Open Access. Powered by Scholars. Published by Universities.®

Physical Sciences and Mathematics Commons

Open Access. Powered by Scholars. Published by Universities.®

Computer Sciences

Institution
Keyword
Publication Year
Publication
Publication Type
File Type

Articles 1 - 30 of 40561

Full-Text Articles in Physical Sciences and Mathematics

Exposing And Fixing Causes Of Inconsistency And Nondeterminism In Clustering Implementations, Xin Yin Dec 2021

Exposing And Fixing Causes Of Inconsistency And Nondeterminism In Clustering Implementations, Xin Yin

Dissertations

Cluster analysis aka Clustering is used in myriad applications, including high-stakes domains, by millions of users. Clustering users should be able to assume that clustering implementations are correct, reliable, and for a given algorithm, interchangeable. Based on observations in a wide-range of real-world clustering implementations, this dissertation challenges the aforementioned assumptions.This dissertation introduces an approach named SmokeOut that uses differential clustering to show that clustering implementations suffer from nondeterminism and inconsistency: on a given input dataset and using a given clustering algorithm, clustering outcomes and accuracy vary widely between (1) successive runs of the same toolkit, i.e., nondeterminism ...


Enterprise Environment Modeling For Penetration Testing On The Openstack Virtualization Platform, Vincent Karovič Jr., Jakub Bartaloš, Vincent Karovič, Michal Greguš Dec 2021

Enterprise Environment Modeling For Penetration Testing On The Openstack Virtualization Platform, Vincent Karovič Jr., Jakub Bartaloš, Vincent Karovič, Michal Greguš

Journal of Global Business Insights

The article presents the design of a model environment for penetration testing of an organization using virtualization. The need for this model was based on the constantly increasing requirements for the security of information systems, both in legal terms and in accordance with international security standards. The model was created based on a specific team from the unnamed company. The virtual working environment offered the same functions as the physical environment. The virtual working environment was created in OpenStack and tested with a Linux distribution Kali Linux. We demonstrated that the virtual environment is functional and its security testable. Virtualizing ...


Taxthemis: Interactive Mining And Exploration Of Suspicious Tax Evasion Group, Yating Lin, Kamkwai Wong, Yong Wang, Rong Zhang, Bo Dong, Huamin Qu, Qinghua Zheng Oct 2021

Taxthemis: Interactive Mining And Exploration Of Suspicious Tax Evasion Group, Yating Lin, Kamkwai Wong, Yong Wang, Rong Zhang, Bo Dong, Huamin Qu, Qinghua Zheng

Research Collection School Of Computing and Information Systems

Tax evasion is a serious economic problem for many countries, as it can undermine the government’s tax system and lead to an unfair business competition environment. Recent research has applied data analytics techniques to analyze and detect tax evasion behaviors of individual taxpayers. However, they have failed to support the analysis and exploration of the related party transaction tax evasion (RPTTE) behaviors (e.g., transfer pricing), where a group of taxpayers is involved. In this paper, we present TaxThemis, an interactive visual analytics system to help tax officers mine and explore suspicious tax evasion groups through analyzing heterogeneous tax-related ...


Qlens: Visual Analytics Of Multi-Step Problem-Solving Behaviors For Improving Question Design, Meng Xia, Reshika P. Velumani, Yong Wang, Huamin Qu, Xiaojuan Ma Oct 2021

Qlens: Visual Analytics Of Multi-Step Problem-Solving Behaviors For Improving Question Design, Meng Xia, Reshika P. Velumani, Yong Wang, Huamin Qu, Xiaojuan Ma

Research Collection School Of Computing and Information Systems

With the rapid development of online education in recent years, there has been an increasing number of learning platforms that provide students with multi-step questions to cultivate their problem-solving skills. To guarantee the high quality of such learning materials, question designers need to inspect how students’ problem-solving processes unfold step by step to infer whether students’ problem-solving logic matches their design intent. They also need to compare the behaviors of different groups (e.g., students from different grades) to distribute questions to students with the right level of knowledge. The availability of fine-grained interaction data, such as mouse movement trajectories ...


