Scalable And Efficient Probabilistic Topic Model Inference For Textual Data

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Scalable and Efficient Probabilistic Topic Model Inference for Textual Data

Author : Måns Magnusson
Publisher : Linköping University Electronic Press
ISBN 13 : 9176852881
Total Pages : 75 pages
Book Rating : 4.1/5 (768 download)

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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 and published by Linköping University Electronic Press. This book was released on 2018-04-27 with total page 75 pages. 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.

Orchestrating a Resource-aware Edge

Author : Klervie Toczé
Publisher : Linköping University Electronic Press
ISBN 13 : 9180757480
Total Pages : 122 pages
Book Rating : 4.1/5 (87 download)

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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 and published by Linköping University Electronic Press. This book was released on 2024-09-02 with total page 122 pages. 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

Author : Sofia Thunberg
Publisher : Linköping University Electronic Press
ISBN 13 : 9180755747
Total Pages : 175 pages
Book Rating : 4.1/5 (87 download)

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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 and published by Linköping University Electronic Press. This book was released on 2024-05-06 with total page 175 pages. 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

Empirical Studies in Machine Psychology

Author : Robert Johansson
Publisher : Linköping University Electronic Press
ISBN 13 : 9179295061
Total Pages : 201 pages
Book Rating : 4.1/5 (792 download)

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Book Synopsis Empirical Studies in Machine Psychology by : Robert Johansson

Download or read book Empirical Studies in Machine Psychology written by Robert Johansson and published by Linköping University Electronic Press. This book was released on 2024-10-09 with total page 201 pages. Available in PDF, EPUB and Kindle. Book excerpt: This thesis presents Machine Psychology as an interdisciplinary paradigm that integrates learning psychology principles with an adaptive computer system for the development of Artificial General Intelligence (AGI). By synthesizing behavioral psychology with a formal intelligence model, the Non-Axiomatic Reasoning System (NARS), this work explores the potential of operant conditioning paradigms to advance AGI research. The thesis begins by introducing the conceptual foundations of Machine Psychology, detailing its alignment with the theoretical constructs of learning psychology and the formalism of NARS. It then progresses through a series of empirical studies designed to systematically investigate the emergence of increasingly complex cognitive behaviors as NARS interacts with its environment. Initially, operant conditioning is established as a foundational principle for developing adaptive behavior with NARS. Subsequent chapters explore increasingly sophisticated cognitive capabilities, all studied with NARS using experimental paradigms from operant learning psychology: Generalized identity matching, Functional equivalence, and Arbitrarily Applicable Relational Responding. Throughout this research, Machine Psychology is demonstrated to be a promising framework for guiding AGI research, allowing both the manipulation of environmental contingencies and the system’s intrinsic logical processes. The thesis contributes to AGI research by showing how using operant psychological paradigms with NARS can enable cognitive abilities similar to human cognition. These findings set the stage for AGI systems that learn and adapt more like humans, potentially advancing the creation of more general and flexible AI. Denna avhandling introducerar Maskinpsykologi som ett tvärvetenskapligt område där principer från inlärningspsykologi integreras med ett adaptivt datorsystem. Genom att kombinera forskning från beteendepsykologi med en formell modell för intelligens (Non-Axiomatic Reasoning System; NARS), undersöker avhandlingen hur operant betingning kan användas för att driva utvecklingen av Artificiell General Intelligens (AGI) framåt. Avhandlingen börjar med att förklara grunderna i Maskinpsykologi och hur dessa relaterar till både inlärningspsykologi och NARS. Därefter presenteras en serie experiment som systematiskt undersöker hur allt mer komplexa kognitiva beteenden kan uppstå när NARS interagerar med sin omgivning. Till att börja med etableras operant betingning som en central metod för att utveckla adaptiva beteenden med NARS. I de följande kapitlen utforskas hur NARS, genom experiment inspirerade av operant inlärningspsykologi, kan utveckla mer avancerade kognitiva förmågor som till exempel generaliserad identitetsmatchning, funktionell ekvivalens och så kallade arbiträrt applicerbara relationsresponser. Denna forskning visar att Maskinpsykologi är ett lovande verktyg för att vägleda AGI-forskning, eftersom det möjliggör att både påverka omgivningsfaktorer och styra systemets interna logiska processer. Avhandlingen bidrar till AGI-forskning genom att visa hur operanta psykologiska metoder, tillämpade på NARS, kan möjliggöra kognitiva förmågor som liknar mänskligt tänkande. Dessa insikter öppnar nya möjligheter för att utveckla AI-system som kan lära sig och anpassa sig på ett mer mänskligt sätt, vilket kan leda till skapandet av mer generell och flexibel AI.

