Reviewerly/exHarmony huggingface dataset:
Designing Online Public Education Contents in Korean Medicine Using the Rapid-Prototyping Instructional Systems Design Model

Objectives: The purpose of this study is to design Korean-themed online public education content in Korean medicine using rapid prototyping instructional systems design (RPISD). This study presents cases of developing and converting face-to-face general education programs designed to increase the interest in and understanding of Korean medicine for the public into online programs within a short timeframe.Methods: This qualitative study is design and development research, which used the RPISD model to analyze the available resources utilized in the rapid development of public educational content and propose systematization and optimization measures by analyzing the needs of clients, learners, and the environment. The ＜Treasured Mirror ofEastern Medicine(DUBG)Open Course＞ was developed according to the model procedure, which involved needs analysis, development of course materials and manuscript, and storyboard creation and its filming and editing. Usability tests were conducted at all stages, and the opinions of clients, instructors, experts, and instructional designers were accommodated and reflected at each stage.Results: Using the rapid prototyping model, ＜Treasured Mirror of Eastern Medicine(DUBG)Open Course＞ was organized into five classes of 20 minutes each. Each class was developed in Korean and included English, Chinese, and Japanese subtitles in addition to Korean under the cooperative instructional design among clients, subject-matter experts, instructional designer and learners.Conclusion: The cooperative instructional design of stakeholders is significant in developing Korean medicine public education content online through extensive interaction and feedback from stakeholders in the early stage of educational content development.
CMRUTU: Code Mixed Roman Urdu (Roman Urdu and English) to Urdu Translator

Urdu is the official language of Pakistan and a familiar language in the South Asian countries. It is spoken as the first language by nearly 70 million people and as a second language by more than 100 million people, mainly in Pakistan and India. Most of the textual communication is not pure Roman Urdu. There are words of actual English in between those Roman Urdu sentences. It is necessary to have a translator that can translate these code-mixed sentences into Urdu because the purpose of any language is to communicate. It can be difficult for a machine to understand the shift of languages in between a sentence. In the past, researchers have worked on Urdu transliteration and rule-based translation. However, a pure translation of mixed Roman Urdu to Urdu with such accuracy is novel. In this research, we have introduced Mixed Language (Roman Urdu and English) to the Urdu translator. A deep learning pre-trained model “g2p_multilingual_byT5_small” is fine-tuned with a newly created corpus of Mixed Roman Urdu sentences and their translations in pure Urdu. With a BLEU score of 66.73, It can translate text messages, paragraphs, or any descriptions from Roman Urdu to Urdu. We have carried out this research using Python programming language and the model training on Google Colab.
Comparison Analysis Of K-Nearest Neighbor (K-Nn) Algorithm With Naive Bayes For Fire Source Detection Mitigation

Fire is one of the disasters that often occur in Indonesia. One of the consequences of fires that occur in Indonesia is forest fires. In 2014 and 2015 alone, 2.6 million ha of forest fires were reported in Indonesia. One way to detect a fire source is by developing machine learning that is used for information processing in the event of a fire by utilizing patterns or information from large data sets. This research will develop an algorithm to detect fires by comparing the accuracy of the two algorithms, that is K-Nearest Neighbor (K-NN) and Naive Bayes. The dataset was obtained from a fire simulation using NodeMCU ESP8266 and IR Flame Sensor, MQ7, and DHT 11. Based on the composition of the training and test data, this research found the best algorithm is K-Nearest Neighbor tuning using GridSearch CV, where the best metric parameters are ‘Minkowski’, K = 1, p = 1, and weights ‘Uniform’, with a composition of 75% training data and 25% test data with accuracy 96.44%, precision 96.48%, recall 96.44%, and F1-Score is 96.33%.
Long-term tracking algorithm of soccer player based on kernelized correlation filter

The performance of player object tracking in soccer match video is seriously affected by challenges like occlusion, out-of-view, similarity interference, low resolution, etc. To solve this problem, we propose a long-term tracking algorithm based on kernelized correlation filter. Firstly, the algorithm fuses shape, color and grayscale features to enhance the object representation ability and introduces scale filter to realize real-time scale estimation to improve the tracking accuracy and robustness. Secondly, the algorithm monitors tracking status in real time by the tracking result evaluation function. If the status judged good, the multi-peak re-detection of response map is used to review the tracking result. Otherwise, the object is re-detected through sliding windows to realize long-term tracking. The experimental results tested on Benchmark for Soccer Player Tracking (BSPT) demonstrate that the proposed tracker can achieve an accurate, real-time and long-term visual tracking for soccer player while runs at speed near 80 FPS.
Collective Behavior in Quantum Interference: A Supplementary Superposition Principle

