Reviewerly/exHarmony huggingface dataset:
Safety Evaluation and Effect Deconstruction of Blood Lipid Test in the Diagnosis of Cardiovascular Disease Patients

With the increase in the number of cardiovascular patients in China, this paper must use better detection methods to examine these patients. Blood lipid testing is a quantitative method for the determination of lipids contained in blood (plasma). Blood lipids are the general term for neutral fats (triglycerides and cholesterol) and lipids (phospholipids, glycolipids, sterols, and steroids) in plasma, which are widely present in the human body. At the same time, this paper also applies this technology to different examination procedures at various current stages. Cardiovascular disease, also known as circulatory system disease, is a series of diseases involving the circulatory system. The circulatory system refers to the organs and tissues that transport blood in the human body, mainly including the heart and blood vessels (arteries, veins, and microvessels), which can be subdivided into acute and chronic and are generally related to arteriosclerosis. In recent years, the most widely used is blood lipid measurement. Therefore, this paper proposes a study on the safety evaluation and effect analysis of blood lipid testing in the diagnosis of cardiovascular disease patients. This article focuses on the causes of cardiovascular disease and introduces the purpose, methods, results, and conclusions of blood lipid detection. Combined with the deep learning method combined with wavelet analysis and support vector machine (SVM), the in-depth analysis is carried out, and suitable patients are selected for blood lipid detection. The final experimental results showed that apolipoprotein B (APOB), total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), triglyceride (TG), and other indicators were significantly different from the control group. The values of apolipoprotein A1 (APOA1) and high-density lipoprotein cholesterol (HDL-C) were significantly different from those in the control group, <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mi>P</mi> <mo>&lt;</mo> <mn>0.05</mn> </math> . This shows that blood lipid examination has a good effect on the diagnosis of cardiovascular patients; it can make accurate diagnosis and prediction of the patient’s condition and has good clinical application value.
Model-Agnostic Scoring Methods for Artificial Intelligence Assurance

State of the art Artificial Intelligence Assurance (AIA) methods validate AI systems based on predefined goals and standards, are applied within a given domain, and are designed for a specific AI algorithm. Existing works do not provide information on assuring subjective AI goals such as fairness and trustworthiness. Other assurance goals are frequently required in an intelligent deployment, including explainability, safety, and security. Accordingly, issues such as value loading, generalization, context, and scalability arise; however, achieving multiple assurance goals without major trade-offs is generally deemed an unattainable task. In this manuscript, we present two AIA pipelines that are model-agnostic, independent of the domain (such as: healthcare, energy, banking), and provide scores for AIA goals including explainability, safety, and security. The two pipelines: Adversarial Logging Scoring Pipeline (ALSP) and Requirements Feedback Scoring Pipeline (RFSP) are scalable and tested with multiple use cases, such as a water distribution network and a telecommunications network, to illustrate their benefits. ALSP optimizes models using a game theory approach and it also logs and scores the actions of an AI model to detect adversarial inputs, and assures the datasets used for training. RFSP identifies the best hyper-parameters using a Bayesian approach and provides assurance scores for subjective goals such as ethical AI using user inputs and statistical assurance measures. Each pipeline has three algorithms that enforce the final assurance scores and other outcomes. Unlike ALSP (which is a parallel process), RFSP is user-driven and its actions are sequential. Data are collected for experimentation; the results of both pipelines are presented and contrasted.
Students’ Opinion on the Quality of Distance Learning during the Ukrainian Pandemic Reality

This article shows the opinion of Ukrainian students (from six different cities of Ukraine) about the quality of distance learning after having one-year experience with such type of education during Pandemic times. The survey was conducted in several main directions: attitudes towards distance learning, key factors influencing the quality of distance learning and forms of teaching that students would prefer in the future.
Unsupervised learning‐based recognition and extraction for intelligent automatic video retrieval

Abstract Image queries‐based automatic recognition is a crucial element of video retrieval. Many recognition systems were proposed with different illustrations in image‐based video retrieval domains. However, intelligent automatic video retrieval recognition (IAVRR) systems development and implementation usage on video retrieval are recurring only at long intervals. Because software and hardware schemes and their updating strategies are used to necessitate automatic recognition of video retrieval image queries, to manage such functions as automated recognition of the image queries in video retrieval with a reduced degree of system memory usage, spots detection, maintenance time, identification of exact duplicate videos, etc. Simultaneously, to solve such problems, it is necessary to use video retrieval and recognition methods and algorithms that allow recognising image queries with a high degree of accuracy in a sufficient time in the conditions of interference and distortion in images. This research determination demonstrates a modified R‐ratio with Viola‐Jones (Mr. VJ) framework with the relevance of developing techniques and algorithms for automatic recognition of image queries. The proposed methods and their relevant algorithms are used to retrieve the highest real videos and ensure video retrieval's mathematical operation in the comparable protected system's operation. The protected system controls over the localisation, normalisation, parsing, segmentation and recognition are accomplished. Hence, the proposed R‐ratio analysis showed high efficiency in experiments on automatic recognition of image queries. Its application made it possible to provide a reasonably short time and high image recognition accuracy in conditions of their different locations. When using the proposed R‐ratio analysis methods with Viola‐Jones, the recognition time is about &lt; 1000 ms. The retrieval accuracy is from 90% to 100% at the various quality of the image pixels.
Supply chains are playing games: A review literature on Gamification in supply chain

