Numerical Study Of A Buckling Restrained Brace (Brb) In Steel Structures And Comparison With A Convergent Ordinary Brace (Ocb) Under Static And Dynamic Loading,
2021
Lecturer, Faculty of Civil Engineering, Ayandegan Institute of Higher Education, Tonekabon, Iran
Numerical Study Of A Buckling Restrained Brace (Brb) In Steel Structures And Comparison With A Convergent Ordinary Brace (Ocb) Under Static And Dynamic Loading, Fatemeh Habibpour
Makara Journal of Technology
Bulk metallic glass (BMG) has good mechanical strength, high hardness, wear resistance, and corrosion resistance with promising application in various industries. However, for the industrial production of BMG, the main issue is how to overcome limitations of joining with other materials. The present study focuses on solder processing at low operating temperature to avoid exceeding the recrystallization temperature. A feasible joining process for BMG was developed using lead-free solders. The BMG surface is pre-plated with copper, nickel, or titanium as a wetting layer. The reaction temperature is set between the glass transition temperature of BMG and the melting point of …
Understanding And Avoiding Ai Failures: A Practical Guide.,
2021
University of Louisville
Understanding And Avoiding Ai Failures: A Practical Guide., Robert Max C Williams
Electronic Theses and Dissertations
As AI technologies increase in capability and ubiquity, AI accidents are becoming more common. Based on normal accident theory, high reliability theory, and open systems theory, we create a framework for understanding the risks associated with AI applications. In addition, we also use AI safety principles to quantify the unique risks of increased intelligence and human-like qualities in AI. Together, these two fields give a more complete picture of the risks of contemporary AI. By focusing on system properties near accidents instead of seeking a root cause of accidents, we identify where attention should be paid to safety for current …
Data-Efficient Machine Learning With Focus On Transfer Learning,
2021
Embry-Riddle Aeronautical University
Data-Efficient Machine Learning With Focus On Transfer Learning, Shuteng Niu
Doctoral Dissertations and Master's Theses
Machine learning (ML) has attracted a significant amount of attention from the artificial intelligence community. ML has shown state-of-art performance in various fields, such as signal processing, healthcare system, and natural language processing (NLP). However, most conventional ML algorithms suffer from three significant difficulties: 1) insufficient high-quality training data, 2) costly training process, and 3) domain discrepancy. Therefore, it is important to develop solutions for these problems, so the future of ML will be more sustainable. Recently, a new concept, data-efficient ma- chine learning (DEML), has been proposed to deal with the current bottlenecks of ML. Moreover, transfer learning (TL) …
Electrostatic Design And Characterization Of A 200 Kev Photogun And Wien Spin Rotator,
2021
Old Dominion University
Electrostatic Design And Characterization Of A 200 Kev Photogun And Wien Spin Rotator, Gabriel G. Palacios Serrano
Electrical & Computer Engineering Theses & Dissertations
High-energy nuclear physics experiments at the Jefferson Lab Continuous Electron Beam Accelerator Facility (CEBAF) require high spin-polarization electron beams produced from strained super-lattice GaAs photocathodes activated to negative electron affinity in a high voltage photogun operating at 130 kV dc. A pair of Wien filter spin rotators in the injector provides precise control of the electron beam polarization at the end station target. An upgrade of the CEBAF injector to better support the upcoming Moller experiment requires increasing the electron beam energy to 200 keV, resulting in better transmission through injector apertures and improved photocathode lifetime. In addition, the energy …
Learning Discriminative And Efficient Attention For Person Re-Identification Using Agglomerative Clustering Frameworks,
2021
University of Nebraska-Lincoln
Learning Discriminative And Efficient Attention For Person Re-Identification Using Agglomerative Clustering Frameworks, Kshitij Nikhal
Department of Electrical and Computer Engineering: Dissertations, Theses, and Student Research
Recent advancements like multiple contextual analysis, attention mechanisms, distance-aware optimization, and multi-task guidance have been widely used for supervised person re-identification (ReID), but the implementation and effects of such methods in unsupervised person ReID frameworks are non-trivial and unclear, respectively. Moreover, with increasing size and complexity of image- and video-based ReID datasets, manual or semi-automated annotation procedures for supervised ReID are becoming labor intensive and cost prohibitive, which is undesirable especially considering the likelihood of annotation errors increase with scale/complexity of data collections. Therefore, this thesis proposes a new iterative clustering framework that incorporates (a) two attention architectures that learn …
Internet Of Things Based Messaging Protocols
For Aquaculture Applications - A Bibliometric
Analysis And Review,
2021
Symbiosis Institute of Technology, Symbiosis International (Deemed University), Pune
Internet Of Things Based Messaging Protocols For Aquaculture Applications - A Bibliometric Analysis And Review, Rupali Shete, Anupkumar M. Bongale Dr., Arunkumar Bongale Dr.
