Computer Engineering Commons^™

Which Variables Should I Log?, Zhongxin Liu, Xin Xia, David Lo, Zhenchang Xing, Ahmed E. Hassan, Shanping Li

Research Collection School Of Computing and Information Systems

Developers usually depend on inserting logging statements into the source code to collect system runtime information. Such logged information is valuable for software maintenance. A logging statement usually prints one or more variables to record vital system status. However, due to the lack of rigorous logging guidance and the requirement of domain-specific knowledge, it is not easy for developers to make proper decisions about which variables to log. To address this need, in this work, we propose an approach to recommend logging variables for developers during development by learning from existing logging statements. Different from other prediction tasks in software …

Information Models Of "Complex-Analytical Information System Of Scientific Degrees" In The Field Of Attestation Of Higher Qualified Scientific And Scientific-Pedagogical Personnel, Hakimjon Nasiridinovich Zaynidinov, Orif Kudratovich Makhmanov, Feruz Mukhammadjon Ogli Latifov, Djamshid Bakhodirovich Sultanov

Chemical Technology, Control and Management

In our country, comprehensive systemic measures are being taken to develop the digital economy and improve the widespread introduction of modern information and communication technologies in all sectors and industries, including education and science. This strategy requires improving the quality and efficiency of the system of training and attestation of highly qualified scientific and scientific-pedagogical personnel, further development of the industry through digitalization and adaptation to advanced world practice. This paper presents models and descriptions of scenarios for the use of "Complex-analytical information system of scientific degrees" developed for the exchange of electronic documents and the digitization of their attestation …

An Automated Method To Enrich Consumer Health Vocabularies Using Glove Word Embeddings And An Auxiliary Lexical Resource, Mohammed Ibrahim, Susan Gauch, Omar Salman, Mohammed Alqahtani

Computer Science and Computer Engineering Faculty Publications and Presentations

Background

Clear language makes communication easier between any two parties. A layman may have difficulty communicating with a professional due to not understanding the specialized terms common to the domain. In healthcare, it is rare to find a layman knowledgeable in medical terminology which can lead to poor understanding of their condition and/or treatment. To bridge this gap, several professional vocabularies and ontologies have been created to map laymen medical terms to professional medical terms and vice versa.

Objective

Many of the presented vocabularies are built manually or semi-automatically requiring large investments of time and human effort and consequently the …

Multilateration Index., Chip Lynch

Electronic Theses and Dissertations

We present an alternative method for pre-processing and storing point data, particularly for Geospatial points, by storing multilateration distances to fixed points rather than coordinates such as Latitude and Longitude. We explore the use of this data to improve query performance for some distance related queries such as nearest neighbor and query-within-radius (i.e. “find all points in a set P within distance d of query point q”). Further, we discuss the problem of “Network Adequacy” common to medical and communications businesses, to analyze questions such as “are at least 90% of patients living within 50 miles of a covered emergency …

Thunderrw: An In-Memory Graph Random Walk Engine, Shixuan Sun, Yuhang Chen, Shengliang Lu, Bingsheng He, Yuchen Li

Research Collection School Of Computing and Information Systems

As random walk is a powerful tool in many graph processing, mining and learning applications, this paper proposes an efficient inmemory random walk engine named ThunderRW. Compared with existing parallel systems on improving the performance of a single graph operation, ThunderRW supports massive parallel random walks. The core design of ThunderRW is motivated by our profiling results: common RW algorithms have as high as 73.1% CPU pipeline slots stalled due to irregular memory access, which suffers significantly more memory stalls than the conventional graph workloads such as BFS and SSSP. To improve the memory efficiency, we first design a generic …

Context-Aware Outstanding Fact Mining From Knowledge Graphs, Yueji Yang, Yuchen Li, Panagiotis Karras, Anthony Tung

Research Collection School Of Computing and Information Systems

An Outstanding Fact (OF) is an attribute that makes a target entity stand out from its peers. The mining of OFs has important applications, especially in Computational Journalism, such as news promotion, fact-checking, and news story finding. However, existing approaches to OF mining: (i) disregard the context in which the target entity appears, hence may report facts irrelevant to that context; and (ii) require relational data, which are often unavailable or incomplete in many application domains. In this paper, we introduce the novel problem of mining Contextaware Outstanding Facts (COFs) for a target entity under a given context specified by …

