Reviewerly/exHarmony huggingface dataset:
Image Denoising Based on the Asymmetric Gaussian Mixture Model

In recent years, image restoration has become a huge subject, and finite hybrid model has been widely used in image denoising because of its easy modeling and strong explanatory results. The gaussian mixture model is the most common one. The existing image denoising methods usually assume that each component of the natural image is subject to the gaussian mixture model (GMM). However, this approach is not entirely reasonable. It is well known that most natural images are complex and their distribution is not entirely gaussian. As a result, there are still many problems that GMM cannot solve. This paper tries to improve the finite mixture model and introduces the asymmetric gaussian mixture model into it. Since the asymmetric gaussian mixture model can simulate the asymmetric distribution on the basis of the gaussian mixture model, it is more consistent with the natural image data, so the denoising effect of the natural complex image is better. We carried out image denoising experiments under different noise scales and types, and found that the asymmetric gaussian mixture model has better denoising effect and performance.
Hierarchical Latent Context Representation for Context-Aware Recommendations

In this paper, we propose a hierarchical representation of latent contextual information that captures contextual situations in which users are recommended particular items. We also introduce an algorithm that converts unstructured latent contextual information into structured hierarchical representations. In addition, we present two general context-aware recommendation algorithms that extend collaborative filtering (CF) approaches and utilize structured and unstructured latent contextual information. In particular, the first algorithm utilizes structured latent contexts and the second one combines the structured and the unstructured latent contextual representations. By using latent contextual information in a recommendation model, we capture and represent both the structure of the latent context in the form of a hierarchy and the values of contextual variables in the form of an unstructured vector. We tested the two proposed methods with two CF-based methods on several context-rich datasets under different experimental settings. We show that using hierarchical latent contextual representations leads to significantly better recommendations than the baselines for the datasets having high- and medium-dimensional contexts. Although this is not the case for the low-dimensional contextual data, the hybrid approach, combining structured and unstructured latent contextual information, significantly outperforms other baselines across all the experimental settings and dimensions of contextual data.
Investigation into the Efficacy of Various Machine Learning Techniques for Mitigation in Credit Card Fraud Detection

An effective credit card fraud detection model is the most challenging issue for the financial organizations. Statistical and machine learning (ML) techniques are widely explored in financial applications. But there is no thumb rule which technique gives better performance. Recent studies conclude that ensemble learning may be the right approach in this problem domain. In this paper, we aim to develop a novel fraud detection system using an ensemble model. In the proposed model, initially the imbalanced credit card dataset is balanced using random under-sampling technique, then the performance of the model is evaluated using both single base classifiers and ensemble of classifiers. In the proposed model, AdaBoost, random forest (RF), extreme gradient boosting and gradient boosting decision tree (GBDT) are used as ensemble models. The experimental result shows that the combination of RF and GBDT for the ensemble model is superior in performance as compared to other combinations.
Overview of the ImageCLEF 2020: Multimedia Retrieval in Medical, Lifelogging, Nature, and Internet Applications

This paper presents an overview of the ImageCLEF 2020 lab that was organized as part of the Conference and Labs of the Evaluation Forum - CLEF Labs 2020. ImageCLEF is an ongoing evaluation initiative (first run in 2003) that promotes the evaluation of technologies for annotation, indexing and retrieval of visual data with the aim of providing information access to large collections of images in various usage scenarios and domains. In 2020, the 18th edition of ImageCLEF runs four main tasks: (i) a medical task that groups three previous tasks, i.e., caption analysis, tuberculosis prediction, and medical visual question answering and question generation, (ii) a lifelog task (videos, images and other sources) about daily activity understanding, retrieval and summarization, (iii) a coral task about segmenting and labeling collections of coral reef images, and (iv) a new Internet task addressing the problems of identifying hand-drawn user interface components. Despite the current pandemic situation, the benchmark campaign received a strong participation with over 40 groups submitting more than 295 runs.
Development of Automatic Speech Recognition for Kazakh Language using Transfer Learning

