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2021

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Articles 811 - 840 of 3476

Full-Text Articles in Computer Sciences

Exploiting Group Structures To Infer Social Interactions From Videos, Maksim Bolonkin Sep 2021

Exploiting Group Structures To Infer Social Interactions From Videos, Maksim Bolonkin

Dartmouth College Ph.D Dissertations

In this thesis, we consider the task of inferring the social interactions between humans by analyzing multi-modal data. Specifically, we attempt to solve some of the problems in interaction analysis, such as long-term deception detection, political deception detection, and impression prediction. In this work, we emphasize the importance of using knowledge about the group structure of the analyzed interactions. Previous works on the matter mostly neglected this aspect and analyzed a single subject at a time. Using the new Resistance dataset, collected by our collaborators, we approach the problem of long-term deception detection by designing a class of histogram-based features …

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Logics Of Resource And Justification, Hirohiko Kushida Sep 2021

Logics Of Resource And Justification, Hirohiko Kushida

Dissertations, Theses, and Capstone Projects

It is a well-known result by G ̈odel in 1933 that the Intuitionistic Logic can be embedded into a system which is essentially equivalent to the modal logic S4. This can be considered to be an attempt to provide a provability semantics to the Intuitionistic Logic. This work had caused some problems: the exact arithmetical meaning of the Intuitionistic Logic and S4, the exact axiomatization of formal provability in formal arithmetic and the standard model of arithmetic. These days the arithmetical interpretation has been extended and generalized to epistemological interpretation for various modal logics, which resulted in various systems of …

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Adversarial Training For Skill Learning In A Mobile Robot, Todd W. Flyr Sep 2021

Adversarial Training For Skill Learning In A Mobile Robot, Todd W. Flyr

Dissertations, Theses, and Capstone Projects

Machine Learning in mobile robotics is sometimes hampered by the difficulties associated with the creation of a large corpus of labeled data that most neural network based learning algorithms demand. In recent years, advances in the field of machine learning have been facilitated via the creation of large collaboratively-created labeled training datasets that researchers can use as the basis for experiments to validate and improve their candidate neural network architectures. For the field of robotics, however, tasks are so disparate and the physical devices so varied that in most cases the creation of collaborative benchmark datasets are impractical. Obtaining data …

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Piecewise Linear Manifold Clustering, Artyom Diky Sep 2021

Piecewise Linear Manifold Clustering, Artyom Diky

Dissertations, Theses, and Capstone Projects

This work studies the application of topological analysis to non-linear manifold clustering. A novel method, that exploits the data clustering structure, allows to generate a topological representation of the point dataset. An analysis of topological construction under different simulated conditions is performed to explore the capabilities and limitations of the method, and demonstrated statistically significant improvements in performance. Furthermore, we introduce a new information-theoretical validation measure for clustering, that exploits geometrical properties of clusters to estimate clustering compressibility, for evaluation of the clustering goodness-of-fit without any prior information about true class assignments. We show how the new validation measure, when …

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Molecular Dynamics Simulations Of Self-Assemblies In Nature And Nanotechnology, Phu Khanh Tang Sep 2021

Molecular Dynamics Simulations Of Self-Assemblies In Nature And Nanotechnology, Phu Khanh Tang

Dissertations, Theses, and Capstone Projects

Nature usually divides complex systems into smaller building blocks specializing in a few tasks since one entity cannot achieve everything. Therefore, self-assembly is a robust tool exploited by Nature to build hierarchical systems that accomplish unique functions. The cell membrane distinguishes itself as an example of Nature’s self-assembly, defining and protecting the cell. By mimicking Nature’s designs using synthetically designed self-assemblies, researchers with advanced nanotechnological comprehension can manipulate these synthetic self-assemblies to improve many aspects of modern medicine and materials science. Understanding the competing underlying molecular interactions in self-assembly is always of interest to the academic scientific community and industry. …

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Solving Multiple Inference In Graphical Models, Cong Chen Sep 2021

