Reviewerly/exHarmony huggingface dataset:
Mobile-assisted pronunciation learning with feedback from peers and/or automatic speech recognition: a mixed-methods study

Although social networking apps and dictation-based automatic speech recognition (ASR) are now widely available in mobile phones, relatively little is known about whether and how these technological affordances can contribute to EFL pronunciation learning. The purpose of this study is to investigate the effectiveness of feedback from peers and/or ASR in mobile-assisted pronunciation learning. 84 Chinese EFL university students were assigned into three conditions, using WeChat (a multi-purpose mobile app) for autonomous ASR feedback (the Auto-ASR group), peer feedback (the Co-non-ASR group), or peer plus ASR feedback (the Co-ASR group). Quantitative data included the pronunciation pretest, posttest, and delayed posttest, and students’ perception questionnaires, while qualitative data included students’ interviews. The main findings are: (a) all three groups improved their pronunciation, but the Co-non-ASR and the Co-ASR groups outperformed the Auto-ASR group; (b) the three groups showed no significant difference in perception questionnaires; and (c) the interviews revealed some common and unique technical, social/psychological, and educational affordances and concerns about the three mobile-assisted learning conditions.
Efficient high-dimension feature selection based on enhanced equilibrium optimizer

Feature selection (FS) is an important task in any classification process and aims to choose the smallest features number that yields higher classification accuracy. FS can be formulated as a combinatorial NP-hard problem for which robust metaheuristics are used as efficient wrapper-based FS approaches. However, when applied for high dimensional datasets that present large features number and few samples, the effectiveness of such wrapper-metaheuristics degraded, and their computation costs increased. To tackle this problem, we propose in this paper a hybrid FS approach based on the ReliefF filter method and a novel metaheuristic Equilibrium Optimizer (EO). The proposed method, called RBEO-LS, is composed of two phases. In the first phase, the ReliefF algorithm is used as a preprocessing step to assign weights for features, which estimate their relevance to the classification task. In the second phase, the binary EO (BEO) is used as a wrapper search approach. The features are ranked according to their weights and are used for the initialization of the BEO population. We embedded the BEO with a local search strategy to improve its performance by adding relevant features and removing redundant ones from the features subset guided by the features ranking and the Pearson coefficient correlation. The performance of the developed algorithm has been evaluated on sixteen UCI datasets and ten high dimensional biological datasets. The UCI datasets contain a high number of samples and a small or medium number of features. The biological datasets present a high number of features with few samples. The results demonstrate that the use of the ReliefF algorithm and the local search strategy improves the performance of the EO algorithm. The results also show the superiority of the RBEO-LS, among other state-of-the-art approaches.
Enhanced multi-swarm cooperative particle swarm optimizer

In this paper, a novel multi-swarm particle swarm optimizer driven by delayed-activation (DA) strategy and repulsive mechanism, named as enhanced multi-swarm cooperative particle swarm optimizer (EMCPSO) is proposed. EMCPSO is designed to make use of the advantage of multi-swarm technique and overcome the problem of premature convergence of original PSO. In this algorithm, the whole population is partitioned into four identical sub-swarms. The best particle of each sub-swarm, sbest, is used to estimate the evolutionary state of the group. If the sbest can continuously improve its solution's quality, that sub-swarm evolves independently without communicating with other counterparts. Otherwise, based on a non-ascending sequence, a delayed-activation (DA) strategy will be triggered. With information sharing among multi-swarm, activating exemplar is constructed to promote the stagnant sub-swarm to search for better solutions again. On the other hand, a repulsive mechanism is introduced to prevent the whole population from gathering together prematurely. In this way, more potential regions of the search space can be explored by EMCPSO. The experiment results on CEC 2017 problem set demonstrate the superior performance of the proposed EMCPSO in terms of solution accuracy and convergence speed.
Deep Learning and Pathomics Analyses Reveal Cell Nuclei as Important Features for Mutation Prediction of BRAF-Mutated Melanomas

Image-based analysis as a method for mutation detection can be advantageous in settings when tumor tissue is limited or unavailable for direct testing. In this study, we utilize two distinct and complementary machine-learning methods of analyzing whole-slide images for predicting mutated BRAF. In the first method, whole-slide images of melanomas from 256 patients were used to train a deep convolutional neural network to develop a fully automated model that first selects for tumor-rich areas (area under the curve = 0.96) and then predicts for mutated BRAF (area under the curve = 0.71). Saliency mapping was performed and revealed that pixels corresponding to nuclei were the most relevant to network learning. In the second method, whole-slide images were analyzed using a pathomics pipeline that first annotates nuclei and then quantifies nuclear features, showing that mutated BRAF nuclei were significantly larger and rounder than BRAF‒wild-type nuclei. Finally, we developed a model that combines clinical information, deep learning, and pathomics that improves the predictive performance for mutated BRAF to an area under the curve of 0.89. Not only does this provide additional insights on how BRAF mutations affect tumor structural characteristics, but machine learning‒based analysis of whole-slide images also has the potential to be integrated into higher-order models for understanding tumor biology.
Learning from Disagreement: A Survey