Beyond Recognition

Author : Le Minh-Ha
Publisher : Linköping University Electronic Press
ISBN 13 : 918075676X
Total Pages : 103 pages
Book Rating : 4.1/5 (87 download)

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Book Synopsis Beyond Recognition by : Le Minh-Ha

Download or read book Beyond Recognition written by Le Minh-Ha and published by Linköping University Electronic Press. This book was released on 2024-05-06 with total page 103 pages. 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.

Machine Learning-Based Bug Handling in Large-Scale Software Development

Author : Leif Jonsson
Publisher : Linköping University Electronic Press
ISBN 13 : 9176853063
Total Pages : 149 pages
Book Rating : 4.1/5 (768 download)

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Book Synopsis Machine Learning-Based Bug Handling in Large-Scale Software Development by : Leif Jonsson

Download or read book Machine Learning-Based Bug Handling in Large-Scale Software Development written by Leif Jonsson and published by Linköping University Electronic Press. This book was released on 2018-05-17 with total page 149 pages. Available in PDF, EPUB and Kindle. Book excerpt: This thesis investigates the possibilities of automating parts of the bug handling process in large-scale software development organizations. The bug handling process is a large part of the mostly manual, and very costly, maintenance of software systems. Automating parts of this time consuming and very laborious process could save large amounts of time and effort wasted on dealing with bug reports. In this thesis we focus on two aspects of the bug handling process, bug assignment and fault localization. Bug assignment is the process of assigning a newly registered bug report to a design team or developer. Fault localization is the process of finding where in a software architecture the fault causing the bug report should be solved. The main reason these tasks are not automated is that they are considered hard to automate, requiring human expertise and creativity. This thesis examines the possi- bility of using machine learning techniques for automating at least parts of these processes. We call these automated techniques Automated Bug Assignment (ABA) and Automatic Fault Localization (AFL), respectively. We treat both of these problems as classification problems. In ABA, the classes are the design teams in the development organization. In AFL, the classes consist of the software components in the software architecture. We focus on a high level fault localization that it is suitable to integrate into the initial support flow of large software development organizations. The thesis consists of six papers that investigate different aspects of the AFL and ABA problems. The first two papers are empirical and exploratory in nature, examining the ABA problem using existing machine learning techniques but introducing ensembles into the ABA context. In the first paper we show that, like in many other contexts, ensembles such as the stacked generalizer (or stacking) improves classification accuracy compared to individual classifiers when evaluated using cross fold validation. The second paper thor- oughly explore many aspects such as training set size, age of bug reports and different types of evaluation of the ABA problem in the context of stacking. The second paper also expands upon the first paper in that the number of industry bug reports, roughly 50,000, from two large-scale industry software development contexts. It is still as far as we are aware, the largest study on real industry data on this topic to this date. The third and sixth papers, are theoretical, improving inference in a now classic machine learning tech- nique for topic modeling called Latent Dirichlet Allocation (LDA). We show that, unlike the currently dominating approximate approaches, we can do parallel inference in the LDA model with a mathematically correct algorithm, without sacrificing efficiency or speed. The approaches are evaluated on standard research datasets, measuring various aspects such as sampling efficiency and execution time. Paper four, also theoretical, then builds upon the LDA model and introduces a novel supervised Bayesian classification model that we call DOLDA. The DOLDA model deals with both textual content and, structured numeric, and nominal inputs in the same model. The approach is evaluated on a new data set extracted from IMDb which have the structure of containing both nominal and textual data. The model is evaluated using two approaches. First, by accuracy, using cross fold validation. Second, by comparing the simplicity of the final model with that of other approaches. In paper five we empirically study the performance, in terms of prediction accuracy, of the DOLDA model applied to the AFL problem. The DOLDA model was designed with the AFL problem in mind, since it has the exact structure of a mix of nominal and numeric inputs in combination with unstructured text. We show that our DOLDA model exhibits many nice properties, among others, interpretability, that the research community has iden- tified as missing in current models for AFL.

