Miracle-dz/UniFAR-FTUs huggingface dataset:
Grounding human vocabulary in robot perception through interaction

This thesis addresses the problem of word learning in computational agents. The motivation behind this work lies in the need to support language-based communication between service robots and their human users, as well as grounded reasoning using symbols relevant for the assigned tasks. The research focuses on the problem of grounding human vocabulary in robotic agent’s sensori-motor perception. Words have to be grounded in bodily experiences, which emphasizes the role of appropriate embodiments. On the other hand, language is a cultural product created and acquired through social interactions. This emphasizes the role of society as a source of linguistic input. Taking these aspects into account, an experimental scenario is set up where a human instructor teaches a robotic agent the names of the objects present in a visually shared environment. The agent grounds the names of these objects in visual perception. Word learning is an open-ended problem. Therefore, the learning architecture of the agent will have to be able to acquire words and categories in an openended manner. In this work, four learning architectures were designed that can be used by robotic agents for long-term and open-ended word and category acquisition. The learning methods used in these architectures are designed for incrementally scaling-up to larger sets of words and categories. A novel experimental evaluation methodology, that takes into account the openended nature of word learning, is proposed and applied. This methodology is based on the realization that a robot’s vocabulary will be limited by its discriminatory capacity which, in turn, depends on its sensors and perceptual capabilities. An extensive set of systematic experiments, in multiple experimental settings, was carried out to thoroughly evaluate the described learning approaches. The results indicate that all approaches were able to incrementally acquire new words and categories. Although some of the approaches could not scale-up to larger vocabularies, one approach was shown to learn up to 293 categories, with potential for learning many more.
ONLINE LEARNING WITH BANDITS FOR COVERAGE

With the rapid growth in velocity and volume, streaming data compels decision support systems to predict a small number of unique data points in due time that can represent a massive amount of correlated data without much loss of precision. In this work, we formulate this problem as the online set coverage problem and propose its solution for recommendation systems and the patrol assignment problem. We propose a novel online reinforcement learning algorithm inspired by the Multi-Armed Bandit problem to solve the online recommendation system problem. We introduce a graph-based mechanism to improve the user coverage by recommended items and show that the mechanism can facilitate the coordination between bandits and therefore, reduce the overall complexity. Our graph-based bandit algorithm can select a much smaller set of items to cover a vast variety of users choices for recommendation systems. We present our experimental results in a partially observable real-world environment. We also study the patrol assignment as an online set coverage problem, which presents an additional level of difficulty. Along with covering the susceptible routes by learning the diversity of attacks, unlike in recommendation systems, our technique needs to make choices against actively engaging adversarial opponents. We assume that attacks over those routes are posed by intelligent entities, capable of reacting with their best responses. Therefore, to model such attacks, we used the Stackelberg Security Game. We augment our graph-based bandit defenders with adaptive adjustment of reward coming from this game to perplex the attackers and gradually succeed over them by maximizing the confrontation. We found that our graph bandits can outperform other Multi-Arm bandit algorithms when a simulated annealing-based scheduling is incorporated to adjust the balance between exploration and exploitation. ONLINE LEARNING WITH BANDITS FOR COVERAGE by Mahmuda Rahman Bachelors, North South University, 2004 Masters, University of Dhaka, 2006 Dissertation Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Computer & Information Science & Engineering. Syracuse University December 2017 Copyright c ©Mahmuda Rahman 2017 All Rights Reserved
Prototyping Material Programming Using Internet of Things

This thesis explore material programming as a proposed design practice for computational composites. A computational composite is an envisioned future material as dynamic, interactive and capable of computation. Computational composites as a concept understand the computer as a material in and of itself and predict current technological growth will lead to such materials in the not too distance future. Material programming is the design practice used for exploring the interactive and dynamic properties of such materials. This thesis will create a physical proof of concept for material programming using today’s internet of things.
Looper-gauge Integrated Control in Hot Strip Finishing Mill Using Inverse Linear Quadratic Theory

Hot strip rolling is a significant process in the fields of manufacturing and processing. In order to further improve the control accuracy of looper angle, strip tension and gauge in hot strip finishing mill, an innovative looper-gauge integrated control scheme is developed in this paper. Based on the inverse linear quadratic (ILQ) theory, a proposed control scheme is designed for the looper-gauge integrated system. First, considering the numerous interactions between looper angle, strip tension and gauge, and the disturbances from several sources, a dynamic 6th order state space model is established and validated. Then, the control scheme based on ILQ theory is imported into gauge-looper integrated system. The desired poles are placed according to the dynamic characteristics requirements. The state feedback optimal control law is determined by an improved ILQ design method. Then the multivariable looper-gauge integrated control system is constructed. The proposed control scheme has the explicit ability to achieve desired looper and gauge control performances, with less external disturbances and no sensitivity of strip dimension changing. The effectiveness of the proposed looper-gauge integrated control scheme compared with traditional control strategies is shown in the simulation results. The interactions between looper control and guage control are also minimized.
A Hybrid Approach to Generating Adjective Polarity Lexicon and its Application to Turkish Sentiment Analysis