Development of an automatic speech recognition system for the Kazakh language is a challenging task due to the lack of audio data and specificity and complexity of the language itself.In this paper, we propose a new method which gets a pre-trained model of the russian language and uses the weight values of the pre-trained model in the proposed neural network.The main reason for choosing the Russian language model is that the pronunciation of the Kazakh and Russian languages is very similar in many respects, because they account for 78% of the total letters and there is a rather large corpus of the Russian speech dataset.The dataset of Kazakh speech with transcriptions was formed by the university's faculty.In general, 50 native speakers were involved who generated about 400 sentences.A special technology has been created for the automatic expansion of the database.The data was extracted from well-known Kazakh books such as "Abai zholy", "Kara sozder", etc.
GSST-based photonic memory multilevel perceptron

Here we demonstrate an on-chip programmable multi-level non-volatile photonic memory based on an ultra-compact (&lt;4pm) hybrid GSST-silicon Mach Zehnder modulator, with low insertion losses (3dB), used as node in a photonic neural network that effortlessly perform inference.
Mapping digital forensic application requirement specification to an international standard

A potential security incident may go unsolved if standardized forensic approaches are not applied during lawful investigations. This paper highlights the importance of mapping the digital forensic application requirement specification to an international standard, precisely ISO/IEC 27043. The outcome of this work is projected to contribute to the problem of secure DF tool creation, and in the process address Software Requirements Specification (SRS) as a process of digital evidence admissibility.
Automatic evaluation system for bulk defects in optics

In the high frequency laser system, the internal defects of optics will affect their damage threshold and downstream modulation, and even lead to subsequent surface damage. In order to efficiently and reliably evaluate the internal defects in optics, an automatic evaluation method of scattering defects in optics is proposed, and the corresponding three-dimensional (3D) precision measurement system is established. The detection principle, image acquisition, image processing and bulk defects (BDs) information extraction methods of the system are studied. Based on a 3D dark-field microscopic imaging (DFMI) technology, samples are scanned with multi-dimensional motion devices. Meanwhile, the scattered beam from the BD was collected by a detector, and finally combined with the digital image processing technology, the acquired images are processed in real time. Through real-time focusing and improved 3D reconstruction algorithm, the images with full aperture BD features are obtained, and the position and size information of defects are extracted. The system has detected the BD inside the fused silica samples. The results show that our system can distinguish the 612.7 lp/mm. Furthermore, the 3D distributions of the BD were also reconstructed.
A randomized exponential canonical correlation analysis method for data analysis and dimensionality reduction

Canonical correlation analysis (CCA) is a famous data analysis method that has been successfully used in many areas. CCA extracts meaningful information from a pair of data sets, by seeking pairs of linear combinations from two sets of variables with maximum correlation. Mathematically, CCA resorts to solving a large-scale generalized eigenvalue problem. However, as the dimension of the data sets is much larger than the number of samples, CCA may suffer from the small-sample-size (SSS) problem and the over-fitting problem. In order to overcome these difficulties, the regularized technique is often applied, but it is difficult to choose the optimal parameter in advance. In this work, we propose an Exponential Canonical Correlation Analysis (ECCA) method based on matrix exponential, which is parameter-free and can overcome the over-fitting and the SSS problems fundamentally. However, the computational overhead of the ECCA method is very high in practice. Based on the randomized singular value decomposition (RSVD), we then propose a Randomized Exponential Canonical Correlation Analysis (RECCA) method for data analysis and dimensionality reduction. Theoretical results are given to show the rationality of this randomized method, and establish the relationship between RECCA and ECCA. Numerical experiments are performed on some real-world, high-dimensional and large-sample data sets, which illustrate the superiority of the proposed algorithms over many state-of-the-art CCA algorithms.
A study of Grammar Analysis in English Teaching With Deep Learning Algorithm

In English teaching, grammar is a very important part. Based on the seq2seq model, a grammar analysis method combining the attention mechanism, word embedding and CNN seq2seq was designed using the deep learning algorithm, then the algorithm training was completed on NUCLE, and it was tested on CoNIL-2014. The experimental results showed that of seq2seq+attention improved 33.43% compared to the basic seq2seq; in the comparison between the method proposed in this study and CAMB, the P value of the former was 59.33% larger than that of CAMB, the R value was 8.9% larger, and the value of was 42.91% larger. Finally, in the analysis of the actual students' grammar homework, the proposed method also showed a good performance. The experimental results show that the method designed in this study is effective in grammar analysis and can be applied and popularized in actual English teaching.
Impact of supply chain integration and intelligent information systems in achieving supply chain innovation: A study of retail trading SMEs in Abu Dhabi, UAE