Many tasks in Natural Language Processing (NLP) and Computer Vision (CV) offer evidence that humans disagree, from objective tasks such as part-of-speech tagging to more subjective tasks such as classifying an image or deciding whether a proposition follows from certain premises. While most learning in artificial intelligence (AI) still relies on the assumption that a single (gold) interpretation exists for each item, a growing body of research aims to develop learning methods that do not rely on this assumption. In this survey, we review the evidence for disagreements on NLP and CV tasks, focusing on tasks for which substantial datasets containing this information have been created. We discuss the most popular approaches to training models from datasets containing multiple judgments potentially in disagreement. We systematically compare these different approaches by training them with each of the available datasets, considering several ways to evaluate the resulting models. Finally, we discuss the results in depth, focusing on four key research questions, and assess how the type of evaluation and the characteristics of a dataset determine the answers to these questions. Our results suggest, first of all, that even if we abandon the assumption of a gold standard, it is still essential to reach a consensus on how to evaluate models. This is because the relative performance of the various training methods is critically affected by the chosen form of evaluation. Secondly, we observed a strong dataset effect. With substantial datasets, providing many judgments by high-quality coders for each item, training directly with soft labels achieved better results than training from aggregated or even gold labels. This result holds for both hard and soft evaluation. But when the above conditions do not hold, leveraging both gold and soft labels generally achieved the best results in the hard evaluation. All datasets and models employed in this paper are freely available as supplementary materials.
Research of Probability-Time Characteristics of the Wireless Sensor Networks for Remote Monitoring Systems

A number of scientific researches are being carried out aimed at creating a remote monitoring system through wireless sensor networks to ensure a stable and high-quality power supply for telecommunications equipment in the world. To solve a number of problems in this area, including Smart Grid systems, including a remote monitoring system for wireless sensors, a two-way information system, energy consumption control, an increase in energy demand, reliability and safety models, algorithms, software and technical solutions, special attention is paid to paid. At the same time, research of characteristics of the wireless sensor networks for monitoring systems is considered relevant. This paper researches of probability-time characteristics of the wireless sensor networks for remote monitoring systems of telecommunication objects. In this case, a graph model reflecting the wireless sensor network was created and the characteristics were determined using the theory of the Pubic service system. The wireless sensor network was also simulated using Anylogic software and the results obtained were compared with the analytical results.
Social media and online hate

Social media host alarming degrees of hate messages directed at individuals and groups, threatening victims' psychological and physical well-being. Traditional approaches to online hate often focus on perpetrators' traits and their attitudes toward their targets. Such approaches neglect the social and interpersonal dynamics that social media afford by which individuals glean social approval from like-minded friends. A theory of online hate based on social approval suggests that individuals and collaborators generate hate messages to garner reward, for their antagonism toward mutually hated targets, by providing friendship and social support that enhances perpetrators' well-being as it simultaneously deepens their prejudices. Recent research on a variety of related processes supports this view, including notions of moral grandstanding, political derision as fun, and peer support for interpersonal violence.
Novel and Secure Blockchain Framework for Health Applications in IoT

Internet of Things (IoT) has grown increasingly in the past decade. This growth brings up several challenging issues for a successful continuous operation of IoT applications. Some of these challenges that need to be taken care of are resource constraints, central server overload, and the risk of illegal use of private data. On the other side, Blockchain technology is increasingly popular and has gained huge success in cryptocurrencies. It offers numerous vital qualities such as a technique for consensus, peer communications, confidence-building without a trustworthy third party, and a transaction controlled by conditions and functions using the intelligent contract technique. Blockchain is an excellent candidate to establish a decentralized, autonomous IoT system addressing the above issues. This study proposes an IoT-Blockchain integration architecture using an Ethereum Blockchain infrastructure within a rich-thin client IoT approach to address the challenges created by the limited IoT resources while implementing the Blockchain mining technique in IoT systems. The architecture depends on load distribution between the resources. Limited resource devices are the thin-clients, while the higher resource devices are assigned as rich-clients. Both clients can access the blockchain and collect the data, but rich-client can only execute the mining process. In addition, we implement a healthcare system based on the proposed architecture in which surgical process management is carried out. We also prove our solution’s efficiency by testing and comparing the architecture against other well-known IoT-based blockchain architectures. The obtained results show that proposed blockchain-IoT architecture is suited for many IoT applications while avoiding difficulties created by IoT devices’ limitations.
Virtual Reality based Metaverse of Gamification