Distributed Moving Base Driving Simulators

Author : Anders Andersson
Publisher : Linköping University Electronic Press
ISBN 13 : 9176850900
Total Pages : 60 pages
Book Rating : 4.1/5 (768 download)

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Book Synopsis Distributed Moving Base Driving Simulators by : Anders Andersson

Download or read book Distributed Moving Base Driving Simulators written by Anders Andersson and published by Linköping University Electronic Press. This book was released on 2019-04-30 with total page 60 pages. Available in PDF, EPUB and Kindle. Book excerpt: Development of new functionality and smart systems for different types of vehicles is accelerating with the advent of new emerging technologies such as connected and autonomous vehicles. To ensure that these new systems and functions work as intended, flexible and credible evaluation tools are necessary. One example of this type of tool is a driving simulator, which can be used for testing new and existing vehicle concepts and driver support systems. When a driver in a driving simulator operates it in the same way as they would in actual traffic, you get a realistic evaluation of what you want to investigate. Two advantages of a driving simulator are (1.) that you can repeat the same situation several times over a short period of time, and (2.) you can study driver reactions during dangerous situations that could result in serious injuries if they occurred in the real world. An important component of a driving simulator is the vehicle model, i.e., the model that describes how the vehicle reacts to its surroundings and driver inputs. To increase the simulator realism or the computational performance, it is possible to divide the vehicle model into subsystems that run on different computers that are connected in a network. A subsystem can also be replaced with hardware using so-called hardware-in-the-loop simulation, and can then be connected to the rest of the vehicle model using a specified interface. The technique of dividing a model into smaller subsystems running on separate nodes that communicate through a network is called distributed simulation. This thesis investigates if and how a distributed simulator design might facilitate the maintenance and new development required for a driving simulator to be able to keep up with the increasing pace of vehicle development. For this purpose, three different distributed simulator solutions have been designed, built, and analyzed with the aim of constructing distributed simulators, including external hardware, where the simulation achieves the same degree of realism as with a traditional driving simulator. One of these simulator solutions has been used to create a parameterized powertrain model that can be configured to represent any of a number of different vehicles. Furthermore, the driver's driving task is combined with the powertrain model to monitor deviations. After the powertrain model was created, subsystems from a simulator solution and the powertrain model have been transferred to a Modelica environment. The goal is to create a framework for requirement testing that guarantees sufficient realism, also for a distributed driving simulation. The results show that the distributed simulators we have developed work well overall with satisfactory performance. It is important to manage the vehicle model and how it is connected to a distributed system. In the distributed driveline simulator setup, the network delays were so small that they could be ignored, i.e., they did not affect the driving experience. However, if one gradually increases the delays, a driver in the distributed simulator will change his/her behavior. The impact of communication latency on a distributed simulator also depends on the simulator application, where different usages of the simulator, i.e., different simulator studies, will have different demands. We believe that many simulator studies could be performed using a distributed setup. One issue is how modifications to the system affect the vehicle model and the desired behavior. This leads to the need for methodology for managing model requirements. In order to detect model deviations in the simulator environment, a monitoring aid has been implemented to help notify test managers when a model behaves strangely or is driven outside of its validated region. Since the availability of distributed laboratory equipment can be limited, the possibility of using Modelica (which is an equation-based and object-oriented programming language) for simulating subsystems is also examined. Implementation of the model in Modelica has also been extended with requirements management, and in this work a framework is proposed for automatically evaluating the model in a tool.