Abstract An interferometer in which all of its components are treated as quantum bodies is examined with the standard interpretation and with a model in which its uncoupled spatially separated components act collectively. These models utilize superposition principles that differ when applied to systems composed of three or more bodies. Interferometric discrepancies between these models that involve frequency shifts and recoil are shown to be difficult to measure. More pronounced differences involve quantum correlated interference. The collective model provides a missing connection between quantum and semiclassical theories. Scattering from an entangled state, which cannot be divided into disjoint parts, is proposed to involve such collective recoil. Collective scattering offers a viable supplement to the standard model, thereby providing insight into constructing tests of the superposition principle in systems with three or more bodies.
Exploiting global context and external knowledge for distantly supervised relation extraction

Distantly supervised relation extraction aims to obtain relational facts from unstructured texts. Although distant supervision can automatically generate labeled training instances, it inevitably suffers from the wrong-label problem. Most of the current work is based on the bag-level for solving the noise problem, where a bag is composed of multiple sentences containing mentions of the same entity pair. However, previous studies mostly represent sentences from a single perspective, wherein insufficient modeling of global information restricts the effectiveness of denoising. In this study, we propose a novel distantly supervised relation extraction approach that incorporates the global contextual information of sentences to guide the denoising process and generate an effective bag-level representation. Simultaneously, knowledge-aware word embeddings were generated to enrich sentence-level representations by introducing both structured knowledge from external knowledge graphs and semantic knowledge from the corpus. The experimental results demonstrate that our proposed approach outperforms state-of-the-art methods on both versions of the large-scale benchmark New York Times dataset. In addition, the differences between the two versions of the dataset were investigated through further comparative experiments.
Learning to restore multiple image degradations simultaneously

Image corruptions are common in the real world, for example images in the wild may come with unknown blur, bias field, noise, or other kinds of non-linear distributional shifts, thus hampering encoding methods and rendering downstream task unreliable. Image upgradation requires a complicated balance between high-level contextualised information and spatial specific details. Existing approaches to solving the problems are designed to focus on single corruption, which unavoidably results in poor performance when the acquisitions suffer from multiple degradations. In this study, we investigate the possibility of handling multiple degradations and enhancing the quality of images via deblurring, bias field correction, and denoising. To tackle the problems with propagating errors caused by independent learning, we propose a unified and scalable framework, which consists of three special decoders. Two decoders learn artifact attention from provided images thereby generating realistic individual artifact and multiple artifacts on single image; the third decoder is trained towards removing artifact on the synthetic image with multiple corruptions thereby generating high quality image. We additionally provide improvements over previous image degradation synthesis approaches by modelling multiple image degradations directly from data observations. We first create a toy MNIST dataset and investigate the properties of the proposed algorithm. We then use brain MRI datasets to demonstrate our method's robustness, including both simulated (where necessary) and real-world artifacts. In addition, our method can be used for single/or multiple degradation(s) synthesis by implementing the learned degradation operators in a new domain from a given dataset. The code will be released upon acceptance of the paper.
Holographic traffic signal control system based on multi-source data fusion

Adaptive control algorithms can adjust the signal period in real time and have a high calculation rate. This paper uses the holographic traffic signal control system of multi-source data fusion to fuse the original traffic data obtained by a variety of sensors, adopts adaptive control algorithm and artificial intelligence conversion technology, combines the fused traffic data information with the OpenATC open source signal machine independently developed in China, and designs a signal control scheme that conforms to the current road network. In the sumo simulation environment, the comparison and verification were carried out in coordination with the traditional static two-way green wave, and the comparison results showed that the average length of the maximum queue at a single intersection decreased by 25.03%, and the average parking delay at a single intersection decreased by 61.51%. This method can effectively alleviate the occurrence of traffic congestion events in the road network and improve the efficiency of road network traffic.
Design and implementation of information maintenance management system for mountainous rural highway slope