The Metaverse is a podium of user mesmerizing interactions in the virtual world with the support of the Internet, software, and hardware required to create the exceptional immersive user experience. Gamification is becoming part of our life, and with the growing technology and digitalization, it has become a prevalent part of different user activities. The increasing reputation of Metaverse is opening the more user immersive online interaction in the virtual internet world where users can meet, participate, and collaborate for a specific purpose such as social interaction. The author discusses the gamified relevant details frequently used in Metaverse since there is currently a lack of study on Gamification in Metaverse surroundings. The approach taken in this paper is understanding the concepts, followed by essential components and future contributions from the Metaverse. This paper concludes the associated future challenges which may get aroused with Metaverse for the current and future generations.
Logistic Regression

Logistic regression is a supervised machine learning algorithm developed for learning classification problems. A classification learning problem is when the target variable is categorical. The goal of logistic regression is to map a function from the features of the dataset to the targets to predict the probability that a new example belongs to one of the target classes. Figure 20-1 is an example of a dataset with categorical targets.
Healthcare Applications Using Blockchain Technology: Motivations and Challenges

Blockchain technology is one of the most important inventions and creative advancements that play a crucial role in today's business world. Blockchain technology is heading in the direction of systematic innovation and revolution. It is a digital ledger of transactions, and every block contains information of transactions linked by cryptographic references. Every block covers information and maintains trust among people based on how far they are. Blockchain is a system used for storing data, and it ensures the security of the system. The resurgence in blockchain technology has encouraged scholars and specialists over the past couple of years to carefully examine new ways of implementing blockchain technology with a wide range in the domain of healthcare. This rapid increase in blockchain technology has generated many endless possibilities. In this article, we provide a review of blockchain technology in healthcare. We present a detailed introduction, history, technical information, and types of blockchain technology. Motivations behind this technology and top healthcare projects completed using this technology are also discussed. This article is classified into three groups based on blockchain applications with their use cases. The evaluation of medical care technologies and relevant applications based on blockchain technology, such as sharing electronic medical records, remote patient monitoring, and supply chain management, are also discussed. In revolutionizing the healthcare industry, we illustrate the potential of blockchain technology. We have also focused on identifying the limitations of previous approaches. Finally, this article is concluded with some open research issues and future research direction.
Scaling-up assessment from a contextual behavioral science perspective: Potential uses of technology for analysis of unstructured text data

With technological advancement, we have increased access to unstructured text data and the means to analyze it. Such information is available from electronic health records, blogs, social media posts, and other sources and is being used in business applications and social science research. These approaches can open up new areas for analysis, summarize massive amounts of information, and provide rapid feedback. Contextual behavioral science research in this area remains extremely limited. This manuscript provides an overview of techniques suitable for beginners interested in such analyses, potential applications, and resources for learning more.
Efficient evaluation of the Number of False Alarm criterion

This paper proposes a method for computing efficiently the significance of a parametric pattern inside a binary image. On the one hand, a-contrario strategies avoid the user involvement for tuning detection thresholds and allow one to account fairly for different pattern sizes. On the other hand, a-contrario criteria become intractable when the pattern complexity in terms of parametrization increases. In this work, we introduce a strategy which relies on the use of a cumulative space of reduced dimensionality, derived from the coupling of a classic (Hough) cumulative space with an integral histogram trick. This space allows us to store partial computations which are required by the a-contrario criterion and to evaluate the significance with a lower computational cost than by following a straightforward approach. The method is illustrated on synthetic examples on patterns with various parametrizations up to five dimensions. In order to demonstrate how to apply this generic concept in a real scenario, we consider a difficult crack detection task in still images, which has been addressed in the literature with various local and global detection strategies. We model cracks as bounded segments, detected by the proposed a-contrario criterion, which allow us to introduce additional spatial constraints based on their relative alignment. On this application, the proposed strategy yields state-of the-art results and underlines its potential for handling complex pattern detection tasks.
Sensitivity to visual speed modulation in head-mounted displays depends on fixation

