Reviewerly/exHarmony huggingface dataset:
Here . . . but Disappeared

Other| November 01 2021 Here . . . but Disappeared Cosmo Whyte Cosmo Whyte Cosmo Whyte (whose work also appears on the covers of this issue) was born in Jamaica in 1982. He has exhibited nationally and internationally. He is the recipient of the Edge Award (2010); the International Sculpture Center’s Outstanding Student Achievement in Contemporary Sculpture Award (2015); the Artadia Award (2016); the Drawing Center’s Open Sessions Fellowship (2018); the Working Artist Award (2018); the Art Matters Award (2019); and the Louis Comfort Tiffany Foundation Award (2019). His work is in the public collection of the National Gallery of Jamaica; the High Museum, Atlanta; the 21c Collection; the Hallmark Art Collection; and the Pérez Art Museum Miami. He is an assistant professor in the Department of Art at Florida State University, Tallahassee. Search for other works by this author on: This Site Google Small Axe (2021) 25 (3 (66)): 147–158. https://doi.org/10.1215/07990537-9583502 Cite Icon Cite Share Icon Share Twitter Permissions Search Site Citation Cosmo Whyte; Here . . . but Disappeared. Small Axe 1 November 2021; 25 (3 (66)): 147–158. doi: https://doi.org/10.1215/07990537-9583502 Download citation file: Zotero Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu nav search search input Search input auto suggest search filter Books & JournalsAll JournalsSmall Axe Search Advanced Search The text of this article is only available as a PDF. Copyright © Cosmo Whyte2021 Article PDF first page preview Close Modal You do not currently have access to this content.
Research on comprehensive benefit evaluation model of municipal engineering based on triangular fuzzy number and fuzzy comprehensive evaluation

On the background of more and more demands for the evaluation of municipal engineering projects, the benefits of municipal engineering are paid more and more attention, and, at present, the research focus in the field of engineering is more on the benefit of single index than comprehensive evaluation benefits, this paper puts forward the concept of &#x201C;comprehensive benefit&#x201D; evaluation of municipal engineering based on various indicators of comprehensive engineering benefits. This paper puts forward an evaluation model of comprehensive benefits of municipal projects based on triangular fuzzy number and fuzzy comprehensive evaluation method, which can carry out post evaluation of comprehensive benefits of various municipal projects under various modes such as PPP, BT and BOT. Through example demonstration, it is proved that the evaluation model is scientific, practical and operable, which provides favorable engineering measures for the sustainable development of the city and the construction of a resource-saving and City friendly society.
Image style transfer based on improved convolutional neural

Image style transfer is a hot issue in the field of computer vision. It refers to the process that an image (content image) has a specified artistic style (style image) through the algorithm, which has an important application value. The complex spatial structure in the process of style transfer will make the details blurred and the local structure of the image distorted. In this paper, a new feature detection network is introduced for feature extraction of style content and style content, which has fewer simple parameters. In the transformation network, the adaptive Instance standardization layer is added after the convolution layer to improve the retention ability of the spatial structure of the content image. Large convolution kernels are replaced to reduce the number of model parameters while maintaining the same receptive field. The network model built in this paper can realize the rapid migration of various styles, strengthen the structural characteristics of images, and improve the detail effect of stylized images significantly.
A Multiple Object Tracking Method Based on Optimized FairMOT

In order to solve the issue of missed detection that is easy to occur in the multi object tracking algorithm FairMOT when the target appearance is similar to the background, and to improve the accuracy of multi-object tracking algorithm in pedestrian tracking, we proposed a pedestrian tracking algorithm termed as DA_FairMOT, based on FairMOT algorithm. At different levels of its feature extraction network DLA34, we added two self-attention modules, the spatial module and channel module. DA_FairMOT combined the two attention feature maps to further improve the representational capability of the model. In the experiment, we use the CLEAR MOT evaluation metric. As a result, the proposed DA_FairMOT algorithm improves IDP (the ID precision) by 1.59&#x0025; on the MOT17 dataset, compared with the benchmark FairMOT algorithm. DA_FairMOT achieves 66.44 for MOTA, and 70.03 for IDF1.
Research on initial trust mechanism of structural equation model based on data

Based on the data research, this paper constructs and verifies the initial trust research model based on structural equation. Firstly, the influencing factors of initial trust are analyzed and verified by analytic hierarchy process. Finally, the corresponding countermeasures are provided to enhance the path of initial trust.
Analysis of the Management Strategy of Liquor Enterprises Using Microsoft's PowerBI Tool: —Taking Kweichow Moutai Enterprises as an Example

