Empirical analysis of rough set categorical clustering techniques based on rough purity and value set

Clustering a set of objects into homogeneous groups is a fundamental operation in data mining. Recently, attention has been put on categorical data clustering, where data objects are made up of non-numerical attributes. The implementation of several existing categorical clustering techniques is challenging as some are unable to handle uncertainty and others have stability issues. In the process of dealing with categorical data and handling uncertainty, the rough set theory has become well-established mechanism in a wide variety of applications including databases. The recent techniques such as Information-Theoretic Dependency Roughness (ITDR), Maximum Dependency Attribute (MDA) and Maximum Significance Attribute (MSA) outperformed their predecessor approaches like Bi-Clustering (BC), Total Roughness (TR), Min-Min Roughness (MMR), and standard-deviation roughness (SDR). This work explores the limitations and issues of ITDR, MDA and MSA techniques on data sets where these techniques fails to select or faces difficulty in selecting their best clustering attribute. Accordingly, two alternative techniques named Rough Purity Approach (RPA) and Maximum Value Attribute (MVA) are proposed. The novelty of both proposed approaches is that, the RPA presents a new uncertainty definition based on purity of rough relational data base whereas, the MVA unlike other rough set theory techniques uses the domain knowledge such as value set combined with number of clusters (NoC). To show the significance, mathematical and theoretical basis for proposed approaches, several propositions are illustrated. Moreover, the recent rough categorical techniques like MDA, MSA, ITDR and classical clustering technique like simple K-mean are used for comparison and the results are presented in tabular and graphical forms. For experiments, data sets from previously utilized research cases, a real supply base management (SBM) data set and UCI repository are utilized. The results reveal significant improvement by proposed techniques for categorical clustering in terms of purity (21%), entropy (9%), accuracy (16%), rough accuracy (11%), iterations (99%) and time (93%). vi

Reconceptualising the Instructional Roles of Academic Librarians in Order to Better Serve Underserved Students at a California Public University

The need to equip society with information literacy (IL) has become essential, as evidenced by the 2016 and 2020 U.S. elections, COVID 19 pandemic, and QAnon. A deficiency in information provenance and credibility, combined with internet users’ poor information-seeking habits, has fostered the perfect environment for misinformation. In this atmosphere, higher education institutions (HEIs) must take the lead in developing a citizenry with the necessary IL skills to make informed judgments. The need to impart IL is even more crucial among the underserved student population (i.e., low-income, first-generation college students, and students of colour) who suffer from a deficiency in IL, because of the digital divide, when arriving at HEIs. The problem of practice (PoP) addressed here concerns the impact of Golden State Academy – Valley (GSA-V) not implementing an academic librarian (AL) taught IL credit-bearing course, crucial for its large underserved student population. GSA-V continues to underutilize its AL concerning the development of such courses, despite their expertise in IL and the literature demonstrating the positive impact on academic success. As a proponent of the critical paradigm, I envision this PoP as an opportunity for empowering marginalized voices. Using Kotter’s eight-stage process, combined with distributed and servant leadership principles, this Organizational Improvement Plan (OIP) proposes the development of an experimental AL-taught IL credit-bearing course. The aim is to utilize this course as an entryway for improving AL instructional roles and developing the IL skills of GSA-V’s underserved student population. The hope is that the experimental course can act as a catalyst for creating a general education IL requirement, thereby significantly increasing the reach and impact of such instruction

Translating Necessity Modality Verbs in Governmental Contracts Texts from Arabic into English

The present paper goes to discover the notion and types of contracts. Moreover, it tries to find the employment of necessity modality verbs in the governmental contract’s texts. The methodology of analysis employed in this research is Fairclough\u27s (1989) linguistic model, which is used in the analysis and interpretation of the necessity verbs in governmental contracts texts. The Data of this study have been rendered by 15 M.A. students in the department of translation at the College of Arts, University of Tikrit. The translational model suggested by Catford (1965) is also employed

Data Quality Management in Large-Scale Cyber-Physical Systems

Cyber-Physical Systems (CPSs) are cross-domain, multi-model, advance information systems that play a significant role in many large-scale infrastructure sectors of smart cities public services such as traffic control, smart transportation control, and environmental and noise monitoring systems. Such systems, typically, involve a substantial number of sensor nodes and other devices that stream and exchange data in real-time and usually are deployed in uncontrolled, broad environments.Thus, unexpected measurements may occur due to several internal and external factors, including noise, communication errors, and hardware failures, which may compromise these systems quality of data and raise serious concerns related to safety, reliability, performance, and security. In all cases, these unexpected measurements need to be carefully interpreted and managed based on domain knowledge and computational models.Therefore, in this research, data quality challenges were investigated, and a comprehensive, proof of concept, data quality management system was developed to tackle unaddressed data quality challenges in large-scale CPSs. The data quality management system was designed to address data quality challenges associated with detecting: sensor nodes measurement errors, sensor nodes hardware failures, and mismatches in sensor nodes spatial and temporal contextual attributes. Detecting sensor nodes measurement errors associated with the primary data quality dimensions of accuracy, timeliness, completeness, and consistency in large-scale CPSs were investigated using predictive and anomaly analysis models via utilising statistical and machine-learning techniques. Time-series clustering techniques were investigated as a feasible mean for detecting long-segmental outliers as an indicator of sensor nodes’ continuous halting and incipient hardware failures. Furthermore, the quality of the spatial and temporal contextual attributes of sensor nodes observations was investigated using timestamp analysis techniques.The different components of the data quality management system were tested and calibrated using benchmark time-series collected from a high-quality, temperature sensor network deployed at the University of East London. Furthermore, the effectiveness of the proposed data quality management system was evaluated using a real-world, large-scale environmental monitoring network consisting of more than 200 temperature sensor nodes distributed around London.The data quality management system achieved high accuracy detection rate using LSTM predictive analysis technique and anomaly detection associated with DBSCAN. It successfully identified timeliness and completeness errors in sensor nodes’ measurements using periodicity analysis combined with a rule engine. It achieved up to 100% accuracy in detecting potentially failed sensor nodes using the characteristic-based time-series clustering technique when applied to two days or longer time-series window. Timestamp analysis was adopted effectively for evaluating the quality of temporal and spatial contextual attributes of sensor nodes observations, but only within CPS applications in which using gateway modules is possible

A Computer Program for Calculating the Circular Product Dimensions During Deep Drawing Operations Steps

Deep drawing process is an important industrial processes where it has been extensively used. It is a process for converting the blank metal to make cylindricalshape in most of the cases. A research program for performing basic calculations related to the product shape was utilized. Eighteen geometric shapes have been selected for the production in this process. The program is include the general equations for this controlling process for each form. When choosing one of the shapes in the program, the dimensions of the desired product are selected. In other words, the inputs to the program are the required dimensions of the product before reaching the required deportation. In addition, diameter, height and other related dimension are computed which were chosen for each stage throughout the operation. Visual Basic language was used to implement this program because of its ability to deal with graphical interface. The program also accounts for a large number of mathematical equations. Computer program is aimed to help designers templates deep drawing quickly through the implementation of the process, and this exceeds the designer experience that is required to perform the require calculations