Reviewerly/exHarmony huggingface dataset:
Association rules and decision rules

Abstract Determining association rules of significant interest is an essential task within data mining and statistical analysis. In this paper, we first precisely define the notion of association rule. For this, we introduce a general model, which includes the usual transaction model, and which allows many operations on the association rules. Then, we interpret association rules as statistical decision rules. This interpretation leads to four decisional measures, one of them being the usual confidence. Then, we give some strategies based on the use of these four decisional measures in order to select or to construct association rules with a given consequent. We finally present an experimental study to illustrate these strategies. This study is carried out in R language, with the R‐package we specifically built for association rules mining.
Black Box Variational Inference with a Deterministic Objective: Faster, More Accurate, and Even More Black Box

Automatic differentiation variational inference (ADVI) offers fast and easy-to-use posterior approximation in multiple modern probabilistic programming languages. However, its stochastic optimizer lacks clear convergence criteria and requires tuning parameters. Moreover, ADVI inherits the poor posterior uncertainty estimates of mean-field variational Bayes (MFVB). We introduce "deterministic ADVI" (DADVI) to address these issues. DADVI replaces the intractable MFVB objective with a fixed Monte Carlo approximation, a technique known in the stochastic optimization literature as the "sample average approximation" (SAA). By optimizing an approximate but deterministic objective, DADVI can use off-the-shelf second-order optimization, and, unlike standard mean-field ADVI, is amenable to more accurate posterior covariances via linear response (LR). In contrast to existing worst-case theory, we show that, on certain classes of common statistical problems, DADVI and the SAA can perform well with relatively few samples even in very high dimensions, though we also show that such favorable results cannot extend to variational approximations that are too expressive relative to mean-field ADVI. We show on a variety of real-world problems that DADVI reliably finds good solutions with default settings (unlike ADVI) and, together with LR covariances, is typically faster and more accurate than standard ADVI.
IFGDS: An Interactive Fraud Groups Detection System for Medicare Claims Data

Safeguarding medicare fund security is a key concern in the medical and healthcare fields. Effectively detecting the fraud groups from massive amounts of medicare claims data is technically challenging. Therefore, we developed an interactive fraud groups detection system named IFGDS. It can screen out suspicious claims data from a large volume of medicare claims data and then detect fraud groups with "ganging up" medical visit behaviors and abnormal treatment characteristics. IFGDS provides abundant visual monitoring and analysis tools as well as a user-friendly interaction experience. It facilitates users to view patients-related information. Moreover, IFGDS has high cross-platform-portability to handle a comprehensive range of data resources. In practice, IFGDS has been applied to medicare regulatory scenarios, and has successfully recovered medicare fund among several cities.
Dynamic Mixture Vector Autoregressions with Score-Driven Weights

We propose a novel dynamic mixture vector autoregressive (VAR) model in which time-varying mixture weights are driven by the predictive likelihood score. Intuitively, the state weight of the k-th component VAR model in the subsequent period is increased if the current observation is more likely to be drawn from this particular state. The model is not limited to a specific distributional assumption and allows for straight-forward likelihood-based estimation and inference. We conduct a Monte Carlo study and find that the score-driven mixture VAR model is able to adequately filter and predict the mixture dynamics from a variety of different data generating processes, which other observation-driven dynamic mixture VAR models cannot appropriately handle. Finally, we illustrate our approach by an application where we model the conditional joint distribution of economic and financial conditions and derive generalized impulse responses.
Automated Measurement Techniques In Robotics And Quality Control With Digital-Close Range Photogrammetry

3D Image Metrology, traditionally called Photogrammetry, is a technique to measure three-dimensional coordinates using images and the principle of triangulation. By taking images from at least two different positions and measuring points of interest in each of the images, one can develop lines of sight from each location where in image was taken to the corresponding points on the object. The intersection of the lines of sight from each of the images for a particular point can then be triangulated, resulting in three-dimensional coordinates. The technology has been available for many years, but the time to develop film or glass plates before measurements could be performed hindered its widespread use. The advent of CCD-sensors and powerful computers have led to rapid developments in digital terrestrial mapping and digital close range applications.<br> Initial digital close-range photogrammetry (DCRP) development relied on manual measurements of each image and provided the capability for quick, accurate, and reliable in-place measurements of static or moving objects in vibrating or unstable environments (Atkinson K. B. 1996). The latest implementation techniques focus on the use of auto-processing targets to automate the measurement task for processing all the images as one task. This feature greatly improves the speed at which object coordinates can be measured and calculated. Today the high degree of automation, the very fast response, and very high accuracy of 3D Image Metrology systems lead to wider acceptance of the technology. They are viewed today as flexible and highly accurate coordinate machines.
Research on the Classification Algorithm of Chinese Language and Literature System Based on Artificial Intelligence Technology