Solving Multiple Inference In Graphical Models, Cong Chen

Dissertations, Theses, and Capstone Projects

For inference problems in graphical models, much effort has been directed at algorithms for obtaining one single optimal prediction. In practice, the data is often noisy or incomplete, which makes one single optimal solution unreliable. To address this problem, multiple Inference is proposed to find several best solutions, M-Best, where multiple hypotheses are preferred for advanced reasoning. People use oracle accuracy as an evaluation criterion expecting one of the solutions has high accuracy with the ground truth. It has been shown that it is beneficial for the top solutions to be diverse. Approaches for solving diverse multiple inference are proposed …

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Routing Policy Choice Prediction In A Stochastic Network: Recursive Model And Solution Algorithm, Tien Mai, Xinlian Yu, Song Gao, Emma Frejinger Sep 2021

Routing Policy Choice Prediction In A Stochastic Network: Recursive Model And Solution Algorithm, Tien Mai, Xinlian Yu, Song Gao, Emma Frejinger

Research Collection School Of Computing and Information Systems

We propose a Recursive Logit (STD-RL) model for routing policy choice in a stochastic time-dependent (STD) network, where a routing policy is a mapping from states to actions on which link to take next, and a state is defined by node, time and information. A routing policy encapsulates travelers’ adaptation to revealed traffic conditions when making route choices. The STD-RL model circumvents choice set generation, a procedure with known issues related to estimation and prediction. In a given state, travelers make their link choice maximizing the sum of the utility of the outgoing link and the expected maximum utility until …

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Enterprise Resource Allocation For Intruder Detection And Interception, Adam B. Haywood Sep 2021

Enterprise Resource Allocation For Intruder Detection And Interception, Adam B. Haywood

Theses and Dissertations

This research considers the problem of an intruder attempting to traverse a defender's territory in which the defender locates and employs disparate sets of resources to lower the probability of a successful intrusion. The research is conducted in the form of three related research components. The first component examines the problem in which the defender subdivides their territory into spatial stages and knows the plan of intrusion. Alternative resource-probability modeling techniques as well as variable bounding techniques are examined to improve the convergence of global solvers for this nonlinear, nonconvex optimization problem. The second component studies a similar problem but …

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Determining Physical Characteristics Through Information Leakage In 802.11ac Beamforming, Albert D. Taglieri Sep 2021

Determining Physical Characteristics Through Information Leakage In 802.11ac Beamforming, Albert D. Taglieri

Theses and Dissertations

The risk of information leakage in 802.11ac allows an eavesdropper to monitor wireless traffic and correlate physical locations between devices, as well as environment changes such as the motion of a person. Previous pattern-analysis mitigation methods, which used nonexistent devices to fool an eavesdropper, are not effective in an 802.11ac network, because devices on the network can be correlated to their physical location, which a nonexistent device does not have. Further, additional information about motion in the target environment can be observed and analyzed, providing a new potential for pattern analysis and sensing. 802.11ac makes it possible to plug in …

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Characterizing Convolutional Neural Network Early-Learning And Accelerating Non-Adaptive, First-Order Methods With Localized Lagrangian Restricted Memory Level Bundling, Benjamin O. Morris Sep 2021

Characterizing Convolutional Neural Network Early-Learning And Accelerating Non-Adaptive, First-Order Methods With Localized Lagrangian Restricted Memory Level Bundling, Benjamin O. Morris

Theses and Dissertations

This dissertation studies the underlying optimization problem encountered during the early-learning stages of convolutional neural networks and introduces a training algorithm competitive with existing state-of-the-art methods. First, a Design of Experiments method is introduced to systematically measure empirical second-order Lipschitz upper bound and region size estimates for local regions of convolutional neural network loss surfaces experienced during the early-learning stages. This method demonstrates that architecture choices can significantly impact the local loss surfaces traversed during training. Next, a Design of Experiments method is used to study the effects convolutional neural network architecture hyperparameters have on different optimization routines' abilities to …

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The Promise And Limits Of Lawfulness: Inequality, Law, And The Techlash, Salomé Viljoen Sep 2021

The Promise And Limits Of Lawfulness: Inequality, Law, And The Techlash, Salomé Viljoen