Many approaches to sentiment analysis benefit from polarity lexicons. Existing methods proposed for building such lexicons can be grouped into two categories: (1) Lexicon based approaches which use lexicons such as dictionaries and WordNet, and (2) Corpus based approaches which use a large corpus to extract semantic relations among words. Adjectives play an important role in polarity lexicons because they are better polarity estimators compared to other parts of speech. Among natural languages, Turkish, similar to other non-English languages suffers from the shortage of polarity resources. In this work, a hybrid approach is proposed for building adjective polarity lexicon, which is experimented on Turkish combines both lexicon based and corpus based methods. The obtained classification accuracies in classifying adjectives as positive, negative, or neutral, range from 71% to 91%.
Prioritizing Design Goals for a Medical Simulator Using Pairwise Comparisons

A simulator is being developed to teach psychomotor skills for the lateral canthotomy and cantholysis procedure to medical providers, filling a current training gap at military medical training facilities. Subject Matter Experts (SMEs) agree on training system design goals; however, there is a lack of consensus on the relative order and magnitude of the priorities of each goal. The designs and implementations to achieve each goal have several interdependencies and tradeoffs, which require a comparison between different sets of design goals. A survey of pairwise comparisons was distributed to SMEs and two methods (a simple method and the Bradley-Terry model) were used to develop a unidimensional scale of prioritizations for each design goal. There was agreement in results across the simple method (calculating the proportion of times an alternative was chosen as preferable) and the Bradley-Terry model. The Bradley-Terry method offered a means to calculate measures of uncertainty, showing nuances where scores may overlap. This prioritization method enabled the research and development team to reconcile differing SME opinions. This also allowed for informed design decisions to ensure that the training system met expectations of SMEs and end users. Future efforts will involve developing scores for relevant sets of demographics to determine if there is concurrence and dissent in priorities.
Re-centring peripheries along ring roads under a smart growth agenda: case studies of Espoo Innovation Garden and Vantaa Aviapolis in Finland

ABSTRACT The study expands on the recent cluster-oriented smart growth vision in Greater Helsinki, Finland, and examines with two case studies the pertinent locational factor and locality-specific issues. The contemporary regional development tendencies of smart clustering have reframed place-making schemes with an aim of placing large concentrations of business activities on the periphery. The case studies of Espoo Innovation Garden and Vantaa Aviapolis draw on descriptive before-and-after analysis over a defined timeframe. The study addresses the formation and transformation of the spatial structure and its relative and relational qualities on an aggregated scale. It demonstrates the potential, challenges, tools, and strategies of the operating smart growth agenda generating spatial-economic competitiveness and attractiveness factors in these particular projects. The findings explain how the locational factor along ring roads parallel with the land-use management and former model of spatial and economic concentration and centralization are hampering the restructuring of new urban centralities.
REAL-TIME, ADAPTIVE, SELF-LEARNING MANAGEMENT OF LAKES

Lakes and reservoirs are increasingly threatened by anthropogenic activities, with serious environmental and financial consequences. In particular, nutrient loadings are increasing due to expanding human populations and food demand, and thermal stratification is increasing due to global warming. For deep lakes, this is leading to an increase of the seasonal water column stability, extending the duration of seasonal deoxygenation of hypolimnetic waters, as well as increasing the nutrient inventory within the bottom sediments. The large volume of deoxygenated hypolimnetic water and nutrient-enriched bottom sediments increase nutrient releases. This overturn brings the deoxygenated, nutrient rich hypolimnetic water into the euphotic zone, leading to increased primary productivity, organic enrichment, and providing a feedback mechanism for further degradation of the fauna and flora living in the epilimnion, potentially culminating in total death of aerobic organisms by asphyxiation. Reservoirs and shallow lakes are increasingly subjected to toxic algal blooms in response to changing patterns of stratification in combination with the increasing nutrient loadings. We use two examples to illustrate the problems presently encountered, the range of control strategies available to manage the consequences and then show how adaptive, real-time, self-learning technologies may be used to dynamically optimize the ecosystem health, as both the impacts and the system change with time. The first example is deep Lake Iseo in Italy. The period between overturns has increased from around every ten years to twenty years in Lake Iseo over the last 50 years. We show that the water column stratification may be controlled with solar-powered impellers, allowing the frequency of overturning to be regulated so as to prevent the buildup of large volumes of low-oxygen hypolimnetic waters. The second is shallow Lake Ypacarai in Paraguay. It has undergone rapid eutrophication, resulting in severe toxic algal blooms that are having a devastating impact on the economy of Paraguay. Using the numerical simulations of the lake ecosystem, we carried out a sensitivity analysis of the available controls for the mitigation of the algal blooms. The simulations demonstrate how a decrease in the nutrient loadings, decrease in the water levels, flushing of the lake with bore or river water and increase in water opacity provide multiple potential bloom control mechanisms. The results for both lakes indicate that a real-time adaptive management system using model forecasts could optimize environmental controls on attributes (e.g. cyanobacteria and dissolved oxygen) that would otherwise seriously deteriorate and impact the ecological processes throughout the lake ecosystems.
The Effects of Mortality Salience and Autonomy Priming on Worldview Defensiveness

