Reviewerly/exHarmony huggingface dataset:
Weakly supervised semantic segmentation for MRI: exploring the advantages and disadvantages of class activation maps for biological image segmentation with soft boundaries

Fully supervised semantic segmentation models require pixel-level annotations that are costly to obtain. As a remedy, weakly supervised semantic segmentation has been proposed, where image-level labels and class activation maps (CAM) can detect discriminative regions for specific class objects. In this paper, we evaluated several CAM methods applied to different convolutional neural networks (CNN) to highlight tissue damage of cod fillets with soft boundaries in MRI. Our results show that different CAM methods produce very different CAM regions, even when applying them to the same CNN model. CAM methods that claim to highlight more of the class object do not necessarily highlight more damaged regions or originate from the same high discriminatory regions, nor do these damaged regions show high agreement across the different CAM methods. Additionally, CAM methods produce damaged regions that do not align with external reference metrics, and even show correlations contrary to what can be expected.
Security Mechanisms and Log Correlation Systems

This study proposes several security measures to protect the integrity of the information and avoid threats using in-depth defense mechanisms against cyber threats, generally organizations do not have security mechanisms at the depth level, integrating centralized monitoring tools and prevention against computer attacks based on log correlation systems. The methodology used for this research was PPDIOO, the same one that contemplates the phases of: Preparation, Planning, Design, Implementation, Operation and Optimization, applied to the Security Information and Event Management (SIEM) event correlation systems using the AlienVault system. OSSIM (Open Source Security Information Manager), which allows comparing, integrating and managing security incidents in real time, implementing defense strategies. As a result of the investigation, automated components are established to strengthen the network infrastructure against computer attacks, proposing five possible scenarios that allowed validating the information presented, as a conclusion the use of the AlienVault OSSIM tool, made it possible to improve the cybersecurity mechanisms guaranteeing the integrity, security, and availability of information, preventing anomalies in the network and failures in its services, mechanisms combined with different integrated monitoring and detection tools allowing centralized security management.
Spread Virus: Usability Evaluation on a Mobile Augmented Reality Videogame

In recent years, Augmented reality (AR) videogames have not met user expectations. This is due to a lack of innovative content, unimaginative gameplay, and that there are not many AR videogames readily available to users. We present Spread Virus, a tower defense game for the AR era, implemented with Usability in mind to make sure that the user interface and in the gameplay mechanics better appeal to players. After its implementation, we used System Usability Scale (SUS) to determine its score within the scale of 0 to 100. Spread Virus was evaluated by twenty users and obtained a score of 74.63, which indicated an acceptable level. The results of the experiment, as well as the techniques used, can be used as a baseline for future research focused on the usability of AR videogames.
Learning Performance Indicators a Statistical Analysis on the Subject of Natural Sciences During the COVID-19 Pandemic at the Tulcán District

Learning systems during the COVID-19 period has been modified in terms of methodology strategies as well as teachers’ remote teaching emergency approach at primary education and higher education institutions. As a consequence, educators had to limitedly teach the basics from prioritized academic curriculums during the health emergency. Natural Sciences was not an exception, and the majority of educators in this field of study have notably identified low-academic performance during the COVID-19 pandemic. In Ecuador, learning expected results was obtained through the evaluation of performance indicators, so in this research project a statistical analysis was performed using scores for these indicators obtained from Middle School samples of students of the Carchi province, with the aim of identifying significantly affected population strata by the application of remote learning and characteristics leading to low-academic performance. Data gathered was statistically evaluated and the test was calibrated using the Item Response Theory; significative difference among variables and performance indicators were analyzed via students’ scores using ANOVA, Pairwise T-Tests, and T-tests. Difference tests were carried out using the weighted score of each student for each indicator as continuous variables and the categorical variables were the internet availability, students’ residence location and quintile they belong to. Results proved that there exist significant differences in the student scores depending on the internet availability and the zone where they live, where the academic performance was significantly higher on those students that had stable internet connection in their homes and resided in urban zones during the pandemic.
On the MLE of the Waring distribution

The two-parameter Waring is an important heavy-tailed discrete distribution, which extends the famous Yule-Simon distribution and provides more flexibility when modelling the data. The commonly used EFF (Expectation-First Frequency) for parameter estimation can only be applied when the first moment exists, and it only uses the information of the expectation and the first frequency, which is not as efficient as the maximum likelihood estimator (MLE). However, the MLE may not exist for some sample data. We apply the profile method to the log-likelihood function and derive the necessary and sufficient conditions for the existence of the MLE of the Waring parameters. We use extensive simulation studies to compare the MLE and EFF methods, and the goodness-of-fit comparison with the Yule-Simon distribution. We also apply the Waring distribution to fit an insurance data.
How can we communicate (visually) what we (usually) mean by collocation and keyness?