Robust Stream Reasoning Under Uncertainty

Author : Daniel de Leng
Publisher : Linköping University Electronic Press
ISBN 13 : 9176850137
Total Pages : 234 pages
Book Rating : 4.1/5 (768 download)

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Book Synopsis Robust Stream Reasoning Under Uncertainty by : Daniel de Leng

Download or read book Robust Stream Reasoning Under Uncertainty written by Daniel de Leng and published by Linköping University Electronic Press. This book was released on 2019-11-08 with total page 234 pages. Available in PDF, EPUB and Kindle. Book excerpt: Vast amounts of data are continually being generated by a wide variety of data producers. This data ranges from quantitative sensor observations produced by robot systems to complex unstructured human-generated texts on social media. With data being so abundant, the ability to make sense of these streams of data through reasoning is of great importance. Reasoning over streams is particularly relevant for autonomous robotic systems that operate in physical environments. They commonly observe this environment through incremental observations, gradually refining information about their surroundings. This makes robust management of streaming data and their refinement an important problem. Many contemporary approaches to stream reasoning focus on the issue of querying data streams in order to generate higher-level information by relying on well-known database approaches. Other approaches apply logic-based reasoning techniques, which rarely consider the provenance of their symbolic interpretations. In this work, we integrate techniques for logic-based stream reasoning with the adaptive generation of the state streams needed to do the reasoning over. This combination deals with both the challenge of reasoning over uncertain streaming data and the problem of robustly managing streaming data and their refinement. The main contributions of this work are (1) a logic-based temporal reasoning technique based on path checking under uncertainty that combines temporal reasoning with qualitative spatial reasoning; (2) an adaptive reconfiguration procedure for generating and maintaining a data stream required to perform spatio-temporal stream reasoning over; and (3) integration of these two techniques into a stream reasoning framework. The proposed spatio-temporal stream reasoning technique is able to reason with intertemporal spatial relations by leveraging landmarks. Adaptive state stream generation allows the framework to adapt to situations in which the set of available streaming resources changes. Management of streaming resources is formalised in the DyKnow model, which introduces a configuration life-cycle to adaptively generate state streams. The DyKnow-ROS stream reasoning framework is a concrete realisation of this model that extends the Robot Operating System (ROS). DyKnow-ROS has been deployed on the SoftBank Robotics NAO platform to demonstrate the system's capabilities in a case study on run-time adaptive reconfiguration. The results show that the proposed system - by combining reasoning over and reasoning about streams - can robustly perform stream reasoning, even when the availability of streaming resources changes.

Designing for Resilience

Author : Vanessa Rodrigues
Publisher : Linköping University Electronic Press
ISBN 13 : 9179298672
Total Pages : 165 pages
Book Rating : 4.1/5 (792 download)

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Book Synopsis Designing for Resilience by : Vanessa Rodrigues

Download or read book Designing for Resilience written by Vanessa Rodrigues and published by Linköping University Electronic Press. This book was released on 2020-05-05 with total page 165 pages. Available in PDF, EPUB and Kindle. Book excerpt: Services are prone to change in the form of expected and unexpected variations and disruptions, more so given the increasing interconnectedness and complexity of service systems today. These changes require service systems to be resilient and designed to adapt, to ensure that services continue to work smoothly. This thesis problematises the prevailing view and assumptions underpinning the current understanding of resilience in services. Drawing on literature from service management, service design, systems thinking and social-ecological resilience theory, this work investigates how service design can foster resilience in service systems. Supported by empirical input from three research projects in healthcare, the findings show service design can contribute to the adaptability and transformability of service systems through its holistic, human-centred, participatory and experimental approaches. Through the analysis, this research identifies key intervention points for cultivating service systems resilience through service design, including the design of service interactions, processes, enabling structures and multi-level governance. The study makes two important contributions. First, it extends the understanding of service systems resilience as the collective capacity for intentional action in responding to ongoing change, coordinated across scales in order to create value. This is supported by offering alternative assumptions about resilience in service. Second, it positions service design as an enabler of service resilience by explicitly linking design practice(s) to processes that contribute to resilience. By extending the understanding of service systems resilience, this thesis lays the groundwork for future research at the intersection of service design, systemic change and resilience.