A Configurable Mining Approach For Learning Services Customization, Aya M. Mostafa Amm, Yehia M. Helmy Ymh, Amira M. Idrees Ami

Future Computing and Informatics Journal

There is no doubt that this age is the age of data and technology. Moreover, there is tremendous development in all fields. The personalized material is a good approach in the different fields. It provides a fit material that matches the styles of readers. It supports readers in various reading domains. This research paper aims to support students in the educational system. Additionally, the research paper designs to increase education values for students. Furthermore, the research paper builds the smart appropriate materials through Egyptian Knowledge Banking (EKB) based on the learner question. The Egyptian Knowledge Bank (EKB) is a rich …

Review Of Data Mining Techniques For Detecting Churners In The Telecommunication Industry, Mahmoud Ewieda, Mohamed Ismail Roushdy, Essam Shaaban

Future Computing and Informatics Journal

The telecommunication sector has been developed rapidly and with large amounts of data obtained as a result of increasing in the number of subscribers, modern techniques, data-based applications, and services. As well as better awareness of customer requirements and excellent quality that meets their satisfaction. This satisfaction raises rivalry between firms to maintain the quality of their services and upgrade them. These data can be helpfully extracted for analysis and used for predicting churners. Researchers around the world have conducted important research to understand the uses of Data mining (DM) that can be used to predict customers' churn. This …

Forecasting Stock Exchange Data Using Group Method Of Data Handling Neural Network Approach, Marzieh Faridi Masouleh, Ahmad Bagheri

Knowledge Engineering and Data Science

The increasing uncertainty of the natural world has motivated computer scientists to seek out the best approach to technological problems. Nature-inspired problem solving approaches include meta-heuristic methods that are focused on evolutionary computation and swarm intelligence. One of these problems significantly impacting information is forecasting exchange index, which is a serious concern with the growth and decline of stock as there are many reports on loss of financial resources or profitability. When the exchange includes an extensive set of diverse stock, particular concepts and mechanisms for physical security, network security, encryption, and permissions should guarantee and predict its future needs. …

Backpropagation Neural Network With Combination Of Activation Functions For Inbound Traffic Prediction, Purnawansyah Purnawansyah, Haviluddin Haviluddin, Herdianti Darwis, Huzain Azis, Yulita Salim

Knowledge Engineering and Data Science

Predicting network traffic is crucial for preventing congestion and gaining superior quality of network services. This research aims to use backpropagation to predict the inbound level to understand and determine internet usage. The architecture consists of one input layer, two hidden layers, and one output layer. The study compares three activation functions: sigmoid, rectified linear unit (ReLU), and hyperbolic Tangent (tanh). Three learning rates: 0.1, 0.5, and 0.9 represent low, moderate, and high rates, respectively. Based on the result, in terms of a single form of activation function, although sigmoid provides the least RMSE and MSE values, the ReLu function …

Do Missing Link Community Smell Affect Developers Productivity: An Empirical Study, Toukir Ahammed, Sumon Ahmed, Mohammed Shafiul Alam Khan

Knowledge Engineering and Data Science

Missing link smell occurs when developers contribute to the same source code without communicating with each other. Existing studies have analyzed the relationship of missing link smells with code smell and developer contribution. However, the productivity of developers involved in missing link smell has not been explored yet. This study investigates how productivity differs between smelly and non-smelly developers. For this purpose, the productivity of smelly and non-smelly developers of seven open-source projects are analyzed. The result shows that the developers not involved in missing link smell have more productivity than the developers involved in smells. The observed difference is …

Indonesian Sentence Boundary Detection Using Deep Learning Approaches, Joan Santoso, Esther Irawati Setiawan, Christian Nathaniel Purwanto, Fachrul Kurniawan

Knowledge Engineering and Data Science

Detecting the sentence boundary is one of the crucial pre-processing steps in natural language processing. It can define the boundary of a sentence since the border between a sentence, and another sentence might be ambiguous. Because there are multiple separators and dynamic sentence patterns, using a full stop at the end of a sentence is sometimes inappropriate. This research uses a deep learning approach to split each sentence from an Indonesian news document. Hence, there is no need to define any handcrafted features or rules. In Part of Speech Tagging and Named Entity Recognition, we use sequence labeling to determine …