Abstract Corpus linguistic methods can now be easily employed in a wide range of studies within sub-disciplines of linguistics and well beyond. In a two-part paper, Gries ( 2022a , 2022b ) challenges some of the most widely used ‘association measures’ of what many might feel to be powerful aspects of text patterning: collocation and key words. While the additional association measure offers some new possibilities, this paper highlights the strong influence of another frequency parameter on odds ratio and Gries’s suggested association measure, and questions the applicability of his cautions for many different kinds of corpus research. Nevertheless, having been inspired to look at different aspects of association and dispersion more carefully, the author presents some new visualizations which were designed to communicate some of the important lessons to be learned from Gries’s papers, especially for learners and teachers using corpus tools in Second Language classrooms.
TKRM: Learning a Transfer Kernel Regression Model for Cross-Database Micro-Expression Recognition

Cross-database micro-expression recognition (MER) is a more challenging task than the conventional one because its labeled training (source) and unlabeled testing (target) micro-expression (ME) samples are from different databases. In this circumstance, a large feature-distribution gap may exist between the source and target ME samples due to the different sample sources, which decreases the recognition performance of existing MER methods. In this paper, we focus on this challenging task by proposing a simple yet effective method called the transfer kernel regression model (TKRM). The basic idea of TKRM is to find an ME-discriminative, database-invariant and common reproduced kernel Hilbert space (RKHS) to bridge MEs belonging to different databases. For this purpose, TKRM has the ME discriminative ability of learning a kernel mapping operator to generate an RKHS and build the relationship between the kernelized ME features and labels in such RKHS. Meanwhile, an additional novel regularization term called target sample reconstruction (TSR) is also designed to benefit kernel mapping operator learning by improving the database-invariant ability of TKRM while preserving the ME-discriminative one. To evaluate the proposed TKRM method, we carried out extensive cross-database MER experiments on widely used micro-expression databases, including CASME II and SMIC. Experimental results obtained proved that the proposed TKRM method is indeed superior to recent state-of-the-art domain adaptation methods for cross-database MER.
Predictive lithology mapping using semisupervised learning: Practical insights using a case study from New South Wales, Australia

We develop a comprehensive study involving three different types of machine learning (unsupervised, supervised, and semisupervised, which we emphasize) for bedrock-lithology classification using a publicly available data set from New South Wales, Australia. The goal of this work is to demonstrate (1) the value each different type of machine learning can provide and (2) which machine learning type(s) may be preferable under different circumstances. Training data are characteristically limited for geoscience problems, which makes supervised techniques susceptible to overfitting; we explore if semisupervised methods can perform better in these circumstances. Using the geophysical data and geologic map provided for the study area, we compare the performance of two supervised methods (the Light Gradient Boosting Machine and eXtreme Gradient Boosting) with one semisupervised algorithm (label propagation [LP]) in three scenarios with varied limited a priori lithologic constraints (i.e., the training data). Hyperparameter tuning is an essential component of supervised and semisupervised techniques, and the default procedure is to choose the hyperparameter combination with the largest mean cross-validation score. However, we use a new hyperparameter selection strategy that simultaneously uses the mean and standard deviation scores, and we test this new tactic for supervised and semisupervised methods. The results indicate (1) that the new hyperparameter selection technique can slightly improve the performance for supervised and semisupervised methods by 1%–2% compared with the standard selection approach and (2) that LP can outperform the two supervised methods by up to 10%, but it depends on how the training data are distributed. As for the unsupervised analysis, the clusters indicate heterogeneous regions that correlate well with the high-entropy areas in the supervised and semisupervised results. The clustering provides complementary results to the other two types of machine learning and is a source of supporting evidence for suggesting where more in-depth field mapping may be needed.
Etherless Ethereum tokens: Simulating native tokens in Ethereum

Standardized Ethereum tokens, e.g., ERC-20 tokens, have become the norm in fundraising (through ICOs) and kicking off blockchain-based DeFi applications. However, they require the user's wallet to hold both tokens and ether to pay the gas fee for making a transaction. This makes for a cumbersome user experience, and complicates, from the user perspective, the process of transitioning to a different smart-contract enabled blockchain, or to a newly launched blockchain. We formalize, instantiate, and analyze in a composable manner a system that we call Etherless Ethereum Tokens (in short, EETs), which allows the token users to transact in a closed-economy manner, i.e., having only tokens on their wallet and paying any transaction fees in tokens rather than Ether/Gas. In the process, we devise a methodology for capturing Ethereum token-contracts in the Universal Composability (UC) framework, which can be of independent interest.
Improving proximal policy optimization with alpha divergence