Applications of Partial Polymorphisms in (Fine-Grained) Complexity of Constraint Satisfaction Problems

Author : Biman Roy
Publisher : Linköping University Electronic Press
ISBN 13 : 9179298982
Total Pages : 57 pages
Book Rating : 4.1/5 (792 download)

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Book Synopsis Applications of Partial Polymorphisms in (Fine-Grained) Complexity of Constraint Satisfaction Problems by : Biman Roy

Download or read book Applications of Partial Polymorphisms in (Fine-Grained) Complexity of Constraint Satisfaction Problems written by Biman Roy and published by Linköping University Electronic Press. This book was released on 2020-03-23 with total page 57 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this thesis we study the worst-case complexity ofconstraint satisfaction problems and some of its variants. We use methods from universal algebra: in particular, algebras of total functions and partial functions that are respectively known as clones and strong partial clones. The constraint satisfactionproblem parameterized by a set of relations ? (CSP(?)) is the following problem: given a set of variables restricted by a set of constraints based on the relations ?, is there an assignment to thevariables that satisfies all constraints? We refer to the set ? as aconstraint language. The inverse CSPproblem over ? (Inv-CSP(?)) asks the opposite: given a relation R, does there exist a CSP(?) instance with R as its set of models? When ? is a Boolean language, then we use the term SAT(?) instead of CSP(?) and Inv-SAT(?) instead of Inv-CSP(?). Fine-grained complexity is an approach in which we zoom inside a complexity class and classify theproblems in it based on their worst-case time complexities. We start by investigating the fine-grained complexity of NP-complete CSP(?) problems. An NP-complete CSP(?) problem is said to be easier than an NP-complete CSP(?) problem if the worst-case time complexity of CSP(?) is not higher thanthe worst-case time complexity of CSP(?). We first analyze the NP-complete SAT problems that are easier than monotone 1-in-3-SAT (which can be represented by SAT(R) for a certain relation R), and find out that there exists a continuum of such problems. For this, we use the connection between constraint languages and strong partial clones and exploit the fact that CSP(?) is easier than CSP(?) when the strong partial clone corresponding to ? contains the strong partial clone of ?. An NP-complete CSP(?) problem is said to be the easiest with respect to a variable domain D if it is easier than any other NP-complete CSP(?) problem of that domain. We show that for every finite domain there exists an easiest NP-complete problem for the ultraconservative CSP(?) problems. An ultraconservative CSP(?) is a special class of CSP problems where the constraint language containsall unary relations. We additionally show that no NP-complete CSP(?) problem can be solved insub-exponential time (i.e. in2^o(n) time where n is the number of variables) given that theexponentialtime hypothesisis true. Moving to classical complexity, we show that for any Boolean constraint language ?, Inv-SAT(?) is either in P or it is coNP-complete. This is a generalization of an earlier dichotomy result, which was only known to be true for ultraconservative constraint languages. We show that Inv-SAT(?) is coNP-complete if and only if the clone corresponding to ? contains essentially unary functions only. For arbitrary finite domains our results are not conclusive, but we manage to prove that theinversek-coloring problem is coNP-complete for each k>2. We exploit weak bases to prove many of theseresults. A weak base of a clone C is a constraint language that corresponds to the largest strong partia clone that contains C. It is known that for many decision problems X(?) that are parameterized bya constraint language ?(such as Inv-SAT), there are strong connections between the complexity of X(?) and weak bases. This fact can be exploited to achieve general complexity results. The Boolean domain is well-suited for this approach since we have a fairly good understanding of Boolean weak bases. In the final result of this thesis, we investigate the relationships between the weak bases in the Boolean domain based on their strong partial clones and completely classify them according to the setinclusion. To avoid a tedious case analysis, we introduce a technique that allows us to discard a largenumber of cases from further investigation.