Articles

In response to widespread skepticism about the recent rise of “tech ethics”, many critics have called for legal reform instead. In contrast with the “ethics response”, critics consider the “lawfulness response” more capable of disciplining the excesses of the technology industry. In fact, both are simultaneously vulnerable to industry capture and capable of advancing a more democratic egalitarian agenda for the information economy. Both ethics and law offer a terrain of contestation, rather than a predetermined set of commitments by which to achieve more democratic and egalitarian technological production. In advancing this argument, the essay focuses on two misunderstandings common …

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Dynamic Heterogeneous Graph Embedding Via Heterogeneous Hawkes Process, Yugang Ji, Tianrui Jia, Yuan Fang, Chuan Shi Sep 2021

Dynamic Heterogeneous Graph Embedding Via Heterogeneous Hawkes Process, Yugang Ji, Tianrui Jia, Yuan Fang, Chuan Shi

Research Collection School Of Computing and Information Systems

Graph embedding, aiming to learn low-dimensional representations of nodes while preserving valuable structure information, has played a key role in graph analysis and inference. However, most existing methods deal with static homogeneous topologies, while graphs in real-world scenarios are gradually generated with different-typed temporal events, containing abundant semantics and dynamics. Limited work has been done for embedding dynamic heterogeneous graphs since it is very challenging to model the complete formation process of heterogeneous events. In this paper, we propose a novel Heterogeneous Hawkes Process based dynamic Graph Embedding (HPGE) to handle this problem. HPGE effectively integrates the Hawkes process into …

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Does Bert Understand Idioms? A Probing-Based Empirical Study Of Bert Encodings Of Idioms, Minghuan Tan, Jing Jiang Sep 2021

Does Bert Understand Idioms? A Probing-Based Empirical Study Of Bert Encodings Of Idioms, Minghuan Tan, Jing Jiang

Research Collection School Of Computing and Information Systems

Understanding idioms is important in NLP. In this paper, we study to what extent pre-trained BERT model can encode the meaning of a potentially idiomatic expression (PIE) in a certain context. We make use of a few existing datasets and perform two probing tasks: PIE usage classification and idiom paraphrase identification. Our experiment results suggest that BERT indeed can separate the literal and idiomatic usages of a PIE with high accuracy. It is also able to encode the idiomatic meaning of a PIE to some extent.

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Artificial Intelligence And Work: Two Perspectives, Steven Miller, Thomas H. Davenport Sep 2021

Artificial Intelligence And Work: Two Perspectives, Steven Miller, Thomas H. Davenport

Research Collection School Of Computing and Information Systems

One of the most important issues in contemporary societies is the impact of intelligent technologies on human work. For an empirical perspective on the issue, we recently completed 30 case studies of people collaborating with AI-enabled smart machines. Twenty-four were from North America, mostly in the US. Six were from Southeast Asia, mostly in Singapore. We compare some of our observations to one of the broadest academic examinations of the issue. In particular, we focus on our case study observations with regard to key findings from the MIT Task Force on the Work of the Future report.

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The Empathetic Car: Exploring Emotion Inference Via Driver Behaviour And Traffic Context, Shu Liu, Kevin Koch, Zimu Zhou, Simon Foll, Xiaoxi He, Tina Menke, Elgar Fleisch, Felix Wortmann Sep 2021

The Empathetic Car: Exploring Emotion Inference Via Driver Behaviour And Traffic Context, Shu Liu, Kevin Koch, Zimu Zhou, Simon Foll, Xiaoxi He, Tina Menke, Elgar Fleisch, Felix Wortmann

Research Collection School Of Computing and Information Systems

An empathetic car that is capable of reading the driver’s emotions has been envisioned by many car manufacturers. Emotion inference enables in-vehicle applications to improve driver comfort, well-being, and safety. Available emotion inference approaches use physiological, facial, and speech-related data to infer emotions during driving trips. However, existing solutions have two major limitations: Relying on sensors that are not built into the vehicle restricts emotion inference to those people leveraging corresponding devices (e.g., smartwatches). Relying on modalities such as facial expressions and speech raises privacy concerns. By contrast, researchers in mobile health have been able to infer affective states (e.g., …

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Feeder Vessel Routing And Transshipment Coordination At A Congested Hub Port, Jiangang Jin, Qiang Meng, Hai Wang Sep 2021

Feeder Vessel Routing And Transshipment Coordination At A Congested Hub Port, Jiangang Jin, Qiang Meng, Hai Wang