Terror Management Theory posits that people are motivated to defend against death awareness by maintaining cultural beliefs and behaviors that transcend mortality— sometimes motivating hostile, even militaristic, defenses of one’s culture. In contrast, self-determination theory suggests that autonomous regulation (self-determination) serves as a platform for personal growth and well-being. However, the present thesis suggests that, in addition to fueling growth, self-determination may also help buffer against the awareness of mortality, thus mitigating the impact of death awareness on hostile cultural worldview defense. To test this hypothesis, American participants were randomly assigned to be reminded of mortality or a control topic, then randomly assigned to be reminded of feelings of autonomy or being controlled, and then lastly completed a measure of one possible form of worldview defense: support for militaristic defense of American foreign policy interests in Syria. The present analysis found that death reminders increased that form of worldview defense, unless participants were first prompted to recall self-determination experiences.
Performance analysis of heat treated AISI 1020 steel samples on the basis of various destructive mechanical testing and microstructural behaviour

ABSTRACT The present work deals with assessment of heat treated AISI 1020, a low carbon steel (0.2%-C), on the basis of various destructive testing and microstructural behaviour. The steels samples were heated above the lower critical temperature range, i.e. up to 750°C. The cooling rate of all the samples was different. Mainly, three different cooling medium were taken. They are: water, air and sand. These three heat treated samples were compared with as received sample. Hence the final condition of all the four samples is: as received; normalised; sand cooled; water quenched. After a proper preparation, samples were undergone through various mechanical tests, such as, tension test, shear test, hardness test, and impact test. In addition, the microstructural images, captured by an optical microscope, of all the samples were observed and analysed. The mechanical properties and microstructural attributes obtained in ‘heat treated’ samples were compared with those results of ‘as received’ samples. From the present experimental analysis, it was found that heat treatment processes significantly changed/improved the desired properties of the AISI 1020 steel samples.
Routing Queries to the Right Expert in Online CQA

: The popular websites such as Stack Overflow and Yahoo provides the question and answer which is posted by the anonymous user by all over the world. In these sites, the programmers from all over the world will answer for the queries which are posted on the websites. In the existing system, if any user wants to know the exact answer they have to mine the answer between the thousands of answer posted on the website and still it was very complicated to know the exact answer and it was very time consuming. So that user has to apply all the answer to get the exact answer. So to overcome from this situation, we proposing the technique in which user can view the exact answer from the websites as it will be displayed as a first answer for that question through post voting prediction. In the voting process, the voting will be given based on the conditions. If that answer satisfies the conditions, it will be up voted. In this way, our proposed technique provides the effectiveness and efficiency, optimality, correctness and complexity.
ENHANCEMENT OF REAL-TIME SCAN MATCHING FOR UAV INDOOR NAVIGATION USING VEHICLE MODEL

Abstract. Autonomous Unmanned Aerial Vehicles (UAVs) have drawn great attention from different organizations, because of the various applications that save time, cost, effort, and human lives. The navigation of autonomous UAV mainly depends on the fusion between Global Navigation Satellite System (GNSS) and Inertial Measurement System (IMU). Navigation in indoor environments is a challenging task, because of the GNSS signal unavailability, especially when the utilized IMU is low-cost. Light Detection and Ranging Radar (LIDAR) is one of the mainly utilized sensors in the indoor environment for localization through scan matching of successive scans. The process of calculating the rotation and translation from successive scans can employ different approaches, such as Iterative Closest Point (ICP) with its variants, and Hector SLAM. ICP and Hector SLAM iterative fashion can greatly increase the matching time, and the convergence is not guaranteed in case of harsh maneuvers, moving objects, and short-range LIDAR as it may get stuck in local minima. This paper proposes enhanced real-time ICP and Hector SLAM algorithms based on vehicle model (VM) during sharp maneuvers. The vehicle model serves as initialization step (coarse alignment) then the ICP/Hector serve as fine alignment step. Test cases of quadcopter flight with harsh maneuvers were carried out with LIDAR to evaluate the proposed approach to enhance the ICP/Hector convergence time and accuracy. The proposed algorithm is convenient for UAVs where there are limitations regarding the size, weight, and power limitations, as it is a stand-alone algorithm that does not require any additional sensors.
Tonhöhenverteilungen im klassischen Orchesterrepertoire

