Miracle-dz/UniFAR-FTUs huggingface dataset:
Distributed Coordination Theory for Ground and Aerial Robot Teams

Distributed Coordination Theory for Ground and Aerial Robot Teams Ashton Roza Doctor of Philosophy Graduate Department of Electrical and Computer Engineering University of Toronto 2019 This thesis investigates distributed coordination problems for two important classes of robots. One class corresponds to ground-based mobile robots, each modelled as a kinematic unicycle. The second corresponds to flying robots, each propelled by a thrust vector and endowed with an actuation mechanism producing torques about three orthogonal body axes. The following coordination problems are studied in this thesis: rendezvous, formation control, linear and circular formation flocking and formation path following. For rendezvous of kinematic unicycles, a smooth, time-independent control law is presented that drives the unicycles to a common position from arbitrary initial conditions, under the assumption that the sensing digraph is time-invariant and contains a globally reachable node. The proposed feedback is very simple and is local and distributed. For rendezvous of flying robots, a control strategy is presented that makes the centres of mass of the vehicles converge to an arbitrarily small neighborhood of one another. The convergence is global, and each vehicle can compute its own control input using local and distributed feedback. For formation control, the objective is to make an ensemble of kinematic unicycles achieve pre-defined inter-agent spacings with parallel heading angles. We consider scenarios where the formation either stops or moves with a final collective motion. In the latter case, problems of linear and circular formation flocking and formation path following are ii studied. A control law is presented in each case that solves the problem for almost all initial conditions. For stopping and flocking formations, the proposed control laws are local and distributed while for formation path following, the control laws additionally require each agent to measure its displacement from the path. The idea used to solve the formation control problems is to rigidly attach an offset vector to the body frame of each unicycle. It is shown that stabilizing the desired formation amounts to achieving consensus of the endpoints of the offset vectors, and simultaneously synchronizing the unicycles’ heading angles. Extension of formation control to flying robots using strictly local and distributed feedback is not addressed in this work and remains a challenging open problem.
Platform Configurations within Information Systems Research: A Literature Review on the Example of IoT Platforms

This contribution aims to shed light on the usage and application of different platform constructs within the context of IoT platforms. The main motivation is that scholars and practitioners use the term platform frequently as an unspecific or vague construct. Different research streams shape and influence the concept of a platform. They range from economic practices like the Two-Sided Market (TSM), Multi-Sided Platform (MSP) or platform business models, over technical platform aspects including standardization and modularization, to the platform ecosystem and the concept of an IS platform fostering value co-creation. Within those concepts, the upcoming phenomenon of IoT platforms represents a specific case to analyze what constructs are used to which extent. Thus, the study helps future scholars to use the term platform more precisely and reveals the interrelatedness of the identified constructs. However, the literature review is only a first step towards demystifying the concept of a platform, due to the limited context of IoT platforms.
Using Machine Learning to Predict Decisions on EEG Data

Many studies have been conducted to determine how decisions are made in the brain. The Drift Diffusion Model (DDM) proposes an explanation for this. Studies have then tried to apply machine learning classification to determine whether decision-making processes exhibit activity as described by the DDM. Although these studies find classification accuracies above chance, they still tend to be low. Hence, this study investigates whether these results can be improved using different machine learning classification techniques. This study classifies intracranial EEG (iEEG) data collected while participants performed perceptual and memory decision making tasks. Fisher’s Linear Discriminant Analysis (LDA), a Support Vector Machine (SVM) with Radial Basis Function (RBF) kernel and an SVM with Polynomial kernel were fit onto this data and their classification was evaluated. It was found that classification was done above chance only for a few participants. The classifiers did not perform well, their F1 scores only ranged between 0.52 to 0.54 across the tasks. Polynomial SVM classified perceptual decisions best and RBF SVM classified memory decisions best compared to the other classifiers based on their ROC curves and F1-scores. The reason for a low classification rate is because of low amounts of data and because of the inconsistency of the data (difference in electrode placements). In conclusion, this study was not able to classify decisions with significant accuracy.
Assessment of Occupancy Estimators for Smart Buildings