A primary cause of simulator sickness in head-mounted displays (HMDs) is conflict between the visual scene displayed to the user and the visual scene expected by the brain when the user’s head is in motion. It is useful to measure perceptual sensitivity to visual speed modulation in HMDs because conditions that minimize this sensitivity may prove less likely to elicit simulator sickness. In prior research, we measured sensitivity to visual gain modulation during slow, passive, full-body yaw rotations and observed that sensitivity was reduced when subjects fixated a head-fixed target compared with when they fixated a scene-fixed target. In the current study, we investigated whether this pattern of results persists when (1) movements are faster, active head turns, and (2) visual stimuli are presented on an HMD rather than on a monitor. Subjects wore an Oculus Rift CV1 HMD and viewed a 3D scene of white points on a black background. On each trial, subjects moved their head from a central position to face a 15° eccentric target. During the head movement they fixated a point that was either head-fixed or scene-fixed, depending on condition. They then reported if the visual scene motion was too fast or too slow. Visual speed on subsequent trials was modulated according to a staircase procedure to find the speed increment that was just noticeable. Sensitivity to speed modulation during active head movement was reduced during head-fixed fixation, similar to what we observed during passive whole-body rotation. We conclude that fixation of a head-fixed target is an effective way to reduce sensitivity to visual speed modulation in HMDs, and may also be an effective strategy to reduce susceptibility to simulator sickness.
Effective Content-Aware Chroma Reconstruction Method for Screen Content Images

In this paper, we propose an effective novel content-aware chroma reconstruction (CACR) method for screen content images (SCIs). After receiving the decoded downsampled YUV image on the client side, our fast chroma-copy approach reconstructs the missing chroma pixels in the flat regions of SCI. Then, for non-flat regions, a non-flat region-based winner-first voting (NRWV) strategy is proposed to identify the chroma subsampling scheme used on the server side prior to compression. Further, an effective adaptive hybrid approach is proposed to reconstruct each missing chroma pixel in the non-flat region by fusing the two reconstructed results, one from our modified NRWV-based chroma subsampling-binding and luma-guided chroma reconstruction scheme, which favors the sharp edges in SCI, as well as the other from the bicubic interpolation scheme, which favors blurred and continuous-tone textures. Further, based on the identified chroma subsampling scheme, a geometry alignment-based error compensation approach is proposed to enhance the reconstructed chroma image. Based on typical test SCIs and JCT-VC screen content videos, comprehensive experiments are carried out in HEVC-16.17 to demonstrate that in terms of quality, visual effect, and quality-bitrate tradeoff of the reconstructed SCIs, our CACR method significantly outperforms the existing state-of-the-art methods.
An effective privacy preserving mechanism for 1: M microdata with high utility

Privacy-preserving data publishing (PPDP) aims at providing an anonymized view of a private microdata to the recipients, e.g., researchers, pharmaceutical companies etc. This private data contains sensitive information about individuals that needs to be protected. In the literature, it is generally assumed that there exists a single record for one individual in any given microdata (1:1 dataset). However, more practically, there are many instances in which an individual can have multiple records in microdata (termed as 1: M datasets). Several techniques have been proposed for the 1:1 microdata but, a few researchers paid attention towards 1:M microdata problems, that perhaps led to new privacy disclosures. A novel privacy model (k, l)-diversity was proposed to cater such disclosure risks and based on this model, an algorithm named 1: M generalization was proposed. Although it was efficient than several other techniques; still has a drawback of huge information loss. In this paper, we propose a hybrid approach named as l-anatomy for 1: M microdata and prove that l-anatomy ensures the privacy of given individuals. Also, experiments performed on two real-world datasets (namely INFORMS and YOUTUBE) reveal that the proposed scheme exhibits higher efficiency and effectiveness as compared to its counterpart.
Ontology-Based Ambiguity Resolution of Manufacturing Text for Formal Rule Extraction

Manufacturing companies maintain manufacturing knowledge primarily as unstructured text. To facilitate formal use of such knowledge, previous efforts have utilized natural language processing (NLP) to classify manufacturing documents or extract manufacturing concepts/relations. However, extracting more complex knowledge, such as manufacturing rules, has been evasive due to the lack of methods to resolve ambiguities. Specifically, standard NLP techniques do not address domain-specific ambiguities that are due to manufacturing-specific meanings implicit in the text. To address this important gap, we propose an ambiguity resolution method that utilizes domain ontology as the mechanism to incorporate the domain context. We demonstrate its feasibility by extending our previously implemented manufacturing rule extraction framework. The effectiveness of the method is demonstrated by resolving all the domain-specific ambiguities in the dataset and an improvement in correct detection of rules to 70% (increased by about 13%). We expect that this work will contribute to the adoption of semantics-based technology in manufacturing field, by enabling the extraction of precise formal knowledge from text.