Applications of Topic Models

Author : Jordan Boyd-Graber
Publisher : Now Publishers
ISBN 13 : 9781680833089
Total Pages : 163 pages
Book Rating : 4.8/5 (33 download)

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Book Synopsis Applications of Topic Models by : Jordan Boyd-Graber

Download or read book Applications of Topic Models written by Jordan Boyd-Graber and published by Now Publishers. This book was released on 2017-07-13 with total page 163 pages. Available in PDF, EPUB and Kindle. Book excerpt: Describes recent academic and industrial applications of topic models with the goal of launching a young researcher capable of building their own applications of topic models.

Parameterized Verification of Synchronized Concurrent Programs

Author : Zeinab Ganjei
Publisher : Linköping University Electronic Press
ISBN 13 : 9179296971
Total Pages : 192 pages
Book Rating : 4.1/5 (792 download)

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Book Synopsis Parameterized Verification of Synchronized Concurrent Programs by : Zeinab Ganjei

Download or read book Parameterized Verification of Synchronized Concurrent Programs written by Zeinab Ganjei and published by Linköping University Electronic Press. This book was released on 2021-03-19 with total page 192 pages. Available in PDF, EPUB and Kindle. Book excerpt: There is currently an increasing demand for concurrent programs. Checking the correctness of concurrent programs is a complex task due to the interleavings of processes. Sometimes, violation of the correctness properties in such systems causes human or resource losses; therefore, it is crucial to check the correctness of such systems. Two main approaches to software analysis are testing and formal verification. Testing can help discover many bugs at a low cost. However, it cannot prove the correctness of a program. Formal verification, on the other hand, is the approach for proving program correctness. Model checking is a formal verification technique that is suitable for concurrent programs. It aims to automatically establish the correctness (expressed in terms of temporal properties) of a program through an exhaustive search of the behavior of the system. Model checking was initially introduced for the purpose of verifying finite‐state concurrent programs, and extending it to infinite‐state systems is an active research area. In this thesis, we focus on the formal verification of parameterized systems. That is, systems in which the number of executing processes is not bounded a priori. We provide fully-automatic and parameterized model checking techniques for establishing the correctness of safety properties for certain classes of concurrent programs. We provide an open‐source prototype for every technique and present our experimental results on several benchmarks. First, we address the problem of automatically checking safety properties for bounded as well as parameterized phaser programs. Phaser programs are concurrent programs that make use of the complex synchronization construct of Habanero Java phasers. For the bounded case, we establish the decidability of checking the violation of program assertions and the undecidability of checking deadlock‐freedom. For the parameterized case, we study different formulations of the verification problem and propose an exact procedure that is guaranteed to terminate for some reachability problems even in the presence of unbounded phases and arbitrarily many spawned processes. Second, we propose an approach for automatic verification of parameterized concurrent programs in which shared variables are manipulated by atomic transitions to count and synchronize the spawned processes. For this purpose, we introduce counting predicates that related counters that refer to the number of processes satisfying some given properties to the variables that are directly manipulated by the concurrent processes. We then combine existing works on the counter, predicate, and constrained monotonic abstraction and build a nested counterexample‐based refinement scheme to establish correctness. Third, we introduce Lazy Constrained Monotonic Abstraction for more efficient exploration of well‐structured abstractions of infinite‐state non‐monotonic systems. We propose several heuristics and assess the efficiency of the proposed technique by extensive experiments using our open‐source prototype. Lastly, we propose a sound but (in general) incomplete procedure for automatic verification of safety properties for a class of fault‐tolerant distributed protocols described in the Heard‐Of (HO for short) model. The HO model is a popular model for describing distributed protocols. We propose a verification procedure that is guaranteed to terminate even for unbounded number of the processes that execute the distributed protocol.