Chinese liquor is a conventional fermented alcoholic beverage that plays an important role in East Asian traditional culture. Each person who lives in or wants to come to East Asia is familiar with Kweichow Moutai. Moreover, not only the adults know it, but also the small children do not feel strange with the brand of Kweichow Moutai, although they may not know what it is. There are so many different brands for liquor, why is the Kweichow Moutai so famous as the Chinese national liquor. This will raise questions, why Kweichow Moutai can reach such a height, both price and status, which has become the theme of research. Digging the reason why Kweichow Moutai can be successful can help other companies to learn experience from Kweichow Moutai so that they can improve themself. In order to explore this problem, this article analyzes the whole economical environment of China, the company&#x0027;s financial data and the decision made by Kweichow Moutai using Microsoft&#x0027;s PowerBI tool to perform a visual analysis of data. Data for this research were selected from the Tushare website. liquor as a special industry which has high level requirements for the process and resources makes each company have a chance to have their own special aspect to attract the customers. With the shock of the whole liquor industry around 2013 year, the veteran company Wuliangye lost control of the liquor industry due to their exposed problems. At the same time, Kweichow Moutai focuses on several main famous products and keeps them at a luxury level. The healthy financial data and clear decisions make Kweichow Moutai become Chinese national liquor.
Multi-source Multi-label Causal Feature Selection

Multi-label causal feature selection plays a vital role in dealing with high-dimensional multi-label data, since the selected causal features signify the causal mechanism with respect to the class attribute. At present, the descriptive information of data is collected from different data sources in many practical applications. Therefore, there is a need of developing multi-source multi-label causal feature selection algorithms. In addition, existing multi-label causal feature selection algorithms cannot directly address multi-source heterogeneous distribution feature spaces. To address these problems, in this paper, we utilize the concept of causal invariance in causal inference. Firstly, we formulate the multi-label causal feature selection problem for multiple data sources as an invariant set search problem across the data sources. Secondly, we present the upper and lower bounds of the causal invariant set. Finally, we design a novel Multi-source Multi-label Causal Feature Selection (MMCFS) algorithm. Extensive experiments are conducted to verify the effectiveness of the proposed algorithm by comparing it with state-of-the-art methods on synthetic datasets.
Effective Image Pre-Processing Techniques with Deep Learning for Leukemia Detection

International Journal of Computer Sciences and Engineering (A UGC Approved and indexed with DOI, ICI and Approved, DPI Digital Library) is one of the leading and growing open access, peer-reviewed, monthly, and scientific research journal for scientists, engineers, research scholars, and academicians, which gains a foothold in Asia and opens to the world, aims to publish original, theoretical and practical advances in Computer Science,Information Technology, Engineering (Software, Mechanical, Civil, Electronics & Electrical), and all interdisciplinary streams of Computing Sciences. It intends to disseminate original, scientific, theoretical or applied research in the field of Computer Sciences and allied fields. It provides a platform for publishing results and research with a strong empirical component. It aims to bridge the significant gap between research and practice by promoting the publication of original, novel, industry-relevant research.
SEMANTIC COMPARISON OF TEXTUAL INFORMATION USING ARTIFICIAL INTELLIGENCE MODELS

The means of artificial intelligence models, providing the users with the specialized algorithms enabling adequate and efficient comparison of the textual information content have been described in the article. The research conducted made it possible to accomplish analysis of the existing models of semantic comparison of the textual information and suggest the innovative way of developing the new algorithm, based on conducting semantic text analysis, applying models of artificial intelligence. The analyzed algorithm enables to extract the main content from the compared texts in the form of a semantic network, which in its turn allows to reliably assess the degree of their identity and as a result to make an objective comparison.
PREDIKSI TINGKAT PENGGUNA NARKOBA DENGAN METODE REGRESI LINEAR BERGANDA BERBASIS WEB

Drug cases, are not decreasing from day to day but are increasing, both as dealers, users, sellers, even as dealers. Drug users range from the elderly to the younger generation and children. Based on these problems, the researcher intends to help people related to law and ordinary people by creating an application to predict the level of drug users with multiple linear regression methods. Data on drug users used from 2015 to 2019 were taken at the Aceh Tamiang Police (Polres). To perform calculations using this multiple linear regression method using 619 user data. In its application, the multiple linear regression method results in an equation Y’ = -4,492312 + 1,018444 X 1 + 1,181143 X 2 where X1 is methamphetamine and X2 is marijuana. If the number of methamphetamine users is 108 and marijuana is 76, it can be predicted that the number of drug users is 195 cases and MAPE is 68.48%. Keyword : drugs, predictions, multiple linear regression .
An Alternative Method of Backward Fuzzy Interpolation based on Areas of Fuzzy Sets

