Reviewerly/exHarmony huggingface dataset:
Weighting and thresholding-based detail preserving image enhancement of gastrointestinal images with noise suppression

Endoscopy is a widely practiced technique across the globe, utilized to envision the gastrointestinal (GI) tract of the human body without causing harm. In GI endoscopy, images affected by noise may not be helpful to support a visual diagnosis of the mucosal surface at good quality. In such advanced imaging systems, image quality enhancement becomes particularly beneficial. In this study, a non-invasive numerical method of image enhancement is examined to improve the image quality metrics as is critical in GI imaging. A unique approach of weighting and thresholding is examined in this study, where the weighting and thresholding play a significant role in image enhancement. Deviations from the proposed weighting scheme results in the reduction of the peak signal to noise ratio (PSNR) among other key metrics. The proposed method evaluated on eight datasets are expounded to produce optimal performance, particularly in that of the PSNR, entropy and contrast over the state-of-the-art (SOA) enhancement algorithms. The proposed method yields a universal image quality index of 93 %, a structural similarity of 86 % and that of 25 %, 11 %, 19 % and 30 % of PSNR, entropy, contrast, and distortion metric enhancements on average in comparison with the SOA, contributing to the improved quality of the resultant frame aiding to view image features at higher detail and overall quality, both quantitively and qualitatively. This in conjunction with higher optical magnification and faster graphics processing units (GPU’s) may be beneficial for a real-time examination of the mucosal surface of the GI tract at superior quality.
LARF: Two-Level Attention-Based Random Forests with a Mixture of Contamination Models

This paper provides new models of the attention-based random forests called LARF (leaf attention-based random forest). The first idea behind the models is to introduce a two-level attention, where one of the levels is the “leaf” attention, and the attention mechanism is applied to every leaf of trees. The second level is the tree attention depending on the “leaf” attention. The second idea is to replace the softmax operation in the attention with the weighted sum of the softmax operations with different parameters. It is implemented by applying a mixture of Huber’s contamination models and can be regarded as an analog of the multi-head attention, with “heads” defined by selecting a value of the softmax parameter. Attention parameters are simply trained by solving the quadratic optimization problem. To simplify the tuning process of the models, it is proposed to convert the tuning contamination parameters into trainable parameters and to compute them by solving the quadratic optimization problem. Many numerical experiments with real datasets are performed for studying LARFs. The code of the proposed algorithms is available.
Theory and Applications of Web 3.0 in the Media Sector

We live in a digital era, with vast technological advancements, which, among others, have a major impact on the media domain. More specifically, progress in the last two decades led to the end-to-end digitalization of the media industry, resulting in a rapidly evolving media landscape. In addition to news digitization, User-Generated Content (UGC) is dominant in this new environment, also fueled by Social Media, which has become commonplace for news publishing, propagation, consumption, and interactions. However, the exponential increase in produced and distributed content, with the multiplied growth in the number of plenary individuals involved in the processes, created urgent needs and challenges that need careful treatment. Hence, intelligent processing and automation incorporated into the Semantic Web vision, also known as Web 3.0, aim at providing sophisticated data documentation, retrieval, and management solutions to meet the demands of the new digital world. Specifically, for the sensitive news and media domains, necessities are created both at the production and consumption ends, dealing with content production and validation, as well as tools empowering and engaging audiences (professionals and end users). In this direction, state-of-the-art works studying news detection, modeling, generation, recommendation, evaluation, and utilization are included in the current Special Issue, enlightening multiple contemporary journalistic practices and media perspectives.
Hardware Error Correction for MZI-Based Matrix Computation

With the rapid development of artificial intelligence, the electronic system has fallen short of providing the needed computation speed. It is believed that silicon-based optoelectronic computation may be a solution, where Mach-Zehnder interferometer (MZI)-based matrix computation is the key due to its advantages of simple implementation and easy integration on a silicon wafer, but one of the concerns is the precision of the MZI method in the actual computation. This paper will identify the main hardware error sources of MZI-based matrix computation, summarize the available hardware error correction methods from the perspective of the entire MZI meshes and a single MZI device, and propose a new architecture that will largely improve the precision of MZI-based matrix computation without increasing the size of the MZI's mesh, which may lead to a fast and accurate optoelectronic computing system.
Biostratigraphy v. geophysics; correlation of Middle Turonian chalks in the Anglo-Paris Basin