Dense distributed sensor systems deployed to monitor and control the built environment open up wide application areas for improving the quality of life through smart spaces. By implementing these systems in residential or commercial buildings, more efficient, pervasive, real-time and finally more robust decision support is achieved. As one of the key tasks, high accuracy occupancy detection and estimation within buildings offers the potential to improve HVAC utilization while reducing maintenance needs, enhancing energy savings for building operators as well as a more suitable thermal comfort for occupants. In this context the main contribution of the paper consists of an assessment of supervised machine learning techniques namely random forests and extreme machine learning neural networks for indirect occupancy profile estimation. The methods are applied on a benchmarking dataset of indoor carbon dioxide measurements and ventilation damper positions as collected from the building information system. The impact of data preprocessing through filtering and smoothing as well as hardware constraints and cloud distributed processing for algorithm deployment are also discussed. We highlight key challenges and discuss how the learned models can be integrated for online operation to improve energy efficiency.
A SURVEY ON COMPREHENSIVE TRENDS IN RECOMMENDATION SYSTEMS & APPLICATIONS

Recommendation System (RS) gains considerable popularity from the past decade in E-Commerce and other allied fields. This paper investigates the various traditional Recommendation System like Content-based (CB), Collaboration Filtering-based (CF), Demographic-based, Knowledge-based and discussed current trends in recommendation system like location-aware, context-aware, and Deep Learning techniques. Various improvements and limitations in Recommendation systems have been listed out with evolution metrics for analyzing the accuracy of the algorithms. This paper well-elaborated for the past, present and future scope of the Recommendation System which would be useful for researchers to get familiarity with this domain. To cite this document: Ssvr Kumar Addagarla and Anthoniraj Amalanathan, " A SURVEY ON COMPREHENSIVE TRENDS IN RECOMMENDATION SYSTEMS & APPLICATIONS ", International Journal of Electronic Commerce Studies, Vol.10, No.1, pp.45-88, 2019. Permanent link to this document: http://dx.doi. org/10.7903/ijecs.1705
Word frequency of the CHILDES corpus: Another perspective of child language features

Based on the corpus of the Child Language Data Exchange System (CHILDES), the current research explores the grammatical composition of child language in terms of word classes. Unlike past studies, the research reported in this article examines not only nouns and verbs but also adjectives, adverbs, prepositions, pronouns, determiners, and interjections. We investigated the word frequency patterns of these word classes in both child and maternal language in order to explore the correlation between input word frequency on the mother’s side and the output word frequency on the child’s side. Our results show that the frequency patterns for word classes differ between child language and maternal language. Due to children’s mental development, young children use many more words with concrete and imageable referents such as nouns and pronouns than less concrete or imageable words such as adjectives, adverbs, prepositions, interjections and conjunctions. Data prove that nouns are the most frequently used word class in child language. In addition, children acquire monosyllabic words more easily and use them much more frequently than multi-syllabic words except for those with extremely high frequency. Moreover, our study reveals a positive correlation between the token volume of maternal language output and that of child language output, suggesting the important role of maternal language in children’s language acquisition. A principle of comprehensible input should be highlighted in adults’ speech to children in order to make them achieve larger vocabulary. This discovery in first language acquisition will hopefully help further studies in second or foreign language acquisition, learning and teaching. ICAME Journal No. 35 96
Analysis and Research on Stock Price of LSTM and Bidirectional LSTM Neural Network

The stock market has the characteristics of large fluctuations and high dimensions, and can be regarded as a nonlinear time series system, so the traditional time series method is not applicable. For the study of deep learning methods, this paper proposed a bidirectional long-short term memory (BLSTM) neural network. Compared with ARIMA model and LSTM neural network, the (BLSTM) neural network is used to predict the accuracy of GREE stock price. Firstly, the stock data is normalized and pre-processing, and then the processed data is input into one-way and two-way LSTM respectively. In the neural network, Dropout is used as an optimization term to prevent over-fitting of the network, and then the three evaluation criteria of RMSE, MAE and Loss are selected to comprehensively analyze the rationality of a single bidirectional LSTM neural network. The experimental results show that the RMSE and MAE are reduced by about 24.2% and 19.4% respectively, and the deviation accuracy is increased by 0.13%. The network complexity is not high, and the error is effectively reduced. It can provide reference value for short-term market investors.
Amplifying the Analyst: Machine Learning Approaches for Buried Utility Characterization

The Netherlands has a complex underground infrastructure comprising various types of cables and pipelines. Given inadequate registration procedures from the past, large deviations occur between the estimated and actual positions of such utilities. Annually, this results in many utility strikes during excavation work. By consequence, a business model has emerged for utility mapping companies that identify the actual positions of cables and pipelines for their customers. The analyst's job, which is essentially a classification problem with respect to utility material type, is however repetitive and labor-intensive. Fortunately, today, human intellect can be amplified by machines through Machine Learning. In this work, three classes of Convolutional Neural Networks are designed and trained for this purpose harnessing Ground Penetrating Radar data, with promising results. It subsequently presents a prototypical web application that embeds the models, allowing them to be used in practice. Early validation activities confirm its business value.
Opportunities and Pitfalls in Applying Emotion Recognition Software for Persons With a Visual Impairment: Simulated Real Life Conversations