Proximal policy optimization (PPO) is a recent advancement in reinforcement learning, which is formulated as an unconstrained optimization problem including two terms: accumulative discount return and Kullback–Leibler (KL) divergence. Currently, there are three PPO versions: primary, adaptive, and clipping. The most widely used PPO algorithm is the clipping version, in which the KL divergence is replaced by a clipping function to measure the difference between two policies indirectly. In this paper, we revisit this primary PPO and improve it in two aspects. One is to reformulate it as a linearly combined form to control the trade-off between two terms. The other is to substitute a parametric alpha divergence for KL divergence to measure the difference of two policies more effectively. This novel PPO variant is referred to as alphaPPO in this paper. Experiments on six benchmark environments verify the effectiveness of our alphaPPO, compared with clipping and combined PPOs.
Internet Resources for Supporting English Language Learning as Target Language

The internet has a huge impact on language teaching and learning as a target language, Language teachers have increased their use of it for teaching language education. This study investigates how language teachers use online resources and applications to help English language learners to achieve their aims through language education. Language educators always use Google, YouTube, and Facebook as examples of Internet technology. These techniques are used to help teaching, self-directed learning and communication. Language training has been impacted by technological advancements and globalization. Technological improvements and globalization have a great influence on language instruction. Furthermore, the Internet connects people all over the world.
Tympanic Membrane Shape Measurement by Miniaturized High-Speed Fringe Projection Shape Measurement Using MEMS Scanning Mirror

Mammals possess an auditory system that carries out a number of functions that allow for sound perception from the surroundings. The hearing process is initiated when the tympanic membrane (TM) transfers acoustic waves from the air into mechanical vibration. The information pertaining to TM shape is essential to the expansion of our understanding of the middle ear. This will allow for better hearing protection, clinical diagnosis, and treatment of damaged or diseased ears. This chapter proposes a shape measurement approach with the use of a miniaturized fringe projection system having a scanning microelectromechanical systems (MEMS) mirror. This method utilizes a single fringe to scan the entire field of view while a series of images with spatially varied single fringe is captured by the high-speed camera. An individual multifringe image is then formed and reconstructed by summing up the single-fringe images numerically. Individually scanning with one fringe boosts optical efficiency and image quality during high-speed image capture. A National Institute of Standards and Technology (NIST)-traceable gauge helps validate shape measurements obtained using our methodologies. This shape measurement method is then applied to determine the shape of human eardrums.
The rings of real numbers modulo n and complex numbers modulo n

Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Twitter Facebook Reddit LinkedIn Tools Icon Tools Reprints and Permissions Cite Icon Cite Search Site Citation Amjad H. Alhusiny; The rings of real numbers modulo n and complex numbers modulo n. AIP Conference Proceedings 13 February 2023; 2414 (1): 040012. https://doi.org/10.1063/5.0114457 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAIP Publishing PortfolioAIP Conference Proceedings Search Advanced Search |Citation Search
Community Detection Based on Deep Dual Graph Autoencoder

Recently, researchers try to use graph neural networks (GNNs) to solve community detection, which is a fundamental task in social network analysis. We can promote targeted products and detect abnormal users by mining the community structure in social network. In this paper, we propose the Community Detection based on Deep Dual Graph Autoencoder (CDDGA). Our model consists of the deep dual graph autoencoder module and clustering layer module. The autoencoder module can simultaneously decode the graph structure and node content. The extension path between encoder and decoder is helpful to learn higher-order structural features. We use clustering layer module to achieve better clustering performance. Finally, both modules are jointly optimized to divide communities. The experimental results show that our algorithm outperforms several state-of-the-art community detection methods.
Unsupervised Online Concept Drift Detection Based on Divergence and EWMA

Concept drift problem is a common challenge for data stream mining, while the underlying distribution of incoming data unpredictably changes over time. The classifier model in data stream mining must be self-adjustable to the concept drift, otherwise it will get terrible classification results. To detect concept drift timely and accurately, this paper proposes an unsupervised online Concept Drift Detection algorithm based on Jensen-Shannon Divergence and EWMA(CDDDE), which detects concept drift through measuring the difference of data distribution within sliding windows and calculating the drift threshold dynamically by Exponentially Weighted Moving Average (EWMA), during the detection without the use of labels. Once concept drift is detected, a new classifier would be trained using the current and subsequent data. Experiments on artificial and real-world datasets show that CDDDE algorithm can efficiently detect the concept drift, and the retrained classifier effectively improves the classification accuracy for the subsequent data. Compared with some supervised algorithms, the detection accuracy and classification accuracy are higher for most datasets.
WSNet: A Wrapper-Based Stacking Network for Multi-scenes Classification of DApps

