process to find the best activation frequencies against the attacker's time/cost optimal strategies. For the first time, MTD activation frequencies are analyzed in a state-of-the-art DAG-based representation. Moreover, this is the first paper that considers the specificity of MTDs in the automatic analysis of attack modeling formalisms. Finally, we present some experimental results using Uppaal Stratego to demonstrate its applicability and relevance.
Mask Detection Algorithm Based on Improved Lightweight YOLOv3

In the object detection task, in order to reduce the reasoning time of the mask detection task, reduce the computing cost, and avoid a certain degree of computing resource waste caused by the full convolution network adopted by YOLOv3, this paper proposes an Mdwneck network, which improves the Bottleneck in the original algorithm and changes its stacking mode to achieve a more lightweight effect. The experimental results show that the mAP index of the improved algorithm is only 0.8% lower than that of the original algorithm, and there is almost no difference in the actual detection scenario. At the same time, the computing cost is greatly reduced and the expected effect is achieved.
Automatic Detection of Fake Key Attacks in Secure Messaging

Popular instant messaging applications such as WhatsApp and Signal provide end-to-end encryption for billions of users. These applications often rely on a centralized, application-specific server to distribute public keys and relay encrypted messages between the users. As a result, they prevent passive attacks but are vulnerable to some active attacks. A malicious or hacked server can distribute fake keys to users to perform man-in-the-middle or impersonation attacks. While typical secure messaging applications provide a manual method for users to detect these attacks, this burdens users, and studies show it is ineffective in practice. This paper presents KTACA, a completely automated approach for key verification that is oblivious to users and easy to deploy. We motivate KTACA by designing two approaches to automatic key verification. One approach uses client auditing (KTCA) and the second uses anonymous key monitoring (AKM). Both have relatively inferior security properties, leading to KTACA, which combines these approaches to provide the best of both worlds. We provide a security analysis of each defense, identifying which attacks they can automatically detect. We implement the active attacks to demonstrate they are possible, and we also create a prototype implementation of all the defenses to measure their performance and confirm their feasibility. Finally, we discuss the strengths and weaknesses of each defense, the load they impose on clients and service providers, and their deployment considerations.
Quadrature squeezing and temperature estimation from the Fock distribution

We present a method to estimate the amount of squeezing and temperature of a single-mode Gaussian harmonic oscillator state based on the weighted least-squares estimator applied to measured Fock state populations. Squeezing and temperature, or equivalently the quadrature variances, are essential parameters states used in various quantum communication and sensing protocols. They are often measured with homodyne-style detection, which requires a phase reference such as a local oscillator. Our method allows estimation without a phase reference, by using for example a photon-number-resolving detector. To evaluate the performance of our estimator, we simulated experiments with different values of squeezing and temperature. From 10,000 Fock measurement events, we produced estimates for states whose fidelities to the true state are greater than 99.99% for small squeezing ( $$r<1.0$$ ), and for high squeezing ( $$r=2.5$$ ), we obtain fidelities greater than 99.9%. We also report confidence intervals and their coverage probabilities.
On Fitness Landscape Analysis of Permutation Problems: From Distance Metrics to Mutation Operator Selection

In this paper, we explore the theory and expand upon the practice of fitness landscape analysis for optimization problems over the space of permutations. Many of the computational and analytical tools for fitness landscape analysis, such as fitness distance correlation, require identifying a distance metric for measuring the similarity of different solutions to the problem. We begin with a survey of the available distance metrics for permutations, and then use principal component analysis to classify these metrics. The result of this analysis aligns with existing classifications of permutation problem types produced through less formal means, including the A-permutation, R-permutation, and P-permutation types, which classifies problems by whether absolute position of permutation elements, relative positions of elements, or general precedence of pairs of elements, is the dominant influence over solution fitness. Additionally, the formal analysis identifies subtypes within these problem categories. We see that the classification can assist in identifying appropriate metrics based on optimization problem feature for use in fitness landscape analysis. Using optimization problems of each class, we also demonstrate how the classification scheme can subsequently inform the choice of mutation operator within an evolutionary algorithm. From this, we present a classification of a variety of mutation operators as a counterpart to that of the metrics. Our implementations of the permutation metrics, permutation mutation operators, and associated evolutionary algorithm, are available in a pair of open source Java libraries. All of the code necessary to recreate our analysis and experimental results are also available as open source.
Machine learning and quantum computing for 5G/6G communication networks - A survey

Recently fifth generation (5G) and beyond applications are evolving, which demands more computational and complex data processing. Quantum computing and quantum learning algorithms are incorporated to enhance processing capabilities and data computation compared to conventional machine learning approaches. This study presents the significance of quantum computing and quantum machine learning models and their research challenges concerning 5G and beyond applications. The researchers focus on global coverage, enhanced spectrum support, increased energy and cost efficiency, high security, and dynamic intelligence, along with big data processing that demands complex data structures and algorithms.
An illustrated tutorial for logarithmic scales and decibels in acoustics