Face Images Classification Using Vgg-Cnn, I Nyoman Gede Arya Astawa, Made Leo Radhitya, I Wayan Raka Ardana, Felix Andika Dwiyanto

Knowledge Engineering and Data Science

Image classification is a fundamental problem in computer vision. In facial recognition, image classification can speed up the training process and also significantly improve accuracy. The use of deep learning methods in facial recognition has been commonly used. One of them is the Convolutional Neural Network (CNN) method which has high accuracy. Furthermore, this study aims to combine CNN for facial recognition and VGG for the classification process. The process begins by input the face image. Then, the preprocessor feature extractor method is used for transfer learning. This study uses a VGG-face model as an optimization model of transfer learning …

Detection Of Disease And Pest Of Kenaf Plant Based On Image Recognition With Vggnet19, Diny Melsye Nurul Fajri, Wayan Firdaus Mahmudy, Titiek Yulianti

Knowledge Engineering and Data Science

One of the advantages of Kenaf fiber as an environmental management product that is currently in the center of attention is the use of Kenaf fiber for luxury car interiors with environmentally friendly plastic materials. The opportunity to export Kenaf fiber raw material will provide significant benefits, especially in the agricultural sector in Indonesia. However, there are problems in several areas of Kenaf's garden, namely plants that are attacked by diseases and pests, which cause reduced yields and even death. This problem is caused by the lack of expertise and working hours of extension workers as well as farmers' knowledge …

Privacy-Preserving Cloud-Assisted Data Analytics, Wei Bao

Graduate Theses and Dissertations

Nowadays industries are collecting a massive and exponentially growing amount of data that can be utilized to extract useful insights for improving various aspects of our life. Data analytics (e.g., via the use of machine learning) has been extensively applied to make important decisions in various real world applications. However, it is challenging for resource-limited clients to analyze their data in an efficient way when its scale is large. Additionally, the data resources are increasingly distributed among different owners. Nonetheless, users' data may contain private information that needs to be protected.

Cloud computing has become more and more popular in …

A Method For Monitoring Operating Equipment Effectiveness With The Internet Of Things And Big Data, Carl D. Hays Iii

Master's Theses

The purpose of this paper was to use the Overall Equipment Effectiveness productivity formula in plant manufacturing and convert it to measuring productivity for forklifts. Productivity for a forklift was defined as being available and picking up and moving containers at port locations in Seattle and Alaska. This research uses performance measures in plant manufacturing and applies them to mobile equipment in order to establish the most effective means of analyzing reliability and productivity. Using the Internet of Things to collect data on fifteen forklift trucks in three different locations, this data was then analyzed over a six-month period to …

Data Mining Of Unstructured Textual Information In Transportation Safety Domain: Exploring Methods, Opportunities And Limitations, Keneth Morgan Kwayu

Dissertations

The unprecedented increase in volume and influx of structured and unstructured data has overwhelmed conventional data management system capabilities in organizing, analyzing, and procuring useful information in a timely fashion. Structured data sources have a pre-defined pattern that makes data preprocessing and information retrieval tasks relatively easy for the current technologies that have been designed to handle structured and repeatable data. Unlike structured data, unstructured data usually exists in an unorganized format that offers no or little insight unless indexed and stored in an organized fashion. The inherent format of unstructured data exacerbates difficulties in data preprocessing and information extraction. …

Inventory Locating With Quuppa: The Design And Development Of A Real-Time Process Monitoring Web Application Solution, Dylan C. Moreland, Trevor J. Howell, John W. Takiff, Patrick S. Dillon, Theo E. Fritz, William K. Mcintyre

Industrial and Manufacturing Engineering

Viasat, Inc. requires precise inventory tracking at their production facility in San Diego, CA. Viasat has installed the Quuppa indoor real-time locating system (RTLS), which it uses to track the real-time position of high-value work-in-process items. In its current state, the system only displays in-the-moment location information, with no available functionality for storing historical data for review, analysis, or visualization. In addition, the data displayed is noisy and prone to significant random error. This paper provides an overview of RTLS methods and technologies, assesses alternative solutions to Viasat’s issue, demonstrates our RTLS integrated web app solution, analyzes its impact, and …