Tradao: A Visual Analytics System For Trading Algorithm Optimization, Ka Wing Tsang, Haotian Li, Fuk Ming Lam, Yifan Mu, Yong Wang, Huamin Qu Oct 2021

Tradao: A Visual Analytics System For Trading Algorithm Optimization, Ka Wing Tsang, Haotian Li, Fuk Ming Lam, Yifan Mu, Yong Wang, Huamin Qu

Research Collection School Of Computing and Information Systems

With the wide applications of algorithmic trading, it has become critical for traders to build a winning trading algorithm to beat the market. However, due to the lack of efficient tools, traders mainly rely on their memory to manually compare the algorithm instances of a trading algorithm and further select the best trading algorithm instance for the real trading deployment. We work closely with industry practitioners to discover and consolidate user requirements and develop an interactive visual analytics system for trading algorithm optimization. Structured expert interviews are conducted to evaluateTradAOand a representative case study is documented for illustrating the system ...


Visual Analysis Of Discrimination In Machine Learning, Qianwen Wang, Zhenghua Xu, Zhutian Chen, Yong Wang, Yong Wang, Huamin Qu Oct 2021

Visual Analysis Of Discrimination In Machine Learning, Qianwen Wang, Zhenghua Xu, Zhutian Chen, Yong Wang, Yong Wang, Huamin Qu

Research Collection School Of Computing and Information Systems

The growing use of automated decision-making in critical applications, such as crime prediction and college admission, has raised questions about fairness in machine learning. How can we decide whether different treatments are reasonable or discriminatory? In this paper, we investigate discrimination in machine learning from a visual analytics perspective and propose an interactive visualization tool, DiscriLens, to support a more comprehensive analysis. To reveal detailed information on algorithmic discrimination, DiscriLens identifies a collection of potentially discriminatory itemsets based on causal modeling and classification rules mining. By combining an extended Euler diagram with a matrix-based visualization, we develop a novel set ...


Splash: Learnable Activation Functions For Improving Accuracy And Adversarial Robustness, Mohammadamin Tavakoli, Forest Agostinelli, Pierre Baldi Aug 2021

Splash: Learnable Activation Functions For Improving Accuracy And Adversarial Robustness, Mohammadamin Tavakoli, Forest Agostinelli, Pierre Baldi

Publications

We introduce SPLASH units, a class of learnable activation functions shown to simultaneously improve the accuracy of deep neural networks while also improving their robustness to adversarial attacks. SPLASH units have both a simple parameterization and maintain the ability to approximate a wide range of non-linear functions. SPLASH units are: (1) continuous; (2) grounded (f(0)=0"); (3) use symmetric hinges; and (4) their hinges are placed at fixed locations which are derived from the data (i.e. no learning required). Compared to nine other learned and fixed activation functions, including ReLU and its variants, SPLASH units show superior performance ...


A Bert-Based Two-Stage Model For Chinese Chengyu Recommendation, Minghuan Tan, Jing Jiang, Bingtian Dai Aug 2021

A Bert-Based Two-Stage Model For Chinese Chengyu Recommendation, Minghuan Tan, Jing Jiang, Bingtian Dai

Research Collection School Of Computing and Information Systems

In Chinese, Chengyu are fixed phrases consisting of four characters. As a type of idioms, their meanings usually cannot be derived from their component characters. In this paper, we study the task of recommending a Chengyu given a textual context. Observing some of the limitations with existing work, we propose a two-stage model, where during the first stage we re-train a Chinese BERT model by masking out Chengyu from a large Chinese corpus with a wide coverage of Chengyu. During the second stage, we fine-tune the retrained, Chengyu-oriented BERT on a specific Chengyu recommendation dataset. We evaluate this method on ...