Fuzzy rule interpolation techniques can reduce the complexity of fuzzy systems and make inferences of conclusions in sparse rule-based systems. However, when certain crucial antecedents are missing in the observations and the subsequent interpolation inference process involves these missing antecedents, conventional fuzzy interpolation methods cannot obtain inferred conclusions. To tackle this problem, Jin et al. proposed the method of backward fuzzy rule interpolation, which allows the missing antecedents to be deduced or interpolated from the known antecedents and given conclusion, extending the research field of fuzzy interpolation techniques. In order to extend the generality of backward fuzzy rule interpolation inference, this paper proposes a backward fuzzy rule interpolation approach based on the CCL algorithm, which utilizes the triangular fuzzy membership functions and verifies the effectiveness of the approach by examples.
Elaboration of an Interactive Electronic Book of Descriptive Measures

These are the advantages of technology: multimedia presentation, interaction, personalization, etc. Within this context an interactive digital book of the characteristics of the information was realized. In a data set, the following characteristics are usually of paramount importance: 1. the center, 2. the variation, 3. the distribution, 4. the atypical values and 5. The characteristics of the data that change over time. It is important to understand the standard deviation values using tools such as the general rule of rank. Because technology allows us to get many of these statistics automatically, it is not so important to memorize formulas or perform complex arithmetic calculations by hand on the other hand, so that the students can concentrate on understanding and interpreting the values that are obtained from them.The interactive digital book of measures of central tendency contemplates that each chapter indicates prerequisites, learning objectives, written development of the subject, videos with explanation, interactive exercises, widget (html 5. Interactive galleries, interactive images, etc.) review questions.The interactive digital book will provide students with full-screen experience with galleries, videos, interactive diagrams, 3D objects, and math expressions and more; these books give life to the content in ways that a printed page can not do. Students will no longer be limited to static images that illustrate traditional texts, but can now immerse themselves in an image with interactive subtitles, rotate a 3D object or make an answer come to life in a chapter review. You can flip through a book with a single finger on the screen. They can also highlight text, take notes, look up content and find definitions in a glossary very easily. In addition, they can take them wherever they go, which will allow students not only to learn between the walls of the classroom, but also in the virtual space that make up these books.
Kluster Rata-Rata Lama Sekolah (RLS) Menurut Jenis Kelamin di Provinsi Jawa Tengah dengan K-Means

The average length of school (RLS) describes the level of achievement in school activities of each citizen in a given area. The higher the number of years of schooling, the higher the population's level of education, hence this indicator is critical since it can reveal the quality of a region's human resources. Furthermore, numerous studies have found that the average length of schooling has a major impact on economic growth. This indicates that when the average length of schooling rises, the number of unemployed and poor people in a given area declines, resulting in a positive and considerable impact on economic growth. The goal of the study was to use artificial intelligence to undertake an analysis in the form of a cluster mapping of the Average Length of Schooling in Regencies and Cities in Central Java (AI). This is necessary in order to gain a macro picture of the average years of schooling's progress over the last few years through regional mapping. The data was taken from the Central Java Statistics Agency (BPS) website, and it was based on the subject Average Length of School (RLS) by gender from 2017 to 2019. The k-means method, which is part of clustering data mining, was employed as the solution method. There were two types of clusters used in this study: high and low clusters. RapidMiner software is used to aid the analyzing process. Preprocessing is done before the k-means approach by taking the average value of the number of RLS based on gender from 2017 to 2019. K-means will be used to process the results of the average value obtained. According to the findings, eight provinces (23 percent) were in the high cluster (cluster 1) while 27 provinces (77 percent) were in the low cluster (cluster 0). According to the findings, RLS levels are still low in over 70% of Central Java's locations.
Visualisasi Data Program Vaksinasi Covid-19 di Kota Depok dengan Big Data Analytics

Indonesia and various country in the world are facing the problem of the Covid-19 pandemic. In Indonesia, the suspect Covid-19 was found in March 2020 in Depok City, West Java. Until February 2021, the number of positive COVID patients was 1,527,524. The need for supervision of the administration of vaccines is carried out by the Depok City Health Office on a number of health facilities that are trusted to administer vaccines to the public. This supervision is carried out using surveillance through visualization of vaccine administration data from the community. So far, the number of vaccines given to all health facilities in Depok City from January to August is around 613276 times which includes the administration of dose 1, dose 2 and dose 3. The amount of existing data can be managed and visualized properly using big data analytics. To get a good shape and visualization in decision making, several data cleansing processes are carried out up to the visualization stage. The use of big data analytics can be used to visualize descriptive data that is able to describe the rhythm of vaccination in Depok City, categorization of vaccine recipients, the type of vaccine given to the number of doses given. So it can be estimated that every month, vaccine recipients will continue to increase, both receiving dose 1, dose 2 and dose 3. This is in line with the Depok government's target which will complete the provision of vaccines to the people of Depok by the end of 2021
Pedestrian and vehicle detection via port surveillance video