On M-Impact Regions And Standing Top-K Influence Problems, Bo Tang, Kyriakos Mouratidis, Mingji Han

Research Collection School Of Computing and Information Systems

In this paper, we study the ��-impact region problem (mIR). In a context where users look for available products with top-�� queries, mIR identifies the part of the product space that attracts the most user attention. Specifically, mIR determines the kind of attribute values that lead a (new or existing) product to the top-�� result for at least a fraction of the user population. mIR has several applications, ranging from effective marketing to product improvement. Importantly, it also leads to (exact and efficient) solutions for standing top-�� impact problems, which were previously solved heuristically only, or whose current solutions face …

Hierarchical Reinforcement Learning: A Comprehensive Survey, Shubham Pateria, Budhitama Subagdja, Ah-Hwee Tan, Chai Quek

Research Collection School Of Computing and Information Systems

Hierarchical Reinforcement Learning (HRL) enables autonomous decomposition of challenging long-horizon decision-making tasks into simpler subtasks. During the past years, the landscape of HRL research has grown profoundly, resulting in copious approaches. A comprehensive overview of this vast landscape is necessary to study HRL in an organized manner. We provide a survey of the diverse HRL approaches concerning the challenges of learning hierarchical policies, subtask discovery, transfer learning, and multi-agent learning using HRL. The survey is presented according to a novel taxonomy of the approaches. Based on the survey, a set of important open problems is proposed to motivate the future …

Minimum Coresets For Maxima Representation Of Multidimensional Data, Yanhao Wang, Michael Mathioudakis, Yuchen Li, Kian-Lee Tan

Research Collection School Of Computing and Information Systems

Coresets are succinct summaries of large datasets such that, for a given problem, the solution obtained from a coreset is provably competitive with the solution obtained from the full dataset. As such, coreset-based data summarization techniques have been successfully applied to various problems, e.g., geometric optimization, clustering, and approximate query processing, for scaling them up to massive data. In this paper, we study coresets for the maxima representation of multidimensional data: Given a set �� of points in R �� , where �� is a small constant, and an error parameter �� ∈ (0, 1), a subset �� ⊆ �� …

Framework For Collecting Data From Iot Device, Md Saiful Islam

Symposium of Student Scholars

The Internet of Things (IoT) is the most significant and blooming technology in the 21st century. IoT has rapidly developed by covering hundreds of applications in the civil, health, military, and agriculture areas. IoT is based on the collection of sensor data through an embedded system, and this embedded system uploads the data on the internet. Devices and sensor technologies connected over a network can monitor and measure data in real-time. The main challenge is to collect data from IoT devices, transmit them to store in the Cloud, and later retrieve them at any time for visualization and data analysis. …

Multilayer Perceptron With Auto Encoder Enabled Deep Learning Model For Recommender Systems, Subhashini Narayan

Future Computing and Informatics Journal

In this modern world of ever-increasing one-click purchases, movie bookings, music, health- care, fashion, the need for recommendations have increased the more. Google, Netflix, Spotify, Amazon and other tech giants use recommendations to customize and tailor their search engines to suit the user’s interests. Many of the existing systems are based on older algorithms which although have decent accuracies, require large training and testing datasets and with the emergence of deep learning, the accuracy of algorithms has further improved, and error rates have reduced due to the use of multiple layers. The need for large datasets has declined as well. …

A Literature Review For Contributing Mining Approaches For Business Process Reengineering, Noha Ahmed Bayomy Nab, Ayman E. Khedr Aek, Laila A. Abd-Elmegid Laa, Amira M. Idrees Ami

Future Computing and Informatics Journal

Due to the changing dynamics of the business environment, organizations need to redesign or reengineer their business processes in order to provide services with the lowest cost and shortest response time while increasing quality. Thence, Business Process Re-engineering (BPR) provides a roadmap to achieve operational goals that leads to enhance flexibility and productivity, cost reduction, and quality of service/product. In this paper, we propose a literature review for the different proposed models for Business Process Reengineering. The models specify where the breakdowns occur in BPR implementation, justifies why such breakdowns occur, and propose techniques to prevent their occurrence again. The …