Logbert: Log Anomaly Detection Via Bert, Haixuan Guo Aug 2021

Logbert: Log Anomaly Detection Via Bert, Haixuan Guo

All Graduate Theses and Dissertations

When systems break down, administrators usually check the produced logs to diagnose the failures. Nowadays, systems grow larger and more complicated. It is labor-intensive to manually detect abnormal behaviors in logs. Therefore, it is necessary to develop an automated anomaly detection on system logs. Automated anomaly detection not only identifies malicious patterns promptly but also requires no prior domain knowledge. Many existing log anomaly detection approaches apply natural language models such as Recurrent Neural Network (RNN) to log analysis since both are based on sequential data. The proposed model, LogBERT, a BERT-based neural network, can capture the contextual information in ...


Hierarchical Mapping For Crosslingual Word Embedding Alignment, Ion Madrazo Azpiazu, Maria Soledad Pera Jul 2021

Hierarchical Mapping For Crosslingual Word Embedding Alignment, Ion Madrazo Azpiazu, Maria Soledad Pera

Computer Science Faculty Publications and Presentations

The alignment of word embedding spaces in different languages into a common crosslingual space has recently been in vogue. Strategies that do so compute pairwise alignments and then map multiple languages to a single pivot language (most often English). These strategies, however, are biased towards the choice of the pivot language, given that language proximity and the linguistic characteristics of the target language can strongly impact the resultant crosslingual space in detriment of topologically distant languages. We present a strategy that eliminates the need for a pivot language by learning the mappings across languages in a hierarchicalway. Experiments demonstrate that ...


Blockchain For Automotive: An Insight Towards The Ipfs Blockchain-Based Auto Insurance Sector, Nishara Nizamuddin, Ahed Abugabah Jun 2021

Blockchain For Automotive: An Insight Towards The Ipfs Blockchain-Based Auto Insurance Sector, Nishara Nizamuddin, Ahed Abugabah

All Works

The advancing technology and industrial revolution have taken the automotive industry by storm in recent times. The auto sector’s constantly growing demand has paved the way for the automobile sector to embrace new technologies and disruptive innovations. The multi-trillion dollar, complex auto insurance sector is still stuck in the regulations of the past. Most of the customers still contact the insurance company by phone to buy new policies and process existing insurance claims. The customers still face the risk of fraudulent online brokers, as policies are mostly signed and processed on papers which often require human supervision, with a ...


Learn Biologically Meaningful Representation With Transfer Learning, Di He Jun 2021

Learn Biologically Meaningful Representation With Transfer Learning, Di He

Dissertations, Theses, and Capstone Projects

Machine learning has made significant contributions to bioinformatics and computational biol­ogy. In particular, supervised learning approaches have been widely used in solving problems such as bio­marker identification, drug response prediction, and so on. However, because of the limited availability of comprehensively labeled and clean data, constructing predictive models in super­ vised settings is not always desirable or possible, especially when using data­hunger, red­hot learning paradigms such as deep learning methods. Hence, there are urgent needs to develop new approaches that could leverage more readily available unlabeled data in driving successful machine learning ap­ plications in this ...


An Empirical Study Of Refactorings And Technical Debt In Machine Learning Systems, Yiming Tang, Raffi T. Khatchadourian, Mehdi Bagherzadeh, Rhia Singh, Ajani Stewart, Anita Raja May 2021

An Empirical Study Of Refactorings And Technical Debt In Machine Learning Systems, Yiming Tang, Raffi T. Khatchadourian, Mehdi Bagherzadeh, Rhia Singh, Ajani Stewart, Anita Raja

Publications and Research

Machine Learning (ML), including Deep Learning (DL), systems, i.e., those with ML capabilities, are pervasive in today’s data-driven society. Such systems are complex; they are comprised of ML models and many subsystems that support learning processes. As with other complex systems, ML systems are prone to classic technical debt issues, especially when such systems are long-lived, but they also exhibit debt specific to these systems. Unfortunately, there is a gap of knowledge in how ML systems actually evolve and are maintained. In this paper, we fill this gap by studying refactorings, i.e., source-to-source semantics-preserving program transformations, performed ...


