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检索条件"主题词=Theory and Algorithms"

共 27 条记录，以下是11-20 订阅

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Deep Learning On Graphs Using Graph Convolutional Networks

Deep Learning On Graphs Using Graph Convolutional Networks

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作者： Saurabh Mithe San Jose State University

学位级别：硕士

Graphs are a powerful way to model network data with the objects as nodes and the relationship between the various objects as links. Such graphs contain a plethora of valuable information about the underlying data which can be extracted, analyzed, and visualized using Machine Learning (ML). The challenge to this task is that graphs are non-Euclidean structures which means that they cannot be directly used with ML techniques because ML techniques only work with Euclidean structures like grids or sequences. In order to overcome this challenge, the graph structure first needs to be encoded into an equivalent Euclidean representation in the form of a low-dimensional vector. This low-dimensional vector is called an embedding vector, and the encoding process is called node embedding. Traditionally, user-defined heuristics and matrix- factorization based methods were used for node embedding. However, these methods are slow and perform poorly on large and complex graphs. During the recent years, various ML techniques have been developed that learn the encoding of the graph automatically, and in a faster and more efficient way. A few of these techniques called Graph Convolutional Networks (GCNs) use variants of the convolutional neural networks adapted for graphs, and are implemented using deep neural networks. The aim of this project is two-fold. Firstly, to develop a unified framework focusing on three major GCN techniques in order to analyze, evaluate, and compare their performance on select benchmark datasets for the task of node classification. And secondly, to implement a new aggregator for one of the techniques â GraphSAGE, and compare the performance of the aggregator with the existing GCN methods as well as the other aggregators provided by GraphSAGE.

关键词： Node embedding machine learning graph convolutional net- work node classification. Artificial Intelligence and Robotics theory and algorithms

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Gogo: An Improved Algorithm To Measure The Semantic Similarity Between Gene Ontology Terms

Gogo: An Improved Algorithm To Measure The Semantic Similari...

引用

作者： Chenguang Zhao University of Southern Mississippi

学位级别：硕士

Measuring the semantic similarity between Gene Ontology (GO) terms is an essential step in functional bioinformatics research. We implemented a software named GOGO for calculating the semantic similarity between GO terms. GOGO has the advantages of both information-content-based and hybrid methods, such as Resnikâs and Wangâs methods. Moreover, GOGO is relatively fast and does not need to calculate information content (IC) from a large gene annotation corpus but still has the advantage of using IC. This is achieved by considering the number of children nodes in the GO directed acyclic graphs when calculating the semantic contribution of an ancestor node giving to its descendent nodes. GOGO can calculate functional similarities between genes and then cluster genes based on their functional similarities. Evaluations performed on multiple pathways retrieved from the saccharomyces genome database (SGD) show that GOGO can accurately and robustly cluster genes based on functional similarities. We release GOGO as a web server and also as a stand-alone tool, which allows convenient execution of the tool for a small number of GO terms or integration of the tool into bioinformatics pipelines for large-scale calculations. GOGO can be freely accessed or downloaded from http://***/GOGO/.

关键词： semantic similarity functional similarity gene ontology gene function prediction gene cluster Bioinformatics theory and algorithms

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Online Local Communities

Online Local Communities

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作者： Mrudula Murali San Jose State University

学位级别：硕士

A community in a network is a group of nodes that are densely and closely connected to each other, get sparsely connected to the nodes outside the community. Finding communities in a large network helps solve many real-world problems. But detecting such communities in a complex network by focusing on the whole network is not feasible. Instead, we focus on finding communities around one or more seed node(s) of interest. Therefore, in this project, we find local communities. Moreover, we consider the online setting where the whole graph is unknown in the beginning and we get a stream of edges, i.e., pair of nodes, or a stream of higher order structures, i.e., triangles of nodes. We created a new dataset that consists of web pages and their links by using the Internet Archive. We extended an existing online local graph community detection algorithm, called COEUS, for higher order structures such as triangles of nodes. We provide experimental results and comparison of the existing method and our proposed method using two public datasets, the Amazon and the DBLP as well as for our new Webpages dataset. In the experimental results, we see that the proposed method performs better than the existing method for one out of three test cases for the public dataset but not for our Webpages dataset. This is because the Webpages dataset has a large number of nodes with degree 1 which poses a problem for modified COEUS because it takes triangles as an input stream.