Data Forgery Detection In Automatic Generation Control: Exploration Of Automated Parameter Generation And Low-Rate Attacks, Yatish R. Dubasi

Computer Science and Computer Engineering Undergraduate Honors Theses

Automatic Generation Control (AGC) is a key control system utilized in electric power systems. AGC uses frequency and tie-line power flow measurements to determine the Area Control Error (ACE). ACE is then used by the AGC to adjust power generation and maintain an acceptable power system frequency. Attackers might inject false frequency and/or tie-line power flow measurements to mislead AGC into falsely adjusting power generation, which can harm power system operations. Various data forgery detection models are studied in this thesis. First, to make the use of predictive detection models easier for users, we propose a method for automated generation …

Machine Learning-Based Recognition On Crowdsourced Food Images, Aditya Kulkarni

Honors Scholar Theses

With nearly a third of the world’s population suffering from food-induced chronic diseases such as obesity, the role of food in community health is required now more than ever. While current research underscores food proximity and density, there is a dearth in regard to its nutrition and quality. However, recent research in geospatial data collection and analysis as well as intelligent deep learning will help us study this further.

Employing the efficiency and interconnection of computer vision and geospatial technology, we want to study whether healthy food in the community is attainable. Specifically, with the help of deep learning in …

Securing Fog Federation From Behavior Of Rogue Nodes, Mohammed Saleh H. Alshehri

Graduate Theses and Dissertations

As the technological revolution advanced information security evolved with an increased need for confidential data protection on the internet. Individuals and organizations typically prefer outsourcing their confidential data to the cloud for processing and storage. As promising as the cloud computing paradigm is, it creates challenges; everything from data security to time latency issues with data computation and delivery to end-users. In response to these challenges CISCO introduced the fog computing paradigm in 2012. The intent was to overcome issues such as time latency and communication overhead and to bring computing and storage resources close to the ground and the …

Interactive Search Vs. Automatic Search: An Extensive Study On Video Retrieval, Phuong-Anh Nguyen, Chong-Wah Ngo

Research Collection School Of Computing and Information Systems

This article conducts user evaluation to study the performance difference between interactive and automatic search. Particularly, the study aims to provide empirical insights of how the performance landscape of video search changes, with tens of thousands of concept detectors freely available to exploit for query formulation. We compare three types of search modes: free-to-play (i.e., search from scratch), non-free-to-play (i.e., search by inspecting results provided by automatic search), and automatic search including concept-free and concept-based retrieval paradigms. The study involves a total of 40 participants; each performs interactive search over 15 queries of various difficulty levels using two search modes …

Mapping Renewal: How An Unexpected Interdisciplinary Collaboration Transformed A Digital Humanities Project, Elise Tanner, Geoffrey Joseph

Digital Initiatives Symposium

Funded by a National Endowment for Humanities (NEH) Humanities Collections and Reference Resources Foundations Grant, the UA Little Rock Center for Arkansas History and Culture’s “Mapping Renewal” pilot project focused on creating access to and providing spatial context to archival materials related to racial segregation and urban renewal in the city of Little Rock, Arkansas, from 1954-1989. An unplanned interdisciplinary collaboration with the UA Little Rock Arkansas Economic Development Institute (AEDI) has proven to be an invaluable partnership. One team member from each department will demonstrate the Mapping Renewal website and discuss how the collaborative process has changed and shaped …

Second Version On A Centralized Approach To Reducing Burnouts In The It Industry Using Work Pattern Monitoring Using Artificial Intelligence Using Mongodb Atlas And Python, Sasibhushan Rao Chanthati

Harrisburg University Other Works

Industry burnout is interlinked with cultural, individual, physical, or emotional exhaustion, and social factors, the resolution of which requires the technology-driven trends in the workplace and the technologies such as work pattern monitoring and Artificial Intelligence that can deal with large amounts of data. Industries face a gigantic problem i.e., employee burnout which can charge a firm loss in numerous hours and thousands of dollars every year. The more advanced companies use work pattern monitoring using Artificial Intelligence to make their employees work more professionally. In this research my attempts to understand the development and leadership, on the effects of …