Using An Integrative Machine Learning Approach To Study Microrna Regulation Networks In Pancreatic Cancer Progression, Roland Madadjim May 2021

Using An Integrative Machine Learning Approach To Study Microrna Regulation Networks In Pancreatic Cancer Progression, Roland Madadjim

Computer Science and Engineering: Theses, Dissertations, and Student Research

With advances in genomic discovery tools, recent biomedical research has produced a massive amount of genomic data on post-transcriptional regulations related to various transcript factors, microRNAs, lncRNAs, epigenetic modifications, and genetic variations. In this direction, the field of gene regulation network inference is created and aims to understand the interactome regulations between these molecules (e.g., gene-gene, miRNA-gene) that take place to build models able to capture behavioral changes in biological systems. A question of interest arises in integrating such molecules to build a network while treating each specie in its uniqueness. Given the dynamic changes of interactome in chaotic ...


High-Order Flexible Multirate Integrators For Multiphysics Applications, Rujeko Chinomona May 2021

High-Order Flexible Multirate Integrators For Multiphysics Applications, Rujeko Chinomona

Mathematics Theses and Dissertations

Traditionally, time integration methods within multiphysics simulations have been chosen to cater to the most restrictive dynamics, sometimes at a great computational cost. Multirate integrators accurately and efficiently solve systems of ordinary differential equations that exhibit different time scales using two or more time steps. In this thesis, we explore three classes of time integrators that can be classified as one-step multi-stage multirate methods for which the slow dynamics are evolved using a traditional one step scheme and the fast dynamics are solved through a sequence of modified initial value problems. Practically, the fast dynamics are subcycled using a small ...


Musical Gesture Through The Human Computer Interface: An Investigation Using Information Theory, Michael Vincent Blandino May 2021

Musical Gesture Through The Human Computer Interface: An Investigation Using Information Theory, Michael Vincent Blandino

LSU Doctoral Dissertations

This study applies information theory to investigate human ability to communicate using continuous control sensors with a particular focus on informing the design of digital musical instruments. There is an active practice of building and evaluating such instruments, for instance, in the New Interfaces for Musical Expression (NIME) conference community. The fidelity of the instruments can depend on the included sensors, and although much anecdotal evidence and craft experience informs the use of these sensors, relatively little is known about the ability of humans to control them accurately. This dissertation addresses this issue and related concerns, including continuous control performance ...


Ieee Access Special Section Editorial: Software-Defined Networks For Energy Internet And Smart Grid Communication, Mubashir Husain Rehmani, Alan Davy, Brendan Jennings, Zeeshan Kaleem, Akhilesh S. Thyagaturu, Hassnaa Moustafa, Al-Sakib Khan Pathan May 2021

Ieee Access Special Section Editorial: Software-Defined Networks For Energy Internet And Smart Grid Communication, Mubashir Husain Rehmani, Alan Davy, Brendan Jennings, Zeeshan Kaleem, Akhilesh S. Thyagaturu, Hassnaa Moustafa, Al-Sakib Khan Pathan

Publications

A new network paradigm of software-defined networks (SDNs) is being widely adapted to efficiently monitor and manage the communication networks with a global perspective. SDN has a key networking feature that separates control and data plane. Today, due to its inherent benefits, SDN has been widely applied to various networking domains, including data centers, 5G Access and Core network functions, wide area network (WAN), enterprise, optical networks, underwater sensor networks (UWSNs), energy Internet (EI), and smart grid (SG).