Library Philosophy and Practice (e-journal)
Internet of Things (IoT) which connects real-world physical objects with various identities involves different technologies and research areas. As it is an integration of different standards and technologies with numerous capabilities, the implementation phase needs to consider important parameters of communication. In IoT this is achieved through messaging protocols. Each object has its own limitations in terms of sensing capability, storage capacity, connectivity, power utilization, etc. And hence when such objects are deployed for different applications, they need to perform well in terms of their various capabilities. Messaging protocols at this stage need to consider these diverse elements. One of …
Newslink: Empowering Intuitive News Search With Knowledge Graphs,
2021
Singapore Management University
Newslink: Empowering Intuitive News Search With Knowledge Graphs, Yueji Yang, Yuchen Li, Anthony Tung
Research Collection School Of Computing and Information Systems
News search tools help end users to identify relevant news stories. However, existing search approaches often carry out in a "black-box" process. There is little intuition that helps users understand how the results are related to the query. In this paper, we propose a novel news search framework, called NEWSLINK, to empower intuitive news search by using relationship paths discovered from open Knowledge Graphs (KGs). Specifically, NEWSLINK embeds both a query and news documents to subgraphs, called subgraph embeddings, in the KG. Their embeddings' overlap induces relationship paths between the involving entities. Two major advantages are obtained by incorporating subgraph …
Feature Extraction And Design In Deep Learning Models,
2021
Old Dominion University
Feature Extraction And Design In Deep Learning Models, Daniel Perez
Computational Modeling & Simulation Engineering Theses & Dissertations
The selection and computation of meaningful features is critical for developing good deep learning methods. This dissertation demonstrates how focusing on this process can significantly improve the results of learning-based approaches. Specifically, this dissertation presents a series of different studies in which feature extraction and design was a significant factor for obtaining effective results. The first two studies are a content-based image retrieval system (CBIR) and a seagrass quantification study in which deep learning models were used to extract meaningful high-level features that significantly increased the performance of the approaches. Secondly, a method for change detection is proposed where the …
The Design Of Dynamic Probabilistic Caching With Time-Varying Content Popularity,
2021
University of Waterloo
The Design Of Dynamic Probabilistic Caching With Time-Varying Content Popularity, Jie Gao, Shan Zhang, Lian Zhao, Xuemin Shen
Electrical and Computer Engineering Faculty Research and Publications
In this paper, we design dynamic probabilistic caching for the scenario when the instantaneous content popularity may vary with time while it is possible to predict the average content popularity over a time window. Based on the average content popularity, optimal content caching probabilities can be found, e.g., from solving optimization problems, and existing results in the literature can implement the optimal caching probabilities via static content placement. The objective of this work is to design dynamic probabilistic caching that: i) converge (in distribution) to the optimal content caching probabilities under time-invariant content popularity, and ii) adapt to the time-varying …
Measurements And Analysis Of Propagation Channels In Vehicle-To-Infrastructure Scenarios,
2021
Huazhong University of Science and Technology
Measurements And Analysis Of Propagation Channels In Vehicle-To-Infrastructure Scenarios, Wei Li, Xiaoya Hu, Jie Gao, Lian Zhao, Xuemin Shen
Electrical and Computer Engineering Faculty Research and Publications
In this paper, we present measurements and analysis of propagation channels in vehicle-to-infrastructure (V2I) scenarios, which are the basis of designing vehicular communication systems. Firstly, we propose a deterministic geometry-based method to classify V2I links into three types, i.e., line-of-sight beneath (LOS-B), non-LOS (NLOS), and line-of-sight above (LOS-A), based on the environmental features, where roadside row of trees constitute the main obstacles. Secondly, for each link, we investigate the large-scale fading effect on V2I channels, including the path loss exponent and shadowing components. Subsequently, we validate the empirical path loss model using extensive measurements and two classical channel models. The …
Dram Failure Prediction In Aiops: Empirical Evaluation, Challenges And Opportunities,
2021
Singapore Management University