As a traditional discipline, the classification of books is an important and tedious task, and with the increasing number of books, it is difficult to rely solely on librarians to classify and proofread the huge number of books, especially for students to find books related to Chinese language and literature. Therefore, in order to promote the realization of automatic classification of Chinese language and literature books and to make the practical requirements of universities satisfied, this paper improves the traditional feature selection algorithm based on artificial intelligence technology, proposes a new kind of feature selection algorithm based on category differentiation, and designs and verifies the automatic text classification system. The results show that this feature selection algorithm has higher classification accuracy and is feasible and effective compared with the traditional feature selection algorithm.
FRM: A novel Faster-RCNN Mutant network for breast lesions screening

In recent years, the Faster-RCNN network is widely used for DICOM images pathological screening in medicine. In the breast field, the Faster-RCNN algorithm model was trained with mammogram image data to screen mass and calcification, respectively. Not only reducing the workload on doctors but also lifting efficiency and lowering patient costs. While Focal loss (FL) is a loss function that can keep the balancing of the learning ability between SHL (sample which is hard to learn) and SEL (sample which is easy to learn), and implemented the evolution from two-stage to one-stage named Retinanet based on the standard Faster-RCNN network for object detection. Although FL can enhance the generalization ability of CNN to SHL, the learning weight of each category is a non-sensitive parameter. Inspired by the FL function, to make the learning weight a sensitive parameter, we modified the Faster-RCNN network structure named the Faster-RCNN-Mutant and designed a mini-dataset dynamic weights (MDDW) loss function with a regularization item based on FL by calculating the distribution of SHL and SEL from the result of RPN network and to carry out the sensitivity of parameters. In this work, compared to the results of Faster-RCNN on the VOC2007 open-source dataset, F-SHL and FA-SHL were improved by 35.29% and 30%, respectively. The precision of benignCalcification and cancerMass improved by 0.48% and 0.73% on the DDSM dataset, but the benignMass has no changes the precision is 0%, and Faster-RCNN-Mutant ultimate recognition precision of cancerMass, benignMass, and benign-Calcification are 26.17%, 0.00%, and 9.68%, respectively.
Preparing quantum many-body scar states on quantum computers

question. We present protocols both for individual scar states in a particular model, as well as superpositions of them that give rise to coherent dynamics. For superpositions of scar states, we present both a system-size-linear depth unitary and a finite-depth nonunitary state preparation protocol, the latter of which uses measurement and postselection to reduce the circuit depth. For individual scarred eigenstates, we formulate an exact state preparation approach based on matrix product states that yields quasipolynomial-depth circuits, as well as a variational approach with a polynomial-depth ansatz circuit. We also provide proof of principle state-preparation demonstrations on superconducting quantum hardware.
Non-uniformity and Quantum Advice in the Quantum Random Oracle Model

QROM (quantum random oracle model), introduced by Boneh et al. (Asiacrypt 2011), captures all generic algorithms. However, it fails to describe non-uniform quantum algorithms with preprocessing power, which receives a piece of bounded classical or quantum advice. As non-uniform algorithms are largely believed to be the right model for attackers, starting from the work by Nayebi, Aaronson, Belovs, and Trevisan (QIC 2015), a line of works investigates non-uniform security in the random oracle model. Chung, Guo, Liu, and Qian (FOCS 2020) provide a framework and establish non-uniform security for many cryptographic applications. Although they achieve nearly optimal bounds for many applications with classical advice, their bounds for quantum advice are far from tight. In this work, we continue the study on quantum advice in the QROM. We provide a new idea that generalizes the previous multi-instance framework, which we believe is more quantum-friendly and should be the quantum analog of multi-instance games. To this end, we match the bounds with quantum advice to those with classical advice by Chung et al., showing quantum advice is almost as good/bad as classical advice for many natural security games in the QROM. Finally, we show that for some contrived games in the QROM, quantum advice can be exponentially better than classical advice for some parameter regimes. To our best knowledge, it provides an evidence of a general separation between quantum and classical advice relative to an unstructured oracle.
Pengembangan Media Permainan Monopoli Guna Meningkatkan Hasil Belajar IPS Kelas V Sekolah Dasar

