Reviewerly/exHarmony huggingface dataset:
65 Preventing digital overdiagnosis

<h3>Introduction</h3> Rapid advances in computing power and machine learning techniques, coupled with the widespread adoption of connected consumer devices and sensors (smartphones, smartwatches) and health information systems (electronic health records, personal health records), have created ideal conditions for the emergence of new digital diagnostic algorithms. These are pattern-searching programs that use routinely collected health data to predict the appearance of future diseases or to detect early manifestations of subclinical diseases. Such algorithms are being applied in an increasing range of cases, including the use of smartwatches to screen for atrial fibrillation, the use of typing patterns on a keyboard to forecast Parkinson’s Disease, and the analysis of electronic health record data to predict which patients will develop sepsis in the hospital. They can be developed and deployed with relative ease, both by medical specialists and data scientists. While on the one hand, such digital tools may help to better detect and prevent real disease before it harms patients or becomes less treatable, on the other hand, these tools will increasingly be applied to healthy individuals to detect conditions that may never have harmed them, leading to an increase in digital overdiagnosis. <h3>Structure</h3> In this talk, we will a) describe the concept of digital overdiagnosis as an emerging phenomenon, b) highlight the lack of awareness and consensus in the medical and data analytics communities and the need for greater dialogue between them, c) underline the need to provide a data-centered definition of overdiagnosis, and d) propose strategies to quantify the risk of digital overdiagnosis as the first essential step to address and mitigate it during development of digital diagnosis tools. <h3>Expected Results</h3> We aim to raise awareness of the nature and risk of an important new form of overdiagnosis—digital overdiagnosis—and to establish a preliminary research agenda to address the problem. This research agenda involves exploring how data and machine learning can itself be used to mitigate the overdiagnosis risks caused by the explosion in clinical data availability and diagnostic algorithms. The benefits of preventing digital overdiagnosis include helping reduce a precipitous rise in public health expenditure and limiting individual patient and population exposure to the risks of over-investigation and overtreatment, which are likely to be increasingly generated by this techno-medical phenomenon.
Various Techniques for the Consensus Mechanism in Blockchain

A blockchain is fundamentally a distributed database of the records of all transactions or electronic events that are executed and shared with the parties involved. Blockchain is a peer-to-peer network that is a digital, decentralized, secure, and incorruptible distributed ledger containing information about transactions in financial terms. Blockchain technology is becoming more ubiquitous in various applications of information technology. Blockchain is indeed far more than a digital platform for payment. Blockchain can provide support to smart devices that are generally low powered and have less capabilities with computation. Huge amounts of computation are required for performing the mining task of block in blockchain. Blockchain is gaining worldwide acceptance nowadays because of its use in most of the current technologies and a large variety of applications. Applications that are based on blockchain benefit greatly as they have several unique characteristics when compared with standard databases.
Hate speech online: the government as regulator and as speaker

Acknowledging that the government is a user of social networks may help us to better understand its attempt to regulate them. This paper draws on the French case to show different ways of regulating hate speech online. It then turns to the peculiar case of hate speech expressed by the government. There are good reasons to consider that government hate speech can be restricted in Europe as well as in the United States. Europe and the United States however pull apart when the government regulate the discussion space below its online speech. Here, European governments are under an obligation to fight hate speech, when U.S. government infringes the First Amendment when it attempts to do so.
Strategi Dakwah Bubohu sebagai Objek Wisata Dakwah di Bumi Gorontalo pada Masa Pandemi

The Bubohu Kingdom is a symbol of Islam and Gorontalo culture, empowering the community, cultivating a micro-economy and a centre for the spread of Islam. This study examines the Islamic da'wah of the Bubohu Kingdom which developed into a da'wah tourism location in Gorontalo. This research is qualitative with a field research method. This study identifies the strategy and process of spreading da'wah that occurs in Bubohu Tourism so that it has an allure as a tourist attraction with a variable base of Islam, da'wah and history in Gorontalo. The results of this study were the strategy of preaching the Kingdom of Bubohu by conveying Islamic teachings to the people of Bubohu Gorontalo, cultivating customary principles based on syara, syara with scriptures. The missionary tourism of the Bubohu Kingdom began with the construction of a site and has since developed with several facilities, namely a natural boarding school, a museum of Indonesian wood fossils, a Bubohu Tourism Vocational School, and an organic agriculture laboratory.
A neural network based algorithm for classification of sets of human body keypoints