Supply chains are changing due to changes in the global environment and to make the strategies effective most of the companies are striving towards the trends in the supply chain. Among the trends gamification is one of those that are creating an impact in the workspace with the techniques that are associated with it. Gamification in the supply chain had the least application and this study gave a road map for the decision makers like supply chain managers with the help of reviewing papers from the databases that are available. Findings say that giants like Amazon and Starbucks are applying gamification techniques to bring more transparency and visibility to reduce errors and to mitigate errors. This paper is providing evidence from the different activities like order management, warehousing activities with case applications are discussed. We conducted systematic literature review with 118 papers on gamification and 22 found relevant to the supply chain and its activities.
3D Global Path Planning Optimization for Cellular-Connected UAVs under Link Reliability Constraint

This paper proposes an effective global path planning technique for cellular-connected UAVs to enhance the reliability of unmanned aerial vehicles' (UAVs) flights operating beyond the visual line of sight (BVLOS). Cellular networks are considered one of the leading enabler technologies to provide a ubiquitous and reliable communication link for UAVs. First, this paper investigates a reliable aerial zone based on an extensive aerial drive test in a 4G network within a suburban environment. Then, the path planning problem for the cellular-connected UAVs is formulated under communication link reliability and power consumption constraints. To provide a realistic optimization solution, all constraints of the optimization problem are defined based on real-world scenarios; in addition, the presence of static obstacles and no-fly zones is considered in the path planning problem. Two powerful intelligent optimization algorithms, the genetic algorithm (GA) and the particle swarm optimization (PSO) algorithm, are used to solve the defined optimization problem. Moreover, a combination of both algorithms, referred to as PSO-GA, is used to overcome the inherent shortcomings of the algorithms. The performances of the algorithms are compared under different scenarios in simulation environments. According to the statistical analysis of the aerial drive test, existing 4G base stations are able to provide reliable aerial coverage up to a radius of 500 m and a height of 85 m. The statistical analysis of the optimization results shows that PSO-GA is a more stable and effective algorithm to rapidly converge to a feasible solution for UAV path planning problems, with a far faster execution time compared with PSO and GA, about two times. To validate the performance of the proposed solution, the simulation results are compared with the real-world aerial drive test results. The results comparison proves the effectiveness of the proposed path planning method in suburban environments with 4G coverage. The proposed method can be extended by identifying the aerial link reliability of 5G networks to solve the UAV global path planning problem in the current 5G deployment.
Using a Two-Stage Scheme to Map Toxic Metal Distributions Based on GF-5 Satellite Hyperspectral Images at a Northern Chinese Opencast Coal Mine

Toxic metals have attracted great concern worldwide due to their toxicity and slow decomposition. Although metal concentrations can be accurately obtained with chemical methods, it is difficult to map metal distributions on a large scale due to their inherently low efficiency and high cost. Moreover, chemical analysis methods easily lead to secondary contamination. To address these issues, 110 topsoil samples were collected using a soil sampler, and positions for each sample were surveyed using a global navigation satellite system (GNSS) receiver from a coal mine in northern China. Then, the metal contents were surveyed in a laboratory via a portable X-ray fluorescence spectroscopy (XRF) device, and GaoFen-5 (GF-5) satellite hyperspectral images were used to retrieve the spectra of the soil samples. Furthermore, a Savitzky–Golay (SG) filter and continuous wavelet transform (CWT) were selected to smooth and enhance the soil reflectance. Competitive adaptive reweighted sampling (CARS) and Boruta algorithms were utilized to identify the feature bands. The optimum two-stage method, consisting of the random forest (RF) and ordinary kriging (OK) methods, was used to infer the metal concentrations. The following outcomes were achieved. Firstly, both zinc (Zn) (68.07 mg/kg) and nickel (Ni) (26.61 mg/kg) surpassed the regional background value (Zn: 48.60 mg/kg, Ni: 19.5 mg/kg). Secondly, the optimum model of RF, combined with the OK (RFOK) method, with a relatively higher coefficient of determination (R2) (R2 = 0.60 for Zn, R2 = 0.30 for Ni), a lower root-mean-square error (RMSE) (RMSE = 12.45 mg/kg for Zn, RMSE = 3.97 mg/kg for Ni), and a lower mean absolute error (MAE) (MAE = 9.47 mg/kg for Zn, MAE = 3.31mg/kg for Ni), outperformed the other four models, including the RF, OK, inverse distance weighted (IDW) method, and the optimum model of RF combined with IDW (RFIDW) method in estimating soil Zn and Ni contents, respectively. Thirdly, the distribution of soil Zn and Ni concentrations obtained from the best-predicted method and the GF-5 satellite hyperspectral images was in line with the actual conditions. This scheme proves that satellite hyperspectral images can be used to directly estimate metal distributions, and the present study provides a scientific base for mapping heavy metal spatial distribution on a relatively large scale.
A Wolf in Sheep’s Clothing: Reuse of Routinely Obtained Laboratory Data in Research

