Reviewerly/exHarmony huggingface dataset:
Textual Analysis of Cities’ Disclosures: Computational Linguistic Approach for Public Accounting Information

This study aims to clarify linguistic features of public accounting information disclosed by cities. Based on analyses such as morphological analysis, TF-IDF (Term frequency inverse document frequency), DP (Deviation of Proportion), N-gram, and FOG measurement, first, it reveals that traditional accounting terms such as asset, revenue, and cash represent the city public accounting information report. Second, according to an inter-city comparison of important terms, the explanation items emphasized by cities differ. Third, the readability level of the urban report is, on average, approximately at college junior level of reading difficulty. This study is significant in that it is the first to elucidate the actual state of the public accounting information of major cities and it uses a unique presentation method of character information analysis of public accounting.
On the Compressive Power of Boolean Threshold Autoencoders

An autoencoder is a layered neural network whose structure can be viewed as consisting of an encoder, which compresses an input vector of dimension $D$ to a vector of low dimension $d$, and a decoder which transforms the low-dimensional vector back to the original input vector (or one that is very similar). In this paper we explore the compressive power of autoencoders that are Boolean threshold networks by studying the numbers of nodes and layers that are required to ensure that the numbers of nodes and layers that are required to ensure that each vector in a given set of distinct input binary vectors is transformed back to its original. We show that for any set of $n$ distinct vectors there exists a seven-layer autoencoder with the smallest possible middle layer, (i.e., its size is logarithmic in $n$), but that there is a set of $n$ vectors for which there is no three-layer autoencoder with a middle layer of the same size. In addition we present a kind of trade-off: if a considerably larger middle layer is permissible then a five-layer autoencoder does exist. We also study encoding by itself. The results we obtain suggest that it is the decoding that constitutes the bottleneck of autoencoding. For example, there always is a three-layer Boolean threshold encoder that compresses $n$ vectors into a dimension that is reduced to twice the logarithm of $n$.
SISTEM INFORMASI AKUNTANSI PEMBAYARAN UTANG USAHA PADA PT. PERTAMINA LUBRICANTS

This Final Project Report aims to determine the ability of students to develop theories that have been obtained during lectures in the University with existing practices in the company, and is useful for measuring quality in the world of work done at PT. Pertamina Lubricants which is located at Kav.55 Floor, Oil Center, Jalan M.H. Thamrin, Jakarta Pusat, This Final Project Report aims to learn more about accounting information systems for business debt payments within the company, from Recording Recognition of Accounts Payable within the company to Payment of Accounts Payable, in the implementation of business debt payments, by using several supporting documents, among others Invoice, Request to Post Invoice, to Proposal Payment. PT.Pertamina Lubricants uses the SAP Application System to support payment activities within the company.
Techniques of Writing Chapter 1 for Research and Development in Public Health

Chapter 1 of general research and research and development have the main structure similar but different and similar some internal details that must point out the condition of the research problem by showing information confirming the problem then identify ideas for solving problem determine the purposes of research, scopes of research and the benefits expected to be received clearly. The objective of this article is to create a template for the preparation of the research and development proposal in public health in part of Chapter 1 that there is a distinctive feature that divides the scope of research into four areas that consist of content scope, data resource scope, variable scope, and instrument scope to be a guideline for researchers to create clarity in the preparation of the research proposal.
Complexity of Eccentricities and All-Pairs Shortest Paths in the Quantum CONGEST Model

Computing the distance parameters of a network, including the diameter, radius, eccentricities and the all-pairs shortest paths (APSP) is a central problem in distributed computing. This paper investigates the distance parameters in the quantum CONGEST models and establishes almost linear lower bounds on eccentricities and APSP, which match the classical upper bounds. Our results imply that there is not quantum speedup for these two problems. In contrast with the diameter and radius, exchanging quantum messages is able to save the communication when the networks have low diameters [F. L. Gall and F. Magniez, Sublinear-time quantum computation of the diameter in CONGEST networks, in Proc. 2018 ACM Symp. Principles of Distributed Computing (PODC), (2018), pp. 337–346; F. Magniez and A. Nayak, arXiv:2002.11795 ]. We obtain the lower bounds via a reduction from the two-way quantum communication complexity of the set intersection [A. A. Razborov, Izv. Math. 159 (2003)].
Impact of Knowledge Management Processes on Organizational Performance in Tamil Nadu Higher Education: Mediating Role of Organizational Commitment