Automated Analysis Of Rfps Using Natural Language Processing (Nlp) For The Technology Domain, Sterling Beason, William Hinton, Yousri A. Salamah, Jordan Salsman May 2021

Automated Analysis Of Rfps Using Natural Language Processing (Nlp) For The Technology Domain, Sterling Beason, William Hinton, Yousri A. Salamah, Jordan Salsman

SMU Data Science Review

Much progress has been made in text analysis, specifically within the statistical domain of Term Frequency (TF) and Inverse Document Frequency (IDF). However, there is much room for improvement especially within the area of discovering Emerging Trends. Emerging Trend Detection Systems (ETDS) depend on ingesting a collection of textual data and TF/IDF to identify new or up-trending topics within the Corpus. However, the tremendous rate of change and the amount of digital information presents a challenge that makes it almost impossible for a human expert to spot emerging trends without relying on an automated ETD system. Since the U ...


The Social Market Economy As A Formula For Peace, Prosperity, And Sustainability, Almuth D. Merkel May 2021

The Social Market Economy As A Formula For Peace, Prosperity, And Sustainability, Almuth D. Merkel

Doctor of International Conflict Management Dissertations

The social market economy was developed in Germany during the interwar period amidst political and economic turmoil. With clear demarcation lines differentiating it from socialism and laissez-faire capitalism, the social market economy became a formula for peace and prosperity for post WWII Germany. Since then, the success of the social market economy has inspired many other countries to adopt its principles. Drawing on evidence from economic history and the history of economic thought, this thesis first reviews the evolution of the fundamental principles that form the foundation of social-market economic thought. Blending the micro-economic utility maximization framework with traditional growth ...


Airbnb Price Prediction With Sentiment Classification, Peilu Liu May 2021

Airbnb Price Prediction With Sentiment Classification, Peilu Liu

Master's Projects

Airbnb is an online platform that provides arrangements for short-term local home renting services. It is a challenging task for the house owner to price a rental home and attract customers. Customers also need to evaluate the price of the rental property based on the listing details. This paper demonstrates several existing Airbnb price prediction models using machine learning and external data to improve the prediction accuracy. It also discusses machine learning and neural network models that are commonly used for price prediction. The goal of this paper is to build a price prediction model using machine learning and sentiment ...


2vt: Visions, Technologies, And Visions Of Technologies For Understanding Human Scale Spaces, Ville Paanen, Piia Markkanen, Jonas Oppenlaender, Haider Akmal, Lik Hang Lee, Ava Fatah Gen Schieck, John Dunham, Konstantinos Papangelis, Nicolas Lalone, Niels Van Berkel, Jorge Goncalves, Simo Hosio May 2021

2vt: Visions, Technologies, And Visions Of Technologies For Understanding Human Scale Spaces, Ville Paanen, Piia Markkanen, Jonas Oppenlaender, Haider Akmal, Lik Hang Lee, Ava Fatah Gen Schieck, John Dunham, Konstantinos Papangelis, Nicolas Lalone, Niels Van Berkel, Jorge Goncalves, Simo Hosio

Presentations and other scholarship

Spatial experience is an important subject in various fields, and in HCI it has been mostly investigated in the urban scale. Research on human scale spaces has focused mostly on the personal meaning or aesthetic and embodied experiences in the space. Further, spatial experience is increasingly topical in envisioning how to build and interact with technologies in our everyday lived environments, particularly in so-called smart cities. This workshop brings researchers and practitioners from diverse fields to collaboratively discover new ways to understand and capture human scale spatial experience and envision its implications to future technological and creative developments in our ...


Analysis Of Students’ Multi-Representation Ability In Augmented Reality-Assisted Learning, Sri Jumini, Edy Cahyono, Muhamad Miftakhul Falah May 2021

Analysis Of Students’ Multi-Representation Ability In Augmented Reality-Assisted Learning, Sri Jumini, Edy Cahyono, Muhamad Miftakhul Falah

Library Philosophy and Practice (e-journal)

Not all learning sources can directly and cheaply be presented, so augmented reality media is needed to be applied to students with various talents and intelligence. This study aims to analyze students’ multi-representation ability through the use of augmented reality media. The research method was carried out through pre-experiment with one group posttest only design. Test question items were given to see the students’ multi-representation ability. Data analysis was carried out through the percentage of the number of students achieving test scores of more than or equal to 80 on a scale of 100. The results showed that 88% (28 ...


