Reviewerly/exHarmony huggingface dataset:
Model-driven deep unfolding approach to underwater image enhancement

We propose a model-driven deep learning approach to underwater image enhancement that can take advantage of both model- and learning-based approaches. We first formulate a joint optimization problem with physical priors to estimate the transmission map and latent clear image. Then, we solve the optimization problem iteratively. At each iteration, the optimization variables and image priors are updated by closed-form solutions and learned deep neural networks, respectively. Experimental results show that the proposed algorithm outperforms state-of- the-art underwater image enhancement algorithms.
Quantum entanglement for continuous variables sharing in an expanding spacetime

Abstract Detecting the structure of spacetime with quantum technologies has always been one of the frontier topics of relativistic quantum information. Here, we analytically study the generation and redistribution of Gaussian entanglement of the scalar fields in an expanding spacetime. We consider a two-mode squeezed state via a Gaussian amplification channel that corresponds to the time-evolution of the state from the asymptotic past to the asymptotic future. Therefore, the dynamical entanglement of the Gaussian state in an expanding universe encodes historical information about the underlying spacetime structure, suggesting a promising application in observational cosmology. We find that quantum entanglement is more sensitive to the expansion rate than the expansion volume. According to the analysis of quantum entanglement, choosing the particles with the smaller momentum and the optimal mass is a better way to extract information about the expanding universe. These results can guide the simulation of the expanding universe in quantum systems.
Gray and White Matters Segmentation in Brain CT Images using Multi-Task Learning from Paired CT and MR Images

Computer tomography (CT) has been routinely used for decades in clinical neuroimaging. Compared to Magnetic resonance imaging (MR), CT is more readily available and more cost-effective, but the soft tissue contrast is much lower. In brain CT images, the unclear soft-tissue boundaries and the high noise level hamper accurate segmentation of the gray and white matter, leaving obstacles for the subsequent geometrical quantification of brain structures. To address this challenge, this paper specifically acquires same-patient CT and MR image pairs and proposes a multi-task learning model for simultaneous tissue segmentation and modality transfer. We aim to use the modality transfer task to learn corresponding MR and CT features which assists the segmentation task to achieve more accurate results than single modality learning. Moreover, we add a Shannon entropy loss function to the training loss to further combat the high noise influence and reduce the fragmentation problem of the segmentation results. Experimental results proved that our multi-task framework achieves more accurate segmentation than the single segmentation task, and the Shannon entropy loss results in much fewer broken brain regions than the state-of-the-art (SOTA) U-net method. Our study provides a useful tool for clinical neural CT image analysis.
Enriching computing simulators by generating realistic serverless traces

Abstract Serverless computing is stepping forward to provide a cloud environment that mainly focuses on managing infrastructure, resources and configurations on the behalf of a user. Research in this field can’t rely on commercial providers such as AWS and Azure, as their inflexibility and cost often limits the required levels of reproducibility and scalability. Therefore, simulators have been opted as an alternative solution by the research community. They offer a reduced-cost and easy-setup environment. To get respectable precision, simulators use real traces collected and offered by commercial providers. These traces represent comprehensive information of executed tasks that reflect user behaviour. Due to serverless computing’s recency, typical workload traces employed by IaaS simulators are not well adoptable to the new computing model. In this paper, we propose an approach for generating realistic serverless traces. We enhance our previous generator approach that was based on the Azure Functions dataset. Our new, genetic algorithm based approach improves the statistical properties of the generated traces. We also enabled arbitrary scaling of the workload, while maintaining real users’ behaviour. These advances further support reproducibility in the serverless research community. We validated the results of our generator approach using the coefficient of determination ( $$R^2$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mi>R</mml:mi> <mml:mn>2</mml:mn> </mml:msup> </mml:math> ), which shows that our generated workload closely matches the original dataset’s characteristics in terms of execution time, memory utilisation as well as user participation percentage. To demonstrate the benefits of the reusability of the generated traces, we applied them with a diverse set of simulators and shown that they offer reproducible results independently of the simulator used.
An Intelligent Recognition Model of English Translation Teaching Method Based on Improved GLR Algorithm

