Scalable and Efficient Probabilistic Topic Model Inference for Textual Data

2018-04-27

Author : Måns Magnusson
Release : 2018-04-27
Genre :
Kind : eBook
Book Rating : 881/5 ( reviews)

Book Synopsis Scalable and Efficient Probabilistic Topic Model Inference for Textual Data by : Måns Magnusson

Download or read book Scalable and Efficient Probabilistic Topic Model Inference for Textual Data written by Måns Magnusson. This book was released on 2018-04-27. Available in PDF, EPUB and Kindle. Book excerpt: Probabilistic topic models have proven to be an extremely versatile class of mixed-membership models for discovering the thematic structure of text collections. There are many possible applications, covering a broad range of areas of study: technology, natural science, social science and the humanities. In this thesis, a new efficient parallel Markov Chain Monte Carlo inference algorithm is proposed for Bayesian inference in large topic models. The proposed methods scale well with the corpus size and can be used for other probabilistic topic models and other natural language processing applications. The proposed methods are fast, efficient, scalable, and will converge to the true posterior distribution. In addition, in this thesis a supervised topic model for high-dimensional text classification is also proposed, with emphasis on interpretable document prediction using the horseshoe shrinkage prior in supervised topic models. Finally, we develop a model and inference algorithm that can model agenda and framing of political speeches over time with a priori defined topics. We apply the approach to analyze the evolution of immigration discourse in the Swedish parliament by combining theory from political science and communication science with a probabilistic topic model. Probabilistiska ämnesmodeller (topic models) är en mångsidig klass av modeller för att estimera ämnessammansättningar i större corpusar. Applikationer finns i ett flertal vetenskapsområden som teknik, naturvetenskap, samhällsvetenskap och humaniora. I denna avhandling föreslås nya effektiva och parallella Markov Chain Monte Carlo algoritmer för Bayesianska ämnesmodeller. De föreslagna metoderna skalar väl med storleken på corpuset och kan användas för flera olika ämnesmodeller och liknande modeller inom språkteknologi. De föreslagna metoderna är snabba, effektiva, skalbara och konvergerar till den sanna posteriorfördelningen. Dessutom föreslås en ämnesmodell för högdimensionell textklassificering, med tonvikt på tolkningsbar dokumentklassificering genom att använda en kraftigt regulariserande priorifördelningar. Slutligen utvecklas en ämnesmodell för att analyzera "agenda" och "framing" för ett förutbestämt ämne. Med denna metod analyserar vi invandringsdiskursen i Sveriges Riksdag över tid, genom att kombinera teori från statsvetenskap, kommunikationsvetenskap och probabilistiska ämnesmodeller.

Scalable and Efficient Probabilistic Topic Model Inference for Textual Data

2018

Author : Måns Magnusson
Release : 2018
Genre :
Kind : eBook
Book Rating : /5 ( reviews)

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Book Synopsis Scalable and Efficient Probabilistic Topic Model Inference for Textual Data by : Måns Magnusson

Download or read book Scalable and Efficient Probabilistic Topic Model Inference for Textual Data written by Måns Magnusson. This book was released on 2018. Available in PDF, EPUB and Kindle. Book excerpt: Probabilistic topic models have proven to be an extremely versatile class of mixed-membership models for discovering the thematic structure of text collections. There are many possible applications, covering a broad range of areas of study: technology, natural science, social science and the humanities. In this thesis, a new efficient parallel Markov Chain Monte Carlo inference algorithm is proposed for Bayesian inference in large topic models. The proposed methods scale well with the corpus size and can be used for other probabilistic topic models and other natural language processing applications. The proposed methods are fast, efficient, scalable, and will converge to the true posterior distribution. In addition, in this thesis a supervised topic model for high-dimensional text classification is also proposed, with emphasis on interpretable document prediction using the horseshoe shrinkage prior in supervised topic models. Finally, we develop a model and inference algorithm that can model agenda and framing of political speeches over time with a priori defined topics. We apply the approach to analyze the evolution of immigration discourse in the Swedish parliament by combining theory from political science and communication science with a probabilistic topic model.

Orchestrating a Resource-aware Edge

2024-09-02

Author : Klervie Toczé
Release : 2024-09-02
Genre :
Kind : eBook
Book Rating : 480/5 ( reviews)

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Book Synopsis Orchestrating a Resource-aware Edge by : Klervie Toczé