System-Level Design of GPU-Based Embedded Systems

Author : Arian Maghazeh
Publisher : Linköping University Electronic Press
ISBN 13 : 9176851753
Total Pages : 81 pages
Book Rating : 4.1/5 (768 download)

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Book Synopsis System-Level Design of GPU-Based Embedded Systems by : Arian Maghazeh

Download or read book System-Level Design of GPU-Based Embedded Systems written by Arian Maghazeh and published by Linköping University Electronic Press. This book was released on 2018-12-07 with total page 81 pages. Available in PDF, EPUB and Kindle. Book excerpt: Modern embedded systems deploy several hardware accelerators, in a heterogeneous manner, to deliver high-performance computing. Among such devices, graphics processing units (GPUs) have earned a prominent position by virtue of their immense computing power. However, a system design that relies on sheer throughput of GPUs is often incapable of satisfying the strict power- and time-related constraints faced by the embedded systems. This thesis presents several system-level software techniques to optimize the design of GPU-based embedded systems under various graphics and non-graphics applications. As compared to the conventional application-level optimizations, the system-wide view of our proposed techniques brings about several advantages: First, it allows for fully incorporating the limitations and requirements of the various system parts in the design process. Second, it can unveil optimization opportunities through exposing the information flow between the processing components. Third, the techniques are generally applicable to a wide range of applications with similar characteristics. In addition, multiple system-level techniques can be combined together or with application-level techniques to further improve the performance. We begin by studying some of the unique attributes of GPU-based embedded systems and discussing several factors that distinguish the design of these systems from that of the conventional high-end GPU-based systems. We then proceed to develop two techniques that address an important challenge in the design of GPU-based embedded systems from different perspectives. The challenge arises from the fact that GPUs require a large amount of workload to be present at runtime in order to deliver a high throughput. However, for some embedded applications, collecting large batches of input data requires an unacceptable waiting time, prompting a trade-off between throughput and latency. We also develop an optimization technique for GPU-based applications to address the memory bottleneck issue by utilizing the GPU L2 cache to shorten data access time. Moreover, in the area of graphics applications, and in particular with a focus on mobile games, we propose a power management scheme to reduce the GPU power consumption by dynamically adjusting the display resolution, while considering the user's visual perception at various resolutions. We also discuss the collective impact of the proposed techniques in tackling the design challenges of emerging complex systems. The proposed techniques are assessed by real-life experimentations on GPU-based hardware platforms, which demonstrate the superior performance of our approaches as compared to the state-of-the-art techniques.

Big Data

Author : Kuan-Ching Li
Publisher : CRC Press
ISBN 13 : 1498760406
Total Pages : 444 pages
Book Rating : 4.4/5 (987 download)

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Book Synopsis Big Data by : Kuan-Ching Li

Download or read book Big Data written by Kuan-Ching Li and published by CRC Press. This book was released on 2015-09-15 with total page 444 pages. Available in PDF, EPUB and Kindle. Book excerpt: As today’s organizations are capturing exponentially larger amounts of data than ever, now is the time for organizations to rethink how they digest that data. Through advanced algorithms and analytics techniques, organizations can harness this data, discover hidden patterns, and use the newly acquired knowledge to achieve competitive advantages. Presenting the contributions of leading experts in their respective fields, Big Data: Algorithms, Analytics, and Applications bridges the gap between the vastness of Big Data and the appropriate computational methods for scientific and social discovery. It covers fundamental issues about Big Data, including efficient algorithmic methods to process data, better analytical strategies to digest data, and representative applications in diverse fields, such as medicine, science, and engineering. The book is organized into five main sections: Big Data Management—considers the research issues related to the management of Big Data, including indexing and scalability aspects Big Data Processing—addresses the problem of processing Big Data across a wide range of resource-intensive computational settings Big Data Stream Techniques and Algorithms—explores research issues regarding the management and mining of Big Data in streaming environments Big Data Privacy—focuses on models, techniques, and algorithms for preserving Big Data privacy Big Data Applications—illustrates practical applications of Big Data across several domains, including finance, multimedia tools, biometrics, and satellite Big Data processing Overall, the book reports on state-of-the-art studies and achievements in algorithms, analytics, and applications of Big Data. It provides readers with the basis for further efforts in this challenging scientific field that will play a leading role in next-generation database, data warehousing, data mining, and cloud computing research. It also explores related applications in diverse sectors, covering technologies for media/data communication, elastic media/data storage, cross-network media/data fusion, and SaaS.