At present, machine translation is widely used in English translation, and the GLR algorithm has great practical value in the translation process. Usually, the number of recognition results calculated by the GLR algorithm has a lot of chance, and in the process of recognition, the accuracy of the algorithm is not very high, and for students, this method can also improve the teaching effect of English translation. However, the recognition speed of this algorithm is slow, and there are still some problems in practical application. This paper proposes an improved GPS algorithm to intelligently recognize it. This paper firstly proposes an optimized GLR algorithm based on intelligent recognition, and then and applies the neural network method to English translation teaching. Then, the phrase structure is constructed by the center point of the phrase, and then the improved GPS algorithm is used for translation, and the method is applied to English learning. It is proved by experiments that the neural network based on intelligent recognition can recognize English and Chinese words. Finally, the neural network model is applied to English translation teaching, and the intelligent recognition performance of the method is tested. The results show that the improved GPS algorithm has a certain contribution in improving students' learning efficiency.
The Impact of Digital Transformation in Higher Education: The Case Study from Vietnam

The education sector in general and higher education, in particular, has paid great attention to digital transformation into teaching. Digital transformation is not only a general development trend but also helps to change the traditional teaching method to a new active teaching method that helps learners to be active and creative when participating in the learning process. The traditional classroom model, which has existed for a long time, is gradually shifting to classrooms that apply information technology to teaching and learning to reduce lectures and transfer knowledge to develop the initiative, creativity, and capacity of students to learn. Create opportunities for learners to learn anytime, anywhere, learners can be active in the learning process, contributing to building a learning society. The article clarifies the impact of digital transformation on higher education in Vietnam. From there, offer an oriented solution to help digital transformation become an effective tool in higher education in Vietnam today.
The effect of augmented reality mobile learning in microeconomic course

Recently, there has been a surge of interest for students to use technology while engaging in their learning. Augmented reality is one of the technologies found suitable for use in the educational field, such as in science, mathematics, and engineering. However, it is not yet being explored in the Microeconomics course. Therefore, this study investigated the effectiveness of using augmented reality, namely Augmented Reality Mobile Learning in Microeconomic courses (ARMLAAPPS). This study used a quasi-experimental design, and two groups were involved: the control and experimental groups. They are undergoing teaching and learning sessions using ARMLAAPPS and conventional teaching methods. This study indicates that students in the experimental group showed a significantly higher visualization skills level than those in the control group after teaching and learning. Besides, this study also found that ARMLAAPPS can assist in highlighting student-centered learning, stimulating student interest and curiosity, increasing student cognitive, affective, and psychomotor processes, and increasing student involvement in the information-seeking process. Educators are suggested to use augmented reality in their teaching and learning since it has effectively enhanced students’ visualization skills and promoted a better understanding of knowledge.
PM $^{2}$ VE: Power Metering Model for Virtualization Environments in Cloud Data Centers

Virtualization technologies provide solutions for cloud computing. Virtual resource scheduling is a crucial task in data centers, and the power consumption of virtual resources is a critical foundation of virtualization scheduling. Containers are the smallest unit of virtual resource scheduling and migration. Although many practical models for estimating the power consumption of virtual machines (VMs) have been proposed, few power estimation models of containers have been put forth. In this paper, we propose a fast-training piecewise regression model based on a decision tree for VM power metering and estimate the power of containers configured on the VM by treating the container as a group of processes on the VM. We select appropriate features from the collected metrics of VMs/containers to help our model fit the nonlinear relationship between power and features well. Besides, we optimize the leaf nodes of the regression tree, realizing the effective power metering of virtualization environments. We evaluate the proposed model on 13 tasks in PARSEC and compare it with several commonly used models in data centers. The experimental results prove the effectiveness of the proposed model, and the estimated power of containers is in line with expectations.
AN ANALYSIS OF CODE-SWITCHING IN DANIEL MANANTA NETWORK YOUTUBE CHANNEL

This paper investigates code-switching based on the types and functions in Daniel Mananta Network YouTube Channel while interacting with each other in informal situations. A qualitative approach was applied to the descriptive study in this paper. The data were gathered through a qualitative method by observing and transcribing the selected YouTube videos. The researcher transcribed, coded, analyzed, and classified the gathered data as the main instrument to determine the classifications and functions of code-switching. The study focused on the classifications and functions of code-switching used within Indonesian-based conversation. The sample of the study involved 10 video playlists of Daniel Tetangga Kamu on the Daniel Mananta Network YouTube Channel. The duration of the 10 videos is 12 hours and 53 minutes. The findings indicate that the Daniel Mananta Network YouTube Channel’s participants frequently used intra-sentential code-switching rather than inter-sentential code-switching, tag switching, and intra-word code-switching. In addition, the researcher also found a new phenomenon of code-switching that emerged where intra-sentential code-switching occurred within inter-sentential code-switching. The findings of the study are not only code-switching but also code-mixing, which is a reduplication that occurred due to the common usage in Indonesian speech. Subsequently, the most frequent function of code-switching used by the participants in the Daniel Mananta Network YouTube Channel is lexical need. Furthermore, the participants in Daniel Mananta Network YouTube Channel code-switch are not only from Indonesian to English but also from English to Indonesian in the Indonesian-based conversation.
Fine-Grained Egocentric Action Recognition with Multi-Modal Unsupervised Domain Adaptation