Electronic health records (EHRs) contain valuable data for reuse in science, quality evaluations, and clinical decision support. Because routinely obtained laboratory data are abundantly present, often numeric, generated by certified laboratories, and stored in a structured way, one may assume that they are immediately fit for (re)use in research. However, behind each test result lies an extensive context of choices and considerations, made by both humans and machines, that introduces hidden patterns in the data. If they are unaware, researchers reusing routine laboratory data may eventually draw incorrect conclusions. In this paper, after discussing health care system characteristics on both the macro and micro level, we introduce the reader to hidden aspects of generating structured routine laboratory data in 4 steps (ordering, preanalysis, analysis, and postanalysis) and explain how each of these steps may interfere with the reuse of routine laboratory data. As researchers reusing these data, we underline the importance of domain knowledge of the health care professional, laboratory specialist, data manager, and patient to turn routine laboratory data into meaningful data sets to help obtain relevant insights that create value for clinical care.
Secure fragile watermarking based on Huffman encoding and optimal embedding strategy

&lt;span lang="EN-US"&gt;Image watermarking is one of the most popular techniques used to assure information security, integrity, and authenticity. Watermarking algorithms can be categorised, according to the domain of insertion, as either spatial or spectral domain-based watermarking approaches. The resulted watermark can also be classified as either strong designed to withstand malicious attacks or fragile designed to detect every possible alteration. In order to combine the advantages of the two categories of watermarking algorithms, this paper proposes a new fragile hybrid domain-based watermarking scheme to get both robustness and imperceptibility using an optimal embedding strategy according to the entropy values of the host images blocs. To enhance security and safety, the watermark undergoes an encryption using Huffman encoding to produce a scrambled watermark. This scheme is evaluated based on different metrics like the peak signal to noise ratio, the structural index similarity, and the normalized correlation coefficient, satisfactory results are attained. The experimental results show that Huffman encryption and optimal blocs selection offer good features of security and imperceptibility.&lt;/span&gt;
A Framework for Robot Self-Assessment of Expected Task Performance

We propose a self-assessment framework which enables a robot to estimate how well it will be able to perform a known or possibly novel task. The robot simulates the task to generate a state distribution of possible outcomes and determines (1) the likelihood of overall success, (2) the most probable failure location, and (3) the expected time to task completion. We evaluate the framework on the “FetchIt!” mobile manipulation challenge which requires the robot to fetch a variety of parts around a small enclosed arena. By comparing the simulated and actual task resulting state distributions, we show that the robot can effectively assess its expected performance which can be communicated to humans.
Using set visualisation to find and explain patterns of missing values: a case study with NHS hospital episode statistics data

Objectives Missing data is the most common data quality issue in electronic health records (EHRs). Missing data checks implemented in common analytical software are typically limited to counting the number of missing values in individual fields, but researchers and organisations also need to understand multifield missing data patterns to better inform advanced missing data strategies for which counts or numerical summaries are poorly suited. This study shows how set-based visualisation enables multifield missing data patterns to be discovered and investigated. Design Development and evaluation of interactive set visualisation techniques to find patterns of missing data and generate actionable insights. The visualisations comprised easily interpretable bar charts for sets, heatmaps for set intersections and histograms for distributions of both sets and intersections. Setting and participants Anonymised admitted patient care health records for National Health Service (NHS) hospitals and independent sector providers in England. The visualisation and data mining software was run over 16 million records and 86 fields in the dataset. Results The dataset contained 960 million missing values. Set visualisation bar charts showed how those values were distributed across the fields, including several fields that, unexpectedly, were not complete. Set intersection heatmaps revealed unexpected gaps in diagnosis, operation and date fields because diagnosis and operation fields were not filled up sequentially and some operations did not have corresponding dates. Information gain ratio and entropy calculations allowed us to identify the origin of each unexpected pattern, in terms of the values of other fields. Conclusions Our findings show how set visualisation reveals important insights about multifield missing data patterns in large EHR datasets. The study revealed both rare and widespread data quality issues that were previously unknown, and allowed a particular part of a specific hospital to be pinpointed as the origin of rare issues that NHS Digital did not know exist.
Local-Moment-Driven Robust Reversible Data Hiding