Dram Failure Prediction In Aiops: Empirical Evaluation, Challenges And Opportunities, Zhiyue Wu, Hongzuo Xu, Guansong Pang, Fengyuan Yu, Yijie Wang, Songlei Jian, Yongjun Wang
Research Collection School Of Computing and Information Systems
DRAM failure prediction is a vital task in AIOps, which is crucial to maintain the reliability and sustainable service of large-scale data centers. However, limited work has been done on DRAM failure prediction mainly due to the lack of public available datasets. This paper presents a comprehensive empirical evaluation of diverse machine learning techniques for DRAM failure prediction using a large-scale multisource dataset, including more than three millions of records of kernel, address, and mcelog data, provided by Alibaba Cloud through PAKDD 2021 competition. Particularly, we first formulate the problem as a multiclass classification task and exhaustively evaluate seven popular/stateof-the-art …
Dbl: Efficient Reachability Queries On Dynamic Graphs,
2021
Shandong University
Dbl: Efficient Reachability Queries On Dynamic Graphs, Qiuyi Lyu, Yuchen Li, Bingsheng He, Bin Gong
Research Collection School Of Computing and Information Systems
Reachability query is a fundamental problem on graphs, which has been extensively studied in academia and industry. Since graphs are subject to frequent updates in many applications, it is essential to support efficient graph updates while offering good performance in reachability queries. Existing solutions compress the original graph with the Directed Acyclic Graph (DAG) and propose efficient query processing and index update techniques. However, they focus on optimizing the scenarios where the Strong Connected Components (SCCs) remain unchanged and have overlooked the prohibitively high cost of the DAG maintenance when SCCs are updated. In this paper, we propose DBL, an …
Dycuckoo: Dynamic Hash Tables On Gpus,
2021
Singapore Management University
Dycuckoo: Dynamic Hash Tables On Gpus, Yuchen Li, Qiwei Zhu, Zheng Lyu, Zhongdong Huang, Jianling Sun
Research Collection School Of Computing and Information Systems
The hash table is a fundamental structure that has been implemented on graphics processing units (GPUs) to accelerate a wide range of analytics workloads. Most existing works have focused on static scenarios and occupy large GPU memory to maximize the insertion efficiency. In many cases, data stored in hash tables get updated dynamically, and existing approaches use unnecessarily large memory resources. One naïve solution is to rebuild a hash table (known as rehashing) whenever it is either filled or mostly empty. However, this approach renders significant overheads for rehashing. In this paper, we propose a novel dynamic cuckoo hash table …
Boundary Precedence Image Inpainting Method Based On Self-Organizing Maps,
2021
Singapore Management University
Boundary Precedence Image Inpainting Method Based On Self-Organizing Maps, Haibo Pen, Quan Wang, Zhaoxia Wang
Research Collection School Of Computing and Information Systems
In addition to text data analysis, image analysis is an area that has increasingly gained importance in recent years because more and more image data have spread throughout the internet and real life. As an important segment of image analysis techniques, image restoration has been attracting a lot of researchers’ attention. As one of AI methodologies, Self-organizing Maps (SOMs) have been applied to a great number of useful applications. However, it has rarely been applied to the domain of image restoration. In this paper, we propose a novel image restoration method by leveraging the capability of SOMs, and we name …
Distance-Based Formation Control Using Decentralized Sensing With Infrared Photodiodes,
2021
Louisiana State University
Distance-Based Formation Control Using Decentralized Sensing With Infrared Photodiodes, Steven Williams
LSU Master's Theses
This study presents an onboard sensor system for determining the relative positions of mobile robots, which is used in decentralized distance-based formation controllers for multi-agent systems. This sensor system uses infrared photodiodes and LEDs; its effective use requires coordination between the emitting and detecting robots. A technique is introduced for calculating the relative positions based on photodiode readings, and an automated calibration system is designed for future maintenance. By measuring the relative positions of their neighbors, each robot is capable of running an onboard formation controller, which is independent of both a centralized controller and a global positioning-like system (e.g., …
How The Power Of Machine – Machine Learning, Data Science And Nlp Can Be Used To Prevent Spoofing And Reduce Financial Risks,
2021
Harrisburg University of Science and Technology
How The Power Of Machine – Machine Learning, Data Science And Nlp Can Be Used To Prevent Spoofing And Reduce Financial Risks, Sasibhushan Rao Chanthati