The development of monopoly game media to improve the learning outcomes of Class V students based on several gaps including: 1) students haven’t carried out learning interesting educational media, 2) there is no available learning that provides flexibility for students, 3) The average KKM has not the standard criteria. The development of media uses the Borg and Gall model modified as needed. There are 10 processes that implemented study, namely, 1) potential and problems, 2) gathering information, 3) product design, 4) design validation, 5) design improvement, 6) product testing, 7) product revision, 8 ) use trial, 9) final product revision, 10) mass production. The results research show that the percentage from the instrument validator is 93.8% very valid category, media validator is 92% very valid and suitable for use without improvement, material validator is 83.75% valid category and suitable for use without improvement but there are still revisions. The test results of educators are 87.4% eligible category. The results of small group trials were conducted 6 students percentage of 87.3% in the very attractive category, field trials were carried out 15 students percentage of 87.6% in the very attractive category. The results T-Test from the small group for the Pre-Test results were 63.3 and the Post-Test results obtained an average of 99.3. Meanwhile, for the second group, the average Pre-Test was 64.1 and the Post-Test result was 98.6. Based on the results of the overall data analysis, it can be concluded that the development of media monopoly game is feasible to be used as a learning medium.&#x0D; Keywords: Monopoly media, Social Science
Spremembe, ki jih prinašajo nove različice v družini standardov za informacijsko varnost ISO/IEC 27000

The family of standards ISO /IEC 27000 represents the most comprehensive series of standards in the field of information security. Their advantage is their general applicability, as they can be implemented quickly and efficiently in any organisation, regardless of its sector or size. The ISO /IEC 27000 family provides organisations with a practical and established framework for information security assessment and certification. As a result, the use of these standards in practise is widespread globally. In 2022, the ISO /IEC 27000 family underwent significant changes, with two of the most important standards receiving new versions. A new version of ISO /IEC 27002 was published in February 2022, and ISO /IEC 27001 in October 2022. The revisions are very important for organisations implementing the requirements of the standards as part of information security management. The aim of this paper is to provide a comprehensive overview of the changes introduced by the new versions ISO /IEC 27001:2022 and ISO /IEC 27002:2022 compared to the 2013 versions. We found that most of the changes were made to Annex A of the ISO /IEC 27001, which required a completely new structure for the ISO /IEC 27002. We have briefly summarized the impact of these changes on organizations applying these standards in their business operations.
A Modern Web Architecture

The degree of influence that the search purveyors have imposed on the development landscape has been significant given that the search engines have become the proxy for the will of humanity. It is the masses that dictate relevance through their interaction with online content that places this flux upon this voting system that all subscribe to and must subsequently abide by. This flux that has been placed on online content entails that the manner by which web content is to be built in the future must change given technological progress and the fact that it must conform to this voting power of the masses that is held in stewardship by the search purveyors. It will subsequently be the burden of development teams to embrace a new doctrine to their development effort whereby this leverage that was previously not harnessed can now be wielded to create value.
Identifying Patient Populations in Texts Describing Drug Approvals Through Deep Learning–Based Information Extraction: Development of a Natural Language Processing Algorithm

Background New drug treatments are regularly approved, and it is challenging to remain up-to-date in this rapidly changing environment. Fast and accurate visualization is important to allow a global understanding of the drug market. Automation of this information extraction provides a helpful starting point for the subject matter expert, helps to mitigate human errors, and saves time. Objective We aimed to semiautomate disease population extraction from the free text of oncology drug approval descriptions from the BioMedTracker database for 6 selected drug targets. More specifically, we intended to extract (1) line of therapy, (2) stage of cancer of the patient population described in the approval, and (3) the clinical trials that provide evidence for the approval. We aimed to use these results in downstream applications, aiding the searchability of relevant content against related drug project sources. Methods We fine-tuned a state-of-the-art deep learning model, Bidirectional Encoder Representations from Transformers, for each of the 3 desired outputs. We independently applied rule-based text mining approaches. We compared the performances of deep learning and rule-based approaches and selected the best method, which was then applied to new entries. The results were manually curated by a subject matter expert and then used to train new models. Results The training data set is currently small (433 entries) and will enlarge over time when new approval descriptions become available or if a choice is made to take another drug target into account. The deep learning models achieved 61% and 56% 5-fold cross-validated accuracies for line of therapy and stage of cancer, respectively, which were treated as classification tasks. Trial identification is treated as a named entity recognition task, and the 5-fold cross-validated F1-score is currently 87%. Although the scores of the classification tasks could seem low, the models comprise 5 classes each, and such scores are a marked improvement when compared to random classification. Moreover, we expect improved performance as the input data set grows, since deep learning models need to be trained on a large enough amount of data to be able to learn the task they are taught. The rule-based approach achieved 60% and 74% 5-fold cross-validated accuracies for line of therapy and stage of cancer, respectively. No attempt was made to define a rule-based approach for trial identification. Conclusions We developed a natural language processing algorithm that is currently assisting subject matter experts in disease population extraction, which supports health authority approvals. This algorithm achieves semiautomation, enabling subject matter experts to leverage the results for deeper analysis and to accelerate information retrieval in a crowded clinical environment such as oncology.
EFL Students’ Perception of Extensive Reading through MALL during the Covid-19 Pandemic