In this paper, a local-moment-driven robust reversible data hiding (LM-RRDH) scheme is proposed, which can provide security to hidden messages against unintentional modifications. The proposed LM-RRDH decomposes an image into LSB and MSB planes and then embeds the secret information into the MSB image so that intrusion by unintentional modifications can be avoided. In addition, the proposed scheme utilizes the prevalent correlation among the pixels on the MSB plane for optimal embedding. In the proposed scheme, a cover image is partitioned into sub-blocks at first, and pixel groups in the sub-block are formed according to local moment and moment-of-moment so that similar-intensity pixels can be grouped into the same group. Next, the secret data is embedded into the pixels of each group by selecting a pairwise embedding strategy adaptively which is based on the number of pixels in each group. As a result, the proposed LM-RRDH can limit the distortion while providing a decent embedding capacity. Further, a protection against non-malicious attacks such as Joint Photographic Experts Group (JPEG) compression is also provided. The experimental results show that the proposed scheme provides a superior quality to the previous works while providing a comparable embedding capacity.
DESIGNING LITERACY E-COACHING MODEL FOR ENGLISH LANGUAGE TEACHERS OF JUNIOR HIGH SCHOOLS IN INDONESIA

This multi-year study which took place in 2019-2021 aims to design a literacy e-coaching model for EFL Junior High School teachers in Indonesia in support of the government-initiated school-based literacy movement program. The study involved 150 English as a Foreign Language (EFL) teachers from 16 provinces in Indonesia in the need analysis part, 12 teachers in the module development part and 41 teachers in the implementation part. Two kinds of modules were developed during the study, namely: (1) a literacy enrichment module to reinforce the participants’ literacy content knowledge; and (2) a literacy learning module to enhance the participants’ competence in teaching literacy. Afterwards, two coaching cycles using the modules were implemented. The coaching cycles were designed by adapting the coaching models from Rogers and Rogers (2007) and Trinh et al. (2011), as well as adopting the Sheltered Instruction Observation Protocol (2017) model. Data was collected from questionnaires, tests, observations, videos, expert judgment, and discussion groups and analyzed using model validation, descriptive and inferential statistics. The study found that the literacy e-coaching model enhanced the participants’ competences in literacy teaching, as the results demonstrated the amalgamation of the models to be effective.
Teachers' Perception towards DELIMa Learning Platform for Mathematics Online Teaching and Learning in Sibu District

This study aims to identify teachers' perceptions of the DELIMa platform towards the implementation of Teaching and Learning (T&L).This quantitative study in the form of a survey involved 136 Chinese primary school mathematics teachers using a simple random sampling technique.An online questionnaire was utilised as the data collection tool.Data collected were analysed descriptively and inferentially.The findings indicated that teachers were generally receptive and prepared to use the DELIMa Learning Platform to apply T&L online with a high mean value.Based on the age factor, the inference analysis findings indicated a substantial difference in the mean level of teachers' acceptance and preparation to adopt T&L online.Teachers should take this challenge by using DELIMa as one of the learning platforms in their teaching and learning process in order to get themselves accustomed to the fields of technology.
Hubungan Pengetahuan dengan Sikap Kesiapsiagaan Bencana Banjir di Desa Koripan Kecamatan Polanharjo Kabupaten Klaten

Indonesia which is located in an area prone to natural disasters. The impact of floods experienced by the community such as diarrhea, itching, digestive tract, fever, upper respiratory tract infections and other diseases. Klaten city is said that every year there will be the most severe floods last year 2021. The knowledge possessed is very helpful in disaster management, and attitudes towards healthy and healthy living behavior are able to prevent environmental-based diseases. Knowing the relationship between knowledge and attitude towards flood disaster preparedness in Koripan village, Polanharjo district, Klaten regency. This research uses analytical methods, data collection techniques with stratified ramdom sampling, a sample of 89 respondents, the instrument of this research is a questionnaire. The results of the univariate analysis showed that the knowledge level of the people in polanharjo sub-district was in the good category. Community attitudes in the positive category. The results of the bivaariate analysis show that there is on relationship between knowledge and attitude towards flood disaster preparedness. The characteristics of respondents in polanharjo sub-district are the majority aged &gt;40 yeard and the majority are male as many as 49 respondents. The level of public knowledge about flood disaster preparedness in polanharjo is mostly in the good category. The public attitude about flood disaster preparedness in the polanharjo district, the majority of the positive results, there is no relationship between knowledge and attitude.