The study aims to explore supply chain innovation in retail trading of Small and Medium Enterprises (SMEs) in different regions of Abu Dhabi by considering the contribution of supply chain integration, supply chain orientation, and competition capability, along with the mediating role of intelligent information systems.Online questionnaires were used to collect the relevant information from managerial employees at target firms.A total of 357 responses were considered suitable for the purpose of the study.All the constructs were internally and externally estimated to be reliable and valid based on factor loading values, Cronbach's alpha, composite reliability, average variance extracted, and discriminant validity.Supply chain integration, orientation, and competition capability were considered as the key contributors to supply chain innovation.However, in the case of supply chain orientation towards intelligent information systems and of intelligent information systems towards supply chain innovation, the association was weak.Intelligent information systems had a weak relationship with competition capability, supply chain integration, orientation, and innovation.The results have real-world implications for management and policymakers of retail trade SMEs in Abu Dhabi.Using the results and conclusions, upper management can shape corporate policy to improve supply chain integration, supply chain orientation, and competition capability, resulting in supply chain innovation in retail trading SMEs..
Working with Prometheus Query Language (PromQL)

PromQL (Prometheus Query Language) is a functional query language provided by Prometheus to enable the user to query data stored in real time and perform all sorts of analysis, aggregations, and operations. In this chapter, we will provide hands-on steps to the readers that will enable them to use PromQL.
Permutation-Based Causal Structure Learning with Unknown Intervention Targets

We consider the problem of estimating causal DAG models from a mix of observational and interventional data, when the intervention targets are partially or completely unknown. This problem is highly relevant for example in genomics, since gene knockout technologies are known to have off-target effects. We characterize the interventional Markov equivalence class of DAGs that can be identified from interventional data with unknown intervention targets. In addition, we propose a provably consistent algorithm for learning the interventional Markov equivalence class from such data. The proposed algorithm greedily searches over the space of permutations to minimize a novel score function. The algorithm is nonparametric, which is particularly important for applications to genomics, where the relationships between variables are often non-linear and the distribution non-Gaussian. We demonstrate the performance of our algorithm on synthetic and biological datasets. Links to an implementation of our algorithm and to a reproducible code base for our experiments can be found at this https URL.
Spectral Color Management in Virtual Reality Scenes

Virtual reality has reached a great maturity in recent years. However, the quality of its visual appearance still leaves room for improvement. One of the most difficult features to represent in real-time 3D rendered virtual scenes is color fidelity, since there are many factors influencing the faithful reproduction of color. In this paper we introduce a method for improving color fidelity in virtual reality systems based in real-time 3D rendering systems. We developed a color management system for 3D rendered scenes divided into two levels. At the first level, color management is applied only to light sources defined inside the virtual scene. At the second level, we applied spectral techniques over the hyperspectral textures of 3D objects to obtain a higher degree of color fidelity. To illustrate the application of this color management method, we simulated a virtual version of the Ishihara test for color blindness deficiency detection.
PROMISE

Service robots, a type of robots that perform useful tasks for humans, are foreseen to be broadly used in the near future in both social and industrial scenarios. Those robots will be required to operate in dynamic environments, collaborating among them or with users. Specifying the list of requested tasks to be achieved by a robotic team is far from being trivial. Therefore, mission specification languages and tools need to be expressive enough to allow the specification of complex missions (e.g., detailing recovery actions), while being reachable by domain experts who might not be knowledgeable of programming languages. To support domain experts, we developed PROMISE, a Domain-Specific Language that allows mission specification for multiple robots in a user-friendly, yet rigorous manner. PROMISE is built as an Eclipse plugin that provides a textual and a graphical interface for mission specification. Our tool is in turn integrated into a software framework, which provides functionalities as: (1) automatic generation from specification, (2) sending of missions to the robotic team; and (3) interpretation and management of missions during execution time. PROMISE and its framework implementation have been validated through simulation and real-world experiments with four different robotic models.
A Multilayered Informative Random Walk for Attributed Social Network Embedding