Based on knowledge management view, the study observed the impact of knowledge management processes in Tamil Nadu Universities and tests the direct association among knowledge management processes and organizational (University) performance. This study further inspects the mediating role of Organizational Commitment between Knowledge Management processes and organizational performance.  This study used a sample frame of 419 academic leaders from Tamil Nadu Universities. Furthermore, the assumed logical associations were analyzed using AMOS 20. Regardless of improved stature of knowledge management in higher education institutions, there is a lack of studies that examine the interrelationship of knowledge management process, organizational commitment and organizational performance. The results show that, KM processes (Creation, Acquisition, Storage, Sharing, and Utilization) and further ascertain the mediating role of organizational performance on the relationship between KM processes and organizational performance of higher education institutions.
The Learning of Narrative Writing Skills through Experiential Learning Model in Elementary School Students

This research was motivated by the low skills of writing narrative text in the fifth-grade elementary school students. Students have difficulty getting ideas in putting them into narrative text. The experiential learning model was chosen because it can make the students easier to find ideas in writing a narrative text. The purpose of this study was to describe the learning process by using the experiential learning model to improve students' narrative writing skills. This research is a qualitative study and using descriptive methods. The instrument data used was a written test of writing narrative text. Participants in this study were 31 students of V-B class at SDN Bedahan 01 Cibinong, Bogor. The results showed that the learning process using an experiential learning model consisting of concrete experience steps, reflective observation, abstract conceptualization, and active experimentation can improve students' narrative writing skills. The pre-test average score written by the student's narrative was 53 while the post-test average score of the students was 80. This research concluded that the learning process through the experiential learning model can improve the narrative writing skills of fifth-grade elementary school students.
Music Education is the Soul Baptism of College Students

short-paper Music Education is the Soul Baptism of College Students Share on Author: Xiaoguang Jia Baotou Teachers College Baotou Teachers CollegeView Profile Authors Info & Claims IPEC2021: 2021 2nd Asia-Pacific Conference on Image Processing, Electronics and ComputersApril 2021 Pages 703–706https://doi.org/10.1145/3452446.3452616Online:25 April 2021Publication History 0citation7DownloadsMetricsTotal Citations0Total Downloads7Last 12 Months7Last 6 weeks1 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Formation of a student's culture of intelligence: Experience of the Nazarbayev University Library

The article substantiates and analyzes the participation of a university library in developing of “the culture of the intellect” of a student, a concept proposed by John Newman in his book “The Idea of a University”. The author refers both to publications of other authors and to the experience of Nazarbayev University Library for an illustration of his views.
Image Segmentation and Object Recognition

The work focuses on object recognition to perform a set of closely related tasks in the field of computer vision, involving object detection and identification. Image classification involves an array of tasks like identifying the class of an object in an image. An object that is identified can also be precisely localized and a bounding box is drawn around it. Object detection combines both localization and identification and classifies one or more objects in image. For Object recognition, a technique called ‘Mask Region-based Convolution Neural Networks’ or Mask R-CNN is used. It is an extremely efficient approach for object localization and recognition tasks. It is an extension over Faster RCNN method by adding a parallel process for the prediction of a highly accurate object segmentation mask that conforms to the bounds of each detected object in the Region of Interest (RoI) by performing pixel by pixel analysis and classification while still staying highly performant. By using the above methods, individual objects can be identified more precisely than with precise localization. It is also easy to generalize the algorithm to other tasks like estimating the pose of a human in the image or applying a color filter to the image selectively on any object
Actividades prácticas con enfoque problémico para la asignatura redes informáticas utilizando un simulador

Computer networks constitute an essential element for the educational teaching process since they contribute to the need for teachers in their training to acquire a group of skills necessary for networking, in order to contribute to a more dynamic and developer learning. This work offers practical activities with a problem approach for the Computer Networks subject taught in the 4th year of the Computer Science Degree using the Packet Tracer simulator, a network simulation program that allowed students to experiment with the behavior of the network and solve problems for the advanced design of the same. From the application of these activities, the students were able to face and solve educational problems in the installation of networks, thus achieving greater motivation for the subject and also allowing a notable improvement in learning, since they became familiar with the physical components of the network and with its configuration, where they could also check if it works properly through pinging and sending messages between their workstations. During three courses, the students faced the practices presented in this proposal, which prepared them to design and configure simple networks for educational purposes.
volesti: Volume Approximation and Sampling for Convex Polytopes in R