Benchmarking Clustering And Classification Tasks Using K-Means, Fuzzy C-Means And Feedforward Neural Networks Optimized By Pso, Adam Pickens, Adam Pickens May 2021

Benchmarking Clustering And Classification Tasks Using K-Means, Fuzzy C-Means And Feedforward Neural Networks Optimized By Pso, Adam Pickens, Adam Pickens

Honors College Theses

Clustering is a widely used unsupervised learning technique across data mining and machine learning applications and finds frequent use in diverse fields ranging from astronomy, medical imaging, search and optimization, geology, geophysics and sentiment analysis to name a few. It is therefore important to verify the effectiveness of the clustering algorithms in question and to make reasonably strong arguments for the acceptance of the end results generated by the validity indices that measure the compactness and separability of clusters. This work aims to explore the successes and limitations of popular clustering mechanisms such as K-Means and Fuzzy C-Means by comparing ...


Standard Non-Uniform Noise Dataset, Andres Imperial, John M. Edwards May 2021

Standard Non-Uniform Noise Dataset, Andres Imperial, John M. Edwards

Browse all Datasets

Fixed Pattern Noise Non-Uniformity Correction through K-Means Clustering

Fixed pattern noise removal from imagery by software correction is a practical approach compared to a physical hardware correction because it allows for correction post-capture of the imagery. Fixed pattern noise presents a unique challenge for de-noising techniques as the noise does not present itself where large number statistics are effective. Traditional noise removal techniques such as blurring or despeckling produce poor correction results because of a lack of noise identification. Other correction methods developed for fixed pattern noise can often present another problem of misidentification of noise. This problem can result ...


Collaborative City Digital Twin For The Covid-19 Pandemic: A Federated Learning Solution, Junjie Pang, Yan Huang, Zhenzhen Xie, Jianbo Li, Zhipeng Cai May 2021

Collaborative City Digital Twin For The Covid-19 Pandemic: A Federated Learning Solution, Junjie Pang, Yan Huang, Zhenzhen Xie, Jianbo Li, Zhipeng Cai

Tsinghua Science and Technology

The novel coronavirus, COVID-19, has caused a crisis that affects all segments of the population. As the knowledge and understanding of COVID-19 evolve, an appropriate response plan for this pandemic is considered one of the most effective methods for controlling the spread of the virus. Recent studies indicate that a city Digital Twin (DT) is beneficial for tackling this health crisis, because it can construct a virtual replica to simulate factors, such as climate conditions, response policies, and people’s trajectories, to help plan efficient and inclusive decisions. However, a city DTsystem relies on long-term and high-quality data collection to ...


A Data-Driven Clustering Recommendation Method For Single-Cell Rna-Sequencing Data, Yu Tian, Ruiqing Zheng, Zhenlan Liang, Suning Li, Fang-Xiang Wu, Min Li May 2021

A Data-Driven Clustering Recommendation Method For Single-Cell Rna-Sequencing Data, Yu Tian, Ruiqing Zheng, Zhenlan Liang, Suning Li, Fang-Xiang Wu, Min Li

Tsinghua Science and Technology

Recently, the emergence of single-cell RNA-sequencing (scRNA-seq) technology makes it possible to solve biological problems at the single-cell resolution. One of the critical steps in cellular heterogeneity analysis is the cell type identification. Diverse scRNA-seq clustering methods have been proposed to partition cells into clusters. Among all the methods, hierarchical clustering and spectral clustering are the most popular approaches in the downstream clustering analysis with different preprocessing strategies such as similarity learning, dropout imputation, and dimensionality reduction. In this study, we carry out a comprehensive analysis by combining different strategies with these two categories of clustering methods on scRNA-seq datasets ...