Network representation learning (also known as Graph embedding) is a technique to map the nodes of a network to a lower dimensional vector space. Random walk based representation techniques are found to be efficient as they can easily preserve different orders of proximities between the nodes in the embedding space. Most of the social networks now-a-days have some content (or attributes) associated with each node. These attributes can provide complementary information along with the link structure of the network. But in a real life network, the information carried by the link structure and that by the attributes vary significantly over the nodes. Most of the existing unsupervised attributed network embedding algorithms do not distinguish between the link structure and the attributes of a node depending on their informativeness. In this work, we propose an unsupervised node embedding technique that exploits both the structure and attributes by intelligently prioritizing one of them, in the random walk, for each node separately. We convert the network into a multi-layered graph and propose a novel random walk based on the informativeness of a node in different layers. This unified approach is simple and computationally fast, yet able to use the content as a complement to structure and viceversa. Experimental evaluations on four real world publicly available datasets show the merit of our approach (up to 168.75 improvement) compared to the state-of-the-art algorithms in the domain. We make the source code available to download. Â© 2020 The authors and IOS Press.
eTher – An Assistive Virtual Agent for Acrophobia Therapy in Virtual Reality

This paper presents the design, a pilot implementation and validation of eTher, an assistive virtual agent for acrophobia therapy in a Virtual Reality environment that depicts a mountain landscape and contains a ride by cable car. eTher acts as a virtual therapist, offering support and encouragement to the patient. It directly interacts with the user and changes its voice parameters – pitch, tempo and volume – according to the patient’s emotional state. eTher identifies the levels of relaxation/anxiety compared to a baseline resting recording and provides three modalities of relaxation - by determining the user to look at a favorite picture, listen to an enjoyable song or read an inspirational quote. If the relaxation modalities fail to be effective, the virtual agent automatically lowers the level of exposure. We have validated our approach with a number of 10 users who played the game once without eTher’s intervention and three times with assistance from eTher. The results showed that the participants succeeded to finish the game quicker in the last gameplay session where the virtual agent intervened. Moreover, their biophysical data showed significant improvements in terms of relaxation state.
Recognition of Reconstructed Frontal Face Images Using FFT-PCA/SVD Algorithm

Face recognition has gained prominence among the various biometric-based methods (such as fingerprint and iris) due to its noninvasive characteristics. Modern face recognition modules/algorithms have been successful in many application areas (access control, entertainment/leisure, security system based on biometric data, and user-friendly human-machine interfaces). In spite of these achievements, the performance of current face recognition algorithms/modules is still inhibited by varying environmental constraints such as occlusions, expressions, varying poses, illumination, and ageing. This study assessed the performance of Principal Component Analysis with singular value decomposition using Fast Fourier Transform (FFT-PCA/SVD) for preprocessing the face recognition algorithm on left and right reconstructed face images. The study found that average recognition rates for the FFT-PCA/SVD algorithm were 95% and 90% when the left and right reconstructed face images are used as test images, respectively. The result of the paired sample <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M1"><mml:mi>t</mml:mi></mml:math>-test revealed that the average recognition distances for the left and right reconstructed face images are not significantly different when FFT-PCA/SVD is used for recognition. FFT-PCA/SVD is recommended as a viable algorithm for recognition of left and right reconstructed face images.
Confidence Intervals for Subjective Tests and Objective Metrics That Assess Image, Video, Speech, or Audiovisual Quality

This report describes a methodology that measures the precision of objective metrics that assess image quality, video quality, speech quality, or the overall audiovisual quality. We assess the confidence intervals of 60 subjective tests and use a confusion matrix to classify the conclusions reached when two subjective test labs perform the same experiment. This allows us to compute the metric’s confidence interval and, when confidence intervals are used to make decisions, to prove whether the metric performs similarly to a subjective test with 15 or 24 subjects. When confidence intervals are not used, the metric’s precision is likened to a certain number of people in an ad-hoc quality assessment. The methods in this report are developed and evaluated using speech quality, video quality, image quality, and audiovisual quality datasets.
Quality Assessment of SAR-to-Optical Image Translation

Synthetic aperture radar (SAR) images contain severe speckle noise and weak texture, which are unsuitable for visual interpretation. Many studies have been undertaken so far toward exploring the use of SAR-to-optical image translation to obtain near optical representations. However, how to evaluate the translation quality is a challenge. In this paper, we combine image quality assessment (IQA) with SAR-to-optical image translation to pursue a suitable evaluation approach. Firstly, several machine-learning baselines for SAR-to-optical image translation are established and evaluated. Then, extensive comparisons of perceptual IQA models are performed in terms of their use as objective functions for the optimization of image restoration. In order to study feature extraction of the images translated from SAR to optical modes, an application in scene classification is presented. Finally, the attributes of the translated image representations are evaluated using visual inspection and the proposed IQA methods.