There has been growing interest in the study of extensive reading in this decade, especially in the area of students' responses by using mobile-assisted language learning (MALL). However, of those studies, only a few explored the effects of assisted learning from the students' internal lens. To fill the gap, this study investigates the students' perceptions of implementing extensive reading through mobile-assisted language learning (MALL) during the Covid-19 pandemic viewed from an internal perspective. The subjects were students' batch 2019 at English Language Study Program at Lambung Mangkurat University. The researchers used purposive sampling to select 20 students based on specific criteria. The study used a qualitative approach that focused on a case study. The instruments were semi-structured interviews. The interview data were interpreted in narration. Based on the findings, students positively perceive implementing extensive reading through MALL during the Covid-19 pandemic. It was discovered four themes involve enhancement of self-students motivations, encouragement of students' feelings, fulfilling their language needs, and reflecting students' perspectives. This study calls for the inclusion of students to read extensively using mobile devices as a good learning strategy to improve their Language skills. However, they must find ways to overcome the challenges
Investigating cognitive presence in the enactment of Blended Learning for learning English Speaking: The perspective from the higher education in Indonesian Context

Blended learning is experienced by many educators widespread all over the world, especially during this pandemic time. Even so, not many of them can ensure the effectiveness of the classroom process in providing meaningful learning and achieving learning aims. Even though a number of studies of blended learning in teaching English in the EFL context have been conducted, there is a scarcity of research that explores the students’ cognitive presence in attending the course with the learning mode of delivery. Understanding the elements of blended learning, such as cognitive presence, is crucial in creating interactive learning that contributes to the success of both learning modes, either face-to-face or online learning. Furthermore, the need of exploring cognitive presence in the implementation of blended learning is to encourage students' critical thinking and depth of knowledge. To fill the void, this qualitative study aims at investigating the students’ perspective on their cognitive presence in learning English speaking by involving 6 preservice teachers in their sophomore years of study using interviews to dig into the phenomenon and collect the needed data. The data were analyzed by using an interactive model in which the researchers carry on the data condensation, data display, and drawing conclusions. This study envisages recommendations regarding the plethora of ways shown by students in constructing and confirming meaning in shaping meaningful learning while doing blended learning. Furthermore, this study contributes to educators, language learners, and language policymakers in providing and paving the working principles in carrying out blended learning to get a successful instructional process
The Role of Online Media for Students’ Reading Skills: A Case Studies

Online media is the activities which are done throughout the internet in order to take benefits from it: such as to post, exchange, and search for online materials using symbols, text, voice, and other. Online media is known as published media over the internet, such as in web-sites, social media, and blog. However, it is self-evident that the internet not only connects people and entertains them, but also gives intellectual and scientific knowledge. The purpose of this study is to explore the students’ perception on the use of online media for their learning activities and the students perceive the use of online learning media on the development of their reading comprehension. This study used a descriptive method with qualitative design to observe and obtain information about a current phenomenon as much as possible. The instrument of this study is a semi-structured interview and questionnaire. For the data collection, the researcher gave some questions as an interview and explanation about how to answer the questionnaire. The results of research showed that online media is proven to help students in understanding the lesson. From questionnaires that have been submitted can be concluded that online media is very influential in the learning process because it makes it easier for teachers to explain and answer student questions, getting students' attention to achieve learning goals. Thus, using online media to increase students’ skills in reading comprehension is necessary as it is believed to help teachers and students to expand and support the English teaching and learning activities.