Handbook of Probabilistic Models

Author : Pijush Samui
Publisher : Butterworth-Heinemann
ISBN 13 : 0128165464
Total Pages : 592 pages
Book Rating : 4.1/5 (281 download)

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Book Synopsis Handbook of Probabilistic Models by : Pijush Samui

Download or read book Handbook of Probabilistic Models written by Pijush Samui and published by Butterworth-Heinemann. This book was released on 2019-10-05 with total page 592 pages. Available in PDF, EPUB and Kindle. Book excerpt: Handbook of Probabilistic Models carefully examines the application of advanced probabilistic models in conventional engineering fields. In this comprehensive handbook, practitioners, researchers and scientists will find detailed explanations of technical concepts, applications of the proposed methods, and the respective scientific approaches needed to solve the problem. This book provides an interdisciplinary approach that creates advanced probabilistic models for engineering fields, ranging from conventional fields of mechanical engineering and civil engineering, to electronics, electrical, earth sciences, climate, agriculture, water resource, mathematical sciences and computer sciences. Specific topics covered include minimax probability machine regression, stochastic finite element method, relevance vector machine, logistic regression, Monte Carlo simulations, random matrix, Gaussian process regression, Kalman filter, stochastic optimization, maximum likelihood, Bayesian inference, Bayesian update, kriging, copula-statistical models, and more. - Explains the application of advanced probabilistic models encompassing multidisciplinary research - Applies probabilistic modeling to emerging areas in engineering - Provides an interdisciplinary approach to probabilistic models and their applications, thus solving a wide range of practical problems

Authorship Attribution

Author : Patrick Juola
Publisher : Now Publishers Inc
ISBN 13 : 160198118X
Total Pages : 116 pages
Book Rating : 4.6/5 (19 download)

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Book Synopsis Authorship Attribution by : Patrick Juola

Download or read book Authorship Attribution written by Patrick Juola and published by Now Publishers Inc. This book was released on 2008 with total page 116 pages. Available in PDF, EPUB and Kindle. Book excerpt: Authorship Attribution surveys the history and present state of the discipline, presenting some comparative results where available. It also provides a theoretical and empirically-tested basis for further work. Many modern techniques are described and evaluated, along with some insights for application for novices and experts alike.

Uncertainty in Artificial Intelligence

Author : Laveen N. Kanal
Publisher : North Holland
ISBN 13 : 9780444700582
Total Pages : 509 pages
Book Rating : 4.7/5 (5 download)

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Book Synopsis Uncertainty in Artificial Intelligence by : Laveen N. Kanal

Download or read book Uncertainty in Artificial Intelligence written by Laveen N. Kanal and published by North Holland. This book was released on 1986 with total page 509 pages. Available in PDF, EPUB and Kindle. Book excerpt: Hardbound. How to deal with uncertainty is a subject of much controversy in Artificial Intelligence. This volume brings together a wide range of perspectives on uncertainty, many of the contributors being the principal proponents in the controversy.Some of the notable issues which emerge from these papers revolve around an interval-based calculus of uncertainty, the Dempster-Shafer Theory, and probability as the best numeric model for uncertainty. There remain strong dissenting opinions not only about probability but even about the utility of any numeric method in this context.