Decentralized applications (DApps) are growing rapidly with the prevalence of blockchain, but security and performance issues plague network managers and developers. Encrypted network traffic classification (ETC) plays a fundamental role in application management, security detection, and QoS improvement and requires different granularity for different scenarios. Existing work focuses on a single scenario, and objects of them are traditional centralized applications (Apps). Since DApps use similar encrypted traffic settings and the same communication interface, the traffic is more complex than Apps. Under the premise of manual-design features, sophisticated architecture, or lots of training data, existing methods have good results, otherwise suffering from low accuracy. In this paper, we propose Wrapper-based Stacking Network (WSNet). According to traffic characteristics of different scenarios, WSNet adaptively selects optimal features for different algorithms to filter out irrelevant and redundant features without human intervention, thereby improving classification efficiency. Combining with stacking technology to integrate advantages of primary learners, hence it has good performance in complex traffic scenarios. Our comprehensive experiments on two real-world datasets show that WSNet adapts to and outperforms the state-of-the-art methods.
MCSketch: An Accurate Sketch for Heavy Flow Detection and Heavy Flow Frequency Estimation

Accurately finding heavy flows in data streams is challenging owing to limited memory availability. Prior algorithms have focused on accuracy in heavy flow detection but cannot provide the frequency of a heavy flow exactly. In this paper, we designed a two-mode counter, called Matthew Counter, for the efficient use of memory and an accurate record flow frequency. The key ideas in Matthew Counter are the use of idle high-bits in the counter and the adoption of a power-weakening method. Matthew Counter allows sufficient competition during the early stages of identifying heavy flows and amplifying the relative advantage when the counter is sufficiently large to ensure the level of accuracy. We also present an invertible sketch, called MCSketch, for supporting heavy-flow detection with small and static memory based on Matthew Counter. The experiment results show that MCSketch achieves a higher accuracy than existing algorithms for heavy flow detection. Moreover, MCSketch reduces the average relative error by approximately 1 to 3 orders of magnitude in comparison to other state-of-art approaches.
LSM-Subgraph: Log-Structured Merge-Subgraph for Temporal Graph Processing

Temporal graphs, with a time dimension, are attracting increasing interest from research communities. Existing temporal graph storage formats mainly include copy-based models, log-based models, and hybrid models that have emerged in recent years. Neither the copy-based model nor the log-based model can trade-off storage and query time well. Hybrid models try to find a compromise between the above two models, but existing models do not consider the skewness of vertex degree in temporal graphs is changing over time. Based on these considerations, we propose LSM-Subgraph, a hybrid storage format that only stores snapshots divided by the fluctuation-aware method and in-between logs. First, LSM-Subgraph uses a PMA-based snapshot creation model to store snapshots based on packed memory arrays (PMA), avoiding rebuilding the whole data structure. Second, LSM-Subgraph uses a select-timepoint method based on fluctuation-aware to divide shards during the update, which achieves a good tradeoff between storage overhead and query time cost. Extensive experimental evaluations over various real-world graphs illustrate that LSM-Subgraph outperforms state-of-the-art temporal graph systems in both memory and time consumption.
OntoCA: Ontology-Aware Caching for Distributed Subgraph Matching

With the growing applications of knowledge graphs in diverse domains, the scale of knowledge graphs is dramatically increasing. Based on the fact that a high percentage of queries in practice is similar to previous queries, extensive caching methods have been proposed to accelerate subgraph matching queries by reusing the results of previous queries. However, most existing methods show poor performance when dealing with distributed subgraph matching queries, as numerous intermediate results from the caching should be transmitted to the worker nodes for further validation, leading to extra communication and computation overhead. In this paper, we propose a novel ontology-aware caching method, called OntoCA, which leverages ontology information for efficient distributed queries. Unlike the existing caching methods, our approach fully employs semantic reasoning to filter intermediate results at an early stage, thus improving the query performance. Furthermore, a workload-adaptive prefetching strategy is proposed to increase the hit ratio of OntoCA. The experimental results show that our proposed OntoCA and prefetching strategy outperforms the existing state-of-the-art distributed query method, reducing the query times by 56.16%.