High-resolution correlations between boreholes are commonly based on a combination of geophysical well-log data and biostratigraphy, and when these conflict resolution can be contentious and difficult. Such a situation exists in the Middle Turonian chalks of the Anglo-Paris Basin, where electrical resistivity logs, registering thin but seemingly laterally continuous beds of clay-rich chalk (marls) in mostly uncored boreholes, provide apparently straightforward correlations across broad regions. In contrast, detailed biostratigraphical analysis using microcrinoids provides very different results, showing the presence of a major hiatus associated with a hardground and differing significantly from the geophysical correlations. Detailed re-examination of this contentious problem, utilizing exposures adjacent to boreholes, demonstrates the supremacy of detailed biostratigraphical analysis, independently supported by regional thickness patterns, cyclostratigraphy and geochemistry. Although closely spaced geophysical well logs can afford convincing correlations, their use in correlation over broader geographical regions leads to erroneous and misleading conclusions.
Evolution of Scientific Information Services in the Context of Open Digital Science

The article is devoted to the information infrastructure of scientific research in the European Union, which is the world leader in this field of organization of science. The main factors determining the development of infrastructure are the digitalization of scientific communications and the movement of open science. The scientific infrastructure ecosystem has replaced most scientific information resources and services. The general structure of this ecosystem in the European Union is described, the navigator of the Knowledge section is provided. Selected scientific and infrastructure projects of the Horison 2020 program are briefly described. An annotated list of services offered by the specialized infrastructure organization OpenAIRE is provided.
Anomaly detection of vectorized time series on aircraft battery data

The power supply system, as an indispensable electronic hardware module in most vehicles, needs the highest level of security and reliability to ensure the normal operation of the vehicle. Efficiently identifying any faulty battery at the earliest stage would prevent potential safety hazards. This paper aims to detect anomalous batteries using a time series analysis of their internal resistance. To identify the most meaningful patterns and extract their features, we propose a method named Pattern-based Vectorization for Time series (PVT). The PVT first encodes the local sequential shapes as unique symbols by sliding window, then maps each time series into a sequence of representative symbols, and finally generates the symbolic feature matrix for the whole time-series data via a TF-IDF statistical method. The effectiveness of PVT has been systematically evaluated with 7 classifiers on a large real civil aviation battery dataset collected from an uninterruptible power system. Our results show that PVT can significantly improve the performance of existing classifiers in detecting abnormal batteries. In particular, the combination with the RUBT classification model achieves the highest performance due to the random undersampling and boosting techniques that suit imbalanced data in anomaly detection scenarios.
Hypercomplex context guided interaction modeling for scene graph generation

Intuitively, humans can consciously and subjectively attend to the interactions between objects, and thus infer reasonable visual relations. However, mainstream approaches of Scene Graph Generation (SGG) strive to alleviate the long-tailed distribution problem with various complicated re-weighting strategies, where a simple concatenation of the refined object features is treated as the final representation of visual relations. In spite of their remarkable progress, such an operation overlooks the importance of interaction on relation recognition. To tackle the problem, this work devises a hyperComplex- Context guided Interaction Modeling (CCIM for short) plug-in, which can be successfully assimilated by the existing methods for performance improvement. Specifically, we first extract the contextual relation feature determined by the constraint relation≈union(head,tail)−headobject−tailobject. Then, we encode the features of relations and objects into hypercomplex space, with three imaginary components, to learn more expressive representations for SGG. Next, guided by the context, we can capture the interaction between a head or tail object and their relation through the Hamilton product. We further reinforce the interaction between enhanced hypercomplex-valued representations of the two entities with Quaternion inner product. At last, the concatenation of all components from the learned hypercomplex feature is adopted as our final relation representation. Extensive experiments on the popular benchmark Visual Genome in various existing approaches demonstrate the effectiveness and generalization of our proposed model-agnostic method under comprehensive evaluation metrics.
The Application of the Gesture Analysis Method Based on Hybrid RF and CNN Algorithms in an IoT–VR Human–Computer Interaction System

With the development of the Internet of Things (IoT) and virtual reality (VR) technology, the demand for high-precision gesture intelligent analysis of a human–machine interaction module for IoT–VR systems is increasing. Therefore, random forest (RF) and convolution neural network (CNN) algorithms are used in this study to build an intelligent gesture recognition model. The experiments were conducted to test the application performance of the design model. The test results show that the qualification rate of the analytical model designed in this study is significantly higher than that of the comparative model. When the threshold is determined to be 43.26 mm, the analytical qualification rates of the RF-CNN (the method of combining RF with CNN algorithms), faster regions with CNN features (Faster-RCNN), and RF models are 82.41%, 76.10%, and 59.10%, respectively. The calculation time of the RF–CNN model is between the two comparative models. From the test data, it can be observed that the research results have certain significance for improving the accuracy of gesture machine recognition technology in China’s VR Internet of Things (IoT) system.
Improving logical flow in English-as-a-foreign-language learner essays by reordering sentences

