Multidomain Fusion Data Privacy Security Framework

With the collaborative collection of the Internet of Things (IoT) in multidomain, the collected data contains richer background knowledge. However, this puts forward new requirements for the security of data publishing. Furthermore, traditional statistical methods ignore the attributes sensitivity and the relationship between attributes, which makes multimodal statistics among attributes in multidomain fusion data set based on sensitivity difficult. To solve the above problems, this paper proposes a multidomain fusion data privacy security framework. First, based on attributes recognition, classification, and grading model, determine the attributes sensitivity and relationship between attributes to realize the multimode data statistics. Second, combine them with the different modal histograms to build multimodal histograms. Finally, we propose a privacy protection model to ensure the security of data publishing. The experimental analysis shows that the framework can not only build multimodal histograms of different microdomain attribute sets but also effectively reduce frequency query error.

Analysis and Recognition Based on Citrus Color Grading Model considering Computer Vision Technology

With the continuous advancement of smart agriculture, the introduction of robots for intelligent harvesting in modern agriculture is one of the crucial methods for the picking of fruits, vegetables, and melons. In this paper, three different illuminations, including front lighting, normal lighting, and back lighting, are first applied to citrus based on the computer vision technology. Secondly, the image data of the fruits, fruit stems, and leaves of the citrus are collected. The color component distributions of citrus based on different color models are analyzed according to the corresponding characteristic values, and an exploratory data analysis process for the image data of citrus is established. In addition, 300 citrus images are selected, and the citrus fruits are segmented from the background through the simulation experiment. The results of the study indicate that the recognition rate for the maturity of citrus has exceeded 98%, which has proved the effectiveness of the method proposed in this paper.

An Atmospheric Visibility Grading Method Based on Ensemble Learning and Stochastic Weight Average

In order to adequately characterize the visual characteristics of atmospheric visibility and overcome the disadvantages of the traditional atmospheric visibility measurement method with significant dependence on preset reference objects, high cost, and complicated steps, this paper proposed an ensemble learning method for atmospheric visibility grading based on deep neural network and stochastic weight averaging. An experiment was conducted using the scene of an expressway, and three visibility levels were set, i.e., Level 1, Level 2, and Level 3. Firstly, the EfficientNet was transferred to extract the abstract features of the images. Then, training and grading were performed on the feature sets through the SoftMax regression model. Subsequently, the feature sets were ensembled using the method of stochastic weight averaging to obtain the atmospheric visibility grading model. The obtained datasets were input into the grading model and tested. The grading model classified the results into three categories, with the grading accuracy being 95.00%, 89.45%, and 90.91%, respectively, and the average accuracy of 91.79%. The results obtained by the proposed method were compared with those obtained by the existing methods, and the proposed method showed better performance than those of other methods. This method can be used to classify the atmospheric visibility of traffic and reduce the incidence of traffic accidents caused by atmospheric visibility.

A Comparison of an Alternative Weight-Grading Model Against Chronological Age Group Model for the Grouping of Schoolboy Male Rugby Players

ObjectivesConcerns regarding marked differences in the weights and body composition of young rugby players competing within the same age groups have led to the suggestion of alternative models for grouping young players. The aims of this study were (1) to compare variance in the body size and body composition of schoolboy rugby players (9 to 14 years), across weight- and age-grading models, and (2) to identify morphotypes for the weight model using Hattori’s body composition chart.Materials and MethodsSkinfold thickness measurements were used to assess body fat mass (BF), fat-free mass (FFM), body fat mass index (BFMI), and fat-free mass index (FFMI). Standardized measure of height and weight were taken for all participants. Data were grouped according to the age categories of the French Rugby Federation (U11: Under 11 years, U13: Under 13 years, and U15: Under 15 years), and to the weight categories (W30–44.9; W45–59.9; and W60–79.9) carried out from 25th and 75th weight percentile in each age category. Body mass index status (NW normal-weight versus OW/OB overweight/obese) was considered. Extreme morphotypes are characterized from BFMI and FFMI in the weight-grading model on Hattori’s body composition chart.ResultsThe dispersion of anthropometric characteristics decreased significantly for the weight model, except for height in all groups and BFMI for U13. Among NW, 3, 1.8, and 0% upgraded; 18.2, 68.7, and 45.5% downgraded; among OW, 50, 21.5, and 12.5%; and among OB, 91.3, 83.3, and 74.6% upgraded, respectively, in U11, U13, U15. FFMI/BFMI were correlated in U11 (r = 0.80, p < 0.001), U13 (r = 0.66, p < 0.001), and U15 (r = 0.77, p < 0.001). There was no significant correlation in W45–59.9 and low correlations in W30–44.9 (r = 0.25, p < 0.001) and W60–79.9 (r = 0.29, p < 0.001). Significant grading difference between the centroids (p < 0.05) and the distribution deviates from centroids of BFMI and FFMI (p < 0.0001) were noted between the two models. Thirteen players were located in adipo-slender, twenty-three in adipo-solid, twenty-two in lean-slender, and two located in the lean-solid morphotype in weight model.ConclusionA weight-grading model should be considered to limit mismatches in anthropometric variables. However, variations of body composition also persisted for this model. Hattori’s body composition chart allowed more detailed examination of morphological atypicalities among schoolboy rugby players.

Points, Points, and More Points: High School Grade Inflation and Deflation when Homework and Employability Scores are Incorporated

With a strong movement of schools starting to use standards-based grading practices, one of the aims of this study was to learn if traditional grading practices communicate grades that are accurate based on the students’ learning of the course objectives. The purpose of this study was to determine the extent to which employability and homework scores within a traditional points- and percentages-weighted grading model inflates or deflates grades. This study analyzed 795 students’ semester math grades at an urban high school to see if, and to what extent, students’ grades were inflated or deflated due to including homework and employability scores in the grade. Final grades, which included homework and employability points, were compared to each student’s overall summative assessment scores to determine grade inflation or deflation. The study also analyzed how changing grading practices to eliminate homework and employability points would impact the number of students that ultimately passed or failed the course. Results of this study indicated 336 (43.2%) students had their grades inflated or deflated by 5% or more and 97 (12.6%) students had their grades inflated or deflated by 10% or more, which is equivalent to moving up or down a full letter grade. School leaders should consider separately communicating academic and non-academic factors to minimize grade inflation/deflation in order to make decisions based upon grades more justifiable.

Peer Assessment in EFL Writing Classes: Investigating Kurdish Students’ Attitudes

Peer assessment refers to students assessing their peers' work and providing grades and/or feedback. This study investigates Kurdish EFL students' attitudes toward peer assessment. To this end, the peer assessment technique will be utilised in teaching academic writing to second-year students in the English Department, College of Basic Education. Participants will be trained on how to assess their peers' writing by using Jacobs et al 's (1981) writing scale as a descriptor grading model. This scale provides five subscales for evaluating a written paragraph, namely: content, organization, vocabulary, language use, and mechanics. After implementing this model for 16 weeks, the students have shown positive attitudes toward implementing peer assessment in their writing classes as a practical assessment technique.