Human action recognition has attracted a lot of attention due to rapid development of surveillance systems and intelligent medical applications. There are multiple approaches to action recognition, such as processing images as a whole or performing analysis of a specific set of features. One of the prominent approaches is to use pose estimation to perform posture analysis. In this paper we consider the latter and present a neural network based algorithm for classification of sets of human body keypoints. The key feature of the algorithm is data preprocessing approach, which allows to increase generalization of the neural network performing classification. We also present a new dataset for the task of human body keypoints classification. The algorithm was evaluated on the presented dataset achieving 89.6% accuracy. The dataset will be available at https://github.com/oKatanaaa/SSAU-Human-Pose-Classification.
Linear Fusion with Element-Wise Knowledge

The process of combining data and estimates is inherent in estimation problems. This paper focuses on the linear fusion under the assumption that only some elements of the cross-correlation matrix of the estimation errors are known. Configurations of the knowledge are discussed individually for up to five estimates. For an arbitrary number of estimates, a general construction of upper bounds of the joint mean square error matrix is proposed. Last, the relation with the Split Covariance Intersection fusion is discussed.
Deep Refinement Network for Target based Aspect-level Sentiment Analysis on User Generated Content in Social Media

Target based aspect-level sentiment analysis (TBASA) is a most popular approach which seeks to evaluate the aspect polarity in a text. Although the sentence level sentiment analysis provides more and deeper knowledge, the aspect-category strategy is effective in providing richer and deeper information. The primary problem with sentiment analysis at aspect level is that multiple aspect categories may exhibit polarity in the sentence. Previously, the convolutional and the Recurrent Neural Network (RNN) based approaches have been utilized to predict the sentiment aspect of a based-on its category. However, the existing approaches fail in extracting significant features from the text. To address these limitations, a novel approach called Deep Refinement Network (DRN) is proposed in this paper, for performing sentiment classification in TBASA. The proposed DRN method extracts the features from both sentence and aspects tokens with the inclusion of various deep learning models to focus on the most important characteristics for each aspect category. The experimental findings show that the proposed model surpasses conventional approaches for comparison in sentiment analysis of aspects and categories.
Are We There Yet? Novices' Code Smells linked to Loop Constructs

Research on novice programmers has focused heavily on identifying errors and misconceptions and helping novices to correct them. In contrast, there is a sparse but growing effort into helping students to evaluate and improve the quality of their code once it is functionally correct. One way to improve quality is to simplify code structure by getting rid of poorly written code patterns, or so called code smells. Although code smells for software developers are well catalogued, novices exhibit a different range of code smells compared to experts. A catalogue of novices' code smells and their transformations will help instructors to discuss code quality issues with their students. Smells related to conditional expressions and conditional statements have been reported both in novice and expert studies. Our poster focuses on lesser-known novice code smells related to iteration from the literature and it also introduces a new iterative pattern, named "AskFirstOrLast", and how to refactor it.
Strype

Frame-Based Editing (FBE) is a novel programming style which encompasses the good features of both Block and Text based editing. Moreover, FBE is designed as a middle step between Block and Text based editing, in order to ease the transition from the first to the latter. Strype is a new online editor which implements the FBE idea for Python and is used for programming the micro:bit. This demo will showcase the unique characteristics of Strype and demonstrate its use. Also, this demo will introduce Strype, and FBE generally, to the educational community in order to initiate fruitful discussions, get feedback and also promote its adoption to the educational community.
Grading Mastery: Calculating Grades from Domain-Law Violations

Adaptive learning exercises intended for mastery learning are performed until the learner reaches the intended mastery level. But how to grade them if all passing students reach the same mastery at the end? Our goal was to calculate grades from the number of wrong steps and correct steps while solving the last questions in the exercise. This approach is implemented in an intelligent tutoring system and its behavior was observed during the system's evaluation. The faster the students learned, the higher were their grades. This technique can be used to grade students directly using the results of solving problems in the training system without separate summative tests. This lets students receive feedback on how well they are learning without performing more exercises.
The CCLA

The Doane University Center for Computing in the Liberal Arts (CCLA) provides a collaborative and supportive environment for Doane students, faculty, and staff, who are interested in incorporating computing into their schoolwork, projects, or research. Our operating analogy is that the CCLA is like a Writing Center, but for computing instead of prose. Ultimately, the CCLA aims to foster a culture of computing at Doane -- all of Doane. Therefore, the CCLA supports any academic discipline on campus: STEM disciplines, Business, Art, Social Sciences, Theater, and so on. Likewise, the CCLA supports a wide range of computing skills, from spreadsheets and word processing through databases and programming and into more advanced technologies such as machine learning and high-performance computing. The CCLA's mission is to be available to help any student, faculty, or staff member with any computer-related problem-solving task. Creating this culture of computing at the university will help equip our students with the computing skills necessary to become outstanding contributors and leaders in both the public and private sectors.
Mineralogical and geochemical characteristics for exploration of gold-polysulfide-quartz mineralization from ore through weathering crust to placer