A Computer-Aided System For Ocular Myasthenia Gravis Diagnosis, Guanjie Liu, Yan Wei, Yunshen Xie, Jianqiang Li, Liyan Qiao, Ji-Jiang Yang May 2021

A Computer-Aided System For Ocular Myasthenia Gravis Diagnosis, Guanjie Liu, Yan Wei, Yunshen Xie, Jianqiang Li, Liyan Qiao, Ji-Jiang Yang

Tsinghua Science and Technology

The current mode of clinical aided diagnosis of Ocular Myasthenia Gravis (OMG) is time-consuming and laborious, and it lacks quantitative standards. An aided diagnostic system for OMG is proposed to solve this problem. The values calculated by the system include three clinical indicators: eyelid distance, sclera distance, and palpebra superior fatigability test time. For the first two indicators, the semantic segmentation method was used to extract the pathological features of the patient’s eye image and a semantic segmentation model was constructed. The patient eye image was divided into three regions: iris, sclera, and background. The indicators were calculated based ...


Robust Segmentation Method For Noisy Images Based On An Unsupervised Denosing Filter, Ling Zhang, Jianchao Liu, Fangxing Shang, Gang Li, Juming Zhao, Yueqin Zhang May 2021

Robust Segmentation Method For Noisy Images Based On An Unsupervised Denosing Filter, Ling Zhang, Jianchao Liu, Fangxing Shang, Gang Li, Juming Zhao, Yueqin Zhang

Tsinghua Science and Technology

Level-set-based image segmentation has been widely used in unsupervised segmentation tasks. Researchers have recently alleviated the influence of image noise on segmentation results by introducing global or local statistics into existing models. Most existing methods are based on the assumption that the distribution of image noise is known or observable. However, real-time images do not meet this assumption. To bridge this gap, we propose a novel level-set-based segmentation method with an unsupervised denoising mechanism. First, a denoising filter is acquired under the unsupervised learning paradigm. Second, the denoising filter is integrated into the level-set framework to separate noise from the ...


Efficient Scheduling Mapping Algorithm For Row Parallel Coarse-Grained Reconfigurable Architecture, Naijin Chen, Zhen Wang, Ruixiang He, Jianhui Jiang, Fei Cheng, Chenghao Han May 2021

Efficient Scheduling Mapping Algorithm For Row Parallel Coarse-Grained Reconfigurable Architecture, Naijin Chen, Zhen Wang, Ruixiang He, Jianhui Jiang, Fei Cheng, Chenghao Han

Tsinghua Science and Technology

Row Parallel Coarse-Grained Reconfigurable Architecture (RPCGRA) has the advantages of maximum parallelism and programmable flexibility. Designing an efficient algorithm to map the diverse applications onto RPCGRA is difficult due to a number of RPCGRA hardware constraints. To solve this problem, the nodes of the data flow graph must be partitioned and scheduled onto the RPCGRA. In this paper, we present a Depth-First Greedy Mapping (DFGM) algorithm that simultaneously considers the communication costs and the use times of the Reconfigurable Cell Array (RCA). Compared with level breadth mapping, the performance of DFGM is better. The percentage of maximum improvement in the ...


Game Theoretical Approach For Non-Overlapping Community Detection, Baohua Sun, Richard Al-Bayaty, Qiuyuan Huang, Dapeng Wu May 2021

Game Theoretical Approach For Non-Overlapping Community Detection, Baohua Sun, Richard Al-Bayaty, Qiuyuan Huang, Dapeng Wu

Tsinghua Science and Technology

Graph clustering, i.e., partitioning nodes or data points into non-overlapping clusters, can be beneficial in a large varieties of computer vision and machine learning applications. However, main graph clustering schemes, such as spectral clustering, cannot be applied to a large network due to prohibitive computational complexity required. While there exist methods applicable to large networks, these methods do not offer convincing comparisons against known ground truth. For the first time, this work conducts clustering algorithm performance evaluations on large networks (consisting of one million nodes) with ground truth information. Ideas and concepts from game theory are applied towards graph ...