关键词： Community detection Local graph clustering Online com- munity theory and algorithms

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High Dimensional Outlier Detection

High Dimensional Outlier Detection

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作者： Omid Khormali Montana Tech

学位级别：硕士

In statistics and data science, outliers are data points that differ greatly from other observations in a data set. They are important attributes of the data because they can dramatically influence patterns and relationships manifested by non-outliers. It is therefore very important to detect and adequately deal with outliers. Recently, a novel algorithm, the ROMA algorithm, has been proposed [11]. In this paper, we propose a modification of the ROMA algorithm that reduces its computational complexity from $O(n^2 m)$ to $O((n/(2^m-o(1)))^2 m)$ where $n$ is the number of data points and $m$ is the dimension of the space. And as a consequence, if $\log(n) <2^m$, then the improved complexity is $O((n/\log(n))^2 m)$.

关键词： Outlier Computational complexity High dimesional dataset Applied Statistics Probability theory and algorithms

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Dish: Democracy In State Houses

Dish: Democracy In State Houses

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作者： Nicholas A Russo Cal Poly

学位级别：硕士

In our current political climate, state level legislators have become increasingly impor- tant. Due to cuts in funding and growing focus at the national level, public oversight for these legislators has drastically decreased. This makes it difficult for citizens and activists to understand the relationships and commonalities between legislators. This thesis provides three contributions to address this issue. First, we created a data set containing over 1200 features focused on a legislatorâs activity on bills. Second, we created embeddings that represented a legislatorâs level of activity and engagement for a given bill using a custom model called Democracy2Vec. Third, we provided a case study focused on the 2015-2016 California State Legislator and had our results verified by a political expert. Our results show that our embeddings can explain relationships between legislator and how they will likely act during the legislative process.

关键词： Digital Democracy Machine Learning Embeddings Neural Networks Clusterings American Politics Artificial Intelligence and Robotics Databases and Information Systems Software Engineering theory and algorithms

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Network Alignment In Heterogeneous Social Networks

Network Alignment In Heterogeneous Social Networks

引用

作者： Priyanka Kasbekar San Jose State University

学位级别：硕士

Online Social Networks (OSN) have numerous applications and an ever growing user base. This has led to users being a part of multiple social networks at the same time. Identifying a similar user from one social network on another social network will give in- formation about a userâs behavior on different platforms. It further helps in community detection and link prediction tasks. The process of identifying or aligning users in multiple networks is called Network Alignment. More the information we have about the nodes / users better the results of Network Alignment. Unlike other related work in this field that use features like location, timestamp, bag of words, our proposed solution to the Network Alignment problem primarily uses information that is easily available which is the topology of the given network. We look to improve the alignment results by using more information on users like username and profile image features. Experiments are performed to compare the proposed solution in both unsupervised and supervised setting.

关键词： Online Social Networks Network Alignment Supervised learning Unsupervised learning OS and Networks theory and algorithms

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Cyber Security- a New Secured Password Generation Algorithm with Graphical Authentication, and Alphanumeric Passwords Along with Encryption

Cyber Security- a New Secured Password Generation Algorithm ...

引用

作者： Rao, Akash Old Dominion University

学位级别：M.S.

Graphical passwords are always considered as an alternative of alphanumeric passwords for their better memorability and usability [1]. Alphanumeric passwords provide an adequate amount of satisfaction, but they do not offer better memorability compared to graphical passwords [1]. On the other hand, graphical passwords are considered less secured and provide better memorability [1]. Therefore many researchers have researched on graphical passwords to overcome the vulnerability. One of the most significant weaknesses of the graphical passwords is "Shoulder Surfing Attack," which means, sneaking into a victim's computer to learn the whole password or part of password or some confidential information. Such kind of attacks is called as Shoulder Surfing Attack. Many researchers have presented various ideas to curb the shoulder surfing attack. However, graphical passwords are still vulnerable to this attack. Therefore, in the present thesis, the solution for shoulder surfing attack is analyzed and a new algorithm is developed to provide better algorithm with memorability as well as very strong password using the encryption. For alphanumeric passwords, dictionary attack, and brute force attack are critical potential threats to be taken care off. Dictionary attacks mean, attacking every word from the dictionary to crack the password, whereas, brute force attack means, applying all different kind of combinations to crack the password. Thus, both protection methods have their pros and cons and, therefore in this thesis, the possible solution has been researched to provide more secure technique. Encryption is another essential technique in the field of cybersecurity. The history of encryption dates back to World War 2, where German forces used its encryption technique for the first time, and this encryption has been developed a lot with the consistent contribution of many researchers. Starting from the German encryption technique, the present encryption field has evolved a lot

关键词： Cybersecurity Encryption Password Security Unbreakable Whirlpool Computer Engineering Information Security theory and algorithms

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The deserving patient: Blame, dependency, and impairment in discourses of chronic pain and opioid use

The deserving patient: Blame, dependency, and impairment in ...