Background A large part of the communication cues exchanged between persons is nonverbal. Persons with a visual impairment are often unable to perceive these cues, such as gestures or facial expression of emotions. In a previous study, we have determined that visually impaired persons can increase their ability to recognize facial expressions of emotions from validated pictures and videos by using an emotion recognition system that signals vibrotactile cues associated with one of the six basic emotions. Objective The aim of this study was to determine whether the previously tested emotion recognition system worked equally well in realistic situations and under controlled laboratory conditions. Methods The emotion recognition system consists of a camera mounted on spectacles, a tablet running facial emotion recognition software, and a waist belt with vibrotactile stimulators to provide haptic feedback representing Ekman’s six universal emotions. A total of 8 visually impaired persons (4 females and 4 males; mean age 46.75 years, age range 28-66 years) participated in two training sessions followed by one experimental session. During the experiment, participants engaged in two 15 minute conversations, in one of which they wore the emotion recognition system. To conclude the study, exit interviews were conducted to assess the experiences of the participants. Due to technical issues with the registration of the emotion recognition software, only 6 participants were included in the video analysis. Results We found that participants were quickly able to learn, distinguish, and remember vibrotactile signals associated with the six emotions. A total of 4 participants felt that they were able to use the vibrotactile signals in the conversation. Moreover, 5 out of the 6 participants had no difficulties in keeping the camera focused on the conversation partner. The emotion recognition was very accurate in detecting happiness but performed unsatisfactorily in recognizing the other five universal emotions. Conclusions The system requires some essential improvements in performance and wearability before it is ready to support visually impaired persons in their daily life interactions. Nevertheless, the participants saw potential in the system as an assistive technology, assuming their user requirements can be met.
Spatio-temporal Adaptive Pricing for Balancing Mobility-on-Demand Networks

Pricing in mobility-on-demand (MOD) networks, such as Uber, Lyft, and connected taxicabs, is done adaptively by leveraging the price responsiveness of drivers (supplies) and passengers (demands) to achieve such goals as maximizing drivers’ incomes, improving riders’ experience, and sustaining platform operation. Existing pricing policies only respond to short-term demand fluctuations without accurate trip forecast and spatial demand-supply balancing, thus mismatching drivers to riders and resulting in loss of profit. We propose CAPrice, a novel adaptive pricing scheme for urban MOD networks. It uses a new spatio-temporal deep capsule network (STCapsNet) that accurately predicts ride demands and driver supplies with vectorized neuron capsules while accounting for comprehensive spatio-temporal and external factors. Given accurate perception of zone-to-zone traffic flows in a city, CAPrice formulates a joint optimization problem by considering spatial equilibrium to balance the platform, providing drivers and riders/passengers with proactive pricing “signals. ” We have conducted an extensive experimental evaluation upon over 4.0× 108 MOD trips (Uber, Didi Chuxing, and connected taxicabs) in New York City, Beijing, and Chengdu, validating the accuracy, effectiveness, and profitability (often 20% ride prediction accuracy and 30% profit improvements over the state-of-the-arts) of CAPrice in managing urban MOD networks.
Efficiency of Extreme Gradient Boosting for Imbalanced Land Cover Classification Using an Extended Margin and Disagreement Performance

Imbalanced learning is a methodological challenge in remote sensing communities, especially in complex areas where the spectral similarity exists between land covers. Obtaining high-confidence classification results for imbalanced class issues is highly important in practice. In this paper, extreme gradient boosting (XGB), a novel tree-based ensemble system, is employed to classify the land cover types in Very-high resolution (VHR) images with imbalanced training data. We introduce an extended margin criterion and disagreement performance to evaluate the efficiency of XGB in imbalanced learning situations and examine the effect of minority class spectral separability on model performance. The results suggest that the uncertainty of XGB associated with correct classification is stable. The average probability-based margin of correct classification provided by XGB is 0.82, which is about 46.30% higher than that by random forest (RF) method (0.56). Moreover, the performance uncertainty of XGB is insensitive to spectral separability after the sample imbalance reached a certain level (minority:majority > 10:100). The impact of sample imbalance on the minority class is also related to its spectral separability, and XGB performs better than RF in terms of user accuracy for the minority class with imperfect separability. The disagreement components of XGB are better and more stable than RF with imbalanced samples, especially for complex areas with more types. In addition, appropriate sample imbalance helps to improve the trade-off between the recognition accuracy of XGB and the sample cost. According to our analysis, this margin-based uncertainty assessment and disagreement performance can help users identify the confidence level and error component in similar classification performance (overall, producer, and user accuracies).
A Surrogate Model for Vortex Generator Flows