Sampling from high dimensional distributions and volume approximation of convex bodies are fundamental operations that appear in optimization, finance, engineering and machine learning. In this paper we present volesti, a C++ package with an R interface that provides efficient, scalable algorithms for volume estimation, uniform and Gaussian sampling from convex polytopes. volesti scales to hundreds of dimensions, handles efficiently three different types of polyhedra and provides non existing sampling routines to R. We demonstrate the power of volesti by solving several challenging problems using the R language.
Teaching Students with Down Syndrome: Perspectives of Special School Teachers & Psychologists

Imparting education to students with Down syndrome is a difficult process since it requires the use of a variety of pedagogical strategies. This study used qualitative case study method and aimed to develop an understanding of the learning difficulties faced by students with Down syndrome through the perspectives of special school teachers and psychologists. This study also aimed to understand the pedagogical practices used by teachers to manage the learning difficulties of students with Down syndrome. The criterion sampling technique was used to select participants. Data were gathered through a self-constructed semi-structured interview guide containing seven dimensions. Qualitative content analysis of these seven dimensions clearly showed that teachers and psychologists perceived that students with Down syndrome certainly face many problems and hurdles in their learning such as lack of required skills, low IQ level, memory storage issues, communication problem, behavior problems, passivity, and distraction issues of students with Down syndrome. Moreover, the findings also showed that these teachers and psychologists employ a variety of pedagogical strategies to teach and manage students with Down syndrome. However, some aspects need to be addressed to make the education of these students effective.
Learning How and Why: Causal Learning and Explanation from Physical, Interactive, and Communicative Environments

Author(s): Edmonds, Mark | Advisor(s): Zhu, Song-Chun | Abstract: Artificial agents expected to operate alongside humans in daily life will be expected to handle novel circumstances and explain their behavior to humans. In this dissertation, we examine these two concepts from the perspective of generalization and explanation. Generalization relies on having a learning algorithm capable of performing well in unseen circumstances and updating the model to handle the novel circumstance. In practice, learning algorithms must be equipped with mechanisms that enable generalization. Here, we examine the generalization question from multiple perspectives, namely imitation learning and causal learning. We show that generalization performance benefits from understanding abstract high-level task structure and low-level perceptual inductive biases. We also examine explanations in imitation learning and communicative learning paradigms. These explanations are intended to foster human trust and address the value alignment problem between humans and machines. In the imitation learning setting, we show that the model components that best contribute to fostering human trust do not necessarily correspond to the model components contributing most to task performance. In the communicative learning paradigm, we show how theory of mind can align a machine's values to the preferences of a human user. Taken together, this dissertation helps address two of the most critical problems facing AI systems today: machine performance in unseen scenarios and human-machine trust.
Research Guides: IDT 4170 and 7170: Mobile Learning: Module 4

Your CECH Library course guide for IDT 4170 and 7170. This guide links to course readings by module. For research support, see the Technology and Instructional Design research guide linked from the home section of this course guide.
Cost-Sensitive Selective Classification and its Applications to Online Fraud Management

Fraud is defined as the utilization of deception forillegal gain by hiding the true nature of the activity. Whileorganizations lose around $3.7 trillion in revenue due to financialcrimes and fraud worldwide, they can affect all levels of societysignificantly. In this dissertation, I focus on credit card fraudin online transactions. Every online transaction comes with a fraudrisk and it is the merchant's liability to detect and stopfraudulent transactions. Merchants utilize various mechanisms toprevent and manage fraud such as automated fraud detection systemsand manual transaction reviews by expert fraud analysts. Manyproposed solutions mostly focus on fraud detection accuracy andignore financial considerations. Also, the highly effective manualreview process is overlooked. First, I propose Profit OptimizingNeural Risk Manager (PONRM), a selective classifier that (a)constitutes optimal collaboration between machine learning modelsand human expertise under industrial constraints, (b) is cost andprofit sensitive. I suggest directions on how to characterizefraudulent behavior and assess the risk of a transaction. I showthat my framework outperforms cost-sensitive and cost-insensitivebaselines on three real-world merchant datasets. While PONRM isable to work with many supervised learners and obtain convincingresults, utilizing probability outputs directly from the trainedmodel itself can pose problems, especially in deep learning assoftmax output is not a true uncertainty measure. This phenomenon,and the wide and rapid adoption of deep learning by practitionersbrought unintended consequences in many situations such as in theinfamous case of Google Photos' racist image recognition algorithm;thus, necessitated the utilization of the quantified uncertaintyfor each prediction. There have been recent efforts towardsquantifying uncertainty in conventional deep learning methods(e.g., dropout as Bayesian approximation); however, their optimaluse in decision making is often overlooked and understudied. Thus,I present a mixed-integer programming framework for selectiveclassification called MIPSC, that investigates and combines modeluncertainty and predictive mean to identify optimal classificationand rejection regions. I also extend this framework tocost-sensitive settings (MIPCSC) and focus on the criticalreal-world problem, online fraud management and show that myapproach outperforms industry standard methods significantly foronline fraud management in real-world settings.
Safer Illinois and RokWall: Privacy Preserving University Health Apps for COVID-19