In terms of current status of the maintenance and management of mountainous rural highway slope, the characteristics and problems in maintenance and management of mountainous rural highway slope are analyzed. In order to improve the efficiency of slope maintenance, an informational maintenance management system of mountainous rural highway slope is developed, and the pilot application is carried out based on this system. The system is composed of the WEB management system and the terminal APP for intelligent inspection and maintenance, which integrates functions such as inspection and maintenance of slope, evaluation of security protection grade, monitoring and early warning, and statistical analysis. With the aid of informational technology, the system can realize a timely and effective maintenance and management of damages and danger of mountainous rural highway slope for the administration, and promote the informatization level of inspection and maintenance management of mountainous rural highway slope. The pilot application of the system has achieved good effects, which provides a scientific and effective decision-making basis for the inspection and maintenance of mountainous rural highway slope, and improves the level and efficiency of maintenance and management of mountainous rural highway slope.
Influence of different environmental aging tests on image quality attenuation of optical imaging system

Optical imaging systems are widely used in various video surveillance sites, which may endure various harsh environmental aging conditions such as salt mist, change of temperature. However, there are few studies on the impact of various environmental tests on the overall imaging performance of the system. In this paper, several common environmental tests are selected to analyze their quantitative impact on the imaging quality of typical imaging systems. The imaging resolution, color revent and gray scale value are selected as the main imaging parameters. The study found that the salt mist test, gas corrosion, damp heat test, high and low temperature shock, and ultraviolet test have a great impact on the optical imaging system. Long-term exposure will cause the imaging quality of the imaging system to decrease significantly, with typical parameters attenuated by more than 10%. High temperature test, low temperature test, vibration test, waterproof test, low air pressure, shock and dust test have little effect on its image quality, the system has good tolerance to it, and the typical parameter attenuation is below 5%. This paper will provide some reference for the rational use of optical imaging systems and the compilation of test standards.
A Hybrid Model for Cloud Data Security Using ECC-DES

An online data storage and retrieval system known as a cloud computing environment makes it easier for users to access data from virtually anywhere at any time. However, according to the CIA triad, data kept on the cloud is vulnerable to data breaches. Data integrity and authentication can be compromised since end users and third parties are both permitted access to the data. With the help of cryptographic algorithms like elliptic curve cryptography (ECC), numerous methods and protocols have been developed to ensure the security and integrity of data. A popular symmetric key block cypher method is the data encryption standard (DES). Up until it was proven unsafe, the security of DES was a sensitive and resolved topic. In this research, we present a method for protecting data transmission among users in cloud computing that, when combined with ECC, can address the security issue with DES. We present a hybrid approach that combines two cryptographic methods. We propose a solution to reduce the key size issue. In comparison to existing encryption systems, our system ensures data confidentiality and authentication integrity. By retaining more space, our plan reduces computational complexity.
Lexical Normalization of Roman Urdu

Roman Urdu is an informal form of writing the Urdu language which is written in Latin script. It is the language most widely used on the internet, social media, and text messaging by native Urdu speakers. The problem that arises with Roman Urdu is an inconsistent way of writing by different people. No standard rules are defined for writing Roman Urdu which makes it very difficult to perform Natural Language Processing. To overcome this issue, the text needs to be normalized to perform effective analysis. Therefore, this work provides a Roman Urdu dictionary that works as the foundation for processing Roman Urdu. It also proposes a model for the lexical normalization of Roman Urdu text.
Constructiveness-Based Product Review Scoring Using Machine Learning

To make the internet a more productive environment, it is vital to promote constructiveness in online discussion forums. Customers are regularly offered the chance to share their thoughts and experiences with a product on online marketplaces. Generally, online products have fewer constructive reviews, and some of them are unrelated to the product. Existing approaches focus on textual features to classify a product's constructiveness and ignore semantic and contextual information about the reviews. The directed graph model has been utilized in this study to represent information about the product. Also, the node and graph level features like average in-degree, out-degree, and clustering coefficients are used to model constructiveness in product evaluation to encourage the most informative reviews. Graph embedding techniques are used to depict each node as a vector into low-dimensional space and preserve the structure and properties of the graph as well. The topic modeling approach has been used to contextualize the reviews with the appropriate product. Additionally, we employed logistic regression, random forest, Gaussian naive Bayes, support vector machine (SVM), and Gradient Boosting Machine models trained on Amazon product reviews and constructive news corpus for constructiveness. These ML models outperform the baseline approach, achieving a 90% F1-Score.
Exploring and Describing the Evidence in Graphics

Subject Biomathematics and Statistics Collection: Oxford Scholarship Online
Further Issues Involved in the Modeling of Counts