Acoustics is generally defined as the science that deals with the production, transmission, and reception of sound and the understanding and control of its effects. In fact, the fields of acoustics cover an especially broad range of subjects and domains, and comprehensive acoustics textbooks are usually quite thick as a consequence. While they are valuable resources for researchers, these books might appear a little daunting for a young audience or for people who are new to acoustics. This paper is an example of how educational comics can be designed and used to introduce one of the most commonly discussed topics when the basics of acoustics are taught: decibel level. Seven drawn pages constitute a visual support to explain the origin and history of the decibel, together with examples from acoustics and other domains on the use of logarithmic scales and classical decibel calculations. Several comments and comprehensive bibliographical references are also provided for each drawn page to enlarge the range of subjects or exercises that can be discussed in courses and foster further readings.
Generating representative, live network traffic out of millions of code repositories

In theory, any network operator, developer, or vendor should have access to large amounts of live network traffic for testing their solutions. In practice, though, that is not the case. Network actors instead have to use packet traces or synthetic traffic, which is highly suboptimal: today's generated traffic is unrealistic. We propose a system for generating live application traffic leveraging massive codebases such as GitHub.
An Improved Probability Density Function (PDF) for Face Skin Detection

Face Detection by skin color in the field of computer vision is a difficult challenge. Detection of human skin focuses on the identification of pixels and skin-colored areas of a given picture. Since skin colors are invariant in orientation and size and rapid to process, they are used in the identification of human skin. In addition features like ethnicity, sensor, optics and lighting conditions that are different are sensitive factors for the relationship between surface colors and lighting (an issue that is strongly related to color stability). This paper presents a new technique for face detection based on human skin. Three methods of Probability Density Function (PDF) were applied to detect the face by skin color; these are the Extreme Value Distribution Function and the Exponential Distribution Function methods, in addition to a new proposed model, over the HSV (Hue, Saturation, and Value) color space. The suggested technique aims to enhance skin pixel detection and improve the detection accuracy of a colored region in the human skin in a specific photo. The new model has proved to be 96.05% more accurate than the Extreme value distribution function and Exponential distribution function according to the selected region of the face during experiments. The images used in this paper were 380 color images from CalTech (California Technology Institute) dataset.
The key to an effective AI-powered digital pathology: Establishing a symbiotic workflow between pathologists and machine

Pathology is a fundamental element of modern medicine that determines the final diagnosis of medical conditions, leads medical decisions, and portrays the prognosis. Due to continuous improvements in AI capabilities (e.g., object recognition and image processing), intelligent systems are bound to play a key role in augmenting pathology research and clinical practices. Despite the pervasive deployment of computational approaches in similar fields such as radiology, there has been less success in integrating AI in clinical practices and histopathological diagnosis. This is partly due to the opacity of end-to-end AI systems, which raises issues of interoperability and accountability of medical practices. In this article, we draw on interactive machine learning to take advantage of AI in digital pathology to open the black box of AI and generate a more effective partnership between pathologists and AI systems based on the metaphors of parameterization and implicitization.
Look back, look around: A systematic analysis of effective predictors for new outlinks in focused Web crawling

Small and medium enterprises rely on detailed Web analytics to be informed about their market and competition. Focused crawlers meet this demand by crawling and indexing specific parts of the Web. Critically, a focused crawler must quickly find new pages that have not yet been indexed. Since a new page can be discovered only by following a new outlink, predicting new outlinks is very relevant in practice. In the literature, many feature designs have been proposed for predicting changes in the Web. In this work we provide a structured analysis of this problem, using new outlinks as our running prediction target. Specifically, we unify earlier feature designs in a taxonomic arrangement of features along two dimensions: static versus dynamic features, and features of a page versus features of the network around it. Within this taxonomy, complemented by our new (mainly, dynamic network) features, we identify best predictors for new outlinks. Our main conclusion is that most informative features are the recent history of new outlinks on a page itself, and of its content-related pages. Hence, we propose a new ‘look back, look around’ (LBLA) model, that uses only these features. With the obtained predictions, we design a number of scoring functions to guide a focused crawler to pages with most new outlinks, and compare their performance. The LBLA approach proved extremely effective, outperforming other models including those that use a most complete set of features. One of the learners we use, is the recent NGBoost method that assumes a Poisson distribution for the number of new outlinks on a page, and learns its parameters. This connects the two so far unrelated avenues in the literature: predictions based on features of a page, and those based on probabilistic modeling. All experiments were carried out on an original dataset, made available by a commercial focused crawler.
Target detection algorithm based on improved homomorphic filter in haze days

With the frequent occurrence of smog weather, traffic accidents occur frequently. It poses new challenges to the transportation system. Aiming at the serious fog weather, the paper proposes a YOLO-Demist algorithm based on the homomorphic filter function model which adopted the linear improvements factor e <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m</sup> . By using the improved homomorphic filter for image enhancement and the maximum inhibition processing on photographs or videos, the results show that the average detection rate is increased from 66.01% to 83.16%, the detection rate is more stable, and the leakage and error detection rate are significantly improved compared with YOLO-V3. The algorithms run with OpenCV for target detection, and the main objects detected are cars and birds. Compared with HLE (Histogram Equalization), SSR(Single Scale Retinex), and other traditional algorithms, the results show that the algorithm can effectively detect the visual disability of road traffic objects under haze weather and reduce the frequency of traffic accidents under severe weather.