Fine-grained egocentric action recognition has made significant progress because of the advancement of supervised learning. Some real-world applications require the network trained on one dataset to perform well on another unlabeled dataset due to the difficulty of annotating new data. However, due to the disparity in dataset distributions, i.e. domain shift, the network is unable to retain its good performance across datasets. Therefore, in this paper, we use unsupervised domain adaptation to address this difficult challenge, i.e. training a model on labeled source data such that it can be directly used on unlabeled target data with the same categories. First, we use Transformer to capture spatial information, and then we propose a temporal attention module to model temporal interdependence. In consideration of the fact that multi-modal data provides more kinds of important information, we build a tri-stream network for spatio-temporal information fusion. Finally, we align source data with target data using adversarial learning. Our network outperforms the baselines on the largest egocentric dataset, the EPIC-KITCHENS-100 dataset.
Factors Influencing Cryptocurrency Adoption in Georgia

Georgia is top of the list due to its tax restrictions on cryptocurrency mining activities. Furthermore, the overall costs of running a mining operation are substantially lower in Georgia. as Georgia takes place as the third biggest mining country in the world. The goal of this paper is to fill a deficiency in the existing literature that examines the present level of cryptocurrency acceptance, and adoption-influencing factors, offering an in-depth interpretation of these aspects and outlining certain problems associated with cryptocurrency adoption in Georgia. &#x0D; This study employs a quantitative approach, a total of 175 people did take part in the poll (out of 200 questionnaire respondents). The survey was held in a hybrid manner, with the questionnaire being distributed in April and May 2022 in various cryptocurrency-selling locations throughout Tbilisi, the capital of Georgia. A structural equation model was applied in the paper (SEM). The findings of the study confirm that the measurement items and constructs in the proposed model are reliable and valid. The result of the study found that there is a positive relationship between future reward, innovativeness, knowledge, and cryptocurrency adoption. Furthermore, it was found that innovativeness is significant as a mediator between future rewards and cryptocurrency adoption.&#x0D; &#x0D;
Unmanned Aircraft Systems with Autonomous Navigation

Unmanned aerial systems play an increasingly remarkable role in widely diffused application fields, from military defense programs and strategies to civil and commercial utilization [...]
SAR time series despeckling via nonlocal matrix decomposition in logarithm domain

Advanced satellite and signal processing techniques have made Synthetic Aperture Radar (SAR) capable of monitoring the dynamic processes of the earth with high temporal frequency through co-registered time series. There are several challenges in interpreting these three-dimensional data cubes, including the inherent speckle effect and outliers caused by abrupt changes or sporadically appearing objects. In this paper, we propose a novel despeckling method, LogSAR-NLMD, based on nonlocal matrix decomposition in the logarithm domain to despeckle SAR time series without producing the artifacts caused by outliers. A maximum a posteriori (MAP) problem is developed by taking into account smoothness and low-rank prior knowledge in addition to the likelihood of SAR reflectivities following the Fisher-Tippett distribution. Additionally, nonlocal self-similarity is exploited to construct time series matrices that satisfy the prior better than the original data. In order to solve the MAP problem, the variable splitting procedure and the alternating direction method of multipliers (ADMM) framework are used. The proposed method has been validated by multiple experiments on synthetic and real data by comparing it with other state-of-the-art methods for despeckling SAR time series.
Data Fabric Within an Enterprise Architecture

Any data architecture, and therefore also our Data Fabric architecture, needs to be looked at in conjunction with the implemented application architecture in an existing enterprise landscape. Many organizations are in the process to modernize and digitalize their application and data landscape. Applications have different requirements with respect to data characteristics, which may recommend a particular data architecture implementation over another, for example, characterized by data access based on data virtualization or data replication and transformation.
Efficient Deep Learning Models for Privacy-Preserving People Counting on Low-Resolution Infrared Arrays