Abstract This chapter discusses the use of offsets to account for heterogeneity in the sampling effort and incidence in count GLMs. The use of offsets is related to modeling counts or rates using the Poisson and negative binomial distributions. It goes on to discuss log-linear models as special cases of count GLMs, and provides a detailed example of model selection using a log-linear model. All statistical models are fitted in both the frequentist and Bayesian frameworks.
Post-Handover Quality Management Index of Electric Housing Work

Since housing electrical work is essential in the aspect of electricity use and electrical accidents, it is necessary to properly design, construct, and manage it to avoid quality problems. However, disputes are intensifying in Korea due to numerous quality problems after the house is handed over to the owner. Although related standards have been established and some studies have been conducted, there is a lack of discussion on housing electrical work because the housing quality problem is varied. In this study, the cases of quality problems that occurred in housing electrical works were investigated, while 52 quality control indices were presented and classified according to detailed work category, object, and problematic occurrences. In addition, the number of cases of quality problems for each quality management index and the repair cost ratio were used as a scale to understand the status of quality management. Finally, 11 quality indices were suggested as candidates of important indices which needed to be controlled to focus on the housing electrical work by comparing the above 52 indices. The comparison was made on which one was best suited to the Pareto principle, a quality management strategy, among the candidates for the critical index and the indices suggested by relevant standards and previous studies. The comparison results showed that the candidates of the essential indices suggested in this study conformed to the Pareto principle, while other indices hardly conformed to it. Therefore, if the quality control strategy of choice and concentration for housing electrical works is used, focusing on these vital index candidates can effectively assist in handling the occurrence of quality problems.
The World as Hypertext

Abstract Chapter 1 introduces phenomenalism as a surprising but promising inversion of ordinary ways of thinking about the relationship between mind and matter.
Choose Your Own Adventure

Abstract Chapter 10 concludes the book with a summary of my overall case for phenomenalism, and a comparison between phenomenalism and David Lewis’s Humean supervenience, a prominent theory whose main sources of anxiety---chance and experience---are the phenomenalist’s metaphysical foundations.
A Signal in the Noise

Abstract Chapter 3 develops the following argument for phenomenalism: the physical world is whatever explains the regularity of experience; the explanation is that there are objective tendencies for experiences to exhibit such regularity; therefore, the physical things we perceive are such tendencies.
Dedication

Extract If it looks like a duck, swims like a duck, and quacks like a duck, then it is probably a duck. —unknown
Using R and RStudio: The Bare-Bones Basics

Subject Biomathematics and Statistics Collection: Oxford Scholarship Online
Possibilities for What?

Abstract Chapter 4 develops a notion of an ideal spacetime in which the experiences of different conscious subjects can occur, and defines ideal objects and events in terms of interdependent experiences co-located in ideal spacetime.
A Revealing Correspondence

Abstract Chapter 6 develops the following argument for phenomenalism: there is an exceptionless correlation between physical things and certain possibilities of sensation; the best explanation for this correlation is that physical things just are such possibiities; so, physical things are possibilities of sensation.
Moderators

Abstract Chapter 2 investigates the epistemology of online content moderation, arguing that current corporate practices promote epistemic injustice and exploit workers in traumatizing ways. The chapter argues that the labor model of commercial content moderation prevents workers from developing necessary epistemic virtues. It provides an account of the epistemic exploitation of moderators and suggests ways to make the work less exploitative. Then it draws on this account and internet studies scholars’ use of the metaphor of digital garbage to provide an account of a novel kind of epistemic injustice, which is called ‘epistemic dumping.’ In epistemic dumping, the labor of sorting, handling, and disposing of the toxic epistemic trash of epistemic communities is disproportionately performed by some (typically traditionally oppressed) groups, while other (typically traditionally dominant) groups benefit from this labor though usually being oblivious to its performance or dismissive of the epistemic labor involved in handling the trash.
Internet Research Ethics for Philosophers: Privacy, Positionality, and Power

Subject Moral Philosophy Collection: Oxford Scholarship Online
Fakers

Abstract Chapter 4 addresses fake news. It argues that normative social epistemology helps us understand why the fake news problem is a problem, a point that more descriptive studies lack the tools to show. This chapter shows how racism shapes online disinformation and how disinformation fuels racism. Fake news does not just generate a generic form of ignorance; instead, it often feeds a specifically racist form of ignorance: white ignorance. This chapter argues that feminist accounts of objectivity can provide tools for platforms to avoid a flight to neutrality that prevents them from accepting responsibility for their role in the fake news problem.