Argumentation is ubiquitous in everyday discourse, and it is a skill that can be learned. In our society, it is also one that must be learned: education systems all over the world agree on the importance of argumentation skills. However, writing effective argumentation is difficult, and even more so if it has to be expressed in a foreign language. Existing artificial intelligence systems for language learning can help learners: they can provide objective feedback (e.g., concerning grammar and spelling), as well as providing learners with opportunities to identify errors and subsequently improve their texts. Even so, systems aiming at higher discourse-level skills, such as persuasiveness and content organisation, are still limited. In this article, we propose the novel task of sentence reordering for improving the logical flow of argumentative essays. To train such a computational system, we present a new corpus called ICNALE-AS2R, containing essays written by English-as-foreign-language learners from various Asian countries, that have been annotated with argumentative structure and sentence reordering. We also propose a novel method to automatically reorder sentences in imperfect essays, which is based on argumentative structure analysis. Given an input essay and its corresponding argumentative structure, we cast the reordering task as a traversal problem. Our sentence reordering system first determines the pairwise ordering relation between pairs of sentences that are connected by argumentative relations. In the second step, the system traverses the argumentative structure that has been augmented with pairwise ordering information, in order to generate the final output text. Empirical evaluation shows that in the task of reconstructing the final reordered essays in the dataset, our reordering system achieves .926 and .879 in longest common subsequence ratio and Kendall's Tau metrics, respectively. The system is also able to perform the reordering operation selectively, that is, it reorders sentences when necessary and retains the original input order when it is already optimal.
Watermarking Protocols: A Short Guide for Beginners

Watermarking protocols, in conjunction with digital watermarking technologies, make it possible to trace back digital copyright infringers by identifying who has legitimately purchased digital content and then illegally shared it on the Internet. Although they can act as an effective deterrent against copyright violations, their adoption in the current web context is made difficult due to unresolved usability and performance issues. This paper aims at providing researchers with the basics needed to design watermarking protocols suited to the web context. It is focused on two important aspects. The first concerns the basic requirements that make a protocol usable by both web users and content providers, whereas the second concerns the security primitives and how they have been used to implement the most relevant examples of watermarking protocols documented in the literature. In this way, researchers can rely on a quick guide to getting started in the field of watermarking protocols.
Time series forecasting based on a novel ensemble‐based network and variational mode decomposition

Abstract Statistical and computational intelligence methods contain weaknesses in handling nonlinearity, non‐stationarity and noise. This research develops a novel decomposition ensemble‐based network named VMD‐DENetwork for time series forecasting over different horizons. A robust decomposition technique called variational mode decomposition (VMD) is applied to decompose the input sequence into several intrinsic modes in a non‐recursive manner. The optimal number of intrinsic modes is selected based on a comprehensive analysis to ensure the stability of the framework. The proposed DENetwork is developed based on stacking architecture and constitutes heterogeneous learners to model the nonlinear and complex relationships. It combines a convolutional neural network, long short‐term memory and an extreme learning machine. A firefly optimization algorithm is adopted for utilizing hyperparameters of the proposed model to enhance the efficiency of VMD‐DENetwork. The forecasting performance is verified by using six real‐world data sets from the New York Mercantile and International Petroleum Exchange. The final obtained results are compared with several peer‐advanced algorithms using the root mean squared error (RMSE), mean absolute error (MAE), Theil inequality coefficient (TIC) and correlation coefficient (R) metrics. The experimental results confirm that the proposed model demonstrates outstanding prediction performance. The employed optimization algorithm is compared with three frequently used bio‐inspired optimization algorithms, and their performance is tested using standard CEC benchmarks.
Use of Web Tools in University Students for the Elaboration of Their Own Learning Environments (PLE)

This article highlights the importance of guiding the student towards the search for new tools that help him to fulfil tasks oriented to work and study. For this, a review of research articles that rescue active methodologies and the implementation of strategies by the teacher to guide students to autonomous learning is carried out. Among the objectives are: to define new ways of coping with a teaching methodology through a flipped classroom, identifying integrating activities that can be developed in a virtual environment to finally design evaluation activities that allow the student to self-evaluate and identify which it is your cognitive conflict and overcome it. The results are based on the coincidences and discrepancies of 11 articles that demonstrate the existing limitations. It is concluded that the responsibility lies with the students to take control and manage their learning.
A Semantic Partitioning Method for Large-Scale Training of Knowledge Graph Embeddings