We studied the peculiarities of the stockwork type gold-polysulfide-quartz mineralization in intrusive massifs and their modification from ore through weathering crust to placer on the examples of deposits from the Middle and Northern Urals. This allowed us to demonstrate the transformation of native gold features under the supergene conditions.
Banking Credit Risk Analysis with Naive Bayes Approach and Cox Proportional Hazard

Credit is needed for some people for certain purposes. In credit, it takes a party that can be used as an intermediary such as a bank. The debtor may not be able to make payments according to the original policy or even cause losses where the Bank may lose the opportunity to earn interest, causing a decrease in total income. This problem is included in the case of non-performing loans. In statistics, the duration of time between a person not making a payment on time until a non-current loan occurs can be predicted using survival analysis. Meanwhile, to predict credit status, you can use classification or prediction methods in machine learning to find out how much influence the predictor variable has. In this study, with a different case, focusing on the credit risk case of how a bank decides to provide credit to prospective debtors using the classifier method found in Machine Learning, namely Naive Bayes and Cox regression from survival analysis. Through the evaluation test of the naive bayes classifier model using accuracy values, confusion matrix and ROC, it can be concluded that this model is a model with good performance for predicting credit status. Multinomial nave Bayes in this study has a higher performance value than Gaussian Naïve Bayes and Bernoulli Naïve Bayes which is 92%. Through cox regression, it is obtained that income factors and loan history have a major influence on determining credit status.
Parallel intelligent command decision-making technology based on combat prior knowledge and reinforcement learning algorithm

Intelligent command decision-making technology based on enhanced learning algorithm has gradually become a new trend in the field of intelligent chess. However, in the complex chess environment, relying solely on intensive learning algorithms, it is difficult to gain effective experience quickly in the initial stage of the opening, convergence speed is very slow. We present the WKP-BCQ (War-Knowledge-Prior Batch-constrained deep Q-learning) algorithm based on prior knowledge to solve the problem of long-term ineffective exploration of agents. Our model generate a knowledge-based cache in the same reward environment according to the rules of experts and the actual per-person competition data. Based on this cache library, combined with discrete BCQ algorithms to train directly from cached data, an effective strategy is generated to solve the cold start problem of relying solely on intensive learning. The proposed WKP-BCQ algorithm is verified by tactical-level chess projection and confrontation system, and the experiment proves that the algorithm can efficiently generate intelligent decision suggestions and have stronger intelligence.
RF-Care: RFID-based Human Pose Estimation for Nursing-care Applications

Continuously and accurately measuring the human posture of the care receiver while the human body is partially obscured, is one of the major challenges in many future smart nursing-care scenarios. To address human posture estimation when visual information is not available, we introduce an RFID-based method to estimate human pose without introducing extra burden to the subject, named the "RF-Care" system. Multiple wearable RFID tags on human clothing and a dual-antenna reader system are used to extract multiple phase features to estimate the 3D pose of the human body. We developed an NLOS (Non-Line of Sight) human posture estimation method to estimate limb angles by modeling the signals of multiple wearable tags moving in space and then combined with a human skeleton model to reconstruct the human posture. Experimental data validate the developed method and system, and show that the RFID system can effectively monitor human posture in real time, especially when the human body is covered by a blanket. When uncovered, the average angular error of the limbs is about 6&#x00B0;, or the average positional error of the limbs is about 4 cm with Kinect 2.0 as the standard; when covered, the angular standard error of the limbs is about 6&#x00B0;, or the positional standard error of the limbs is about 4 cm. The developed system would benefit various nursing-care applications such as nursing-care robots and untethered monitoring at home.
Multi-Relational Data and Knowledge Graphs

In Chapter 3 we discussed approaches for learning low-dimensional embeddings of nodes. We focused on so-called shallow embedding approaches, where we learn a unique embedding for each node. In this chapter, we will continue our focus on shallow embedding methods, and we will introduce techniques to deal with multi-relational graphs.
Implementation of Open Source 3D Modeler Including CAM Workbench into Machine Practice Subject

Kobe City College of Technology department of mechanical engineering was starting the new curriculum from 2017. In the new curriculum, 3D CAD and 3D printing lecture in machine practice subject was start for the second grade students from 2018 because of necessary skill for mechanical engineer. On the other hand, it is difficult to introduce a large number of full-scale CNC machines into the machine practice subject because of their size and price. In this study, we attempt to establish CAM and small CNC milling machine education environment by adopting open source 3D modeler software CAM workbench for machine practice subject.
Current situation of the preservation and utilization of the items recognized as the Mechanical Engineering Heritage