Copyright applies. A non-exclusive, non-transferable and limited right to use is granted. This document is intended solely for personal, non-commercial use. Terms of Use Quiring, Roman; Weinzierl, Stefan (2016): Tonhöhenverteilungen im klassischen Orchesterrepertoire. In: Proceedings of the Inter-Noise 2016 : 45th International Congress and Exposition on Noise Control Engineering : towards a quiter future : August 21-24, 2016, Hamburg. Berlin: Deutsche Gesellschaft für Akustik e.V. pp. 1486–1489. Roman Quiring, Stefan Weinzierl
Filtrage collaboratif en ligne: application à la publicité programmatique

Internet advertising has become a major economic challenge for online selling companies that have to optimise their catalogs in real time, in order to propose to users products that fit the best to their interests and preferences. Conventional collaborative filtering engines cannot be applied in this context as there are very few ordinal information, corresponding mainly to past purchases, linking users to the products of their interests. In the other hand relevant information, mainly quantitative, can be obtained from the analyses of user sessions. The particularity of past purchases and the real-value indicators obtained from such analyses is their interdependency and also their nature, as ordinal outputs can be considered as ground truth while real-valued outputs are predicted from user profiling tools and may contain noise. The main objective of this project is to develop the new generation of recommendation system for online advertisement that can handle both ordinal and quantitative information, associating users to products. To attain this goal, we first propose to establish a theoretical framework for multi-target learning for recommendation systems that takes into account the interdependencies between the two types of outputs, relating users to products, and also their nature. The interdependency between the outputs could be studied using the hierarchical decomposition of products and a statistical tool for empirical processes extending the Hoeffding inequality. From this analysis we expect to obtain a transformation allowing to reduce the primal multi-target learning problem into simpler dual multi-label classification and regression problems that will path the way for the development of new generation recommendation engines. The user profiling has to be done using the very few labeled data available and by exploiting the graph of users having the same interest, this requires the development of a graph-based semi-supervised model to propagate information over similar users. The overall model needs to be scalable having very low recommendation time. The thesis is a common project with Kelkoo and Best of Media which will provide the data necessary for developing the framework and the models. For this position, we are looking for highly motivated people, with a passion to work in machine learning, information retrieval and the skills to develop algorithms for prediction in real-life applications. We are looking for an inquisitive mind with the curiosity to use a new and challenging technology that requires a rethinking visual processing to achieve a high payoff in terms of speed and efficiency.
Economic effects of the liquid biofuel industry in South Korea using input–output analysis

Bio-energy is a research field that is of worldwide interest. South Korea, which imports all of its heavy fuel oil for consumption, passed a new law allowing bio-heavy oil made from animal fat, by-product of biodiesel processes, palm oil, and other leftover oil to be used to generate electricity in place of heavy fuel oil. As there is lack of policy research with respect to liquid biofuels, the purpose of this study is to define the bio-heavy oil industry in South Korea and to investigate the economic effects of bio-heavy oil. An input–output analysis model was used and demonstrated that the production-, value-added-, import-, and employment-induced effects of the bio-heavy oil industry were larger than those induced by the heavy fuel oil industry. As the import of fuel by the heavy fuel oil industry was greater than the bio-heavy oil industry, the import substitution effect of the bio-heavy oil industry was found to be greater. This resulted in a positive value for the net-induced effect of the bio-heavy oil industry. When considering the global concern with respect to the development and expansion of biofuel feedstock, this study shows the possibility of transforming heavy fuel oil plants distributed around the world into renewable energy sources.
A fuzzy logic based method for fault tolerant hierarchical load management of more electric aircraft

With the increasing number of electrical loads, load management of the more electric aircraft becomes crucial for reliability and efficiency. One of the major challenge is to develop an optimal and reliable adaptive power control. This paper presents a three-level load management method with dedicated time steps for fault tolerance and increasing calculation efficiency. Both the operative mode and the health level of the loads are taken into account in the control using fuzzy logic. The electrical system of a V-tail more electric aircraft that consists of a generator, an auxiliary power unit, and several AC/DC buses and loads is examined by the proposed method in normal and faulty cases. Compared with some conventional methods, the proposed load management method has the advantage of efficiently shedding loads according to the power imbalance and the fault situation.