A surrogate model for vortex generator flows has been created. This was done by developing a framework to generate a reduced order approximation of a discrete optimized source-term on a uniform grid. The discrete optimized source term uses the flow field of a vortex generator simulation with a body-fitted mesh (BFM) as objective. The reduced order approximation yields very similar results to the optimized solution and shows that it can be used to train the surrogate model. The surrogate model was trained by varying the inflow angle and Reynolds number in 24 training simulations. The shape factor, velocity profile and circulation downstream of the vortex generator are better in the proposed surrogate model than in the jBAY model, showing that the surrogate model is more accurate. Additionally, unlike the jBAY model, this new surrogate model is independent of the mesh refinement.
Compiling a “classical” explanatory combinatorial lexicographic description into a relational database

This paper deals with the processing of a formal electronic dictionary of French centered around the description of semantic derivations and collocations. The purpose of our project was to obtain a more flexible format for this dictionnary, the DiCo, which has a traditional structure. By compiling the DiCo, we get an object model which, in turn, can be stored in an SQL database. We show how the object model allows us to more easily access the data and gives us a new perspective on the structure of dictionary. This latter can be completly reorganized following new axes like lexical function links.
Asymptotics of Replication and Matching in Large Caching Systems

We consider a generic model of distributed caching systems, where the cache servers are constrained by two main resources: memory size and bandwidth. Content distribution networks (CDNs) providing video contents and peer-to-peer video-on-demand services are a few examples of such systems. The throughput of these systems crucially depends on how these resources are managed, i.e., how contents are replicated across servers and how requests of specific contents are matched to servers storing the contents. In this paper, we formulate the problem of computing the replication policy and the matching policy, which jointly maximizes the throughput of the caching system. It is shown that computing the optimal replication policy for a given finite system is an NP-hard problem. A greedy replication scheme is then proposed and is shown to achieve a constant factor approximation guarantee when combined with the optimal matching policy. We note that the optimal matching policy has the problem of interruption in service of the ongoing requests due to re-assignment or repacking of the existing requests. To avoid this problem, we propose a simple randomized online matching scheme and analyze its performance in conjunction with the proposed replication scheme. We consider a limiting regime, where the number of servers is large and the arrival rates of the contents are scaled proportionally, and show that the proposed policies achieve asymptotic optimality. Extensive simulation results are presented to evaluate the performance of different policies and study the behavior of the caching system under different service time distributions of the requests.
On the use of prior and external knowledge in neural sequence models

Neural sequence models have recently achieved great success across various natural language processing tasks. In practice, neural sequence models require massive amount of annotated training data to reach their desirable performance; however, there will not always be available data across languages, domains or tasks at hand. Prior and external knowledge provides additional contextual information, potentially improving the modelling performance as well as compensating the lack of large training data, particular in low-resourced situations. In this thesis, we investigate the usefulness of utilising prior and external knowledge for improving neural sequence models. We propose the use of various kinds of prior and external knowledge and present different approaches for integrating them into both training and inference phases of neural sequence models. The followings are main contributions of this thesis which are summarised in two major parts: We present the first part of this thesis which is on Training and Modelling for neural sequence models. In this part, we investigate different situations (particularly in low resource settings) in which prior and external knowledge, such as side information, linguistic factors, monolingual data, is shown to have great benefits for improving performance of neural sequence models. In addition, we introduce a new means for incorporating prior and external knowledge based on the moment matching framework. This framework serves its purpose for exploiting prior and external knowledge as global features of generated sequences in neural sequence models in order to improve the overall quality of the desired output sequence. The second part is about Decoding of neural sequence models in which we propose a novel decoding framework with relaxed continuous optimisation in order to address one of the drawbacks of existing approximate decoding methods, namely the limited ability to incorporate global factors due to intractable search. We hope that this PhD thesis, constituted by two above major parts, will shed light on the use of prior and external knowledge in neural sequence models, both in their training and decoding phases.