Research Collection School Of Computing and Information Systems

With increasing container-shipping traffic, congestion at transshipment hub ports happens from time to time incurring longer-than-expected waiting time for vessels and loss of transshipment connections. This situation is even worse for feeder companies, due to their relatively lower berthing priority. It is essential to design the feeder vessel routes and schedules in response to hub port congestion while ensuring efficient transshipment connection with their connecting long-haul services. In this paper, we study the vessel routing and transshipment coordination problem for a feeder liner company where only limited choices of berthing time slots are available at the hub port. We proposed …

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Novel Hybrid Resampling Algorithms For Parallel/Distributed Particle Filters, Xudong Zhang Sep 2021

Novel Hybrid Resampling Algorithms For Parallel/Distributed Particle Filters, Xudong Zhang

Dissertations, Theses, and Capstone Projects

Particle filters, also known as sequential Monte Carlo (SMC) methods, use the Bayesian inference and the stochastic sampling technique to estimate the states of dynamic systems from given observations. Parallel/Distributed particle filters were introduced to improve the performance of sequential particle filters by using multiple processing units (PUs). The classical resampling algorithm used in parallel/distributed particle filters is a centralized scheme, called centralized resampling, which needs a central unit (CU) to serve as a hub for data transfers. As a result, the centralized resampling procedures produce extra communication costs, which lowers the speedup factors in parallel computing. Even though some …

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Robust And Ethical Data Governance Critical To Growth In Digital Age, Themin Suwardy, Melvin Yong Sep 2021

Robust And Ethical Data Governance Critical To Growth In Digital Age, Themin Suwardy, Melvin Yong

Research Collection School Of Accountancy

With increasing digitalisation, and companies collecting an ever-increasing amount of their customer and business data, organisations have to become more accountable to stakeholders such as regulators, customers and investors on the issue of data. Observers say expectations are also increasing, with incidents of data breaches capturing much media attention. Just as corporate governance encompasses more than just compliance, experts say data governance is more than just data protection and security but also about creating value.

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Characterization And Automatic Updates Of Deprecated Machine-Learning Api Usages, Stefanus Agus Haryono, Thung Ferdian, David Lo, Julia Lawall, Lingxiao Jiang Sep 2021

Characterization And Automatic Updates Of Deprecated Machine-Learning Api Usages, Stefanus Agus Haryono, Thung Ferdian, David Lo, Julia Lawall, Lingxiao Jiang

Research Collection School Of Computing and Information Systems

Due to the rise of AI applications, machine learning (ML) libraries, often written in Python, have become far more accessible. ML libraries tend to be updated periodically, which may deprecate existing APIs, making it necessary for application developers to update their usages. In this paper, we build a tool to automate deprecated API usage updates. We first present an empirical study to better understand how updates of deprecated ML API usages in Python can be done. The study involves a dataset of 112 deprecated APIs from Scikit-Learn, TensorFlow, and PyTorch. Guided by the findings of our empirical study, we propose …

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Biasheal: On-The-Fly Black-Box Healing Of Bias In Sentiment Analysis Systems, Zhou Yang, Harshit Jain, Jieke Shi, Muhammad Hilmi Asyrofi, David Lo Sep 2021

Biasheal: On-The-Fly Black-Box Healing Of Bias In Sentiment Analysis Systems, Zhou Yang, Harshit Jain, Jieke Shi, Muhammad Hilmi Asyrofi, David Lo

Research Collection School Of Computing and Information Systems

Although Sentiment Analysis (SA) is widely applied in many domains, existing research has revealed that the unfairness in SA systems can be harmful to the welfare of less privileged people. Several works propose pre-processing and in-processing methods to eliminate bias in SA systems, but little attention is paid to utilizing post-processing methods to heal bias. Postprocessing methods are particularly important for systems that use third-party SA services. Systems that use such services have no access to the SA engine or its training data and thus cannot apply pre-processing nor in-processing methods. Therefore, this paper proposes a black-box post-processing method to …

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Which Variables Should I Log?, Zhongxin Liu, Xin Xia, David Lo, Zhenchang Xing, Ahmed E. Hassan, Shanping Li Sep 2021