In recent years, knowledge graph embeddings have achieved great success. Many methods have been proposed and achieved state-of-the-art results in various tasks. However, most of the current methods present one or more of the following problems: (i) They only consider fact triplets, while ignoring the ontology information of knowledge graphs. (ii) The obtained embeddings do not contain much semantic information. Therefore, using these embeddings for semantic tasks is problematic. (iii) They do not enable large-scale training. In this paper, we propose a new algorithm that incorporates the ontology of knowledge graphs and partitions the knowledge graph based on classes to include more semantic information for parallel training of large-scale knowledge graph embeddings. Our preliminary results show that our algorithm performs well on several popular benchmarks.
RealGraph+: A High-Performance Single-Machine-Based Graph Engine that Utilizes IO Bandwidth Effectively

This paper proposes RealGraph+, an improved version of RealGraph that processes large-scale real-world graphs efficiently in a single machine. Via a preliminary analysis, we observe that the original RealGraph does not fully utilize the IO bandwidth provided by NVMe SSDs, a state-of-the-art storage device. In order to increase the IO bandwidth, we equip RealGraph+ with three optimization strategies to issue more-frequent IO requests: (1) User-space IO, (2) Asynchronous IO, and (3) SIMD processing. Via extensive experiments with four graph algorithms and six real-world datasets, we show that (1) each of our strategies is effective in increasing the IO bandwidth, thereby reducing the execution time; (2) RealGraph+ with all of our strategies improves the original RealGraph significantly; (3) RealGraph+ outperforms state-of-the-art single-machine-based graph engines dramatically; (4) it shows performance comparable to or even better than those of other distributed-system-based graph engines.
Locating Faulty Applications via Semantic and Topology Estimation

With the explosion of Internet product users, how to locate the faulty ones from numerous back-end applications after a customer complaint has become an essential issue in improving user experience. However, existing solutions mostly rely on manual testing to infer the fault, severely limiting their efficiency. In this paper, we transform the problem of locating faulty applications into two subproblems and propose a fully automated framework. We design a scorecard model in one stage to evaluate the semantic relevance between applications and customer complaints. Then in the other stage, topology graphs that reflect the actual calling relationship and engineering connection relationship between applications are utilized to evaluate the topology relevance between applications. Specifically, we employ a multi-graph co-learning framework constrained by consistency-independence loss and an engineering-theory-driven clustering strategy for the unsupervised learning of graphs. With semantic and topology relevance, we can comprehensively locate relevant faulty applications. Experiments on the Alipay dataset show that our method gains significant improvements in both model performance and efficiency.
Deep Intention-Aware Network for Click-Through Rate Prediction

E-commerce platforms provide entrances for customers to enter mini-apps that can meet their specific shopping requirements. Trigger items displayed on entrance icons can attract more entering. However, conventional Click-Through-Rate (CTR) prediction models, which ignore user instant interest in trigger item, fail to be applied to the new recommendation scenario dubbed Trigger-Induced Recommendation in Mini-Apps (TIRA). Moreover, due to the high stickiness of some customers to mini-apps, existing trigger-based methods that over-emphasize the importance of triggers, are undesired for TIRA, since a large portion of customer entries are because of their routine shopping habits instead of triggers. We identify that the key to TIRA is to extract customers’ personalized entering intention and weigh the impact of triggers based on this intention. To achieve this goal, we convert CTR prediction for TIRA into a separate estimation form, and present Deep Intention-Aware Network (DIAN) with three key elements: 1) Intent Net that estimates user’s entering intention, i.e., whether he/she is affected by the trigger or by the habits; 2) Trigger-Aware Net and 3) Trigger-Free Net that estimate CTRs given user’s intention is to the trigger-item and the mini-app respectively. Following a joint learning way, DIAN can both accurately predict user intention and dynamically balance the results of trigger-free and trigger-based recommendations. Experiments show that DIAN advances state-of-the-art performance in a large real-world dataset, and brings a 9.39% lift of online Item Page View and 4.74% CTR for Juhuasuan, a famous mini-app of Taobao.
Efficient Fair Graph Representation Learning Using a Multi-level Framework

Graph representation learning models have demonstrated great capability in many real-world applications. Nevertheless, prior research reveals that these models can learn biased representations leading to unfair outcomes. A few works have been proposed to mitigate the bias in graph representations. However, most existing works require exceptional time and computing resources for training and fine-tuning. In this demonstration, we propose a framework FairMILE for efficient fair graph representation learning. FairMILE allows the user to efficiently learn fair graph representations while preserving utility. In addition, FairMILE can work in conjunction with any unsupervised embedding approach based on the user’s preference and accommodate various fairness constraints. The demonstration will introduce the methodology of FairMILE, showcase how to set up and run this framework, and demonstrate our effectiveness and efficiency to the audience through both quantitative metrics and visualization.