Download or read book Orchestrating a Resource-aware Edge written by Klervie Toczé. This book was released on 2024-09-02. Available in PDF, EPUB and Kindle. Book excerpt: More and more services are moving to the cloud, attracted by the promise of unlimited resources that are accessible anytime, and are managed by someone else. However, hosting every type of service in large cloud datacenters is not possible or suitable, as some emerging applications have stringent latency or privacy requirements, while also handling huge amounts of data. Therefore, in recent years, a new paradigm has been proposed to address the needs of these applications: the edge computing paradigm. Resources provided at the edge (e.g., for computation and communication) are constrained, hence resource management is of crucial importance. The incoming load to the edge infrastructure varies both in time and space. Managing the edge infrastructure so that the appropriate resources are available at the required time and location is called orchestrating. This is especially challenging in case of sudden load spikes and when the orchestration impact itself has to be limited. This thesis enables edge computing orchestration with increased resource-awareness by contributing with methods, techniques, and concepts for edge resource management. First, it proposes methods to better understand the edge resource demand. Second, it provides solutions on the supply side for orchestrating edge resources with different characteristics in order to serve edge applications with satisfactory quality of service. Finally, the thesis includes a critical perspective on the paradigm, by considering sustainability challenges. To understand the demand patterns, the thesis presents a methodology for categorizing the large variety of use cases that are proposed in the literature as potential applications for edge computing. The thesis also proposes methods for characterizing and modeling applications, as well as for gathering traces from real applications and analyzing them. These different approaches are applied to a prototype from a typical edge application domain: Mixed Reality. The important insight here is that application descriptions or models that are not based on a real application may not be giving an accurate picture of the load. This can drive incorrect decisions about what should be done on the supply side and thus waste resources. Regarding resource supply, the thesis proposes two orchestration frameworks for managing edge resources and successfully dealing with load spikes while avoiding over-provisioning. The first one utilizes mobile edge devices while the second leverages the concept of spare devices. Then, focusing on the request placement part of orchestration, the thesis formalizes it in the case of applications structured as chains of functions (so-called microservices) as an instance of the Traveling Purchaser Problem and solves it using Integer Linear Programming. Two different energy metrics influencing request placement decisions are proposed and evaluated. Finally, the thesis explores further resource awareness. Sustainability challenges that should be highlighted more within edge computing are collected. Among those related to resource use, the strategy of sufficiency is promoted as a way forward. It involves aiming at only using the needed resources (no more, no less) with a goal of reducing resource usage. Different tools to adopt it are proposed and their use demonstrated through a case study.

Companion Robots for Older Adults

2024-05-06

Author : Sofia Thunberg
Release : 2024-05-06
Genre :
Kind : eBook
Book Rating : 747/5 ( reviews)

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Book Synopsis Companion Robots for Older Adults by : Sofia Thunberg

Download or read book Companion Robots for Older Adults written by Sofia Thunberg. This book was released on 2024-05-06. Available in PDF, EPUB and Kindle. Book excerpt: This thesis explores, through a mixed-methods approach, what happens when companion robots are deployed in care homes for older adults by looking at different perspectives from key stakeholders. Nine studies are presented with decision makers in municipalities, care staff and older adults, as participants, and the studies have primarily been carried out in the field in care homes and activity centres, where both qualitative (e.g., observations and workshops) and quantitative data (surveys) have been collected. The thesis shows that companion robots seem to be here to stay and that they can contribute to a higher quality of life for some older adults. It further presents some challenges with a certain discrepancy between what decision makers want and what staff might be able to facilitate. For future research and use of companion robots, it is key to evaluate each robot model and potential use case separately and develop clear routines for how they should be used, and most importantly, let all stakeholders be part of the process. The knowledge contribution is the holistic view of how different actors affect each other when emerging robot technology is introduced in a care environment. Den här avhandlingen utforskar vad som händer när sällskapsrobotar införs på omsorgsboenden för äldre genom att titta på perspektiv från olika intressenter. Nio studier presenteras med kommunala beslutsfattare, vårdpersonal och äldre som deltagare. Studierna har i huvudsak genomförts i fält på särskilda boenden och aktivitetscenter där både kvalitativa- (exempelvis observationer och workshops) och kvantitativa data (enkäter) har samlats in. Avhandlingen visar att sällskapsrobotar verkar vara här för att stanna och att de kan bidra till en högre livskvalitet för vissa äldre. Den visar även på en del utmaningar med en viss diskrepans mellan vad beslutsfattare vill införa och vad personalen har möjlighet att utföra i sitt arbete. För framtida forskning och användning av sällskapsrobotar är det viktigt att utvärdera varje robotmodell och varje användningsområde var för sig och ta fram tydliga rutiner för hur de ska användas, och viktigast av allt, låta alla intressenter vara en del av processen. Kunskapsbidraget med avhandlingen är en helhetssyn på hur olika aktörer påverkar varandra när ny robotteknik introduceras i en vårdmiljö

Beyond Recognition

2024-05-06

Author : Le Minh-Ha
Release : 2024-05-06
Genre :
Kind : eBook
Book Rating : 76X/5 ( reviews)

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Book Synopsis Beyond Recognition by : Le Minh-Ha

Download or read book Beyond Recognition written by Le Minh-Ha. This book was released on 2024-05-06. Available in PDF, EPUB and Kindle. Book excerpt: This thesis addresses the need to balance the use of facial recognition systems with the need to protect personal privacy in machine learning and biometric identification. As advances in deep learning accelerate their evolution, facial recognition systems enhance security capabilities, but also risk invading personal privacy. Our research identifies and addresses critical vulnerabilities inherent in facial recognition systems, and proposes innovative privacy-enhancing technologies that anonymize facial data while maintaining its utility for legitimate applications. Our investigation centers on the development of methodologies and frameworks that achieve k-anonymity in facial datasets; leverage identity disentanglement to facilitate anonymization; exploit the vulnerabilities of facial recognition systems to underscore their limitations; and implement practical defenses against unauthorized recognition systems. We introduce novel contributions such as AnonFACES, StyleID, IdDecoder, StyleAdv, and DiffPrivate, each designed to protect facial privacy through advanced adversarial machine learning techniques and generative models. These solutions not only demonstrate the feasibility of protecting facial privacy in an increasingly surveilled world, but also highlight the ongoing need for robust countermeasures against the ever-evolving capabilities of facial recognition technology. Continuous innovation in privacy-enhancing technologies is required to safeguard individuals from the pervasive reach of digital surveillance and protect their fundamental right to privacy. By providing open-source, publicly available tools, and frameworks, this thesis contributes to the collective effort to ensure that advancements in facial recognition serve the public good without compromising individual rights. Our multi-disciplinary approach bridges the gap between biometric systems, adversarial machine learning, and generative modeling to pave the way for future research in the domain and support AI innovation where technological advancement and privacy are balanced.

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