Ultra-low-resolution Infrared (IR) array sensors offer a low-cost, energy-efficient, and privacy-preserving solution for people counting, with applications such as occupancy monitoring. Previous work has shown that Deep Learning (DL) can yield superior performance on this task. However, the literature was missing an extensive comparative analysis of various efficient DL architectures for IR array-based people counting, that considers not only their accuracy, but also the cost of deploying them on memory- and energy-constrained Internet of Things (IoT) edge nodes. In this work, we address this need by comparing 6 different DL architectures on a novel dataset composed of IR images collected from a commercial 8x8 array, which we made openly available. With a wide architectural exploration of each model type, we obtain a rich set of Pareto-optimal solutions, spanning cross-validated balanced accuracy scores in the 55.70-82.70% range. When deployed on a commercial Microcontroller (MCU) by STMicroelectronics, the STM32L4A6ZG, these models occupy 0.41-9.28kB of memory, and require 1.10-7.74ms per inference, while consuming 17.18-120.43 $\mu$J of energy. Our models are significantly more accurate than a previous deterministic method (up to +39.9%), while being up to 3.53x faster and more energy efficient. Further, our models' accuracy is comparable to state-of-the-art DL solutions on similar resolution sensors, despite a much lower complexity. All our models enable continuous, real-time inference on a MCU-based IoT node, with years of autonomous operation without battery recharging.
Smart Contract Vulnerability Detection Model Based on Siamese Network

Blockchain is experiencing the transition from the first generation to the second generation, and smart contract is the symbol of the second generation blockchain. Under the background of the explosive growth of the second-generation blockchain platform and applications represented by smart contracts, frequent smart contract vulnerability events seriously threaten the ecological security of the blockchain, reflecting the importance and urgency of smart contract vulnerability detection. In this paper, we proposed a smart contract vulnerability detection method based on a Siamese network. We combined the Siamese network with Long Short-Term Memory (LSTM) Network neural network to complete the task of smart contract vulnerability detection. The Siamese network used in this paper consists of two subnetworks that share the same parameters onto a low dimension and easily separable feature space. Siamese network is now widely used in the field of image similarity and target tracking. In this paper, we improve the Siamese network so that it can be used for smart contract vulnerability detection. By comparing with previous research results, the model has better vulnerability detection performance and a lower false-positive rate.
An Efficient Detection Model for Smart Contract Reentrancy Vulnerabilities

We propose a novel smart contract re-entry vulnerability detection model based on BiGAS. The model combines a BiGRU neural network that introduces an attention mechanism with an SVM. We start from the data features of smart contracts, learn the model layer by layer to achieve feature extraction and vulnerability identification, introduce batch normalization, Dropout processing and use improved model classifiers to improve the vulnerability identification accuracy, model convergence speed and generalization capability of smart contracts. We had conducted numerous experiments, and the experimental results showed that BiGAS Detection Model has a strong vulnerability detection ability. The accuracy of vulnerability detection reached 93.24%, and the F1-score was 93.17%. We compared our approach with advanced automated audit tools and other deep learning-based vulnerability detection methods. The conclusion was that our method is significantly better than the existing advanced methods in detecting smart contract reentrancy vulnerabilities.
An Enhanced Prototypical Network Architecture for Few-Shot Handwritten Urdu Character Recognition

Few shot models have started to gain a lot of popularity in the past few years.This is mostly because these models grant the ability to structure the representation space (classes) using a very less amount of examples for each class.Such models are usually trained on a wide range of different classes and their examples, which allows them to form and learn a decision-based metric in the process.Non-Latin languages, especially languages such as Urdu, have a bi-linear direction of writing and are context-sensitive in nature, and are hard to recognize.Also, unlike traditional English, there is a very small amount of clean, collated, and usable data that is available for the Urdu language.In this paper, we explore a prototypical network for k-shot classification on handwritten Urdu characters.The prototypical network learns the Euclidean embeddings of the provided images and uses clusters to classify newer examples.Our improved method is able to outperform other methods of few-shot learning and is able to accurately classify both Urdu characters as well as numerals using a minimal number of examples.After comprehensive qualitative and quantitative evaluation and comparison of our proposed approach with other methods to classify handwritten text in few-shot settings, we found out that our proposed approach was typically able to beat other methods by a margin of 1% -2% while relying on a small training set.
White blood cell segmentation by using vector color approach

White blood cells play an important role in protecting the body from various types of diseases, therefore blood tests are done to determine a person’s disease. Diagnosis of blood diseases is done by analyzing and counting the white blood cells in the blood. Optimal calculations and analysis of white blood cell are important, so research on white blood cell detection is a rapidly growing topic. Many detection methods are proposed using the existing segmentation techniques. In this paper, the detection method is proposed using vector operation approach instead general segmentation technique. From the experiments performed, it is shown that the proposed method can detect white blood cells on smear images. Further, this method extracts the nucleus from white blood cells well.