In order to discuss the preserve and utilization of the items recognized as The Mechanical Engineering Heritage by JSME, which has been done since 2008fy, seven recognized items located in Fukuoka Prefecture are surveyed and reported. All the items are good condition and satisfied situation of the utilization except for two items. In spite of the existence of these exceptions, the activity on MEH are considered to be still significant to understand mechanical engineering and the history of machine and that of mechanical engineering.
FALSIFIABILITY OF SCIENTIFIC THEORIES AND THE DUHEM PROBLEM. (ANALYTICAL REVIEW)

The criterion of falsifiability is the cornerstone of the methodological doctrine of K. Popper. According to Popper, a theory is falsifiable and, therefore, scientific if there is at least one statement describing some phenomena in the world with which the prediction derived from this theory is incompatible. If falsified, the theory must be rejected. However, P. Duhem showed that scientific theories are tested experimentally only in conjunction with a number of auxiliary assumptions, the truth of which is implicitly assumed by the experimenter. At the same time, the result of the experiment, which contradicts the predictions of the system «theory and auxiliary assumptions», does not in itself say which component of this system is false. Identifying faulty components is what constitutes the Duhem problem. A promising way to solution of this problem may be associated with the application of Bayes' theorem and the methods of mathematical probability theory and statistics.
Analysis of Current Practice

The broad range of machine learning applications in architecture has produced an equally diverse array of strategies to select learning models, optimize implementation strategies, and adapt to task-specific needs. In this chapter, we revisit works introduced in Chapter 3 but, this time, we analyze varying techniques employed in these works. These comparisons identify potentially useful design practices and strategies for future work.
An energy saving strategy based on M/M/c queuing model with preemptive priority and asynchronous working vacation

In order to reduce the idle energy consumption of virtual machines (VMs), the strategy of asynchronous working vacation of partial servers is introduced. Considering that users have different priorities in practice, we introduce the preemptive priority strategy and give the high-priority users the preemptive priority to ensure its service quality. A virtual machine (VM) scheduling strategy that based on queueing theory is proposed. By establishing a three-dimensional continuous time Markov stochastic model, the expressions of the mean sojourn time of user requests and the default rate of system are obtained by using matrix-geometric solution method and Gauss-Seidel iterative method. Then we define the total energy consumption, the influence of system parameters on each performance indicator is analysed through numerical experiments. Finally, Nash equilibrium and social optimal behaviours of users are studied, and we obtain the optimal arrival rate of user requests by numerical experiments.
A Study on the Perception of Teachers on Field Experience Learning during the Corona Pandemic

Field trip is one of the important learning methods that can be learned while doing it with two meanings: field trip and experience. However, due to the influence of infectious diseases, most field trips have been reduced or postponed in front-line schools. Accordingly, the Busan Metropolitan Office of Education developed and distributed the
Conclusion

In this book, we presented the design and implementation of DBMS architectures that are explicitly tailored for NVM. It focused on three aspects of a DBMS: (1) logging and recovery, (2) storage and buffer management, and (3) indexing.
Relationship among Movement Trajectory of Body Parts, Center of Gravity Movement and Standing Postures while Chucking of an Expert and Non-Expert

The participants are an expert is 85 years has been working 70 years in lathe and the non-expert is 16 years that only 1 year of experience. The standing posture aligned with the chuck had the rotation pattern movement. It had a most distance movement of wrists, shoulders, elbows and hands. The standing on the right of the chuck was not a rotation pattern but trend line movement was a straight line. The standing posture of right side of the chuck always used his waist as like the base of movement. The standing postures did not influence the balance movement between left and right side of body. The standing postures affect the body movement on XZ plane. The expert always bent his elbow both left and right side and stretched it out of the body not only the standing aligned with the chuck but also the standing on the right of the chuck. The difference of hand holding position does not affect the standing posture on the right side of the chuck. The expert always sidle away from the machine around 45 degrees. The differences of hand holding position affect the distance and pattern movement.
Information Security

chapter Share on Information SecurityDemocratizing Cryptography: The Work of Whitfield Diffie and Martin HellmanAugust 2022 Pages 307–340https://doi.org/10.1145/3549993.3550005Published:26 August 2022Publication History 0citation4DownloadsMetricsTotal Citations0Total Downloads4Last 12 Months4Last 6 weeks2 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access