has fundamentally disrupted the way we live.Government bodies, universities, and companies worldwide are rapidly developing technologies to combat the COVID-19 pandemic and safely reopen society.Essential analytics tools such as contact tracing, super-spreader event detection, and exposure mapping require collecting and analyzing sensitive user information.The increasing use of such powerful data-driven applications necessitates a secure, privacy-preserving infrastructure for computation on personal data.In this paper, we analyze two such computing infrastructures under development at the University of Illinois at Urbana-Champaign to track and mitigate the spread of COVID-19.First, we present Safer Illinois, a system for decentralized health analytics supporting two applications currently deployed with widespread adoption: digital contact tracing and COVID-19 status cards.Second, we introduce the RokWall architecture for privacy-preserving centralized data analytics on sensitive user data.We discuss the architecture of these systems, design choices, threat models considered, and the challenges we experienced in developing production-ready systems for sensitive data analysis.
Specifics of the autonomous energy power unit dynamic processes’ modeling

Significant features of modern autonomous power units, along with increasing complexity of structures and modes and increasing requirements for quality of operation, are the heterogeneity of their composition, the presence of links with distributed parameters, changing parameters during operation, the presence of computer components control, monitoring and diagnostics. A characteristic feature of controlled autonomous power units is the diversity of elements that are part of them. This, in turn, usually creates inhomogeneity in the mathematical description of the system. Physical heterogeneity is caused by the presence of elements that are different in physical essence (mechanical, electrical, optical, magnetic and electromagnetic, etc.). Functional heterogeneity is manifested in the presence of different purpose signals (control, measuring, duplicating, auxiliary, etc.). Thus, modern autonomous power units belong to the class of complex inhomogeneous dynamic systems.These features cause the emergence of new requirements for methods and tools for mathematical modeling of dynamic processes in autonomous power units, in particular the need to move from common in practice autonomous study of individual components, to a comprehensive study of the system as a whole using macromodels, model building according to experimental data, increasing the adequacy of mathematical models by rationally taking into account the distribution of parameters of the relevant links, the possibility of rapid changes in the composition of computer models, taking into account physical and functional heterogeneity.The article considers the methods of construction of mathematical models of dynamic processes in modern autonomous power units and in their control systems, diagnostics and testing on the basis of expansion of forms of mathematical descriptions with involvement of integral operators and Volterra equations.
Forensic Accounting and Tax Fraud Detection in the Nigerian Public Sector

Financial fraud had for long been seen as a menace that led to the collapse of many organizations in the world. The Federal Government in a bid to reduce the cases of financial fraud, established many anti-graft agencies but yet the efforts seemed not to have yielded any results. Recently, the Federal Government approved the establishment of a new anti-graft agency, “The Proceeds of Crime Recovery and Management Agency. This goes to buttress that the issue of financial fraud, tax fraud inclusive has overwhelmed the government. Forensic accounting has been conceived as one of the most effective ways of reducing cases of tax fraud in Nigeria. Forensic accounting and tax fraud in the public sector was examined. The results showed that forensic accounting reduces the occurrence of tax fraud in the public sector and recommended that forensic accounting should be adopted by the public sector to meet up with the demand of technology.
LibGuides: Diversity in STEM Education: Specialized Databases

This Library Guide contains resources for students interested in Diversity in STEM education (Science, Technology, Engineering, and Mathematics)
LibGuides: Computer Keyboards: A Beginner’s Guide to Basics, Customization, and Safety: Home

This guide is geared to help educate anyone on the topic of computer keyboards, their proper use, safety, and picking the right keyboard for you.