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 …

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Unified And Incremental Simrank: Index-Free Approximation With Scheduled Principle, Fanwei Zhu, Yuan Fang, Kai Zhang, Kevin C.-C. Chang, Hongtai Cao, Zhen Jiang, Minghui Wu Sep 2021

Unified And Incremental Simrank: Index-Free Approximation With Scheduled Principle, Fanwei Zhu, Yuan Fang, Kai Zhang, Kevin C.-C. Chang, Hongtai Cao, Zhen Jiang, Minghui Wu

Research Collection School Of Computing and Information Systems

SimRank is a popular link-based similarity measure on graphs. It enables a variety of applications with different modes of querying (e.g., single-pair, single-source and all-pair modes). In this paper, we propose UISim, a unified and incremental framework for all SimRank modes based on a scheduled approximation principle. UISim processes queries with incremental and prioritized exploration of the entire computation space, and thus allows flexible tradeoff of time and accuracy. On the other hand, it creates and shares common “building blocks” for online computation without relying on indexes, and thus is efficient to handle both static and dynamic graphs. Our experiments …

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Orthogonal Inductive Matrix Completion, Antoine Ledent, Rrodrigo Alves, Marius Kloft Sep 2021

Orthogonal Inductive Matrix Completion, Antoine Ledent, Rrodrigo Alves, Marius Kloft

Research Collection School Of Computing and Information Systems

We propose orthogonal inductive matrix completion (OMIC), an interpretable approach to matrix completion based on a sum of multiple orthonormal side information terms, together with nuclear-norm regularization. The approach allows us to inject prior knowledge about the singular vectors of the ground-truth matrix. We optimize the approach by a provably converging algorithm, which optimizes all components of the model simultaneously. We study the generalization capabilities of our method in both the distribution-free setting and in the case where the sampling distribution admits uniform marginals, yielding learning guarantees that improve with the quality of the injected knowledge in both cases. As …

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Holistic Prediction For Public Transport Crowd Flows: A Spatio Dynamic Graph Network Approach, Bingjie He, Shukai Li, Chen Zhang, Baihua Zheng, Fugee Tsung Sep 2021

Holistic Prediction For Public Transport Crowd Flows: A Spatio Dynamic Graph Network Approach, Bingjie He, Shukai Li, Chen Zhang, Baihua Zheng, Fugee Tsung

Research Collection School Of Computing and Information Systems

This paper targets at predicting public transport in-out crowd flows of different regions together with transit flows between them in a city. The main challenge is the complex dynamic spatial correlation of crowd flows of different regions and origin-destination (OD) paths. Different from road traffic flows whose spatial correlations mainly depend on geographical distance, public transport crowd flows significantly relate to the region’s functionality and connectivity in the public transport network. Furthermore, influenced by commuters’ time-varying travel patterns, the spatial correlations change over time. Though there exist many works focusing on either predicting in-out flows or OD transit flows of …

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Semi-Supervised Semantic Visualization For Networked Documents, Delvin Ce Zhang, Hady W. Lauw Sep 2021

Semi-Supervised Semantic Visualization For Networked Documents, Delvin Ce Zhang, Hady W. Lauw

Research Collection School Of Computing and Information Systems

Semantic interpretability and visual expressivity are important objectives in exploratory analysis of text. On the one hand, while some documents may have explicit categories, we could develop a better understanding of a corpus by studying its finer-grained structures, which may be latent. By inferring latent topics and discovering keywords associated with each topic, one obtains a semantic interpretation of the corpus. One the other hand, by visualizing documents, latent topics, and category labels on the same plot, one gains a bird’s eye view of the relationships among documents, topics, and various categories. Semantic visualization is a class of methods that …

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Adversarial Attacks And Mitigation For Anomaly Detectors Of Cyber-Physical Systems, Yifan Jia, Jingyi Wang, Christopher M. Poskitt, Sudipta Chattopadhyay, Jun Sun, Yuqi Chen Sep 2021

Adversarial Attacks And Mitigation For Anomaly Detectors Of Cyber-Physical Systems, Yifan Jia, Jingyi Wang, Christopher M. Poskitt, Sudipta Chattopadhyay, Jun Sun, Yuqi Chen