In view of the diversity and complexity of the landscape environment and spatial layout of the port, the behaviors of relevant personnel and vehicles are relatively hidden, which leads to the problem that they can&#x0027;t be accurately identified in the surveillance video. In this paper, we used an improved object detection algorithm for personnel and vehicles applied in port environment, which combines the feature pyramid networks for feature extraction based on ResNet-101 and Faster R-CNN object detection network. The experimental results show that the algorithm has good real-time performance, and effectively improves the accuracy of related personnel and vehicle location detection and recognition in the port environment.
A Proposal to Model Wargames in the MDA Framework

This work aims to define meaningful actions that players can take in a wargame. Starting from the premise that wargames are (serious) games, we wondered if a wargame and its actions could be well modeled as a game. We looked at formal approaches and decided to model wargames in MDA since this framework analyzes the actions in games as mechanics or dynamics. The proposed model links emotions with instincts that may arouse in players with mechanics and dynamics from wargames. Afterward, we indirectly evaluated the model through a survey among wargames experts. Although most research participants agree with the suitability of the proposed mechanics and dynamic, they suggested other actions that players could perform in wargames. The model matched most emotions and instincts selected by the participants and the results allowed us to improve the model mainly in mechanics and dynamics. An important contribution of this work is to recognize the emotions and instincts that are triggered by the dynamics and mechanics of wargames. The participants’ answers on instincts agree with our understanding from the literature, but their answers on emotions contradict some views on wargames. Many participants indicated that wargames can evoke fear, anger and sadness, but wargames have limitations to arouse these emotions in players. Most military see wargames primarily as training activities. However, few participants find enjoyment in wargames.
The Innovative Model of Family Education Guidance of Guiding First, then Supplementing and Further Solidifying

In the new era a new more effective family education model can ease unbalanced and inadequate education development. In the new era, parents look forward to a more personalized, active and interactive form of family education guidance. This new guidance model will start with improving behavior by focusing on emotional behavior and cognition. The Roast can ease parents' anxiety, lectures and reading groups can supplement parents' knowledge, and mutual discussion, vicarious practice and teaching others can solidify a scientific and effective family education behavior. The family education guidance model of "guiding first, then supplementing and further solidifying" focuses on the participation of parents in the whole process, which can improve the effectiveness of family education guidance.
Investigation of the Properties of Digital Chromosome Microscopy Images for the Telemedicine Applications

Deep cytogenetic examination of chromosomes properties is very specialized test that can be performed only in some scientific laboratories. Many problems occurred with sending the cytohistological micro preparations to laboratories of other countries because of COVID-19 pandemic. The only one way was available – to use telecommunication systems and send digital images of micro preparations to laboratories for their analysis in the limited time. Due to technical features of digital images of microslides, their informative value can vary significantly. Subjective and qualitative estimations of the parameters of microobjects are also a significant factor, which leads to decreasing the accuracy of laboratory diagnostics and complicating the repeatability of the results in scientific research. The aim of this article is to investigate the chromosome image parameters that carry the fullest informative value out of the images.
An Optimistic Security Model for Improving Cyber Security Using Adaptive Algorithms to Prevent SQL Injection Attacks

Throughout today's economic and social life, the use of web-based services (such as e-commerce, online banking, and web-based communications, to name a few) has become a common habit. Countless applications operate worldwide on millions of servers, and their numbers are steadily increasing. It has become a focus of attackers and hackers for the attacks because of the huge growth of internet usage. It is necessary for all companies to develop and protect their applications in order to maintain their credibility and keep their products relevant for users. Web applications have brought in new classes of computer security vulnerabilities, such as SQL injection (SQLIA), and it has exceeded previously prominent vulnerability classes in recent years. SQL injection is the instance of the broader class of vulnerabilities that are based on input validation. The primary purpose of this research is to study the vulnerabilities of SQL injection and to propose an optimistic security model for secure data transmission. In this work, the authors proposed an adaptive algorithm to prevent SQL injections.
Playing to Program: Towards an Intelligent Programming Tutor for RUR-PLE

Intelligent tutoring systems (ITSs) provide students with a one-on-one tutor, allowing them to work at their own pace, and helping them to focus on their weaker areas. The RUR1–Python Learning Environment (RUR-PLE), a game-like virtual environment to help students learn to program, provides an interface for students to write their own Python code and visualize the code execution (Roberge 2005). RUR-PLE provides a fixed sequence of learning lessons for students to explore. We are extending RUR-PLE to develop the Playing to Program (PtP) ITS, which consists of three components: (1) a Bayesian student model that tracks student competence, (2) a diagnosis module that provides tailored feedback to students, and (3) a problem selection module that guides the student’s learning process. In this paper, we summarize RUR-PLE and the PtP design, and describe an ongoing user study to evaluate the predictive accuracy of our student modeling approach.