引用

作者： Nickerson, Maureen Antioch University

学位级别：Psy.D.

Negative stereotypes about people with chronic pain pose a barrier in the delivery of care; contribute to worsening symptoms of physical and psychological distress; and play a role in policy decisions that adversely affect patients and providers. Pain-care seekers may be accused of malingering, laziness, mental aberration, attention seeking, and drug seeking. The propagation of stigmatizing attitudes was explored in this Critical Discourse Analysis of online-reader-comments responding to a series of pain-care policy articles published by a large metropolitan newspaper. Results suggest that framing pain patients as legitimate and deserving can inadvertently reproduce the inequities advocates seek to redress. Ascriptions of deservingness were associated with the locus of choice and agency. Assignments of blameworthiness were used to distinguish the legitimate pain patient from the illegitimate care seeker. Motivation for seeking pain care, as much as the effects of opioids, provided crucial determinants in evaluating legitimacy claims and blame ascriptions. Evaluations of deservingness were predicated on the valence of social regard. Compassion, empathy, respect and believability were rewards of positive social regard. The subjects of addiction and drug abuse were maligned to the detriment of people with pain and people with opioid addiction alike. The disease-entity model of chronic pain was associated with psychiatric discourses of mental illness through a narratives inaccurate reality perception. Loss of independence, rationality, and respectability were semantically linked to negative stereotypes of pain patients, drug addicts, and mentally ill groups. Medical discourses drawing on empirical materialist traditions assert taken-for-granted population categories (e.g. chronic noncancer pain patient) with little acknowledgment of confounding variables, lack of evidence, or their social impact. For the benefit of people seeking care, there is a critical need for moral

关键词： Glycan Identification Tandem Mass Spectrometry Glycopeptide algorithms Bioinformatics theory and algorithms

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Optimal Reallocation under Additive and Ordinal Preferences 16

Optimal Reallocation under Additive and Ordinal Preferences

引用

International Conference on Autonomous Agents and Multiagent Systems

作者： Haris Aziz Peter Biro Jerome Lang Julien Lesca Jerome Monnot Data61 and UNSW Hungarian Academy of Sciences LAMSADE Universite Paris-Dauphine

ISBN: (纸本)9781510855083

Reallocating resources to get mutually beneficial outcomes is a fundamental problem in various multi-agent settings. In the first part of the paper we focus on the setting in which agents express additive cardinal utilities over objects. We present computational hardness results as well as polynomial-time algorithms for testing Pareto optimality under different restrictions such as two utility values or lexicographic utilities. In the second part of the paper we assume that agents express only their (ordinal) preferences over single objects, and that their preferences are additively separable. In this setting, we present characterizations and polynomial-time algorithms for possible and necessary Pareto optimality.

关键词： Economics theory and algorithms Game theory (cooperative and non-cooperative) Social choice theory Pareto optimality economics Preferences Additives Ordinal solidification Social theory Agents Public utilities

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Fractional Hedonic Games 14

Fractional Hedonic Games

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International Conference on Autonomous Agents and Multiagent Systems

作者： Haris Aziz Felix Brandt Paul Harrenstein NICTA and UNSW TU Munchen University of Oxford

ISBN: (纸本)9781634391313

An important issue in multi-agent systems is the exploitation of synergies via coalition formation. We initiate the formal study of fractional hedonic games. In fractional hedonic games, the utility of a player in a coalition structure is the average value he ascribes to the members of his coalition. Among other settings, this covers situations in which there are several types of agents and each agent desires to be in a coalition in which the fraction of agents of his own type is minimal. Fractional hedonic games not only constitute a natural class of succinctly representable coalition formation games, but also provide an interesting framework for network clustering. We propose a number of conditions under which the core of fractional hedonic games is non-empty and provide algorithms for computing a core stable outcome.

关键词： Economics theory and algorithms Game theory (cooperative and non-cooperative) Teamwork Coalition formation Coordination

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