Research Collection School Of Computing and Information Systems

The threats faced by cyber-physical systems (CPSs) in critical infrastructure have motivated research into a multitude of attack detection mechanisms, including anomaly detectors based on neural network models. The effectiveness of anomaly detectors can be assessed by subjecting them to test suites of attacks, but less consideration has been given to adversarial attackers that craft noise specifically designed to deceive them. While successfully applied in domains such as images and audio, adversarial attacks are much harder to implement in CPSs due to the presence of other built-in defence mechanisms such as rule checkers (or invariant checkers). In this work, we …

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Precision Public Health Campaign: Delivering Persuasive Messages To Relevant Segments Through Targeted Advertisements On Social Media, Jisun An, Haewoon Kwak, Hanya M. Qureshi, Ingmar Weber Sep 2021

Precision Public Health Campaign: Delivering Persuasive Messages To Relevant Segments Through Targeted Advertisements On Social Media, Jisun An, Haewoon Kwak, Hanya M. Qureshi, Ingmar Weber

Research Collection School Of Computing and Information Systems

Although established marketing techniques have been applied to design more effective health campaigns, more often than not, the same message is broadcasted to large populations, irrespective of unique characteristics. As individual digital device use has increased, so have individual digital footprints, creating potential opportunities for targeted digital health interventions. We propose a novel precision public health campaign framework to structure and standardize the process of designing and delivering tailored health messages to target particular population segments using social media–targeted advertising tools. Our framework consists of five stages: defining a campaign goal, priority audience, and evaluation metrics; splitting the target audience …

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Redesigning Patient Flow In Paediatric Eye Clinic For Pandemic Using Simulation, Kar Way Tan, Bee Keow Goh, Aldy Gunawan Sep 2021

Redesigning Patient Flow In Paediatric Eye Clinic For Pandemic Using Simulation, Kar Way Tan, Bee Keow Goh, Aldy Gunawan

Research Collection School Of Computing and Information Systems

This study proposes a systematic approach to the construction of a simulation model to support decision-making concerning the capacity limit and staffing configurations at the paediatric eye clinic in Singapore under the COVID-19 pandemic situation. During the pandemic, the clinic must ensure that the operations are aligned to the safe-distancing regulations put in place by the Ministry of Health while coping with the demand. We developed simulation models to examine the ‘asis’ process and proposed numerous ‘to-be’ processes for new clinic configurations to operate under the pandemic conditions. We combined scenario-thinking and simulation optimization to determine the additional manpower and …

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Learning And Evaluating Chinese Idiom Embeddings, Minghuan Tan, Jing Jiang Sep 2021

Learning And Evaluating Chinese Idiom Embeddings, Minghuan Tan, Jing Jiang

Research Collection School Of Computing and Information Systems

We study the task of learning and evaluating Chinese idiom embeddings. We first construct a new evaluation dataset that contains idiom synonyms and antonyms. Observing that existing Chinese word embedding methods may not be suitable for learning idiom embeddings, we further present a BERT-based method that directly learns embedding vectors for individual idioms. We empirically compare representative existing methods and our method. We find that our method substantially outperforms existing methods on the evaluation dataset we have constructed.

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Secure And Verifiable Outsourced Data Dimension Reduction On Dynamic Data, Zhenzhu Chen, Anmin Fu, Robert H. Deng, Ximeng Liu, Yang Yang, Yinghui Zhang Sep 2021

Secure And Verifiable Outsourced Data Dimension Reduction On Dynamic Data, Zhenzhu Chen, Anmin Fu, Robert H. Deng, Ximeng Liu, Yang Yang, Yinghui Zhang

Research Collection School Of Computing and Information Systems

Dimensionality reduction aims at reducing redundant information in big data and hence making data analysis more efficient. Resource-constrained enterprises or individuals often outsource this time-consuming job to the cloud for saving storage and computing resources. However, due to inadequate supervision, the privacy and security of outsourced data have been a serious concern to data owners. In this paper, we propose a privacypreserving and verifiable outsourcing scheme for data dimension reduction, based on incremental Non-negative Matrix Factorization (NMF) method. We emphasize the importance of incremental data processing, exploiting the properties of NMF to enable data dynamics in consideration of data updating …

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