Harrisburg University Other Works
This paper discusses the potential of machine learning, data science, and natural language processing (NLP) in mitigating the incidence of spoofing and financial risks hinged on cyber threats. Another one is spoofing; it is the act of impersonating legitimate entities to gain unauthorized information and it is indeed a threat to the public and companies to some extent. The research introduces two primary methodologies to combat spoofing: an email filtering system using a machine learning algorithm and an encryption and decryption system using a Caesar Cipher and Python programming language. It distinguishes between approved domains and unapproved domains by using …
Heterogeneous Resources Cost-Aware Geo-Distributed Data Analytics,
2021
University of Nebraska at Omaha
Heterogeneous Resources Cost-Aware Geo-Distributed Data Analytics, Minmin Zhang
UNO Student Research and Creative Activity Fair
Many popular cloud service providers deploy tens of data centers (DCs) around the world to reduce user-perceived latency for better user experiences, in which a large amount of data is generated and stored in a geo-distributed manner. Geo-distributed Data Analytics (GDA) has gained great popularity in meeting the growing demand to mine meaningful and timely knowledge from such highly dispersed data. Since GDA systems require a large data migration between DCs via a wide area network (WAN), many existing works invested significant effort to optimize data transfer strategies to efficiently use limited WAN by considering the network pricing policies on …
"When They Say Weed Causes Depression, But It's Your Fav Antidepressant": Knowledge-Aware Attention Framework For Relationship Extraction,
2021
University of South Carolina - Columbia
"When They Say Weed Causes Depression, But It's Your Fav Antidepressant": Knowledge-Aware Attention Framework For Relationship Extraction, Shweta Yadav, Usha Lokala, Raminta Daniulaityte, Krishnaprasad Thirunarayan, Francois Lamy, Amit Sheth
Publications
With the increasing legalization of medical and recreational use of cannabis, more research is needed to understand the association between depression and consumer behavior related to cannabis consumption. Big social media data has potential to provide deeper insights about these associations to public health analysts. In this interdisciplinary study, we demonstrate the value of incorporating domain-specific knowledge in the learning process to identify the relationships between cannabis use and depression. We develop an end-to-end knowledge infused deep learning framework (Gated-K-BERT) that leverages the pre-trained BERT language representation model and domain-specific declarative knowledge source (Drug Abuse Ontology (DAO)) to jointly extract …
Bibliometric Survey For Stock Market Prediction Using Sentimental Analysis And Lstm,
2021
Symbiosis International University
Bibliometric Survey For Stock Market Prediction Using Sentimental Analysis And Lstm, Pooja Bagane, Nimit Mehta Mr, Parth Kakde Mr, Nisarg Bramhbhatt Mr, Ishansh Sahni Mr, Sirbi Kotrappa Dr
Library Philosophy and Practice (e-journal)
Creating an overview of the flow fundamentals of the global market trading and sublimation of equities into a superimposed system of economic agendas. Furthermore this leads to dynamic overlapping with the current technological advancements to create a platform for information exchange and inculcations. This created a new field of access points where we could enhance and analyse the data available and create an interface to predict the rise and fall trends involved with the stock market. These help create a sense of control and format over the public personification over the economic impacts and use social media and involve discrete …
The Role Of Institutional Repositories In Advancing Open Scholarship: A Case Study From The United Arab Emirates University,
2021
UAE University
The Role Of Institutional Repositories In Advancing Open Scholarship: A Case Study From The United Arab Emirates University, Amina Itani, Linda Östlundh Mrs.
The Journal of Electronic Theses and Dissertations
The digitization of theses and dissertations at the United Arab Emirates University (UAEU) began five years ago with the Digital Commons institutional repository (IR) platform, Scholarworks, employed for the purpose. The project, initiated by the University Library, exemplifies how academic libraries can take the lead in advocating for digital preservation and open access publishing of institutional research materials. This case study describes how the library’s Electronic Theses and Dissertations (ETD) initiative has provided an excellent model to the UAEU for it to start disseminating its research output and how the library’s copyright and open access policies have been crucial for …
